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Series 2No. 126

National Health InterviewSurvey: Research for the1995–2004 Redesign

July 1999

Vital andHealth StatisticsFrom the CENTERS FOR DISEASE CONTROL AND PREVENTION / National Center for Health Statistics

U.S. DEPARTMENT OF HEALTH AND HUMAN SERVICESCenters for Disease Control and Prevention

National Center for Health Statistics

Copyright information

All material appearing in this report is in the public domain and may bereproduced or copied without permission; citation as to source, however, isappreciated.

Suggested Citation

National Center for Health Statistics. National Health Interview Survey:Research for the 1995–2004 redesign. Vital Health Stat 2(126). 1999.

Library of Congress Cataloging-in-Publication Data

National Health Interview Survey : research for the 1995–2004 redesign.p. cm. — (Vital and health statistics. Series 2, Data evaluation and

methods research ; no. 126) (DHHS publication ; no. (PHS) 99-1326)Includes bibliographical references.ISBN 0-8406-0557-9. — ISBN 0-8406-0557-91. Health surveys—United States. 2. Medical statistics—United States.

3. Health status indicators—United States. I. Series. II. Series : DHHSpublication ; no. (PHS) 99-1326.

[DNLM: 1. National Health Interview Survey (U.S.) 2. HealthSurveys—United States. 3. Data Collection—methods—United States.4. Sampling Studies—United States. 5. Statistics—United States.W2 AN148vb no. 126 1999]RA409.U45 no. 126[RA407.3]362.1'0723 s—dc21[362.1'0723]DNLM/DLC 99-15863for Library of Congress CIP

For sale by the U.S. Government Printing OfficeSuperintendent of DocumentsMail Stop: SSOPWashington, DC 20402-9328Printed on acid-free paper.


Series 2:Data Evaluation and MethodsResearchNo. 126

Hyattsville, MarylandJuly 1999DHHS Publication No. (PHS) 99-1326

Vital andHealth Statistics

U.S. DEPARTMENT OF HEALTH AND HUMAN SERVICESCenters for Disease Control and PreventionNational Center for Health Statistics

National Center for Health Statistics

Edward J. Sondik, Ph.D.,Director

Jack R. Anderson,Deputy Director

Jack R. Anderson,Acting Associate Director forInternational Statistics

Lester R. Curtin, Ph.D.,Acting Associate Director forResearch and Methodology

Jennifer H. Madans, Ph.D.,Acting Associate Director forAnalysis, Epidemiology, and Health Promotion

P. Douglas Williams,Acting Associate Director for DataStandards, Program Development, and Extramural Programs

Edward L. Hunter,Associate Director for Planning, Budget,and Legislation

Jennifer H. Madans, Ph.D.,Acting Associate Director forVital and Health Statistics Systems

Douglas L. Zinn,Acting Associate Director forManagement

Charles J. Rothwell,Associate Director for DataProcessing and Services

Office of Research and Methodology

Lester R. Curtin, Ph.D.,Acting Associate Director

Lester R. Curtin, Ph.D.,Chief, Statistical Methods Staff

Trena Ezzati-Rice,Chief, Survey Design Staff

Jimmie D. Givens,Acting Chief, Statistical Technology Staff

Kenneth W. Harris,Acting Manager, Questionnaire DesignResearch Laboratory Staff

Primary contributors:

David JudkinsDavid MarkerJoseph WaksbergSteven BotmanJames Massey

Other contributors:

John EdmondsMansour FahimiHüseyin GökselDoris Northrup (CODA)Svetlana RyaboyValerija SmithDavid Wright

Prepared under Contract number 200–89–7021 byWestat, Inc.1650 Research BoulevardRockville, Maryland 20850

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Preface

This report presents a detaileddescription of a major portion ofthe research undertaken to

redesign the National Health InterviewSurvey (NHIS) for the period1995–2004.

The NHIS is one of the major datacollection programs of the NationalCenter for Health Statistics (NCHS).Through NHIS, information concerningthe health of the U.S. civiliannoninstitutionalized population iscollected in household interviewsthroughout the United States. NHIS habeen in continuous operation since1957, and its sample design has beenreevaluated and modified following eacof the last four decennial censuses ofthe U.S. population.

The 1995–2004 redesign of NHISwas a major undertaking that involvednumber of government agencies as weas several private contractors. TheSurvey Design Staff in the Office ofResearch and Methodology (ORM),NCHS, in collaboration with theDivision of Health Interview Statistics(DHIS), NCHS, had overallresponsibility for the development andimplementation of the 1995–2004 NHISredesign. Monroe Sirken, formerly theORM Associate Director, played a majorole in the conceptualization andplanning of the research program. Thelate Jim Massey, former Chief of theSurvey Design Staff, and Don Malec,formerly of the Survey Design Staff, hathe primary responsibility for directingthe redesign research and coordinatingthe research activities conducted byNCHS, the U.S. Bureau of the Censusand Westat, Inc. The late Steve Botmaformerly of the Survey Design Staff, hathe lead responsibility in helping toimplement the 1995–2004 NHISredesign, and also carried out manyimportant steps leading to thepublication of this report. Van Parsonsof the Statistical Methods Staff, ORM,contributed to the development ofsubdesigns for the 1995–2004 NHISredesign. Keith Hoffman and Trena

,

Ezzati-Rice of the Survey Design Staffparticipated in the research andevaluation of the Social SecurityAdministration files as a potentialsupplemental sampling frame for NHISas described inchapter 6of this report.Chris Moriarity of the Survey DesignStaff carried out the final steps of StevBotman’s work that led to thepublication of this report. The late OweThornberry, formerly the Director,DHIS, and John Horm, formerly theacting Chief of the Survey Planning anDevelopment Branch, DHIS, were theleading DHIS participants in the1995–2004 NHIS redesign work.

Under contract with NCHS betwee1989 and 1993, Westat, Inc., conducteresearch for the 1995–2004 NHISredesign. The primary researchers atWestat, Inc., included David Judkins,David Marker, and Joseph Waksberg.Other contributors included JohnEdmonds, Mansour Fahimi, HüseyinGöksel, Doris Northup (CODA),Svetlana Ryaboy, Valerija Smith, andDavid Wright.

Additional research was conductedby the U.S. Bureau of the Census andcoordinated through the Task Force onHousehold Survey Redesign, assembland directed by the late Maria Gonzaleof the Office of Management andBudget (OMB). The task force wasformed to coordinate and monitor theU.S. Bureau of the Census’ssimultaneous redesign of all of thehousehold surveys the Bureau conducfor other Federal government agenciesThe task force played an important rolin coordinating the technical andfunding requirements for the redesignsof all of the surveys.

The U.S. Bureau of the Census habeen the primary data collector for theNHIS since its inception. The Bureaualso has been involved with the researand implementation of the NHISredesigns. For the 1995–2004 redesigthe Demographic Statistical MethodsDivision (DSMD) had the primaryresponsibility for evaluating alternativeprimary sampling unit definitions forNHIS, and for implementing theredesigned sample. DSMD staff alsoparticipated in the research and

evaluation of the Social SecurityAdministration files as a potentialsupplemental sampling frame for NHISas described inchapter 6of this report.Persons at the U.S. Bureau of theCensus that deserve special recognitiofor their contribution to the 1995–2004NHIS redesign effort include PrestonJay Waite, formerly Chief of DSMD;Thomas Moore, Chief of the HealthSurveys and Supplements Branch inDSMD; Robert Mangold, formerly Chieof the Health Surveys Branch in theDemographic Surveys Division; PatriciWilson of DSMD; and Lloyd Hicks,formerly of DSMD.

The primary focus of the1995–2004 NHIS redesign research wto explore sample design options toimprove the reliability of NHIS statisticsfor racial, ethnic, economic, andgeographic domains. Another objectivewas to assess the integration of thesample designs of the NCHS surveyshealth care providers and NHIS.

The decisions for the 1995–2004NHIS redesign were based on a numbof factors including technical soundnescompeting analytical objectives,operational feasibility, costs, andavailable resources. The research resupresented in this report were the primabasis for assessing the technicalsoundness and costs for a number ofdesign alternatives. In particular, as paof Westat, Inc.’s, research, a sampledesign referred to as the ‘‘alpha’’ desigwas developed, which assumed a50-percent data collection budgetincrease to permit oversampling ofracial and ethnic minorities. Westat, Inresearchers also developed amodification of the ‘‘alpha’’ design, the‘‘beta’’ design, which assumed nochange in NHIS data collection budgetThe ‘‘beta’’ design, which became thedesign implemented in 1995, isdescribed inchapter 17. The researchresults in this report also led toadditional research, primarily in the areof survey integration.

A more detailed description of the1995–2004 NHIS design will appear ina forthcoming NCHS Series 2 reporttitled Design and Estimation for the

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1995–2004 National Health InterviewSurvey.

As required by the contract betweeNCHS and Westat, Inc., a draftmanuscript of this report was submittedto NCHS by Westat, Inc., in early 1994.A revised manuscript was submitted toNCHS by Westat, Inc., in early 1996.This report is very similar to the 1996manuscript, although personnel fromWestat, Inc., and NCHS made additionarevisions before publication.

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Contents

Preface. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii

Abstract. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

Executive Summary. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1Research Objectives. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2Research Methodology. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

Part I. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

Chapter 1. Background on the National Health Interview Survey. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8Schedule and Content. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8Sample Design for 1985–94. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

Chapter 2. New Objectives for 1995 Redesign. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9Statistics for Racial, Ethnic, and Economic Domains. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9Statistics for Geographic Domains. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9Integration With Surveys of Health Care Providers. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

Part II. Statistics for Racial, Ethnic, and Economic Domains. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

Chapter 3. Current Precision. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12Variances in Current Design. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12Components of Variance. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

Chapter 4. Potential Techniques for Increasing Sample Sizes for Selected Domains. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16Sample Sizes With Simple Expansion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16Oversampling Classes of Primary Sampling Units. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18Oversampling Classes of Blocks or Block Groups. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18Screening With Subsampling. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20Network Sampling by Nomination of Relatives. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21Dual-Frame Sampling With Administrative Lists. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24Multiyear Aggregation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24Carrying Samples Over From Prior Years. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24Cost Models. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

Chapter 5. Best Techniques for Sampling the Nonelderly Black and Hispanic Populations. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26Oversampling Small Decennial Census Geographic Units Without Screening. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26Screening a National Equiprobability Sample. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28Oversampling at the Block Level With Screening for Race, Ethnicity, Age, and Sex. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28Oversampling at the Block Level With Screening for Race and Ethnicity Only. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

Chapter 6. Choosing a Sampling Technique for the Elderly Black and Hispanic Populations. . . . . . . . . . . . . . . . . . . . . . . . . . . . 33Area Sampling With Oversampling at the Block Level With Screening for Race, Ethnicity, Age, and Sex. . . . . . . . . . . . . . . 33Dual-Frame Sampling With Social Security Administration Lists. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

Chapter 7. Prospects for Statistics on Detailed Hispanic Subdomains. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37Design-Based Techniques. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37Model-Based Techniques. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37

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Chapter 8. Prospects for Statistics on Other Minority Groups. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38Asians and Pacific Islanders. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38American Indians, Eskimos, and Aleuts. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38

Chapter 9. The Poor. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39

Part III. Statistics for Geographic Domains . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41

Chapter 10. Subnational Estimates for State Groupings and Hispanic Persons. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42Subnational Domains With Current Design. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42Subnational Domains for Hispanic Persons. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43Oversampling Hispanic Persons. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43

Chapter 11. Design-Based Approaches for State Estimation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47Estimation Based Solely on the National Health Interview Survey. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47Dual-Frame Estimation Using Random Digit Dialing Supplementation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47

Chapter 12. Empirical Comparison of Model-Dependent Estimators. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58Estimators. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58State-Specific Mean Square Errors. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60Empirical Results. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62Summary of Empirical Results. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71

Part IV. Coordination With Surveys of Health Care Providers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73

Chapter 13. Coordination at the Primary Sampling Unit Level. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74Cost Savings. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74Analysis Enrichment. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74Variance Increases. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74Conclusion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75

Chapter 14. Network Sampling of Providers Through the National Health Interview Survey. . . . . . . . . . . . . . . . . . . . . . . . . . . . 76Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76Model for Costs and Variance. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77Comparison With List-Based Sampling. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79Conclusion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80

Chapter 15. The National Health Interview Survey as a Tool for Linking Beneficiary and Provider Perspectives onHealth Care. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81

Other Surveys. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81National Health Interview Survey as a Baseline or as a Follow Up?. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81

Part V. Decisions on Sample Design and Contingency Options. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83

Chapter 16. Decisions on Sample Design. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84Reconciliation of Objectives. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84Primary Sampling Unit Definition and Measure of Size. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84Primary Sampling Unit Stratification. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85Primary Sampling Unit Selection. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85Selection of Blocks. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85Within-Block Selection. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86Screening. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87New Construction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88Dual-Frame Sampling for Elderly Minorities. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88Panel Construction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88Summary of Design Characteristics. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92

Chapter 17. Contingency Plans. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93The Beta Option. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93New Construction Sampling. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97Restoration of an Equiprobability Design at Level Cost and Other Contingency Plans. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98

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Chapter 18. Recommendations for Continuing Research. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100Intercensal Tracking of Minority Populations Across Density Strata. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100Updating Minority Density Areas. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100Coverage Improvement. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100

References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101

Appendixes

I. List of the National Health Interview Survey Redesign Memorandums. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102II. National Health Interview Survey Estimates and Coefficients of Variation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105

Text Figure

1. Summary of sample sizes for new design (alpha option). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92

Text Tables

A. Effective adult sample sizes by redesign option and population domain relative to the 1988 NHIS design. . . . . . . . . . . . 4B. Sample sizes for the black and Hispanic populations, aged 65 and older. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

Detailed Tables

1. Items selected for direct variance estimation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122. Coefficients of variation for selected NHIS items: 1988. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133. Domain-specific required sample sizes for a 30-percentcv. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 144. Projections of the population by age, sex, race, and ethnicity to 2000. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 165. Projections of undercoverage by age, sex, race, and ethnicity. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 176. Sample sizes for equiprobability sample of 50,000 interviewed households. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 187. Sample sizes for equiprobability sample of 50,000 interviewed households with one adult per household

(corrected for within-household design effect). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 198. Distributions in 1990 of minority populations across density strata. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 209. Distributions in 1990 of each density stratum that is other. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

10. Aspects of the optimal mix of screening and oversampling for various cost assumptions and racial and/or ethnicsubdomains. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

11. Distribution of the black population across density strata in 1990. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2612. Distribution of the Hispanic population across density strata in 1990. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2613. Distribution of the other population across density strata in 1990. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2614. Distribution of the total population across density strata in 1990. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2715. Distribution of the black population across density strata in 2000. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2716. Distribution of the Hispanic population across density strata in 2000. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2717. Distribution of the other population across density strata in 2000. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2718. Distribution of the total population across density strata in 2000. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2819. The limits of oversampling without screening. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2920. Oversampling rates for the alpha design (relative to a design that would yield 50,000 interviews). . . . . . . . . . . . . . . . . . 3021. Persons in screened households for the alpha option. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3022. Percent of other households discovered during screening to be kept for full interviews under the alpha design. . . . . . . . 3123. Effective sample sizes for alpha design when all household members are sampled. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3124. Effective sample sizes for alpha design when one adult is sampled per household. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3225. Required sample sizes by sampling method and precision target. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3426. Geographic concentration of the poor in 1990. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3927. State groupings to produce State estimates every 3 years. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4228. Oversampling rates to produce subnational estimates for Hispanics. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4329. Oversampling rates when the HCFA frame is used as a sampling frame for the elderly. . . . . . . . . . . . . . . . . . . . . . . . . . . 4430. Summary of number screened and interviewed, assuming two different screening costs. . . . . . . . . . . . . . . . . . . . . . . . . . . 4531. Summary of number screened (in the Southwest) and interviewed, assuming two different screening costs. . . . . . . . . . . . 4632. State sample sizes with and without oversampling of blacks and Hispanics; States ranked by population

size in 2000. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4833. Number of States for which estimates would have the desired accuracy for three different sample sizes. . . . . . . . . . . . . 49

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34. State sample sizes (ranked by population size in 2000) for collapsed ages with and without oversampling ofblacks and Hispanics. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50

35. RDD supplementation sample sizes for age groups with one adult sampled per household. . . . . . . . . . . . . . . . . . . . . . . . . 5236. Number of States for which all cells meet the desired level of accuracy. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5337. Variances of an RDD sample of 1,000 and 2,000 households per year for two designs, two different percentages,

and for all adults and the rarest cell. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5338. The number of States, under the proposed redesign andp = 0.1, for which the unbiased composite estimates for

certain cells will havecv’s greater than 30 or 15 percent. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5439. Number of States, by the size RDD supplement needed to derive a dual-frame NHIS estimator of accuracy similar

to stand-alone 1,000 RDD interviews. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5440. Summary of the number of States and percent reduction in bias corresponding to different size RDD supplementation

of a dual-frame estimator. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5541. Differences between telephone and nontelephone households for percent of persons who smoke, and persons under

65 years old without private health insurance. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5542. Comparison of 1,000 RDD interviews alone (sampling one adult per household), with dual-frame estimators

(from proposed design), andp = 30 percent. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5643. Values ofbj for each of the 16 age/race/sex subgroups used in estimator 5. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5944. Values ofbj for each of the 32 central city/age/race/sex subgroups used in estimator 6. . . . . . . . . . . . . . . . . . . . . . . . . . . . 6045. Average number of doctor visits, by State. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6346. Percentage with perceived poor health status, by State. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6447. State-specific mean square errors for number of doctor visits, using estimators 2 and 5. . . . . . . . . . . . . . . . . . . . . . . . . . . 6548. Groupings of States by common expected biases. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6549. Average number of doctor visits, with States grouped by percentages of nonblack population in central cities. . . . . . . . . 6650. Average number of doctor visits, with States grouped by percentage of black population in central cities. . . . . . . . . . . . . 6751. Variation in State estimates for average number of doctor visits. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6852. Percentage with self-perceived poor health status, with States grouped by percentage of nonblack population in

central cities. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6853. Percentage with perceived poor health status, with States grouped by percentage of black population in

central cities. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6954. Robustness of the generalized synthetic estimator. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7055. State-specific mean square errors for number of doctor visits, using the synthetic estimator, with States grouped by

percent nonblack population in central cities. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7056. State-specific mean square errors for percentage with perceived poor health using the generalized synthetic estimator

with 16 balanced variances, with States grouped by percentage of black population in central cities. . . . . . . . . . . . . . . . 7057. State-specific mean square errors for percentage with perceived poor health using the synthetic estimator, with

States grouped by percentage of nonblack population in central cities. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7158. Reduction in estimate of the MSE of the generalized synthetic estimator (16 subgroups) relative to

the MSE of the synthetic estimator for percent with perceived poor health. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7159. Initial cluster sizes (in terms of placeholders). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8660. Percent of initially selected strings to be retained for actual listing (all others are put aside). . . . . . . . . . . . . . . . . . . . . . . 8661. Sample sizes for elderly minorities. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8862. Oversampling rates for the beta design (relative to a design that would yield 50,000 interviews). . . . . . . . . . . . . . . . . . . 9363. Percent of total sample reserved for alpha option to be retained for the beta option. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9364. Desired cluster sizes for beta design (in terms of placeholders). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9465. Percent of strings reserved for the alpha option to be retained for the beta option. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9466. Percent of listing lines reserved for NHIS within strings retained for the beta option that are actually to be screened

for the beta option. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9467. Persons in screened households for the beta option. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9568. Percentage of other households discovered during screening to be kept for full interviews, beta option. . . . . . . . . . . . . . . 9569. Effective sample sizes for beta design when all household members are sampled. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9670. Effective sample sizes for beta design when one adult is sampled per household. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9771. Effective adult sample sizes for the year 2000 by redesign option and population domain relative to the 1988 NHIS

design when just one adult is sampled per sample household. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9872. Percent of sample originally selected that would be retained for an equiprobability design. . . . . . . . . . . . . . . . . . . . . . . . . 9873. Percentage of strings to keep in conjunction with post-listing subsampling for equiprobability design. . . . . . . . . . . . . . . . 9974. Percentage of listed HU’s to keep in retained strings in conjunction with string subsampling for equiprobability

design. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99

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ObjectivesThis report presents a major portion

of the research carried out for the1995–2004 redesign of the NationalHealth Interview Survey. The primaryfocus of the 1995–2004 NHIS redesignresearch was to explore sample designoptions to improve the reliability ofNHIS statistics for racial, ethnic,economic, and geographic domains.Another objective was to assess theintegration of the sample designs of theNCHS surveys of health care providersand NHIS.

MethodsA number of research tasks were

carried out by Westat, Inc., to explorethe feasibility and cost of varioussample design options for the1995–2004 National Health InterviewSurvey redesign. This report provides adetailed description of the research thatwas carried out.

ResultsThe research results presented in

this report were the primary basis forassessing the technical soundness andcosts for a number of designalternatives. The first option, called thealpha option, was developed under theassumption of a 50-percent datacollection budget increase. The secondoption, called the beta option, wasdeveloped under the assumption of nochange in the data collection budget.The beta option was the designimplemented in 1995.

Other important research itemsdescribed in this report includeoversampling methods for minoritypopulations and dual-frame samplingmethods. The research results in thisreport also led to additional research,primarily in the area of surveyintegration.

A more detailed description of the1995–2004 NHIS design will appear ina forthcoming NCHS Series 2 reporttitled Design and Estimation for the1995–2004 National Health InterviewSurvey.

Keywords: integrated survey designc model-based estimation c dual-frame estimation c network sampling

Executive Summary

IntroductionThe sample design for the Nationa

Health Interview Survey (NHIS) hastraditionally been revised following eacdecennial census. The redesigns servetwo purposes. The first is simply toupdate the sampling material throughthe use of data from the recentlycompleted census. The second is toredirect the sample toward new goals,making the NHIS responsive to theexpected needs of health statistics in thfollowing decade.

The timing of the planning andintroduction of the revised sample iscoordinated with similar revisions theU.S. Bureau of the Census makes for iother demographic surveys, includingthe Current Population Survey, theAmerican Housing Survey, the Surveyof Income and Program Participation,and the National Crime Survey. Thecoordination is necessary to ensure thahouseholds are not part of the samplemore than one survey; it is also anefficient way of selecting samples formultiple surveys. As a result, however,changing the design is an extremelycomplex task, even during the traditionpostcensus window.

The U.S. Bureau of the Censusrequires a 4-year lead time to changeprocedures for selecting blocks, thebasis for the area sample employed forNHIS. A 3-year lead time is required tochange primary sampling units (PSU’s)and an additional year is needed to listthe housing in sample blocks and selecthe households that will be asked toparticipate in NHIS. Cuts in sample siz

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can be made much more quickly,usually in a matter of months, providedthat they follow certain guidelines, butexpansions take at least 2 years—longefor expansions that go beyond limits seduring the redesign. This problem canbe solved by an initial selection of avery large sample, but there aresubstantial financial costs to be bornefor selecting more sample than isactually needed. Because of thecoordination requirement, the 1995redesign is the only opportunity that theNational Center for Health Statistics(NCHS) will have to make fundamentalchanges in the sample design of NHISat a reasonable cost until the year 2005

NCHS put considerable effort intodebating and consolidating ideas aboutthe most critical objectives for NHISstatistics. A number of objectives wereidentified—more than it appeared likelycould be simultaneously realized, givenbudget prospects. Westat, Inc., wasawarded a contract to assist in thedevelopment of a new design that woulmeet these objectives to the greatestextent feasible, given various fundingassumptions and revised priorities. Worto be done by Westat, Inc., consisted ofthe statistical and cost aspects of sampdesign options. The contract did notinclude any provision for research onthe instrument content or design. WestaInc.’s, full final report synthesizes 4years of research, meetings, and 119technical memorandums (a list of theirtitles is included as appendix 1) on themost efficient sampling methods forachieving the objectives. From thisresearch a proposed design for 1995 wdeveloped. In case budgets did notpermit the introduction of the

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Page 2 [ Series 2, No. 126

proposed design, a backup design wasalso developed.

This executive summary brieflyreviews the objectives and the methodsstudied. More discussion is devoted todescribing the proposed design anddiscussing the extent to which it meetseach of the objectives. For more detailthe reader is referred to the full report.

Research ObjectivesThe highest priority requirements

for the redesigned NHIS, as specified bNCHS, were:

+ Improved precision of healthstatistics about demographicallydefined domains, includingspecifically those defined by theinteraction of age and sex with raceethnicity, and poverty status.

+ Improved precision of healthstatistics for subnational geographicareas—in particular, States.

+ Decreased bias by eliminating proxyreporting.

+ An ability to quickly and radicallychange the size of NHIS in responsto funding opportunities andcutbacks.

+ Improved analytic potential and/orcost reduction through linkage withthe provider surveys sponsored byNCHS.

+ A continued ability for NHIS toserve as a vehicle for follow-onsurveys such as the National Surveyof Family Growth (NSFG).

Research Methodology

Improved Precision forDemographically DefinedDomains

A wide range of techniques wasstudied that might be used to improveprecision for racial and ethnic minoritiesand other demographically defineddomains. These techniques includedoversampling geographic areas withstrong concentrations of targeteddomains, screening a general sample toidentify members of the targeteddomains (and subsequently subsamplinothers), sampling from lists such as the

Medicare list, network sampling (whermembers of the targeted domains areasked to nominate their relatives forinclusion in the sample), and usingsimple techniques such as carryingsample cases of targeted domains ovfrom 1 year to the next or aggregatingstatistics across years.

Important considerations in thesestudies were the prevalence of thedomain, the extent of geographicclustering of domain members, thecontribution of each stage of samplingto the variance for a domain, coveragof lists of domain members, thevariances and potential biases ofnetwork sampling, and the power ofmultiyear estimates, given that eachyear’s sample is not independent ofother years. Another importantcomponent of this work was theprojection of costs for alternativedesigns. An attempt was made to forman extremely general cost model baseon interviewer time sheets for NHISfrom the U. S. Bureau of the Census,but most cost projections were actualdone in a more informal manner, baseon information supplied by personsexperienced in the management ofsurvey operations.

To study the prevalence andgeographic clustering of each domaindata were examined from the NHISitself, the 1980 and 1990 decennialcensuses, and demographic models opopulation growth from the U. S.Bureau of the Census. To study thecontribution of each stage of samplingan analysis of components of variancwas conducted on the 1988 NHIS witnew methodology. The main listsconsidered were the master beneficialists maintained by the Social SecurityAdministration (SSA) and Health CareFinancing Administration (HCFA) foradministration of Social Security andMedicare. Most of the work done onnetwork sampling of rare domains waconceptual in nature withrecommendations for further researchThe power of multiyear estimates andcarryover procedures was based on twork on components of variance andU. S. Bureau of the Census data on thfrequency of residential moves byminority persons.

Improved Precision forGeographically DefinedDomains

This research started from ananalysis by NCHS staff (1) indicatingthat State-level statistics from NHIShave very poor precision for mostStates. Based on U. S. Bureau of theCensus projections of State populationsin the year 2000 (the midpoint of theperiod in which the redesigned samplewould be used), estimates were madethe size of sample supplements thatwould be needed to improve theprecision of State-level statistics to moracceptable levels. These estimates werprepared under the assumptions that thsupplemental samples would be of thetraditional area-permit type and thesamples would be obtained by means orandom digit dialing (RDD). Variousgroupings of States for which acceptabprecision could be more easily obtainedwere also studied. Finally, there was anintensive study of the potential ofvarious small-area techniques, includinmodel-based statistics.

Decreased Bias Because ofProxy Reporting

Work by NCHS staff had identifiedproxy reporting as a major cause ofresponse bias. The size of the bias tento vary among items but appears to beparticularly large for the more subjectivitems, such as self-perceived healthstatus. Obtaining response rates nearlyas good as the current response rateswhile disallowing proxy response isquite expensive. To offset the increasedcost of this requirement, the idea wasput forward to interview just onerandomly chosen adult per family.Although the merits of this approachwere mostly outside the scope of thisresearch, the potential that this systemwould be imposed conditioned all theother research and required study ofdesign options that assumed all adultswould be interviewed in each samplehousehold and that just one adult woulbe interviewed per family within thehousehold.

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Series 2, No. 126 [ Page 3

Flexibility for Rapid SampleExpansions and Contractions

Research on rapid sampleexpansions consisted mostly ofdiscussions about the cost of preparingand maintaining extra samples. The taof a rapid expansion is not as easy asseems it should be. Given requiremenfrom the Office of Management andBudget for minimization of responseburden, the complexity of the designsother current surveys at the U. S.Bureau of the Census, and the NHISrequirement for no recourse to decenncensus address registers, issues ofcoordination are extremely complex.Also, there are costs for fieldwork doneto keep a sample ready to use (even iis never used), for example, collectingbuilding permit information fromselected building permit offices. Finallyit is difficult to select additional sampleefficiently for data collection instrumentand procedures that have not beendesigned yet.

Rapid contractions are considerabeasier to design. Research in this areafocused on alternate panel constructiomethodologies.

Integrated Survey Design

Integration of the NHIS design withthe design for the National Health CareSurvey (NHCS), an umbrella term for acollection of independent surveys ofhealth care providers was an importanissue for the NCHS staff. There wasinterest in the possibility of enhancedanalytic strength from an integrated seof surveys and the possibility ofoperational synergies that might lead tlower overall cost for the integratedsystem.

Research in this area focused mosstrongly on the implications ofintegration at the PSU level, where allthe surveys would be located in thesame set of PSU’s or where the NHCSPSU’s were at least nested within theNHIS PSU’s. Given the paucity ofcounty-level health and health care dathe research that could be conducted ithis area was limited. The research thawas done focused on the compatibilityof optimal PSU-level measures of sizefor the various surveys and on the

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implications for the provider surveys otwo-stage versus three-stage design.former work was based on tabulationsof the National Master FacilityInventory and other NCHS sources.Similarity of the set of PSU’s to beincluded with certainty for each surveywas closely examined. Also, thecorrelation in the measure of size fornoncertainty PSU’s was examined. Thlatter set of work was based on NationHospital Discharge Survey (NHDS) anNational Ambulatory Medical CareSurvey (NAMCS) data. The NHDSwork was based on a review of internNCHS documents. The NAMCS workinvolved the calculation of componentof variance using new methodology.

Follow-on Surveys

A number of NCHS surveys userespondents from NHIS samples fromprevious years as screening samplefind domains of special interest forintensive interviews on topics thatcannot be included in NHIS becauseof time or other constraints. The besknown example of this sort ofsampling approach is NSFG. Theflexibility to be able to use retiredNHIS samples for this purpose wasthe reason for the switch in 1985 froan NHIS sample based on a mix ofdecennial census address registers,manual block listing in areas withpoor quality registers, and newconstruction building permits, to asample based only on manual blocklisting and building permits. The cosof this switch was far from trivialbecause of the requirement for muchmore listing. This extra listing costcontinues to be a factor for the 1995redesign. To preserve the benefits othis expense for extra listing, a factoin all discussions about the NHISdesign was the potential impact ofdesign changes on these follow-onsurveys. Particular tension was notebetween the large numbers of sampPSU’s needed for improved precisiofor State estimates and the smallnumber of sample PSU’s usuallyneeded for follow-on surveys.

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Major Findings andRecommendations

The major outcome of this contracwas a new recommended design forNHIS. This recommended design islabeled the ‘‘alpha option.’’ Thespecifications for this design floweddirectly from the research conducted bWestat, Inc., with several iterations ofrefinement in response to feedback froNCHS and the U. S. Bureau of theCensus. The alpha option is predicatedon the assumption of an ongoing budgfor 1995 and beyond that is 50 percenlarger than would be required to sustathe current design at a strength of50,000 completed household interviewper year. Because the current NHIS habeen running close to 47,500 completehousehold interviews per year, thisimplies a need for an increased budgeon the order of 58 percent. If anadditional option regarding elderlyminority groups is exercised, the totalbudget increase becomes about62 percent. There is also the assumptithat 1994 monies can be found for theextra listing of blocks and recruiting antraining of interviewers required by thealpha option.

Recognizing the uncertainties of thFederal appropriation process, a ‘‘betaoption’’ was also identified that wouldhave the same long-term costs as thecurrent design with 47,500 annualhousehold interviews. NCHS set thegoals for the beta option of maintainingcurrent precision for statistics for blackpeople; and improving precision onstatistics for Hispanic persons enoughachieve parity with those for blackpersons.

Improved Precision forDemographically DefinedDomains

The alpha option responds to all othe goals of the 1995 design to someextent. The primary achievement of thidesign, however, is to providesubstantial improvements in theprecision of statistics about the blackand Hispanic populations. Theimprovements for Hispanic persons arparticularly strong. The alpha optionprovides considerable improvement in

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the precision of statistics on the blackand Hispanic populations at the cost ofprecision for other demographicallydefined domains. The tradeoff is sharpefor the beta option because it improvesthe precision of statistics on Hispanicpersons and holds the precision ofstatistics on black persons the same, aat current budget levels.Table Aquantifies these projected precisionchanges in terms of changes in effectivsample sizes. (The effective sample sizshown here correct the raw sample sizfor the design effects resulting fromdifferential sampling rates across theminority density strata and across adulin households of different sizes.) Thesample sizes do not add up across thedomains because of different designeffects. The deterioration in precision fototals and for other categories waswithin a zone with which NCHS stafffelt comfortable.

Statistics on the Black and HispanicPopulations

These improvements in theprecision of statistics for the black andHispanic populations were achieved bya mix of two techniques: Stratificationon minority density withnonproportional allocation, andscreening. Stratification on minoritydensity with nonproportional allocationmeans that blocks are stratified by thepercentage of the local population thatblack and by the percent that isHispanic, and that a disproportionateshare of the initial sample is selected inthe strata with high black and/orHispanic density. Screening means thabrief set of questions is firstadministered to each household todetermine whether any of the residentsare black or Hispanic; if the householdis black or Hispanic, the household isretained with certainty for a fullinterview; otherwise, the household isonly retained for a full interview with aprobability less than one.

Table A. Effective adult sample sizes by redesign

Option

1988 NHIS . . . . . . . . . . . . . . . . . . . . . . .Alpha option . . . . . . . . . . . . . . . . . . . . . .Beta option . . . . . . . . . . . . . . . . . . . . . . .

1Budget for 1988, adjusted for inflation, equals 100.

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The stratification and oversamplingby minority density is a technique thatwas also used for the 1985 design, albto a lesser extent. For that designenumeration districts and block groups(higher levels of geographic units thanblocks) were stratified by percentage othe local population in 1980 that wasblack. Also, the degree of oversamplingof the strongly black areas wasconstricted (by plans for the formationof interviewer assignments) in such amanner that there was almost nooversampling in rural southern PSU’swith strong black concentrations. Thenew design employs finer geographicdetail and an alternate plan for buildinginterviewer assignments to make theoversampling much more efficient. Thenew design also focuses more onHispanic density than on black density,given the fact that the Hispanicpopulation is currently smaller than theblack population in the United States.

Screening is a technique that hasbeen used at private survey researchfirms for many years but has beenemployed only once at the U. S. Bureaof the Census (for the Survey of Incomand Education in the 1960’s). Given thlong length of time since the last use oscreening at the U. S. Bureau of theCensus, the Bureau decided to conducfield test of screening procedures. Thisfield test, conducted by the Bureau in1993 with very favorable results,focused on a particularly sensitiveaspect of the plan for screening (thedecision to contact neighbors to keepresponse rates high and costs low).

The beta option also leads toimprovements for statistics on Hispanicpersons, but not as much as the alphaoption. It provides this improvementwith no change in the precision ofstatistics on the black population andwith no increase in cost. As alreadynoted, the beta option provides for eveworse precision than the alpha optionfor the population that is neither black

option and population domain relative to the 1988 N

Budget1 Total Blac

100 35,600 4,50158 +8% +97%100 –12% +4%

nor Hispanic, although the difference inthis area between the alpha and betaoptions is minor.

An alternative with either the alphaor the beta option is to supplement thearea-permit sample with a sample ofblack and likely Hispanic SSAbeneficiaries. (The word ‘‘likely’’ is usedbecause the SSA files do not actuallyhave a variable that identifies Hispanicpersons. Research has shown, howevethat there is a set of surnames that isassociated strongly enough with beingHispanic to serve as the basis forsampling.)Table Bshows the increasesin the effective sample sizes for elderlyblack and Hispanic persons that couldbe achieved with supplements from SSlists of the indicated sizes.

Other Demographically DefinedDomains

Besides the black and Hispanicpopulations, the other domains ofinterest that were studied include Asianand Pacific Islander Americans,American Indians, Eskimos, and Aleuts,the poor, the rural population, teenagersthe extreme elderly (85 years of age anover), and Hispanic subgroups such asPuerto Ricans.

A study of 1990 decennial censusresults indicated that the Asian andPacific Islander population is toodispersed for oversampling to be veryeffective and too small for substantialimprovement via screening to bepractical. The same conclusion appliesto the Native American population. Itwas noted, however, that if interest werfocused solely on the Native Americanpopulation living on reservations,trustlands, tribal jurisdiction statisticalareas, and native villages, the relativelyfew numbers of other racial and ethnicgroups living in these areas would makscreening highly feasible.

With improved precision forestimates on the black and Hispanicpopulations, there is no question that th

HIS design

k Hispanic Other

0 2,200 30,900+299% –4%+113% –21%

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Table B. Sample sizes for the black and Hispanic populations, aged 65 and older

Domain Desired

Effective sample sizes

Projectedunder alpha

option(one household)


option(all household)

With extra2,800 cases

to be screenedfrom SSA

(one household)

With extra5,800 cases

to be screenedfrom SSA

(one household)

Black

Male . . . . . . . . . . . . . . . . . . . . . . . . . . . 1,000 500 1,150 1,000 1,000Female . . . . . . . . . . . . . . . . . . . . . . . . . 1,000 740 1,710 1,000 1,000

Hispanic

Male . . . . . . . . . . . . . . . . . . . . . . . . . . . 1,000 350 910 770 1,000Female . . . . . . . . . . . . . . . . . . . . . . . . . 1,000 495 1,270 920 1,000


alpha option would also improve theprecision for statistics about the poorblack and poor Hispanic populations,because the oversampling of stronglyblack and/or Hispanic neighborhoodsmeans oversampling many of theNation’s poorest neighborhoods.Precision for other poor Americans,however, will be worse after theredesign. The effect on precision forstatistics on all poor persons has notbeen quantified but is probably slight.

For statistics on the ruralpopulation, it was noted that there are anumber of competing definitions of theterm. The U.S. Bureau of the Censusdefinition of rural applies at the blocklevel. Rural areas exist withinmetropolitan areas, and urban areas exiwithin nonmetropolitan areas.Population counts per block arenecessary for the classification of areasas urban or rural under this definition.Because such counts are only collectedin the decennial census, statistics aboutrural areas become outdated fairly soonafter each census. The U.S. Departmenof Agriculture has several definitions ofrural counties, but none of these seemeto reflect NCHS needs. On consideratioof the various alternatives, the NCHSstaff decided that its analytic needswould be met best by compiling data fornonmetropolitan areas rather than for thareas encompassed by one of thedefinitions of a rural area.

Although precision for NHISnonmetropolitan statistics is alreadyfairly good, it was decided to furtherimprove the precision by increasing thetotal number of PSU’s for NHIS from198 in the 1985 full design to 358 in the

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1995 full design. Also, it was decidedthat metropolitan status should be astrict stratifier in the selection of PSU’sand that at least one nonmetropolitanPSU would be selected from each of th50 States (except New Jersey, which isall metropolitan). These improvementswould be counterbalanced to someextent; however, because theoversampling of the black and Hispanicpopulations would tend to shift samplepersons from nonmetropolitan areas tometropolitan areas. Also, the reductionin the total number of completedinterviews (to pay for the screenerinterviews) and the design effect due todifferential sampling of black, Hispanic,and other persons would tend to degrathe precision of nonmetropolitanstatistics. The net effect of these changis that, although there would be morenonmetropolitan PSU’s in the sample inthe 1995–2004 decade than during the1985–1994 decade, the total number ononmetropolitan interviews would bereduced.

With regard to teenagers, a planwas considered that would haveinvolved screening for age andretaining households with members intargeted age domains at higher ratesbut this plan was rejected. At any ratethis plan was only meant to providefor oversampling of the black andHispanic populations in targeted agedomains. At the planned budget levelan important increase in the samplesize of teenagers could only beachieved by scrapping theoversampling of densely black andHispanic blocks. This goal (teenagestatistics) was essentially put aside.

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Oversampling of the extremelyelderly population would be quiteefficient with Medicare or SSA lists.However, there was no way ofaccomplishing this goal whilesimultaneously achieving competingneeds for precision. The main reasonthat this group is predominantly white,and statistics on white people, ingeneral, will be less precise with the1995 design than with the 1985 design

With regard to Hispanic subgroupsit was realized that however desirablethis goal might be in light of thediversity of the Hispanic population, itwould simply require more funds thanNCHS foresees being available.Model-based techniques wereinvestigated that might involve specialsamples in only blocks with very highconcentrations of the targeted subgroubut the investigation was inconclusive.

Improved Precision forGeographically DefinedDomains

The 1995 design will provideimproved flexibility for samplesupplements that would lead toState-level statistics with acceptableprecision, but the design will not byitself provide such statistics for morethan three States—none if theone-adult-per-family rule is used forinterviewing. This flexibility wasachieved by stratifying the PSU samplby State and by metropolitan status.This stratification will make it far easieto integrate the regular NHIS samplewith any State-specific sample. Also, aleast two PSU’s (one metropolitan and

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one nonmetropolitan) were selectedfrom each State that containsmetropolitan and nonmetropolitan areaHaving at least this marginalrepresentation in every State willprovide an anchor for State supplemenwhether such supplements are selecteby RDD or by area-sampling methodsIt also enhances the prospects formodel-based State-specific estimates;intensive study of techniques for suchestimates, however, left us ratherdubious about long-term prospects inthis area.

Decreased Bias by Eliminationof Proxy Reporting

The alpha option does providestrongly improved precision for theblack and Hispanic populationscompared with the 1985 design,assuming that in both cases theone-adult-per-household rule is used finterviewing. However, there is still anet decline in the precision of thesestatistics comparing the old design witall adults interviewed in each samplehousehold to the new design with onlyone adult interviewed in each samplehousehold. The decision of how manyadults to interview in each samplehousehold is independent of the otherdesign features. The one-adult-per-household rule has already been usedfor a number of NHIS questionnairesupplements in the 1985 design.Conversely, there is nothing in thedesign proposed for 1995 that isinconsistent with interviewing all adultsin every sample household. It is thusapparent that the one-adult-per-household rule should be structured inway to at least obtain a limited set ofcore data for all household members.

Flexibility for Rapid SampleExpansions and Contractions

The new design does not reallyprovide improved flexibility for rapidexpansions. The cost of carrying areserve sample was judged by NCHSbe too great. However, general systemchanges being made at the U. S. Bureof the Census for all of its currentsurveys will make such expansionseasier. Nonetheless, 2–4 years will stil

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be needed for expansions. Rapidcontractions are easier. The new desighas a more flexible panel structure thawas used previously. It allows cuts ofwhole PSU’s by sixths, in addition tocuts by fourths. Of course, there is stillthe same capability for across-the-boarcuts (where there is a small reductionthe sample size in each PSU), althougit is well known that such cuts savevery little money.

Integrated Survey Design

The main conclusions in this areawere with respect to integration at thePSU level. One product of the researchhowever, was a fairly detailed plan forhow network sampling theory could beused to provide unbiased estimates forprovider surveys when the providersample is based on those providersidentified in NHIS interviews. Thedifferential sampling of black, Hispanic,and other people makes estimation witsuch a network-based approach morecomplicated, but it was demonstrated tbe theoretically possible. More researcon this topic is being done under asuccessor contract (2).

With regard to PSU’s, oneconclusion was that the PSU’s shoulddefined much as they have always beeconsisting of counties, groups ofcounties, metropolitan statistical areas(MSA’s) and New England countymetropolitan areas (NECMA’s), with thprimary consideration for groupingcounties consisting of feasibleinterviewer travel. Plans wereconsidered for defining PSU’s in termsof health care commuting patterns, butthese plans were rejected as increasinwithin-PSU travel costs to anunwarranted extent.

Another conclusion was that themeasure of size for each PSU should bdefined in terms of total population, nosome measure of health care providedconsumed. Health care consumption wfound to be substantially correlated witpopulation. On the other hand, a type omeasure of size based on racial andethnic composition that would havebrought more heavily black andHispanic PSU’s into the sample wasrejected as being unnecessarily harmfuto the health care provider surveys tha

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do not, as a rule, oversample health caproviders according to the racial orethnic mix of their clientele.

Another conclusion was that thehealth care provider surveys could moslikely be restricted to a special subset othe NHIS PSU’s with little effect ontheir precision or cost, although such asubset would have to be defineddifferently than the subsets planned forreductions in the NHIS. This area is alsa focus of the successor contract formore research.

Follow-on Surveys

It was believed that the policy ofoversampling the black and Hispanicpopulations for the NHIS would also bebeneficial to such follow-on surveys asthe NSFG. Even though some of thesesurveys have traditionally onlyoversampled the black population, it wathought that oversampling Hispanicpersons is becoming more important fomost demographic surveys. The largernumber of PSU’s and the smallernumber of interviews with householdsthat are neither black nor Hispanic mayreduce efficiency for some of thesesurveys, but it was believed that suchlosses were justified, given theimportance of improving the precisionof statistics on the black and Hispanicpopulations for NHIS and of creatingthe needed flexibility for Statesupplemental samples.

Part I.Introduction

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Chapter 1.Background on theNational HealthInterview Survey

The National Health InterviewSurvey (NHIS) is an ongoingsurvey of the population of the

United States on health issues. Thesurvey has been in continuous operatiosince 1957. NHIS data are obtainedthrough personal interviews of thecivilian, noninstitutionalized populationconducted by the U.S. Bureau of theCensus. The official goal for the samplesize traditionally has been around50,000 interviewed households per yeabut the actual number of households isusually smaller, sometimes muchsmaller, depending on funding.

Schedule and ContentThe interviews are conducted durin

the year in weekly samples (eachweekly sample consists of a probabilitysample of the population), so thatseasonal trends can be followed. Thecore interview focuses on nonfinancialaspects of health that householdrespondents usually know, for examplevisits to doctors, visits to hospitals,chronic ailments, and recent acuteconditions are all covered in the coreinterview. Financial data, such as annuhousehold income and whether theperson is covered by health insurance,are also collected. The collection of daon diet, the cost of medical care, anddetails of diagnoses and treatments isleft to other surveys conducted by theU.S. Department of Health and HumanServices. Each year supplements to thcore interview cover topics such assmoking behavior, knowledge aboutacquired immunodeficiency syndrome,and disabilities. Many of thesesupplements require a self-responserather than rely on a householdrespondent.

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Sample Design for 1985–94The first stage of the NHIS sample

consists of 198 multicounty PSU’s.These sample PSU’s were selected,using a stratified probability design,from a much larger set of PSU’s thatcovers the United States. Forstratification purposes, the PSU’s weregrouped before selection to ensure thathe chosen PSU’s would be broadlyrepresentative in terms of severaldemographic and economiccharacteristics. Some of these PSU’s aso populous that they were included inthe sample with certainty. These arecalled self-representing (SR) PSU’s. Thfull NHIS design contains 52 SR PSU’sThe remaining 146 PSU’s had a chancof not being selected. These PSU’s,which represent themselves and otherPSU’s that were not selected, are callenonself-representing (NSR) PSU’s.

Within each sample PSU, a sampleof blocks (or small groups of blocks)was selected as the second stage of thsample. In PSU’s in which the blackpopulation consisted of 5 to 50 percentof the total in the 1980 census, blocksenumeration districts (ED’s) with highblack populations were selected with ahigher probability than other blocks. Thselection was made in the early 1980’swith a planned, slow rotation throughblocks as they become exhausted. Forthe third stage, within each block, acluster of eight housing units (HU’s) isselected each year after fresh listing orupdating of potential residentialstructures. These HU’s are spreadthroughout the block as evenly aspossible.

To gain better control over the sizeof the sample, HU’s constructed sincethe 1980 census are selected throughsample of building permits rather thanthrough area sampling. These units areselected in clusters of four instead ofeight.

To provide continuous coverage ofthe population throughout the year, thesample of households is spread over th52 weeks of the year; each week’s

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sample is representative of the U.S.population. Each year a totally newsample of households is selected. Thoshouseholds tend to be neighbors of thehouseholds interviewed the previousyear, however.

As a result of the NHIS sampleredesign of 1985, it became possible foNCHS to transmit data on NHIS samplehouseholds to private contractors for usin conducting follow-on surveys. Before1985 a significant portion of the NHISsample was drawn from lists created fothe previous decennial census. In 1985all of the NHIS sample was obtained byarea and permit sampling, without anyrecourse to decennial census lists. Thefollow-on surveys are then said to belinked to NHIS. The confidentiality ofthe transmitted data is protected undersection 308(d) of the Public HealthService Act. This feature of the designwill have been well utilized between1986 and 1994 and is a feature thatNCHS wants to maintain. Thus,decisions about NHIS design must bemade with the needs of follow-onsurveys in mind.

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Chapter 2.New Objectives for1995 Redesign

For the 1995 survey, it was desireto improve the precision of basichealth statistics about various

domains defined in terms of race, ethnorigin, poverty status, and geography.Accompanying this desire was therealization that it would be difficult andexpensive to improve the precision ofstatistics for these domains whilemaintaining the precision of statisticsabout the prevalence of rare healthconditions. Accordingly, the sorts ofstatistics that were identified asimportant for domain analysis includeself-assessed health status, havingvisited a doctor in the prior year, havinbeen hospitalized in the past year,knowledge of AIDS, and smokingbehavior. Statistics about the prevalenof specific chronic conditions such ascancer and about the occurrence ofacute conditions such as automobileaccidents were given a lower priority.

Statistics for Racial,Ethnic, and EconomicDomains

The goal of this part of the researcwas to develop sampling methods forNHIS that would provide data ofacceptable precision for the following:

+ National estimates for race and/orethnicity, age, and sex subdomainsMore specifically, the subdomainsconsisted of the complete crossclassification of the following:

+ Four racial and/or ethnic groups(i.e., black persons, Hispanicpersons, Asians and PacificIslanders, and all others).

+ Males and females.+ Six age groups (i.e., under 5 years

5–17 years, 18–24 years, 25–44years, 45–64 years, and 65 yearsand over).

+ National estimates for the threemajor Spanish-origin subgroups ofHispanic persons in the United

States (i.e., Mexican-American,Puerto Rican, and Cuban-American)Sex and age subdomains for thesesubgroups were also desirable,although with more limited agedetail than for the broaderrace/ethnicity groups. Thesesubdomains consisted of under 18years, 18–44 years, 45–64 years, an65 years and over. As an alternativeto the production of data for thethree subgroups, sample designimplications were also examined fordata on all Hispanic persons in thethree geographic areas in which thesubgroups are concentrated (i.e., fivSouthwestern States, the greaterNew York City area, and Florida).

+ Statistics on the poor, where thepoor can alternately be defined ashaving incomes below the povertylevel, below 125 percent of thepoverty level, below 150 percent ofthe poverty level, or below someother cutoff.

Statistics for GeographicDomains

A second goal was to developmethods of producing State data by sexand three age groups: Under 18 years,18–64 years, and 65 years and over. Asan alternative to State data, thefeasibility of producing data for Stategroupings was to be explored. Asdescribed subsequently in this report, aconflict exists between achieving thisgoal and the improvement of race and/oethnicity statistics: Most members of theminority groups reside in the larger,urbanized States, whereas the smallerStates would require samplesupplementation to provide adequatelyprecise State data. Another conflictexists between this goal and therequirements of follow-on surveys.Improved precision for State datarequires the use of many more samplePSU’s than would usually be desirablefor a follow-on survey.

Integration With Surveysof Health Care Providers

The third set of issues to beexamined were those involved in

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integrating NHIS with surveys of healthcare providers. Some issues focused oanalytic and operational benefits ofusing the same set of sample PSU’s.Others focused on integration at theperson-event level.

Integration at the PSU level seemelikely to have operational efficienciesbecause both sets of surveys areconducted by the U.S. Bureau of theCensus. Integration might achievecertain synergies that could reducetravel and other expenses. It would alsprovide some analytic enhancements,because a relationship probably existsbetween the practices of health careproviders within a PSU and thehealth-related characteristics of theresidents of the PSU. Integration at theperson-event level is even moreinteresting because it allows directlinking of individual practices andoutcomes. Penalties for integration alsoexist, however. The optimal designs fordifferent surveys are not necessarilyidentical. A major goal of this researchwas to determine the relative advantagand disadvantages of the differentlinkage possibilities.

Reducing Response ErrorsThrough Greater Use ofSelf-Response on the CoreInterview

Prior research (3) had shown thatproxy response has a significant impacon a number of statistics, includingthose for health status. That impact isparticularly strong for men. Apparently,wives (the typical proxy respondents)tend to assess their husbands’ health abetter than the men themselves do.Although this bias is not very troublingfor tracking health statistics over time(under the reasonable assumption thatthe bias due to proxy response is fairlystable over time), it is very worrisomefor one-time questionnaire supplementsthat are designed to compare populatiodomains. Unfortunately, obtainingself-response for all sample persons isconsiderably more expensive than thepolicy of requesting self-response butaccepting proxy response whenobtaining self-response presentsdifficulties. The most common difficulty

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is failing to find all household membershome at the same time. To keep the cper interview under control whereself-response is necessary, plans forrejecting proxy response involve theinterview of just one randomly chosenadult per household. If the designatedadult cannot be interviewed byself-response, the household is classifias a noninterview. This procedure iscurrently used for most NHISquestionnaire supplements. It is verylikely that the number of supplementsrequiring self-response will be increasein the future. This increased number wmake it necessary to reduce the lengthof the core interview to restrain costsand to minimize response burden. Withthis idea in mind, almost all of theresearch on how to improve theprecision for selected domains wasrepeated under two assumptions: Thatdata are collected on all householdmembers as is currently the case withthe core instrument; and that data arecollected on only one random adult pehousehold (and possibly on all childrenin the household).

Part II.Statistics for Racial, Ethnic, andEconomic Domains

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Table 1. Items selected for direct variance estimation

Statistics based on nonbinary person-level data about the prior 2 weeks

Average number of doctor’s visitsAverage number of bed daysAverage number of chronic conditions

Statistics based on binary person-level data from the full sample

Percent reporting poor health statusPercent reporting at least two hospitalizations in the past yearPercent reporting at least one doctor’s visit in the past yearPercent unable to carry on major activityPercent with more than 5 years since last doctor’s visit

Statistics based on binary person-level data from one-sixth of the sample

Number with chronic arthritisNumber with color blindnessNumber with chronic epilepsy

Statistics based on (almost) binary person-level data about the prior 2 weeks

Number of acute digestive system conditionsNumber of acute bronchitis conditionsNumber of acute urinary conditionsNumber injured in moving motor vehicle accidents


Chapter 3. CurrentPrecision

To determine the level of precisionin the 1985–94 NHIS and thesample size necessary to achiev

satisfactory levels after the redesign, itwas necessary to examine the currentNHIS variances. Furthermore, estimateof variance components would indicatehow any necessary increases in samplsizes should be implemented. An earlyresearch step therefore was to calculatvariances for the current NHIS design.

Variances in CurrentDesign

As a guide to the reliability that canbe achieved with the multistage sampledesign and estimation procedure usedNHIS, Westat, Inc., computed estimateof the sampling variances in 1988 for agroup of variables collected in NHIS.The methodology is explained in greatdetail in National Health InterviewSurvey: Report on Variance Estimation(4), which is also listed as RedesignMemorandum #40 inappendix 1. Inbrief, a variation due to Fay on balancerepeated replications was used toestimate the variances, both total andwithin PSU.

The variables covered a range ofitems, and variances were calculated fostatistics relating to the total populationand to the age categories. The 15 itemselected from NHIS for direct varianceestimation are listed intable 1. Theyrepresent several parameter sets ofcharacteristics in NHIS. The first set ofitems is based on nonbinary person-levdata; that is, the measurement variableat the person level can take on valuesother than 1 (yes) and 0 (no). It is alsoimportant to note that within the first seof items, the reference period for therespondent is the 2 weeks preceding thinterview. Because the NHIS sample isspread out over the 52 weeks of theyear, only 1/26th of the eventsexperienced by a person within a yearcan be reported.

The second set of items is based obinary person-level data or recodes of

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multinomial responses into simple yesand no categories. The reference periofor these questions is either irrelevantit is a year.

The third set of items is composedof counts of chronic conditions reportedby targeted respondents. Six differentflash cards of chronic conditions areused in NHIS. The sample is dividedinto (nearly) equal sixths, and each sixis shown a different prompting flashcard. Thus, these statistics are based oonly one-sixth of the full sample.

The fourth set of items consists ofcounts of acute conditions that haveafflicted the respondents within theprevious 2 weeks. Although a fewrespondents report multiple attacks ofthe same acute condition within the2-week reference period, this questionbasically binary, that is, either theperson has experienced the condition ithe last 2 weeks or has not experienceit. The varying reference periods andsampling fractions have importanteffects on reliability, as will be seenpresently.

Variances of these items wereestimated from the 1988 NHIS forseveral subdomains. The subdomainsthe two-way classifications involvingage and sex, race, census region, orcensus division. Age is classified into

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six categories: Under 5 years, 5–17years, 18–24 years, 25–44 years, 45–6years, and 65 years and over. Sex hastwo classifications: Male and female.Race is categorized twice: First, in threclassifications—Hispanic, black, but noHispanic, and others; and second, in twclassifications—Asian or Pacific origin,and others. There are four censusregions and nine census divisions.

Appendix 2shows the estimatedvariances. For each item, there are threpages of estimates and coefficients ofvariation for breaks by the variousdomains. (Thecv, or coefficient ofvariation, is the ratio of standard error tmean, expressed as a percentage.) Fothose subdomains where the estimatedmean is 0, thecv is given as 0, eventhough thecv is undefined. Thecv’svary widely. For example, thecv on thepercent of the total population reportingat least one doctor visit in the past yeais just 0.27 percent, but thecv on thenumber of moving motor vehicleinjuries to young adults between 18 an24 years of age in the West NorthCentral Census Division is117.04 percent.

Because it was evident that therewas considerable instability in thevariance estimates, the decision wasmade to generalize some of them.

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Variance generalization is a techniquefor improving the stability of varianceestimates that is based on the fact thatthe variance of the mean of a set ofindependent and identically distributed(iid) Bernoulli random variables isfunctionally related to the commonmean of the variables.

In complex surveys many statisticsof interest are means of Bernoullirandom variables that are not quiteindependent and not quite identicallydistributed. Experience has shown thatthe variance of these means can oftenwell predicted by functions of theoverall mean of the variables (5). Infact, if there are few degrees of freedofor direct variance estimates, the meansquare error of the generalized variancestimates can be lower than that of thedirect variance estimates (6). Variousattempts have been made over the yeato develop similar models for statistics

Table 2. Coefficients of variation for selected NHIS

Estim

Publis

Quantitative items based on 2-week data

Doctor visits per person . . . . . . . . . . . . . . . 5.4Bed days per person . . . . . . . . . . . . . . . . . 6.3Conditions per person . . . . . . . . . . . . . . . . .

Rates based on full sample

Percent with poor health . . . . . . . . . . . . . . . 0.0Percent with 2 or more hospitalizations . . . . . . 0.0Percent with no doctor visits . . . . . . . . . . . . 0.2Percent with 1 or more doctor visits . . . . . . . . .Percent unable major activity . . . . . . . . . . . . 0.0Percent 5+ years last doctor visit . . . . . . . . . 0.0

Generic full sample items

p = .01 . . . . . . . . . . . . . . . . . . . . . . . . . .p = .1 . . . . . . . . . . . . . . . . . . . . . . . . . .p = .2 . . . . . . . . . . . . . . . . . . . . . . . . . .p = .5 . . . . . . . . . . . . . . . . . . . . . . . . . .

Rates based on 1/6 sample

Percent with chronic arthritis . . . . . . . . . . . . 0.1Percent with chronic color blindness . . . . . . . 0.0Percent with chronic epilepsy . . . . . . . . . . . . 0.0

Rates based on 2-week data

Percent with acute digestive condition . . . . . . 0.0Percent with acute bronchitis . . . . . . . . . . . . 0.0Percent with acute urinary condition . . . . . . . . 0.0Percent injured in motor vehicle accident . . . . 0.0

. . . Category not applicable.1Some domains have population estimates with zero variance due

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that are not sums of Bernoulli variablesbut these have generally beenunsuccessful (and thus, not published)Accordingly, for this report, of the itemsshown intable 1, generalized varianceestimates were only prepared for thesecond, third, and fourth sets of itemSeeNational Health Interview Survey:Report on Variance Estimation(4) fordetails of how the generalizedvariance estimates were prepared forthis study.

Table 2contains a variety ofestimatedcv’s for the selected variablesin total and by age. The table shows thcv’s as the following:

+ Calculated from generalizedvariance functions published byNCHS.

+ Calculated from generalizedvariance functions fitted by Westat,Inc., when the domain to which astatistic is restricted is subject to

items: 1988

ates Coefficient of variation (%)

hedWestat, Inc.

tabulated

Frompublished

curves

From new curvesDirect

byBRR

Controlledmargin1

Interiorcell1

00 3.863 . . . . . . . . . 0.900 5.344 . . . . . . . . . 2.0. . 4.298 . . . . . . . . . 0.7

27 0.027 2.4 1.7 2.0 2.016 0.016 3.2 2.3 2.6 2.733 . . . 1.0 . . . . . . . . .. . 0.747 . . . 0.2 0.2 0.340 0.040 2.0 1.4 1.6 1.536 0.034 2.1 1.5 1.8 2.0

3.5 2.9 3.31.2 0.9 1.00.8 0.6 0.70.4 0.3 0.3

30 0.126 2.2 1.9 2.0 2.212 0.011 6.7 6.6 7.0 6.904 0.004 11.6 11.5 12.1 12.0

63 0.063 6.9 6.0 6.0 6.934 0.034 9.3 8.4 8.4 7.828 0.028 10.1 9.2 9.1 10.017 0.017 12.5 11.9 11.7 13.7

to poststratification. Such a domain is referred to as a controlled marg

poststratification (e.g., thepercentage of females who reportpoor health).

+ Calculated from generalizedvariance functions fitted by Westat,Inc., when the domain to which astatistic is restricted is not subject topoststratification (e.g., percentage ofthose with low income who reportpoor health).

+ Estimated directly with Fay’s variantof balanced repeated replication(BRR).

Poststratification is an estimationtechnique used to bring survey estimatesinto conformity on certain dimensionswith known or more accuratelyestimated population totals. NHISestimation routinely involvespoststratification on age, sex, and race.

The generalized variances aresomewhat lower than the ones reportedin the published NHIS reports. After

cv’s (%) for limited age ranges direct by BRR

Age0.5

Age6–17

Age18–24

Age25–44

Age45–64

Age 65years

and over

1.7 1.8 2.4 1.8 2.3 2.33.6 2.7 3.7 2.5 3.6 4.12.1 1.6 1.8 1.1 1.2 1.3

16.1 13.9 15.6 4.6 3.4 2.49.6 11.5 9.4 4.7 4.5 4.1

0.4 0.6 0.7 0.4 0.5 0.413.0 11.6 6.3 3.3 2.3 2.6

undefined 6.6 6.2 2.7 3.1 4.1

100.4 32.3 18.7 5.1 3.2 3.3undefined 17.0 27.2 10.4 12.6 17.1

40.5 33.1 44.0 21.0 21.1 28.9

17.4 12.7 16.9 12.1 16.2 18.016.5 17.6 35.8 15.7 26.0 27.144.9 38.7 30.6 14.7 19.8 18.999.9 28.2 28.0 20.6 34.6 46.1

in. Other domains are referred to as interior cells.

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discussions were held with NCHS staffthe differences were ascribed to the mof items that formed the basis for thegeneralization process. It was agreedthat the new set of items was morerepresentative of the sets of items in thpublished reports than the ones used ithe NCHS computations of generalizedvariance functions. It was thus decidedto use the new generalized variancefunctions in the decision-making procefor determining required sample sizes.

Normally in a sample survey, thecvdepends mainly on the statistic (i.e., thvalue ofp) and the size of thesubdomain for which the statistic isestimated. However, in NHIS thecv’salso differ sharply among the threeclasses of statistics into which the ratehave been classified intable 2; ratesbased on the full sample, those basedthe one-sixth sample used for chronicconditions, and rates based on 2-weekdata. (The 2-week data can be

Table 3. Domain-specific required sample sizes fo

Quantitative items based on 2-week data

Doctor visits per person . . . . . . . . . . . . . . . . . .Bed days per person . . . . . . . . . . . . . . . . . . . .Conditions per person . . . . . . . . . . . . . . . . . . .

Rates based on full sample

Percent poor health . . . . . . . . . . . . . . . . . . . . .Percent with 2 or more hospitalizations . . . . . . . . .Percent with no doctor visits . . . . . . . . . . . . . . .Percent with 1 or more doctor visits . . . . . . . . . . .Percent unable major activity . . . . . . . . . . . . . . .Percent 5+ years last doctor visit . . . . . . . . . . . .

Generic full sample items

p = .01 . . . . . . . . . . . . . . . . . . . . . . . . . . . . .p = .1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . .p = .2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . .p = .5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Rates based on one-sixth sample

Percent with chronic arthritis . . . . . . . . . . . . . . .Percent with chronic color blindness . . . . . . . . . .Percent with chronic epilepsy . . . . . . . . . . . . . . .

Rates based on 2-week data

Percent with acute digestive condition . . . . . . . . .Percent with acute bronchitis . . . . . . . . . . . . . . .Percent with acute urinary condition . . . . . . . . . . .Percent injured in motor vehicle accident . . . . . . .

. . . Category not applicable.1Because of an observed p = 0, the estimated required sample siz

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considered as approximately based on1/26-sample of NHIS.) For the samevalue ofp and the same subdomain, thcv’s for the three classes of items reflethe different effective sample sizesresulting from the subsampling forcondition items and 2-week data.

Desired levels of precision forblack; Hispanic; Asian andPacific Islander; AmericanIndian, Eskimo, and Aleutpopulations; and persons withlow income

For this research on the 1995redesign, the required level of precisionwas a 30-percentcv. Taking intoaccount the observedcv’s for the currentdesign fromtable 2and known NHISsample sizes for 1988 (not shown),table 3was developed; it shows therequired sample sizes for many differen

r a 30-percent cv

General population

DirectBRR

Size calculated using cv from:

Publishedcurves

Controlledmargin

Interiorcell1

Age0–5

. . . . . . . . . 116 30

. . . . . . . . . 541 137

. . . . . . . . . 59 48

760 397 520 522 2,7421,351 701 917 967 964

132 . . . . . . . . .. . . 4 5 9 1

528 266 348 307 1,783582 314 411 552 (1)

1,974 1,104 1,444179 100 131

80 45 5820 11 15

639 464 515 610 106,2485,923 5,792 6,437 6,227 (1)

17,754 17,417 19,357 18,898 17,316

6,282 4,825 4,712 6,236 3,19011,412 9,420 9,201 7,955 2,85513,459 11,178 10,918 13,202 21,25020,616 18,608 18,174 24,627 105,130

es to achieve the desired precision are infinite.

statistics to have a 30-percentcv. Thisassumes the same extent of clusteringthe 1988 NHIS but without thedifferential sampling by racialcomposition of neighborhood. Theformula used was

nR = nDScvD

30D2 × 1

1.03

wherenD is the sample size for thedomain in 1988,cvD is an estimatedcvfor the statistic on the domain in 1988,nR is the required sample size, anddivision by 1.03 corrects for the designeffect because of the slight oversamplithat was done of heavily blackneighborhoods in the 1985–94 design.

As is evident from inspection of thtable, this guideline of a flat 30-percencv did not really provide much guidancon the magnitude of an appropriatesample size for each domain. For acvguideline to determine a sample size,

Limited age domains(Total sample size required over all domains

to be found by summing across domains)

Age6–17

Age18–24

Age25–44

Age45–64

Age 65and over

86 76 126 138 83192 179 263 333 272

66 44 48 36 26

5,063 3,162 861 294 943,458 1,141 892 514 276

8 6 7 6 33,515 520 447 137 1111,152 501 293 241 280

27,127 4,505 1,069 261 1777,529 9,540 4,446 3,965 4,743

28,541 24,986 18,109 11,170 13,618

4,207 3,709 5,985 6,621 5,2818,116 16,554 10,080 16,953 12,000

39,022 12,069 8,896 9,835 5,80420,771 10,154 17,427 30,041 34,630

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single statistic for which the designmust provide the requiredcv must bespecified. Specifying a group ofstatistics allows the rarest characteristito drive the sample size. Of course,across the age domains, the raresthealth-related characteristic changes. Idid not seem to make sense todetermine a different required samplesize for each age domain. The roundfigures of 1,000 as a required effectivesample size were the result of areasonable compromise. A sample of1,000 persons selected with clusteringbut equal probabilities provides thedesiredcv of 30 percent for an estimateproportion of 1.438 percent.

This standard of 1,000 was used fomost of the research. It is referred toloosely as an effective sample size of1,000, although this is a slight abuse ostandard terminology. A sampleclustered in the same manner as thecurrent NHIS but selected with equalprobabilities will not provide as goodprecision as a simple random sample o1,000 cases.

Current sample sizes are quitedifferent for estimates based on datafrom the core interview than for thosebased on items from most questionnairsupplements. Most supplements areadministered to only one adult perhousehold to reduce the burden on thehousehold. Crossing the desired agebreaks by sex and by race and ethnicitleads to the finding that sample sizes ainadequate (i.e., fewer than 1,000) onsupplements for almost every minorityage-by-sex domain.

For the core interview, most blackdomains have adequate sample sizes.The exceptions are the domains of blamales and females under age 5 andblack males ages 65 and over. A fewHispanic domains have adequate sampsizes for the core, but most areinadequate. For the other groups, nodomains have adequate sample sizes,even for the core interview.

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Components of VarianceOn average, the study found that

about 6 percent of the total variance in1988 resulted from differences amongPSU’s, with the rest resulting fromdifferences between and within segmenwithin PSU’s. Considerable variationwas found in the estimated proportiondue to between-PSU variance, howeveSome of this variation was attributableto sampling error on the varianceestimates themselves, and some wasattributable to real differences. Inparticular, because estimates pertainingto large domains tend to have higheraverage cluster size and thus greaterbetween-PSU variance, the size of theestimate has some relationship to theproportion of the total variancecontributed by the between-PSUcomponent.

An average between-PSU varianceof 6 percent is perhaps a little smallerthan common wisdom among samplingstatisticians would have predicted for asurvey with only 198 sample PSU’s.This indicates that the stratificationperformed by the U.S. Bureau of theCensus in the early 1980’s was quiteeffective.

Between-PSU variances are evensmaller for minority groups. The studyfound that between-PSU varianceaccounts for between 2 and 3 percent ototal variance for black persons andessentially 0 percent for Hispanicpersons. Components of variance werenot calculated for minorities other thanthe black and Hispanic populations, buit seems reasonable to believe thatbetween-PSU variance is also notimportant for Asians and PacificIslanders. On the other hand, there isgood reason to suspect thatbetween-PSU variance is larger forAmerican Indians, Eskimos, and Aleutsbecause of their large nonmetropolitanpopulations. Between-PSU variance fornonmetropolitan estimates was a notablarge 15 percent of total variance, morethan twice as large as for statistics for
the United States as a whole.

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Table 4. Projections of the population by age, sex, race, and ethnicity to 2000

Race, Hispanic origin, and age Male Female Total

Black

Under 5 . . . . . . . . . . . . . . . . . . . . . . . 1,358 1,301 2,6595–17 . . . . . . . . . . . . . . . . . . . . . . . . . 3,910 3,732 7,64218–24 . . . . . . . . . . . . . . . . . . . . . . . . 1,888 1,910 3,79825–44 . . . . . . . . . . . . . . . . . . . . . . . . 4,920 5,479 10,39945–64 . . . . . . . . . . . . . . . . . . . . . . . . 2,933 3,538 6,47065 years and over . . . . . . . . . . . . . . . . . 1,238 1,793 3,031Total . . . . . . . . . . . . . . . . . . . . . . . . . 16,247 17,753 33,999

Hispanic

Under 5 . . . . . . . . . . . . . . . . . . . . . . . 1,685 1,612 3,2975–17 . . . . . . . . . . . . . . . . . . . . . . . . . 4,043 3,861 7,90318–24 . . . . . . . . . . . . . . . . . . . . . . . . 1,943 1,805 3,74825–44 . . . . . . . . . . . . . . . . . . . . . . . . 5,423 4,841 10,26345–64 . . . . . . . . . . . . . . . . . . . . . . . . 2,405 2,524 4,92965 years and over . . . . . . . . . . . . . . . . . 774 1,086 1,860Total . . . . . . . . . . . . . . . . . . . . . . . . . 16,271 15,729 32,000

Other


Total


NOTE: Figures may not add to totals because of rounding.


Chapter 4. PotentialTechniques forIncreasing SampleSizes for SelectedDomains

This chapter reviews the basictechniques that can be used foroversampling racial, ethnic, and

economic domains. Some of thesetechniques are not discussed further insubsequent chapters because it is cleathey do not meet NCHS requirementsfor NHIS.

Sample Sizes With SimpleExpansion

The least complicated approach tomeeting the new precision requirementfor NHIS would be to simply increasethe sample size. This option wouldrequire no changes in methodology. Toevaluate this option, it is useful to firstproject sample sizes that would beobtained for each domain given anequiprobability design of 50,000interviewed households, the goal that t1985 design was built around. There aseveral steps to making this projection.The first step is to project the populatiointo the future. Given that the 1995design will be used from 1995 through2004, it seemed reasonable to evaluatesizes as of the year 2000.Table 4showssuch projections.

The projections for the total andblack populations were based on tableof Projections of the Population of theU.S. by Age, Sex, and Race: 1988 to2080 (7). The projections for theHispanic population were based onSeries 17 fromtables 2and3 ofProjections of the Hispanic Population:1983 to 2080(8). The projections of‘‘other’’ populations were computed bysubtraction, ignoring the existence ofblack Hispanic persons. Because all ofthe U.S. Bureau of the Censusprojections were made before the 1990decennial census, those projections wemultiplied by correction factors toreflect the error in the projections for1990. Thus, the numbers intable 4

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(above) reflect an adjustment of−3.2 percent for black populations,+11.5 percent for Hispanic populations,and −0.7 percent for ‘‘other’’populations. Clearly, the potential forerror exists in all these projections. TheHispanic projections are particularlysensitive to trends in immigration. Moscritically, the projections are sensitive tchanges in ethnic identification. Studieby the U.S. Bureau of the Census haveshown repeatedly that question wordincan have a major impact on estimatesthe Hispanic population.

The second step in projectingsample sizes is to account forundercoverage. Historically, populationestimates from NHIS have been lowerthan those from the decennial census,particularly for the black and Hispanicpopulations. (This is true for practicallyall surveys.)Table 5shows projectionsof undercoverage for typical

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demographic surveys. The numbers arefrom Redesign Memorandum #9(9) andare averaged over years. From year toyear, coverage rates can varysubstantially, depending on the size ofthe survey and survey procedures.

The third step in projecting samplesizes is to project household sizes, thatis, the number of persons per householdGiven historical trends it was decided topredict household sizes of 2.71, 3.40,and 2.36 for black, Hispanic, and‘‘other’’ households, respectively. Thesenumbers are sensitive to assumptionsabout the causes of undercoverage aswell as changes in trends for youngadults to set up separate householdsfrom their parents.

Given these steps the sample sizesby race, ethnicity, age, and sex can beobtained by reducing the eligiblepopulation by predicted undercoverage,

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Table 5. Projections of undercoverage by age, sex, race, and ethnicity

Race, Hispanic origin, and age

Percent missed

Male Female

Black

Under 5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 0 05–17 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 118–24 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 1225–44 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 1045–64 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 965 years and over . . . . . . . . . . . . . . . . . . . . . . . . 7 5

Hispanic


Other



summing across age and sex, and thedividing by household size (specific torace and ethnicity), thereby yieldingestimates of the counts of householdsthat can be subject to discovery by atypical demographic survey in the year2000. Yields in an equiprobabilitysurvey of 50,000 interviewed householare then obtained by proportionality.Table 6shows the results of thesecalculations.

Different projections could havebeen obtained by assuming interactionbetween household relationships andcoverage. For example, there is someevidence to support the proposition thaundercoverage of occupied housing isless severe than the undercoverage ofpersons within listed households becauof less severe undercoverage ofhousehold heads than of other househmembers, even after controlling for agand sex. However, definitive studies onthe relative importance of within-household undercoverage versusundercoverage of whole households(as a result of mistakes in thelisting/screening process such asthinking homes unfit for habitation orvacant) are not available. It was decidethat it would be adequate for planningpurposes to simply divide estimates of

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covered persons by projected househsizes to get estimates of coveredpersons.

Not shown in the table areprojections for Asian and PacificIslanders nor for American Indians,Eskimos, and Aleuts. Projections forthese racial groups are just 3,600 and1,000, respectively, in a survey of50,000 interviewed households. Thosenumbers are for population of any ageand sex.

Given NCHS interest insubsampling just one adult perhousehold, as discussed inchapter 2,projections were also made for theexpected yield for such a proceduregiven an equiprobability sample of50,000. Such a procedure reduces thsample size directly and also indirectlThe sample size is directly reducedbecause fewer people are interviewedis indirectly reduced in the sense thatthe sample is no longer anequiprobability sample. For example,adults in three-adult households end uwith weights three times as large asadults in single-adult households. Thedirect reductions can be obtained bysimply dividing by the projectednumbers of adults per household. Forthis research it was assumed that the

would be 1.81 adults per blackhousehold, 2.06 per Hispanic househoand 1.82 per ‘‘other’’ household. Astudy of data from theCurrentPopulation Surveyyielded projectionsthat the departure from equiprobabilitywould induce additional design effectsof 1.28, 1.25, and 1.19 for black,Hispanic, and ‘‘other’’ adults,respectively.Table 7shows projectionsof effective sample sizes that have beeadjusted for these design effects but nothe design effects resulting fromclustering. Because sampling only oneadult per household would reduce thecomponent of the design effect due towithin-household intraclass correlation,these adjustments might be a little toosevere, but they do give a good idea othe precision that could be expectedwith such a design.

As can be readily inferred fromtables 6and7, simple expansion of thesample is an extremely expensivemethod for satisfying the precisionrequirements discussed inchapter 3.Because the projected sample sizes aralways larger for black domains than foHispanic domains, the expansion ratiorequired for Hispanic goals would alsomeet black goals. Thus, it is enough tolook at the expansion ratios for Hispanpersons. To meet the precisionrequirement for elderly Hispanic malesby this technique alone would requirebetween a threefold and fourfoldincrease in the NHIS sample size, evenif NCHS were willing to take all adultsin minority households. (The requiredprecision is achieved with 1,000interviews, but the equiprobabilitydesign yields just 323.) In otherwords, instead of interviewing 50,000households per year, it would benecessary to interview 150,000 to200,000 households per year. A vitalcategory such as Hispanic males18–24 years of age would require afull fourfold increase if only one adultis interviewed per household. Thenumbers are even larger for elderlyAsian Americans and NativeAmericans. Clearly, this is not afeasible technique.

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Table 6. Sample sizes for equiprobability sample of 50,000 interviewed households

Race, Hispanic origin, and age Male Female Total

Black

Under 5 years . . . . . . . . . . . . . . . . . . . . . 667 639 1,3065–17 . . . . . . . . . . . . . . . . . . . . . . . . . . . 1,900 1,813 3,71318–24 . . . . . . . . . . . . . . . . . . . . . . . . . . 705 825 1,52925–44 . . . . . . . . . . . . . . . . . . . . . . . . . . 1,908 2,420 4,37245–64 . . . . . . . . . . . . . . . . . . . . . . . . . . 1,223 1,580 2,80365 years and over . . . . . . . . . . . . . . . . . . . 565 836 1,401Total . . . . . . . . . . . . . . . . . . . . . . . . . . . 6,968 8,112 15,079Total 18 years and over . . . . . . . . . . . . . . . 4,401 5,660 10,061Households . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5,564

Hispanic

Under 5 years . . . . . . . . . . . . . . . . . . . . . 785 751 1,5365–17 . . . . . . . . . . . . . . . . . . . . . . . . . . . 1,885 1,801 3,68618–24 . . . . . . . . . . . . . . . . . . . . . . . . . . 715 691 1,40625–44 . . . . . . . . . . . . . . . . . . . . . . . . . . 1,996 1,948 3,94345–64 . . . . . . . . . . . . . . . . . . . . . . . . . . 896 1,016 1,91265 years and over . . . . . . . . . . . . . . . . . . . 323 453 776Total . . . . . . . . . . . . . . . . . . . . . . . . . . . 6,600 6,659 13,259Total 18 years and over . . . . . . . . . . . . . . . 3,930 4,107 8,037Households . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3,900

Other

Under 5 years . . . . . . . . . . . . . . . . . . . . . 2,800 2,654 5,4545–17 . . . . . . . . . . . . . . . . . . . . . . . . . . . 8,460 8,023 16,48318–24 . . . . . . . . . . . . . . . . . . . . . . . . . . 4,069 4,106 8,17525–44 . . . . . . . . . . . . . . . . . . . . . . . . . . 14,045 14,245 28,29045–64 . . . . . . . . . . . . . . . . . . . . . . . . . . 11,539 11,980 23,51965 years and over . . . . . . . . . . . . . . . . . . . 5,695 8,232 13,927Total . . . . . . . . . . . . . . . . . . . . . . . . . . . 46,608 49,240 95,848Total 18 years and over . . . . . . . . . . . . . . . 35,348 38,563 73,911Households . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40,614

Total

Under 5 years . . . . . . . . . . . . . . . . . . . . . 4,252 4,044 8,2965–17 . . . . . . . . . . . . . . . . . . . . . . . . . . . 12,245 11,636 23,88118–24 . . . . . . . . . . . . . . . . . . . . . . . . . . 5,488 4,622 11,11025–44 . . . . . . . . . . . . . . . . . . . . . . . . . . 17,948 18,612 36,56145–64 . . . . . . . . . . . . . . . . . . . . . . . . . . 13,659 14,575 28,23465 years and over . . . . . . . . . . . . . . . . . . . 6,584 9,521 16,104Total . . . . . . . . . . . . . . . . . . . . . . . . . . . 60,176 64,011 124,186Total 18 years and over . . . . . . . . . . . . . . . 43,679 48,330 92,009Households . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50,078

. . . Category not applicable.



Oversampling Classes ofPrimary Sampling Units

It is well known that somenonself-representing PSU’s havedisproportionate numbers of Hispanicpersons and others have disproportionnumbers of black persons. This led tothe idea that it might be advantageousdeliberately assign a larger measure ofsize to such PSU’s so that more of thewould appear in the sample. This woulreduce between-PSU variance fortargeted minority statistics and make iteasier to oversample targeted minoritieat the block level while maintaining

te

o

reasonable PSU workloads. (The PSUworkload is the total number ofhouseholds interviewed in a PSU overthe course of a year.) With thistechnique, however, the between-PSUvariance is increased for the majoritypopulation and for untargeted otherminorities as well as for statistics for thtotal population. Also, the set of samplPSU’s becomes less suitable for thehealth care provider surveys and forfollow-on surveys, such as the NationaSurvey of Family Growth, thatreinterview former NHIS samplepersons or housing units.

Because large majorities of theblack and Hispanic populations live inlarge metropolitan areas that are notsubject to between-PSU variance (aspecial tabulation of the 1988 NHISfound 62 percent of black persons and69 percent of Hispanic persons living inSR PSU’s), and between-PSU varianceis already small for these domains, andbecause equal workloads are not animportant consideration for NHIS withits traveling interviewers, it was decidenot to oversample PSU’s with largeminority populations. Equal monthlyworkloads are important, but they canbe controlled by the number of timesthe PSU is visited in the course of theyear. More details can be found inPSUProbabilities Given DifferentialSampling at Second Stage(10).

Oversampling Classes ofBlocks or Block Groups

Using this technique, small areasthat are known from the prior decenniacensus to be rich in targeted minoritiesare deliberately oversampled. Unless thtargeted minority is completely isolatedresidentially, this technique naturallygives rise to an oversample of thosenonminority persons who happen toreside in blocks with heavyconcentrations of minorities. Thisoversampling is usually undesirable asthere are no fixed precision requiremenfor such nonminority persons.Furthermore, the resulting variation inprobabilities of selection for theuntargeted demographic domains leadsto larger-than-necessary design effectsfor the total estimates and for themajority population. For these reasons,oversampling of blocks and blockgroups is most effective when combinewith screening (see the next section).Nonetheless, this technique can and habeen used by itself and so wasconsidered as a possible approach in tresearch.

The demographic domain is oftendefined in terms of age, sex, race, orethnic origin. The underlying theory forthis approach is the same regardless othe definition. Stratify the blocks in eacPSU intoM strata according to thepercentage of the population within the

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Table 7. Sample sizes for equiprobability sample of 50,000 interviewed households with oneadult per household (corrected for within-household design effect)


One adult per household

Male Female Total

Black

Under 5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .5–17 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .18–24 . . . . . . . . . . . . . . . . . . . . . . . . . . 304 356 66025–44 . . . . . . . . . . . . . . . . . . . . . . . . . . 823 1,044 1,86745–64 . . . . . . . . . . . . . . . . . . . . . . . . . . 528 682 1,21065 years and over . . . . . . . . . . . . . . . . . . . 244 361 605Total . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .Total 18 years and over . . . . . . . . . . . . . . . 1,900 2,443 4,343Households . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.559

Hispanic

Under 5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .5–17 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .18–24 . . . . . . . . . . . . . . . . . . . . . . . . . . 278 268 54625–44 . . . . . . . . . . . . . . . . . . . . . . . . . . 775 756 1,53145–64 . . . . . . . . . . . . . . . . . . . . . . . . . . 348 394 74265 years and over . . . . . . . . . . . . . . . . . . . 126 176 302Total . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .Total 18 years and over . . . . . . . . . . . . . . . 1,526 1,595 3,121Households . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3,901

Other

Under 5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .5–17 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .18–24 . . . . . . . . . . . . . . . . . . . . . . . . . . 1,879 1,896 3,77525–44 . . . . . . . . . . . . . . . . . . . . . . . . . . 6,485 6,577 13,06245–64 . . . . . . . . . . . . . . . . . . . . . . . . . . 5,328 5,531 10,85965 years and over . . . . . . . . . . . . . . . . . . . 2,630 3,801 6,431Total . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .Total 18 years and over . . . . . . . . . . . . . . . 16,321 17,805 34,127Households . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40,610

Total

Under 5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .5–17 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .18–24 . . . . . . . . . . . . . . . . . . . . . . . . . . 2,460 2,520 4,98025–44 . . . . . . . . . . . . . . . . . . . . . . . . . . 8,083 8,378 16,46145–64 . . . . . . . . . . . . . . . . . . . . . . . . . . 6,204 6,608 12,81265 years and over . . . . . . . . . . . . . . . . . . . 2,999 4,338 7,337Total . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .Total 18 years and over . . . . . . . . . . . . . . . 19,746 21,844 41,590Households . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50,070




block belonging to the targeted domainThere does not appear to be any theoon the optimal number of such strata oon how to choose the cut-points thatdefine them. As a general rule, it woulseem that more strata are better thanfewer. The limiting constraints are thecost of the computer runs to classifyblocks into the strata and the burden overifying that sampling is done for eacstratum correctly.

Let P1−PM indicate the proportionof the total population living in each of

these strata. LetD1−DM indicate thecorresponding proportions of thetargeted domain population. LetSI1−SIMindicate corresponding samplingintervals for theM strata. The problemis to determine the optimal set ofsampling intervals for producingstatistics about the targeted domainpopulation.

Assuming equal unit variances in astrata, the design effects for totalstatistics and for statistics about thetargeted domain will be, respectively,

DE = 3∑i = 1

M

Pi SIi4 • 3∑i = 1

M

Pi

SIi4 and

(1)

DE = 3∑i = 1

M

Di SIi4 • 3∑i = 1

M

Di

SIi4

(2)

The average sampling fractions forthe overall sample and for the targeteddomain are, respectively,

O Pi

SIi

andODi

SIi

Thus, the ratio of the samplingfraction for the targeted domain to thesampling fraction for the generalpopulation (henceforth referred to as the‘‘nominal oversampling rate’’) is

O Di

SIiO Pi

SIi(3)

Division of this nominaloversampling rate by the design effectfor the targeted domain gives theeffective oversampling ratio:

1[∑ (Pi /SIi)] [∑Di SIi]

(4)

The problem is to maximizeequation 4 subject to the constraint of aconstant sample size. LetSI indicate thesampling interval that would yield theallowable fixed sample size with asimple random sample. Then the optimalsampling interval for thei-th stratum is

SIi = SIŒPi

Di∑j = 1

M

√ Dj Pj[ ](5)

The optimal sampling intervals fromequation 5 can be substituted back intothe earlier equations to obtain thenominal and effective oversamplingratios and the design effects. These basicformulas are used to discuss thesuitability of this approach compared

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Table 8. Distributions in 1990 of minority populations across density strata

Stratum (designated minority aspercent of block)

Percent of designated minority in each stratum

Black Hispanic

Asian andPacific

Islander

AmericanIndian,Eskimo,

and Aleut

< 10 . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.5 14.8 37.0 46.8< 5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.6 19.4 34.65–10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.1 17.7 12.110–30 . . . . . . . . . . . . . . . . . . . . . . . . . . 13.9 22.1 32.1 15.930–60 . . . . . . . . . . . . . . . . . . . . . . . . . . 16.2 23.3 18.0 7.760 and over . . . . . . . . . . . . . . . . . . . . . . 61.4 39.8 13.0 29.6


SOURCE: 1990 STF1B file

Table 9. Distributions in 1990 of each density stratum that is other

Percent of block population constituted bya specific racial and/or ethnic group Black Hispanic

Asian andPacific

Islander

AmericanIndian,Eskimo,

and Aleut

< 10 . . . . . . . . . . . . . . . . . . . . . . . . . . . 86.6 83.5 77.6 76.0< 5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84.6 78.2 76.05–10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75.9 71.5 73.310–30 . . . . . . . . . . . . . . . . . . . . . . . . . . 63.6 62.9 59.3 70.330–60 . . . . . . . . . . . . . . . . . . . . . . . . . . 40.7 37.8 35.0 50.060 years and over . . . . . . . . . . . . . . . . . . . 8.1 11.8 15.4 7.7

NOTE: Other is defined as not black, Hispanic, Asian, Pacific Islander, American Indian, Eskimo, or Aleut. In most tables in thisreport, other is defined as not black or Hispanic.


with others for each of the targeteddomains inchapters 5through 9. Someforeshadowing of those results can beobtained fromtable 8, however.Oversampling integrated andpredominantly minority blocks can beseen to have much more potential forblack persons than for the other groupsbecause the black population is muchmore highly concentrated. It isimmediately apparent that Asian andPacific Islanders are not concentratedenough for the technique to be effectiveThis is particularly obvious when oneconsiders the large boost in the effectivsample size that is required for thedomain. Among American Indians,Eskimos, and Aleuts, there are twodistinct patterns. Those who live onreservations are extremely segregatedand thus easy to oversample; those whlive off reservations tend to be onlyslightly segregated.

Table 9shows the percentage ofeach density stratum that is not black,Hispanic, Asian, or Native American.The table basically shows thepervasiveness of non-Hispanic whitepersons (the only racial and/or ethnicdomain with excellent precision) in allstrata. The table shows that blocks withstrong concentrations of black personsor Native Americans are the least likelyblocks to contain non-Hispanic whitepersons. Even in these blocks, howevethe numbers of non-Hispanic whitepersons imply that a sampleconcentrated in the densest minorityblocks will pick up substantial numbersof non-Hispanic white persons. Becausthere is no desire to improve theprecision of statistics about this groupnon-Hispanic white persons,

.

oversampling classes of blocks is awasteful technique by itself. It is farmore efficient when combined withscreening, as is discussed in the nextsection.

Screening WithSubsampling

With screening, a larger samplethan actually desired is selected. A verbrief, inexpensive questionnaire is thenadministered to the sample to determinthe members of the domain to beoversampled. Those units (persons orhousing units) that are not part of thatdomain are subsampled. It is possibleset up multiple cells with differentsubsampling rates. The subsampling cbe done centrally after the entirescreening operation is completed, or itcan be done by the interviewer on thefly. Techniques have been developedthat make the subsampling process veeasy for the interviewer (11).Interviewers do not need randomnumbers. Instead, they are givenhouse-by-house preinterview instructioabout which domains can be interviewe

at which households. These instructionare randomized centrally beforescreening to yield the desired samplingrates. Alternatively, with computer-assisted personal interviewing (CAPI),the sampling can be programmed andperformed automatically in the laptopcomputer; the computer notifies theinterviewer which households to retainfor the full interview and which ones toreject as a result of subsampling.

Experience at Westat, Inc., hasshown that it is possible to obtain basicinformation on sex, race, and ethnicityfor the occupants of more than99 percent of housing units. A successrate this high requires persistent,well-trained, door-to-door interviewersand some recourse to proxy informatiofrom neighbors.

The U.S. Bureau of the Censusestimates that each screening interviewthat does not result in a household ofthe desired type costs as much asone-third of the cost of interviewing thehousehold with standard NHISinstruments. This estimate isparameterized in this report asR= 3,meaning that a full standard interviewcosts three times as much as a screeninterview. For example, 51,000 extraattempted screeners should cost as muas 17,000 completed interviews. In facthe Bureau made an estimate this lowonly with great hesitancy. Some at theBureau believed thatR= 2 or evenR= 1.5 would be better projections.(The cost estimates are staff memberjudgments because the Bureau has nohad any recent direct experience withscreening of this type.) The actual costwill be sensitive to the requiredresponse rate, the rules allowing the usof information from neighbors, and the

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complexity of the screening rules.Because the U.S. Bureau of the Censuhas not attempted screening inhousehold interviews since the 1960’s,decision was made to field testprocedures in 1993. A field test wascarried out by the Bureau with goodresults (12).

Although screening can be done onan equiprobability sample, it is oftenmore effective when combined withoversampling at the block level, as isfurther explored inchapter 5. Screeningcan be used either at the person level oat the household level. Given the verylow marginal cost in NHIS of collectingdata on extra members of a household,most of the attention for this study wason screening whole households either ior out of the sample for full interviews.

The formulas in the previoussection for optimal sampling intervalsneed to be changed when screening isconducted in tandem with oversamplingThose formulas assumed constant costper unit across the strata. Whenscreening is used, cost is no longerconstant across the strata. Each minoriperson found in one of the less densestratum costs more than a similarminority person in the most densestratum because more screeninginterviews with untargeted domains hadto be conducted to find him or her.

Let N be the size of the totalpopulation andND be the size of thetargeted domain. Letc be the cost ofone screening interview. An interviewwith a member of the targeted domainin stratumi costs (R-1)c plus

NPi

NDDic for all the screening interviews

that did not yield households from thetargeted domain. The cost per interview

is thus proportional toND

N(R−1) +

Pi

Di

Under optimal allocation, assumingequal unit variances in all strata, thesampling interval in stratumi should bedetermined by the equation

SIi αŒ Pi

Di+

ND

N(R−1)

(6)

where the constant of proportionalityk

r

is determined by either a cost constrainor a sample size demand. This formulaassumes that no interviews areconducted with anyone outside thetargeted domain. However, a subsampof the ‘‘other’’ population can be keptfrom the screened households withoutaffecting the optimal allocation verymuch. If there are multiple targeteddomains, it appears difficult to obtain anoptimum allocation formula.

If R = 1, (i.e., screening is asexpensive as interviewing), thisproportionality gives rise to the samerelative allocation of sample as equatio5. If the cost of screening is far lessthan the cost of interviewing (i.e.,R isapproaching infinity) and the domain isnot extremely rare (i.e.,ND/N is notclose to 0), this relationship results inclose to a flat set of sampling intervals,which is equivalent to allocation inproportion to total population.Table 10illustrates this relationship well. As thecost of interviewing increases inproportion to the cost of screening, thebenefits of oversampling decrease. Forexample, with a value ofR = 40, thepercent reduction in total cost forstatistics on the black population in theyear 2000 for screening withoversampling, compared with screeningwith uniform sampling intervals, is just1.5 percent. On the other hand, when tpopulation is extremely rare, as is thecase with Native Americans, the benefiof oversampling are still substantial(19-percent cost reduction for sameprecision) even when a full interviewcosts 60 times as much as a screenerinterview.

Network Sampling byNomination of Relatives

Prior applications of networksampling involved having an initialprobability sample of persons (obtainedby area or list sampling) report on thenumber of relatives they have outsidethe household and whether any of themhave certain characteristics of interest,such as diabetes (13). A variant of thismethod was studied as part of the NHIredesign research. It was not believedthat most persons would be able toanswer most of the questions in NHIS

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accurately for relatives not living in thehousehold. In fact, for many NHISsupplements, even a householdrespondent is considered unsatisfactoryinterviewers are instructed to acceptnonresponse instead of proxy responseself-response is impossible. Thus, thescheme proposed and studied for NHISwas to have interviewers track thenominated relatives and administer theNHIS instruments directly to them. Useof the telephone for the tracking andfollow-on interviews was a possiblerefinement considered.

This scheme is still underconsideration for future redesigns butwas dropped from active considerationfor 1995 for several reasons. First, thescheme is very new. No publisheddescriptions came to light in whichnetwork sampling had been used on alarge-scale survey in which thenominees had to be tracked andinterviewed within a short time. Also,available data on the sizes of familialnetworks by race and ethnicity arelimited. The risks of implementationwithout extensive pretesting would beconsiderable. Because there wasinsufficient time to pretest the method,and reasonable alternatives wereidentified at least for the black andHispanic populations, the idea was puton the shelf until future redesigns.

Detailed Considerations

The areas carefully consideredinclude the following:

+ The racial and/or ethnic groups forwhich network sampling might bemost advantageous.

+ Whether open or closed networkswould be more appropriate.

+ The adequacy of existinginformation on multiplicitiesassociated with different networkdefinitions.

+ The biases resulting frommisreporting of multiplicities andhow to reduce those biases.

+ The use of the telephone to reducecosts.

+ Unduplication with other currentsurveys run by the U.S. Bureau ofthe Census.

+ Control over sample sizes.

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Table 10. Aspects of the optimal mix of screening and oversampling for various cost assumptions and racial and/or ethnic subdomains

Cost assumption

Ratio of interview cost to screening cost

1:1 1.5:1 2:1 3:1 4:1 5:1 10:1 20:1 40:1 60:1

Based on 1990 distributions Optimal ratios of screening-to-interview sample

Black . . . . . . . . . . . . . . . . . . . . . . . . . 2.6 2.8 3.0 3.3 3.5 3.7 4.3 5.1 5.9 6.4Hispanic . . . . . . . . . . . . . . . . . . . . . . . 3.7 4.0 4.2 4.5 4.8 5.0 5.7 6.6 7.6 8.2Asian and Pacific Islander . . . . . . . . . . . . 10.1 10.8 11.3 12.1 12.8 13.3 15.4 18.1 21.2 23.2American Indian, Eskimo, Aleut . . . . . . . . 15.8 18.5 20.5 23.7 26.1 28.2 35.8 45.4 57.2 65.2

Based on projections to 2000

Black . . . . . . . . . . . . . . . . . . . . . . . . . 3.1 3.4 3.6 4.0 4.2 4.5 5.2 5.9 6.6 6.9Hispanic . . . . . . . . . . . . . . . . . . . . . . . 3.9 4.2 4.4 4.7 4.9 5.1 5.8 6.5 7.1 7.4

Based on 1990 distributions Optimal oversampling rates for densest stratum

Black . . . . . . . . . . . . . . . . . . . . . . . . . 8.2 6.8 6.0 5.0 4.4 3.9 2.9 2.2 1.7 1.5Hispanic . . . . . . . . . . . . . . . . . . . . . . . 9.7 8.2 7.3 6.1 5.3 4.8 3.5 2.6 2.0 1.7Asian and Pacific Islander . . . . . . . . . . . . 10.7 9.1 8.1 6.8 6.0 5.4 3.9 2.9 2.2 1.9American Indian, Eskimo, Aleut . . . . . . . . 20.4 16.2 13.9 11.2 9.7 8.6 6.1 4.4 3.2 2.6


Black . . . . . . . . . . . . . . . . . . . . . . . . . 5.5 4.6 4.0 3.4 3.0 2.7 2.0 1.6 1.3 1.2Hispanic . . . . . . . . . . . . . . . . . . . . . . . 5.6 4.7 4.1 3.4 3.0 2.8 2.1 1.6 1.4 1.3

Based on 1990 distributions Screener sample sizes for precision equivalent to simple random sample of 1,000

Black . . . . . . . . . . . . . . . . . . . . . . . . . 3,900 3,900 4,000 4,100 4,200 4,300 4,700 5,300 6,000 6,400Hispanic . . . . . . . . . . . . . . . . . . . . . . . 5,500 5,500 5,600 5,700 5,800 5,900 6,300 7,000 7,800 8,300Asian and Pacific Islander . . . . . . . . . . . . 18,000 18,000 18,000 18,000 19,000 19,000 20,000 21,000 23,000 25,000American Indian, Eskimo, Aleut . . . . . . . . 61,000 61,000 61,000 61,000 62,000 63,000 65,000 69,000 75,000 79,000


Black . . . . . . . . . . . . . . . . . . . . . . . . . 4,700 4,700 4,800 4,900 5,000 5,100 5,600 6,100 6,700 7,000Hispanic . . . . . . . . . . . . . . . . . . . . . . . 5,500 5,500 5,500 5,600 5,700 5,800 6,200 6,700 7,200 7,500

Based on 1990 distributions Interviewed sample sizes for precision equivalent to simple random sample of 1,000

Black . . . . . . . . . . . . . . . . . . . . . . . . . 1,500 1,400 1,300 1,200 1,200 1,200 1,100 1,000 1,000 1,000Hispanic . . . . . . . . . . . . . . . . . . . . . . . 1,500 1,400 1,300 1,300 1,200 1,200 1,100 1,000 1,000 1,000Asian and Pacific Islander . . . . . . . . . . . . 1,800 1,700 1,600 1,500 1,500 1,400 1,300 1,200 1,100 1,100American Indian, Eskimo, Aleut . . . . . . . . 3,800 3,300 3,000 2,600 2,400 2,200 1,800 1,500 1,300 1,200

Based on projections to 2000Black . . . . . . . . . . . . . . . . . . . . . . . . . 1,500 1,400 1,300 1,200 1,200 1,200 1,100 1,000 1,000 1,000Hispanic . . . . . . . . . . . . . . . . . . . . . . . 1,400 1,300 1,300 1,200 1,200 1,100 1,100 1,000 1,000 1,000

Based on 1990 distributions Percent reduction in cost compared to screening without oversampling

Black . . . . . . . . . . . . . . . . . . . . . . . . . 53 48 43 36 31 27 16 7.6 3.1 1.7Hispanic . . . . . . . . . . . . . . . . . . . . . . . 51 47 43 38 33 30 19 10.2 4.6 2.7Asian and Pacific Islander . . . . . . . . . . . . 47 45 43 41 38 36 28 18.8 10.4 6.6American Indian, Eskimo, Aleut . . . . . . . . 52 51 50 48 47 46 40 33.1 24.3 19.0


Black . . . . . . . . . . . . . . . . . . . . . . . . . 41 35 32 26 21 18 10 4.1 1.5 0.8Hispanic . . . . . . . . . . . . . . . . . . . . . . . 35 31 27 22 19 16 9 3.7 1.2 0.5


Domains of Application—Satisfactory techniques had already beidentified for the black and Hispanicpopulations (seechapter 5). AmericanIndians, Eskimos, and Aleuts seemedtotally out of reach with anydesign-unbiased sampling technique(chapter 8). The techniques identified fothe black and Hispanic populationswould be too expensive to use forAsians and Pacific Islanders (also

nchapter 8). Network sampling, whencoupled with multiyear aggregation(discussed later in this chapter),however, seems to offer the possibilityof achieving the desired precision forstatistics on Asians and Pacific Islandeat a reasonable cost. Thus, there isreason to continue to study the potentuses of network sampling foroversampling demographic domains foNHIS.

Open Versus Closed Networks—Aclosed network is defined as one inwhich anyone in the network cannominate anyone else in the network.An open network is defined as any othetype of network. An example of a closenetwork is induced by the sibling rule,and an example of an open network isinduced by the parent rule. (SeeNational Network Surveys ofDiabetes(13) for descriptions of both

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rules.) The multiplicity of a rule is theaverage number of persons that someomay nominate, including himself orherself.

Existing data on multiplicities—Unpublished tabulations of a specialexperiment on the 1976 NHIS revealthat the standard open network, allowinnomination of parents and siblings,yields a multiplicity of 5.1 and that thestandard closed network, allowingnomination only of siblings, yields amultiplicity of 3.6. Because of increasedvariation in weights, design effectsincrease by 30 to 260 percent for thesibling rule. The networks also lead toan increase in design effects as a resulof clustering; this effect has not beenestimated. (The ‘‘design effects’’ inNational Network Surveys ofDiabetes(13) need to be inverted andmultiplied by 3.6 to obtain the morecommon design effects that indicate anincrease in variance over a simplerandom sample of the same size.)Obviously, if the multiplicity is 3.6 butthe design effect increases by260 percent, there is no net gain inprecision. Several other caveats must bnoted here. First, it is difficult to predictthe number of households over whichnominated persons will be spread. Manof the nominated persons will be in theoriginal sample household. On the othehand, if each spouse nominates bloodrelations, the multiplicity may increasestrongly. Second, these numbers applyto the total population; results might bevery different for the Asian Americanpopulation. Third, these networks werenot geographically restricted to be in thsame PSU or in any sample PSU.

Biases—Misreporting of multiplicityresults in biased and inconsistentestimators. Of course, all sample surveestimators are biased and inconsistentsome degree because of listing errors,misclassification of the occupancy ofHU’s, incomplete household rosters,nonresponse, and other nonsamplingerrors. However, there is an additionalpotential for bias in multiplicityestimators based on network samples.The actual level of bias dependsstrongly on how the networks aredefined. If the network is defined interms of ambiguous relationships, thebias will be worse. For example, if the

econcept ‘‘friend’’ is used to define thenetwork, it is easy to see that manypeople will have no real idea of howmany other people would regard themas friends. Without that knowledge, anindividual cannot inform the surveytaker of his or her personal multiplicityWithout knowledge of that multiplicity,it is not possible to form an unbiasedweight for the respondent.

Even when the network is definedin terms of less ambiguous relationshisuch as those of siblings or parents,uncertainties will remain because of srelationships, adoptions, fosterrelationships, and other specialrelationships. Furthermore, it is notsufficient to know how many personscould have nominated the sampleperson. Rather, it is necessary to knowhow many persons would havenominated the sample person had thebeen selected in the basic NHIS sampFor example, if brother Ken wouldnever provide to interviewersinformation on brother Harry, Ken doenot count in computing the multiplicityof Harry. Would Harry know whether onot Ken would nominate him?

Biases attributable to poorrespondent estimation of personalmultiplicity that are common in manynetwork sampling applications can besharply reduced for a closed networknominated persons are also intervieweand asked to make nominations of theown. Depending upon the effectivenesof procedures for resolvingdiscrepancies between nominations bvarious members of the network, biascould be largely eliminated. Of coursethis circular nomination and resolutionwould be time consuming, in terms ofperson hours and calendar time. Currschedules for completing assignmentswould have to be relaxed.

The only closed network to havebeen tried in the past is the sibling rulGreat care must be taken withhalf-siblings to make even this networclosed. It might be possible to deviselarger closed network by following onlmale or only female bloodlines. Forexample, if an eldest son couldnominate only his father, father’s fathefather’s father’s father, eldest son, eldson’s eldest son, eldest son’s eldestson’s eldest son, the network appears

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be closed; everyone in the network canominate everyone else in the networkIn general, this appears to be anextremely difficult task of dubiousutility.

Biases attributable to poorrespondent estimation of personalmultiplicity in open networks are muchmore difficult to avoid. One possibleway of avoiding them is to check withall potential nominators to see whethethey would indeed have nominated thesample person. This would be a costlyoperation because unless the potentianominators can also be nominatedthemselves, there is no other reason ttrace and contact them. Furthermore,there is no way to determine whetherthere are other people (in addition tothose mentioned by the sample personwho might have nominated the sampleperson.

Use of The Telephone—Some of thedifficulties of timing could be reducedby using telephone interviews fornominated households (or persons).Also, use of the telephone would makeit possible to allow nationwidenominations instead of only localnominations. On the negative side,telephoning could introduce another bibecause some households do not havtelephones and others might refuse toprovide telephone numbers.

Unduplication—It would beextremely difficult, perhaps impossible, toprevent duplication of network samplecases by other census surveys. Duplicawould, of course, be very rare, but a fewcases would be nearly certain.

Control Over Sample Sizes—Network sampling would result in poorcontrol over sample sizes. Controllingthe mix of ages would also be difficult.It is likely that a network sample thatyielded enough elderly cases for one adomain would yield far more cases froother age domains than would optimalbe desired. Subsampling withinhouseholds is possible, but thisintroduces a whole new dimension ofoperational complexities. If a siblingrule were used, it might be possible—brestricting the ages of persons whocould make nominations—to target thenominated sample more closely by agOf course, such a rule would change aclosed network to an open one.

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Further Research

The first step in extending researchforward on this idea would be to add asupplement to some major nationalsurvey that would fill in informationgaps about multiplicities for racial andethnic domains. NHIS is a possiblevehicle for such a supplement, but theCurrent Population Surveywould bepreferable given its larger size. Thesupplement should be developed todetermine the multiplicity for varioushousehold rules—with and without locarestrictions, and with and withoutrestriction to persons with listedtelephone service. The next step wouldbe to design an actual pretest. No suchdesign work was performed as part ofthis project. Clearly, planning will needto closely involve NCHS and the U.S.Bureau of the Census.

Dual-Frame SamplingWith Administrative Lists

The only lists of persons that aremaintained by the Federal Governmenand made available for sampling forhealth-related research are the list ofSocial Security and Railroad RetiremenAct beneficiaries maintained by SSAand the list of Medicare beneficiariesmaintained by HCFA. Use of these listsfalls under provisions of the Privacy Acof 1974, which permits transfer of thedata (including names and addresses)within the Public Health Service forhealth-related research. Westat, Inc., hused the Medicare list for a number ofsurveys (including the Medicare CurrenBeneficiary Survey for HCFA); the listhas been easy to work with and WestaInc., staff members have been able tolocate almost all of the sample personson it.

Commercial lists of other segmentsof the population do exist, but thesewere not thoroughly investigated as paof this research.

Integration of samples frommultiple frames into a single microdatafile with a single weight requires, at aminimum, the ability to tell which unitshad more than one chance of selectionThis is enough to create unbiasedweights. Furthermore, if it is possible todetermine the sampling stratum for eac

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unit in each of the frames, the precisiof the estimators can be improved. Iffull joint probabilities of selection for aunits within and across all frames canbe determined, even more preciseestimators are possible. Unbiasedmultiplicity-based weights can bedeveloped without knowing the overalprobability of selection for a unit. If thioverall probability of selection isknown, however, the Horvitz-Thompsoestimator will usually be more precise

Multiyear AggregationThe least costly way to improve th

precision of core statistics for raredomains is to aggregate statistics ovemultiple years. The precision thusachieved is not quite as great as wouresult from independent samples,because each year’s sample is in thesame set of PSU’s and segments, buprecision loss is only moderate.Combining statistics over 2 years addabout 10 to 15 percent to design effecA 3-year combination adds about 19 t26 percent. For rare domains quite ayears of aggregation might be necessThis method is clearly not suitable forquestionnaire supplements that arecarried for only a single year, as is oftthe case. Still, some data on raredomains can be collected this way. Inthe absence of any other data for sucdomains, NCHS should have atabulation program to produce theseaggregations.

Carrying Samples OverFrom Prior Years

For questionnaire supplements, amethod that is closely related tomultiyear aggregation is to carrysamples over from previous years.Clearly, the least costly source ofsamples for rare domains consists ofthose samples that have already beenused. The drawbacks to this method ahigher nonresponse and higherrespondent burden. High response isvery difficult to maintain in the face ofphysical moves and declining interestparticipation.

This method can be made even leexpensive by not following movers, bu

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this reduces the available sample sizeand increases design effects. InternalU.S. Bureau of the Census’ data indicathat when movers are not followed, atotal response rate of 70 percent can bobtained, where all the movers arecounted as nonrespondents. Ifd years ofprevious sample are used, this meansthat the nominal sample size increasesby a factor of 1 + (.7)d. The recentmovers that are identified in the newsample must be weighted up by a factoof d + 1 to obtain unbiased weights.This means that a design effect will beincurred of

DE =d + 1

(1 + .7d)2 [.7(d + 1) +.3

d + 1](7)

Thus, the effective sample size onlyincreases by a factor of

Effective multiyear sample sizeOne year sample size

=

(1 + .7d)3

.7 (d + 1)2 + .3 (8)

Adding all stationary persons of atargeted domain from 1 prior year thusresults in an increase in the effectivesample size for the targeted domain ofabout 60 percent. Two prior years yieldan increase of 110 percent. Three prioryears yield an increase of 160 percent.Of course, if members of the targeteddomain tend to move at higher or lowerates than the general population, thesprojections will either be too optimisticor too pessimistic. Also, there are recaissues that could affect how unbiasedthe weights are. To the extent that recemovers make errors in reporting the faor timing of their moves, the weightswill suffer bias. It seems to make sensethat longer periods of recall willincrease the potential for that sort ofbias.

A solution that avoids the designeffects and possible biases of notfollowing movers is to retain addressesof targeted domains rather than theoccupants themselves. Further study othis option would require data on thefrequency with which successoroccupants of minority households arealso members of the same minority.Such data were not readily available,and so this option was not seriously

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studied as part of this research.However, it is an option that may beworthy of further study. Data could beassembled on change in the minoritystatus of household members throughlongitudinal address surveys such as thAmerican Housing Survey.

Cost ModelsMaking rational choices between

alternative techniques clearly requiressome understanding of the costs of thealternatives. An effort was made todevelop a comprehensive cost model fNHIS (14); unfortunately, the robustnesof the model was uncertain. Existingdata are thin and unlikely to beimproved upon. Instead of relying oncomprehensive cost models, the reseadescribed in this report proceeded bydrafting fairly specific plans for intuitivecosting by survey administration expert

A number of steps that could betaken to improve the quality of cost datat the U.S. Bureau of the Census wereoutlined, but these steps would be rathexpensive. It is not enough to know homuch each interviewer charged. Acareful accounting of all interviewers’actions and their associated costs woube a useful start, but even that wouldnot be enough to allow majorextrapolations from current practice.NCHS would need either to raisebudgets or reduce the collection ofsubstantive data enough to allow thecollection of cost data that are goodenough to support reliable costmodeling. It is doubtful that the gains insample design resulting from theimproved cost models would be greatenough to compensate for the high cosof the data collection required to createsuch models.

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Table 11. Distribution of the black population across density strata in 1990

Percent of block that is black

Percent of block that is Hispanic

< 5 5–10 10–30 30–60 60+ Total

< 10 . . . . . . . . . . . . . . . . . . . . 4.6 1.2 1.5 0.7 0.5 8.510–30 . . . . . . . . . . . . . . . . . . . 6.6 2.1 2.8 1.3 1.0 13.930–60 . . . . . . . . . . . . . . . . . . . 8.4 1.7 2.8 2.5 0.7 16.260+ . . . . . . . . . . . . . . . . . . . . . 50.0 4.5 5.6 1.3 0.1 61.4Total . . . . . . . . . . . . . . . . . . . . 69.6 9.6 12.7 5.8 2.2 100.0

SOURCE: 1990 decennial census, tabulated by Westat, Inc.


Table 12. Distribution of the Hispanic population across density strata in 1990



< 5 5–10 10–30 30–60 60+ Total

< 10 . . . . . . . . . . . . . . . . . . . . 5.2 6.1 15.1 14.6 32.5 73.410–30 . . . . . . . . . . . . . . . . . . . 0.7 1.2 3.8 4.4 5.5 15.530–60 . . . . . . . . . . . . . . . . . . . 0.3 0.4 1.6 3.4 1.7 7.460+ . . . . . . . . . . . . . . . . . . . . . 0.5 0.5 1.7 0.9 0.1 3.6Total . . . . . . . . . . . . . . . . . . . . 6.6 8.1 22.1 23.3 39.8 100.0



Table 13. Distribution of the other population across density strata in 1990



< 5 5–10 10–30 30–60 60+ Total

< 10 . . . . . . . . . . . . . . . . . . . . 68.5 8.8 8.1 2.1 0.7 88.310–30 . . . . . . . . . . . . . . . . . . . 4.7 1.4 1.6 0.5 0.1 8.330–60 . . . . . . . . . . . . . . . . . . . 1.6 0.3 0.4 0.1 0.0 2.560+ . . . . . . . . . . . . . . . . . . . . . 0.8 0.1 0.1 0.0 0.0 0.9Total . . . . . . . . . . . . . . . . . . . . 75.6 10.6 10.2 2.8 0.8 100.0

0.0 Quantity more than zero but less than 0.05




Chapter 5. BestTechniques forSampling theNonelderly Black andHispanic Populations

Oversampling SmallDecennial CensusGeographic Units WithoutScreening

In the 1985–94 design, enumeratiodistricts (ED’s) and block groups (BG’swith strong concentrations of blackpersons were oversampled, a randomsample of households within these EDand BG’s was selected, and all residenof selected households were retainedfull interviews regardless of race. Thisprocedure is not very helpful inincreasing the effective sample size foblack persons because it is coupled wa policy of using total population as themeasure of size for the selection ofPSU’s and keeping tight constraints ontotal PSU workloads. This yields asituation in which strongly black ED’sand BG’s in some rural southern PSU’are not oversampled, because to do sowould result in unacceptably largePSU-level workloads. This concernabout total workloads is probablymistaken; the U.S. Bureau of the Censhas the latitude to handle the largeworkloads by scheduling more visits byits traveling interviewers to such PSU’sEven if the workload constraints wereviewed as absolutely necessary, the laworkloads in some PSU’s could havebeen prevented if the PSU size measuhad emphasized the presence of blackpersons.

This strategy has another inherentproblem: It results in an oversample ofwhite persons, Asians, NativeAmericans, and others who live inpredominantly black neighborhoods.Although a relatively small proportionof the population that is not black livesin such neighborhoods, that proportionis a substantial part of the population ithese neighborhoods. People who arenot black in these neighborhoods are n

of greater policy interest than otherpeople who are not black; thisoversampling, therefore, is largely awaste of effort.

To quantify the limitations of thisapproach (and to serve as the basis forother research), new tabulations of the1990 decennial census were madeshowing how the various domains aredistributed across strata defined by thedensity of minorities within blocks.Tables 11–14summarize key results.Table 13is of particular interest; itshows that a little less than 1 percent othe population that is neither black norHispanic lives in blocks that are morethan 60-percent Hispanic and that acomparable proportion of this populationlives in blocks that are more than60-percent black. Although these

proportions may seem small, the‘‘other’’ population is so large (about80 percent of total population) thatabout 14 percent of the total populationis ‘‘other’’ in the blocks where Hispanicpersons exceed 60 percent. Similarly,about 9 percent of the total population‘‘other’’ in the blocks that are more than60-percent black.

By the time the new design isimplemented, the results from the 1990decennial census will no longer becurrent. The prevalence of each groupeach stratum has been projected forwato 2000, however, the midpoint of thelife of the new design, based upon studof the deterioration in strata observedbetween 1980 and 1988 (15).

Despite the prevalence of the‘‘other’’ population in the blocks with

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Table 14. Distribution of the total population across density strata in 1990



< 5 5–10 10–30 30–60 60+ Total

< 10 . . . . . . . . . . . . . . . . . . . . 55.3 7.7 8.0 3.1 3.5 77.510–30 . . . . . . . . . . . . . . . . . . . 4.6 1.4 1.9 0.9 0.7 9.630–60 . . . . . . . . . . . . . . . . . . . 2.3 0.5 0.8 0.7 0.2 4.560+ . . . . . . . . . . . . . . . . . . . . . 6.6 0.6 0.9 0.2 0.0 8.4Total . . . . . . . . . . . . . . . . . . . . 68.9 10.3 11.5 4.9 4.4 100.0



Table 15. Distribution of the black population across density strata in 2000



< 5 5–10 10–30 30–60 60+ Total

< 10 . . . . . . . . . . . . . . . . . . . . 11.5 3.3 2.8 0.9 0.8 19.310–30 . . . . . . . . . . . . . . . . . . . 6.3 1.6 3.0 1.4 1.0 13.330–60 . . . . . . . . . . . . . . . . . . . 8.1 1.9 3.1 2.8 0.4 16.260+ . . . . . . . . . . . . . . . . . . . . . 44.9 2.6 3.1 0.6 0.0 51.2Total . . . . . . . . . . . . . . . . . . . . 70.8 9.3 12.0 5.7 2.2 100.0

SOURCE: Westat, Inc., projection.


Table 16. Distribution of the Hispanic population across density strata in 2000



< 5 5–10 10–30 30–60 60+ Total

< 10 . . . . . . . . . . . . . . . . . . . . 15.3 10.0 15.5 13.0 23.5 77.310–30 . . . . . . . . . . . . . . . . . . . 1.0 1.4 3.0 3.5 5.0 13.930–60 . . . . . . . . . . . . . . . . . . . 0.2 0.4 2.5 3.0 0.6 6.760+ . . . . . . . . . . . . . . . . . . . . . 0.5 0.3 0.9 0.3 0.1 2.0Total . . . . . . . . . . . . . . . . . . . . 17.0 12.1 22.0 19.8 29.2 100.0



Table 17. Distribution of the other population across density strata in 2000



< 5 5–10 10–30 30–60 60+ Total

< 10 . . . . . . . . . . . . . . . . . . . . 73.9 8.1 7.3 1.7 0.3 91.210–30 . . . . . . . . . . . . . . . . . . . 4.3 0.7 0.8 0.2 0.1 6.130–60 . . . . . . . . . . . . . . . . . . . 1.7 0.3 0.3 0.0 0.0 2.360+ . . . . . . . . . . . . . . . . . . . . . 0.4 0.1 0.0 0.0 0.0 0.5Total . . . . . . . . . . . . . . . . . . . . 80.2 9.1 8.4 1.8 0.4 100.0

0.0 Quantity more than zero but less than 0.05.




high concentrations of black or Hispanipersons, oversampling without screenincan be used to achieve modest increasin effective sample sizes for the blackand Hispanic populations, particularly ionly one of the groups is targeted.(Table 19illustrates this.) The tabledisplays the same strata and populatiodistributions that are displayed intables 15–18, but its strata are arrayedvertically instead of in matrix form. Theoversampling rates are relative to thesampling fraction implied by anequiprobability sample of 50,000households. (An oversampling rate of lesthan 1 means that the sampling fractionthat stratum is less than that for anequiprobability sample.) The oversamplinrates were derived with the formulasprovided inchapter 4, in the section onoversampling classes of blocks, under thassumption of constant cost.

Examination oftable 19reveals thatoversampling without screening canboost the nominal sample for blackpersons by 157 percent. Unfortunately,the design effect jumps up to 1.51; thusthe increase in the effective sample sizis only 71 percent. The cost of such astrategy is a 28-percent decline in theeffective sample size for the totalpopulation, a 19-percent decline for theHispanic population, and a 35-percentdecline for the other population. Thebest that can be done for Hispanicpersons is a 54-percent increase in theeffective sample size, accompanied bydeclines of 28 percent, 48 percent, and29 percent for the total population, blacpersons, and others, respectively. Thelast four columns of the table showvarious compromise allocations of thesample across the strata. Compromiseand B reflect optimal allocations for anartificial population that is part blackand part Hispanic. Compromise C takean oversampling rate for each minoritystratum that is the maximum of thoserequired for either black or Hispanicpersons and then dramaticallyundersamples the first stratum that hasthe lowest concentrations of the blackand Hispanic populations. CompromiseD sets oversampling rates that areproportional to the maximums requiredby the black and Hispanic populations.None of these compromises offers thedesired level of improvement in

effective sample sizes for minorities.Compromises A and B seem reasonabbut still fall short of goals. CompromiseD is unacceptable, and compromise Ccounterproductive even for the blackand Hispanic populations. The problemcomes from the objective ofoversampling both minorities. Thesegroups tend not to share the same are

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of high concentration. The blocks withstrong concentrations of black personhave only a small proportion of theHispanic population, and vice versa.Because oversampling with screeningoffers much larger improvements (seethe discussion later in this chapter), thtechnique of oversampling withoutscreening was dropped from further

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Table 18. Distribution of the total population across density strata in 2000



< 5 5–10 10–30 30–60 60+ Total

< 10 . . . . . . . . . . . . . . . . . . . . 59.2 7.8 7.7 2.9 3.1 80.710–30 . . . . . . . . . . . . . . . . . . . 4.2 0.9 1.3 0.7 0.8 7.830–60 . . . . . . . . . . . . . . . . . . . 2.3 0.5 0.9 0.7 0.1 4.560+ . . . . . . . . . . . . . . . . . . . . . 6.0 0.4 0.5 0.1 0.0 7.0Total . . . . . . . . . . . . . . . . . . . . 71.7 9.5 10.4 4.4 4.0 100.0





consideration for the black and Hispanpopulations.

Screening a NationalEquiprobability Sample

Screening without oversamplingblocks is probably the only techniquethat makes sense for oversamplingdomains that are fairly evenly spreadthroughout the land, such as teenagerFor black and Hispanic persons,however, screening by itself is muchmore costly than screening inconjunction with oversampling blocks;was thus dropped from furtherconsideration.

Oversampling at the BlockLevel With Screening forRace, Ethnicity, Age, andSex

Given that black and Hispanicpopulations tend to be separatelysegregated, that there is no particularinterest in those members of themajority population who happen to livein blocks where they are the minority,and that screening interviews areexpensive, the most clearly efficientprocedure is to combine oversamplingof blocks with screening. The term‘‘screening’’ is used here to include thestandard subsampling of untargeteddomains that immediately follows theshort interview that reveals the domainof a household or person.

By screening households inoversampled blocks, it is possible toeliminate the undesired oversample ofothers living there. If screening isconducted even in the stratum with the

lowest concentration of black andHispanic persons (and thus the smallesampling fraction), it is possible toincrease the effective sample sizesfurther for black and Hispanic persons.Fromtable 10, it is apparent thatminorities in the only-slightly-integratedblocks should not be sampled at rates lethan 4 to 5 times those used in highlysegregated blocks. Finally, with screeninit is possible to oversample each agegroup at a separate rate. This is animportant capability, because the agebracket of 18–24 years is criticallyimportant for some current public healthissues (such as acquiredimmunodeficiency syndrome) yet is quitenarrow; thus it is difficult to achieve therequired precision for young black adultsand Hispanic persons withoutsimultaneously getting larger-than-necessary samples of the more broadlydefined age brackets such as 25–44 yea

Accordingly, a detailed plan wasdeveloped to oversample blocks andscreen sample households by race,ethnicity, sex, and age. To do this, theplan included screening whole householdin or out of the sample that will be askedto go through the full interview dependinon their compositions. An alternativewould have been to screen somehousehold members in and others out; bthis did not make sense, given the NHIScore instrument’s structure, which makesthe interview of additional householdmembers very inexpensive.

The plan was ultimately rejected fotwo reasons. The first was a concernabout the complexity of the screeningprocess. In those cases where theoccupants of a sample housing unitcannot be directly contacted during thescreening phase, it is often fairly easyget neighbors to provide occupants’ ra

.

and sex. Ethnicity (Hispanic or not) is alittle more difficult to obtain, and age isthe most difficult.

The second reason for rejecting theplan was a feeling that, even thoughstatistics about the age bracket of 18–2years are very important, it wassufficient just to increase the totalminority sample size. There was greatreluctance to spend money on screeninto find minority households only todiscard some of those householdsbecause they did not contain anoccupant of the desired age and sex.

Oversampling at the BlockLevel With Screening forRace and Ethnicity Only

Once the decision had been made togive up on the required effective samplesize of 1,000 for the age bracket of 18–2years by sex for each minority, theproposed design had to be alteredsignificantly. This relaxation ofrequirements led to substantialsimplifications and cost savings. The bascombination of oversampling blocks withhigh concentrations of black and Hispanipersons and then following with screeninis still by far the most efficient method ofincreasing total minority effective samplesizes, however. The reasons for this arethe same as discussed in the previoussection.

To set the details of the design,NCHS delineated the following newrequirements and assumptions:

+ An assumption of a budget50-percent larger than is needed foa sample of 50,000 households inthe current design.

+ Tolerance of a reduction in thesample size for the population thatis neither black nor Hispanic, ifnecessary, to obtain the requiredeffective sample sizes for black andHispanic populations.

+ A requirement of roughly equaleffective sample sizes for black andHispanic persons of 8,000 adultseach, given the random selection oone adult per household.

+ The assumption thatR = 3, meaningthat a full NHIS interview costs 3times as much as a screening

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Table 19. The limits of oversampling without screening

Projections to the year 2000 Oversampling rates by stratum subject to fixed total sample size

Densitystratum

Percentblack

in 1990

PercentHispanicin 1990

Percentof total

population

Percentof black

population

Percentof Hispanicpopulation

Percentof other

population

To minimizevariance for

blacks

To minimizevariance for

HispanicCompromise

ACompromise

BCompromise

CCompromise

D

1 . . . . . . . . < 10 < 5 59.2 11.5 15.3 73.9 0.58 0.63 0.58 0.56 0.10 0.482 . . . . . . . . < 10 5–9 7.8 3.3 10.0 8.1 0.86 1.41 1.23 1.09 1.41 1.083 . . . . . . . . < 10 10–29 7.7 2.8 15.5 7.3 0.79 1.76 1.50 1.28 1.76 1.344 . . . . . . . . < 10 30–59 2.9 0.9 13.0 1.7 0.73 2.63 2.20 1.82 2.63 2.005 . . . . . . . . < 10 60+ 3.1 0.8 23.5 0.3 0.66 3.40 2.83 2.32 3.40 2.596 . . . . . . . . 10–29 < 5 4.2 6.3 1.0 4.3 1.60 0.61 0.86 1.10 1.60 1.227 . . . . . . . . 10–29 5–9 0.9 1.6 1.4 0.7 1.74 1.55 1.49 1.52 1.74 1.338 . . . . . . . . 10–29 10–29 1.3 3.0 3.0 0.8 1.98 1.89 1.78 1.79 1.98 1.519 . . . . . . . . 10–29 30–59 0.7 1.4 3.5 0.2 1.85 2.78 2.44 2.20 2.78 2.1210 . . . . . . . 10–29 60+ 0.8 1.0 5.0 0.1 1.51 3.21 2.74 2.35 3.21 2.4411 . . . . . . . . 30–59 < 5 2.3 8.1 0.2 1.7 2.45 0.37 1.11 1.58 2.45 1.8712 . . . . . . . 30–59 5–9 0.5 1.9 0.4 0.3 2.60 1.17 1.49 1.83 2.60 1.9813 . . . . . . . 30–59 10–29 0.9 3.1 2.5 0.3 2.40 2.07 2.01 2.07 2.40 1.8314 . . . . . . . 30–59 30–59 0.7 2.8 3.0 0.0 2.61 2.55 2.40 2.39 2.61 1.9915 . . . . . . . 30–59 60+ 0.1 0.4 0.6 0.0 2.58 3.04 2.76 2.62 3.04 2.3216 . . . . . . . 60+ < 5 6.0 44.9 0.5 0.4 3.57 0.36 1.58 2.29 3.57 2.7217 . . . . . . . 60+ 5–9 0.4 2.6 0.3 0.1 3.40 1.10 1.74 2.29 3.40 2.5918 . . . . . . . 60+ 10–29 0.5 3.1 0.9 0.0 3.25 1.70 1.99 2.37 3.25 2.4819 . . . . . . . 60+ 30–59 0.1 0.6 0.3 0.0 3.20 2.26 2.33 2.54 3.20 2.4420 . . . . . . . 60+ 60+ 0.0 0.0 0.1 0.0 2.92 3.11 2.87 2.70 3.11 2.37

Totals for 2000 268,000,000 34,000,000 32,000,000 203,000,000

Resulting nominal oversampling rates across strataTotal 1.00 1.00 1.00 1.00 1.00 1.00Black 2.57 0.87 1.47 1.85 2.64 2.05Hispanic 1.02 2.18 1.89 1.65 2.17 1.72Other 0.74 0.84 0.79 0.76 0.55 0.72

Design effects from unequal weightsTotal 1.30 1.39 1.30 1.33 5.93 1.49Black 1.51 1.67 1.17 1.25 3.79 1.37Hispanic 1.26 1.41 1.33 1.27 4.01 1.37Other 1.14 1.19 1.16 1.16 4.01 1.24

Effective oversampling ratesTotal 0.72 0.72 0.77 0.75 0.17 0.67Black 1.71 0.52 1.26 1.49 0.69 1.50Hispanic 0.81 1.54 1.42 1.30 0.54 1.25Other 0.65 0.71 0.68 0.65 0.14 0.58



interview. See the fourth section ofchapter 2for the rationale onrequiring only one sample adult pehousehold.

Designing a sample to meet theserequirements was a complexundertaking. The theory inchapter 4guides the allocation of sample acrossminority density strata when there isonly one targeted domain, but it doesnot determine an optimal allocationwhen there are two targeted domains.Also, that theory assumes that allpersons found in the screening who arnot members of the targeted domain wbe dropped from the sample. For thisdesign it was necessary to balance thecost of screening and the design effec

ll

associated with oversampling against thgains in precision for the black andHispanic populations.

The basic procedure used during thdesign process was to experiment withdifferent allocations of the sample acrosthe minority density strata definedearlier in this chapter. For every trialallocation, the nominal oversamplingrates, design effects, and effectiveoversampling rates were calculated forall three domains using equations 3, 2,and 4, respectively. The nominaloversampling rates were then applied tothe sample sizes that could be expectefrom an equiprobability sample of50,000 interviewed households shown itable 6. The costs of the screeninginterviews and of the full interviews for

the black and Hispanic households werthen calculated. This amount of moneywas then subtracted from the totalassumed available given a 50-percentincrease in funding over the levelrequired for an equiprobability sampleof 50,000 interviewed households. Thisthen determined the number of fullinterviews that could be conducted with‘‘other’’ households. The allocation ofthe sample was then chosen that yieldethe required effective sample sizes of8,000 interviewed black adults and8,000 interviewed Hispanic adults (givethe random selection of one adult perhousehold), while maximizing theeffective sample size for other adults.

The resulting oversampling rates areshown intable 20. Recall that these

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Table 20. Oversampling rates for the alpha design[Relative to a design that would yield 50,000 interviews]



< 5 5–10 10–30 30–60 60+

< 10 . . . . . . . . . . . . . . . . . . . . 1.6 2.00 3.20 4.00 4.0010–30 . . . . . . . . . . . . . . . . . . . 1.6 2.00 3.50 4.00 4.0030–60 . . . . . . . . . . . . . . . . . . . 1.7 2.00 3.50 4.00 4.0060+ . . . . . . . . . . . . . . . . . . . . . 2.0 2.00 3.50 4.00 4.00

Table 21. Persons in screened households for the alpha option


All adults in each household

Male Female Total

Black

Under 5 . . . . . . . . . . . . . . . . . . . . . . . 1,475 1,413 2,8885–17 . . . . . . . . . . . . . . . . . . . . . . . . . 4,203 4,010 8,21318–24 . . . . . . . . . . . . . . . . . . . . . . . . 1,559 1,824 3,38325–44 . . . . . . . . . . . . . . . . . . . . . . . . 4,220 5,352 9,57245–64 . . . . . . . . . . . . . . . . . . . . . . . . 2,706 3,494 6,20065 and over . . . . . . . . . . . . . . . . . . . . 1,250 1,850 3,100Total . . . . . . . . . . . . . . . . . . . . . . . . . 15,412 17,943 33,355Total 18 and over . . . . . . . . . . . . . . . . . 9,735 12,520 22,255Households . . . . . . . . . . . . . . . . . . . . . . . . . . . 12,295

Hispanic


Other


Total





ratesarerelative to an equiprobabilitysample that yields 50,000 completedhousehold interviews.

These oversampling rates result innominal oversampling rates for theblack, Hispanic, and ‘‘other’’populations of 2.212, 3.195, and 1.831respectively (equation 3). Applying thenominal oversampling rates totable 6yields table 21, which shows the totalnumber of persons in screenedhouseholds.

Clearly, the number of ‘‘other’’households discovered during screeninis much larger than can be afforded wia 50-percent budget increase. At theconclusion of the screening interview,is necessary to tell some of them,‘‘Thanks for your time and have a niceday.’’ Of course, there is no reason tosubsample at a uniform rate. Given thedifferential oversampling rates shown itable 20, it is much better to design theretention rates in such a manner that a‘‘other’’ household (containing onlynon-Hispanic white persons, Asians, oNative Americans) will have the sameprobability of selection whether it is inpredominantly black, Hispanic, or otheneighborhood.

The computation of retention ratesfor ‘‘other’’ households depends strongon the relative costs of screening andfull interviews. As mentioned already, afundamental assumption underlying thdesign was that a full interview costs 3times as much as a screening intervie(R = 3). The differential oversamplingrates shown intable 20are projected toyield 123,900 addresses for screening(before identification of vacants, otherineligibles, and nonrespondents). Withsample of 50,000 interviewedhouseholds, the number of addressesscreen is just 62,500 (allowing for acombined loss to ineligibility andnonresponse of 20 percent). If thehypothetical survey of 50,000 completewere expanded by 50 percent, thatwould mean 93,750 screened addressThe difference of 30,150 screenedhouseholds means that the number offull interviews must be cut by at least10,050 households from the 75,000 thcould be done with a straight 50-perceincrease over 50,000 interviewedhouseholds. Cutting 10,050 householdfrom the hypothetical 75,000 does not

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Table 22. Percent of other households discovered during screening to be kept for fullinterviews under the alpha design


Percent of black that is Hispanic

< 5 5–10 10–30 30–60 60+

< 10 . . . . . . . . . . . . . . . 54.1 43.3 27.0 21.6 210–30 . . . . . . . . . . . . . . 54.1 43.3 24.7 21.6 230–60 . . . . . . . . . . . . . . 50.9 43.3 24.7 21.6 260+ . . . . . . . . . . . . . . . . 44.4 43.3 24.7 21.6 2

Table 23. Effective sample sizes for alpha design when all household members are sampled



Male Female Total

Black


Hispanic

Under 5 . . . . . . . . . . . . . . . . . . . . . . . 2,209 2,115 4,3255–17 . . . . . . . . . . . . . . . . . . . . . . . . . 5,307 5,068 10,37518–24 . . . . . . . . . . . . . . . . . . . . . . . . 2,012 1,946 3,95725–44 . . . . . . . . . . . . . . . . . . . . . . . . 5,618 5,483 11,10045–64 . . . . . . . . . . . . . . . . . . . . . . . . 2,523 2,859 5,38265 and over . . . . . . . . . . . . . . . . . . . . 910 1,274 2,184Total . . . . . . . . . . . . . . . . . . . . . . . . . 18,579 18,745 37,324Total 18 and over . . . . . . . . . . . . . . . . . 11,063 11,562 22,624Households . . . . . . . . . . . . . . . . . . . . . . . . . . . 10,983

Other


Total





save all the cost associated with thosecontacts, however. There are still thescreening costs. The actual number oftotal households that can be retained fofull interviews is determined by theequation

[50,000 +62,500 − 50,000

3 ] (1.5) =

[ x +(123,900 −x)

3 ]Solving this equation yields the

answer that 59,900 households can beretained for full interviews. Subtractingout the 12,300 black households and th12,500 Hispanic households that areexpected to receive full interviews, thisleaves room for 35,100 ‘‘other’’households to receive full interviews.Given that it is expected that thescreening will yield 74,300 ‘‘other’’households, this implies that the overalretention rate of other householdsdiscovered during screening should be47.3 percent.

Let ri be the retention rate forscreened ‘‘other’’ households ini-thminority density stratum. Also, letki bethe oversampling rate for the stratumfrom table 20, and letQi be theproportion of the national ‘‘other’’population that is in thei-th stratum (intable 17). It is optimal to have the twofollowing relationships both be true:

OQi ri = 0.473 andri ~1ki

The first equation simply requiresthat the average weighted retention ratefor other households across all the strabe correct. The second relationship willensure an equiprobability sample of‘‘other’’ households. Both relationshipscan be satisfied by setting

ri =0.473

ki∑j

(Qj /kj)

The results of this calculation are showin table 22.

The differential weights associatedwith the oversampling shown intable 20leads to design effects of 1.084 and1.135 for the black and Hispanicpopulations, respectively. Aftersubsampling, the design effect for the

‘‘other’’ population will be 1.00 becausethere will be an equiprobability sampleof the ‘‘other’’ population. Thus, theeffective sample sizes for the threedomains can be achieved by adjusting

the numbers of screened persons showin table 21for the design effects andsubsampling. This means dividing by1.084 for the black population, dividingby 1.135 for the Hispanic population,

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Table 24. Effective sample sizes for alpha design when one adult is sampled per household



Male Female Total

Black

Under 5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .5–17 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .18–24 . . . . . . . . . . . . . . . . . . . . . . . . 621 726 1,34725–44 . . . . . . . . . . . . . . . . . . . . . . . . 1,680 2,131 3,81145–64 . . . . . . . . . . . . . . . . . . . . . . . . 1,078 1,391 2,46965 and over . . . . . . . . . . . . . . . . . . . . 498 737 1,234Total . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .Total 18 and over . . . . . . . . . . . . . . . . . 3,876 4,985 8,861Households . . . . . . . . . . . . . . . . . . . . . . . . . . . 11,343

HispanicUnder 5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .5–17 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .18–24 . . . . . . . . . . . . . . . . . . . . . . . . 781 756 1,53725–44 . . . . . . . . . . . . . . . . . . . . . . . . 2,182 2,129 4,31145–64 . . . . . . . . . . . . . . . . . . . . . . . . 980 1,110 2,09065 and over . . . . . . . . . . . . . . . . . . . . 353 495 848Total . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .Total 18 and over . . . . . . . . . . . . . . . . . 4,296 4,490 8,786Households . . . . . . . . . . . . . . . . . . . . . . . . . . . 10,983

OtherUnder 5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .5–17 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .18–24 . . . . . . . . . . . . . . . . . . . . . . . . 1,627 1,642 3,26925–44 . . . . . . . . . . . . . . . . . . . . . . . . 5,616 5,696 11,31345–64 . . . . . . . . . . . . . . . . . . . . . . . . 4,614 4,791 9,40565 and over . . . . . . . . . . . . . . . . . . . . 2,277 3,292 5,569


and multiplying by 0.473 for the‘‘other’’ population. The effectivesample size for the total population isslightly more complicated to calculateand present because the oversamplingblack and Hispanic households meansthat the average household size willincrease for the sample. Aftersubsampling, the nominal oversamplinrate for the total population will be1.278 with a design effect of 1.281.Also, the nominal oversampling rate fohouseholds will be 1.197 with the samdesign effect of 1.281. The effectivesample size for the total population isobtained by multiplying the sample sizshown intable 6by either 1.278 or1.197 and then dividing by 1.28. Theresults of these adjustments are showin table 23.

To get effective sample sizes whenjust one adult is sampled per househoit is necessary to adjust the numbers itable 23in the same manner as thenumbers intable 6were adjusted toobtaintable 7. The results are shown intable 24.

Total . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .Total 18 and over . . . . . . . . . . . . . . . . . 14,135 15,421 29,556Households . . . . . . . . . . . . . . . . . . . . . . . . . . . 35,171

TotalUnder 5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .5–17 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .18–24 . . . . . . . . . . . . . . . . . . . . . . . . 2,488 2,566 5,05325–44 . . . . . . . . . . . . . . . . . . . . . . . . 7,739 8,137 15,87645–64 . . . . . . . . . . . . . . . . . . . . . . . . 5,382 5,901 11,28365 and over . . . . . . . . . . . . . . . . . . . . 2,512 3,631 6,144Total . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .Total 18 and over . . . . . . . . . . . . . . . . . 18,121 20,235 38,356Households . . . . . . . . . . . . . . . . . . . . . . . . . . . 46,794


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Chapter 6. Choosing aSampling Techniquefor the Elderly Blackand HispanicPopulations

Area Sampling WithOversampling at the BlockLevel With Screening forRace, Ethnicity, Age, andSex

As shown intable 20, even with a50-percent budget increase, theoversampling of blocks with highconcentrations of black or Hispanicpopulations and screening (as a resultmortality and immigration patterns),black and Hispanic persons constitute amuch smaller proportion of the elderlypopulation in this country than is thecase in other age groups. There are noenough elderly black and Hispanicpersons to yield sample sizes from theproposed design that meet NCHSrequirements. Although the sample sizefor both sexes have projecteddeficiencies, the problems are especiallacute for males; fewer than 500 areprojected for black males (on aneffective basis) and about 350 forHispanic males (also on an effectivebasis). A budget increase much largerthan 50 percent would be necessary tomeet the goals by oversampling blocksand screening alone. Other methods ofincreasing the effective sample sizes fothese domains were thereforeresearched.

Dual-Frame SamplingWith Social SecurityAdministration Lists

Because the Social SecurityAdministration (SSA) maintains fileswith excellent coverage of the elderlypopulation (at least for those age 66years and over), research was conducton dual-frame sampling that wouldcombine the traditional area-permitsample with a supplemental list sample

While this research was beingconducted, interest arose insupplementing the NHIS sample for1994 (1 year before the plannedredesign) with a list sample of disabledSSA beneficiaries. This work is reporteseparately inA Dual Frame Design forSampling Elderly Minorities andPersons With Disabilities(16). The ideaof oversampling SSA disabledbeneficiaries for NHIS has been droppfor now. A decision to use SSA lists fooversampling either disabledbeneficiaries or elderly minority personhowever, makes it easier, of course, touse the list for the other type ofoversampling as well.

The task of oversampling the elderlyblack population from SSA files is fairlystraightforward because race is indicatefor about 97 percent of the file.Unfortunately, SSA files do not have anindicator of Hispanic origin for personswho are currently elderly. (SSA started tcollect ethnicity data in the 1980’s, toolate to be of use in classifying thecurrent elderly population.) Thus, animportant issue is the identification ofthose who are likely to be Hispanic.This task is possible through the use osurname classification. The followingsections discuss this use of surnameseffective sample sizes that can beachieved with dual-frame sampling, anwhether to use SSA lists to oversampleither addresses or persons. Assumingthat persons are oversampled, the issuof other household members andestimation issues for dual-framesampling are discussed in the last twosections of this chapter.

Using Surnames to IdentifyHispanic Persons

To use the SSA files foroversampling elderly Hispanic personsdespite the lack of an indicator forHispanic origin, a strategy that identifiepersons likely to be Hispanic on thebasis of surname was considered. TheHispanic surname file developed by J.Passel and D. Word at the U. S. Bureaof the Census (17) was used to classifsurnames by ethnic origin. ThePassel-Word file contains 12,497surnames that tend to belong toHispanic persons.

The precision and cost of adual-frame sample based on surnamedepends strongly on the sensitivity andspecificity of the Passel-Word file. Evefalse positive costs money to interviewor screen out; every false negativeincreases the portion of the area-permsample that is not covered by the listand that will therefore have highsampling weights, thereby increasingdesign effects because of unequalweights. (Note, however, that neitherfalse positives nor false negatives makunbiased estimation impossible. This isdiscussed later in this chapter.) Passeand Word indicated that false positives(also called errors of commission) runaround 15 percent and that falsenegatives (also called errors ofomission) run at around 20 percent.Given the time lapse since that originastudy, it was judged prudent to repeatthe study. To that end, the surname ofevery member of the 1988 NHIS wasmatched against the Passel-Word file.This allowed the comparison ofself-reported ethnicity with the ethnicitythat would be imputed on the basis ofsurname using this file. At the sametime, some of the characteristics ofpersons with nonconforming nameswere analyzed; it was thought that thisinformation should be useful in makingdecisions about sample allocation.

The overall false-positive rate was12.6 percent, lower than had beenexpected on the basis of previouslypublished research; the overallfalse-negative rate was 31.6 percent,higher than expected. Passel and Wororiginally reported error rates with theMarch 1976Current Population Surveyof 15.0 percent false-positives and20.7 percent false-negatives. Part of thdiscrepancy involves persons who didnot classify their ethnicity, who hadhyphenated names, or who refused toprovide their names; Passel and Wordtreated all of these differently than wasdone in this research. An inspection ofnonconforming surnames of Hispanicpersons indicates, however, that evenallowing for these variations inmatching procedure, a substantial andunexplained difference exists in thefalse-negative rate. The reason for thepoorer performance could be eitherlower quality recording, transcription,

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Table 25. Required sample sizes by sampling method and precision target

Method SexEffective Hispanic

sample size1Screener

interviews2

SSA list with match to Passel-Word file underobserved error rates . . . . . . . . . . . . . . . . . . . . . .

M 770 1,000F 920 1,000

Total screener interviews required . . . . . . . . . . . . . . 2,000

SSA list without match to Passel-Word file . . . . . . . . . M 770 9,750F 920 10,350


Area sample . . . . . . . . . . . . . . . . . . . . . . . . . . . M 770F 920


SSA list with match to Passel-Word file underobserved error rates . . . . . . . . . . . . . . . . . . . . . .

M 1,000 3,050F 1,000 1,950


SSA list with match to Passel-Word file under1976 error rates . . . . . . . . . . . . . . . . . . . . . . . .

M 1,000 1,280F 1,000 1,120


SSA list with match to Passel-Word file under0 error rates . . . . . . . . . . . . . . . . . . . . . . . . . .

M 1,000 760F 1,000 590


SSA list without match to Passel-Word file . . . . . . . . . M 1,000 15,100F 1,000 12,500


Area sample . . . . . . . . . . . . . . . . . . . . . . . . . . . M 1,000F 1,000


1Nominal Hispanic sample sizes are larger than the effective sizes shown.2List either persons to be tracked and screened or households from area sample to be screened in order to achieve the effectivesample sizes indicated for Hispanics. All of these screener interviews would be in addition to the 99,000 annual householdscreener interviews proposed for the 1995 design.


and keying of names on the 1988 NHISthan on the March 1976 CPS(interviewers, not respondents, recordname spellings) or real change in thepopulation in the relationship betweensurname and self-reported ethnicity.Both rates are slightly higher for theelderly than for the general population:16.6 percent false-positives and33.6 percent false-negatives.

Both types of misclassificationbased on surname are far more commamong women than among men.Intermarriage evidently plays a strongrole in both misclassification rates.Socioeconomic status also plays a strorole in both rates. The general trendseems to be that higher socioeconomicstatus means a weaker associationbetween surname and self-reportedethnic origin. Differences also exist bymetropolitan status, part of the country,and country of origin (for self orancestors). SeeUsing Surnames toOversample Hispanics From a ListFrame (18) for more detail.

Effective Sample Sizes WithDual-Frame Sampling

Although the estimated sensitivityof the Passel-Word Hispanic surnamelist was not as great as had been hopeusing it to create a list sample of elderlHispanic persons still has goodpotential. The technique is far cheaperthan area sampling with screening forthe same precision. These implicationsare explained more fully in this sectionTable 25contrasts some of the numbersthat appear in the following text.

Under current plans for 1995 andbeyond, the area-permit NHIS samplewill yield nominal elderly Hispanicsample sizes of about 500 males and700 females. After accounting for thedesign effect due to disproportionatesampling of heavily Hispanic blocks, theffective sample sizes (compared withsimilarly clustered sample with equalprobabilities) will be only about 350 an500. By adding 1,000 males withHispanic surnames from SSA lists andanother 1,000 females in the samemanner, the nominal sample sizes canbe boosted to around 1,240 and 1,400and the effective sample sizes can beboosted to around 770 and 920. (The

, effective sample sizes do not increasemuch as the nominal sample sizesbecause of the design effect due to thelarge weights that Hispanic personswithout Hispanic surnames will bear.)To get a comparable boost from thearea-permit sample alone would requirthe screening of an additional 100,000households.

For another contrast, suppose thaone used SSA lists as a supplementallist sampling frame without payingattention to surname. In that case, mothan 20,000 persons would have to belocated and screened on ethnicity toobtain comparable boosts for elderlyHispanic persons. Thus, althoughscreening a list of elderly persons is farmore efficient than screening a sample ohouseholds (even if those households wskewed toward heavily Hispanic blocks)it is not as efficient as using a list incombination with the surname list.

Nonetheless, boosting the effectivesample sizes even more sharply forelderly Hispanic persons, to as high as

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1,000 males and 1,000 females, wasdesired. Here it turns out that thefalse-negative rate is too high to makethat economical. About 5,000 listpersons with Hispanic surnames wouldhave to be added to the sample.

Interest in determining ways todecrease the false-negative rate issignificant, even if it means someincrease in the false-positive rate as itlikely would. If, for example, the 1976findings of Passel and Word still held,the supplemental sample size requiredfor the desired effective sample sizes bsex would be 2,400 instead of 5,000.(Even with a perfect indicator ofHispanic origin on the list, asupplemental sample of 1,350 personswould be required to achieve the desireeffective sample sizes of 1,000 by sex.It is suspected that adjustments ininterviewer training and in keypunchingcould reduce the frequency of falsenegatives. Even though the U. S. Bureaof the Census would be sampling froman SSA list that would probably have

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more accurate name spelling, it wouldstill be necessary to classify everyonethe area-permit sample to determineappropriate sampling weights for thedual-frame estimator.

Addresses or Persons?

One of the issues that was discusswith respect to dual-frame sampling wawhether SSA beneficiaries or theiraddresses should be the sampling unitThe field costs would be lower for theaddress option because tracking ofmovers and persons with bad addresse(e.g., addresses of custodians) would nbe necessary. This idea was rejected,however, for two reasons. First, theefficiency of the oversample would bedegraded. No data were available onhow often beneficiaries move or on thecharacteristics of subsequent occupantof their housing units. Second (and moimportant), weighting would be moredifficult with the address-basedapproach. Using this approach, it wouldbe necessary to match every address ithe regular NHIS sample against SSAfiles to determine whether SSA had arecord of an elderly black person or anelderly person with a Hispanic surnameliving at the address. Given the fact thaSSA addresses have neither isolatedcomponents nor standardizedabbreviations, the false-nonmatch ratewould be substantial.

A related approach would be tomake the person the sampling unit butto conduct the interview only if the SSAaddress was current and correct. Thiswould have reduced the cost of thesample without increasing matchingdifficulties. This idea was rejectedbecause it would result in an oversampof a narrower domain than the domainof interest.

Other Household Members

If persons sampled from SSA listsare to be followed, the question ofwhether to collect data about otherhousehold members naturally arises. Aflexible approach was developed thatinvolves collecting data about otherhousehold members for core items andfor some (but not all) questionnairesupplements. When other household

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members are interviewed, a multiplicitestimator would be used to weight the

The recommendation to administethe core questionnaire to all householmembers was based on severalconsiderations. First, plans have beenmade to trim the core to a very shortinstrument. Furthermore, the corequestionnaire structure makes it unlikethat substantial cost savings could begained by restricting the core to theSSA-designated sample person. Ofcourse, if those plans to trim the coreare never realized, a review of thisrecommendation would be appropriate

The decision about any particularsupplement to the core would beinfluenced by several factors. First, arother household members likely to bedomains of interest? Second, is theburden on the household tolerable?Third, is the marginal cost of collectingthe data high?

Some core supplements areadministered to random samples ofhousehold members. (The most commrule is one random adult.) If it isdecided to administer such a supplemin households that are in the SSA listsample, the within-householdsubsampling must be done independeof which person or persons in thehousehold were selected directly fromthe SSA list. The alternatives involvesharp variations in weights.

Dual-Frame Estimation

Dual-frame sampling with SSA listsrequires somewhat more complexweighting than surveys using a singleframe. Extra data are needed for unbiasweighting. Specifically, it must be knownwhether every person in the regular NHarea-permit sample has a chance of beselected through the list. For list samplinof elderly black and Hispanic persons,ideally the following information wouldbe known about members of thearea-permit sample:

+ Whether he or she is a SocialSecurity beneficiary.

+ If so, how race is recorded on SSAfiles for the person (black, other, ounspecified).

+ The exact spelling of surname onSSA files.

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One possible way to obtain thesedata would be to do an exact match oall elderly persons in the area-permitsample against SSA files. However, anexact match would be expensive anddifficult. Several considerations indicatethat such a match is not reallynecessary. First, almost all elderlypersons are SSA beneficiaries. (Someproblems might exist with persons 65years of age in terms of the lag timebetween individuals becomingbeneficiaries and files from SSA beingprocessed through sampling operationThus, it might be best to use the list tooversample only persons aged 66 yeaand over rather than those aged 65 yeand over.) With respect to race andsurname spelling, it is reasonable toassume that both are reportedconsistently enough. (Black versus notblack is the only important racialclassification for weighting. Also, exactconsistency on surname spelling is norequired; it is enough if the misspellinghas the same status with respect topresence on the Hispanic surname fileIf a match were to be planned anywayfor a disabled list sample, however, itwould make sense to match the elderlarea sample at the same time to pickrace and surname spelling from SSAfiles. If no match is done, it might bepossible to harmonize spelling betweeNHIS and SSA by asking to see aSocial Security card.

Details of Weighting the Core

If dual-frame sampling with SSAlists were done, the first step each yeawould be to count the number ofpersons in each NHIS area-permithousehold who are age 65 years or ovand either are black or have a Hispanisurname. Then household base weighcould be developed. These base weighwould not be the traditional inverseprobabilities of selection because itwould be impossible to compute theprobability of selection for a householdacross all possible area-permit and listsamples from the given joint design.(This is because of the systematicmultistage approach being used in thelist sample.) Instead, a multiplicity-based base weight would be determinfor each household in the list sample;

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the standard base weight would beassigned to each household in thearea-permit sample; then the twoweights would be averaged for everysample household. The average wouldmost likely be a weighted average. Forhousehold that is only in one sample,the base weight for the other componenwould be zero. Base weights forarea-permit sample households that donot contain any possible list samplepersons are, of course, not changed asresult of dual-frame estimation.

As a variant, it might be preferableto work out adjustments for nonresponsseparately for each component and thenaverage together the nonresponse-adjusted weights.

Poststratification could be done justas it is now, using demographic modelsby age, race, and sex. Once the weightshave been calculated, the morecomplicated estimation for the dualsample would be irrelevant to users.They would be able to make ordinaryweighted tabulations of the core in thesame way they would have for thearea-permit sample by itself.

Weighting procedures forquestionnaire supplements would depenon the within-household sampling rules.It is anticipated that different weightswould be needed for analysis of the corinterview than for analysis ofsupplements.

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Chapter 7. Prospectsfor Statistics onDetailed HispanicSubdomains

Despite the progress that theproposed design (see Part V)will make in improving the

precision of NHIS statistics aboutHispanic persons, the diversity of theHispanic experience in this country issuch that the analytic potential of NHISwould be improved by additionalbreakdowns in tabulations. Accordinglyseparate statistics on Cuban AmericanPuerto Rican Americans, MexicanAmericans (separately by whether theirancestors lived in the United Statesbefore 1900 or not), Central AmericanAmericans, and other HispanicAmericans appear to be desirable.

Design-Based TechniquesAlthough precise statistics on such

detailed domains would undoubtedly beextremely interesting, limits are imposeby a fixed budget. Given theoverwhelming importance of age andsex for questions related to health, valicontrasts between detailed Hispanicsubgroups would have to be supportedby adequate sample sizes for each ageand sex subgroup within each Hispanicsubgroup. Given the required precisionlevels discussed inchapter 3, eightage-by-sex subdomains for each detailHispanic subgroup would require 8,000persons. Multiplying by 6, detailedHispanic subgroups give a total requiresample size of 48,000 persons, about40 percent of the total sample. Even ifsome way to obtain addresses cheaplyfor so many Hispanic persons existed (does not), devoting such a largeproportion of the total sample toHispanic persons would seriously erodthe utility of NHIS for addressing otherquestions. The research conducted undthis contract yields the conclusion thatis not possible to obtain design-basedstatistics of the desired reliability aboutdetailed Hispanic subgroups with thecurrent budget. Even a doubling or

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tripling of the budget would allow onlymodest precision for each Hispanicsubgroup.

The possibility of breaking Hispanicstatistics geographically instead ofethnically was also investigated. Thisapproach is related to that used in theHispanic Health and NutritionExamination Survey. The regionsinvestigated were the following: TheNew York Consolidated MetropolitanStatistical Area; Florida; and the fivesouthwestern States of California,Arizona, New Mexico, Texas, andColorado. Unfortunately, thisinvestigation also showed the need forbudget increases far larger than NCHSstaff thought were possible. More detaican be found inchapter 11.

Model-Based TechniquesGiven the impracticality of

design-based techniques, research waconducted on model-based techniquesIn this research, the possibility wasconsidered of oversampling only thoseHispanic persons who live in heavilyHispanic blocks and then extrapolatingthe results to all Hispanic persons usinmodels. A key factor here is therelationship between health andsegregation. If health is not related tosegregation or if the relationship can bexplained in terms of socioeconomicvariables that are available for the whosample, the model-based approachwould be much more precise thandesign-based estimators that could beproduced at the same cost. Even ifneither of these conditions applies (andit seems natural to believe that there issome relationship that cannot be entireexplained by other variables), and thusthe model-based estimates would bebiased, it is possible that model-basedestimators would still have anacceptable mean square error.

To study the question, therelationship between segregation andhealth was studied for Hispanic personin the 1988 NHIS. That samplecontained too few Hispanic people tostudy the detailed groups separately, sthey were simply studied as a group.The health characteristics of Hispanicpersons were contrasted across the fivminority density strata that had been

created for the Hispanic population.(The strata were defined as of 1980.)Simple comparisons showed significadifferences. Logistic models were thencreated for three binary health measuThe inclusion of socioeconomicvariables reduced the nominalrelationship between segregation andhealth but did not rule out the existenof important residual relationships. (Fomore detail on this study, seeResidential Segregation and HealthCharacteristics of MinorityPopulations(19).) The sample sizeswere not large enough to allow veryfirm projections of the mean squareerror for model-based estimators. Moryears of NHIS samples need to bestudied to arrive at more definiteconclusions.

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Chapter 8. Prospectsfor Statistics on OtherMinority Groups

Prospects are not good forinexpensive, large samples ofAmerican Indians, Eskimos, and

Aleuts, or of Asians and PacificIslanders.

Asians and PacificIslanders

The main obstacle to providingprecise health statistics for Asians andPacific Islanders is that, despite theconcentrations of population in theChinatowns of New York and SanFrancisco, most Asians and PacificIslanders live in more integratedneighborhoods. Considerable screeningat substantial cost would be necessarylocate them.

Based on statistics from the 1990census on the concentration withinblocks of Asians and Pacific Islanders(tables 5and6), it is estimated that amodest oversample of Asians andPacific Islanders could be achieved byscreening an additional 15,000households (beyond the 99,000 alreadyplanned) and by interviewing anadditional 2,580 households. Theresulting oversample would yield aneffective sample size of around 8,000persons. (Effective sample size heremeans the nominal sample size dividedby the design effect due to unequalprobabilities but not due to clustering.)This sample size of 8,000 persons coulbe achieved only by interviewing allmembers of Asian households. (Asianhouseholds are simply too rare to allowsubsampling within households toreduce household burden.) Also, thedistribution across ages would not beoptimal. Narrow age bands of particularanalytic interest such as persons ages18–24 years and children under age 6would have smaller sample sizes thandesired. It was estimated that thisadditional screening and interviewingwould cost an additional $1.4 millionper year, more than the budget NCHSindicated would be available.

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Some of the other potentialtechniques discussed inchapter 4mayhave some promise for sampling AsianNetwork sampling is particularlyinteresting given large family sizes forthis domain. (Among Asians and PacifiIslanders, the number of persons peroccupied housing unit was 3.59 in 199compared with 2.64 for all races.) Todevelop the idea further would requirethe sort of research described earlier inchapter 4. Multiyear aggregation is alsoa technique to be considered.

Also, at least one or two companieare experimenting with lists of typicalAsian surnames as a means ofidentifying Asians and Pacific IslandersIf such an approach yields afalse-negative rate below 40 percent, aleast modest oversampling would bepossible. Perhaps it could be combinedwith oversampling by block andscreening and with network sampling.Although this option has not beenpursued very vigorously, it probably isworth further effort.

A further problem is the diversity othe populations covered by the Office oManagement and Budget classificationof ‘‘Asian and Pacific Islander.’’ Just asinterest exists in detailed statistics onMexican Americans, Puerto RicanAmericans, Cuban Americans, and soon, some of the analysts interested inAsian statistics stress the need todistinguish among Chinese, Japanese,Filipinos, Polynesians, Indians, MalaysVietnamese, Hmong, Mongolians, andso on. Given the relative prosperity ofsome of these subgroups, a fundamenquestion remains unanswered: If it is nfeasible to create precise statistics foreach subgroup of Asians and PacificIslanders separately (and it is surely nofeasible with any procedure short of aspecial census), are statistics on theamalgam group truly useful? It isassumed in this report that suchstatistics are indeed important, but thisquestion should receive more carefulattention by subject matter specialists.

In conclusion, it was believed thatthe budget for NHIS could not beincreased enough to pay for obtainingtruly useful statistics about this domainbroken down by age and sex.

American Indians,Eskimos, and Aleuts

The main obstacle to providingprecise health statistics about NativeAmericans is their small numbers. Abouone-half of Native Americans live on ornear reservations where they are easyfind (although transportation can bedifficult and expensive in those areas),but the other one-half live in mostlywell-integrated neighborhoods wherefinding them is very expensive. If thegoal were only to provide reliablestatistics about Native Americans livingon reservations, it could probably beachieved with a modest budget increas(perhaps less than 10 percent).

Otherwise, it is estimated that ascreening sample of an additional128,000 households (on top of theplanned 99,000) and an additional 5,86household interviews would be requiredjust to produce an effective sample sizeof 8,000 American Indians, Eskimos,and Aleuts—even if all householdmembers were interviewed. That wouldcost about $9 million per year. With thebudget that NCHS has indicated wouldbe available, this would requiresubstantial cutbacks in other domainscovered by NHIS.

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Table 26. Geographic concentration of the poor in 1990

Percent of blockgroup that is living

below poverty

Percent ofthe poorin such

block groups

Percent oftotal

populationin such

block groups

Percent ofpopulation

in suchblock groups

that isnot poor

< 5 . . . . . . . . . . . . . . . . . . . . . 5.8 33.3 97.75 to 10 . . . . . . . . . . . . . . . . . . . 12.3 22.1 92.710 to 20 . . . . . . . . . . . . . . . . . . 24.8 22.8 85.720 to 30 . . . . . . . . . . . . . . . . . . 19.8 10.7 75.630 to 40 . . . . . . . . . . . . . . . . . . 14.3 5.4 65.540 to 50 . . . . . . . . . . . . . . . . . . 10.0 2.9 55.550+ . . . . . . . . . . . . . . . . . . . . . 13.0 2.8 38.2


Chapter 9. The Poor

The 1985–94 NHIS has areasonable sample size for thepoor (roughly 16,000 persons pe

year); it is sufficient for moderatelydetailed analyses. Because of theoversampling of black and Hispanicpopulations, the sample of the poor wibe even larger after the redesign iscompleted. The research performed dinot reveal any inexpensive method ofincreasing the sample size of the poorany further.

The main obstacle to inexpensive,larger samples of the poor is thatpersons with low income are far lesssegregated than many people believe(seetable 26). (Poverty data areavailable only from the decennial censat the block-group level rather than atthe block level. The poor may be moredramatically segregated at the blocklevel; but no data exist on the subject,nor can an oversampling strategy bebuilt based upon data that do not existPoverty is not sufficiently concentratedat the block-group level to makestratification and oversampling a feasibstrategy for major increases in effectivesample sizes.

As mentioned earlier, oversamplingblack and Hispanic populations willexpand the sample size of the poor alittle. Larger increases require expensiand problematic screening. (Persuadinneighbors to speculate on the financiacondition of sample households is farmore difficult than obtaining proxyreports on race and ethnic origin. Ethicconsiderations about whether neighborshould be asked such questions alsoemerge. Even when a member of thesample household is willing to respondtotal income tends to be inadequatelyreported unless an extensive set ofscreens and probes and encouragemeof record checks are done; and all ofthese are incompatible with the idea tha screening interview should be cheapand quick to administer.)

Dual-frame samples were notseriously considered, given theknowledge that no national database obeneficiaries of any of the welfareprograms has been constructed and th

fact that other surveys (such as theCurrent Population Surveyand theSurvey of Income and ProgramParticipation) have shown repeatedlythat a very large segment of the poor dnot participate in welfare programs.

Network estimators were also notseriously considered. It thus appears ththe only feasible techniques foroversampling the poor for NHIS areeither to retain those found in theregular area-permit sample for extrayears of interviewing or to aggregatestatistics across years.

Part III.Statistics for Geographic Domains

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Table 27. State groupings to produce State estimates every 3 years[Roughly 6,600,000 needed per group]

StatePopulation

(000’s) StatePopulation

(000’s) StatePopulation

(000’s)

Washington . . . . . . . 4,991 North Dakota . . . . . . 629 Florida . . . . . . . . . . 15,415Alaska . . . . . . . . . . 687 South Dakota . . . . . 714Oregon . . . . . . . . . 2,877 Minnesota . . . . . . . . 4,490 South Carolina . . . . . 3,906

8,555 Nebraska . . . . . . . . 1,556 Georgia . . . . . . . . . 7,957Kansas . . . . . . . . . 2,529 11,863

California . . . . . . . . 33,500 9,918Hawaii . . . . . . . . . . 1,345 North Carolina . . . . . 7,483

34,845 Iowa . . . . . . . . . . . 2,549Missouri . . . . . . . . . 5,383 Virginia . . . . . . . . . 6,877

Idaho . . . . . . . . . . . 1,047 7,932Neveda . . . . . . . . . 1,303 West Virginia . . . . . . 1,722Utah . . . . . . . . . . . 1,991 Wisconsin . . . . . . . . 4,784 Maryland . . . . . . . . 5,274Colorado . . . . . . . . 3,813 Illinois . . . . . . . . . . 11,580 Delaware . . . . . . . . 734Montana . . . . . . . . . 794 16,364 District of Columbia . . 610Wyoming . . . . . . . . 489 8,349

9,437 Michigan . . . . . . . . 9,250New Jersey . . . . . . . 8,546

Arizona . . . . . . . . . 4,618 Indiana . . . . . . . . . 5,502New Mexico . . . . . . 1,968 Ohio . . . . . . . . . . . 10,629 Pennsylvania . . . . . . 11,503

6,586 16,131New York . . . . . . . . 17,986

Texas . . . . . . . . . . 20,211 Kentucky . . . . . . . . 3,733Tennessee . . . . . . . 5,266 Maine . . . . . . . . . . 1,271

Oklahoma . . . . . . . . 3,376 8,999 New Hampshire . . . . 1,333Arkansas . . . . . . . . 2,529 Vermont . . . . . . . . . 591Louisiana . . . . . . . . 4,516 Mississippi . . . . . . . 2,877 Massachusetts . . . . . 6,087

10,421 Alabama . . . . . . . . . 4,410 Rhode Island . . . . . . 1,0497,287 Connecticut . . . . . . . 3,445

13,776



Chapter 10.Subnational Estimatefor State Groupingsand Hispanic Persons

Subnational Domains WithCurrent Design

The ability of the current NHISdesign to produce accurate estimatesStates was examined. For estimates tohave the precision specified by theNCHS technical staff, an effectivesample size of at least 1,000 individuais necessary (see the first section ofchapter 3). If the NHIS sample of124,000 individuals (50,000 householdwere distributed according to aself-weighting sample, only 33 of theStates would have the necessary 1,00completed interviews to produceStatewide estimates. The same numbeof States would satisfy the minimumsample size requirement if the samplewere to oversample the black andHispanic populations at twice the rateapplied to persons who are not black oHispanic.

Reasonable analyses of healthcharacteristics, however, requireState-level estimates by age and sex.The four standard age breaks forsubnational statistics are 0–17 years,18–44 years, 45–64 years, and 65 yeaand over. The rarest of the eightage-by-sex categories is males 65 yeaof age and over; that category isestimated to consist of 5.3 percent of tU.S. population in the year 2000. Usina self-weighting design (or one thatoversamples at a 2-to-1 rate), no Statewill contain a sample large enough toinclude 1,000 members of thisage-by-sex category. A fallback positiois simply to divide States by the fourage categories. (Given the strong agepatterns in most health statistics, itappears that control by age is essentia

The smallest category then is the aggroup 65 years and over, constituting13 percent of the population. Only threeStates—California, Texas, and NewYork—are sufficiently large to anticipateincluding 1,000 elderly. (Florida is not

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quite large enough. Given that the elderare a significantly larger percentage of thpopulation in Florida than in the averageState, it is likely that, by the year 2000,Florida will also include 1,000 sampledelderly.) To select a sample of 1,000completed interviews of the elderly fromeach of the 51 States (including theDistrict of Columbia), however, requirestotal sample of approximately 390,000[(1,000/0.13)*51]. This is 3 times thecurrent sample size.

The possibility of producing Statestatistics from a combination of 3 yearof NHIS data was also examined. ManNHIS variables (e.g., chronic conditionrates) are relatively stable from year toyear. Items covered by supplements tothe core questionnaire, however, woulhave to be retained for 3 years. Becauthere are correlations between yearsinduced by remaining in the samePSU’s and many of the same blocks, tannual State sample size must be largthan the 333 appropriate for combiningindependent samples. Based on analy

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of intra-PSU and block correlations, anon the current level of oversampling, itwas estimated that 397 completedinterviews would be required each yearto yield an effective sample of 1,000after 3 years (NHIS RedesignMemorandum #40 (4)). With aself-weighting sample design, only 12 othe States would receive the 397completed interviews per year toproduce State estimates every 3 years.

NCHS suggested examining a set28 State groupings to ascertain whethea sufficient sample would be available tproduce estimates for each groupingevery 3 years. Only 21 of the 28groupings were of the necessary size.An alternative set of 22 groupings inwhich each group met this criterion wasestablished (seetable 27). In addition toproducing estimates every 3 years,two-thirds of these groups couldproduce estimates every 2 years andthree of them every year.

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Table 28. Oversampling rates to produce subnational estimates for Hispanics

AreaIncreasedbase rate

Oversampledcells

Oversamplingsome dense

stratum

Southwest . . . . . . . . . . . . 3 3 7.0:1Florida . . . . . . . . . . . . . . 6 6 8.5:1New York . . . . . . . . . . . . 13 3 9.0:1


Subnational Domains forHispanic Persons

The possibility of producingestimates for Hispanic persons living inthree different areas of the country waalso examined. The three areas are thSouthwest, Florida, and New York.Hispanic persons in these areas arepredominantly of Mexican, Cuban, andPuerto Rican ancestry, respectively. Tharea probability sample would have toproduce an effective sample size of1,000 completed interviews in each of24 cells: Four ages (0–17 years, 18–4years, 45–64 years, and 65 years andover) by two sexes for each of the threareas.

Hispanic growth rates in the threeareas were assumed to be the same afor the dominant subgroup in each areAlso, because no information isavailable on undercoverage losses forHispanic persons by individualgeographic areas, it was assumed thaoverall Hispanic-coverage rates wouldapply in all areas except Florida.Hispanic persons in Florida arepredominantly Cuban Americans, whoare older than other U.S. Hispanicpersons and have higher incomes andeducational attainment. We haveassumed the Hispanic-undercoveragerate in Florida is one-half that ofHispanic persons in general.

Although the results presented herare plausible, they are based on a fairamount of speculation. The research wcarried out before the 1990 censusupdate became available. Theseassumptions should be updated with1990 census data if oversampling thessubdomains is still being seriouslyconsidered. The sampling rates needeand screening workloads are quitesensitive to the geographic distributionof Hispanic persons.

The definitions of the geographicareas were initially assumed to besimilar to those used in the HispanicHealth and Nutrition ExaminationSurvey (HHANES). In HHANES theSouthwest was defined as the followinfive States: California, Arizona, NewMexico, Texas, and Colorado; Floridawas only Dade County; and the NewYork area included Hartford and

Fairfield Counties, Connecticut; Essex,Passaic, Hudson, Middlesex, and UnioCounties, New Jersey; and Nassau,Westchester, and Suffolk Counties, andNew York City, New York. With therapid growth of the Hispanic populationpredicted from 1980 to 2000, it is likelythat the New York and Florida areaswith high Hispanic densities will expandbeyond those used in HHANES.State-level U. S. Bureau of the theCensus’ projections to the year 2000,made on the basis of the 1980 census,have been used for all three areas(assumptions about Hispanic persons adetailed in NHIS RedesignMemorandums #9 and #26). Forpurposes of this research, the same fivStates were used for the Southwest, alof Florida for Florida, and New Yorkand New Jersey (which includes mostthe suburban Hispanic population) forNew York.

Oversampling HispanicPersons

If 24,000 completed interviews areallocated to these 24 cells and the totanumber of interviewed households iskept to 50,000, the base sampling ratefor persons who are not Hispanic (andHispanic persons in other areas of thecountry) is reduced from 0.000497 to0.000414 (see NHIS RedesignMemorandums #16 and #31 forderivation of base sampling rate).Without any oversampling, only 4 of the24 cells will attain the 1,000 interviews.These are males and females 0–17 yeof age and 18–44 years of age in theSouthwest. To reach 1,000 in each ofthe remaining 20 cells requiresoversampling Hispanic persons at a ratof 9.3 in the Southwest, 43.5 in NewYork, and 19.6 in Florida. (Although thenumber of Hispanic persons will besimilar in New York and Florida,Florida has many more elderly Hispani

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persons.) Because 21 percent of theHispanic population lives outside densHispanic blocks, it is not possible toadjust for such large oversampling ratewithout increasing the base samplingrate (see NHIS Redesign Memorandum#26). It is necessary, therefore, toincrease the base sampling rate in allthree areas of the country. Alloversampling, especially increasing thebase sampling rate, will requirescreening significant numbers ofhouseholds that will not be interviewed

Even after tripling the basesampling rate in the Southwest, therestill three cells for which high-densityHispanic strata must be furtheroversampled: Males 65 years and ovefemales 65 years and over, and males45–64 years of age. After a sixfoldincrease in Florida’s base rate, six cellrequire additional oversampling. In NeYork a thirteenfold increase results inonly three cells needing additionaloversampling. These 12 cells stillrequire oversampling of the most densHispanic blocks after increasing the basampling rate in the area. Thedifferential sampling rates would rangefrom 7.0 to 1 in the Southwest, from 8to 1 in Florida, and from 9.0 to 1 inNew York. The oversampling rates werdetermined for each cell to avoidoversampling any stratum at 10 timesthe rate used for other strata in the sageographic area. It was then necessarfor the oversampling to be sufficient fothe expected cell size to exceed 1,000multiplied by the design effect arisingfrom oversampling high-densityHispanic blocks.Table 28summarizesthese findings.

These 24 cells will require datacollected from 30,402 persons,corresponding to all persons in 10,134households (Hispanic households areassumed to average 3.0 persons,compared with non-Hispanichouseholds, which average 2.5 person

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Table 29. Oversampling rates when the HCFA frame is used as a sampling frame for theelderly

AreaIncreasebase rate

Oversampledcells

Oversamplingmost dense

stratum

Southwest . . . . . . . . . . . . 4 2 9.5:1Florida . . . . . . . . . . . . . . 4 6 7.0:1


The screening from oversampling the 1cells, however, will identify 90,339Hispanic households; 80,205 of thesewill not need to be interviewed.

To retain a person sample of thesize provided by a 50,000-householdsample requires 39,866 households(50,000−10,134) from the remainder othe country, including the populationthat is not Hispanic of these three areaThis population of the three areasaccounts for 32.89 percent of theremaining population of the UnitedStates and should therefore include13,112.0 (0.3289 × 39,866) sampledhouseholds. This procedure will screen187,452 households that are notHispanic in these areas, so an extra174,341 households would be screeneThe U.S. Bureau of the Censusestimates that screening costs equalbetween one-third and one-half ofinterviewing costs. Screening the254,546 extra households (80,205 +174,341) in these three areas willtherefore use considerably more monethan the current NHIS data collectionbudget—without conducting a singleinterview. Thus, at the current budget,is impossible to produce design-basedestimates for Hispanic persons living inthese three areas, using a strictlyarea-sample approach.

Oversampling Hispanic PersonsWhen Using the Health CareFinancing AdministrationFrame

Subnational estimates for Hispanicpersons, therefore, can only be producif an alternative more efficientmethodology is available. A possiblealternative is to select the Hispanicelderly from a frame of Social Securityrecipients maintained by the Health CaFinancing Administration (HCFA). Theselection could be carried out in thesampled PSU’s. This would reduce theextensive screening needed for the rarage-sex domains, but it would stillrequire the use of the area sample toconduct 1,000 interviews in 18 cells(sex (male or female) by age (0–17years, 18–44 years, 45–64 years) inthree areas).

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Producing a sufficient number ofinterviews in each of these cells wouldrequire increasing the sample in theSouthwest by a factor of 3.4, in NewYork by 13.5, and in Florida by 12.2.Two cells in the Southwest require thioversampling of high-density Hispanicstrata: Males 45–64 years of age andfemales 45–64 years of age. Afterquadrupling the base rate in Florida, asix cells require further oversampling.After quadrupling the base rate in NewYork, three cells require oversamplingMales 45–64 years, females 45–64years, and males 18–44 years. It isinteresting to note that in Florida themost uncommon cell is females 0–17years, not the older people.

The lower oversampling ratesshown intable 29compared withtable 28significantly reduce the numbeof households that need to be screeneThe number of Hispanic households tbe screened dropped from 90,339 to36,089 and non-Hispanic householdsfrom 187,452 to 70,029.

The size of the Hispanic sample inthe three areas has hardly changed, t30,315 (including the 6,000 comingfrom the HCFA frame). This isequivalent to 10,105 households (ofwhich 8,105 come from the areaprobability frame). Because 36,089Hispanic households will be screenedthe three areas, an unused 27,984Hispanic households will be screened

To retain a 50,000-household samp39,895 households (50,000−10,105) mube sampled from the remainder of thecountry and the population that is notHispanic of these three areas. Thenon-Hispanic sample from the three areshould therefore include 13,121 samplehouseholds (0.3289 × 39,895). Given th70,029 non-Hispanic households will bescreened in these areas, an extra 56,90households will be screened. Again, usithe U.S. Bureau of the Census’ estimat

New York . . . . . . . . . . . . 4

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for screening costs, the 84,893 extrahouseholds (27,984 + 56,909) in thesethree areas will reduce the number ofcompleted interviews, for a constant-costNHIS, by approximately 28,000.

It is therefore necessary to redo thecalculations, beginning with an overallsample size of fewer than 25,000households. This reduces the desirednumber of interviews of people who arenot Hispanic, thereby increasing thenumber of extra households beingscreened and even further reducing themoney available. This iterative processconverges on a total sample (excludingHispanic persons in these three areas)16,771 households, 5,516 of whom arenot Hispanic persons in the Southwest,New York, or Florida.

If retaining a cost equivalent to theexisting budget is desired, it is possibleto produce separate age and sex resulfor Hispanic persons in each of the thregeographic areas from NHIS. There arehowever, two important caveats to thisstatement. First, use of the HCFA frameto sample Hispanic elderly is required.The preceding calculations assumed thcosts are equivalent for the HCFA andarea probability frames. If, for example,it costs 50 percent more to interview asample from HCFA records than froman area sample, the number ofnon-Hispanic households that can besampled will drop by more than 1,000.Second, the non-Hispanic householdsample size has been reduced from the45,985 households (50,000 −(0.000497 × 24,236,000/3)) of anon-oversampled design (such as is usfor the existing NHIS) to 16,771. Thiswill increase the standard error onnon-Hispanic estimates by 66 percent.will also make it impossible to providenational estimates for black persons anfor persons who are not black orHispanic for all 24 age-by-sex-by-racecells.

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Table 30. Summary of number screened and interviewed, assuming two different screeningcosts

Cost ratio3 to 1

Cost ratio2 to 1

Hispanic person sample in three areas . . . . . . . . . . . . . . . . . . . . . 25,532 25,580Hispanic non-HCFA person sample in three areas . . . . . . . . . . . . . . 19,532 19,580Hispanic non-HCFA household sample in three areas . . . . . . . . . . . . 6,511 6,527Remaining desired sample households . . . . . . . . . . . . . . . . . . . . . 41,489 41,473Desired nondense strata sample in three areas . . . . . . . . . . . . . . . 7,926 7,923Final remaining sample households . . . . . . . . . . . . . . . . . . . . . . . 26,411 18,152Final non-Hispanic household sample in three areas . . . . . . . . . . . . 5,046 3,468Total households screened in dense strata1 . . . . . . . . . . . . . . . . . . 56,790 56,637

Of these:Not retained for interviewing . . . . . . . . . . . . . . . . . . . . . . . . . . 45,234 46,643Hispanic households interviewed . . . . . . . . . . . . . . . . . . . . . . . 6,511 6,527Non-Hispanic households interviewed . . . . . . . . . . . . . . . . . . . . 5,046 3,468

Total households interviewed in nondense strata . . . . . . . . . . . . . . . 21,365 14,684Total HCFA households interviewed . . . . . . . . . . . . . . . . . . . . . . . 2,000 2,000Total households interviewed in entire United States . . . . . . . . . . . . 34,922 26,679

Maximum oversampling rate, blocks at least 50-percent Hispanic:Southwest — Male 45–64 years . . . . . . . . . . . . . . . . . . . . . . . 4.93 5.03Florida — Female 0–17 years . . . . . . . . . . . . . . . . . . . . . . . . . 17.65 18.11New York — Male 45–64 years . . . . . . . . . . . . . . . . . . . . . . . . 24.08 24.71

1Dense strata are defined as census blocks with more than 5 percent Hispanics in the most recent census.


Model-Dependent SubnationalHispanic Estimates

Given the significant reduction inaccuracy for estimates of people whoare not Hispanic resulting from thepreceding design, the possibility ofproducing model-based estimates wasexplored. The levels of oversamplingthat would be required were examinedfor all three areas, as well as only for allHispanic persons in the Southwest. Thelatter estimates would closely approximanational estimates for Hispanic persons oMexican descent. The category of perso65 years of age and over is again assumto be sampled from a frame maintainedHCFA.

For this part of the research, it wasassumed that a composite estimatorwould be used, combining a directsample estimator and an estimatordrawn from a model. The sample wouldbe drawn only from blocks with highdensities of Hispanic residents (at leas5 percent). Such an estimate will onlyprovide unbiased estimates of allHispanic persons in the geographic areif the health characteristic has the samexpectation for the 21 percent ofHispanic persons living outsidehigh-density blocks as for the 79 perceliving inside those blocks, within thevariables used in the model. (See thesecond section ofchapter 7for moredetail.)

Table 30summarizes the samplesizes and sampling rates when screeniand subsampling are used. Data areshown for two different ratios of thecost of a completed household intervieto the cost of screening a household.The findings for a 3:1 cost ratio aredescribed first.

Because of the design effect ofdifferential sampling rates in some cellsthe 24 cells (including 65 years andover from HCFA) will requireinterviews with 25,532 individuals; ofthese, 19,533 must come from the areasample (corresponding to 6,511 Hispanhouseholds).

To retain a 50,000-householdsample, 41,489 households (50,000−6,511−2,000 from HCFA) must besampled from the remainder of thecountry, as well as from the populationthat is not Hispanic of these three area

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and the Hispanic population in thenondense stratum of these three areasThe population in nondense Hispanicblocks in the three areas comprises19.10 percent of the remainingpopulation in the United States; thus thremaining sample from the three areasshould include 7,924 sampledhouseholds (0.1910 × 41,489).Identifying these sampled householdsthe dense strata will require screeningan additional 12,516 Hispanic (fromnonrare cells) and 29,837 non-Hispanihouseholds in these three areas. Usinthe U.S. Bureau of the Census’ 3:1 coestimate for screening the 42,353 extrhouseholds (12,516 + 29,837) willreduce the number of remaininginterviews that can be funded, for aconstant-cost NHIS, to 27,372.

It is therefore necessary to redo thcalculations beginning with a smalleraffordable sample size (27,372 insteadof 41,489). This reduces the desirednumber of interviews of persons whoare not Hispanic, thereby increasing thnumber of extra households beingscreened and reducing even further thmoney available. This iterative processconverges on a total sample (excludinHispanic persons in dense strata in thethree areas) of 26,411 households, 5,0of whom are in the Southwest, NewYork, or Florida.

With a 3:1 cost ratio, developingdesign-unbiased estimates for the densblocks of each of the 24 cells willrequire screening 56,790 households. Othese, 45,234 are not retained for thefull interview, and 11,557 areinterviewed. Another 21,365 householdare interviewed from other than thedense strata of the three areas, and2,000 households are interviewed fromHCFA files. This results in a total of34,922 households being interviewed inthe entire United States.

Table 30also shows that, as the coof screening grows even larger, thenumber of Hispanic interviews increaseonly slightly. The reason it increases atall is that as the sample becomes moreconcentrated in the most denselyHispanic blocks, larger design effectsare created, requiring more interviewsfor the same degree of accuracy. Themaximum oversampling rates are showat the bottom oftable 30. The numberof families that are and are not Hispanithat are screened declines only slightlyas the cost of screening increases.Unfortunately, the cost associated withsuch screening increases much fasterthan the number of screened householdecreases. As a result, the total numbeof households included in NHIS dropsfrom 50,000 without screening to 34,92with a 3:1 cost ratio, and to only 26,679

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Table 31. Summary of number screened (in the Southwest) and interviewed, assuming twodifferent screening costs

Cost ratio3 to 1

Cost ratio2 to 1

Hispanic person sample in the Southwest . . . . . . . . . . . . . . . . . . . 17,395 17,409Hispanic non-HCFA person sample in the Southwest . . . . . . . . . . . . 11,395 11,409Hispanic non-HCFA household sample in the Southwest . . . . . . . . . . 3,798 3,803Remaining desired sample households . . . . . . . . . . . . . . . . . . . . . 44,202 44,197Desired nondense strata household sample in the Southwest . . . . . . . 5,591 5,590Final remaining sample households . . . . . . . . . . . . . . . . . . . . . . . 40,478 38,513Final non-Hispanic household sample in the Southwest . . . . . . . . . . . 5,120 4,871Total households screened in dense strata . . . . . . . . . . . . . . . . . . 20,089 20,043

Of these:Not retained for interviewing . . . . . . . . . . . . . . . . . . . . . . . . . . 11,171 11,369Hispanic households interviewed . . . . . . . . . . . . . . . . . . . . . . . 3,798 3,803Non-Hispanic households interviewed . . . . . . . . . . . . . . . . . . . . 5,120 4,871

Total households interviewed in nondense strata . . . . . . . . . . . . . . . 35,358 33,642Total HCFA households interviewed . . . . . . . . . . . . . . . . . . . . . . . 2,000 2,000Total households interviewed in entire United States . . . . . . . . . . . . 46,276 44,316

Maximum oversampling rate, blocks at least 50-percent Hispanic:Southwest — Male 45–64 years . . . . . . . . . . . . . . . . . . . . . . . 4.93 5.03Florida — Female 0–17 years . . . . . . . . . . . . . . . . . . . . . . . . . 1.00 1.00New York — Male 45–64 years . . . . . . . . . . . . . . . . . . . . . . . . 1.00 1.00


with a 2:1 cost ratio. This dropoff insample size is even more pronouncedwhen the Hispanic households in thehigh-density strata are excluded. Thenumber then drops from 26,411 with a3:1 cost ratio to 18,152 with a 2:1 costratio. From these households (i.e.,approximately 55,000 individualrespondents) must come estimates forthe 12 age-by-sex cells for both blackpersons and persons who are neitherblack nor Hispanic.

The potential of model-dependentestimates only for Hispanic persons ofMexican ancestry was also examined.The majority of such Hispanic personsreside in the Southwest and includeapproximately 90 percent of all Hispanpersons in that area. Thus, it is possibto approximate this target population busing model-dependent estimates forHispanic persons in the Southwest.Table 31is of the same format astable 30, but indicates the effect of onlyoversampling high-density Hispanicstrata in the Southwest.

Table 31shows that thisoversampling would have only a minoreffect on the quality of statistics forblack persons or persons who are notblack or Hispanic. The overall samplesize would be reduced only about10 percent (from 50,000 households to46,000 or 44,000). The correspondingreduction in the non-Hispanic samplewould be from 45,985 to 40,478 or38,513.

The same caveats apply as in theprevious section, if it is desired to retaa cost equivalent to the existing budgeThe non-Hispanic household sample shas been reduced to between 26,41118,152, depending on the cost ofscreening. This will increase thestandard error on non-Hispanic estimaby 32 to 59 percent, also depending onthe cost of screening.

Oversampling only in the Southwewould have a much smaller effect on tquality of non-Hispanic estimates. Thereduction in the non-Hispanic samplewould be from 45,985 to somewherebetween 40,478 and 38,513—areduction in accuracy of only 7 or9 percent. The design-unbiased Hispanestimates would, however, be only forthose in dense strata of the Southwest

Determining whether to implementthe foregoing procedure foroversampling high-density Hispanicblocks will obviously depend on anumber of factors, including the qualityof the model-dependent estimators andthe cost of screening. A major factor inthe determination is examining thepotential bias in estimating Hispanichealth characteristics from a sample inhigh-density blocks. This issue wasexamined and is reported inchapter 7inthe section on model-based techniques.

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Chapter 11.Design-BasedApproaches for StateEstimation

Estimation Based Solely onthe National HealthInterview Survey

Table 32shows the expected sampsizes by State for a self-weightingsample of 50,000 households whenblack and Hispanic persons areoversampled at a rate of 2 to 1. (Theseare similar to the rates in the proposedbeta option; seechapter 17for moredetails.) These sizes are shown for theentire State and for the rarest age-by-scell, males age 65 years or over. Thistable assumes that only an area samplis used, that is, there is nosupplementation of persons age 65 anover from HCFA or SSA lists. For theoversampled design, the effective sampsizes are provided, adjusting for thedesign effect resulting from thedifferential survey weights.

In the first section ofchapter 3, therationale was provided for thedetermination that an effective samplesize of 1,000 persons or more wasrequired for an individual analyticdomain. It is clear from the table that nState will have that size for all age andsex domains; and for about one-third othe States, it would be impossible toproduce estimates for the entire Statepopulation.Table 33summarizes thesefindings. Even if the sample size weredoubled or quadrupled, some Stateswould not have the accuracy to be ableto have State totals published; and fewthan 10 would be able to have all ageand sex domains published.

Examination oftable 32indicatesthat, after the redesign, the number ofStates for which the sample size will besufficient to produce design-basedestimates from NHIS will not changesignificantly from that reported intable 33.

Given the preceding results, allfurther research into design-basedestimates assumed that HCFA lists

would be used for persons 65 years ofage or over. The rarest cell in the areasample becomes males 45–64 years oage.Table 34follows a format similar tothat of table 32but now providesestimates for total adults and males ag45–64 years, both when using aself-weighting sample and when usingdesign with oversampling rates similarto those proposed in the alpha option(see chapter 5) for the redesign. (Thealpha option assumes a budget50-percent greater than that of thecurrent design.) To improve theestimates, the final columns examine timpact of combining 3 years of data(the effective sample sizes shown intable 34do not take into account theslight reduction in accuracy resultingfrom using the same PSU’s andneighboring blocks each year). Thefinal column shows the coefficient ofvariation (cv) for the rarest cell for avariable that achieves acv of30 percent from a sample of 1,000persons (see the first section ofchapter 3). With 3 years of data, onlythree States (California, Texas, andNew York) provide the desiredaccuracy for all eight age and sexcells. Florida will provide close to thedesired level of accuracy (acv of32 percent for males 45–64 years ofage). For all remaining States, it willnot be possible to make State-levelage and sex estimates for all of theeight cells.

Dual-Frame EstimationUsing Random DigitDialing Supplementation

States that are interested inproducing age-by-sex estimates for theown population will have to supplementhe NHIS area sample. Given the resuof the previous section and the cost ofarea sampling, it appears that randomdigit dialing (RDD) supplementation isthe method most States would chooseIn this section the size of the RDDsupplement that would be needed toprovide the desired level of accuracy(cv ≤ 30 percent) is determined. Threecomposite estimators are considered:Unbiased with acv= 30 percent;unbiased with an equal-sized RDD

supplement in each State; and aminimum variance, possibly biased,version of the second estimator.

The following assumptions weremade for this section:

+ Close geographic clustering of thesupplemental sample by PSU orsegment is not necessary becauseof the use of telephone datacollection. The only clusteringwould be within sets of 100telephone numbers having thesame area code, prefix, and firsttwo extension digits. TheMitofsky-Waksberg method ofsample selection would be used.(If other RDD methods were to beused, a different design effectwould be needed in the nextsubsection.)

+ The NHIS redesign used intable 34(see NHIS Redesign Memorandum#50R for more details) would be thealpha option plus subsampling ofone person per household, theprocedure generally used in NHISsupplements.

+ The estimated RDD supplementsizes assume that States areinterested only in age and sex cellsFor those States interested in otherdecompositions by race or Hispanicorigin, it may be necessary toenlarge the supplement further. Thiswill depend on whether the size ofthe smallest desired cell is larger orsmaller than the smallest age andsex cell.

+ Telephone coverage is 93 percent ineach State. (Variation among Statesis small, ranging from 83 percent inAlaska to 97 percent in Wisconsin.Using 93 percent for each Statesimplifies the calculation and theresults are subject to only minorerror.)

Design Effects

With an RDD sample, it is notnecessary to use PSU’s, and the onlyclustering is the clusters of 100telephone numbers used in theMitofsky-Waksberg sample selectionmethod. Optimizing the cluster sizegenerally results in a fairly small clustefor interviewing, usually three to five or

Table 32. State sample sizes with and without oversampling of blacks and Hispanics; States ranked by population size in 2000

Sample size with 50,000 household sample andblacks and Hispanics oversampled at 2 to 1

Sample size withself-weighting sample of

50,000 households2 Actual sample3 Effective sample4

Rank State

Populationyear 2000

(000’s)

Percentblack or

Hispanic1Totaladults

Males 65years of age

and olderTotaladults


and olderTotaladults


and older

1 California . . . . . . . . . . . . . . . . . 33,500 38.7 15,444 819 17,564 931 15,701 8322 Texas . . . . . . . . . . . . . . . . . . . 20,211 39.5 9,317 494 10,657 565 9,520 5053 New York . . . . . . . . . . . . . . . . . 17,986 33.7 8,292 439 9,090 482 8,176 4334 Florida . . . . . . . . . . . . . . . . . . . 15,415 33.2 7,106 377 7,761 411 6,987 3705 Illinois . . . . . . . . . . . . . . . . . . . 11,580 27.1 5,338 283 5,563 295 5,063 2686 Pennsylvania . . . . . . . . . . . . . . . 11,502 12.3 5,303 281 4,883 259 4,633 2467 Ohio . . . . . . . . . . . . . . . . . . . . 10,629 14.0 4,900 260 4,580 243 4,320 2298 Michigan . . . . . . . . . . . . . . . . . 9,250 17.0 4,264 226 4,091 217 3,821 2039 New Jersey . . . . . . . . . . . . . . . . 8,546 27.4 3,940 209 4,116 218 2,743 198

10 Georgia . . . . . . . . . . . . . . . . . . 7,957 28.0 3,668 194 3,850 204 3,497 18511 North Carolina . . . . . . . . . . . . . . 7,483 22.0 3,450 183 3,451 183 3,178 16812 Virginia . . . . . . . . . . . . . . . . . . 6,877 20.0 3,170 168 3,119 165 2,888 15313 Massachusetts . . . . . . . . . . . . . . 6,087 7.5 2,806 149 2,473 131 2,391 12714 Indiana . . . . . . . . . . . . . . . . . . 5,502 10.0 2,536 134 2,288 121 2,189 11615 Missouri . . . . . . . . . . . . . . . . . . 5,383 12.0 2,482 132 2,279 121 2,165 11516 Maryland . . . . . . . . . . . . . . . . . 5,274 30.0 2,431 129 2,592 137 2,345 12417 Tennessee . . . . . . . . . . . . . . . . 5,266 17.0 2,428 129 2,329 123 2,175 11518 Washington . . . . . . . . . . . . . . . . 4,991 3.0 2,301 122 1,943 103 1,915 10219 Wisconsin . . . . . . . . . . . . . . . . . 4,784 6.5 2,205 117 1,926 102 1,869 9920 Arizona . . . . . . . . . . . . . . . . . . 4,618 24.6 2,129 113 2,175 115 1,990 10521 Louisiana . . . . . . . . . . . . . . . . . 4,516 33.0 2,082 110 2,270 120 2,044 10822 Minnesota . . . . . . . . . . . . . . . . . 4,490 2.0 2,070 110 1,731 92 1,714 9123 Alabama . . . . . . . . . . . . . . . . . . 4,410 26.0 2,033 108 2,100 111 1,916 10224 South Carolina . . . . . . . . . . . . . . 3,906 30.5 1,801 95 1,927 102 1,742 9225 Colorado . . . . . . . . . . . . . . . . . 3,813 15.2 1,758 93 1,660 88 1,560 8326 Kentucky . . . . . . . . . . . . . . . . . 3,733 8.0 1,721 91 1,524 81 1,470 7827 Connecticut . . . . . . . . . . . . . . . . 3,445 11.5 1,588 84 1,452 77 1,382 7328 Oklahoma . . . . . . . . . . . . . . . . . 3,376 7.5 1,556 82 1,372 73 1,326 7029 Oregon . . . . . . . . . . . . . . . . . . 2,877 2.0 1,326 70 1,109 59 1,098 5830 Mississippi . . . . . . . . . . . . . . . . 2,877 36.5 1,326 70 1,484 79 1,330 7131 Iowa . . . . . . . . . . . . . . . . . . . . 2,549 2.5 1,175 62 988 52 976 5232 Arkansas . . . . . . . . . . . . . . . . . 2,529 16.0 1,166 62 1,109 59 1,039 5533 Kansas . . . . . . . . . . . . . . . . . . 2,529 6.5 1,166 62 1,018 54 988 5234 Utah . . . . . . . . . . . . . . . . . . . . 1,991 1.0 918 49 760 40 756 4035 New Mexico . . . . . . . . . . . . . . . 1,968 37.7 907 48 1,024 54 917 4936 West Virginia . . . . . . . . . . . . . . . 1,722 3.0 794 42 670 36 661 3537 Nebraska . . . . . . . . . . . . . . . . . 1,556 4.0 717 38 612 32 600 3238 Hawaii . . . . . . . . . . . . . . . . . . . 1,345 2.5 620 33 521 28 515 2739 New Hampshire . . . . . . . . . . . . . 1,333 2.0 615 33 514 27 509 2740 Nevada . . . . . . . . . . . . . . . . . . 1,303 8.0 601 32 532 28 513 2741 Maine . . . . . . . . . . . . . . . . . . . 1,271 1.0 586 31 485 26 483 2642 Rhode Island . . . . . . . . . . . . . . . 1,049 6.0 484 26 420 22 409 2243 Idaho . . . . . . . . . . . . . . . . . . . . 1,047 0.0 483 26 396 21 396 2144 Montana . . . . . . . . . . . . . . . . . . 794 0.0 366 19 300 16 300 1645 Delaware . . . . . . . . . . . . . . . . . 734 22.0 338 18 338 18 312 1746 South Dakota . . . . . . . . . . . . . . 714 0.5 329 17 271 14 271 1447 Alaska . . . . . . . . . . . . . . . . . . . 687 3.5 317 17 269 14 264 1448 North Dakota . . . . . . . . . . . . . . . 629 0.5 290 15 239 13 238 1349 Vermont . . . . . . . . . . . . . . . . . . 591 1.0 272 14 226 12 225 1250 Wyoming . . . . . . . . . . . . . . . . . 489 0.0 225 12 185 10 185 10

0.0 Quantity more than zero but less than 0.05.1For total population and blacks, Series P-25 No. 1017; Hispanics from table 5.2Total sample size = population (State)/U.S. population × 124,000; males 65+ = .053 of total sample. The overall sampling rate is (.94) × 124,000/252,668,000 = .000461. The equal-weighted sampleoverall sampling rate is (.94) × 124,000/252,668,000 = .000461.3Blacks and Hispanics will be .222 of the U.S. population in 2000. If they are oversampled at a rate of 2 to 1, the sample rate for nonblack non-Hispanics will be .00378 and the rate for blacks andHispanics will be .00804.4The design effect for oversampling at a rate of 2 to 1 is (1 + p) (1 − p/2) where p is the proportion of blacks and Hispanics in the State. The effective sample size (for statistics of total population) in aState is the actual size divided by the design effect for that State.


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Table 33. Number of States for which estimates would have the desired accuracy for threedifferent sample sizes

Sample size

Self-weighting sample Minorities oversampled 2 to 1

TotalStates

Male65 and over

TotalStates

Male65 and over

50,000 . . . . . . . . . . . . . . 33 0 32 0100,000 . . . . . . . . . . . . . 41 1 40 2200,000 . . . . . . . . . . . . . 50 7 48 5


six households. Intraclass correlationsfor these clusters have not beencalculated for health-related variablebut other social and demographicsurveys report very low intraclasscorrelations. RDD design effects alsohave a component arising fromvariability in weights fromnonresponse adjustments and fromwithin-household clustering. It wasassumed that a design effect of 1.20was a reasonable average for RDDsamples. The current (area sample)design of the NHIS has a design effeof approximately 1.25; thus the RDDdesign effect relative to the currentdesign effect is 1.20/1.25 = 0.96. (If,for example, list-assisted RDDmethods were used, the relative deseffect would be smaller because of areduction in clustering.)

In addition to the current NHISdesign effect, the proposed area samphas a design effect that is computed intwo parts. The first component includethe subsampling of one per householdand differential oversampling rates bydensity strata computed separately forblack persons, Hispanic persons, and‘‘others.’’ The second componentaccounts for the combination of thethree groups with different averagesampling rates, computed separately bState. The effective sample size for tharea sample is then computed bydividing the actual sample size by thetwo design effect components for thatState. The value for the first compone(see NHIS Redesign Memorandum#56R) is close to 1.33 for all threegroups; therefore this value is used foall three. The value of the secondcomponent was computed separatelyeach State; values vary from 1.35 to1.93.

r

Variance of an UnbiasedDual-Frame Estimator

A combined RDD and area samplewill thus have a smaller design effectthan an area sample alone and will nea smaller actual sample size to achievethe accuracy of an effective 1,000 caseneeded for the reliability specified byNCHS. To derive the variance of thedual-frame sample design, one canproceed as follows. Letxa,t andxr be themean for the telephone households in tharea sample and the mean for the RDDsample, respectively, in a particularState. Letxa,n be the mean fornontelephone households in the areasample. The sample estimate of adual-frame sample can be expressed a

x = (0.93)[f xa,t + (1 − f )xr] + 0.07xa,n

where 0.93 is the expected proportionhouseholds with telephones andf is aweight. Any value for the weightbetween 0 and 1 will provide unbiasedestimates if bothxa,t andxr are unbiased,but a value should be selected thatcomes close to minimizing the totalvariance.

First derive the value off thatminimizes the variance of the term inbrackets. Use the following notation:

na andnr are the sample sizes in thearea sample and RDD samplfor the domain of interest

da anddr are the design effects in thearea and RDD samples,relative to the current design

σ2 is the population variance ofthe statistic being considered.The sameσ2 applies toxa,t andxr because the populations

are identical and no mode-of-interview effects areconsidered.

The variance of the term in thebrackets is

σxt

2 = f 2 σxa,t

2 + (1 − f )2 σxr

2

The expression is minimized bytaking weights that are proportional tothe reciprocal of the variances of eachterm; that is,

f =σxr

2

σxa,t

2 + σxr

2

where

σxr

2 = drσ2

nr

σxa,t

2 =daσ

2

.93na

The variance of the full estimator isthen

V(x) = Sda

naD σ2 .93dr na + .07danr

.93dr na + danr(9)

This formula understates thevariance slightly because it assumes ncovariance between the sample oftelephone households and nontelephonhouseholds. Such a covariance will exifor the area sample, but its effect shoube quite small.

Given the size of the area sample,na, the goal is to determine the size ofthe RDD supplemental sample,nr,which will provide the same level ofaccuracy for the composite estimator aprovided by an area sample with aneffective sample size of 1,000 personsfor the same domain. This implies that

V(x) =σ2

1,000

Substituting this into equation 9produces

1 =Sda

naD 1,000S.93dr na + .07danr

.93dr na + danrD

This equation can be simplified to

.93dr na + danr =

1,000Sda

naD (.93dr na + .07danr)

Table 34. State sample sizes (ranked by population size in 2000) for collapsed ages with and without oversampling of blacks and Hispanics

Sample size with self-weighting sample of50,000 households2

Sample size with 50 percent budget increase, one per household andblack and Hispanics oversampled at 2.59 and 3.59 to 1

Populationyear 2000

(000’s)

Actual sample3 Effective sample4Effective sample4

using 3 years of data

Rank StatePercentblack1

PercentHispanic1

Totaladults

Males45–64

Totaladults

Males45–64

Totaladults

Males45–64

Totaladults

Males45–64

cv(%)

1 California . . . . . . . . . . . . 33,500 8.7 32.0 11,660 1,714 9,394 1,381 5,016 737 15,049 2,212 202 Texas . . . . . . . . . . . . . . 20,211 12.1 30.0 7,731 1,136 6,234 916 3,356 493 10,067 1,480 263 New York . . . . . . . . . . . . 17,986 17.7 17.2 6,260 920 4,427 651 2,512 369 7,537 1,108 294 Florida . . . . . . . . . . . . . . 15,415 14.8 14.3 5,365 789 3,529 519 2,050 301 6,150 904 325 Illinois . . . . . . . . . . . . . . 11,580 17.5 10.9 4,030 592 2,577 379 1,525 224 4,576 673 376 Pennsylvania . . . . . . . . . . 11,502 9.8 2.8 4,004 589 2,016 296 1,342 197 4,025 592 397 Ohio . . . . . . . . . . . . . . . 10,629 12.0 1.8 3,699 544 1,876 276 1,248 183 3,744 550 408 Michigan . . . . . . . . . . . . 9,250 16.2 3.0 3,220 473 1,761 259 1,128 166 3,385 498 439 New Jersey . . . . . . . . . . . 8,546 15.8 12.1 2,974 437 1,906 280 1,123 165 3,369 495 43

10 Georgia . . . . . . . . . . . . . 7,957 27.0 1.9 2,769 407 1,678 247 1,040 153 3,119 459 4411 North Carolina . . . . . . . . . 7,483 21.9 1.4 2,604 383 1,478 217 938 138 2,814 414 4712 Virginia . . . . . . . . . . . . . 6,877 19.4 3.3 2,394 352 1,366 201 860 126 2,580 379 4913 Massachusetts . . . . . . . . . 6,087 5.4 6.6 2,119 311 1,091 160 710 104 2,130 313 5414 Indiana . . . . . . . . . . . . . 5,502 9.3 2.5 1,915 282 952 140 638 94 1,914 281 5715 Missouri . . . . . . . . . . . . . 5,383 11.1 1.6 1,874 275 936 138 627 92 1,882 277 5716 Maryland . . . . . . . . . . . . 5,274 27.9 3.3 1,836 270 1,149 169 702 103 2,106 310 5417 Tennessee . . . . . . . . . . . 5,266 16.8 .9 1,833 269 970 143 634 93 1,903 280 5718 Washington . . . . . . . . . . . 4,991 2.3 6.0 1,737 255 849 125 568 84 1,704 251 6019 Wisconsin . . . . . . . . . . . . 4,784 5.7 2.7 1,665 245 792 116 543 80 1,628 239 6120 Arizona . . . . . . . . . . . . . 4,618 2.7 20.8 1,607 236 1,041 153 594 87 1,782 262 5921 Louisiana . . . . . . . . . . . . 4,516 32.2 2.9 1,572 231 1,021 150 619 91 1,858 273 5722 Minnesota . . . . . . . . . . . . 4,490 1.7 1.7 1,563 230 686 101 493 72 1,479 217 6423 Alabama . . . . . . . . . . . . . 4,410 25.8 .8 1,535 226 899 132 565 83 1,696 249 6024 South Carolina . . . . . . . . . 3,906 30.0 1.1 1,360 200 838 123 518 76 1,554 228 6325 Colorado . . . . . . . . . . . . 3,813 4.1 15.5 1,327 195 797 117 474 70 1,421 209 6626 Kentucky . . . . . . . . . . . . 3,733 7.9 .8 1,299 191 610 90 423 62 1,270 187 6927 Connecticut . . . . . . . . . . . 3,445 9.1 8.6 1,199 176 672 99 419 62 1,256 185 7028 Oklahoma . . . . . . . . . . . . 3,376 6.8 3.6 1,175 173 579 85 388 57 1,165 171 7229 Oregon . . . . . . . . . . . . . 2,877 1.7 5.5 1,001 147 480 71 325 48 975 143 7930 Mississippi . . . . . . . . . . . 2,877 36.0 .8 1,001 147 654 96 399 59 1,197 176 7231 Iowa . . . . . . . . . . . . . . . 2,549 2.3 1.8 887 130 394 58 281 41 843 124 8532 Arkansas . . . . . . . . . . . . 2,529 15.7 1.1 880 129 461 68 303 45 909 134 8233 Kansas . . . . . . . . . . . . . 2,529 6.2 5.2 880 129 445 65 294 43 881 129 8334 Utah . . . . . . . . . . . . . . . 1,991 .7 5.9 693 102 330 49 224 33 672 99 9535 New Mexico . . . . . . . . . . 1,968 1.7 41.1 685 101 587 86 304 45 913 134 8236 West Virginia . . . . . . . . . . 1,722 2.6 .7 599 88 260 38 189 28 566 83 10437 Nebraska . . . . . . . . . . . . 1,556 3.7 3.3 542 80 254 37 175 26 525 77 10838 Hawaii . . . . . . . . . . . . . . 1,345 1.8 8.4 468 69 239 35 155 23 466 69 11539 New Hampshire . . . . . . . . 1,333 .8 1.2 464 68 198 29 145 21 435 64 11940 Nevada . . . . . . . . . . . . . 1,303 7.2 13.3 454 67 271 40 163 24 488 72 11241 Maine . . . . . . . . . . . . . . 1,271 .3 .8 442 65 186 27 137 20 412 61 12242 Rhode Island . . . . . . . . . . 1,049 4.3 6.1 365 54 183 27 121 18 363 53 13043 Idaho . . . . . . . . . . . . . . . 1,047 .6 7.1 364 54 178 26 119 17 356 52 13144 Montana . . . . . . . . . . . . . 794 .3 2.1 276 41 120 18 87 13 260 38 15345 Delaware . . . . . . . . . . . . 734 21.1 3.0 255 38 148 22 93 14 278 41 14846 South Dakota . . . . . . . . . 714 .3 1.0 249 37 105 15 77 11 232 34 16247 Alaska . . . . . . . . . . . . . . 687 3.3 3.6 293 35 112 17 77 11 232 34 16248 District of Columbia . . . . . . 634 67.8 7.2 221 32 205 30 128 19 383 56 12649 North Dakota . . . . . . . . . . 629 .6 1.0 219 32 93 14 68 10 205 30 17350 Vermont . . . . . . . . . . . . . 591 .5 .9 206 30 87 13 64 9 192 28 17951 Wyoming . . . . . . . . . . . . 489 .8 7.4 170 25 84 12 56 8 167 25 191

1For total population and blacks, Series P-25 No. 1017. For Hispanics, 1990 census figures are projected to grow 39.6 percent in each State by 2000. This percentage represents the ratio of the high2000 projection to the 1990 figure, since the 1990 figure (P-25 No. 995) exceeded the high projection for 1990.2Total adult sample size = population (State)/U.S. population × 124,000 × 0.755; males 45–64 are 0.147 of the total adult sample. The equal-weighted sample overall sampling rate is(0.94) × 124,000/252,668,000 = 0.000461.3Blacks and Hispanics will be oversampled and screened as described in Memorandum #50R, using the design resulting in 25,000 sampled ‘‘others.’’ The average oversampling rates relative to therate for ‘‘others’’ are 2.59 for blacks and 3.59 for Hispanics.4There are two separate components of the design effect. Each is calculated using the standard formula found in Survey Sampling (20). The first component includes the subsampling of one perhousehold and differential oversampling rates by density strata, computed separately for blacks, Hispanics, and ‘‘others.’’ The second component accounts for the combination of the three groups withdifferent average sampling rates.This design effect will vary from State to State. The effective sample size in a State is the actual sample size divided by the two design effect components for thatState. The effective sample size using 3 years of data could be reduced by an additional 19 to 26 percent to account for the clustering of the sample in the same PSU’s each year. This would increasethe cv’s by a factor of 1:1.


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Solving this equation for the size othe RDD supplement yields

nr =930dadr − (.93)dr na

da − 70 (da2 /na)

=930dr − (.93)dr na

*

1 − (70/na*) (10)

wherena* = na/da is the State-specific

effective size of the area sample.Substituting the RDD design effect

dr = 0.96 and the area effective samplesize into equation 10 only produces apositive value ofnr whenna

* > 70. Whenthe area sample for a cell is less than71, the variability in the area samplecomponent already exceeds the totalvariability allowed in the compositeestimator.

Thus, it is impossible for RDDsupplementation to provide an unbiasecomposite estimator with the desiredaccuracy if the cell has an effectivesample size from the area sample of lethan 71. The reason for this is that whthe sample sizes are very small, thecomponent of variance arising fromnontelephone households is so large tit exceeds the permissible variance frothe total sample.

An examination oftable 35showsthat, using a single year of data, RDDsupplementation can help for every cein only 24 States, but 10 of theserequire more than 20,000 cases. EvenCalifornia it would require 1,762interviews. It can also be noted thatwhen interested in estimates for alladults, it is impossible to provide anunbiased estimator using RDDsupplementation in three of the States

When 3 years’ data are combined,RDD supplements can provide thedesired precision for all cells in 38States, although 3 of them require morthan 20,000 interviews. In the 22 Statethat do not have 1,000 area sample adcases, moderate-size RDD samplesupplementation will provide sufficientprecision.

To summarize, unbiased dual-framestimators with RDD supplementation1 year’s NHIS data are of very limitedutility. With 3 years’ data this approachdoes not help in a number of States, bit can be used in a majority of States.Table 36presents these findings.

t

t

Precision of UnbiasedDual-Frame Estimator With aFixed-Size Random DigitDialing Supplement

The next question examined was,the same RDD resources were availabin every State, in how many (andwhich) States would different conditionprovide estimates for all adults, and foall of the eight age-by-sex cells, withcoefficients of variation below30 percent? Using the proposed desigthe 16 sets for analysis were formed bthe complete crossing of the followingfour dichotomies:

+ 1,000 versus 2,000 RDD householinterviewed per State.

+ One adult versus all adultsinterviewed per RDD samplehousehold.

+ Characteristics withp = 0.5 versusp = 0.1.

+ 1 year versus 3 years of data.

First, the sampling errors providedby use of the RDD sample without thearea probability sample are examined.Then the accuracy of the best unbiasecomposite estimator is examined—where the composite combines the RDsample with the NHIS area sample inthe State. Finally, the impact of limitingthe variation in the weights in thecomposite estimator is examined, alonwith the extent of bias that can beexpected for such biased estimators.

The accuracy of estimates madesolely from the RDD sample will be thesame for every State. Thus only theaccuracy of the estimator for 16conditions need be examined: 1,000 o2,000 households, one adult or all aduin a household,p = 0.5 or 0.1, and 1year’s or 3 years’ data.

The variance, standard error, andcoefficient of variation for each of thes16 sets, for all adults and for males45–64 years of age, are provided intable 37. The average number of adultsper household is assumed to be 1.85(see NHIS Redesign Memorandum #6and because the average number ofadult males between ages 45–64 yearper household is assumed to be one,assumed that there is only a trivialproportion of households with more tha

one male in this age range. It istherefore necessary only to include anintrahousehold correlation for the ‘‘alladult’’ cell. Based on the calculations inNHIS Redesign Memorandum #40 (4),conservative estimate for thiscorrelation,ρ = 0.1, is used.

It was also assumed that the effectof clustering is negligible for RDDsamples (the number of completes percluster and the intracluster correlationare likely to be small). The tableassumes further that an equal probabilsample of households is used for theRDD supplement; if the modifiedWaksberg method (which eliminates thneed for sequential sampling, butintroduces some differential weighting,seeAvoiding Sequential Sampling WithRandom Digit Dialing(21)) is used,then the variation in weights will yieldsome increase incv’s.

Table 37shows that a telephonesample of 1,000 households will provida cv less than 30 percent for all Statesfor each of the age and sex categoriesThis is true for a characteristic with anoccurrence of 0.10 or 0.50, with oneadult interviewed per household or alladults interviewed. It is not necessary tuse more than 1 year’s sample or todouble the number of households.

Unfortunately, these estimates ofcvdo not take into account the bias fromsampling only telephone households.This bias will vary from State to State(the percentage of the 1980 populationin a State that was without telephonesvaried from 3.3 percent to 16.7 percentand can be considerable for some heacharacteristics. These biases are alsolikely to be greater for black personsand Hispanic persons than for thegeneral population because a higherpercentage of minorities than of whitepersons lack telephones.

The minimum variance for anunbiased dual-frame estimator waspresented in equation 9. This variancewas computed for every State for eachof the 16 models. Whenp = 0.5 all ofthe proposed models provide unbiasedcomposite estimators with the desiredcv< 30 percent. In fact, withp = 0.5, allof the models provide estimates withcv< 15 percent. This is not the case fothe eight conditions withp = 0.1; these

Table 35. RDD supplementation sample sizes for age groups with one adult sampled per household

Rank State

Populationyear 2000(000’s)1

Area effective sample3

using 1 year of dataRDD actual sampleusing 1 year of data

Area effective sample3

using 3 years of dataRDD actual sample

using 3 years of data

Totaladults2

Males45–64

Totaladults2

Males45–64

Totaladults2

Males45–64

Totaladults2

Males45–64

1 California . . . . . . . . . . . . 33,500 5,016 737 + 1,762 15,049 2,212 + +2 Texas . . . . . . . . . . . . . . 20,211 3,053 449 + 3,966 9,160 1,347 + +3 New York . . . . . . . . . . . . 17,986 2,512 369 + 4,726 7,537 1,108 + +4 Florida . . . . . . . . . . . . . . 15,415 2,050 301 + 5,527 6,150 904 + 6315 Illinois . . . . . . . . . . . . . . 11,580 1,525 224 + 6,850 4,576 673 + 2,2186 Pennsylvania . . . . . . . . . . 11,502 1,342 197 + 7,558 4,025 592 + 2,8137 Ohio . . . . . . . . . . . . . . . 10,629 1,248 183 + 8,020 3,744 550 + 3,1298 Michigan . . . . . . . . . . . . 9,250 1,128 166 + 8,765 3,385 498 + 3,5519 New Jersey . . . . . . . . . . . 8,546 1,123 165 + 8,804 3,369 495 + 3,570

10 Georgia . . . . . . . . . . . . . 7,957 1,040 153 + 9,492 3,119 459 + 3,88111 North Carolina . . . . . . . . . 7,483 938 138 60 10,635 2,814 414 + 4,28612 Virginia . . . . . . . . . . . . . 6,877 860 126 136 11,887 2,580 379 + 4,62313 Massachusetts . . . . . . . . . 6,087 710 104 287 16,516 2,130 313 + 5,37314 Indiana . . . . . . . . . . . . . 5,502 638 94 363 21,690 1,914 281 + 5,80915 Missouri . . . . . . . . . . . . . 5,383 627 92 374 22,868 1,882 277 + 5,88016 Maryland . . . . . . . . . . . . 5,274 702 103 295 16,927 2,106 310 + 5,41817 Tennessee . . . . . . . . . . . 5,266 634 93 367 22,086 1,903 280 + 5,83418 Washington . . . . . . . . . . . 4,991 568 84 440 34,428 1,704 251 + 6,31719 Wisconsin . . . . . . . . . . . . 4,784 543 80 469 45,597 1,628 239 + 6,53020 Arizona . . . . . . . . . . . . . 4,618 594 87 411 27,949 1,782 262 + 6,11721 Louisiana . . . . . . . . . . . . 4,516 619 91 383 23,899 1,858 273 + 5,93722 Minnesota . . . . . . . . . . . . 4,490 493 72 528 165,993 1,479 217 + 7,01123 Alabama . . . . . . . . . . . . . 4,410 565 83 443 35,278 1,696 249 + 6,33824 South Carolina . . . . . . . . . 3,906 518 76 497 69,407 1,554 228 + 6,75625 Colorado . . . . . . . . . . . . 3,813 474 70 551 # 1,421 209 + 7,22626 Kentucky . . . . . . . . . . . . 3,733 423 62 617 # 1,270 187 + 7,90327 Connecticut . . . . . . . . . . . 3,445 419 62 623 # 1,256 185 + 7,97528 Oklahoma . . . . . . . . . . . . 3,376 388 57 666 # 1,165 171 + 8,51029 Oregon . . . . . . . . . . . . . 2,877 325 48 768 # 975 143 24 10,17130 Mississippi . . . . . . . . . . . 2,877 399 59 651 # 1,197 176 + 8,31231 Iowa . . . . . . . . . . . . . . . 2,549 281 41 855 # 843 124 153 12,22732 Arkansas . . . . . . . . . . . . 2,529 303 45 810 # 909 134 88 11,05833 Kansas . . . . . . . . . . . . . 2,529 294 43 828 # 881 129 116 11,51534 Utah . . . . . . . . . . . . . . . 1,991 224 33 1,008 # 672 99 327 18,78435 New Mexico . . . . . . . . . . 1,968 304 45 806 # 913 134 84 10,98536 West Virginia . . . . . . . . . . 1,722 189 28 1,151 # 566 83 442 35,04237 Nebraska . . . . . . . . . . . . 1,556 175 26 1,227 # 525 77 489 60,02138 Hawaii . . . . . . . . . . . . . . 1,345 155 23 1,372 # 466 69 561 #39 New Hampshire . . . . . . . . 1,333 145 21 1,477 # 435 64 601 #40 Nevada . . . . . . . . . . . . . 1,303 163 24 1,314 # 488 72 534 242,78041 Maine . . . . . . . . . . . . . . 1,271 137 20 1,570 # 412 61 632 #42 Rhode Island . . . . . . . . . . 1,049 121 18 1,861 # 363 53 704 #43 Idaho . . . . . . . . . . . . . . . 1,047 119 17 1,915 # 356 52 715 #44 Montana . . . . . . . . . . . . . 794 87 13 4,248 # 260 38 904 #45 Delaware . . . . . . . . . . . . 734 93 14 3,306 # 278 41 861 #46 South Dakota . . . . . . . . . 714 77 11 8,729 # 232 34 982 #47 Alaska . . . . . . . . . . . . . . 687 77 11 8,738 # 232 34 982 #48 District of Columbia . . . . . . 634 128 19 1,723 # 383 56 674 #49 North Dakota . . . . . . . . . . 629 68 10 # # 205 30 1,079 #50 Vermont . . . . . . . . . . . . . 591 64 9 # # 192 28 1,135 #51 Wyoming . . . . . . . . . . . . 489 56 8 # # 167 25 1,280 #

+ Cells for which the area effective sample size already exceeds 1,000 and do not require RDD supplementation.

# Cells for which the area effective sample size is less than 71 and RDD cannot help achieve the desired level of accuracy.1For total population, Series P-25 No. 1017.2Total adult sample size = population (State)/U.S. population x 124,000 × 0.755; males 18–24 are 0.147 of the total adult sample.3There are two separate components of the design effect. Each is calculated using the standard formula found in Survey Sampling (20). The first component includes the subsampling of one perhousehold and differential oversampling rates by density strata, computed separately for blacks, Hispanics, and ‘‘others.’’ The second component accounts for the combination of the three groups withdifferent average sampling rates. This design effect will vary from State to State. The effective sample size in a State is the actual sample size divided by the two design effect components for thatState.


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Table 36. Number of States for which all cells meet the desired level of accuracy

EstimatorAll 8 age

and sex cellsAll

adults

1 year area sample only . . . . . . . . . . . . . . . . . . . . . . . 0 101 year RDD, less than 20,000 supplement per State . . . . . 14 481 year RDD, unlimited supplement . . . . . . . . . . . . . . . . 24 483 year area sample only . . . . . . . . . . . . . . . . . . . . . . . 3 293 year RDD, less than 20,000 supplement per State . . . . . 35 513 year RDD, unlimited supplement . . . . . . . . . . . . . . . . 38 51


results can be seen intable 38. Thistable indicates many fewer States thanin earlier tables with the desired level oaccuracy. This is because the1,000-interview requirement was basedon acv of 30 percent for a characteristiwith p = 0.014, not 0.1.

Table 38shows that the minimumcondition of acv< 30 percent can beachieved for most States under any ofthe eight conditions. It is achieved forall States when 3 years’ of NHIS dataare used.

The differences are much morepronounced when the number of Statefor which some cells will have estimatewith cv’s exceeding 15 percent isexamined. In general the results showthe impact of increasing the size of thetwo samples. When interviewing all

Table 37. Variances of an RDD sample of 1,000 anand the rarest cell

Variance p = .50

Variance (n = 1,000) . . . . . . . . . . . . . . . 0.00030Standard error . . . . . . . . . . . . . . . . . . . 0.01732cv . . . . . . . . . . . . . . . . . . . . . . . . . . . 0.03464




1The design effect for sampling one adult per household is assumed2Males 45 to 64 years old are the rarest age-by-sex cell and make3From Memorandum #66 the average number of adults per househnoticeable effect of clustering since there is only one such male in aa conservative estimate for the intrahousehold correlation is 0.1. Th

NOTE: These variances are independent of the area sample size in

adults in 1,000 households, the samplesize is quite similar to that wheninterviewing 1 adult from 2,000households, and so are the number ofStates exceeding the 15-percent cutoff.Unless all adults in 2,000 householdsare interviewed by telephone andcombined with 3 years’ of NHIS data,one can expect at least 20 of the Stateto have some of the age-by-sex cells fowhich thecv will exceed 15 percent.

Comparisons of Dual-FrameEstimators With Stand-Alone1,000 Random Digit DialingEstimator

Currently, the Centers for DiseaseControl and Prevention funds each Stato collect health data from RDD survey

d 2,000 households per year for two designs, two

One adult per hous

All adults

1 year 3 years

p = .10 p = .50 p = .10 p =

0.00011 0.00010 0.00004 0.00.01039 0.01000 0.00600 0.00.10392 0.02000 0.06000 0.0

0.00005 0.00005 0.00002 0.00.00735 0.00707 0.00424 0.00.07348 0.01414 0.04243 0.0

All adults per hous

0.00005 0.00005 0.00002 0.00.00727 0.00699 0.00419 0.00.07265 0.01398 0.04195 0.0

0.00003 0.00002 0.00001 0.00.00514 0.00494 0.00297 0.00.05137 0.00989 0.02966 0.0

to be 1.2. This is based on results of 18,500 interviews from the N

up 14.7 percent of all adults (see Memorandum #56R).

old is 1.85. Therefore this design anticipates interviews with 1,850 (3lmost all households. For all adults there is an additional design effeis value was used for the bottom left part of this table.

the State.Therefore these estimates apply to all States using either

of 1,000 individuals. RDD estimatorsare subject to potential biases resultingfrom differences in a healthcharacteristic between households withand without telephones. Therefore themean square error and variance ofdual-frame estimators were comparedwith the same measures of an RDDestimator based on 1,000 interviews peState. For these comparisons only 1 yeof NHIS data was used assuming theproposed alpha option design. Estimatefor all adults and for the six age-by-sexcells for adults were examined. A95-percent response rate was assumedfor NHIS and an 80-percent responserate for the RDD interviews.

Three different incidence rates wereexamined: 0.03, 0.10, and 0.30. Thiscovers most of the incidence levelsfound in NHIS data and provides somemeasure of robustness to the results. Tcomparison of accuracy compared therelative root mean square errors(RRMSE’s) of the two estimators. In thecase of the dual-frame estimator, this isequal to thecv, but for the RDDestimator, it is a function of bothvariance and bias.Trends in UnitedStates Telephone Coverage Across Tim

different percentages, and for all adults

ehold1

45–64 year old males2

1 year 3 years

0.50 p = 0.10 p = 0.50 p = 0.10

0204 0.00073 0.00068 0.000244518 0.02711 0.02608 0.015659035 0.27105 0.05216 0.15649

0102 0.00037 0.00034 0.000123194 0.01917 0.01844 0.011076389 0.19166 0.03689 0.11066

ehold

0092 0.00033 0.00031 0.000113032 0.01819 0.01751 0.010506064 0.18192 0.03501 0.10503

0046 0.00017 0.00015 0.000062144 0.01286 0.01238 0.007434288 0.12864 0.02476 0.07427

ational Household Education Survey.

,700) adults. For the 45- to 64-year-old males there is noct to consider. Based on the calculations in Memorandum #40,

the current or proposed sample designs.

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Table 38. The number of States, under the proposed redesign and p = 0.1, for which theunbiased composite estimates for certain cells will have cv’ s greater than 30 or 15 percent

Sample

Number of States withcv > 30 percent

Number of States withcv > 15 percent

Totaladults

Males45–64

Totaladults

Males45–64

One adult per 1,000 households, 1 year . . . . . . . . . . 0 8 1 47One adult per 2,000 households, 1 year . . . . . . . . . . 0 3 1 42All adults in 1,000 households, 1 year . . . . . . . . . . . 0 3 1 42All adults in 2,000 households, 1 year . . . . . . . . . . . 0 2 0 26One adult per 1,000 households, 3 years . . . . . . . . . 0 0 0 34One adult per 2,000 households, 3 years . . . . . . . . . 0 0 0 24All adults in 1,000 households, 3 years . . . . . . . . . . . 0 0 0 23All adults in 2,000 households, 3 years . . . . . . . . . . . 0 0 0 11


and Subgroups(22) provide estimates othe differences between telephone andnontelephone households for selectedNHIS characteristics. The largest andsmallest percent bias shown for(approximately) each of the threepvalues previously mentioned wasdetermined (and the national bias wasassumed to apply to all States). (Thesbiases assume that no poststratificatioby income is done for the RDDestimator. To the extent that this is donthe impact of these biases will bereduced.) The minimum relative bias foall three incidence levels was near zerThe maximum bias varied by incidencerate. The maximum bias forp = 0.03was 3.6 percent, forp = 0.10 it was28.9 percent, and forp = 0.30 it was16.7 percent.

Table 39shows, forp = 0.10 and0.30, the size RDD supplement needein a State to combine with the NHISarea-permit sample and produceaccuracy equivalent to that of astand-alone RDD sample of 1,000households. The key determinant of thsize RDD supplement required by thedual-frame estimator is the level of thebias. For characteristics with smallbiases, the size supplements for a Sta

Table 39. Number of States, by the size RDD supp1,000 RDD interviews

Size of RDDsupplemental sample

All six collapsed age/se

p = 0.10(Bias = 1.6)

p =(Bias =

None needed . . . . . . . . . . 12 11–999 . . . . . . . . . . . . . . 24 2> 1,000 . . . . . . . . . . . . . 13 1No amount can help . . . . . 2

,

are almost identical, regardless ofincidence rate and of whether all sixcells are wanted or only one. In fact,when the bias is zero, they are identicaFor p = 0.03 the largest bias was only3.6 percent; thus, the results closelymatched the low-bias results intable 39.

When the bias is small, a dozenStates do not require any telephonesupplementation. That is, the NHISsample in those States is more accurathan an RDD sample of 1,000households. In 36 of the 51 States(including the District of Columbia), theentire supplement for all age and sexcells would be less than 1,000households. In another 13 States, thesupplement would have to be larger th1,000; for 1 State it would exceed 7,00households.

For two States, Alaska andWyoming, no telephone sample couldreduce the variability of the estimatorsufficiently to match the accuracy of1,000 telephone interviews. This isbecause the small number of NHISinterviews that must represent all of thenontelephone households in the Statereceive such a large weight that thedesign effect from differential weightskeeps the variance of the dual-frame

lement needed to derive a dual-frame NHIS estimat

Low bias

x cells All adults one combined cell All six collaps

0.300.0%)

p = 0.10(Bias = 1.6%)

p = 0.30(Bias = 0.0%)

p = 0.10(Bias = 28.9%)

2 12 12 204 24 24 253 13 13 62 2 2 0

estimator larger than that of the 1,000RDD interview estimator. To a lesserextent, it is this same variation inweights that causes the other 13 Statessupplements to exceed 1,000households.

For these 13 States whosesupplements exceed 1,000 householdsis important to remember that the extrainterviews are to ensure the desiredlevel of accuracy, regardless of theactual telephone or nontelephone biasthe State. The stand-alone RDDestimator will achieve the desiredaccuracy only if its bias is no greaterthan that reflected by the nationalestimates. Given that this survey colleca great number of variables, each ofwhich has different biases (which mayvary over time), these are likely to beitems, and States in which biases aremuch greater than would be anticipatedfrom studies such asTrends in UnitedStates Telephone Coverage Across Timand Subgroups(22).

For characteristics with high biasesthe supplemental sample sizes for thedual-frame estimator are even smaller.This is because the larger bias inflatesthe RRMSE of the stand-alone RDDestimator. For a given incidence rate anbias, the main determinant of thesupplement size is the telephonecoverage rate, not the NHIS samplesize. In all of the high-bias situations, aleast 45 of the States requiredsupplements smaller than 1,000households, and at least 20 needed nosupplementation at all. When only onecell per State was needed, all Statesrequired supplements smaller than 1,00households.

The next analysis compared theprecision (variance) of the stand-aloneRDD estimator with a minimum

or of accuracy similar to stand-alone

High bias

ed age/sex cells All adults one combined cell

p = 0.30(Bias = 16.7%)

p = 0.10(Bias = 28.9%)

p = 0.30(Bias = 16.7%)

25 33 3522 18 16

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Table 40. Summary of the number of States and percent reduction in bias corresponding todifferent size RDD supplementation of a dual-frame estimator

Dual-frame RDDsupplemental sample size

Numberof

States

Percent ofreduction in bias

of RDD withdual frame

0 . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 1001–500 . . . . . . . . . . . . . . . . . . . . . . . . 15 46–83501–1,000 . . . . . . . . . . . . . . . . . . . . . 24 6–43

Table 41. Differences between telephone and nontelephone households for percent ofpersons who smoke, and persons under 65 years old without private health insurance

CharacteristicNontelephonehouseholds

Telephonehouseholds

Allhouseholds

Persons who currently smoke cigarettes . . . . . . . . . . 49.6 28.8 30.1Persons under 65 years old without private healthinsurance . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64.9 19.8 23.1


variance (but potentially biased)dual-frame estimator. This dual-frameestimator is different from thatconsidered in earlier tables. Allinterviews are given equal weights tominimize the variance. Thisunderrepresents households withouttelephones, however, because they arincluded only in the sample of NHIShouseholds. Thus the estimator will onpartially reduce the bias that is usuallypresent in the stand-alone RDDestimator. (The percent reduction in biis a function of the relative sample sizfrom NHIS and RDD, independent ofthe actual bias in the RDD estimate.) Brestricting the comparison to matchingthe estimators’ variance, the RDDsupplement will be between 0 and 1,0interviews for each State. If the NHISarea sample in the State provides anestimate with smaller variance than thof the stand-alone RDD estimate, noRDD supplement is needed. If the NHarea sample is practically nonexistent,the dual-frame estimator will resemblethe stand-alone estimator and require1,000 interviews.

Table 40summarizes the results. NRDD supplement is required in 12States, because the NHIS area samplthe States provides smaller variancesthan the stand-alone RDD sample. Thare the same 12 States that did notrequire RDD supplementation intable 39under the assumption of lownontelephone bias. When nosupplementation is used, the bias isobviously completely eliminated becauthe NHIS sample is unbiased. Foranother 15 States, the supplementalsample size would be fewer than 500interviews. The bias of the dual-frameestimator in these States will be fromto 83 percent less than that of thestand-alone estimator. In the remaining24 States, where the supplement wourequire more than 500 interviews, thebias reduction is from 6 to 43 percent.

For the final analysis, two variablewere chosen that are of concern as paof the Healthy People 2000 ObjectivesPercent of persons currently smokingcigarettes and percent of persons und65 years of age without private healthinsurance. Estimates from the 1985NHIS are available for both of thesevariables for telephone and

in

e

nontelephone households fromTrends inUnited States Telephone CoverageAcross Time and Subgroups(22). Thelatter variable represents a worst-casescenario for the stand-alone RDDestimator, because it has the highestrelative bias (telephone householdsrelative to all households) for variablesreported with similar incidence rates.Cigarette smoking has a more moderatbias. For variables without anytelephone coverage bias, examination omean square errors would be equivalento the variances used in this analysis.Information for 1985 (in percent) onthese two variables, at the national leveis reported intable 41.

For each State,table 42comparesthe RDD stand-alone estimator with adual-frame estimator for the followingcharacteristics: Coverage rate (telephoncoverage rates are from the 1980 censuand NHIS coverage rate is assumed tobe 100 percent); response rate (aweighted average of RDD and NHISresponse rates weighted by the twoactual sample sizes); variance (assuminp = 0.30, the same relative relationshipbetween the variances would hold if another incidence rate is assumed); meansquare error (MSE) for currentlysmoking cigarettes; and MSE forpersons under 65 years of age withoutprivate health insurance.

The dual-frame estimator used forthe variance comparison is the minimumvariance estimator also used intable 40.If the characteristic to be estimated was

,

known not to suffer from significantbias, a minimum variance (potentiallybiased) dual-frame estimator could beused when comparing MSE’s. If thecharacteristic (such as the twopreceding) was biased, it is not obviouswhether it would be better to use theminimum variance or unbiased versionsof the dual-frame estimator. For thisanalysis the version with the bestvariance or MSE (depending on thetable column) is used based on thetelephone coverage rate and NHISsample size in each State. In all but oncase, the unbiased dual-frame estimatohas a smaller MSE than the potentiallybiased dual-frame estimator. The oneexception is for people currentlysmoking cigarettes in North Dakota.Cigarette smoking is subject to a smallbias than percent without private healthinsurance (4.5, compared with 16.7,percent relative bias). North Dakota alshas the third-smallest NHIS sample sizcombined with a high telephone-coverage rate. Thus, the overall bias issmall, the bias in the State will be lessthan average, and the sample of NHIShouseholds with telephones is small.

Examination oftable 42shows thatfor every State, the use of a dual-frameestimator (including NHIS data) canimprove the estimates that would beobtained from a stand-alone RDDsurvey of 1,000 households. In generalthe dual-frame estimator used incomparing variances intable 40is notthe same dual-frame estimator used in

Table 42. Comparison of 1,000 RDD interviews alone (sampling one adult per household), with dual-frame estimators (from proposeddesign), and p = 30 percent

Rank State

Projectedpopulationyear 2000(000’s)1

Actual NHISsample using1 year’s data

all adults1

Coveragerate (%)

Responserate (%)

Variance(× 1,000)2

Persons who currentlysmoke cigarettesMSE (× 1,000)3

Persons under 65 years oldwithout private health

insurance MSE (× 1,000)3

RDDDualframe RDD

Dualframe RDD

Dualframe RDD

Dualframe RDD

Dualframe

1 California . . . . . . . . . . . . 33,500 9,394 95 100 80 93 0.252 0.036 0.372 0.036 0.778 0.0302 Texas . . . . . . . . . . . . . . 20,211 5,673 91 100 80 94 0.252 0.054 0.634 0.054 2.010 0.0463 New York . . . . . . . . . . . . 17,986 4,427 93 100 80 88 0.252 0.063 0.494 0.063 1.353 0.0534 Florida . . . . . . . . . . . . . . 15,415 3,529 90 100 80 92 0.252 0.073 0.669 0.074 2.174 0.0625 Illinois . . . . . . . . . . . . . . 11,580 2,577 95 100 80 89 0.252 0.089 0.370 0.090 0.768 0.0766 Pennsylvania . . . . . . . . . . 11,502 2,016 96 100 80 91 0.252 0.097 0.327 0.098 0.564 0.0837 Ohio . . . . . . . . . . . . . . . 10,629 1,876 94 100 80 90 0.252 0.101 0.403 0.103 0.923 0.0878 Michigan . . . . . . . . . . . . 9,250 1,761 96 100 80 90 0.252 0.107 0.325 0.108 0.555 0.0929 New Jersey . . . . . . . . . . . 8,546 1,906 95 100 80 87 0.252 0.107 0.349 0.109 0.669 0.092

10 Georgia . . . . . . . . . . . . . 7,957 1,678 88 100 80 88 0.252 0.112 0.853 0.117 3.037 0.09911 North Carolina . . . . . . . . . 7,483 1,478 89 100 80 88 0.252 0.119 0.772 0.124 2.656 0.10512 Virginia . . . . . . . . . . . . . 6,877 1,366 92 100 80 90 0.252 0.124 0.548 0.129 1.604 0.10913 Massachusetts . . . . . . . . . 6,087 1,091 96 100 80 88 0.252 0.136 0.331 0.140 0.584 0.11814 Indiana . . . . . . . . . . . . . 5,502 952 93 100 80 88 0.252 0.143 0.441 0.150 1.104 0.12715 Missouri . . . . . . . . . . . . . 5,383 936 95 100 80 89 0.252 0.144 0.380 0.150 0.815 0.12716 Maryland . . . . . . . . . . . . 5,274 1,149 96 100 80 86 0.252 0.137 0.329 0.141 0.576 0.11917 Tennessee . . . . . . . . . . . 5,266 970 90 100 80 88 0.252 0.143 0.674 0.154 2.198 0.13018 Washington . . . . . . . . . . . 4,991 849 94 100 80 86 0.252 0.150 0.392 0.157 0.871 0.13319 Wisconsin . . . . . . . . . . . . 4,784 792 97 100 80 88 0.252 0.153 0.299 0.157 0.435 0.13320 Arizona . . . . . . . . . . . . . 4,618 1,041 89 100 80 88 0.252 0.147 0.758 0.161 2.591 0.13621 Louisiana . . . . . . . . . . . . 4,516 1,021 89 100 80 88 0.252 0.145 0.762 0.157 2.613 0.13322 Minnesota . . . . . . . . . . . . 4,490 686 97 100 80 87 0.252 0.158 0.302 0.164 0.449 0.13923 Alabama . . . . . . . . . . . . . 4,410 899 87 100 80 88 0.252 0.150 0.973 0.168 3.603 0.14224 South Carolina . . . . . . . . . 3,906 838 87 100 80 86 0.252 0.155 0.935 0.176 3.425 0.14925 Colorado . . . . . . . . . . . . 3,813 797 94 100 80 84 0.252 0.161 0.422 0.172 1.013 0.14626 Kentucky . . . . . . . . . . . . 3,733 610 88 100 80 86 0.252 0.167 0.861 0.194 3.074 0.16427 Connecticut . . . . . . . . . . . 3,445 672 97 100 80 86 0.252 0.168 0.302 0.175 0.450 0.14828 Oklahoma . . . . . . . . . . . . 3,376 579 92 100 80 85 0.252 0.172 0.524 0.192 1.490 0.16329 Oregon . . . . . . . . . . . . . 2,877 480 83 100 80 85 0.252 0.181 1.449 0.240 5.839 0.20330 Mississippi . . . . . . . . . . . 2,877 654 93 100 80 86 0.252 0.170 0.436 0.186 1.080 0.15831 Iowa . . . . . . . . . . . . . . . 2,549 394 96 100 80 85 0.252 0.188 0.315 0.205 0.511 0.17332 Arkansas . . . . . . . . . . . . 2,529 461 87 100 80 85 0.252 0.185 0.933 0.233 3.414 0.19733 Kansas . . . . . . . . . . . . . 2,529 445 95 100 80 85 0.252 0.186 0.363 0.206 0.735 0.17534 Utah . . . . . . . . . . . . . . . 1,991 330 95 100 80 84 0.252 0.199 0.379 0.231 0.809 0.19535 New Mexico . . . . . . . . . . 1,968 587 86 100 80 87 0.252 0.185 1.098 0.238 4.189 0.20236 West Virginia . . . . . . . . . . 1,722 260 89 100 80 84 0.252 0.205 0.745 0.286 2.532 0.24237 Nebraska . . . . . . . . . . . . 1,556 254 96 100 80 83 0.252 0.208 0.314 0.240 0.507 0.20338 Hawaii . . . . . . . . . . . . . . 1,345 239 95 100 80 84 0.252 0.212 0.357 0.260 0.707 0.22039 New Hampshire . . . . . . . . 1,333 198 94 100 80 83 0.252 0.215 0.398 0.276 0.900 0.23440 Nevada . . . . . . . . . . . . . 1,303 271 90 100 80 84 0.252 0.211 0.663 0.301 2.146 0.25441 Maine . . . . . . . . . . . . . . 1,271 186 93 100 80 82 0.252 0.216 0.491 0.301 1.337 0.25542 Rhode Island . . . . . . . . . . 1,049 183 95 100 80 83 0.252 0.220 0.350 0.283 0.672 0.24043 Idaho . . . . . . . . . . . . . . . 1,047 178 93 100 80 83 0.252 0.221 0.471 0.318 1.242 0.26944 Montana . . . . . . . . . . . . . 794 120 92 100 80 82 0.252 0.228 0.520 0.386 1.473 0.32645 Delaware . . . . . . . . . . . . 734 148 95 100 80 82 0.252 0.227 0.356 0.317 0.704 0.26846 South Dakota . . . . . . . . . 714 105 94 100 80 82 0.252 0.231 0.426 0.376 1.032 0.31847 Alaska . . . . . . . . . . . . . . 687 112 83 100 80 81 0.252 0.231 1.459 0.616 5.887 0.52148 District of Columbia . . . . . . 634 205 95 100 80 82 0.252 0.219 0.351 0.278 0.679 0.23549 North Dakota . . . . . . . . . . 629 93 96 100 80 81 0.252 0.233 0.328 0.309 0.572 0.29150 Vermont . . . . . . . . . . . . . 591 87 93 100 80 81 0.252 0.234 0.449 0.426 1.139 0.36051 Wyoming . . . . . . . . . . . . 489 84 92 100 80 81 0.252 0.236 0.554 0.515 1.634 0.435

1The actual NHIS sample sizes come from table 1 of Memorandum #56R.2The RDD design effect for sampling one adult per household is assumed to be 1:2. This is based on results of 18,500 interviews from the National Household Education Survey. The State-specificdesign effects for the NHIS were computed in Memorandum #56R. The dual-frame estimator used for the variance comparison is the minimum variance (potentially biased) estimator also used intable 40.3The dual-frame estimator used in these columns is the minimum of these unbiased and minimum variance estimators.


s


comparing mean square errors (exceptfor North Dakota). The rationale is that,given either basis for choosing anestimator—minimizing variance orminimizing MSE—it is possible toconstruct a dual-frame estimator withsignificantly better properties than thoseof the stand-alone RDD estimator.

The bias discussed in this chapterarises from differences in the healthcharacteristics of households with andwithout telephones. A second source ofbias is adjusting for nonresponse. Anestimator based on a higher responserate will minimize the impact of thissecond source of nonresponse. Thefollowing summarizes the findingscomparing stand-alone RDD anddual-frame estimation:

+ The coverage rate is from 3 to17 percent higher.

+ The response rate is from 1 to14 percent higher.

+ The dual-frame variance is alwayslower; for 12 of the States, it is lessthan one-half that of the RDDestimator.

+ The dual-frame mean square error ialways lower. For cigarette smokersin 32 States, and for those withoutprivate health insurance in everyState, it is less than one-half that ofthe RDD estimator.

l

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a

e

ced

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tics

n

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n

arerr


Chapter 12.Empirical Comparisonof Model-DependentEstimators

This chapter describes an empiricacomparison of model-dependentestimators that could potentially

be used to produce State-level estimatefrom NHIS. Five model-dependentestimators were examined along withthe direct design-unbiased estimator.Their point estimates for each State aswell as their estimated mean squareerrors (MSE’s) were compared using thdata collected in the 1988 NHIS. Thefive estimators were the syntheticestimator originally developed for NHIS(23), a composite estimator of thesynthetic and direct estimators (24), andthree versions of the generalizedsynthetic estimator (GSE) (25).

The GSE loosens the restrictiveassumptions on which the traditionalsynthetic estimator depends in order toproduce unbiased estimates. Inparticular, although the syntheticestimator requires that the mean for apopulation subgroup (for example, whitefemales 18–44 years of age) be thesame in every State (if this assumptionapproximately holds, then the syntheticestimator will be approximatelyunbiased), the GSE requires only thatthey have the same expectation. TheBayesian statistical term for this is thatthe State/subgroup means for aparticular variable be exchangeable (26The GSE also allows one to incorporateprior information into the estimator andallows the subgroup means in someStates to have smaller standarddeviations than in others.

NHIS collects data on dozens ofhealth items. For this analysis two itemswere examined: The mean of a countvariable and a proportion. The countvariable was the average number ofdoctor visits in the past 12 months, andthe proportion was the percentage withself-perceived poor health status. Theverbatim questions on the NHISquestionnaire were as follows:

+ During the past 12 months, howmany times did (person’s name) seor talk to a medical doctor orassistant? (Do not count doctorsseen while an overnight patient in ahospital.)

+ Would you say (your/his/her) healthin general is excellent, very good,good, fair, or poor?

EstimatorsThe exact formulas for the six

estimators are described next.

1. Direct, design-unbiased, inflationestimator

y1i = ∑j

∑k

wijk yijk / ∑j

∑k

wijk

(11)

whereyijk = response for thekth respondent,

in subgroupj, in Statei

wijk = weight for thekth respondent,in subgroupj, in Statei

i = 1, 2, . . .I

j = 1, 2, . . .J

k = 1, 2, . . .Nij

2. Synthetic estimator.Sixteensubgroups were used, four agecategories (0–19 years, 20–44 years,45–64 years, 65 years or over), two racategories (black, other than black), antwo sex categories (male, female).

y2 i. = ∑j = 1

16Nij y.j.

Ni.

(12)

whereNij = 1990 census total population for

subgroupj in Statej

y.j. = ∑i

∑k

wijk yijk / ∑j

∑k

wijk

Ni. = 1990 Census total population forStatei.

The predictive version of the syntheticestimator was also examined. This usethe observed values of the sampledrespondents and the preceding syntheestimator only for the nonsampled case
.
As with almost all nationalhousehold surveys, the sampling fractiofor NHIS is quite small, resulting ininsignificant differences between the twforms of the synthetic estimator. Thus,only the results from the more commonversion given here are reported.

3. Composite estimator.This is alinear combination of the first twoestimators where the weights areproportional to the mean square errorsof the two estimators.

y3i. = ti y1i. + (1 − ti) y2i(13)

where

ti =MSE (y2i.)

Var (y1i.) + MSE (y2i.)

Because many States contain onlyone or two sampled PSU’s, directestimated variances would be highlyunstable. Thus, for these analyses, theVar(y1i) were developed separatelyfor different types of questions (see thefirst section ofchapter 3). Thecalculation of MSE(y2i.) is describedin the second section of this chapter.

Before discussing the final threeestimators, it is necessary to discuss thequation for the GSE. (SeeSmall AreaEstimation: A Bayesian Perspective(25)and Small Area Estimation for theNational Health Interview Survey (27)for more detail.) GSE is derived basedon two assumptions. The first is thatconditional on the mean and variancefor Statei and subgroupj, the Yijk areexchangeable within their State/subgroup and are independent betweeStates/subgroups. The secondassumption is that for each State/subgroup, the mean and variance areexchangeable across States, and theyindependent between subgroups. Undethese assumptions, GSE is the posteriomean for Statei, given the samplescontaining datay.

E (Y.. | (s,y)) =

3ni. yi.. + ∑j = 1

J

(Nij − nij)zµ.j vp + mj

*vs

vp + vs4/Niz

(14)

g

s,

.

t

s

r

Table 43. Values of bj for each of the 16 age/race/sex subgroups used in estimator 5

Age Race Sex bj

0–19 . . . . . . . . . . . . . . . . . . . . Black Male 21.460–19 . . . . . . . . . . . . . . . . . . . . Black Female 21.430–19 . . . . . . . . . . . . . . . . . . . . Nonblack Male 22.260–19 . . . . . . . . . . . . . . . . . . . . Nonblack Female 22.3720–44 . . . . . . . . . . . . . . . . . . . Black Male 20.5620–44 . . . . . . . . . . . . . . . . . . . Black Female 20.6120–44 . . . . . . . . . . . . . . . . . . . Nonblack Male 21.2620–44 . . . . . . . . . . . . . . . . . . . Nonblack Female 22.0045–64 . . . . . . . . . . . . . . . . . . . Black Male 20.2145–64 . . . . . . . . . . . . . . . . . . . Black Female 19.8545–64 . . . . . . . . . . . . . . . . . . . Nonblack Male 23.5045–64 . . . . . . . . . . . . . . . . . . . Nonblack Female 23.4365 and over . . . . . . . . . . . . . . . Black Male 21.8665 and over . . . . . . . . . . . . . . . Black Female 21.3365 and over . . . . . . . . . . . . . . . Nonblack Male 23.0965 and over . . . . . . . . . . . . . . . Nonblack Female 23.51


where

ni = sample size in Statei

yi.. = ∑i

∑j

wijk yijk / ∑i

∑j

wijk

nij = sample size in subgroupj andStatej

µ.j = best linear unbiased estimate(BLUE) for the mean ofsubgroupj

vp = a priori variance on the priormean for subgroupj

mj* = prior mean for subgroupj

vs = expected sampling variance of thesample mean for subgroupj

As with any Bayesian prediction offinite populations, the GSE uses theobserved data and estimates for only thunobserved values. The right-hand sideof equation 14 is also seen to contain aweighted average of a sample estimateand a prior mean separately for eachsubgroup. If the a priori variance,vp, ismuch larger than the expected samplinerror,vs, GSE reduces to

E(Yizz | (s,y) ) =3niz yizz+∑j = 1

J

(Nij − nij) µ.j4 /Niz

(15)

In general, the BLUE for the mean ofsubgroupj is a weighted average ofevery State average for subgroupj, yij., where the weights are inverselyproportional to the sampling variancefor that State/subgroup. If for anysubgroup the elemental variances(σij

2) are assumed to be equal in all Statethe BLUE is simply equal toy.j.. In thiscase the GSE is equal to (the predictiveversion of) the synthetic estimator.

The remaining three estimators, allforms of the GSE, can now be described

4. GSE with heteroscedasticvariances.It is desired to examine therobustness of the traditional syntheticestimator in view of the assumption thathe variances are equal across smallareas. It can be shown that forproportions the State/subgroup variancemust be homoscedastic if their meansare assumed equal. Thus, this estimatoapplies only to the doctor visit variable.Assume the mean response for a given

e

subgroup is the same for all small areas(to reflect the assumption used by thesynthetic estimator) and that the priorvariance is diffuse (at least relativeto vs). Assume that within a subgroup,there are differences in the distributionsof doctor visits between those in centralcities and those who live elsewhere.Estimates for subgroups in States thathave either almost all of their populationin central cities or not in central citieswould be subject to less variation thanfor subgroups in States with a mixture.In particular, assume the mean andvariance of each small area/subgroup is

[µij , σij2 zij (1 − zij)]

where

zij = the proportion of the State’s black(or nonblack) population living incentral cities (capped at 0.95)

Thus,

y4i. = 3ni. yi. + ∑j =1

J

(Nij − nij) µj4 / Niz

(16)

where

µ.j = 3∑i = 1

Inij yij

zij (1 − zij)4

/ 3∑i = 1

Inij

zij (1 − zij)4

5. GSE with balanced prior andsampling variances (16 subgroups).Abasic requirement for all Bayesestimators is that the weights associatedwith the BLUE and the prior mean mustbe nonnegative. Thus,vp andvs must benonnegative. Further, when their exactformulas are examined, the relativemagnitudes of these two terms are alsoconstrained (24, 26), partially as afunction of how evenly the population isspread across all States. Definebj as themidpoint in the allowable range in therelative sizes of the prior and samplingvariances for each subgroupj. It isinteresting to note that when themidpoint in the possible range ofrelationships for the two variances ischosen, the resulting estimator givesgreater weight to the prior mean. Thiswould be most appropriate when theprior is based on more data (or greaterknowledge) than the current survey. Inthis empirical examination, subgroupsare spread across 51 States (thefollowing formula is a function of I-1and (I-1)2, whereI = 51), and the priormean is simply estimated by the meanof the previous year’s NHIS.

y5i = { niyi + ∑j = 1

J

(Nij − nij)z

3µ.j (51/50bj− 1/50) +mj

*(51/50bj)

(51/25bj− 1/50)4} / Niz

(17)

r

ct

ed

sl

l

r

flyy

e

Table 44. Values of bj for each of the 32 central city/age/race/sex subgroups used inestimator 6

Central city Age Race Sex bj

Yes . . . . . . . . . . . . . . . . 0–19 Black Male 18.20Yes . . . . . . . . . . . . . . . . 0–19 Black Female 18.16Yes . . . . . . . . . . . . . . . . 0–19 Nonblack Male 14.83Yes . . . . . . . . . . . . . . . . 0–19 Nonblack Female 14.92Yes . . . . . . . . . . . . . . . . 20–44 Black Male 17.28Yes . . . . . . . . . . . . . . . . 20–44 Black Female 17.12Yes . . . . . . . . . . . . . . . . 20–44 Nonblack Male 14.30Yes . . . . . . . . . . . . . . . . 20–44 Nonblack Female 14.85Yes . . . . . . . . . . . . . . . . 45–64 Black Male 16.43Yes . . . . . . . . . . . . . . . . 45–64 Black Female 15.72Yes . . . . . . . . . . . . . . . . 45–64 Nonblack Male 16.17Yes . . . . . . . . . . . . . . . . 45–64 Nonblack Female 16.18Yes . . . . . . . . . . . . . . . . 65 and over Black Male 18.37Yes . . . . . . . . . . . . . . . . 65 and over Black Female 17.58Yes . . . . . . . . . . . . . . . . 65 and over Nonblack Male 16.81Yes . . . . . . . . . . . . . . . . 65 and over Nonblack Female 17.05No . . . . . . . . . . . . . . . . 0–19 Black Male 18.34No . . . . . . . . . . . . . . . . 0–19 Black Female 18.26No . . . . . . . . . . . . . . . . 0–19 Nonblack Male 25.17No . . . . . . . . . . . . . . . . 0–19 Nonblack Female 25.28No . . . . . . . . . . . . . . . . 20–44 Black Male 18.28No . . . . . . . . . . . . . . . . 20–44 Black Female 18.17No . . . . . . . . . . . . . . . . 20–44 Nonblack Male 24.01No . . . . . . . . . . . . . . . . 20–44 Nonblack Female 24.70No . . . . . . . . . . . . . . . . 45–64 Black Male 18.62No . . . . . . . . . . . . . . . . 45–64 Black Female 18.68No . . . . . . . . . . . . . . . . 45–64 Nonblack Male 25.92No . . . . . . . . . . . . . . . . 45–64 Nonblack Female 25.79No . . . . . . . . . . . . . . . . 65 and over Black Male 18.77No . . . . . . . . . . . . . . . . 65 and over Black Female 18.41No . . . . . . . . . . . . . . . . 65 and over Nonblack Male 24.85No . . . . . . . . . . . . . . . . 65 and over Nonblack Female 25.29


Where for these analyses

µ.j = y.j. from the 1988 NHIS

mj* = y.j. from the 1987 NHIS

Table 43provides the values ofbj foreach of the 16 subgroups. A value nea1 indicates that the subgroup isconcentrated in one State, and a valuenear 51 implies that the subgroup isevenly spread throughout the States.

6. GSE with balanced prior andsampling variances (32 subgroups).This estimator is of the same form asestimator 5. To examine the impact onthe synthetic estimator when it is subjeto greater variability, however, thisestimator uses 32 subgroups. Thisincreases the sampling errors associatwith the subgroup means. The 32subgroups are each of the 16 subgroupused previously, subdivided into centracity and noncentral city. This examinesthe possibility of significant differencesbetween members of a subgroup wholive in a central city versus outside thecentral city. (It was not possible toseparate the 1987 NHIS data by centracity and noncentral city; thus the sameprior means were used for estimator 6as for estimator 5.)Table 44providesthe values ofbj for each of the 32subgroups. It is worth noting thatalthough thebj values used for estimato5 only vary from 19.85 to 23.51, thebj

values used for estimator 6 vary from14.30 to 25.92.

State-Specific Mean SquareErrors

Before discussing the empiricalresults, it is necessary to describe briea procedure for comparing the accuracof the estimators.Estimation of TheError of Synthetic Estimates(28)introduced the average mean squareerror (across small areas) as a measurof accuracy for model-based small areaestimates. Lety1i be the design-unbiased

inflation estimator for small areai;yfi be any model-based estimatorf for small areai; andYi. be the true mean

for small areai.

1I ∑

i = 1

I

(y1i − yf i)2 =

1I ∑

i = 1

I

[(y1i − yi.) − (yf i − yi.)]2

=1I∑

i = 1

I

(y1i − yi.)2 +1I∑

i = 1

I

(yf i − yi.)2

−2I ∑

i = 1

I

(y1i − yi.) (yf i − yi.)

(18)

By taking the expectation of both sidesof equation 18

E31I∑i = 1

I

(y1i − yf i)24= aveMSE (y1) + aveMSE(yf)

− E32I∑i = 1

I

(y1i − yi.) (yf i − yi.)4If the number of areasI is relativelylarge, the covariance-like last term onthe righthand side will be quite small,becauseE (y1i − yiz) = 0 and asI → ∞, y1i

andyfi become approximatelyindependent. Thus, for largeI, thisequation is approximately

E31I∑i = 1

I

(y1i − yfi)24 ≈aveVAR(y1) + aveMSE(yf)

and the approximate (for relatively largeI) average MSE may be written as

aveMSE(yf) ≈ E31I ∑i = 1

I

(y1i − yf i)24− aveVAR(y1)

(19)

te

ge

of

ea

it

h

d

ng

edx

s

.

h

t

s

c

a

;

r


and an approximately unbiased estimaof the average MSE is

1I ∑

i = 1

I

(y1i − yfi)2 − aveVAR(y1)(20)

To estimate the average MSE, the firstterm of equation 20 is computed bytaking the average squared differencebetween the two estimators across allsmall areas. The second term can becomputed for any item by calculatingthe variances of the direct estimator ineach small area and taking their averaWith many sample designs, however,there will be very few PSU’s in eachsmall area; therefore, the estimatedvariances for direct estimators for suchsmall areas will be very unstable.Averaging the estimated variancesacross all small areas will provide astable estimate of average variance.The approximately unbiased estimatethe average MSE is simple to computeand provides a design-based overallmeasure of accuracy. Unfortunately, byaveraging across all small areas, itoverstates the error associated with arwhere the model fits well or for whichthe sampling error is small (comparedwith an average small area). Similarly,understates the error associated withareas where the model fails or for whicthe sampling error is large.It would be far preferable to producesmall area-specific mean square errorsfor model-based estimators. This woulprovide smaller MSE’s in areas wherethe model fits well or for which thesampling error is small. It would alsoprovide larger MSE’s in small areaswhere the model fails or for which thesampling error is large. The followingdevelops a new procedure for estimatiState-specific MSE’s. The definition ofMSE for small areai of model-basedextimatoryf is

MSE(yf i) = Var(yf i) + Bias2(yf i)(21)

Var(yfi) can be calculated usingreplicated variances (jackknife orbalanced repeated replication).Replicated variances are frequently usfor survey estimates based on complesurvey designs and/or when weightsvary within a stratum. The changes in

.

s

the estimated statistic are examined adifferent PSU’s are dropped from theanalysis, with other PSU’ssimultaneously given additional weightThese procedures incorporate thesurvey’s complex sample design into testimated variance and produceestimates of precision for each smallarea. Replicated variances ofmodel-based statistics are computedusing the same procedure as for direcestimates of means or ratios.

Unfortunately, the lack of anestimate of the ‘‘truth’’ for potentiallybiased model-based estimators requirethe use of an average bias, inconjunction with a State-specificvariance. (If the true value were knownfor each small area, MSE’s could becomputed directly.)

aveBias2(yf) = aveMSE(yf)− aveVar(yf)

(22)

Combining equations 19 and 22 gives

aveBias2(yf) = E31I ∑i =1

I

(y1i − yf i)24−aveVAR(y1) − aveVAR(yf) (23)

Using this average bias (equation23), we can produce small area-specifimean square errors by replacing thedefinition of MSE (equation 21) with

MSE(yf) = Var(yfi) + aveBias2 (yf).(24)

To estimate equation 24, thecomputations for the design-based smarea-specific MSE’s involve thefollowing six-step process:

Estimate

E31I ∑i = 1

I

(y1 i − yf i)24with

1I ∑

i = 1

I

(y1i − yf i)2

(25)

e

ll

and aveVar(y1) with1I ∑

i = 1

I

var(y1i)(26)

Var(yf i) are estimated using replicated

variances, call these estimates var(yf i)(27)

Estimate aveVar(yf)with1I ∑

i = 1

I

var(yf i)(28)

Then

aveBias2(yf) = (eq. 25) − (eq. 26)− (eq. 28)

(29)

MSE(yf i) = (eq. 27) + (eq. 29)(30)

An examination of equation 24 revealsthat the six-step procedure will produceimprovements (that is, differentiation inMSE’s for small areas) only when thevariance is a significant proportion ofMSE. This can occur under either oftwo conditions. First, if the model usedto produce the model-based estimates isa close approximation of reality, the biasterm will be small. Second, if themodel-based estimates depend heavilyon the sample in that small area and thesample size of the survey in the smallarea is small, the variances will belarge. It is important to note, however,that these model-based estimators candepend on factors other than the samplethus, a small sample size does notnecessarily imply a large variance. Forexample, if a form of the generalizedsynthetic estimator (equation 14) is usedthat relies heavily on the priordistribution, even when the sample sizefor each subgroup is small, the estimatowill have little variance. Alternatively, ifthe form of the GSE weights the sampledata heavily, the variance of theestimator will increase and will increaseeven more if the number of subgroupsinto which the data are split increases.

In the ideal situation for producingsmall area-specific mean square errors,the variance term will dominate the biasterm. In such situations the root meansquare error can be used to provideapproximate confidence intervals for themodel-based estimates.

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Empirical Results

National Model

Tables 45and46 provide theestimates derived for each State for eaof the estimators for number of doctorvisits and perceived poor health statusrespectively. The States are sorted indescending order of the estimates usinestimator 1. Note that for two of theStates, North Dakota and Nebraska, nsample was collected; and thus, nodirect design-unbiased estimate existsThe largest and smallest estimates areshaded for each estimator. Although thmodel-based estimators are notconsistent with the design-unbiasedestimator, it is striking to note thesimilarities between the differentmodel-based estimators. In particular fdoctor visits, the standard syntheticestimator and all three generalizedversions found Alaska to have thesmallest estimated numbers. All butestimator 6 found Florida to have thelargest estimated number (Florida wassecond largest for estimator 6). Thedifferent versions of the syntheticestimator had very little difference,generally less than 1 percent. Thisrobustness of the synthetic estimator theterogeneous variances and inclusionof data from the prior year would becomforting if this estimator were to beused to produce State-level estimates,and it is probably a result of the largesample sizes from each subgroup.

Similar results are observed forself-perceived poor health status. Thethree versions of the synthetic estimateach found Alaska to have the lowestincidence rate and the District ofColumbia, the highest. It is intriguing tnote that these two States had directestimates that were very similar. Againthe robustness of the synthetic estimafor NHIS is a striking finding.

Many estimators based onexchangeable models produce estimasimilar to those of composite estimatothat is, a weighted average of the direestimator and the estimator developedconditional on the truth of a particularmodel (for example, the syntheticestimator). The GSE assumesexchangeability of State/subgroup meaacross States for a given subgroup. It

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therefore produces estimates that, in tabsence of strong prior information, amore similar to the synthetic than to thcomposite estimator.

One other finding from these tableis the strong shrinkage toward the mein all of the model-dependent estimatoAlthough the direct estimator spans arange of more than 3.0 doctor visits pyear (2.685−6.036), each of themodel-dependent estimators has a spof only 0.4 doctor visits. Similar resultare found for self-perceived poor heal

Table 47shows the results of thesix-step process for computing MSE’sfor estimators 2 and 5 for averagenumber of doctor visits. The third row(the Gonzalez-Waksberg average MSEis calculated by subtracting the seconfrom the first. The fourth row is theaverage of the replicated variances fothe 51 States. The average bias (rowstep 5) is found by subtracting row 4from row 3. Unfortunately, the averagevariance across States is quite smallcompared with the average bias (lessthan 2 percent). As a result the largesand smallest State-specific MSE’s arevery similar to the average MSE. Forthe synthetic estimator, theaveMSE = 0.1703, while the smallestState-specific MSE is 0.1701 (Georgiaand the largest is 0.1732 (the DistrictColumbia). MSE’s of similar magnitudwith small variances were found for thother estimators and for perceived pohealth. These results are not verysurprising as numerous examples havshown that the magnitude of the biasthe synthetic estimator is bigger thanvariability. This is compounded by thefact that the sample size of NHIS is solarge (125,000 respondents) that thesampling variation is quite small. Formost surveys where the sample size ilikely to be much smaller, the samplinvariation can be expected to play a mimportant role, increasing the utility ofState-specific MSE’s.

Subnational Models

Given the large sample size ofNHIS, it is possible to divide the Natiointo groups of States having anticipatesimilar biases with respect to thevariable of interest. When this is true,the differences in MSE’s between thes

.

groups can be large; and, for States ingroupings where the model fits well, thaverage bias will be small and theState-specific MSE’s will vary. Thisallows for significantly smallerapproximate confidence intervals forsome States compared with other StatThe range of estimated mean valuesmay also increase when thecomputations are based on data fromrestricted subset of States.

Three potential groupings of Stateare examined. They are based on thepercent of the State’s population livingbelow the poverty level, the percent ofthe population that is not black living incentral cities, and the percent of theblack population living in central cities.Table 48identifies which States wereclassified in the high, medium, and lowcategories in each of these groupings.

Although each State still only has16 (or 32) subgroups, the national datset is being split into 48 (or 96)subgroups under each of thesesubnational models. Thus, the subgroumeans will be more variable, and onewould expect the State-specific MSE’sto be more variable.

One limitation when using theaverage MSE of M. Gonzalez and J.Waksberg (28) is that its computationdepends on taking the difference of twestimates. It is quite possible that,because of the errors associated withthese two estimates, the estimatedaverage MSE may be negative. BecauMSE’s are by definition nonnegative, imay be impossible to produce estimatMSE’s for some data sets. The sameholds for the six-step proceduredescribed earlier for computingState-specific MSE’s. Unfortunately,this problem is exacerbated when thNation is split into multiple groupingsWhen the Nation is split into threegroups of States (as is the case foreach of the three groupings alreadymentioned), there is a much greaterchance of producing negativeestimated MSE’s than when all Stateare combined into one estimate. Evetime State-specific MSE’s werecalculated for each estimator for bothvariables using the poverty-basedgroupings, at least one of the threegroups of States produced a negativestimated average MSE. Thus, the

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Table 45. Average number of doctor visits, by State

State

Design-unbiasedinflation

estimator(1)

Syntheticestimator

(2)

Compositeestimator

(3)

Hetero-scedasticvariances

(4)

GSE with(16)

balancedvariances

(5)

GSE with(32)

balancedvariances

(6)

Vermont . . . . . . . . . . . . . . . . . . 6.036 3.889 4.600 3.912 3.904 3.893Delaware . . . . . . . . . . . . . . . . . 5.648 3.856 4.178 3.898 3.895 3.873Colorado . . . . . . . . . . . . . . . . . 4.868 3.831 4.477 3.858 3.851 3.859District of Columbia . . . . . . . . . . . 4.706 3.852 4.121 3.967 3.987 4.084Michigan . . . . . . . . . . . . . . . . . 4.594 3.852 4.461 3.890 3.888 3.890

Connecticut . . . . . . . . . . . . . . . . 4.589 3.926 4.343 3.957 3.948 3.956Arizona . . . . . . . . . . . . . . . . . . 4.562 3.889 4.350 3.915 3.906 3.927Nevada . . . . . . . . . . . . . . . . . . 4.530 3.841 4.026 3.871 3.862 3.865Massachusetts . . . . . . . . . . . . . . 4.481 3.933 4.362 3.960 3.951 3.959Rhode Island . . . . . . . . . . . . . . . 4.424 3.959 4.098 3.984 3.974 3.977

Montana . . . . . . . . . . . . . . . . . . 4.394 3.911 4.159 3.933 3.925 3.920Kentucky . . . . . . . . . . . . . . . . . 4.333 3.895 4.179 3.925 3.921 3.913Pennsylvania . . . . . . . . . . . . . . . 4.204 3.967 4.175 4.000 3.993 3.994Ohio . . . . . . . . . . . . . . . . . . . . 4.201 3.896 4.162 3.930 3.926 3.930Wyoming . . . . . . . . . . . . . . . . . 4.156 3.827 3.912 3.849 3.844 3.842

California . . . . . . . . . . . . . . . . . 4.111 3.817 4.093 3.848 3.841 3.850Oklahoma . . . . . . . . . . . . . . . . . 4.104 3.898 4.041 3.927 3.923 3.928New Mexico . . . . . . . . . . . . . . . 4.084 3.842 3.898 3.865 3.860 3.866Maine . . . . . . . . . . . . . . . . . . . 4.075 3.928 3.981 3.949 3.941 3.933Maryland . . . . . . . . . . . . . . . . . 4.045 3.811 3.970 3.863 3.861 3.841

Florida . . . . . . . . . . . . . . . . . . . 3.981 4.003 3.985 4.041 4.030 4.025Tennessee . . . . . . . . . . . . . . . . 3.950 3.876 3.931 3.916 3.915 3.924Washington . . . . . . . . . . . . . . . . 3.869 3.871 3.870 3.896 3.888 3.890Arkansas . . . . . . . . . . . . . . . . . 3.840 3.908 3.867 3.949 3.949 3.937New York . . . . . . . . . . . . . . . . . 3.828 3.902 3.836 3.943 3.937 3.958

Iowa . . . . . . . . . . . . . . . . . . . . 3.821 3.957 3.878 3.980 3.971 3.973New Jersey . . . . . . . . . . . . . . . . 3.806 3.913 3.828 3.950 3.943 3.930West Virginia . . . . . . . . . . . . . . . 3.734 3.975 3.888 3.999 3.992 3.981Oregon . . . . . . . . . . . . . . . . . . 3.704 3.928 3.800 3.951 3.941 3.943Utah . . . . . . . . . . . . . . . . . . . . 3.704 3.740 3.721 3.761 3.761 3.760

Wisconsin . . . . . . . . . . . . . . . . . 3.692 3.894 3.732 3.920 3.912 3.920South Carolina . . . . . . . . . . . . . . 3.677 3.780 3.730 3.838 3.843 3.801Virginia . . . . . . . . . . . . . . . . . . 3.671 3.819 3.704 3.864 3.864 3.859Alabama . . . . . . . . . . . . . . . . . . 3.638 3.846 3.698 3.899 3.903 3.893Hawaii . . . . . . . . . . . . . . . . . . . 3.575 3.850 3.796 3.875 3.864 3.869

Kansas . . . . . . . . . . . . . . . . . . 3.573 3.897 3.690 3.924 3.917 3.922Missouri . . . . . . . . . . . . . . . . . . 3.549 3.913 3.655 3.947 3.942 3.940Mississippi . . . . . . . . . . . . . . . . 3.489 3.763 3.674 3.829 3.840 3.793Texas . . . . . . . . . . . . . . . . . . . 3.450 3.789 3.482 3.824 3.823 3.838Illinois . . . . . . . . . . . . . . . . . . . 3.420 3.862 3.483 3.901 3.897 3.908

Louisiana . . . . . . . . . . . . . . . . . 3.410 3.754 3.502 3.813 3.819 3.810Indiana . . . . . . . . . . . . . . . . . . 3.380 3.885 3.516 3.915 3.910 3.920South Dakota . . . . . . . . . . . . . . 3.334 3.921 3.702 3.924 3.933 3.932New Hampshire . . . . . . . . . . . . . 3.236 3.879 3.798 3.901 3.893 3.893Idaho . . . . . . . . . . . . . . . . . . . . 3.162 3.857 3.616 3.877 3.871 3.864

Alaska . . . . . . . . . . . . . . . . . . . 3.093 3.629 3.542 3.656 3.653 3.665Minnesota . . . . . . . . . . . . . . . . . 3.034 3.885 3.258 3.908 3.900 3.899Georgia . . . . . . . . . . . . . . . . . . 3.000 3.755 3.190 3.809 3.812 3.787North Carolina . . . . . . . . . . . . . . 2.685 3.842 2.949 3.890 3.891 3.874North Dakota . . . . . . . . . . . . . . . . . . 3.908 3.908 3.931 3.920 3.923Nebraska . . . . . . . . . . . . . . . . . . . . 3.913 3.913 3.938 3.931 3.938


NOTE: Shading indicates the largest and smallest estimates in the column.


analyses are restricted to the latter twState groupings.

Tables 49and50 provide theestimates derived for each State for eaof the estimators for number of doctorvisits, based only on data from within

h

that State’s group of States, grouped bpercent of other-than-black and blackpopulations in central cities,respectively. As withtable 46, the Statesare sorted in descending order of theestimates for estimator 1, with the

largest and smallest estimate shaded feach estimator. Again, it is striking tonote the similarities between thedifferent model-based estimators. WithStates grouped by percentage of thepopulation that is not black in central

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Table 46. Percentage with perceived poor health status, by State

State


estimator(1)

Syntheticestimator

(2)

Compositeestimator

(3)

GSE with(16)

balancedvariances

(5)

GSE with(32)

balancedvariances

(6)

West Virginia . . . . . . . . . . . . . . . 7.17 2.87 5.28 2.86 2.87Mississippi . . . . . . . . . . . . . . . . 6.28 3.19 5.28 3.18 3.23Alabama . . . . . . . . . . . . . . . . . . 5.42 3.13 4.94 3.13 3.14Maine . . . . . . . . . . . . . . . . . . . 5.19 2.60 4.01 2.59 2.59Arkansas . . . . . . . . . . . . . . . . . 5.10 3.07 4.49 3.07 3.08

Kentucky . . . . . . . . . . . . . . . . . 5.06 2.70 4.56 2.69 2.70North Carolina . . . . . . . . . . . . . . 4.68 2.99 4.48 2.99 3.01Tennessee . . . . . . . . . . . . . . . . 4.46 2.91 4.23 2.91 2.90Oklahoma . . . . . . . . . . . . . . . . . 3.81 2.75 3.61 2.74 2.74Louisiana . . . . . . . . . . . . . . . . . 3.52 2.96 3.44 2.96 2.97

Georgia . . . . . . . . . . . . . . . . . . 3.44 2.81 3.38 2.81 2.83South Carolina . . . . . . . . . . . . . . 3.42 3.03 3.36 3.03 3.07Indiana . . . . . . . . . . . . . . . . . . 3.40 2.69 3.33 2.68 2.68Arizona . . . . . . . . . . . . . . . . . . 3.29 2.57 3.18 2.56 2.55Virginia . . . . . . . . . . . . . . . . . . 3.29 2.80 3.25 2.80 2.80

Texas . . . . . . . . . . . . . . . . . . . 3.02 2.49 3.00 2.48 2.47Florida . . . . . . . . . . . . . . . . . . . 2.96 3.29 2.97 3.30 3.30District of Columbia . . . . . . . . . . . 2.95 4.34 3.62 4.35 4.25Alaska . . . . . . . . . . . . . . . . . . . 2.87 1.76 2.13 1.74 1.74Idaho . . . . . . . . . . . . . . . . . . . . 2.80 2.42 2.66 2.40 2.41

Ohio . . . . . . . . . . . . . . . . . . . . 2.79 2.81 2.79 2.80 2.80Michigan . . . . . . . . . . . . . . . . . 2.78 2.76 2.78 2.75 2.75Missouri . . . . . . . . . . . . . . . . . . 2.64 2.88 2.66 2.87 2.87Delaware . . . . . . . . . . . . . . . . . 2.61 2.85 2.72 2.85 2.87Oregon . . . . . . . . . . . . . . . . . . 2.50 2.65 2.53 2.64 2.64

Wyoming . . . . . . . . . . . . . . . . . 2.41 2.32 2.36 2.30 2.30Illinois . . . . . . . . . . . . . . . . . . . 2.40 2.82 2.42 2.82 2.81California . . . . . . . . . . . . . . . . . 2.31 2.45 2.31 2.45 2.44New York . . . . . . . . . . . . . . . . . 2.31 2.96 2.33 2.96 2.94Kansas . . . . . . . . . . . . . . . . . . 2.21 2.69 2.28 2.68 2.68

Massachusetts . . . . . . . . . . . . . . 2.21 2.71 2.24 2.70 2.69Montana . . . . . . . . . . . . . . . . . . 2.10 2.59 2.27 2.58 2.58Vermont . . . . . . . . . . . . . . . . . . 2.09 2.45 2.24 2.44 2.44New Jersey . . . . . . . . . . . . . . . . 2.03 2.94 2.07 2.94 2.95South Dakota . . . . . . . . . . . . . . 2.00 2.63 2.23 2.62 2.62

Rhode Island . . . . . . . . . . . . . . . 1.97 2.78 2.19 2.77 2.77Maryland . . . . . . . . . . . . . . . . . 1.84 2.91 1.91 2.92 2.93Utah . . . . . . . . . . . . . . . . . . . . 1.75 1.99 1.79 1.97 1.97Nevada . . . . . . . . . . . . . . . . . . 1.74 2.56 1.92 2.56 2.56Pennsylvania . . . . . . . . . . . . . . . 1.69 3.02 1.72 3.01 3.01

New Mexico . . . . . . . . . . . . . . . 1.67 2.36 1.79 2.35 2.35Iowa . . . . . . . . . . . . . . . . . . . . 1.65 2.77 1.76 2.76 2.76Washington . . . . . . . . . . . . . . . . 1.44 2.50 1.50 2.49 2.49Wisconsin . . . . . . . . . . . . . . . . . 1.38 2.64 1.45 2.63 2.62Connecticut . . . . . . . . . . . . . . . . 1.27 2.82 1.38 2.82 2.81

Colorado . . . . . . . . . . . . . . . . . 1.20 2.36 1.27 2.35 2.35Minnesota . . . . . . . . . . . . . . . . . 1.16 2.51 1.23 2.49 2.49Hawaii . . . . . . . . . . . . . . . . . . . 0.87 2.44 1.08 2.44 2.43New Hampshire . . . . . . . . . . . . . 0.68 2.42 0.86 2.40 2.40North Dakota . . . . . . . . . . . . . . . . . . 2.59 2.59 2.58 2.58Nebraska . . . . . . . . . . . . . . . . . . . . 2.67 2.67 2.66 2.66




cities, the standard synthetic estimatand all three generalized versions finAlaska to have the smallest estimatenumber of doctor visits and WestVirginia to have the largest estimatednumber. The four model-based

estimators again agree on the Stateswith the largest and smallest number ofdoctor visits when States are grouped bpercentage of black population in centracities, but the extreme States are not thsame as with the first grouping; instead

yl

the District of Columbia is always thehighest and Mississippi, the lowest.

A major difference between theestimates intables 49and50 comparedwith those intable 45is that theState-to-State range of model-depende

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Table 47. State-specific mean square errors for number of doctor visits, using estimators 2and 5

Syntheticestimator

GSE with (16)balancedvariances

(1) E (y1i. − y^

fi.)2 . . . . . . . . . . . . . . . .3777 .3754

(2) aveVar (y1) . . . . . . . . . . . . . . . . .2074 .2074aveMSE (yf) . . . . . . . . . . . . . . . .1703 .1680

(4) aveVar (yf) . . . . . . . . . . . . . . . . .0020 .0003

aveVar (yf) . . . . . . . . . . . . . . . 1.2 percent 0.1 percentaveMSE

(5) aveBias2 (yf) . . . . . . . . . . . . . . . .1684 .1678

(6) MSE (yf)Smallest . . . . . . . . . . . . . . . . .1701 (Georgia) .1680 (many)Largest . . . . . . . . . . . . . . . . . .1732 (District of Columbia) .1684 (District of Columbia)

RMSE (yf) . . . . . . . . . . . . . . . . . .412–.416 .410

Table 48. Groupings of States by common expected biases

Percentage of population below poverty in 1988:

0.0–8.7 . . . . . . . . . . . . . . . . . . . . Connecticut, New Jersey, New Hampshire, Wisconsin,Kansas, Vermont, Massachusetts, Delaware, Nevada,Washington

9.4–17.6 . . . . . . . . . . . . . . . . . . . Iowa, Wyoming, Maryland, Rhode Island, Utah, Indiana,Nebraska, Pennsylvania, Oregon, Virginia, Alaska, Hawaii,Minnesota, North Dakota, Michigan, Ohio, Colorado, Idaho,North Carolina, Illinois, Michigan, California, Maine, New York,Florida, Georgia, Arizona, South Dakota, Montana, District ofColumbia, South Carolina, Oklahoma, Kentucky

17.9–27.2 . . . . . . . . . . . . . . . . . . West Virginia, Tennessee, Texas, Alabama, Arkansas,Louisiana, New Mexico, Mississippi

Percentage of 1990 nonblack population in central city:

0–15 . . . . . . . . . . . . . . . . . . . . . . Delaware, Vermont, South Carolina, Mississippi, West Virginia,Georgia, New Jersey, Maryland, Idaho, Maine, Kentucky,Michigan

16–37 . . . . . . . . . . . . . . . . . . . . . All 34 others

38–100 . . . . . . . . . . . . . . . . . . . . Alaska, New York, Texas, Arizona, District of Columbia

Percentage of 1990 black population in central city:

0–35 . . . . . . . . . . . . . . . . . . . . . . Mississippi, South Carolina, Hawaii, Vermont, North Dakota,Idaho, West Virginia, Maine, Georgia, Delaware, NorthCarolina

36–77 . . . . . . . . . . . . . . . . . . . . . Arkansas, Maryland, Montana, Utah, Florida, Nevada, NewMexico, New Jersey, South Dakota, Alabama, Kentucky,Louisiana, Virginia, New Hampshire, Wyoming, Washington,California, Missouri, Oklahoma, Colorado, Alaska, Texas,Rhode Island, Kansas, Ohio, Arizona, Tennessee, Minnesota,Pennsylvania, Connecticut, Iowa

78–100 . . . . . . . . . . . . . . . . . . . . Illinois, Michigan, Oregon, Massachusetts, New York,Nebraska, Indiana, Wisconsin, District of Columbia


point estimates is much larger when thStates are grouped than when all Stateused national subgroup estimates.Table 51demonstrates that for numberof doctor visits, the range ofmodel-dependent estimates across theStates doubles when the States aregrouped. For example, when data fromall States are combined, the GSE with32 subgroups has a range of 0.41 (fro3.67 (Arkansas) to 4.08 (the District ofColumbia). When data only from withina State’s grouping are used, this rangeincreased to 0.76 for groupings that arnot black (3.42 (Arkansas) to 4.18 (WeVirginia)) and to 0.97 for blackgroupings (3.48 (Mississippi) to 4.45(the District of Columbia)). This isimportant because one of the concernsabout the synthetic estimator is thatmany analysts believe it ‘‘overshrinks’’the estimates toward the nationalaverage, underrepresenting the truevariability from small area to small are

Similar results are observed forself-perceived poor health status. WithStates grouped by populations that arenot black in central cities (table 52), thethree versions of the synthetic estimatoeach find Alaska to have the lowestincidence rate and the District ofColumbia the highest. It is againintriguing to note that these two Stateshad direct estimates that were verysimilar. When grouped by blackpopulations in central cities (table 53),Alaska still has the lowest rate, butMississippi has the highest rate.

Table 54examines the robustness ofthe GSE by comparing the variation amothe different model-dependent estimatorsfor each State. Small variation (as is fouwhen national subgroup estimates areused) would indicate that the syntheticestimator is robust to its assumptions thare loosened in the various forms of theGSE that are examined. For doctor visitsthe variation among the model-basedestimators is similar when States aregrouped by population other than blackwhen all States are combined. Whengrouped by percentage of the blackpopulation in central cities, there issignificant variation among themodel-dependent estimates for the 11States with more rural black populations

Grouping the States also increasethe variation among model-dependent

estimates for percent with poor health ineach State. This variation is particularlypronounced for States with largepercentages of its population that are nblack, or small percentages of its blackpopulation, in central cities.

Thus, using subnational groupingsof States results in model-dependentestimates that vary more from State toState and are, in general, still consistenacross the form of estimator that is use

t

.

In some States, however, the form of thGSE can have a significant impact onthe estimate. The remainder of thischapter examines the impact on MSE’sof using the subnational groupings.

The six-step process for computingMSE’s is recalculated averaging onlyacross those States in the same groupingThe subgroup means are calculatedseparately for each grouping.Tables 55–57are demonstrative of results for the two

r ss

Table 49. Average number of doctor visits, with States grouped by percentage of nonblack population in central cities

State


estimator(1)

Syntheticestimator

(2)

Compositeestimator

(3)


(4)

GSE with(16)

balancedvariances

(5)

GSE with(32)

balancedvariances

(6)

Vermont . . . . . . . . . . . . . . . . . . 6.036 4.055 5.241 4.079 4.099 4.097Delaware . . . . . . . . . . . . . . . . . 5.648 3.999 4.653 4.041 4.044 4.042Colorado . . . . . . . . . . . . . . . . . 4.868 3.831 4.100 3.822 3.861 3.876District of Columbia . . . . . . . . . . . 4.706 3.807 3.928 3.971 4.051 4.053

Michigan . . . . . . . . . . . . . . . . . 4.594 3.997 4.554 4.035 4.052 4.090Connecticut . . . . . . . . . . . . . . . . 4.589 3.923 4.099 3.919 3.958 3.969Arizona . . . . . . . . . . . . . . . . . . 4.562 3.767 4.074 3.799 3.713 3.703Nevada . . . . . . . . . . . . . . . . . . 4.530 3.839 3.888 3.832 3.872 3.881Massachusetts . . . . . . . . . . . . . . 4.481 3.931 4.170 3.923 3.961 3.973Rhode Island . . . . . . . . . . . . . . . 4.424 3.956 3.995 3.947 3.982 3.986








Alaska . . . . . . . . . . . . . . . . . . . 3.093 3.456 3.437 3.495 3.416 3.417Minnesota . . . . . . . . . . . . . . . . . 3.034 3.884 3.567 3.872 3.908 3.905Georgia . . . . . . . . . . . . . . . . . . 3.000 3.888 3.090 3.936 3.947 3.944North Carolina . . . . . . . . . . . . . . 2.685 3.837 3.356 3.851 3.897 3.881Nebraska . . . . . . . . . . . . . . . . . . . . 3.910 3.910 3.901 3.937 3.949North Dakota . . . . . . . . . . . . . . . . . . 3.906 3.906 3.893 3.925 3.930




different groupings of States examined foeach of the two variables.

Table 55demonstrates that groupingStates with similar expected biases canresult in State-specific MSE’s where thevariance is a nontrivial component of

the MSE. For States with a highpercentage of their population that isnot black residing in central cities, thevariance of the synthetic estimator ison average 21 percent of the MSE’s.When all States are taken together, th
e
root mean square error (RMSE) varieonly from 0.412 to 0.416. When Stateare grouped by the distribution ofpopulation that is not black, RMSE’svary from 0.190 in Utah to 0.717 inWest Virginia, reflecting the fact that

t

Table 50. Average number of doctor visits, with States grouped by percentage of black population in central cities

State


estimator(1)

Syntheticestimator

(2)

Compositeestimator

(3)


(4)

GSE with(16)

balancedvariances

(5)

GSE with(32)

balancedvariances

(6)

Vermont . . . . . . . . . . . . . . . . . . 6.036 3.471 5.369 3.703 3.856 3.859Delaware . . . . . . . . . . . . . . . . . 5.648 3.336 4.622 3.559 3.699 3.709Colorado . . . . . . . . . . . . . . . . . 4.868 3.897 3.897 3.898 3.893 3.900District of Columbia . . . . . . . . . . . 4.706 4.230 4.342 4.282 4.440 4.449Michigan . . . . . . . . . . . . . . . . . 4.594 3.877 4.410 3.934 3.878 3.873

Connecticut . . . . . . . . . . . . . . . . 4.589 3.992 3.992 3.991 3.991 3.998Arizona . . . . . . . . . . . . . . . . . . 4.562 3.954 3.945 3.953 3.948 3.969Nevada . . . . . . . . . . . . . . . . . . 4.530 3.904 3.904 3.903 3.903 3.907Massachusetts . . . . . . . . . . . . . . 4.481 3.891 4.303 3.953 3.870 3.875Rhode Island . . . . . . . . . . . . . . . 4.424 4.028 4.028 4.026 4.020 4.023








Alaska . . . . . . . . . . . . . . . . . . . 3.093 3.668 3.688 3.692 3.689 3.702Minnesota . . . . . . . . . . . . . . . . . 3.034 3.952 3.952 3.950 3.944 3.942Georgia . . . . . . . . . . . . . . . . . . 3.000 3.182 3.010 3.386 3.534 3.541North Carolina . . . . . . . . . . . . . . 2.685 3.285 2.715 3.499 3.642 3.637Nebraska . . . . . . . . . . . . . . . . . . . . 3.872 3.872 3.930 3.848 3.853North Dakota . . . . . . . . . . . . . . . . . . 3.505 3.505 3.747 3.862 3.836




the model-dependent estimators areable to produce estimates for someStates with much greater accuracythan for others.

Table 56demonstrates results thado not differentiate quite as strongly

between States. Using the generalizedsynthetic estimator with 16 balancedvariances to estimate percent withperceived poor health withoutsubgrouping the States, the variance othe model-based estimator is only

f

0.4 percent of its MSE, and the RMSEvaries only from 1.16 to 1.17 in anyState. When the States are groupedaccording to the percent of the blackpopulation in central cities, however,the State-specific MSE’s are different.

Table 51. Variation in State estimates for average number of doctor visits

Directinflation Synthetic Composite

Hetero-scedatic

variances

GSEwith 16

groupings

GSEwith 32

groupings

All States . . . . . . . . . . . . . . . . . 2.69–6.04 3.63–4.00 2.95–4.60 3.66–4.04 3.65–4.03 3.67–4.08Percent nonblack . . . . . . . . . . . . 2.69–6.04 3.46–4.13 3.09–5.24 3.50–4.16 3.42–4.18 3.42–4.18Percent black . . . . . . . . . . . . . . 2.69–6.04 3.14–4.23 2.72–5.37 3.34–4.28 3.48–4.44 3.48–4.45

Table 52. Percentage with self-perceived poor health status, with States grouped by percentage of nonblack population in central cities

State


estimatorSyntheticestimator

Compositeestimator

GSE with(16)

balancedvariances

GSE with(32)

balancedvariances

West Virginia . . . . . . . . . . . . . . . . . . . . 7.17 3.34 6.02 3.44 3.43Mississippi . . . . . . . . . . . . . . . . . . . . . 6.28 3.37 5.67 3.56 3.56Alabama . . . . . . . . . . . . . . . . . . . . . . . 5.42 3.10 4.71 3.11 3.13Maine . . . . . . . . . . . . . . . . . . . . . . . . 5.19 3.06 4.52 3.13 3.13Arkansas . . . . . . . . . . . . . . . . . . . . . . 5.10 3.00 4.23 3.02 3.04

Kentucky . . . . . . . . . . . . . . . . . . . . . . 5.06 3.10 4.81 3.19 3.19North Carolina . . . . . . . . . . . . . . . . . . . 4.68 2.95 4.36 2.97 3.00Tennessee . . . . . . . . . . . . . . . . . . . . . 4.46 2.84 4.09 2.86 2.85Oklahoma . . . . . . . . . . . . . . . . . . . . . . 3.81 2.65 3.48 2.67 2.67Louisiana . . . . . . . . . . . . . . . . . . . . . . 3.52 2.95 3.40 2.97 2.98

Georgia . . . . . . . . . . . . . . . . . . . . . . . 3.44 3.04 3.42 3.18 3.17South Carolina . . . . . . . . . . . . . . . . . . . 3.42 3.25 3.40 3.40 3.41Indiana . . . . . . . . . . . . . . . . . . . . . . . 3.40 2.59 3.27 2.62 2.61Arizona . . . . . . . . . . . . . . . . . . . . . . . 3.29 2.72 2.81 2.47 2.45Virginia . . . . . . . . . . . . . . . . . . . . . . . 3.29 2.75 3.21 2.77 2.78

Texas . . . . . . . . . . . . . . . . . . . . . . . . 3.02 2.59 2.78 2.34 2.34Florida . . . . . . . . . . . . . . . . . . . . . . . . 2.96 3.19 2.98 3.23 3.24District of Columbia . . . . . . . . . . . . . . . . 2.95 4.07 3.99 3.82 3.81Alaska . . . . . . . . . . . . . . . . . . . . . . . . 2.87 1.88 1.91 1.68 1.68Idaho . . . . . . . . . . . . . . . . . . . . . . . . . 2.80 2.83 2.81 2.90 2.90

Ohio . . . . . . . . . . . . . . . . . . . . . . . . . 2.79 2.72 2.78 2.75 2.74Michigan . . . . . . . . . . . . . . . . . . . . . . 2.78 3.09 2.79 3.20 3.20Missouri . . . . . . . . . . . . . . . . . . . . . . . 2.64 2.79 2.66 2.82 2.82Delaware . . . . . . . . . . . . . . . . . . . . . . 2.61 3.18 2.78 3.30 3.29Oregon . . . . . . . . . . . . . . . . . . . . . . . 2.50 2.53 2.51 2.56 2.56

Wyoming . . . . . . . . . . . . . . . . . . . . . . 2.41 2.20 2.28 2.22 2.22Illinois . . . . . . . . . . . . . . . . . . . . . . . . 2.40 2.75 2.42 2.78 2.77California . . . . . . . . . . . . . . . . . . . . . . 2.31 2.36 2.31 2.39 2.39New York . . . . . . . . . . . . . . . . . . . . . . 2.31 3.03 2.63 2.77 2.78Kansas . . . . . . . . . . . . . . . . . . . . . . . 2.21 2.59 2.29 2.61 2.61

Massachusetts . . . . . . . . . . . . . . . . . . . 2.21 2.60 2.25 2.62 2.62Montana . . . . . . . . . . . . . . . . . . . . . . . 2.10 2.46 2.27 2.49 2.49Vermont . . . . . . . . . . . . . . . . . . . . . . . 2.09 2.89 2.32 2.94 2.93New Jersey . . . . . . . . . . . . . . . . . . . . . 2.03 3.31 2.07 3.43 3.43South Dakota . . . . . . . . . . . . . . . . . . . 2.00 2.51 2.25 2.53 2.54

Rhode Island . . . . . . . . . . . . . . . . . . . . 1.97 2.67 2.24 2.69 2.69Maryland . . . . . . . . . . . . . . . . . . . . . . 1.84 3.16 1.89 3.30 3.30Utah . . . . . . . . . . . . . . . . . . . . . . . . . 1.75 1.89 1.78 1.91 1.91Nevada . . . . . . . . . . . . . . . . . . . . . . . 1.74 2.46 1.97 2.49 2.49Pennsylvania . . . . . . . . . . . . . . . . . . . . 1.69 2.92 1.74 2.95 2.94

New Mexico . . . . . . . . . . . . . . . . . . . . 1.67 2.25 1.82 2.28 2.28Iowa . . . . . . . . . . . . . . . . . . . . . . . . . 1.65 2.65 1.81 2.67 2.67Washington . . . . . . . . . . . . . . . . . . . . . 1.44 2.38 1.52 2.41 2.41Wisconsin . . . . . . . . . . . . . . . . . . . . . . 1.38 2.52 1.48 2.55 2.55Connecticut . . . . . . . . . . . . . . . . . . . . . 1.27 2.72 1.43 2.75 2.74

Colorado . . . . . . . . . . . . . . . . . . . . . . 1.20 2.26 1.31 2.28 2.28Minnesota . . . . . . . . . . . . . . . . . . . . . . 1.16 2.39 1.26 2.41 2.41Hawaii . . . . . . . . . . . . . . . . . . . . . . . . 0.87 2.32 1.16 2.36 2.36New Hampshire . . . . . . . . . . . . . . . . . . 0.68 2.30 0.95 2.32 2.32Nebraska . . . . . . . . . . . . . . . . . . . . . . . . . 2.56 2.56 2.58 2.58North Dakota . . . . . . . . . . . . . . . . . . . . . . . 2.47 2.47 2.50 2.49




s

e

r

she

Table 53. Percentage with perceived poor health status, with States grouped by percentage of black population in central cities

State


estimatorSyntheticestimator

Compositeestimator

GSE with(16)

balancedvariances

GSE with(32)

balancedvariances

West Virginia . . . . . . . . . . . . . . . 7.17 3.94 6.09 4.40 4.39Mississippi . . . . . . . . . . . . . . . . 6.28 4.32 5.82 4.75 4.75Alabama . . . . . . . . . . . . . . . . . . 5.42 3.09 4.79 3.06 3.06Maine . . . . . . . . . . . . . . . . . . . 5.19 3.57 4.62 3.96 3.95Arkansas . . . . . . . . . . . . . . . . . 5.10 3.07 4.34 3.03 3.04

Kentucky . . . . . . . . . . . . . . . . . 5.06 2.72 4.42 2.69 2.70North Carolina . . . . . . . . . . . . . . 4.68 4.06 4.63 4.47 4.40Tennessee . . . . . . . . . . . . . . . . 4.46 2.91 4.15 2.87 2.87Oklahoma . . . . . . . . . . . . . . . . . 3.81 2.77 3.55 2.74 2.74Louisiana . . . . . . . . . . . . . . . . . 3.52 2.91 3.41 2.88 2.89

Georgia . . . . . . . . . . . . . . . . . . 3.44 3.77 3.46 4.16 4.12South Carolina . . . . . . . . . . . . . . 3.42 4.09 3.49 4.50 4.50Indiana . . . . . . . . . . . . . . . . . . 3.40 2.28 2.85 2.21 2.21Arizona . . . . . . . . . . . . . . . . . . 3.29 2.60 3.15 2.58 2.56Virginia . . . . . . . . . . . . . . . . . . 3.29 2.78 3.23 2.75 2.76

Texas . . . . . . . . . . . . . . . . . . . 3.02 2.49 2.99 2.46 2.45Florida . . . . . . . . . . . . . . . . . . . 2.96 3.30 2.98 3.28 3.28District of Columbia . . . . . . . . . . . 2.95 4.04 3.87 3.90 3.95Alaska . . . . . . . . . . . . . . . . . . . 2.87 1.77 2.22 1.74 1.74Idaho . . . . . . . . . . . . . . . . . . . . 2.80 3.32 2.93 3.67 3.66

Ohio . . . . . . . . . . . . . . . . . . . . 2.79 2.82 2.79 2.79 2.79Michigan . . . . . . . . . . . . . . . . . 2.78 2.38 2.65 2.30 2.28Missouri . . . . . . . . . . . . . . . . . . 2.64 2.89 2.67 2.86 2.86Delaware . . . . . . . . . . . . . . . . . 2.61 3.87 3.03 4.28 4.27Oregon . . . . . . . . . . . . . . . . . . 2.50 2.21 2.33 2.13 2.13

Wyoming . . . . . . . . . . . . . . . . . 2.41 2.35 2.38 2.32 2.32Illinois . . . . . . . . . . . . . . . . . . . 2.40 2.44 2.41 2.36 2.36California . . . . . . . . . . . . . . . . . 2.31 2.47 2.31 2.44 2.44New York . . . . . . . . . . . . . . . . . 2.31 2.56 2.36 2.48 2.50Kansas . . . . . . . . . . . . . . . . . . 2.21 2.72 2.31 2.69 2.69

Massachusetts . . . . . . . . . . . . . . 2.21 2.28 2.23 2.20 2.21Montana . . . . . . . . . . . . . . . . . . 2.10 2.63 2.32 2.60 2.60Vermont . . . . . . . . . . . . . . . . . . 2.09 3.35 2.49 3.70 3.71New Jersey . . . . . . . . . . . . . . . . 2.03 2.94 2.09 2.92 2.92South Dakota . . . . . . . . . . . . . . 2.00 2.68 2.30 2.64 2.65

Rhode Island . . . . . . . . . . . . . . . 1.97 2.82 2.26 2.79 2.79Maryland . . . . . . . . . . . . . . . . . 1.84 2.87 1.94 2.86 2.87Utah . . . . . . . . . . . . . . . . . . . . 1.75 2.03 1.81 1.99 1.99Nevada . . . . . . . . . . . . . . . . . . 1.74 2.58 1.98 2.56 2.56Pennsylvania . . . . . . . . . . . . . . . 1.69 3.04 1.74 3.00 3.00

New Mexico . . . . . . . . . . . . . . . 1.67 2.40 1.84 2.37 2.36Iowa . . . . . . . . . . . . . . . . . . . . 1.65 2.82 1.81 2.78 2.78Washington . . . . . . . . . . . . . . . . 1.44 2.53 1.52 2.50 2.50Wisconsin . . . . . . . . . . . . . . . . . 1.38 2.22 1.63 2.14 2.15Connecticut . . . . . . . . . . . . . . . . 1.27 2.84 1.42 2.81 2.81

Colorado . . . . . . . . . . . . . . . . . 1.20 2.39 1.30 2.36 2.35Minnesota . . . . . . . . . . . . . . . . . 1.16 2.54 1.26 2.51 2.51Hawaii . . . . . . . . . . . . . . . . . . . 0.87 3.37 1.09 3.70 3.61New Hampshire . . . . . . . . . . . . . 0.68 2.46 0.93 2.42 2.42North Dakota . . . . . . . . . . . . . . . . . . 3.59 3.59 3.96 3.89Nebraska . . . . . . . . . . . . . . . . . . . . 2.23 2.23 2.16 2.17




For States with a high percentage incentral cities, the average variance is12.8 percent of the MSE. Among theseStates, RMSE’s vary from 0.411 inMichigan to 0.475 in the District ofColumbia, and the State-specific RMSEfor Mississippi (with a low percent of the

black population in central cities) is 1.46,three and one-half times the RMSE forMichigan.

This is the greatest differentiationfound between State RMSE’s using thiestimator. In general, the traditionalsynthetic estimator has a larger varianc

than do forms of GSE that rely on prioinformation. By incorporating priorinformation, the GSE subgroup meansare no longer completely a function ofthe sampled data and therefore are lesvariable than the subgroup means of tsynthetic estimator.

Table 54. Robustness of the generalized synthetic estimator

Variable State groupingRange among estimators

2, 4, 5, and 6

Doctor visits . . . . . . . . . . . . . . . . . . None 6 percent District of Columbia0–2 percent All others6 percent District of Columbia

Nonblack 3 percent Arizona0–2 percent All others

Black . . . . . . . . . . . . . . . . . . . . . . . 10–11 percent All 11 States with < 36 percent4 percent District of Columbia0–2 percent All others

Poor health . . . . . . . . . . . . . . . . . . . None 0–2 percent All statesNonblack 7–12 percent All 5 States with > 37 percent

3–6 percent 9 of 12 States with < 16 percent0–2 percent All others

Black 10–12 percent All 11 States with < 36 percent3–4 percent 8 of 9 States with > 77 percent0–2 percent All others

Table 55. State-specific mean square errors for number of doctor visits, using the synthetic estimator, with States grouped by percentnonblack population in central cities

Nonblack population in central cities1

All States Low Moderate High

(1) E (y1i. − y^

2i.)2 . . . . . . . . . . . . . . . .3777 .7536 .2203 .3238

(2) aveVar (y1) . . . . . . . . . . . . . . . . .2074 .2417 .1842 .2736

aveMSE (y2) . . . . . . . . . . . . . . . .1703 .5119 .0361 .0502

(4) aveVar (y2) . . . . . . . . . . . . . . . . .0020 .0119 .0029 .0106aveVar (y2) . . . . . . . . . . . . . . . 1.2 percent 2.3 percent 8.0 percent 21.1 percentaveMSE

(5) aveBias2 (y2) . . . . . . . . . . . . . . . .1684 .5001 .0333 .0396

(6) MSE (y2)Smallest . . . . . . . . . . . . . . . . .1701 (Georgia) .5097 (Georgia) .0360 (Utah) .0474 (Texas)Largest . . . . . . . . . . . . . . . . . .1732 (District of Columbia) .5141 (West Virginia) .0364 (Iowa) .0586 (District of Columbia)

RMSE (y2)Smallest . . . . . . . . . . . . . . . . .412 .714 .190 .218Largest . . . . . . . . . . . . . . . . . .416 .717 .191 .242

1See table 48 for State groupings.

Table 56. State-specific mean square errors for percentage with perceived poor health using the generalized synthetic estimator with 16balanced variances, with States grouped by percentage of black population in central cities

Black population in central cities1


(1) E (y1i. − y^

6i.)2 . . . . . . . . . . . . . . . 1.727 2.752 1.276 .431

(2) aveVar (y1) . . . . . . . . . . . . . . . . 0.369 0.641 0.312 0.254aveMSE (y6) . . . . . . . . . . . . . . . 1.358 2.111 0.964 0.177


(5) aveBias2 (y6) . . . . . . . . . . . . . . . 1.357 2.022 0.957 0.154

(6) MSE (y6)Smallest . . . . . . . . . . . . . . . . 1.357 (Alaska, Utah) 2.102 (Georgia) 0.962 (Alaska) 0.169 (Michigan)Largest . . . . . . . . . . . . . . . . . 1.360 (District of Columbia) 2.125 (Mississippi) 0.967 (Florida) 0.226 (District of Columbia)

RMSE (y6)Smallest . . . . . . . . . . . . . . . . 1.16 1.45 0.981 0.411Largest . . . . . . . . . . . . . . . . . 1.17 1.46 0.983 0.475


NOTE: All values are times 0.0001, except RMSE, which is times 0.01.


ha

e

te

e

l

is

n

le

iced.

d

e

y

s.

g

al

is

’s

e

Table 57. State-specific mean square errors for percentage with perceived poor health using the synthetic estimator, with States grouped bypercentage of nonblack population in central cities

Nonblack population in central cities1


(1) E (y1i. − y^

2i.)2 . . . . . . . . . . . . . . . 1.724 3.011 1.080 .0651

(2) aveVar (y1) . . . . . . . . . . . . . . . . 0.369 0.552 0.265 0.600aveMSE (y2) . . . . . . . . . . . . . . . 1.355 2.459 0.815 0.051


(5) aveBias2 (y2) . . . . . . . . . . . . . . . 1.350 2.404 0.809 0.011

(6) MSE (y2)Smallest . . . . . . . . . . . . . . . . 1.353 (Alaska) 2.446 (Georgia) 0.813 (Utah) 0.032 (Alaska)Largest . . . . . . . . . . . . . . . . . 1.374 (District of Columbia) 2.475 (West Virginia) 0.819 (Iowa) 0.097 (District of Columbia)

RMSE (y2)Smallest . . . . . . . . . . . . . . . . 1.16 1.56 0.902 0.179Largest . . . . . . . . . . . . . . . . . 1.17 1.57 0.905 0.311


NOTE: All values are times 0.0001, except RMSE, which is times 0.01.

Table 58. Reduction in estimate of the MSE of the generalized synthetic estimator (16subgroups) relative to the MSE of the synthetic estimator for percent with perceived poorhealth

Percent incentral city

Grouped bypercentage black

Grouped bypercentage nonblack

Low . . . . . . . . . . . . . . . . . . . . . . . . . < 1 5–6Medium . . . . . . . . . . . . . . . . . . . . . . . < 1 (1–2)High–District of Columbia . . . . . . . . . . . . 26 (14)High–other States . . . . . . . . . . . . . . . . . 5–7 (128–226)

NOTE: When the MSE of the GSE is larger, it is shown in parentheses.


Table 57shows much morepromising results. Using the syntheticestimator to estimate percent withperceived poor health withoutsubgrouping the States yields results tare almost identical to those in theprevious table. When the States aregrouped according to the percent of thpopulation that is not black in centralcities, the State-specific MSE’s are quidifferent. For States with a highpercentage in central cities, the averagvariance is 78 percent of MSE. Amongthese States, the RMSE’s vary from0.179 in Alaska to 0.311 in the Districtof Columbia; thus, a confidence intervaon the estimate for the District ofColumbia would be almost twice aswide as one for Alaska. TheState-specific RMSE for West Virginia1.57, almost 9 times the RMSE forAlaska.

In addition to examining thevariation of MSE’s from State to State,it is also of interest to examine whenestimates produced by the syntheticestimator or GSE have smaller MSE’s.By incorporating prior information, theGSE is based on more information andcan therefore be expected to havesmaller variance (unless the variance othe prior estimate is large). If the priorestimates are not consistent with thesample means for certain subgroups,however, the GSE will introduce anextra source of bias. Thus, it is possibfor either estimator to produce smaller

t

MSE’s. The data used in this empiricalanalysis produced MSE’s that weresometimes smaller for the GSE and atother times, for the synthetic estimator.Table 58summarizes these findings forself-perceived poor health.

Summary of EmpiricalResults

With a national model, where thesubgroup averages are subject to littlesampling error, the generalized synthetestimator was quite robust to alternativmodel assumptions that were examineThat is, the point estimates of thetraditional synthetic estimator werehardly changed when the small areaand/or subgroup variances were alloweto vary across small areas or when priorinformation (in the form of the previousyear’s survey estimate) was included. Thlarge sample sizes in the national modelresulted in MSE’s that were dominated bthe bias term and thus showed littledifference from State to State.

Doubling the number of subgroupsin the national model from 16 to 32 didnot have much impact on the estimateThe empirical findings were quitedifferent when the sample size persubgroup was reduced by usingsubnational models. The form of theGSE was important in that the pointestimates for a given State could differby approximately 10 percent, dependinon the form of the estimator. All of themodel-dependent estimators had moreState-to-State variation using thesubnational models, reducing theamount of shrinkage toward the nationmean.

By grouping the States on the basof similar expected biases, it waspossible to produce State-specific MSEthat varied markedly from group togroup. For groupings where the averagbias was a small component of MSE,the individual State MSE’s also variedwithin the group. For example, in thoseStates where a high percentage of thepopulation that is not black lives in

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central cities, the variance averaged tbe almost 80 percent of MSE. Thisresulted in RMSE’s for some of theseStates that were almost twice as largethose for other States in the same groAcross all States RMSE’s varied by afactor of 9.

When the GSE incorporated priorinformation, the State-to-State variatioin MSE’s was not as significant. Thisresults from the fact that, by basing thestimates only partially on the sampledata, the variance is reduced. Becausonly the variance component of theMSE is State-specific, this minimizesthe differences between States.

No consistent results were foundwhether the GSE would produce smaMSE’s than the traditional syntheticestimator. When States were groupedpercent of black (or other-than-black)population in central cities, the GSEalways had a smaller MSE forself-perceived poor health. When theywere grouped by percent of black (orother-than-black) population in centralcities, however, the reverse was truetwo groups of States. This analysis walimited by the many occasions when tMSE procedure produced negativeestimates for one or more groups ofStates. Although this limitation alsoarises when using average MSE’s, it imore pronounced the greater the numof groups of small areas.

The GSE has the potential to improsignificantly on the traditional syntheticestimator when the sampling error of thsubgroup mean is not small—for exampwhen the number of subgroups is largesuch cases the synthetic estimator issensitive to the assumptions that aregeneralized in the GSE. When thissituation arises, the GSE can be usedeither to produce estimates based onassumptions with which the analyst feemore comfortable or to test the sensitiviof the traditional estimator to itsassumptions.

s.

The GSE appears to have potentialfor producing State-level estimates fromNHIS for at least some variables. Theprocedure for producing State-specificMSE’s may be useful for detectingdifferences between States. In eithersituation more research is needed on thcovariates for the model-dependentestimators and the effects on theestimates of using different priordistributions.

Part IV.Coordination With Surveys ofHealth Care Providers

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Chapter 13.Coordination at thePrimary SamplingUnit Level

L ocating all the surveys of healthcare providers (knowncollectively as the National

Health Care Survey, or NHCS) in asingle set of PSU’s that is a subset ofthe NHIS PSU’s was a possibility inwhich National Center for HealthStatistics was extremely interested, asdiscussed inchapter 2. Two potentialbenefits of such coordination wereidentified and researched: One is costsavings; the other is enrichments toanalysis. They are discussed separatelyin the following sections. A majorpotential drawback—varianceincreases—was identified andresearched. After advantages anddisadvantages of this approach arediscussed, a conclusion is presented.

Cost SavingsPlacing all the component surveys

of the NHCS into a single subset ofNHIS PSU’s could lead to certainsynergies that could translate into costsavings. To achieve the synergies,however, it would be necessary to limitthe flexibility of the combined design.At a broad level, the coordination wouldsave money if a single small set ofinterviewers could work on all thesurveys. Cost savings would be largestif the interviewers could work only intheir home PSU’s, thereby eliminatingthe cost of long-distance travel. For theU.S. Bureau of the Census to be able tretain such a cadre of local interviewersit would be necessary to schedule theprovider surveys well in advance in apattern that allowed optimal staffutilization. Any breaks in the patterncould lead to loss of that staff, therebyrequiring costly travel by backupinterviewers. Overlaps in the patternwhere local staff were stretched beyondtheir capabilities would also requireexpensive travel by backups. Given theuncertain nature of multiyear funding

for these surveys, this sort ofpredictability seems unlikely; thereforemajor cost savings also appear unlikel

Analysis EnrichmentMost health care policy research

is done at the individual level.However, some research is done at tcommunity level, some at the Statelevel, and some at the national level.Examples of research at the individulevel include studies linking smokingto heart disease, evaluating differenttreatments for breast cancer, andtesting drugs. Examples at the broadlevels consist mostly of trying to linkdifferent policies, such as per capitaspending, with average healthmeasures. The idea behind thisresearch was that it might make sento study linkages at the PSU level. FPSU’s to be an interesting level tostudy, they should be defined asinteresting entities in and ofthemselves.

It would be a promising startif PSU’s could be defined as clearlydistinct units with respect to patternsof health care delivery. Toward thisaim, NCHS staff (29) defined a set ohealth care service areas (HCSA’s)that were fairly well self-contained interms of health care delivery; that is,the HCSA’s were defined in such away that for most of the areas,residents obtained their health careservices from providers located in thsame HCSA’s.

If a sample of HCSA’s wasused as sample PSU’s for NHIS and fthe full set of health care providersurveys (NHCS), an analysis file couldbe set up in which each recordcorresponded to a different HCSA andeach record contained average healthstatistics gathered from NHIS andaverage provider traits collected fromNHCS.

It turned out, however, that HCSA’had to be defined as quite large areasfor them to be self-contained. Thisresulted in a substantial reduction in thnumber of PSU’s in the United States(about 800 rather than the 1,900 PSU’defined for NHIS in 1985). Even at thislevel, persons seeking health careservices still participated in considerab

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crossing of HCSA borders. Furthermorea clear and convincingly urgent set ofhypotheses about linkages at the HCSAlevel was never set forth.

Variance IncreasesConfining NHIS and all the surveys

of health care providers to a single setof PSU’s will inevitably lead toincreases in variance for analysesrestricted to just one of the surveys.This is true because the optimaldefinition, stratification, and selectionprocedure of PSU’s is different for eachsurvey.

Because there are far fewerproviders than households and far morwork to be done by the interviewer ateach sample provider than at eachsample household, the optimal PSU sizfor several provider surveys is far largethan for NHIS. For example, it might bereasonable to define a substantial partSouth Dakota as a single PSU for asurvey of hospitals; for NHIS, however,the travel costs would be extremely higif more than a few rural counties werecollapsed together to form a PSU.

Stratification is quite different, too,because no thought was devoted totrying to produce provider statistics atthe State level. Trying to keep NHISflexible for State expansions means thastrata must respect State boundaries. Tprovider surveys have no parallel needFor example, Cheyenne, Wyoming;Boise, Idaho; Helena, Montana; andBillings, Montana, might make areasonable stratum for one of theprovider surveys.

In particular, the optimal number ofsample PSU’s is very different forvarious surveys. To provide flexibilityfor State expansions, the optimalnumber of PSU’s for NHIS is quitelarge—at least 300. The optimal numbeof sample PSU’s for health careprovider surveys is closer to 100.

Even given the same PSUdefinitions and stratification, the optimaprobabilities of selection are differentacross surveys. For each survey, theoptimal probabilities of selection areproportional to a different measure ofsize. For NHIS, the best measure of sizwould be some weighted combination opopulation by domain, as discussed in

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chapters 4and15. For the NationalHospital Discharge Survey, the optimameasure of size would be a recent couof hospital discharges. For the NationaAmbulatory Medical Care Survey, theoptimal measure of size would be arecent count of ambulatory visits todoctors’ offices. Similar statements canbe made about the other providersurveys.

Unfortunately, it is impossible usingcurrently available data to quantifyaccurately the extent of the varianceincrease for one survey to use thesample PSU’s designed as optimal fordifferent survey. To solve this problem,it would be necessary to have accuratedata on the characteristics of interest athe county level. Other than a questionon disability, the decennial census doenot contain health-related data of anysort, much less health care data. Thehealth-related information that isavailable on a county basis covers sucitems as numbers of physicians, hospibeds, nursing homes, mortality fromvarious causes, and so forth. Past studhas shown that these do not correlatewell with each other, making it unlikelythat they would be efficient stratifiers omeasures of size for different providersurveys. Consequently, research onalternate PSU definitions, stratification,and selection methodology is not veryproductive. The only other alternative ito conduct experiments at the nationallevel where two independent designs aused with independent field staffs; to dso, of course, would be extremelyexpensive.

Nonetheless, a set of PSU’s thatwould be reasonable for NHIS, with ameasure of size that would also bereasonable for NHIS, was examined tosee how satisfactory this set of PSU’swould be for the provider surveys. Theset consisted of those defined for theCurrent Population Surveyoutside NewEngland in 1984 and those defined inNew England for the Survey of Incomeand Program Participation in 1985. Fothis set of PSU’s, the degree of overlaacross the surveys was examined withrespect to the subset of PSU’s thatshould be self-representing; that is,selected with certainty. The correlationbetween the optimal measures of sizethe PSU level were also examined

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among a common set of nonself-representing PSU’s.

A finding of this study was that allthe PSU’s that should be self-representing for one provider survey ormore (given 112 sample PSU’s) wouldbe contained within the set of NHISself-representing PSU’s (assuming 358sample NHIS PSU’s). In addition, highcorrelations were found between thealternate measures of size. Although it itrue that the correlations became ratherlow for the smallest PSU’s (in terms ofpopulation), only a small part of thenational population lives in such PSU’s.From these findings, it was concludedthat the penalties to the provider surveyof being confined to a subset of NHISsample PSU’s would probably bemodest.

ConclusionWith respect to NHIS itself, the

potential for enriching analysis andimproving operational efficiencies forthe provider surveys does not appearconcrete enough to justify picking a setof PSU’s that is less than optimal forNHIS. Furthermore, investigations ofcorrelations of measure of size indicatethat the provider surveys can probablyfit within the NHIS sample PSU’s withlittle precision loss, if that is desired.

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Chapter 14.Network Sampling ofProviders Through theNational HealthInterview Survey

IntroductionIn its report, the Panel on the

National Health Care Survey (30) madetwo recommendations related to netwosampling. The first (recommendation4–1) was that providers other thanshort-term hospitals, office-basedphysicians, and nursing homes besurveyed by network sampling based onominations from patients of theseproviders who happened to be includedin NHIS. The second (recommendation4–2) was that NCHS study the utility ofsampling even the short-term hospitalsand office-based physicians in thismanner. This chapter examines thecircumstances under which networksampling based on NHIS could be morefficient than list-based sampling.

It is important to note thatrecommendations 4–1 and 4–2 by thepanel concern only the types of datatraditionally collected in NCHS providersurveys and not dual patient-providerdata on individual health care eventssuch as were obtained through theNational Medical Expenditure Survey(NMES) of 1987 with its componentMedical Provider Survey (MPS). Otherrecommendations in the panel’s reportdeal with the analytic potential of thosetypes of data and problems involved incollecting such data. These other issueare briefly discussed inchapter 15.

List Quality

For an arbitrary class of health carproviders, it is useful to define threegrades of list quality. In the first case, iis possible that no list exists at all. Thisseems doubtful because the sorts ofhealth care providers listed in thepanel’s report (psychologists, dentists,physical and occupational therapists,pharmacists, podiatrists, chiropractors,nurses, nurse practitioners, physician

assistants, nurse midwives, andoptometrists, among others) are typicaState licensed or are associated withlicensed practitioners or facilities. If alicensing procedure exists, a list can beconstructed. Of course, it may benecessary to negotiate individually witheach State to obtain a list of licensedproviders within that State, and merginthe State lists together into a nationallist would require some effort. (Also,classification and unduplication issuesare likely to be severe, but that is trueof any sampling approach.) The onlytypes of entities that are sometimesviewed as health care providers that arprobably not susceptible to list-basedsampling are those that considerreligious practices a form of health carThe panel did not recommend thatcoverage be extended to this class ofcaregivers, however.

At the next level of list quality, alist exists but contains no indication ofpractice size. This is frequently the caswith membership lists of nationalvoluntary associations of health careproviders. This may be the mostcommon situation for the types of healcare providers under consideration. Atthe highest level of quality, a list existswith a quantification of the number ofpersons served by each provider.

Another dimension of list qualityconcerns coverage of the intendedpopulation. Lists of health careproviders are likely to age fairly quicklyProviders move, retire, enter thebusiness, form new partnerships,dissolve partnerships, and so on. If theState licensing procedure does notrequire annual renewals, the informatiomay become extremely outdated.Furthermore, a small class of unlicenseproviders may exist. Coverage gaps duto out-of-date lists and unlicensedproviders do cause bias. Undercoveragis also a problem with network samplehowever, as is explored below.

Network SampleUndercoverage

A number of possible sources ofundercoverage exist. The first source isundercoverage in NHIS itself. Coveragrates generally run between 60 and100 percent depending on age, sex, ra
,
and ethnic origin, with an overall figurearound 90 percent (relative to theunadjusted decennial census). Thesecond source is unit nonresponse inNHIS. For various reasons about5 percent of eligible sample householdsare not interviewed. The third source isunderreporting of health care events.Little empirical evidence currently existson how often this occurs, but research atHCFA (matching self-reported eventsfrom the Medicare Current BeneficiarySurvey against administrative records)may soon shed additional light in thisarea. This source of undercoverage canclearly be reduced by keeping thereference period short for qualifyingevents. (There could also beoverreporting of events if steps are nottaken to prevent telescoping.)

The fourth source of undercoverageis failure to name the health careprovider and to provide enoughinformation so that the provider can belocated. Experience with the providerfollowup (MPS) to NMES is notdirectly relevant to projecting the rate atwhich this might occur but does containsome clues. NMES respondents wereasked to sign consent forms in additionto naming the health care provider andsupplying address information. Althoughjust 81 percent of the NMES sampleprovided the signed consent forms andprovider information, it could be that anetwork sample based on NHIS wouldencounter much more favorable rates. Areasonable projection of 95 percentoverall coverage can be made on theassumption that consent forms wouldnot be required because a randomsample of patient records would bedrawn at the provider’s office andinformation would presumably berecorded anonymously; it is possiblethat the nominator would not even be inthe sample. Coverage can be anticipatedto be far worse for some providers,however. In particular, separately billingdoctors associated with hospital visitsare likely to be poorly covered becausemany patients do not remember theirphysicians’ names or even know thatthey were in attendance.

All in all, it seems reasonable toproject provider coverage of between 75and 85 percent before providernonresponse. (Implications for response

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rates are discussed in the section entit‘‘Other considerations.’’) Depending onState licensing procedures and/or thefrequency of membership updates forprofessional societies, this range ofcoverage could be better or worse thawhat could be expected from a list.Within a provider type, coverage ofthose providers who servepredominantly black and Hispanicpopulations can be expected to be leswell covered because of theundercoverage of these groups in NHIitself. The decision of which approachwould lead to higher coverage wouldprobably have to be based on athorough review of possible list sourcefor the specific provider to be surveyedOf course, coverage alone will notdetermine the decision; there are stillquestions of variance and cost.

Provider or EventCharacteristics?

The variance structure of a samplesurvey of health care providers dependstrongly on the nature of the survey’sfocus. If primary interest is in personaland/or professional characteristics of thprovider (e.g., office costs, technicalequipment owned, fee for service,income), network sampling will almostalways lead to very large variances. This attributable to the highly variableprobabilities of selection (arising fromthe fact that the probability will dependon the number of visits to the facility).The obstacles to list formation wouldhave to be severe indeed for networksampling with such a substantive focusto be considered. Only if the primaryinterest is on volumes of various typesof services provided (e.g., numbers ofappendectomies, tranquilizerprescriptions, root canals, babiesdelivered at home, thin lenses ground)does network sampling have any appeThis is because network sampling issimilar to probability proportionate-to-size (PPS) sampling.

The range of provider sizes is animportant consideration in whether PPsampling or some other method ofsampling is efficient. PPS sampling ismore likely to be important in a surveyof institutional providers than in asurvey of individual professionals

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because of the much greater room forvariation in workload. Because goodlists are also more likely to be availablfor institutional providers, however, thiscriterion will not usually facilitate thedecision between list-based samplingand network sampling.

Areas With Rich Potential forNetwork Sampling

Taking these factors together, thechief potential for NHIS-based networksampling of providers appears to be insurveys where the focus is on eventcharacteristics; the only available listsproviders have no adequate informatioon measures of practice size; and theproviders are not associated withinstitutions for which good lists areavailable. We will assume that dentistsare a potential domain of interest inorder to simplify language in creating amodel for costs and variances ofnetwork sampling. A structure isdeveloped below for comparingvariances and costs for network andlist-based samples of dental visits.Analyses for other domains wouldfollow similar lines.

Model for Costs andVariance

A Network Sample of Dentists

Suppose that a survey of dentistshas been commissioned in which themain focus is on volumes of servicessuch as prophylaxis, x rays, fillings,crowns, root canals, orthodontia, andgum surgery. Note that this sort of datwould probably be most efficientlyobtained from a dentist by abstractingindividual patient records rather than basking the dentist to keep count.Although gross revenue, expenses, annet income are examples of variablesrelated to service volume where noabstraction is required, at least someabstraction is probably typical ofprovider surveys interested in servicevolumes and will be an assumed featuof the design.

Let

N be the annual number of dentalvisits throughout the United States

t be the length of the referenceperiod in weeks preceding theNHIS sample week

Niv be the number of appointmentskept by thei-th dentist within thet-week reference periodimmediately prior to thevth NHISsample week (It is explicitlyassumed here that appointmentsthe same person cannot reasonabbe unduplicated and will becounted separately.)

n be the number of dental visitsreported in the NHIS sampleduring thet-week period precedingthe vth sample week (assumeconstant across sample weeks forsimplicity)

m be the number of networks thatare established for thet-weekperiod preceding thevth sampleweek (assume constant acrosssample weeks for simplicity)

δ be the intraclass correlation forevents at the same dentist and

λ be the number of events sampledper network (may not include theevent that brought the practice intsample)

The planned average samplinginterval for persons who are neitherblack nor Hispanic in 1995 and beyondis about 1,900. Only 1 part in 52 of thesample can report on anyt-week period,however. Also, onlyt parts in 52 ofannual visits occur during at-weekperiod. Thus, the expected number ofdental visits to be detected in at-weekperiod isn = Nt/ [(522)1,900]. Forexample, suppose thatN = 500,000,000and t = 1. Thenn = 97; that is, it ispossible to form 97 network samplesaround 97 dental visits reported in 1week’s sample. For ease of exposition,each of these network samples isreferred to as a cluster. With 97 dentalvisits identified each week, a maximumof 5,044 clusters can be established ovthe course of a year. That would

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probably be more than necessary toachieve adequate precision.

Note that some dentists will be hit(nominated) more than once. Mostoften, this will occur when a patient hatwo visits during the reference periodfor related services. Siblings may oftenreceive routine dental care in the sameweek. To reduce the number of multiplhits, it may be advantageous to identifa specific subset of NHIS that isallowed to make nominations. Theobjective of that subsample designwould be to minimize the number ofhouseholds with multiple potentialnominators without increasing thevariation in selection probabilities. Thiscan be done with the same sort ofsubsampling that was used for Cycle Iof the National Survey of FamilyGrowth. There, single-eligiblehouseholds were selected at lower ratethan multieligible households, and onlyone eligible was selected per househo(This would not be an option if all visitsdetected in a year had to be retained acluster nuclei, as might be the case wia rarer domain such as endodontists.)

It is reasonable to assume thatprobabilities of selection for providerswill be too difficult to determine, andthat, therefore, it will not be possible touse the Horvitz-Thompson estimator.(The problem is made particularlycomplex by the systematic selection ofblocks for NHIS. To establish theprobability of selection for the providerit would be necessary to geocode allpatients who could have nominated theprovider and then establish their relativpositions in the systematic block sort(and within-block sort).) Instead, amultiplicity estimator will be required,and one is suggested in the following:

Let i be the index dentist andj theindex appointments within thet-weekreference period immediately before thvth NHIS sample week. If a person whmade thejth appointment at theithdentist happens to be interviewed forNHIS within thevth week of the year,let Wivj be the NHIS noninterview-adjusted weight for the person;otherwise, letWivj = 0. Assume thatnonresponse to NHIS is ignorable givethe adjustment cells employed by NHISThen an unbiased weight forvth weekfor the dental provider is

WDiv =52t ∑

j

Wivj Niv

This weight can be averaged overall 52 weeks to obtain an unbiasedweight for the dental provider for theyear. (Note that it is not necessary toknow Niv for weeks where there are nohits becauseΣ

jWivj Niv = 0 for such

periods.)Let miv be the number of networks

that are established at theith practicebased on thev-th interview week. Thenan unbiased weight for all theλmiv

sample events is

WEiv = WDiv

Niv

λmiv

Note that if the number of networksestablished at a practice were equal tothe number of times that it was hit forNHIS, and if NHIS were self-weighting,we would have

WEiv =(52/t) BW

λwhereBWwas the common NHISweight. Note, however, that clients ofdentists with racially or ethnicallyintegrated practices will have differentprobabilities of selection than those atmostly white or mostly minoritypractices.

The actual variance associated withusingWEiv to create a weighted estimatis extremely complicated, depending onpatterns of multiple visits within NHISsegments. Nonetheless, a reasonableapproximation can be obtained bytreating the sample as a self-weightingsample of 52m clusters ofλ events each.The relative variance of such a sampleis well known to be

vw2 =

[1 + δ (λ − 1)] Vw2

52mλwherem = Σi miv, the number ofnetworks per week, is assumed not tovary by week; where 52mλ is thetotal number of sample events; andwhereVw

2 is the relative variance withinthe population of events of thecharacteristic of interest. For example,the interest lies in the proportion ofdental visits that are for root canals, anthe true proportion is 0.05, thenVw

2 = 0.95/0.05 = 19.

To allow for multiple hits and thevariation in the NHIS sample, anadjustment factorξ is introduced:

vw2 =

[1 + δ(λ − 1)] ξVw2

52mλAdding on the between-PSU relativevariance, the final approximation isobtained:

v2 = vw2 + vb

2

=[1 + δ (λ − 1)] ξVw

2

(52mλ)+ vb

2

wherevb2 is the between-PSU relative

variance associated with the selectednumber of PSU’s.

Of course, the most criticalparameter to fix for the subsampling isthe number of clusters to retain. Giveneither a fixed-cost or a fixed-precisionrequirement, that number is fixed by thcluster size. To identify the optimalcluster size, it is necessary to quantifysome cost estimates for the variousphases of data collection. Let

c0 be the cost of developing samplinand data collection procedures atraining interviewers on the newprocedures in the desired numbeof PSU’s

c1 be the extra cost per NHIShousehold interview of screeningfor recent dental visits (equal to 0if NHIS already asks about visitsto this type of provider)

c2 be the cost associated with eachnetwork, such as obtaining fromthe NHIS respondent the nameand address of the dentist and aconsent form to release records,tracing and recruiting thenominated dentist, and samplingfrom his or her records

c3 be the cost per event ofabstraction including dataprocessing and editing

Then the total costs for thehypothetical survey of dentists would b

C = c0 + c1 (55,000) + 52c2m + 52c3mλ

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With these assumptions, for a givefixed costC, the optimal cluster size is

λ =Œ 1 − δ

δ Œ c2

c3

and the optimal number of clusters is

m =C − c0 − 55,000c1

52(c2 + c3λ)

For a fixed-precisionv2, the optimalcluster size is unchanged and theoptimal number of clusters is

m =[1 + δ (λ − 1)] ξVw

2

(v2 − vb2)52λ

Some plausible values for theparameters are shown next. Obviouslyhowever, each parameter would havebe carefully reconsidered for a specificsurvey. For illustrative purposes, then,assume the following cost structure,variance structure, and precisionrequirement:

c0 = $300,000

c1 = $3

c2 = $300

c3 = $15

Vw2 = 99

vb2 = 0.0005

ξ = 1.1

δ = 0.15

v2 = 0.01

(Note thatVw2 is a population-relative

variance, whereasvb2 is the relative

between-PSU variance on a sampleestimator—hence the difference inmagnitude betweenVw

2 andvb2.) Then

λ = 15 andm = 46. The total annualsample size would then be 35,880abstracted events and the total costwould be $1.7 million.

Comparison WithList-Based Sampling

A Comparable List Sample ofDentists

Cost implications are discussed firfollowed by variance implications, andlast by some operational implicationsthat may affect response rates. The ef

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to patch together State lists wouldincreasec0, perhaps by as much as$200,000. On the other hand, costc1

does not apply. Furthermore, costc2

might be lower because State lists mighave better name and addressinformation than what NHISrespondents can supply. State lists willprobably contain enough dirty andincomplete records, however, to offsetmost of these savings. Thus, it seemsreasonable to speculate thatc2 mightdecrease from $300 to $290. It is alsopossible thatc3 would increasesomewhat because of variation in thenumber of cases to be abstracted perdental practice. (Note: It is beingassumed here that the provider class ointerest is one for which lists cannot beexpected to contain useful measures osize.)

Assuming that the list will notcontain any information on the numberof patients served by the dentist, therelative variance from a list samplewould be

v2 =[1 + δ(λ − 1)] ξVw

2

(52mλ)+

VN2

(52m)+ vb

2

whereVN2 is the relative variation in

practice size,λ is now the averagecluster size, andξ is now the extradesign effect attributable to variation inpractice size that persists even when tcharacteristic of interest is a ratio inwhich the numerator and thedenominator are highly correlated withpractice size. If poststratification ispossible (unlikely for a dental survey),or the main interest is in ratios such asthe percent of patients who receive roocanals, then theVN

2/(52m) term willdisappear. (The term cancels out bysubtraction after linearization becauseaffects both numerator anddenominator.) However, ifpoststratification is not possible and themain focus is on totals such as the totanumber of root canals performed in thecountry or on ratios where thedenominator is not correlated withpractice size, such as the averagenumber of root canals per dentist, thenthe VN

2/(52m) term does apply. In eithercase, theξ factor is likely to persist.Also note thatξ does not approach 1 asm becomes large. (This factor is

t

discussed inSample Survey Methodsand Theoryvolumes I and II (31). It iswritten there as the ratio ofV2 with acaret toV2 without a caret.)

For fixed precision, the optimalnumber of clusters is now

m =[1 + δ(λ − 1)] ξVw

2 + λVN2

(v2 − vb2) 52λ

To illustrate, suppose the followingdistribution of weekly practice sizes:

Practices Visits

1–10 10,000 55,00011–20 20,000 310,00021–50 60,000 2,130,00051–100 60,000 4,530,000101–200 10,000 1,515,000201–500 100 35,100501–1,000 2 1,500

Total 160,102 8,576,600

Then the average practice size would b53 and the population variance ofpractice size would be 1,326 for apopulation-relative variance of 0.47.This variation is probably a bit on thehigh side, assuming that, inmultiple-dentist practices, the samplewould be taken of all patients seen byany of the partners. Whether the sampcould be stratified by individual dentistswould depend on how lists aremaintained in the office.

Assume then the following coststructure, variance structure, andprecision requirement:

c0 = $500,000

c1 = $0

c2 = $290

c3 = $18

Vw2 = 99

vb2 = 0.0005

ξ = 0.47

δ = 1.2

v2 = 0.01

Thenλ = 10 andm = 57. The totalannual sample size would then be29,640 abstracted events, and the totacost would be $1.9 million.

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Other Considerations

Besides coverage, cost, and variaimplications, other possible implicatioexist for response rates, othernonsampling errors, and elapsed timefrom survey authorization to release odata. With network sampling, it is mordifficult to plan the work in advance. Iis fairly likely that routine dental visitswill be scheduled on the same day fosome family members. Also possible,but somewhat less likely—neighborsmay be scheduled for the same weekMultiple hits cause operationalnuisances by requiring twice as muchabstracting to keep the sampleself-weighting (ork times as much ifthere arek hits in the same practice).is also possible for the same practicebe hit multiple times over the year,thereby adding to variable andunpredictable response burden andresulting in irritation in some dentiststoward the survey taker. The numbermultiple hits will be difficult to predictand probably will vary considerablyfrom one type of provider to another.For example, multiple hits seemunlikely to be much of a problem forchiropractors but do seem a likelyproblem for emergency rooms. If thisunpredictability is a problem withproviders, one solution would be to wuntil an entire year of NHIS had madenominations before following up any othe nominations. This, of course, impan extra time lag between the time thsurvey is approved and when datacollection may begin. Another problemwith such an approach would be thedestruction or transfer to long-termstorage of service records.

With list sampling, it is possible toplan the visits in advance, possiblyleading to cost savings andimprovements in response rates. Onother hand, if the list does not have agood measure of size, the number ofrecords to be abstracted per practicebecomes even more unpredictable thwith network sampling. This might haunfavorable implications for costs andresponse rates. In the example ofdentists just given, the number couldvary from 1 to more than 100.

One problem inherent in bothsampling approaches is the existence

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joint practices. If the provider named bthe list or nominated by the patient haa joint practice with one provider ormore, establishing the exact definitionthe sampling unit is an important andpossibly difficult task. If separateappointment logs are kept, there is noreal problem. If no separate logs arekept and each patient is simply seen bthe next available provider, the entirepractice may have to be viewed as thsampling unit. Alternatively, it may bepossible to select an oversample ofpatient records and subsample themafter inspection to just those of patienseen by the provider of interest. Thisproblem’s existence does not seem tofavor either network or list-basedsampling.

Last is the consideration of multipprovider surveys. Such surveys wouldundoubtedly involve some economiesscale. The costc0 for the networksample and the list sample would belower per survey if multiple surveyswere done. The reduction might besharper for network sampling, howevethan for list sampling.

ConclusionIf a reasonable list with a

reasonable measure of size can befound, list-based sampling is probablypreferable; otherwise, it appears thatnetwork sampling should be considereFactors that would tilt the balance infavor of network sampling include thefollowing:

+ Primary interest is on eventcharacteristics, not providercharacteristics.

+ Considerable uncertainty existsabout the quality of the lists,particularly with respect to coveragand currentness.

+ Available lists do not havereasonable measures of size; andeither:– The numbers of events per

provider varies widely, controltotals for poststratification are noavailable, and only a smallnumber of providers can besampled or

– The numbers of events perprovider varies widely, and

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variation in the number of recordsto be abstracted per providerincreases abstracting costs ornonresponse rates.

+ The cost of forming a list isprojected to be greater than the cosof collecting nominations fromNHIS.

+ The difficulties in tracing nominatedproviders are thought to be lessonerous than finding providersmentioned on State licensing lists(or lists obtained through othersources).

+ Flexibility in scheduling visits toproviders (including multiple visits)is not problematic, or an extra delayin the survey schedule can beaccommodated.

Formulas have been provided in thireport that will facilitate deciding whichapproach is less expensive for fixedprecision. Some of the parameters willbe extremely difficult to project;however, considerable room forjudgment will remain.

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Chapter 15.The National HealthInterview Survey as aTool for LinkingBeneficiary andProvider Perspectiveson Health Care

A great deal of attention hasrecently been focused on thepossibility of fundamentally

redesigning NHIS in such a manner thait provides a more complete picture ofthe health care process, incorporatingdata from the beneficiaries andproviders. Ideally, health economistswould like to be able to use NHIS as atool for assessing the beneficiaries’satisfaction with different treatmentmodalities for various conditions and thcost of those treatment modalities.

Other SurveysSeveral of the recommendations of

the Panel on the National Health CareSurvey, if implemented, would makeNHIS resemble other Federal surveysmore closely. This brings up thequestion of the proper goals for NHISvis-a-vis those other surveys.Specifically, the panel’s call for detailedmedical cost and utilization data to becollected through longitudinal followups of the health care providers to NHIrespondents would make NHIS resembthe National Medical ExpenditureSurvey (NMES) rather closely. Alongwith its predecessors, the NationalMedical Care and Expenditure Surveyand the National Medical CareUtilization and Expenditure Survey, andthe Medicare Current BeneficiarySurvey conducted by HCFA, NMESallows the linkage of health care costswith the outcomes of health care.

National Health InterviewSurvey as a Baseline or asa Follow Up?

If NHIS is to be used to obtaindual-perspective data on health care(data about the same events from boththe provider and the beneficiary), thequestion arises of whether it is better totrack patients from a sample of providerecords or to track providers from asample of patient nominations. Adifferent research effort (2) funded byNCHS has been underway to study thefeasibility of the first approach. NMESstands as an example of how the secoapproach can work. Both approachesobviously involve problems. The Panelof the National Research Councilfavored the NMES approach. This repotakes no position.

Part V.Decisions on Sample Design andContingency Options

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Chapter 16. Decisionson Sample Design

The recommended design is baseon the assumption that the fieldbudget for NHIS can be boosted

(starting in 1995) by 50 percent (overthe level needed to support 50,000household interviews with the 1985–94design) to pay for improved precisionon health statistics about black andHispanic persons. Starting in 1991, theU. S. Bureau of the Census undertookall necessary preparations for therecommended design. As discussed inchapter 17, there was not a budgetincrease in 1995. Also, the preparationcosts in 1994 for the recommendeddesign are substantial. Thus, analternative was also designed that woulead to some improvement in theprecision of Hispanic statistics under alevel 1995 budget (equal to the 1988budget after adjustment for inflation).The recommended design is referred toas the alpha option; the alternative isknown as the beta option.

Reconciliation ofObjectives

It was recognized early in theresearch that there were somefundamental tensions between theobjectives for the redesign. The desirefor improved statistics on the black andHispanic populations implied a need toincrease the sample in the majormetropolitan areas of the large States.This desire also implied a need tostratify PSU’s by ethnic composition,with a lower priority for respect of Stateboundaries. Furthermore, for thisobjective, nonself-representing PSU’swith significant numbers of minoritypersons should be oversampled. Last,for this objective, all householdmembers should be interviewed in blacand Hispanic households. Pulling in asecond direction, the desire forimproved State statistics implied a neeto allocate a disproportionate share ofthe sample to small States whererelatively few minority persons live, todefine and stratify nonself-representing

PSU’s in such a manner that Stateboundaries were strictly respected, anto select nonself-representing PSU’ssolely in proportion to population withinStates. Pulling in a third direction, thedesire to integrate the provider surveywith NHIS implied a need to definelarge PSU’s as sometimes crossing Stlines (so that most inhabitants of a PSwould receive their medical serviceswithin the boundaries of the PSU) andto oversample PSU’s that tend toprovide disproportionate amounts ofmedical services to nonresidents. Pulliin a fourth direction, the desire forreduced response bias throughself-response implied a need tointerview just one randomly selectedadult per household.

These inherent conflicts wereresolved by placing the highest priorityon improving NHIS statistics about theblack and Hispanic populations despiteinterviewing just one random adult perhousehold. Deviations from optimaldesign features for this objective weretolerated where the effect on the primaobjective was not severe.

Primary Sampling UnitDefinition and Measure ofSize

The definition of PSU’s and themeasure of size used to select samplePSU’s have important impacts onbetween-PSU variances. For PSU’s wcost functions such as NHIS, it isimportant that there be moreheterogeneity within PSU’s than acrosPSU’s. Thus, for example, it would beinefficient to define the suburbs of ametropolitan area as a PSU separatefrom the central city. With respect to thmeasure of size, the between-PSUvariance on a particular statistic isclosely related to the correlationbetween the measure of size and thePSU totals for the characteristic ofinterest. If the correlation is perfect, thbetween-PSU variance will be zero.

Unfortunately, it is extremelydifficult to project the between-PSUvariances that would result fromalternate definitions and/or measures osize. The current survey can provideinformation only about between-PSU

variance for the current definition andmeasure of size. To answer this questiobjectively, it is necessary to haveprecise county-level summaryinformation on the characteristic ofinterest for every county in the Nation.Such information exists forcharacteristics tabulated in the decenncensus, the Census of Agriculture, theEconomic Census, Internal RevenueService studies of payrolls, and othersources. Unfortunately, none of thesesources has much information that isrelevant to NHIS. NCHS also has theU.S. Department of Health and HumanServices Area Reference File thatcontains county-level information onhospitals and doctors as well as otherhealth providers. These, however, stillhave very little to do with the aspects ohealth about which the population isqueried in NHIS. Some conclusions cabe reached using basic demographicdata from the census, but much of thework in this area is highly speculativeand driven by expert opinion.

Primary Sampling UnitDefinition

A study conducted by the U. S.Bureau of the Census showed that usinthe Health Care Service Areas as PSUwould decrease survey precision,assuming that the budget was heldconstant (32). This is because theHCSA’s tend to be much larger in termof land area than traditional PSU’s. Thelarger land area would mean morewithin-PSU travel expense. To offsetthat increase in cost, it would benecessary to reduce the number ofsample PSU’s. The decrease in thenumber of sample PSU’s woulddecrease survey precision. Althoughthere is some question about just howlarge a budget-neutral cut in the numbeof sample PSU’s would have to be andsome question about the impact of thecut on between-PSU variance, Westat,Inc., agreed with the overall thrust ofthis research.

Having ruled out HCSA’s as PSU’sthe next question was whether the PSUdefinitions used for any of the othercurrent surveys at the U. S. Bureau ofthe Census might be suitable for NHISThe PSU’s for theCurrent Population

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Surveyrespect State and metropolitanstatistical area (MSA) boundaries (asdefined in 1990). Given the desire tomake NHIS flexible for Stateexpansions, the NHIS PSU’s need torespect State boundaries. So from thatperspective, the CPS PSU’s wereattractive. Administrative data that mighbe useful in small area estimationprojects, however, are generallyavailable only at the county level, evenin New England, where MSA’s violatecounty boundaries. Thus, the CPSPSU’s in New England were unsuitablefor NHIS. The PSU’s for the Survey ofIncome and Program Participation(SIPP) do not respect State boundariesoutside New England and are thusunsuitable for NHIS, but the SIPPPSU’s in New England respect Stateand NECMA (New England countymetropolitan area) boundaries, whichmeans they also respect countyboundaries.

The final NHIS PSU’s weretherefore defined to be coincident withCPS PSU’s outside New England andSIPP PSU’s within New England. Thisresulted in 1,955 PSU’s.

Primary Sampling UnitMeasure of Size

Based on the research described inchapters 4and13, the recommendationwas made to use total population (allages) as the measure of size. This is thsame measure of size that was used inthe 1985 redesign.

Primary Sampling UnitStratification

The stratification performed for the1985 redesign was extremely efficientfor regional and national statistics.Despite a drop in the number of samplPSU’s from 376 in 1984 to 198 in 1987and beyond (because of budgetproblems, not all of the 198 samplePSU’s were used in 1985 and 1986), thlevel of between-PSU variance stayedabout the same. (Estimates ofbetween-PSU variances are quiteunstable from both surveys; thisinstability may be masking some levelof change.) The 1985 stratification was

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based on multivariate clusteringtechniques and used more than a dozdimensions to define similarity betweePSU’s. This efficiency is particularlyimpressive given the fact that healthcharacteristics were not among thedimensions used for stratification. Theclustering program appears to haveidentified natural clusters that made foefficient strata for a wide range ofstatistics.

One type of statistic was not wellserved by the stratification, however.Statistics for individual States aresubject to very high between-PSUvariances in the 1985–94 design. Thisbecause the strata do not respect Staboundaries. (The strata for the 1973–8design did not respect State boundarieither, leading to similar highbetween-PSU variances for Statestatistics.) PSU’s with similardemographic, social, and economiccharacteristics were grouped togetherfrom different States. Thecv on manyState estimates is 100 percent or wors

Because the flexibility tosupplement NHIS for specific States wa major goal of the 1995 redesign, itwas decided that the strata shouldstrictly respect State boundaries. Tocounteract the increase in between-PSvariances that would affect regional annational statistics, it was also decidedforce the strata to respect metropolitastatus and to increase the number ofsample PSU’s to a level comparable tthat of the 1973–84 design.

Primary Sampling UnitSelection

The methodology used for selectinPSU’s was much the same for 1995 afor 1985. In strata where two PSU’swere selected, the U.S. Bureau of theCensus selected two PSU’s withoutreplacement using the Brewer-Durbinmethodology. The measure of size watotal population (all ages). No attemptwas made to maximize or minimizeoverlap with the old NHIS design orwith the new design for the CPS or another current survey at the U.S. Bureaof the Census. The only difference fro1985 was that some strata were quitesmall as a result of respecting State

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boundaries and metropolitan status. Inthese small strata, just one sample PSwas selected with probabilityproportional to total population.

Selection of BlocksAs discussed inchapter 1, NHIS

utilizes a mix of area and permitsampling rather than the list samplingused by most of the other demographicsurveys conducted by the U.S. Bureauof the Census. To facilitate coordinationacross surveys and the preparation offield materials, dummy entities are setup to represent all the housing units(HU’s) and group-quarter equivalents(GQE’s) reported in the 1990 census toexist in a PSU. (A GQE is a collectionof usually three persons innoninstitutional group quarters. Thiscollection is counted as equivalent to ahousehold for sampling purposes.)Census Bureau headquarters personneselect placeholders rather than HU’s anGQE’s directly for NHIS. CensusBureau field personnel then list theblocks that contain the sampleplaceholders. Census Bureauheadquarters personnel then draw acorrespondence between the sampleplaceholders and the physically listedHU’s and GQE’s to identify the finalsample of NHIS.

The selection of placeholdersinvolves two stages. First, blocks mustbe selected. As the first step in blockselection, these placeholders were sortby the characteristics of their parentblocks and, within that sort, by theirblock labels. One of the blockcharacteristics used in the sort wasminority density stratum, as describedlater.

Blocks with fewer than four knownHU’s and GQE’s were combined withneighboring blocks and given acombined block label. A largerminimum combined block size wasconsidered but rejected by U.S. Bureauof the Census staff because of concernabout coordination with other currentsurveys.

Each block was classified into oneof the 24 minority density strata definedin table 59. A different optimal clustersize was identified for each minoritydensity stratum. The possibilities were

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Table 59. Initial cluster sizes (in terms of placeholders)



< 5 5–10 10–30 30–60 60+

< 10 . . . . . . . . . . . . . . . . . . . . . . . . . 16 16 16 12 810–30 . . . . . . . . . . . . . . . . . . . . . . . . 12 12 12 8 830–60 . . . . . . . . . . . . . . . . . . . . . . . . 12 12 12 8 860+ . . . . . . . . . . . . . . . . . . . . . . . . . . 8 8 8 8 8


8, 12, and 16. Although intermediatesizes, not multiples of 4, might have beeeven better for NHIS, coordination withthe Bureau’s other current surveysrequired the clusters be multiples of 4.The formula used to determine theoptimal cluster size for thei-th stratumwas

λi = 4F 81 − Oi (1 − ri)

× 14

+ 0.5GwhereOi is the proportion of thestratum that is neither black norHispanic (derived fromtables 17and18), andri is the planned retention ratefor such other households (table 22).The idea of the formula is to inflate thecluster size enough so that eighthouseholds are still designated for fullinterviews after screening andsubsampling. Although it would havebeen easier to equalize the number ofplaceholders per cluster, it was thoughthat field operations would be moreefficient with a more uniform number offull interviews per cluster.

The placeholders were strungtogether into initial clusters ofcorresponding lengths. Each such clusis also known as a segment. (The word‘‘segment’’ has two common usages atthe U.S. Bureau of the Census. It is alsused to refer to the block or collectionof blocks from which the HU’s andGQE’s are drawn corresponding to theplaceholders. For example, a blockmight have 35 HU’s, with 8 scheduled

Table 60. Percent of initially selected strings to be


< 10 . . . . . . . . . . . . . . . . . . . . . . . . .10–30 . . . . . . . . . . . . . . . . . . . . . . . .30–60 . . . . . . . . . . . . . . . . . . . . . . . .60+ . . . . . . . . . . . . . . . . . . . . . . . . . .

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for interview in NHIS in a particularyear. The 8 and the 35 are referred toa ‘‘segment.’’) Consecutive groups of 1initial clusters were strung together toprovide enough sample for 10 years ointerviewing. Thus, each string consistof 80, 120, or 160 placeholders.

A systematic sample was thendrawn of the strings from each PSUwith a single sampling interval of40 × Pr{PSU} and a single random star(PSU’s with a probability of selectionless than 1/40 were grouped intorotating clusters.) This sampling planwill yield an overall sample of 1/40th oall U.S. housing. The number 40 waschosen so that the sample would yield10 samples of 200,000 interviewedhouseholds each. The number 200,00was chosen because it corresponds totimes 50,000, and the largestoversampling ratio specified intable 20is 4.0. No measure of size was used inthe selection, so long strings had thesame probability of selection as shortstrings. (Because string length isinversely related to frequency, theprobability of selection for anindividual placeholder is 1/40 whetheit is in a long or short string.) Thisresulted in a random clusteredequiprobability sample comprising2.5 percent of the universe. Theplaceholders in this sample arereserved for the sole use of NHISeven though NHIS will use only asubsample of them. The reason for nsharing the unused portion with othecurrent surveys at the U.S. Bureau o

retained for actual listing (all others are put aside)

Percent of block t

< 5 5–10 10–30

40.0 50.0 80.040.0 50.0 87.542.5 50.0 87.548.8 50.0 87.5

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the Census is that the unused NHISsubsample will not represent the entireU.S. population.

As is discussed inchapter 5, thebest plan identified for nonelderly blackpersons and Hispanic persons involves acombination of oversampling andscreening. The strata and oversamplingrates desired for the oversampling arethose shown intable 20. The initialstring selection previously describedresulted in an oversampling rate of 4 inall the strata. Thus, some strings had tobe dropped in some strata. The stringretention rates are shown intable 60.These rates were calculated by dividingthe desired oversampling rates intable 20by 4.

Within-Block SelectionAt the end of block selection, the

U.S. Bureau of the Census had a list ofsample blocks and, for each block, acount of how many placeholders were tobe allocated to NHIS. They haveprocedures in place to list physically theresidential structures in the block and toline them up with the placeholders.These procedures guarantee that theHU’s and GQE’s assigned to eachsurvey will be nonoverlapping even ifseveral current surveys have selected thblock. To make this possible, all blocksthat were selected by NHIS had to beconverted to area-type samplingprocedures for all the current surveys,even though good address registers exisfor many of the blocks, and the other

hat is Hispanic

30–60 60+

100.0 100.0100.0 100.0100.0 100.0100.0 100.0

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surveys could have used them to selecsamples more cheaply. The listing begasometime in 1994.

Westat, Inc.’s, projection is that thelisting operation for the year 2000 willyield 123,900 HU’s and GQE’s to bescreened. Of course, there is considerabuncertainty when projecting the resultseven a few years into the future. Factorsthat can affect the accuracy of theprojection include new housingconstruction rates, housing demolitionrates, residential/nonresidential conversiorates, and the growth rate of thepopulation living in group quarters such adormitories. The details of the listingprocedures can also affect the accuracythe projections. It is important to note thathe number of units listed each year islikely to increase from 1995 through 200and that as a result, some adjustment ofthe string retention rates might be requireover time.

ScreeningThe new sample was introduced in

January 1995. Screening of the samplehousing units began immediately. Allhouseholds with black or Hispanicoccupants will be retained for fullinterviews. Other households will beretained at the rates shown intable 22.

Of the 123,900 HU’s and GQE’sthat are projected to be designated forscreening in the year 2000, not all willhave cooperative occupants, some willhave no occupants at all, and some winot even be inhabitable. The U.S.Bureau of the Census made a projectiothat in the average year about 80 perceof the listed units will have occupantswho will cooperate with the screeningeffort. These 99,100 successfullyscreened HU’s are expected to consistof 12,300 black households, 12,500Hispanic households, and 74,400‘‘other’’ households.

Turning to the mechanics ofscreening, the exact procedures that aused to cope with HU’s that are difficultto screen will depend on whether laptocomputers are used by the interviewersas they screen. The screening proceduare described separately for CAPI andpaper-based screening.

(The next paragraph describes aproposed procedure for CAPI screenin

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However, when CAPI interviewing wasimplemented in 1997, this procedurewas not implemented; the paper-basedscreening process described later wasretained.)

With CAPI screening, theinterviewer will approach the door withthe computer to enter the race andethnicity of the occupants. The computwill have preloaded instructions to guidthe interviewer through the regularNHIS interview or to terminate theinterview after ascertaining the race anethnicity of all occupants. If any of theoccupants are black or Hispanic, thedecision will always be for a regularinterview. If none of the occupants areeither black or Hispanic, the computerwill draw a random number to decidewhether to proceed with a full interviewor to terminate. The random decisionwill give the correct retentionprobability for these other households,but there will be some variability fromexpected yields. Consideration wasgiven to coordinating the random drawsacross households, but this will probabnot be feasible across interviewers, andit is not clear that it is worth the effortto coordinate across householdsscreened by a single interviewer.

With paper-based procedures,interviewers will have an instruction foreach assigned HU on whether toconduct a regular interview regardlessthe race or ethnicity of the householdoccupants (an ‘‘I’’ instruction forinterview) or whether to only conductthe regular interview if at least one ofthe occupants is either black or Hispan(an ‘‘S’’ instruction for screen). Theseinstructions will be made up in thecentral office after the listing phase forsample blocks and will ensure thedesired retention probabilities.Furthermore, it will be possible tocoordinate the sampling instructionsacross interviewers. However, there wilstill be some variance about expectedyields as can be seen from thepossibility that just by chance, thehouseholds that are neither black norHispanic could happen to fall into the‘‘I’’ category at disproportionate rates.

With either procedure, allowanceneeds to be made for housing units thaare difficult or impossible to interview,even just to get the race and ethnicity o

the occupants. To keep response rateshigh without raising costs exorbitantlythrough many repeat visits, interviewerwill be trained to use information fromneighbors under some circumstances.(Screening using neighbors wasdiscontinued in 1997.) After twounsuccessful attempts on different daysto find anyone at home, interviewerswill call on neighbors. With CAPI,interviewers will be required to obtainconsistent information from twoneighbors before accepting their reporton the race and ethnicity of the targetehousehold. Upon obtaining consistentinformation, the computer will decidewhether additional attempts are neededto conduct a regular interview orwhether the HU can be closed assuccessfully screened and subsampledout. In the latter case, considerableexpense will have been saved. In thefirst case, the interviewer is not muchworse off than prior to contacting theneighbors, and valuable information habeen gathered to inform the weightingprocedures for nonresponse adjustmeneven if the household is neverinterviewed. Note that the base weightsare formed based on the neighborinformation and are unbiased even if thneighbor information turns out to bewrong.

With paper-based procedures, theinterviewer will never bother to contactneighbors for an HU that is marked ‘‘I.’’For an HU marked ‘‘S,’’ the interviewerwill start to contact neighbors after twounsuccessful attempts at direct contacton different days. If the first neighborsays that no one in the targetedhousehold is black or Hispanic, theinterviewer will close the case assuccessfully screened out. Otherwise,the interviewer will try a secondneighbor. If both neighbors say that thehousehold has some members who areblack or Hispanic, the interviewer willresume attempts to contact thehousehold directly. If the interviewereventually succeeds and it turns out ththe neighbor’s information was wrong(there are not any members who areblack or Hispanic), the interviewer willclose the case as successfully screeneout. Note that this procedure results inslight bias in the baseweights, but thisbias should be quite small. To make th

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paper-based procedure unbiased, itwould be necessary to seek neighborinformation on all the ‘‘I’’ householdsthat are difficult to contact. It was theU.S. Bureau of the Census’ decision thusing neighbors for ‘‘I’’ householdswould be expensive andincomprehensible to interviewers.

Because there was some initialskepticism about using neighbors at athe U.S. Bureau of the Census Bureauconducted a field test of screeningprocedures in the summer of 1993. Thfocused in particular on the accuracy oneighbor information on race and ethnorigin. The results were stronglysupportive of the utility of neighborcontacts during screening. Informationabout race from neighbors was shownbe extremely accurate, at least for thedichotomy of black versus not black.Information about Hispanic origin wasless reliable but still useful.

New ConstructionA plan was considered where ne

construction would be treated the saas the stratum for old constructionleast dense with minorities (under10 percent black and under 5 percenHispanic). This would have requiredtaking a considerably larger samplenew construction and then screeningfor minorities. This recommendationwas rejected out of concern that thenew construction sample would bemore expensive to screen because itdifficult to cluster effectively. Therewas also a feeling that the newconstruction universe would containeven fewer minorities than the least

Table 61. Sample sizes for elderly minorities

Domain

Black

Male . . . . . . . . . . . . . . . . . . . . . . . . .Female . . . . . . . . . . . . . . . . . . . . . . .

Hispanic

Male . . . . . . . . . . . . . . . . . . . . . . . . .Female . . . . . . . . . . . . . . . . . . . . . . .

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minority-dense old constructionstratum. (This supposition may betested with the 1995 design afterimplementation. Such a study wouldprovide useful input to the 2005redesign.) Current plans are notspecific on the sampling interval to bused in new construction, but it islikely to be larger than that used inthe 1985–94 design.

Dual-Frame Sampling forElderly Minorities

The oversampling and screeningplans described earlier will not yieldsatisfactory precision for elderly blackpersons or elderly Hispanic personsunless NCHS allows all elderly minoritadults found in a household to beinterviewed, in addition to a randomlyselected adult between the ages of 18and 64. These groups are simply toorare to allow within-householdsubsampling. As discussed inchapter 6,there are attractive dual-frame samplinsystems employing SSA lists that coulimprove the precision for elderly blackpersons and Hispanic persons atreasonable cost, while keeping therestriction that only one adult beinterviewed per household. Such adual-frame system is not part of theformal plan for the 1995 design at thistime. As of this writing, a dual-framesystem may be implemented in 1996Table 61shows the projected effectivesample sizes for elderly black personand Hispanic persons from the planndesign (the alpha option). It alsoshows the desired effective samplesizes, the projected effective sample

Desired

Effe


option(1 household)

Projectedunder alph

option(all househol

1,000 600 1,1501,000 740 1,710

1,000 350 9101,000 495 1,270

sizes that could be attained withdual-frame sampling, and the numbersof additional screening interviews thatwould be necessary to obtain thoseeffective sample sizes with dual-framesampling.

Panel ConstructionThis section begins with a review of

panel formation in 1985 and continueswith a review of the objectives forpanels, a discussion of the additionalcomplexities for 1995, a description ofsome adjustments to the methods usedin 1985, and a couple of brief notes onimplications for weighting.

Panel Design for 1985

Four panels were defined in the1985 design. To support panel definitionself-representing PSU’s were dividedinto three classes: Large, medium, andsmall. The panels were defined at thePSU level for NSR PSU’s and small SRPSU’s. They were defined at theassignment level for large- andmedium-sized SR PSU’s. (This may nothave been done in 1985 itself but was inplace for 1986 and beyond.) Anassignment was a collection of segmentwithin a PSU that were scheduled to beinterviewed in the same week. A PSUwas defined as the first-stage unit insample selection, not as the unit forinterviewer assignment as used for fieldmanagement and control. The panelswere defined by first forming pairs ofstrata and then randomly assigning pandesignations 1 and 2 to the two PSU’sin one stratum and 3 and 4 to the twoPSU’s in the other stratum within the

ctive sample sizes

a

ds)

With extrascreened

2,800 casesfrom SSA

(1 household)

With extrascreened

5,800 casesfrom SSA

(1 household)

1,000 1,0001,000 1,000

770 1,000920 1,000

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superstratum. The random assignment wnot independent across superstrata.Dependence was created to better equapanel sizes. For example, if a PSU withlarge workload was assigned to panel 1one superstratum, a PSU with a smallworkload was chosen for panel 1 in thenext superstratum. The panels wereexhaustive and mutually exclusive. Eachpanel was an equally valid nationalsample. (SeeDesign and Estimation forthe National Health Interview Survey,1985–94(33) for more details on 1985procedures.)

Objectives

Panels had several uses in the 1985design. The first use was samplereduction. When adequate funds were navailable in 1985 and 1986 for the fullsample design, the panels were a quickand efficient way to cut back the samplebecause whole NSR PSU’s and compleassignments in SR PSU’s were dropped

The second use was advanceselection of a conveniently sized PSUsample for occasional surveys such asthe National Hospital Discharge SurveSelecting general purpose designs inadvance saves some time and effort ifsurvey comes along that needsapproximately that number of PSU’s.There are also some operationalefficiencies in terms of knowing wherethe U.S. Bureau of the Census shouldmaintain an interviewing potential.Subsamples could obviously be drawnwhen necessary, but it seemedconvenient to have plans in place forsuch subsampling.

The third use of the panels wassplitting retired NHIS samples upamong competing followup surveys suas the National Survey of FamilyGrowth. By stating that survey A couldhave panels 1 and 3, and survey B cohave panels 2 and 4, none of the samwas wasted or used twice. It is not clewhether this capability was actuallyused. Whether this would have been thbest way to split the sample between ttwo surveys is also unclear. For somepairs of surveys, it would probably havbeen better to randomize segments oreven to split each segment between thcompeting surveys.

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The fourth use of the panels wasconducting randomized experimentswith the NHIS sample. This potentialdoes not appear to have been used. Tpanels would not necessarily haveyielded a good experimental designanyway, because randomization wouldhave been at a high level in thesampling, allowing between-PSU andbetween-interviewer variances toobscure the results. A better design fomany purposes would be obtained bysplitting each work assignment, or eveeach segment, between treatments.

Given the utilization patterns of thpanel structure in the 1985–94 decadeseems clear that the highest priority indesigning panels for the next decadeshould be on the potential for orderlycuts in the sample. (Cutting PSU’smakes sense if a desire exists to reducosts while maintaining the samerelative sampling rates for minorities aproposed for the full design. If, insteadthe decision is made to drop back to aequiprobability sample, dropping PSUis not the best way to cut the sample.Seechapter 17for more details.) Basedon the cuts experienced in 1985 and1986 (25 percent and 50 percent,respectively) and the fact that thenumber of PSU’s in the 1995 designwas chosen based on the assumptiona 50-percent budget increase, it isconvenient to have panels that can mcuts of the following magnitudes easy25 percent, 33 percent, 50 percent,67 percent. The second priority shouldbe to have conveniently sized, first-stadesigns available for the provider surveand other new or occasional surveys; oleast there should be a clear mechanismfor generating appropriately sizedsubsamples of the NHIS first-stage deswith short lead times. The uses forsplitting the sample for either follow-onsurveys or experiments seem lessimportant and should probably not beconsidered at all. It also seems reasonato abandon all support for State estimatin subdesigns.

Problems for 1995

The NSR strata in the 1985 desigwere roughly equal in size. Two PSU’swere selected from each NSR stratum

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Neither of these two statements applieto the 1995 design. To provide theflexibility in the 1995 design for Stateexpansions, the NSR strata were definby mostly State and metropolitan statuIn slightly fewer than one-half of theState-by-metro-status domains, it waspossible to stratify more deeply thanthat. The most strata that were definedwithin a State-by-metro-status domainwas five. In States where either the NSmetropolitan population or thenonmetropolitan population is small,only one PSU was selected from thestratum instead of two. In larger strata,two PSU’s were selected. Some smallemetropolitan areas were selected withcertainty because they made up a largportion of the State metropolitanpopulation. The total number of PSU’sin the 1995 design is 358. (Consolidatemetropolitan areas are just counted onregardless of how many administrativePSU’s they might be divided into. Anumber of smaller metropolitan areasare counted twice, however, becausethey cross State lines and multiplefragments were selected.)

To minimize the variation inbaseweights for a reduced NHIS, it isimportant to form the superstrata in suca manner that the number of full-designsample PSU’s per superstratum isconstant and the original strata within asuperstratum are nearly constant in sizAlso, the full-design sample PSU’sshould be subsampled with equalprobability within a superstratum. Itdoes not matter for this objectivewhether the superstrata vary in size.

For the second objective of havingthe subdesigns serve as appropriatefirst-stage samples for provider surveyshowever, the superstrata for subsampliPSU’s should be nearly equal in size(population, health care providers, heacare events, etc.). This means collapsiunequal numbers of original strata ofvarious sizes together to formsuperstrata. For this objective, thefull-design sample PSU’s should besubsampled with probabilityproportionate to stratum size. If onesuperstratum contains seven samplePSU’s and another contains three, theris no simple way of coming up withexhaustive and mutually exclusive setsof NSR PSU’s.

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Panel Construction for 1995

(This section describes panelstructures created by Westat, Inc., andthe U.S. Bureau of the Census.Subsequently, NCHS created a panelstructure consisting of four subsets; thais the panel structure that has been usin the 1995–2004 redesign.)

It was decided to construct twodistinct panel structures. For the first othese, to be constructed immediately, itwas decided that the same generalprocedures would be followed as in1985, forming superstrata with a fixednumber of sample PSU’s persuperstratum and then subsampling wiequal probabilities. For the secondstructure, to be constructed whenneeded, it was decided to formsuperstrata with equal populations andthen to subsample with probabilityproportionate to stratum population. Thplans for each of these structures isdiscussed in more detail later. The firstpanel structure will serve the first, thirdand fourth objectives just discussed. Thsecond panel structure will efficientlysatisfy the second objective. At theconclusion of this section, the penaltiesthat would be suffered if the decisionwere reached to use the first panelstructure to serve the second objectiveare discussed.

Reduction panels—The generalprocedures used in 1985 need onlyminor adjustments. The most significanof these adjustments is to provide thecapability of cuts of 16.7, 25.0, 33.3,50.0, 66.7, 75.0, and 83.3 percent. Toprovide this capability, two panelstructures will actually be created forreduction purposes—one with fourpanels and one with six. Both will becreated using the same methodology.Only the four-PSU case is discussed indetail here, but the generalization to sixPSU’s is straightforward.

The first adjustment to the oldfour-PSU procedure is that, rather thanpairing only two 2-ps strata (strata inwhich two PSU’s are selected perstratum), it will be necessary to grouptogether either two 2-ps strata, one 2-pstratum and two 1-ps strata, or four 1-pstrata. (If one 2-ps stratum is pairedwith two 1-ps strata, it is important thatthe 1-ps strata be roughly equal in size

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and half the size of the 2-ps stratum.)The second adjustment is that it may bmore difficult to pair strata that areequal in size while still respectingregional and metropolitan categoryboundaries. On the one hand, originalstrata vary much more in size, and onthe other hand, there are more originalstrata to work with. Although adequatedata were not available at the time ofthis writing to project the numbers ofSR PSU’s by size class that theprocedure will yield, this will be aroutine task for staff at the U.S. Bureauof the Census. It is currently anticipatethat the resulting set of panels couldyield nested subdesigns with roughly110 PSU’s, 200 PSU’s, 280 PSU’s, an358 PSU’s. (Of course, the set of sixpanels will provide additional optionsbeyond these.)

Within the large and medium SRPSU’s, the reduction panels can be seup along the same lines as in1985—that is, by systematicallysubsampling assignments. Current plancall for assignments to be all roughlyequal in size, so there will be no needincorporate a measure of size into thesampling. The sort for the subsamplingshould probably include region, PSU,administrative PSU, and an indicator owhether the assignment is predominancentral city or suburb. The systematicsubsampling should probably be doneacross PSU’s with alternating sorts onthe indicators for central city status. Thsubsampling interval should be four sothat the populations in SR and NSRPSU’s have the same probabilities ofselection.

An additional feature of theprocedure for 1985 for assigning panelnumbers to the four subsets of thePSU’s will be retained: Either expectedor actual NHIS sample sizes (in termsof segment counts) by sample PSU wibe used to try to equalize total panelsample sizes.

Subdesigns for other surveys—For anumber of reasons, it was decided todelay forming panel structures for othesurveys. Although this work could bedone in advance, the panel structureswill be more efficient if designed afterthe actual requirements of other surveyare known. That will allow differentsorts and specialized measures of size

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For example, for a survey of hospices,data on local numbers and sizes ofhospices could be used for stratificatioand also be built into the measure ofsize of each PSU to create a moreefficient design.

As was discussed inchapter 3,further research is planned on thebenefits of concentrating all the providsurveys into a single set of PSU’s.Based on current research, however, aassuming that U.S. Bureau of theCensus interviewers will be visiting all358 NHIS PSU’s (i.e., that there are nsample cuts), it is unclear whether theare substantial additional operationalefficiencies from concentrating all theprovider surveys into a single subset oNHIS. This suggests that it might bebest to custom-select a different subseof the NHIS PSU’s for each of theprovider surveys.

It may be determined later thatthere are important benefits in havingthe provider surveys nested within eacother. The rest of this section indicatesreasonable method for creating such aset of subdesigns.

Two surveys that are likely to sharthe NHIS design are NHDS andNAMCS. These have recently (the ear1990’s) been carried out in the 112 PStwo-panel subdesign of the 1985 NHISAnother recurring survey is the NationNursing Home Survey (NNHS). It hastraditionally not been clustered. To easinto clustering so as to avoid large riskin the changeover process, it mightmake sense to have a design with asmany as 200 PSU’s for it. (With morethan 1,200 sample facilities, even 200PSU’s should generate enoughclustering to yield substantialoperational efficiencies.) After havingbeen fielded once with 200 PSU’s, itwould be possible to study thecomponents of variance to see whetha smaller number of PSU’s wouldserve survey objectives adequately.Other surveys of providers are plannbut are brand new, so there is no bafor choosing an optimal number ofPSU’s for them. For demographicsurveys, past (not publiclydocumented) experience has shownthat between-PSU variance onminority estimates is important at 100PSU’s and small at 200 PSU’s.

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Interpolation between those extremeis uncertain and not attempted here.

The 52 largest SR PSU’s in the fu1995 design (358 PSU’s) were identifiwith the thought that they would beappropriate for a national survey ofapproximately 200 PSU’s. AdditionalSR PSU’s were identified for Statepurposes, but there is no guarantee thall the PSU’s that should be SR for anational survey of more than 200 PSUare in the sample. Thus, it isrecommended that the largest subdescontain 200 sample PSU’s. These 200should be selected in such a manner teach sample PSU represents anapproximately equal number of personOnce that subdesign has been selecteit will then be possible to split this setof 200 sample PSU’s into panels in thsame manner as was done for the 198PSU’s in the full 1985 design. In thismanner a set of nested subdesigns cabe created with 62 PSU’s, 113 PSU’s,155 PSU’s, and 200 PSU’s.

The 306 full-design sample PSU’soutside of the 52 largest consolidatedmetropolitan areas should be groupedinto about 74 superstrata. This meansaverage of 4.2 sample PSU’s persuperstratum. The superstrata shouldformed by applying the stratificationsoftware developed at the U.S. Bureauof the Census directly to the 306 PSUThe measure of size for a PSU shouldbe defined as the stratum populationrepresented by the PSU (one-half ofstratum population in the case of PSUfrom 2-ps strata). Substantivecharacteristics of the sample PSU itseshould be used to define similarityrather than stratum characteristics. (Itpermissible and indeed most efficient tuse the characteristics of the samplePSU itself rather than just those of thestratum that it represents. However,unbiased estimates of variance arethen not possible, and reasonableestimates of variance components arelikely to be particularly problematic. Se‘‘Using Sample Information forStratification’’ (34).) The superstratashould be constrained to respect regioand metro status and to be roughlyequal in size. Respecting region andmetro status is important because thesare likely variables in NCHS reports.Keeping the superstrata sizes roughly

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equal is important because it reducesdesign effects and gives greater controover workloads. (Workload control isnot that important for NHIS with itsitinerant interviewers but may be for thprovider surveys.) Of course, it is verylikely that it will be difficult to find astratification that respects all theseconstraints and has internallyhomogeneous strata. The phrase‘‘roughly equal’’ already provides someslack, but it may be necessary to relaxthe constraint on strictly metro andnonmetro superstrata. This would not bcatastrophic because the definitions ofmetropolitan areas change over timeanyway.

Within each superstratum, twoPSU’s should be selected by theBrewer-Durbin method with probabilityproportionate to the measure of sizedefined earlier. Then the stratificationprogram should be applied to thesuperstrata to form hyper-superstrata.The hyper-superstrata should alsorespect regional and metropolitanboundaries to the greatest extentcompatible with roughly equal sizes forthe hyper-superstrata. One superstratushould then be selected from eachhyper-superstratum with probabilityproportionate to superstratum size. Witfour sample PSU’s per hyper-superstrata, the four subpanels can theeasily be identified.

An alternative that is less efficientbut more elegant to describe is to formjust 37 superstrata to begin with andthen select four PSU’s from eachsuperstratum by either theDurbin-Sampford method or theRao-Hartley-Cochran method withprobability proportionate to the measurof size defined earlier. The Durbin-Sampford method is generally moreefficient than the Rao-Hartley-Cochranmethod but is substantially moredifficult to implement forn = 4.

Why not a unified set of panels?Two distinct panel structures have beerecommended in this report becauseeach will be more efficient for aparticular objective and because noserious disadvantages are foreseen. Toplay the devil’s advocate, however,suppose there were a cut in the NHISsample size to 280 PSU’s and therewere a survey of dentists in 200 PSU’s

for which the U.S. Bureau of the Censucould promise substantial budget savinif the sample were concentrated withinNHIS PSU’s. Because some of the 200PSU’s in the national subdesign forprovider surveys would probably havebeen among the 78 PSU’s dropped (inthis example) by NHIS, some efficiencywould have been lost. If the cost savingare great enough, however, it would bepossible to pick a new national designof 200 PSU’s from the 280 still in theNHIS.

If the smaller design effects andmore stable workloads associated withuniform superstratum sizes are notimportant for a particular providersurvey or for some other piggybacksurvey, however, the subdesignsprepared for NHIS cuts could also beused for such a survey. To recapitulatethe sizes that will be available under thplans recommended here, it will be easto reduce the NHIS to (roughly) either85, 110, 140, 200, 250, 280, or 300PSU’s from the planned 358 PSU’s; ansubdesigns readily available for surveywith a requirement for more equalworkloads will have 62, 113, 155, or200 PSU’s.

Note that the two 200-PSUsubdesigns will have quite differentcharacteristics. The NHIS will beself-weighting (within block strata)across the set of 200 PSU’s designed fa reduced NHIS, but NHIS workloadswill vary widely across those 200PSU’s. NHIS will not beself-weighting across the set of 200PSU’s designed for a piggybacksurvey nor will it have equalworkloads across those PSU’s(because of oversampling minoritydensity strata). However, a uniformsurvey of total population could bemade self-weighting with equalworkloads across the set of 200 PSUdesigned for a piggyback survey.

Weighting Implications

If it is necessary to breachmetropolitan boundaries whenassembling superstrata, it wouldprobably make sense to incorporate afirst-stage adjustment with metro andnonmetro cells into the weighting. Also,if a piggyback survey uses one of the

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Figure 1. Summary of sample sizes for new design (alpha option)


fixed subpanels designed formultipurpose use even though the idemeasure of size for the survey is verydifferent from total population, afirst-stage adjustment based on the idmeasure of size should be incorporateinto the weighting for that survey.

Summary of DesignCharacteristics

Figure 1shows the projected sampsizes for the new design. It shows thetotal number of listing lines designatedfor screening, total number of screenehouseholds, number of interviewedhouseholds, and effective person-samsizes for both options of interviewing aadults and for interviewing just oneadult per household.

Primary Sampling Units

PSU’s were selected for the 1995design. The PSU’s are clusters of whoadjacent counties. In nonmetropolitanareas, the clusters were formed subjeto minimum constraints on populationand maximum constraints on land areIn metropolitan areas, the PSU’s arecomplete 1990 MSA’s except whereMSA’s cross State lines and in NewEngland. Multistate MSA’s were splitinto State components. PSU’s in NewEngland were based on NECMA’s.

After the PSU’s had been definedthey were stratified by State andmetropolitan status. Large MSA’s werdesignated SR and placed into separastrata. In States with large numbers oNSR MSA PSU’s or large numbers ofnonmetropolitan PSU’s, the PSU’s wefurther stratified by income with theconstraint that the strata within a Statand metropolitan category beapproximately equal in size.

After stratification, one or twoPSU’s were selected from each stratuwith probability proportionate to size.The measure of size was the 1995estimated total resident population (alages). Where two PSU’s were selectethe selection was done withoutreplacement using the Brewer-Durbinmethod. A total of 358 PSU’s wereselected.

Household Clusters

Placeholders were created for allHU’s reported in the 1990 census in aPSU. These placeholders were sorted bthe characteristics of their parent blocksand, within that sort, by their blocklabels. (Blocks with fewer than fourknown housing units were collapsedwith other blocks and given a combinedblock label.) One of the blockcharacteristics used in the sort waswhether there were substantial additionoperational efficiencies fromconcentrating all the provider surveysinto a single subset of NHIS. Thissuggests that it might be best tocustom-select a different subset of theNHIS PSU’s for each of the providersurveys.

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Chapter 17.Contingency Plans

A t the time the research describein this report was performed, itwas not clear whether the

50-percent budget increase needed toimplement the recommended designwould be available. Consequently, anumber of contingency plans weredeveloped. After intense study anddiscussion, one of these options waschosen as the fallback position. It isreferred to as the beta option. (Theintended redesign described inchapter16 is referred to as the alpha option.)

In fact, in late 1994, a decision wamade to implement the beta optionrather than the alpha option. The betaoption assumes the same level offunding as in 1988 (after adjustment foinflation). It calls for the continuedoversampling of black persons at thecurrent modest rate and for newoversampling of Hispanic persons at arate high enough so that the precisionfor Hispanic estimates is comparable tthat for black estimates.

Several other alternatives that werseriously considered are brieflydescribed later for documentarypurposes.

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Table 62. Oversampling rates for the beta design (r


< 10 . . . . . . . . . . . . . . . . . . . . . . . . .10–30 . . . . . . . . . . . . . . . . . . . . . . . .30–60 . . . . . . . . . . . . . . . . . . . . . . . .60+ . . . . . . . . . . . . . . . . . . . . . . . . . .

NOTE: Compare with table 20 for the alpha design.

Table 63. Percent of total sample reserved for alpha


< 10 . . . . . . . . . . . . . . . . . . . . . . . . .10–30 . . . . . . . . . . . . . . . . . . . . . . . .30–60 . . . . . . . . . . . . . . . . . . . . . . . .60+ . . . . . . . . . . . . . . . . . . . . . . . . . .

The Beta OptionThe beta option is similar to the

recommended design but has a muchlower level of screening. Instead ofdesignating 123,900 HU’s and GQE’sfor screening, only 71,500 will bedesignated. Also, the number of ‘‘otherhouseholds discovered during screeninto be retained for full interviews will bereduced from 35,100 to 28,900. Thesetwo steps will reduce the cost from alevel 50 percent over a survey with50,000 regular interviews, to a level5 percent below such a survey. Becausthe 1988 NHIS had about 47,500 reguinterviews, these reductions will makethe beta option close to budget-neutralrelative to 1988, after correcting forinflation.

This cut was crafted in such a waythat the precision for black statistics wibe about the same as in 1988, theprecision for Hispanic statistics will besharply improved over 1988 levels toabout the same level as the precision oblack statistics, and the precision for aother statistics will drop fairly sharplyrelative to 1988. These decisions wereguided by NCHS perceptions ofprecision needs for the domains. Thebasic directive was to hold the precisiofor black statistics steady, improve theprecision for Hispanic statistics to bringthem into parity with black statistics,and let the precision for other statisticsfall into place as dictated by the budge

elative to a design that would yield 50,000 interview

Percent of block th

< 5 5–10 10–30

1.0 1.5 1.51.0 1.5 1.51.0 1.0 1.01.0 1.0 1.2

option to be retained for the beta option

Percent of block

< 5 5–10 10–3

62.5 75.0 46.962.5 75.0 42.958.8 50.0 28.651.3 50.0 34.3

r

Table 62shows the oversamplingrates that are projected to generate therequired numbers of black and Hispanichouseholds with an approximatelyoptimal tradeoff between cost forscreening and higher design effects forblack and Hispanic statistics. These rateare relative to the sampling rate for ahypothetical equiprobability survey thatwould yield 50,000 completedinterviews. These rates can be comparewith those for the alpha option intable 20. Note that under the betaoption, blocks with heavy concentrationof Hispanic persons and lightconcentrations of black persons areactually sampled more heavily thanblocks with heavy concentrations ofboth minorities. This was arrangeddeliberately to gain parity between blacand Hispanic effective sample sizes.

From a systems engineeringstandpoint, it was important that allsample clusters for the beta design besubset of those selected for the alphadesign. The U.S. Bureau of the Censushad done a tremendous amount of workby the fall of 1994 to select and preparethe clusters for the alpha option. Anyuse of different clusters by the betadesign would have resulted in at least a2-year delay in the 1995 NHIS.Table 63shows the percentages of the sampleselected for the alpha option that will beneeded for the beta option. These werecalculated by the formula

s)

at is Hispanic

30–60 60+

1.6 2.31.5 2.01.5 2.01.5 1.5

that is Hispanic

0 30–60 60+

40.0 57.537.5 50.037.5 50.037.5 37.5

keepi =ki

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where the numerator is the oversamplirate for the beta design fromtable 62and the denominator is the oversamplinrate for the alpha design fromtable 20.

The cut in the designated alphasample will be achieved by a mixture odropping whole clusters before listingand subsampling listed HU’s in otherclusters. Although it would have beenpossible to achieve the cut either byonly dropping whole clusters or by onlysubsampling listed HU’s within thewhole set of clusters selected for thealpha option, the mixture approach willresult in the smallest variation in thenumber of interviewed households percluster.Table 64shows the optimalcluster sizes for the beta design bystratum. These may be compared withthose intable 59and were calculated bythe formulas given inchapter 16.

Let yi be the proportion of clustersselected for the alpha design that will bkept for the beta design. Letzi be the

Table 64. Desired cluster sizes for beta design (in


< 10 . . . . . . . . . . . . . . . . . . . . . . . . .10–30 . . . . . . . . . . . . . . . . . . . . . . . .30–60 . . . . . . . . . . . . . . . . . . . . . . . .60+ . . . . . . . . . . . . . . . . . . . . . . . . . .

NOTE: Compare with table 59.

Table 65. Percent of strings reserved for the alph


< 10 . . . . . . . . . . . . . . . . . . . . . . . . .10–30 . . . . . . . . . . . . . . . . . . . . . . . .30–60 . . . . . . . . . . . . . . . . . . . . . . . .60+ . . . . . . . . . . . . . . . . . . . . . . . . . .

Table 66. Percent of listing lines reserved for NHIoption


< 10 . . . . . . . . . . . . . . . . . . . . . . . . .10–30 . . . . . . . . . . . . . . . . . . . . . . . .30–60 . . . . . . . . . . . . . . . . . . . . . . . .60+ . . . . . . . . . . . . . . . . . . . . . . . . . .

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proportion of listed HU’s (and GQE’s)that would be used for the alpha designthat still need to be used for the betadesign. Letsi andsi' be the optimalcluster sizes for the alpha and betadesigns, respectively. Theny andz areobtained by solving the set of equation

ki' = yi zi ki and

si' = zi si

The results of solving this system ofequations are shown intables 65and66.

Tables 67–70parallel tables 21–24showing persons in screened householby domain, retention rates for ‘‘other’’households by stratum, effective samplsizes by domain when all householdmembers are sampled, and effectivesample sizes by domain when just oneadult is sampled per household,respectively. These tables were derivedusing the same formulas and methodsdiscussed inchapter 5. As in chapter 5,the effective sample sizes reflect thereduced power of the sample (relativea simple random sample of the same

terms of placeholders)

Percent of block

< 5 5–10 10–

12 12 112 12 1

8 88 8

a option to be retained for the beta option

Percent of block t

< 5 5–10 10–3

83.3 100.0 62.562.5 75.0 42.988.2 75.0 42.951.3 50.0 34.3

S within strings retained for the beta option that are a

Percent of block that is

< 5 5–10 10–30

75 75 75100 100 100

67 67 67100 100 100

size) due to differential weights but notthe reduced power due to clustering.The effective sample sizes are bestcompared with an equiprobability desigthat is subject to the same clustering athe 1985–94 design of NHIS.

The total number of screenedhouseholds with cooperative occupantsis projected to be about 57,200 in theyear 2000 with a total of 144,000occupants of all ages, of whom 106,00will be 18 years and over. Thedistribution of these screenedhouseholds across the domains ofinterest is shown intable 67. The totalof 57,200 screened households is toomany to retain for full interviews givenan inflation-adjusted 1988 NHIS budgeAccording to the projection of ascreening cost equal to one-third of afull interview cost, the total number ofhouseholds that can be kept for fullinterviews is 41,400. This means thatonly 64.7 percent of the ‘‘other’’households discovered during screenincan be retained. The retention rates intable 68restore the sample for ‘‘other’’

that is Hispanic

30 30–60 60+

2 12 82 8 88 8 88 8 8

hat is Hispanic

0 30–60 60+

40.0 57.537.5 50.037.5 50.037.5 37.5

ctually to be screened for the beta

Hispanic

30–60 60+

100 100100 100100 100100 100

Table 67. Persons in screened households for the beta option



Male Female Total

Black

Under 5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 743 712 1,4545–17 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2,117 2,019 4,13618–24 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 785 919 1,70425–44 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2,125 2,695 4,82145–64 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1,363 1,760 3,12265 and over . . . . . . . . . . . . . . . . . . . . . . . . . . . 630 932 1,561Total . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7,762 9,036 16,798Total 18 and over . . . . . . . . . . . . . . . . . . . . . . . . 4,903 6,305 11,208Households . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6,192

Hispanic

Under 5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1,275 1,221 2,4965–17 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3,064 2,926 5,98918–24 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1,161 1,123 2,28425–44 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3,243 3,165 6,40845–64 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1,457 1,650 3,10765 and over . . . . . . . . . . . . . . . . . . . . . . . . . . . 525 736 1,261Total . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10,725 10,821 21,546Total 18 and over . . . . . . . . . . . . . . . . . . . . . . . . 6,386 6,674 13,060Households . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6,340

Other

Under 5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3,080 2,919 5,9995–17 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9,306 8,825 18,13118–24 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4,476 4,517 8,99325–44 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15,450 15,670 31,11945–64 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12,693 13,178 25,87165 and over . . . . . . . . . . . . . . . . . . . . . . . . . . . 6,265 9,055 15,320Total . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51,269 54,164 105,433Total 18 and over . . . . . . . . . . . . . . . . . . . . . . . . 38,883 42,419 81,302Households . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44,671

Total




Table 68. Percentage of other households discovered during screening to be kept for full interviews, beta option



< 5 5–10 10–30 30–60 60+

< 10 . . . . . . . . . . . . . . . . . . . . . . . . . 71.2 47.4 47.4 44.5 30.910–30 . . . . . . . . . . . . . . . . . . . . . . . . 71.2 47.4 47.4 47.4 35.630–60 . . . . . . . . . . . . . . . . . . . . . . . . 71.2 71.2 71.2 47.4 35.660+ . . . . . . . . . . . . . . . . . . . . . . . . . . 71.2 71.2 59.3 47.4 47.4


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Table 69. Effective sample sizes for beta design when all household members are sampled


All adults per household

Male Female Total

Black

Under 5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 719 689 1,4085–17 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2,049 1,955 4,00418–24 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 760 889 1,64925–44 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2,057 2,609 4,66745–64 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1,319 1,703 3,02365 and over . . . . . . . . . . . . . . . . . . . . . . . . . . . 610 902 1,511Total . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7,514 8,748 16,262Total 18 and over . . . . . . . . . . . . . . . . . . . . . . . . 4,746 6,104 10,850Households . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5,994

Hispanic

Under 5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1,179 1,129 2,3075–17 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2,831 2,704 5,53518–24 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1,073 1,038 2,11125–44 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2,997 2,925 5,92245–64 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1,346 1,525 2,87265 and over . . . . . . . . . . . . . . . . . . . . . . . . . . . 486 680 1,165Total . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9,912 10,001 19,913Total 18 and over . . . . . . . . . . . . . . . . . . . . . . . . 5,902 6,168 12,070Households . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5,859

Other


Total





households to an equiprobability basisThe design effects implied by theoversampling rates intable 62are 1.033,1.082, 1.000, and 1.095 for the black,Hispanic, ‘‘other,’’ and total populationsrespectively. Application of these desigeffects to the raw sample sizes intable 67and allowing for the randomsubsampling of ‘‘other’’ householdsleads to the effective sample sizesshown intable 69. These are the samplsizes that should be used to predictprecision for the core questionnaireitems that are asked of all household

members. For questionnaire items thaare administered to just one randomselected adult per household,table 70shows the effective sample sizes thatcan be used to predict precision.

Table 71contrasts the alpha andbeta options with both the old design(1988) and equiprobability samples.Note that under either option, theprecision for black and Hispanicestimates will be comparable to eachother. With the beta option, the effectivsample size for ‘‘others’’ drops by abo20 percent, much steeper than the

4-percent drop under the alpha option.The effective sample size for totalpopulation declines by about 10 percenunder the beta option, compared withabout a 10-percent improvement undethe alpha option. There are twoequiprobability designs shown in thetable. One consists of 47,000interviewed households (the same sizeas the 1988 NHIS) and the otherconsists of 50,000 interviewedhouseholds (the design that was thebasis of projections inchapter 4). Theeffective sample sizes for the black

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Table 70. Effective sample sizes for beta design when one adult is sampled per household



Male Female Total

Black

Under 5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .5–17 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .18–24 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 328 384 71225–44 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 888 1,126 2,01445–64 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 569 735 1,30565 and over . . . . . . . . . . . . . . . . . . . . . . . . . . . 263 389 652Total . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .Total 18 and over . . . . . . . . . . . . . . . . . . . . . . . . 2,048 2,635 4,683Households . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5,994

Hispanic

Under 5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .5–17 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .18–24 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 417 403 82025–44 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1,164 1,136 2,30045–64 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 523 592 1,11565 and over . . . . . . . . . . . . . . . . . . . . . . . . . . . 185 264 453Total . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .Total 18 and over . . . . . . . . . . . . . . . . . . . . . . . . 2,292 2,395 4,688Households . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5,859

Other

Under 5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .5–17 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .18–24 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1,337 1,349 2,68625–44 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4,615 4,681 9,29645–64 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3,792 3,937 7,72965 and over . . . . . . . . . . . . . . . . . . . . . . . . . . . 1,871 2,705 4,577Total . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .Total 18 and over . . . . . . . . . . . . . . . . . . . . . . . . 11,616 12,672 24,288Households . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28,902

Total

Under 5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .5–17 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .18–24 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1,942 1,993 3,93525–44 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6,203 6,460 12,66345–64 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4,517 4,874 9,39165 and over . . . . . . . . . . . . . . . . . . . . . . . . . . . 2,144 3,098 5,242Total . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .Total 18 and over . . . . . . . . . . . . . . . . . . . . . . . . 14,805 16,425 31,230




population declines under eitherequiprobability design relative to the1988 NHIS because of the oversamplinof the black population that was done inthe 1985–94 NHIS design. Because thaoversampling was not very effective, thedeclines in the black effective samplesize associated with a switch to anequiprobability design would be modestThe oversampling in the 1985–94 desigalso explains the increases in theeffective sample sizes for the ‘‘other’’and total populations under theequiprobability designs relative to the1988 NHIS. The strong increases in the

Hispanic effective sample sizes undethe equiprobability design are mostlydue to the projected growth of theAmerican Hispanic population betwee1988 and 2000. Finally, it is importanto remember the caveats about theprojections given inchapter 4. All theprojections are very sensitive toundercoverage rates for the black anHispanic populations, and it is wellestablished that these rates fluctuateconsiderably from year to year for noknown reason. Also, the projections othe growth of the Hispanic populationare quite sensitive to immigration

policies and economic conditions abroaand in the United States.

New ConstructionSampling

Sampling of permits for newresidential buildings is being done abouthe same for the 1995 design as for th1985 design. The permits carry noinformation on race or ethnicity offuture occupants. Furthermore, it isextremely difficult to map individualpermits into blocks defined for the 1990decennial census. The main reason for

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Table 71. Effective adult sample sizes for the year 2000 by redesign option and population domain relative to the 1988 NHIS design whenjust one adult is sampled per sample household

Budget Total Black Hispanic Other

1988 NHIS 95 35,600 4,500 2,200 30,900

Projections for the year 2000

Alpha option . . . . . . . . . . . . . . . . . . . . . . . . . . . 150 38,400 8,900 8,800 29,600Beta option . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95 31,230 4,700 4,700 24,300Equiprobability . . . . . . . . . . . . . . . . . . . . . . . . . . 95 39,500 4,100 3,000 32,400Equiprobability . . . . . . . . . . . . . . . . . . . . . . . . . . 100 41,600 4,300 3,100 34,100

Percent change from 1988

Alpha option . . . . . . . . . . . . . . . . . . . . . . . . . . . 150 +8% +97% +299% −4%Beta option . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95 −12% +4% +113% −21%Equiprobability . . . . . . . . . . . . . . . . . . . . . . . . . . 95 +11% −8% +35% +5%Equiprobability . . . . . . . . . . . . . . . . . . . . . . . . . . 100 +17% −3% +42% +10%

NOTE: Budget for 1988, adjusted for inflation, equals 95.


this difficulty is that much newconstruction takes place along newroads that did not exist at the lastcensus. Also, sometimes the permits aidentified in terms of legal language(e.g., plots, land grants) rather than bystreet addresses. Given that theoccupants of newly constructed housinunits tend to be disproportionately whitand not Hispanic, and given the highercost of screening permits, the decisionwas reached to sample permits at thesame rate as ‘‘other’’ housing is samplafter screening. This means that newconstruction permits will be sampled arather low rates and that any black orHispanic households that happen to befound there will have rather largeweights compared with other black andHispanic households in the sample.

Restoration of anEquiprobability Design atLevel Cost and OtherContingency Plans

Another option seriously considereas a contingency plan was to drop bacto an equiprobability sample that wouldcost the same as the 1988 survey afte

Table 72. Percent of sample originally selected tha


< 10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .10–30 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .30–60 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .60+ . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

adjustment for inflation. (The idea herewas an all-or-nothing approach onminority statistics.) One idea was tobring back dropped segments and thedo a uniform cut. Theoretically, thiswould work because (as discussed infifth section ofchapter 16) the U.S.Bureau of the Census initially selectedrandom clustered equiprobability5-percent sample of the universe. Mucof this sample in the lower minoritydensity strata was set aside as not toused. These ‘‘dropped’’ clusters arebeing reserved for NHIS use, butnothing is being done to make themready for quick use. During the courseof the redesign research, the U.S.Bureau of the Census estimated that2 years or more of lead time would berequired to restore any dropped clusteThis is a longer lead time than iscompatible with quick response tofunding contingencies. Thus, indesigning a contingency plan to restoran equiprobability design, it wasimportant to think of a way to do itwithout using the dropped clusters.

This is indeed possible. The key tothe feasibility of restoring anequiprobability sample without using thdropped clusters is the fact that the

t would be retained for an equiprobability design

Percent of bl

< 5 5–10 1

62.5 50.062.5 50.058.8 50.051.3 50.0

proposed redesign includes largeoversamples (relative to a design with50,000 completes) in all strata forscreening even after dropping initialclusters. In fact, after dropping clustersaccording to the alpha option, thesample is still 60 percent higher even inthe lowest minority density stratum thaunder the old design. The onlydrawback to not being able to bringback dropped clusters on short notice ithat the expected segment sizes will belarger than desired in several strata. Thretention rates for an equiprobabilitysample (designed to yield about 48,500completed household interviews) areshown intable 72.

Note that if the subsampling is donon the household level after listing inthe low-minority-density strata ratherthan subsampling whole segments, thevariation in segment size can be reducconsiderably. The drawbacks of such aprocedure would be decreased savingsin listing cost and increased complexityin sampling systems.Tables 73and74show retention rates at the segment anwithin-segment levels that wouldminimize the variation in segment size.

Projections of effective sample sizefor this plan are given intable 71. Note

ock that is Hispanic

0–30 30–60 60+

31.3 25.0 25.028.6 25.0 25.028.6 25.0 25.028.6 25.0 25.0

8

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Table 73. Percentage of strings to keep in conjunction with post-listing subsampling for equiprobability design



< 5 5–10 10–30 30–60 60+

< 10 . . . . . . . . . . . . . . . . . . . . . . . . . 100.0 100.0 62.5 37.5 25.010–30 . . . . . . . . . . . . . . . . . . . . . . . . 93.8 75.0 42.9 25.0 25.030–60 . . . . . . . . . . . . . . . . . . . . . . . . 88.2 75.0 42.9 25.0 25.060+ . . . . . . . . . . . . . . . . . . . . . . . . . . 51.3 50.0 28.6 25.0 25.0

Table 74. Percentage of listed HU’s to keep in retained strings in conjunction with string subsampling for equiprobability design



< 5 5–10 10–30 30–60 60+

< 10 . . . . . . . . . . . . . . . . . . . . . . . . . 62.5 50.0 50.0 66.7 100.010–30 . . . . . . . . . . . . . . . . . . . . . . . . 66.7 66.7 66.7 100.0 100.030–60 . . . . . . . . . . . . . . . . . . . . . . . . 66.7 66.7 66.7 100.0 100.060+ . . . . . . . . . . . . . . . . . . . . . . . . . . 100.0 100.0 100.0 100.0 100.0


that the effective sample size for blackpersons would drop relative to the 198design because they would not beoversampled. Effective sample sizes foHispanic persons would rise simplybecause of their projected populationgrowth.

By using the reduction panelsdescribed inchapter 16in the section on1995 panel construction, it is alsopossible to shrink the whole designproportionately.

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Chapter 18.Recommendations foContinuing Research

Intercensal Tracking ofMinority PopulationsAcross Density Strata

As the decade progresses, it will bpossible to use the NHIS to estimate tproportion of each domain living ineach minority density stratum by usingthe technique used to estimate the 198distributions (as described in ‘‘CostFunctions for NHIS and Implications foSurvey Design’’ (15)). Such trackingmight indicate that adjustments shouldbe made in the allocation of the samplacross the strata. These adjustments cbe made by adding and/or droppingsegments (within the 2.5 percentreserved for NHIS) and by adjustingpostscreening retention rates. A lag wioccur, probably of several years, inimplementing such adjustments. It isrecommended that estimates of theproportion of minorities in the densitystrata be made every year. Furthermorthese should not be viewed as done ofor research purposes but thatconsideration of modification ofsubsampling rates be performed atregular intervals.

Updating Minority DensityAreas

By the year 1998, the informationon the distribution of minorities bydensity strata was 8 years old. Datafrom the 2000 census will not beavailable at the block level for anotheryears. Even when available, it willprobably take several years to updatethe 1990 data about the blocks. Workcomparing 1980 and 1988 showedsignificant movement of black personsout of areas that had strong blackconcentrations in 1980. Many of thesemigrants’ new neighborhoods were alspredominantly black by 1988, althoughthey were mostly white in 1980. Similatrends may be expected for the 1990’sBlack persons are predicted to continu

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to migrate out of heavily segregatedinner-city neighborhoods into lesssegregated city and suburbanneighborhoods. Some white persons alikely to leave these neighborhoods; ifthey do, new predominantly blackneighborhoods will have formed. As thprocess continues, the oversample inold black neighborhoods will yieldsmaller and smaller black samples, anthe black persons who are found in thenew, formerly white, areas will havelarge weights, reducing precision forminorities.

A middecade census with acrosswalk to 1990 blocks would largelysolve this problem, but given the lowprobability of a mid-decade census,alternatives were sought to update theminority density of blocks, blockgroups, or even just tracts.Unfortunately, no viable alternativesare currently apparent. A studyperformed in a number of localjurisdictions found almost all of themdo not maintain data bases that woube helpful (35). The sole exceptionwas a school district involved incourt-ordered desegregation.

It would be useful to repeat thisstudy in 1998–99. It is possible thatlocal area administrative requirementswill make such data bases morecommon. They would permit updatingof the density strata. The cost of thiskind of research is quite modest.

Coverage ImprovementAs the research was underway, an

aspect of NHIS emerged that had notbeen previously identified as ahigh-priority concern. NHIS coveragerates for adult black males (wherecoverage is defined as the proportionthe census ‘‘adjusted’’ total populationthat is covered in the survey) appearsbe in the 60- to 75-percent range formost age groups. Coverage rates forHispanic persons are not computed onregular basis, but the U.S. Bureau of tCensus’ experience withCurrentPopulation Surveyindicates that blackand Hispanic male coverage rates areroughly comparable. Black femalecoverage is not as low as the male ratbut Current Population Surveyevidenceindicates that Hispanic female coverag

is probably not much different than thafor Hispanic males.

It is fairly obvious that the healthand social characteristics of theuncovered population are quite differenfrom those of the covered population.Furthermore, it is unlikely that theweighting procedures fully compensatefor the differences. The resulting biasthe NHIS estimates could be quiteserious, possibly more troubling than tbias due to accepting proxy response.NHIS research may have been focusetoo tightly on sampling error at theexpense of bias. Unfortunately, thisproblem is extremely difficult toalleviate. At a meeting of staff memberof NCHS, the U.S. Bureau of theCensus, and Westat, Inc., a number osuggestions were made for additionalresearch on the causes of theundercoverage. Westat, Inc., staff feltstrongly that enough basic research habeen done, however, and that it wastime to start testing methods ofchanging NHIS procedures, using somof the ideas that had been put forwardpreviously. The U.S. Bureau of theCensus subsequently started toimplement such research. Forpreliminary results, see ‘‘CoverageImprovement from ExperimentalResidence Questions’’ (36).

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16. Ezzati TM, Hoffman K, Judkins DR, etal. A dual-frame design for samplingelderly minorities and persons withdisabilities. Statistics in Medicine.14:571–83. 1995.

17. Passel JS, Word DL. Constructing thelist of Spanish surnames for the 1980census: An application of Bayes’Theorem. Paper presented at the annmeeting of the Population Associationof America. 1980.

18. Judkins D, Massey J, Smith V. Usingsurnames to oversample Hispanics froa list frame. Proceedings of the sectioon survey research methods. AmericaStatistical Association. 847–52. 1992.

19. Wright D, Judkins D, Ryaboy S.Residential segregation and healthcharacteristics of minority populations.In: Proceedings of the section on socistatistics. American StatisticalAssociation. 198–203. 1992.

20. Kish L. Survey sampling. New York:Wiley. 1965.

21. Brick JM, Waksberg J. Avoidingsequential sampling with random digitdialing. Survey Methodology.17:27–41. 1991.

22. Thornberry OT, Massey JT. Trends inUnited States telephone coverage acrtime and subgroups. In: Groves RM,Biemer PP, Lyberg LE, et al., eds.Telephone survey methodology. NewYork: Wiley. 1988.

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24. Schaible WL. A composite estimatorfor small area statistics. NationalInstitute on Drug Abuse ResearchMonograph. 24:36–62. 1978.

25. Marker DA. Small area estimation: ABayesian perspective (Unpublisheddoctoral dissertation). Ann Arbor,Michigan: University of Michigan.1995.

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26. Ericson W. Subjective Bayesian modein sampling finite populations. Journalof the Royal Statistical Society B.31:195–233. 1969.

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28. Gonzalez M, Waksberg J. Estimationthe error of synthetic estimates. Paperpresented at the first meeting of theInternational Association of SurveyStatisticians. Vienna, Austria:International Association of SurveyStatisticians. August 18–25, 1973.

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30. Panel on the National Health CareSurvey. Toward a national health caresurvey: A data system for the 21stcentury. Wunderlich GS, ed.Washington, DC: National AcademyPress. 1992.

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35. NHIS redesign memorandum number98 from D Malec to J Waksberg.Feasibility of updating geographicdistribution of minorities in postcensalyears. August 20, 1992.

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Appendix IList of the National Health Interview Survey Redesign Memorandums

No. Date To1 From Letter/Memo (if memo, subject)

1 October 3, 1989 Malec Judkins (Letter) Draft Specifications for NHIS Extract for Westat2 October 10 Malec Waksberg (Letter) Specifications for NHIS Extract for Westat3 October 17 Malec Westat Research Plan4 October 20 Malec Waksberg Notes on October 12 Meeting Regarding NHIS Redesign5 October 24 Malec Judkins Letter (Draft NHIS Variance Estimation)6 November 19 Malec Judkins Letter (NHIS Variance Estimation)7 November 29 Malec Waksberg Letter8 December 13 Malec Judkins Letter (Merge of NHIS and Payroll Files)9 December 13 Malec Waksberg Preliminary Analysis of Revisions in NHIS Sample Design Required for Subdomain Statistics

10 January 3, 1990 Malec Waksberg Preliminary Analysis of Revisions in NHIS Sample Design Required for State Statistics11 January 11 Malec Waksberg Numbering of Memoranda and Reports12 January 12 Malec Judkins Preliminary Report on Choice of First-Stage Probabilities13 January 16 Malec Waksberg Condition Items in NHIS14 January 16 Malec Judkins Preliminary Report on Network Sampling Application15 January 17 Malec Judkins Corrected Variance Specifications16 January 24 Malec Waksberg Further Analysis of Revision in NHIS Sample Design Required for Subdomain Statistics for

Blacks and Hispanics17 March 26 Malec Waksberg Households as Sampling Units for Subdomain Statistics in the NHIS18 March 28 Malec Waksberg Interim Report on Oversampling Techniques19 March 30 Malec Judkins Preliminary Report on Dual Frame Sampling with Medicare Lists20 April 6 Malec Waksberg NHIS Sample Size at Constant Cost When Screening is Necessary for Oversampling of

Subdomains21 April 30 Malec Waksberg Current Status of Westat’s NHIS Design Research22 June 7 Malec Judkins The limits of oversampling without screening23 June 12 Malec Judkins Research on Minimum Sample Size for Desired Reliability24 June 13 Malec Judkins Supplemental Report on Minimum Sample Size for Desired Reliability25 June 14 Malec Judkins Variance Decomposition and Multi-Year Estimation26 June 18 Malec Waksberg Oversampling of Hispanic Spanish Origin Subgroups27 June 18 Malec Judkins State Grouping28 June 21 Malec Judkins Required Sample Sizes by Age29 June 26 Malec Judkins NHIS Redesign Research — Minutes and Feedback to Requests from Meeting of May 8, 199030 June 26 Massey Waksberg Census Bureau’s Cost Estimates for Using Designated Respondents within Households in

NHIS31 July 3 Malec Marker Hispanic Estimates by Geographic Area32 July 9 Malec Judkins NHIS Redesign Research — Comparison of Oversampling with Screening versus

Oversampling by Itself33 July 12 Malec Waksberg First Draft of Executive Summary of NHIS Redesign Research Report34 July 19 Malec Waksberg Comparison of Sampling Plans that Produce Acceptable Race/Ethnicity Data for NHIS35 July 26 Malec Judkins More Thoughts on Oversampling Procedures36 August 6 Malec Judkins Revisions to Table in Memoranda 22, 32, and 3337 August 23 Malec Judkins Revised Report on Network Sampling Applications38 September 25 Malec Judkins Variation in Segment Size39 September 25 Malec Judkins Comparison of Oversampling Strategies for Several Cost Assumptions40 September 26 Malec Judkins Draft report on variance estimation attached41 October 19 Malec Waksberg Robustness of Race/Ethnic Stratification Based on Decennial Census Data42 November 7 Malec Marker Model-Based Hispanic Estimates by Geographic Area43 October 30 Malec Judkins Draft Report on Subdomain and Design-Based Small Area Estimates44 November 29 Malec Judkins Oversampling Implications Tables45 December 12 Malec Waksberg Unlocatable Building Permit Records46 December 19 Malec Judkins Costs of Using HCFA Lists46A December 19 Malec Marker Subnational Research Plan for FY 9147 December 26 Malec Wright (David) Master Area Reference File48 January 11, 1991 Massey Waksberg Sample Sizes, Screening Levels and Relative Costs of Alternative Sample Designs in Your

December 28 Memo48R January 4, 1991 Massey Waksberg Sample Sizes, Screening Levels and Relative Costs of Alternative Sample Designs in Your

December 28 Memo49 January 11, 1991 Massey Waksberg Information Requested in Your December 28 Memo that Was Not Covered in Our Memo

Number 4849R January 8, 1991 Massey Waksberg Information Requested in Your December 28 Memo that Was Not Covered in Our Memo

Number 48

1The normal distribution list is: Westat, Inc.: Joe Waksberg, Mansour Fahimi, David Judkins, David Marker, and David Wright; CODA: Doris Northrup; NCHS: Dr. JamesMassey (2 copies), Dr. Owen Thornberry, Dr. Monroe Sirken, and Tommy McLemore; Census Bureau: Thomas Moore; and one copy for the chronological file. (Listing goesonly to J.M., J.W., and chronological File.)



50 February 14, 1991 Malec Judkins Design for a 50% Increase in Budget Authority50R February 14, 1991 Malec Judkins NHIS Redesign Research Design for 50% Increase in Budget Authority50A February 15, 1991 Massey Judkins Brief Description of Current Projects51 February 19, 1991 Massey Waksberg Use of Nonhousehold Respondent in NHANES III52 February 19, 1991 Malec Marker Comparison of Model-Based Estimators for States53 March 12, 1991 Malec Fahimi/Judkins Simulation Study on First-Stage Probabilities54 March 27, 1991 Massey Judkins Components of Variance for Minority and Nonmetropolitan Statistics55 April 20, 1991 Massey Judkins Integrated Survey Design56 May 1, 1991 Massey Marker Area Sample-Based and RDD Supplemented State Level Estimates56R June 5, 1991 Malec Marker, Waksberg Area Sample-Based and RDD Supplemented, State-Level Estimates57 May 14, 1991 Massey Waksberg Effect of Oversampling PSU’s with High Proportions of Blacks or on Rural Statistics58 May 30, 1991 Massey Waksberg, Wright,

JudkinsRobustness of Distribution of the Population by Minority Density and Effect on Sample DesignsWhich Oversample Strata with High Minority Density

59 June 3, 1991 Massey Waksberg Additional Tasks on NHIS Research60 June 12, 1991 Massey Waksberg Current Status of Tasks Listed in the Contract61 June 12, 1991 Massey Judkins/Searls Univariate Exploration of Health Characteristics by Minority Density Stratum62 June 14, 1991 Massey Waksberg Cost Estimates for Random Digit Dialing Surveys to Supplement NHIS63 June 19, 1991 Massey Judkins Allocation of Strata and PSU’s given State Stratification64 July 17, 1991 Massey Judkins Use of Phone Number for Medicare Supplement65 July 17, 1991 Massey Judkins Sensitivity and Specificity of SSA Indicators for Institutionalization66 August 2, 1991 Massey Judkins NHIS Redesign Research — Revised Sampling Parameters67 October 8, 1991 Massey Marker Precision of RDD Supplementation for State Level Age/Sex Estimates68 October 9, 1991 Massey Judkins NHIS Redesign Research — Further Results of PL-94 Tabulations69 October 14, 1991 Massey Judkins Decomposition of NHDS Variances70 October 17, 1991 Massey Judkins NHIS Redesign — Census Screening System71 November 12, 1991 Massey Marker Comparison of Stand-Alone RDD with Unbiased Dual Frame Estimators72 November 14, 1991 Massey Waksberg Oversampling Density Strata for Statistics on Asian and American Indian Subdomains73 December 9, 1991 Massey Marker Comparison of stand-alone RDD with biased dual frame estimators74 December 20, 1991 Moore Judkins NHIS Redesign — Sample Selection Specifications for the Medicare Pilot Study75 January 10, 1992 Massey Judkins/Edmonds Documentation of Variance Estimation Software76 January 15, 1992 Massey Göksel Specifications for the Data File to Analyze Measures of Size for the NCHS77 February 3, 1992 Massey Judkins Restoration of an Equi-Probability Sample78 February 4, 1992 Massey Judkins Within Household Sampling Rules78R March 4, 1993 Massey Judkins Within Household Sampling Rules (Revised)79 February 19, 1992 Massey Marker Comparison of Stand-alone RDD with Dual Frame Estimators80 February 21, 1992 Massey Judkins New Construction Sampling81 February 25, 1992 Massey Wright/Judkins Logistic Regressions on Health Characteristics of Blacks and Hispanics by Minority 82 Density

Strata82 March 6, 1992 Massey Judkins Questionnaire Implications of SSA Sampling83 April 20, 1992 Malec Marker Detailed plan for empirical comparison of small area estimators84 April 27, 1992 Thornberry Marker Comments on the Interviewer Assignment Period for the 1995 NHIS85 May 29, 1992 Massey Göksel The correlations among the health care service based and population based measures of size

for NHIS86 June 2, 1992 Massey Judkins NHIS Redesign Research—Interim Report on the Use of List of Surnames to Oversample

Hispanics87 June 11, 1992 Massey Judkins NHIS Redesign: Variance Implications of a Macro-level Incorporation of the SSA Disabled List

Sample88 June 11, 1992 Massey Judkins Comments on Third Working Draft of UCF Specifications89 June 24, 1992 Massey Waksberg Current Status of Westat’s Research on ISD Research Contract90 July 7, 1992 Massey Judkins NHIS Redesign: Utility of Hispanic Surname List for Elderly91 July 31, 1992 Massey Waksberg Interim Report on Oversampling Persons Whose Income Is Below the Poverty Level92 July 8, 1992 Malec Edmonds Gibbs Sampler Software Research93 July 15, 1992 Massey Judkins Interviewing Other Household Members in the 1992 SSA List Pilot for NHIS94 July 15, 1992 Massey Judkins Dual-Frame Estimation for 1992 Pilot95 July 22, 1992 Massey Judkins Construction of Panels for the 1995 NHIS Design96 July 23, 1992 Massey Waksberg NHIS Coverage Research97 July 31, 1992 Massey Göksel Variance Estimation for NAMCS98 August 20, 1992 Malec Waksberg Feasibility of Updating Geographic Distribution of Minorities in Postcensal Years99 August 28, 1992 Sirken Judkins Decisions from Meeting of July 30, 1992, on NHIS Panel Formation

100 August 31, 1992 Malec Marker Update on Empirical Comparison of Model-Based Estimators101 September 8, 1992 Malec Judkins Assistance on Preparation of Sample Size Tables for Questionnaire Design102 September 23, 1992 Shimizu Judkins Network Sampling of Health Care Events Based upon the National Health Interview Survey103 October 5, 1992 Hoffman Judkins Comments on Draft Analytic Plan for SSA List Sample Pilot104 October 6, 1992 Malec Judkins Possible Benefits of a Mid-Design Update to the Minority Density Strata105 October 12, 1992 Sirken Judkins NHIS Redesign: Improvement Due to Screening and Equivalency to Multi-Year Reuse

1The normal distribution list is: Westat, Inc.: Joe Waksberg, Mansour Fahimi, David Judkins, David Marker, and David Wright; CODA: Doris Northrup; NCHS: Dr. JamesMassey (2 copies), Dr. Owen Thornberry, Dr. Monroe Sirken, and Tommy McLemore; Census Bureau: Thomas Moore; and one copy for the chronological file. (Listing goesonly to J.M., J.W., and chronological File.)



106 October 20, 1992 Sirken Judkins NHIS Redesign: Oversampling/Screening Designs for a 15-Percent Increase in BudgetAuthority

107 October 27, 1992 Massey Waksberg Updating Minority Density Areas108 November 12, 1992 Sirken Judkins NHIS Redesign: Changes in Effective Sample Sizes for Several Designs Options relative to the

1985–94 Design109 January 25, 1993 Massey Judkins County-Level Clustering for Surveys of Health Care Facilities109R February 16, 1993 Massey Judkins County-Level Clustering for Surveys of Health Care Facilities110 December 3, 1992 Massey Judkins NHIS Redesign: Changes in Effective Sample Sizes for Several More Design Options Relative

to the 1985–94 Design111 December 7, 1992 Massey Waksberg Undercoverage in Sample Household Surveys112 February 8, 1993 Massey Judkins NHIS Redesign: Draft Outline for Final Report113 February 16, 1993 Massey Judkins Comments by Daniel Horvitz on Memorandum #102114 February 26, 1993 Massey Judkins Tables on Benefits of SSA Sample of Disabled Beneficiaries102R March 1, 1993 Massey Judkins Network Sampling of Health Care Events Based upon the National Health Interview115 August 5, 1993 Massey Göksel Variance Estimation for NAMCS Report, cover letter116 August 17, 1993 Massey Waksberg Distribution of Poverty in Census Block Groups and Implications for Sample Design117 December 6, 1993 Milliken Judkins Draft NHIS Neighbor Screening Plan118 September 20, 1994 Moore Judkins Corrections to Specifications for the 1995 NHIS119 September 27, 1994 Moore Judkins Revised Retention Rates for the 1995 NHIS

1The normal distribution list is: Westat, Inc.: Joe Waksberg, Mansour Fahimi, David Judkins, David Marker, and David Wright; CODA: Doris Northrup; NCHS: Dr. James Massey (2 copies), Dr. OwenThornberry, Dr. Monroe Sirken, and Tommy McLemore; Census Bureau: Thomas Moore; and one copy for the chronological file. (Listing goes only to J.M., J.W., and chronological File.)


Appendix IINational Health Interview Survey Estimates and Coefficients of Variation

Age Category Male Female

Subdomain

Black notHispanic

Other notHispanic Hispanic Asian Not Asian

Substantive variable: Number of chronic arthritis conditions

Aged under 5 years . . . . . . Estimate 10,834.15 0.00 0.00 10,834.15 0.00 0.00 10,834.15cv (%) 100.42 0.00 0.00 100.42 0.00 0.00 100.42

Aged 5 to 17 years . . . . . . Estimate 20,936.29 102,472.95 2,989.95 88,779.63 31,639.65 0.00 123,409.24cv (%) 72.06 36.06 110.05 32.99 85.01 0.00 32.25

Aged 18 to 24 years . . . . . Estimate 185,408.77 312,945.07 57,570.42 428,290.51 12,492.91 0.00 498,353.84cv (%) 28.64 20.29 33.17 19.97 99.51 0.00 18.67

Aged 25 to 44 years . . . . . Estimate 1,704,849.19 2,992,162.51 462,040.58 3,843,956.66 391,014.45 24,536.29 4,672,475.41cv (%) 9.19 5.78 13.80 5.35 17.75 71.47 5.11

Aged 45 to 64 years . . . . . Estimate 4,183,376.07 7,471,621.61 1,114,563.03 10,078,130.83 462,303.82 122,063.96 11,532,933.72cv (%) 4.99 3.85 8.86 3.42 15.44 31.80 3.31

Aged 65 years or older . . . . Estimate 4,467,372.24 8,933,255.70 1,067,112.78 12,030,460.38 303,054.77 74,758.73 13,325,869.20cv (%) 4.72 3.76 8.75 3.52 23.39 48.58 3.31

Total . . . . . . . . . . . . . . . Estimate 10,572,776.71 19,812,457.84 2,704,276.77 26,480,452.17 1,200,505.61 221,358.98 30,163,875.56cv (%) 2.87 2.54 5.24 2.30 10.90 24.08 2.19

Age . . . . . . . . . . . . . . . . Category Northeast Midwest South West New England Mid-Atlantic East North Central

Aged under 5 years . . . . . . Estimate 0.00 0.00 10,834.15 0.00 0.00 0.00 0.00cv (%) 0.00 0.00 100.42 0.00 0.00 0.00 0.00

Aged 5 to 17 years . . . . . . Estimate 32,556.67 24,084.00 45,325.06 21,443.51 21,342.66 11,214.01 2,989.95cv (%) 55.18 103.70 45.66 70.41 71.02 87.10 110.05

Aged 18 to 24 years . . . . . Estimate 90,312.81 117,621.35 153,598.07 136,821.61 12,749.08 77,563.72 93,995.48cv (%) 30.80 33.42 29.18 40.28 93.92 35.07 38.30

Aged 25 to 44 years . . . . . Estimate 942,920.08 1,211,535.27 1,614,182.24 928,374.10 336,063.52 606,856.56 900,800.67cv (%) 12.38 9.10 9.00 13.36 26.65 15.06 11.08

Aged 45 to 64 years . . . . . Estimate 2,241,755.71 3,160,059.56 4,285,532.46 1,967,649.95 463,956.43 1,777,799.28 2,402,975.30cv (%) 7.57 7.24 6.55 7.61 21.64 9.45 9.48

Aged 65 years or older . . . . Estimate 2,742,113.80 3,636,458.13 4,917,022.86 2,105,033.14 709,838.86 2,032,274.94 2,696,136.12cv (%) 6.93 6.38 6.26 8.22 14.83 8.18 7.98

Total . . . . . . . . . . . . . . . Estimate 6,049,659.07 8,149,758.31 11,026,494.85 5,159,322.31 1,543,950.55 4,505,708.52 6,096,897.52cv (%) 4.75 3.85 3.97 5.33 14.69 6.17 5.30

Age . . . . . . . . . . . . . . . . Category West North Central South Atlantic East South Central West South Central Mountain Pacific Total

Aged under 5 years . . . . . . Estimate 0.00 0.00 0.00 10,834.15 0.00 0.00 10,834.15cv (%) 0.00 0.00 0.00 100.42 0.00 0.00 100.42

Aged 5 to 17 years . . . . . . Estimate 21,094.04 22,659.13 0.00 22,665.93 21,443.51 0.00 123,409.24cv (%) 116.94 72.33 0.00 56.20 70.41 0.00 32.25

Aged 18 to 24 years . . . . . Estimate 23,625.87 101,341.64 13,338.67 38,917.76 58,444.31 78,377.30 498,353.84cv (%) 66.51 35.79 101.16 59.05 53.65 49.02 18.67

Aged 25 to 44 years . . . . . Estimate 310,734.60 560,452.09 306,884.03 746,846.13 236,257.40 692,116.70 4,697,011.70cv (%) 21.90 15.87 22.71 14.57 25.39 14.87 5.11

Aged 45 to 64 years . . . . . Estimate 757,084.26 2,013,710.77 922,738.94 1,349,082.75 509,677.94 1,457,972.01 11,654,997.68cv (%) 16.23 10.23 21.37 14.03 19.66 10.86 3.22

Aged 65 years or older . . . . Estimate 940,322.01 2,303,120.77 1,059,233.05 1,554,669.04 646,225.28 1,458,807.86 13,400,627.93cv (%) 13.04 12.19 14.31 12.95 20.29 9.46 3.29

Total . . . . . . . . . . . . . . . Estimate 2,052,860.79 5,001,284.41 2,302,194.69 3,723,015.76 1,472,048.44 3,687,273.87 30,385,234.54cv (%) 12.09 8.89 12.69 11.02 14.03 7.39 2.15



Subdomain

Black notHispanic


Substantive variable: Number of acute digestive system conditions

Aged under 5 years . . . . . . Estimate 1,516,847.23 540,828.34 308,811.96 1,247,309.73 501,553.88 0.00 2,057,675.57cv (%) 22.01 27.00 44.33 19.84 46.42 0.00 17.40

Aged 5 to 1 years . . . . . . . Estimate 1,920,859.80 1,830,977.12 568,326.74 2,810,454.68 373,055.50 183,761.45 3,568,075.47cv (%) 15.11 19.63 28.24 14.95 57.91 61.36 12.30

Aged 18 to 24 years . . . . . . Estimate 865,943.43 835,393.36 453,038.64 1,085,033.40 163,264.75 54,640.27 1,646,696.51cv (%) 26.72 22.64 30.41 21.64 53.71 99.10 17.34

Aged 25 to 44 years . . . . . . Estimate 1,367,065.02 2,675,392.92 955,278.74 2,882,052.71 205,126.50 0.00 4,042,457.94cv (%) 20.76 15.25 23.59 15.09 50.84 0.00 12.09

Aged 45 to 64 years . . . . . . Estimate 552,387.05 1,318,405.62 174,859.12 1,587,611.76 108,321.79 103,691.29 1,767,101.39cv (%) 31.39 18.72 50.71 18.40 70.59 71.34 16.61

Aged 65 years or older . . . . Estimate 750,600.42 1,115,956.27 224,480.32 1,345,371.22 296,705.15 0.00 1,866,556.69cv (%) 28.53 23.45 52.74 20.76 52.73 0.00 17.99

Total . . . . . . . . . . . . . . . Estimate 6,973,702.97 8,316,953.63 2,684,795.51 10,957,833.50 1,648,027.58 342,093.01 14,948,563.58cv (%) 10.00 8.44 11.91 7.74 29.29 42.64 6.78


Aged under 5 years . . . . . . Estimate 174,007.40 660,931.80 762,888.36 459,848.02 49,837.06 124,170.34 567,035.82cv (%) 52.10 37.50 28.41 36.44 99.77 60.55 40.53

Aged 5 to 17 years . . . . . . Estimate 508,359.98 913,315.35 1,458,410.70 871,750.89 93,690.31 414,669.67 626,789.38cv (%) 31.30 29.43 16.58 28.79 64.51 35.49 36.98




Aged 65 years or older . . . . Estimate 411,102.33 214,092.39 891,701.57 349,660.41 0.00 411,102.33 64,588.35cv (%) 48.04 46.51 24.95 27.98 0.00 48.04 71.87

Total . . . . . . . . . . . . . . . Estimate 2,624,574.96 3,485,301.08 6,118,850.36 3,061,930.19 814,582.75 1,809,992.21 2,354,355.97cv (%) 14.01 16.00 10.23 15.71 23.35 17.85 20.55


Aged under 5 years . . . . . . Estimate 93,895.98 198,849.70 133,896.93 430,141.73 99,251.95 360,596.07 2,057,675.57cv (%) 99.42 50.43 55.59 41.35 70.40 42.88 17.40

Aged 5 to 17 years . . . . . . Estimate 286,525.97 452,778.38 374,560.87 631,071.45 137,701.16 734,049.73 3,751,836.92cv (%) 41.11 35.04 56.55 34.02 56.94 31.64 12.70




Aged 65 years or older . . . . Estimate 149,504.04 536,324.46 106,945.02 248,432.09 183,360.34 166,300.07 1,866,556.69cv (%) 58.91 30.98 70.13 51.93 49.92 61.82 17.99

Total . . . . . . . . . . . . . . . Estimate 1,130,945.11 2,200,738.79 1,379,969.75 2,538,141.83 937,874.96 2,124,055.23 15,290,656.60cv (%) 21.58 19.04 31.24 18.68 24.56 19.56 6.87



Subdomain

Black notHispanic


Substantive variable: Number of acute bronchitis conditions

Aged under 5 years . . . . . . Estimate 1,132,721.10 932,992.47 174,278.96 1,681,713.59 209,721.03 0.00 2,065,713.57cv (%) 22.35 23.35 50.24 20.16 41.04 0.00 16.46

Aged 5 to 17 years . . . . . . Estimate 863,924.92 939,198.84 230,337.38 1,480,137.09 92,649.29 0.00 1,803,123.76cv (%) 23.77 24.94 46.15 19.16 62.65 0.00 17.64


Aged 25 to 44 years . . . . . . Estimate 726,719.19 1,313,878.85 198,576.82 1,842,021.22 0.00 0.00 2,040,598.04cv (%) 29.77 21.24 48.82 16.91 0.00 0.00 15.69


Aged 65 years or older . . . . Estimate 604,458.85 349,479.40 0.00 953,938.25 0.00 0.00 953,938.25cv (%) 35.16 36.86 0.00 27.12 0.00 0.00 27.12

Total . . . . . . . . . . . . . . . Estimate 3,723,366.82 4,358,036.99 665,737.40 7,023,891.50 391,774.91 0.00 8,081,403.81cv (%) 11.82 10.42 29.54 8.24 33.03 0.00 7.77


Aged under 5 years . . . . . . Estimate 307,542.36 598,984.08 717,203.55 441,983.59 49,837.06 257,705.30 458,693.50cv (%) 38.13 32.18 27.15 27.27 102.98 40.64 39.54



Aged 25 to 44 years . . . . . . Estimate 347,465.61 1,040,638.57 343,083.46 309,410.40 51,555.03 295,910.58 586,994.94cv (%) 41.49 22.84 39.53 33.61 99.11 45.45 27.21

Aged 45 to 64 years . . . . . . Estimate 202,146.32 260,811.67 49,327.30 250,947.04 0.00 202,146.32 163,370.52cv (%) 51.08 44.90 101.54 44.78 0.00 51.08 57.67


Total . . . . . . . . . . . . . . . Estimate 1,188,777.72 2,642,229.77 2,267,118.57 1,983,277.76 201,549.25 987,228.47 1,772,802.14cv (%) 20.70 14.76 17.43 17.96 48.50 22.96 17.87


Aged under 5 years . . . . . . Estimate 140,290.58 140,473.88 242,401.58 334,328.09 91,121.73 350,861.85 2,065,713.57cv (%) 70.85 58.70 35.88 47.01 69.85 29.01 16.46


Aged 18 to 24 years . . . . . . Estimate 0.00 55,451.51 0.00 178,140.88 0.00 56,120.14 454,797.86cv (%) 0.00 105.88 0.00 58.10 0.00 99.80 35.79

Aged 25 to 44 years . . . . . . Estimate 453,643.63 233,637.93 0.00 109,445.53 96,819.49 212,590.91 2,040,598.04cv (%) 44.82 46.61 0.00 73.03 70.79 50.34 15.69

Aged 45 to 64 years . . . . . . Estimate 97,441.14 0.00 0.00 49,327.30 48,694.13 202,252.91 763,232.33cv (%) 71.30 0.00 0.00 101.54 101.42 50.00 25.97

Aged 65 years or older . . . . Estimate 0.00 157,907.61 0.00 201,021.17 47,540.24 241,149.11 953,938.25cv (%) 0.00 57.71 0.00 50.44 101.31 65.02 27.12

Total . . . . . . . . . . . . . . . Estimate 869,427.62 736,892.24 344,388.95 1,185,837.37 525,222.32 1,458,055.44 8,081,403.81cv (%) 35.44 35.64 38.90 23.40 33.18 20.97 7.77



Subdomain

Black notHispanic


Substantive variable: Number of acute urinary conditions

Aged under 5 years . . . . . . Estimate 49,837.06 201,295.03 0.00 251,132.09 0.00 0.00 251,132.09cv (%) 100.69 49.98 0.00 44.91 0.00 0.00 44.91

Aged 5 to 17 years . . . . . . Estimate 0.00 319,296.08 88,479.72 230,816.36 0.00 0.00 319,296.08cv (%) 0.00 38.68 73.83 44.73 0.00 0.00 38.68


Aged 25 to 44 years . . . . . . Estimate 587,183.97 2,134,905.33 377,161.26 2,081,547.32 263,380.73 49,899.27 2,672,190.03cv (%) 37.06 15.80 34.88 17.20 44.48 99.71 14.91

Aged 45 to 64 years . . . . . . Estimate 444,449.11 974,334.29 72,700.28 1,346,083.12 0.00 0.00 1,418,783.40cv (%) 33.31 23.99 74.84 20.44 0.00 0.00 19.78

Aged 65 years or older . . . . Estimate 444,834.40 807,739.79 138,542.87 1,051,811.77 62,219.54 0.00 1,252,574.19cv (%) 35.96 23.26 48.54 22.00 102.89 0.00 18.86

Total . . . . . . . . . . . . . . . Estimate 1,609,699.47 5,237,013.08 758,311.31 5,649,781.24 438,620.00 49,899.27 6,796,813.28cv (%) 21.15 10.84 23.24 10.65 38.96 99.71 10.02


Aged under 5 years . . . . . . Estimate 58,172.05 96,937.60 46,185.39 49,837.06 0.00 58,172.05 51,115.62cv (%) 99.37 69.27 101.34 100.69 0.00 99.37 99.21

Aged 5 to 17 years . . . . . . Estimate 88,479.72 0.00 135,191.30 95,625.05 0.00 88,479.72 0.00cv (%) 73.83 0.00 57.64 71.04 0.00 73.83 0.00




Aged 65 years or older . . . . Estimate 0.00 298,918.98 612,170.27 341,484.94 0.00 0.00 157,433.20cv (%) 0.00 47.06 24.62 37.48 0.00 0.00 59.42

Total . . . . . . . . . . . . . . . Estimate 869,451.67 1,941,814.83 2,868,121.47 1,167,324.59 208,292.07 661,159.60 1,223,287.78cv (%) 24.97 17.39 14.41 20.08 44.58 32.12 20.27


Aged under 5 years . . . . . . Estimate 45,821.98 0.00 46,185.39 0.00 0.00 49,837.06 251,132.09cv (%) 96.52 0.00 101.34 0.00 0.00 100.69 44.91

Aged 5 to 17 years . . . . . . Estimate 0.00 89,982.98 0.00 45,208.32 51,177.33 44,447.73 319,296.08cv (%) 0.00 70.56 0.00 99.14 100.36 100.32 38.68



Aged 45 to 64 years . . . . . . Estimate 207,258.01 236,053.19 0.00 258,767.82 49,235.51 0.00 1,418,783.40cv (%) 49.67 47.35 0.00 43.82 100.27 0.00 19.78

Aged 65 years or older . . . . Estimate 141,485.79 204,100.43 144,310.85 263,758.98 139,487.22 201,997.72 1,252,574.19cv (%) 74.36 45.11 39.81 40.26 57.76 49.35 18.86

Total . . . . . . . . . . . . . . . Estimate 718,527.05 1,209,532.57 471,564.28 1,187,024.62 340,689.22 826,635.38 6,846,712.55cv (%) 28.48 22.96 39.15 24.64 37.67 26.01 10.00



Subdomain

Black notHispanic


Substantive variable: Number of chronic color blindness

Aged under 5 years . . . . . . Estimate 0.00 0.00 0.00 0.00 0.00 0.00 0.00cv (%) 0.00 0.00 0.00 0.00 0.00 0.00 0.00



Aged 25 to 44 years . . . . . . Estimate 1,011,047.67 94,985.16 42,279.77 978,638.48 85,114.58 26,619.79 1,079,413.04cv (%) 10.65 33.58 55.55 10.79 42.11 70.74 10.47


Aged 65 years or older . . . . Estimate 309,106.73 52,550.96 14,983.71 346,673.97 0.00 0.00 361,657.69cv (%) 19.06 45.98 70.01 17.52 0.00 0.00 17.05

Total . . . . . . . . . . . . . . . Estimate 2,529,741.27 221,907.97 94,593.07 2,520,956.15 136,100.02 38,915.68 2,712,733.56cv (%) 7.08 23.34 33.12 7.17 29.70 57.68 6.97







Aged 65 years or older . . . . Estimate 131,820.55 115,353.27 45,505.75 68,978.12 35,697.90 96,122.65 93,819.43cv (%) 30.64 31.41 50.01 40.51 57.08 36.19 34.93

Total . . . . . . . . . . . . . . . Estimate 664,648.63 964,503.32 583,813.38 538,683.91 161,886.40 502,762.23 691,853.62cv (%) 12.03 11.41 17.06 15.10 27.37 14.37 12.91



Aged 5 to 17 years . . . . . . Estimate 46,005.54 22,949.32 0.00 0.00 56,502.03 53,544.54 371,915.28cv (%) 50.48 70.34 0.00 0.00 44.41 58.94 16.99

Aged 18 to 24 years . . . . . . Estimate 62,927.40 11,849.17 0.00 37,875.13 25,665.47 23,858.27 229,325.04cv (%) 62.06 100.45 0.00 52.18 71.24 68.22 27.17



Aged 65 years or older . . . . Estimate 21,533.84 34,466.02 0.00 11,039.73 33,202.75 35,775.36 361,657.69cv (%) 72.01 57.22 0.00 101.13 58.40 56.96 17.05

Total . . . . . . . . . . . . . . . Estimate 272,649.69 281,521.51 121,197.98 181,093.90 253,628.84 285,055.07 2,751,649.24cv (%) 27.85 27.01 33.11 25.32 23.77 20.82 6.87



Subdomain

Black notHispanic


Substantive variable: Number of chronic epilepsy conditions

Aged under 5 years . . . . . . Estimate 54,167.69 10,658.17 8,326.56 33,322.19 23,177.11 0.00 64,825.86cv (%) 44.47 100.01 104.12 56.63 70.05 0.00 40.54





Aged 65 years or older . . . . Estimate 56,905.22 59,396.44 22,686.32 58,557.18 35,058.16 0.00 116,301.66cv (%) 35.34 44.73 58.03 39.34 58.11 0.00 28.89



Aged under 5 years . . . . . . Estimate 23,177.11 13,084.48 28,564.27 0.00 0.00 23,177.11 13,084.48cv (%) 70.05 99.66 55.81 0.00 0.00 70.05 99.66


Aged 18 to 24 years . . . . . . Estimate 36,733.13 30,836.97 0.00 11,944.75 0.00 36,733.13 20,194.85cv (%) 74.51 60.24 0.00 97.13 0.00 74.51 75.13






Aged under 5 years . . . . . . Estimate 0.00 8,326.56 13,448.12 6,789.59 0.00 0.00 64,825.86cv (%) 0.00 104.12 89.17 92.43 0.00 0.00 40.54

Aged 5 to 17 years . . . . . . Estimate 0.00 11,214.93 23,013.69 29,619.73 0.00 10,661.35 86,471.19cv (%) 0.00 104.51 70.71 55.24 0.00 102.01 33.08

Aged 18 to 24 years . . . . . . Estimate 10,642.12 0.00 0.00 0.00 11,944.75 0.00 79,514.85cv (%) 98.62 0.00 0.00 0.00 97.13 0.00 43.97



Aged 65 years or older . . . . Estimate 0.00 19,042.88 27,415.07 12,171.18 11,125.20 11,286.53 116,301.66cv (%) 0.00 73.19 59.97 80.84 42.73 99.48 28.89




Subdomain

Black notHispanic


Substantive variable: Number of moving motor vehicle injuries

Aged under 5 years . . . . . . Estimate 0.00 91,643.96 0.00 91,643.96 0.00 0.00 91,643.96cv (%) 0.00 99.89 0.00 99.89 0.00 0.00 99.89


Aged 18 to 24 years . . . . . . Estimate 737,646.81 432,467.37 91,416.57 853,135.17 225,562.44 0.00 1,170,114.18cv (%) 37.76 34.30 67.82 34.40 50.45 0.00 28.03

Aged 25 to 44 years . . . . . . Estimate 769,740.68 774,975.18 224,596.54 1,158,288.58 161,830.73 101,395.75 1,443,320.10cv (%) 31.41 26.45 50.96 24.27 61.67 100.30 20.83


Aged 65 years or older . . . . Estimate 203,736.01 109,345.86 48,149.92 208,638.38 56,293.58 0.00 313,081.87cv (%) 46.82 70.54 62.72 50.23 99.49 0.00 46.07

Total . . . . . . . . . . . . . . . Estimate 2,055,956.60 2,104,302.26 467,830.89 3,139,481.21 552,946.77 150,089.88 4,010,168.98cv (%) 20.37 16.62 32.59 16.16 32.18 75.26 13.91


Aged under 5 years . . . . . . Estimate 0.00 0.00 0.00 91,643.96 0.00 0.00 0.00cv (%) 0.00 0.00 0.00 99.89 0.00 0.00 0.00

Aged 5 to 17 years . . . . . . Estimate 97,230.71 46,289.09 366,611.44 97,260.70 45,547.58 51,683.12 0.00cv (%) 65.72 100.48 33.73 70.86 83.53 99.87 0.00




Aged 65 years or older . . . . Estimate 108,160.19 0.00 101,202.21 103,719.48 0.00 108,160.19 0.00cv (%) 99.77 0.00 59.39 71.70 0.00 99.77 0.00

Total . . . . . . . . . . . . . . . Estimate 806,260.56 560,567.74 1,553,798.49 1,239,632.08 356,651.38 449,609.18 457,383.87cv (%) 30.90 31.94 22.18 23.29 44.47 39.64 34.62


Aged under 5 years . . . . . . Estimate 0.00 0.00 0.00 0.00 91,643.96 0.00 91,643.96cv (%) 0.00 0.00 0.00 0.00 99.89 0.00 99.89


Aged 18 to 24 years . . . . . . Estimate 56,894.78 289,788.91 0.00 102,105.05 164,983.75 269,598.78 1,170,114.18cv (%) 117.04 62.89 0.00 70.47 58.48 53.39 28.03

Aged 25 to 44 years . . . . . . Estimate 0.00 59,956.38 123,477.46 362,596.02 150,941.13 257,927.89 1,544,715.85cv (%) 0.00 99.97 99.81 42.94 57.88 52.84 20.63

Aged 45 to 64 years . . . . . . Estimate 0.00 0.00 66,351.30 81,709.72 54,862.26 48,694.13 433,311.06cv (%) 0.00 0.00 99.98 75.82 98.85 100.60 34.57

Aged 65 years or older . . . . Estimate 0.00 0.00 53,052.29 48,149.92 0.00 103,719.48 313,081.87cv (%) 0.00 0.00 99.56 62.72 0.00 71.70 46.07

Total . . . . . . . . . . . . . . . Estimate 103,183.87 397,636.83 345,961.39 810,200.27 513,007.00 726,625.07 4,160,258.86cv (%) 78.71 49.56 48.08 28.20 37.41 30.30 13.66



Subdomain

Black notHispanic


Substantive variable: Percentage unable to carry out major activity


Aged 5 to 17 years . . . . . . Estimate 0.31 0.41 0.48 0.33 0.37 0.00 0.37cv (%) 17.18 15.22 23.77 12.94 30.69 0.00 11.66




Aged 65 years or older . . . . Estimate 11.79 9.71 19.67 9.67 11.91 10.26 10.57cv (%) 4.02 3.29 6.05 2.91 13.30 28.15 2.68

Total . . . . . . . . . . . . . . . Estimate 4.35 3.72 5.89 3.83 3.21 1.88 4.08cv (%) 1.90 2.34 3.08 1.82 7.10 17.81 1.56



















Subdomain

Black notHispanic


Substantive variable: Percentage with interval since last doctor visit more than 5 years


























Subdomain

Black notHispanic


Substantive variable: Percentage reporting poor health


























Subdomain

Black notHispanic


Substantive variable: Percentage reporting at least 2 hospitalizations in the past year


























Subdomain

Black notHispanic


Substantive variable: Percentage reporting at least 1 doctor visit in the past year


























Subdomain

Black notHispanic


Substantive variable: Average number of doctor visits per capita


























Subdomain

Black notHispanic


Substantive variable: Average number of bed days per capita


























Subdomain

Black notHispanic


Substantive variable: Average number of conditions per capita

























Vital and Health Statisticsseries descriptions

SERIES 1. Programs and Collection Procedures —These reportsdescribe the data collection programs of the National Centerfor Health Statistics. They include descriptions of the methodsused to collect and process the data, definitions, and othermaterial necessary for understanding the data.

SERIES 2. Data Evaluation and Methods Research —These reportsare studies of new statistical methods and include analyticaltechniques, objective evaluations of reliability of collecteddata, and contributions to statistical theory. These studies alsoinclude experimental tests of new survey methods andcomparisons of U.S. methodology with those of othercountries.

SERIES 3. Analytical and Epidemiological Studies —These reportspresent analytical or interpretive studies based on vital andhealth statistics. These reports carry the analyses further thanthe expository types of reports in the other series.

SERIES 4. Documents and Committee Reports —These are finalreports of major committees concerned with vital and healthstatistics and documents such as recommended model vitalregistration laws and revised birth and death certificates.

SERIES 5. International Vital and Health Statistics Reports —Thesereports are analytical or descriptive reports that compare U.S.vital and health statistics with those of other countries orpresent other international data of relevance to the healthstatistics system of the United States.

SERIES 6. Cognition and Survey Measurement —These reports arefrom the National Laboratory for Collaborative Research inCognition and Survey Measurement. They use methods ofcognitive science to design, evaluate, and test surveyinstruments.

SERIES 10. Data From the National Health Interview Survey —Thesereports contain statistics on illness; unintentional injuries;disability; use of hospital, medical, and other health services;and a wide range of special current health topics coveringmany aspects of health behaviors, health status, and healthcare utilization. They are based on data collected in acontinuing national household interview survey.

SERIES 11. Data From the National Health Examination Survey, theNational Health and Nutrition Examination Surveys, andthe Hispanic Health and Nutrition Examination Survey —Data from direct examination, testing, and measurement onrepresentative samples of the civilian noninstitutionalizedpopulation provide the basis for (1) medically defined totalprevalence of specific diseases or conditions in the UnitedStates and the distributions of the population with respect tophysical, physiological, and psychological characteristics, and(2) analyses of trends and relationships among variousmeasurements and between survey periods.

SERIES 12. Data From the Institutionalized Population Surveys —Discontinued in 1975. Reports from these surveys areincluded in Series 13.

SERIES 13. Data From the National Health Care Survey —Thesereports contain statistics on health resources and the public’suse of health care resources including ambulatory, hospital,and long-term care services based on data collected directlyfrom health care providers and provider records.

SERIES 14. Data on Health Resources: Manpower and Facilities —Discontinued in 1990. Reports on the numbers, geographicdistribution, and characteristics of health resources are nowincluded in Series 13.

SERIES 15. Data From Special Surveys —These reports containstatistics on health and health-related topics collected inspecial surveys that are not part of the continuing datasystems of the National Center for Health Statistics.

SERIES 16. Compilations of Advance Data From Vital and HealthStatistics —Advance Data Reports provide early release ofinformation from the National Center for Health Statistics’health and demographic surveys. They are compiled in theorder in which they are published. Some of these releasesmay be followed by detailed reports in Series 10–13.

SERIES 20. Data on Mortality —These reports contain statistics onmortality that are not included in regular, annual, or monthlyreports. Special analyses by cause of death, age, otherdemographic variables, and geographic and trend analysesare included.

SERIES 21. Data on Natality, Marriage, and Divorce —These reportscontain statistics on natality, marriage, and divorce that arenot included in regular, annual, or monthly reports. Specialanalyses by health and demographic variables andgeographic and trend analyses are included.

SERIES 22. Data From the National Mortality and Natality Surveys —Discontinued in 1975. Reports from these sample surveys,based on vital records, are now published in Series 20 or 21.

SERIES 23. Data From the National Survey of Family Growth —These reports contain statistics on factors that affect birthrates, including contraception, infertility, cohabitation,marriage, divorce, and remarriage; adoption; use of medicalcare for family planning and infertility; and related maternaland infant health topics. These statistics are based onnational surveys of women of childbearing age.

SERIES 24. Compilations of Data on Natality, Mortality, Marriage,Divorce, and Induced Terminations of Pregnancy —These include advance reports of births, deaths, marriages,and divorces based on final data from the National VitalStatistics System that were published as supplements to theMonthly Vital Statistics Report (MVSR). These reports providehighlights and summaries of detailed data subsequentlypublished in Vital Statistics of the United States. Othersupplements to the MVSR published here provide selectedfindings based on final data from the National Vital StatisticsSystem and may be followed by detailed reports in Series 20or 21.

For answers to questions about this report or for a list of reports publishedin these series, contact:

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