Managing Systems, Managing Systems, Neighborhood and Population Neighborhood and Population

HealthHealth

Eliseo J. Pérez-Stable, MD

EPI 222: Health Disparities Research Methods

April 28, 2011

Conceptual Framework: Multi-level Conceptual Framework: Multi-level Determinants of Health DisparitiesDeterminants of Health Disparities

Psychosocial - beliefs, attitudes, adherence, coping, personality

Behavior - exercise, diet, alcohol, smoking, sexual behavior, substance use

Health care system

Demographics - age, gender, race, ethnicity, education, income

Physical environment

Social environment

HealthHealth& health care& health care

disparitiesdisparities

Biological - genetics,stress, allostatic load, opiate receptors, metabolism, telomeres

Contextual Individual-level

Technical aspects of health care

Communication Communication with clinicianswith clinicians

Economic resources

Health Care SystemHealth Care System

Structure-Process-Outcome ParadigmStructure-Process-Outcome Paradigm

Patient outcomes

Structure Structure of careof care

•Structure - system of care•Technical process - knowledge

and judgment skills •Interpersonal process - the way care is provided

Donabedian A. Quality Review Bulletin, 1992, p. 356

Process of care:Process of care:-technical care-technical care-interpersonal-interpersonal

care care

Hospice Use Differences by Hospice Use Differences by Race/EthnicityRace/Ethnicity

• Medicare Beneficiaries, Diagnosis of heart failure in 2001

• 98,258 patients, age 80, 39% new

% Any Hospice Use% Any Hospice Use OROR

AllAll 3.93.9 ––––––

WhiteWhite 4.14.1 ––––––

BlackBlack 2.82.8 0.590.59

LatinoLatino 2.42.4 0.490.49Gives, Arch Intern Med 2010; 170: 427

Readmission Rates by RaceReadmission Rates by Race• Medicare Beneficiaries, 30-day readmission for MI, CHF and pneumonia, 3.16 million patients

• Minority-serving hospitals worse

% Readmit% Readmit WW BB OROR

MIMI 22.6 24.8 22.6 24.8 1.131.13

CHFCHF 27.1 27.9 1.04 27.1 27.9 1.04

Pneumonia 21.3 23.7Pneumonia 21.3 23.7 1.151.15Joynt KE, Orav EJ, Jha AK, JAMA 2011; 305: 675

Admissions to High-Quality Admissions to High-Quality Hospitals for CHDHospitals for CHD

• Medicare data, 2002-2005, from markets with top-ranked cardiac hospitals

• Evaluate role of race, SES of area, distance to hospital

• Black with acute MI more likely to go to top ranked hospitals (OR = 1.12; 1.08 - 1.16)

• No difference in care for CABG• Blacks from disadvantaged zip codes were

less likely to go to top ranked hospitals (OR = 0.75; 0.64 – 0.86)

Popescu, Arch Intern Med 2010; 170: 1209

Limited English Proficiency is a Risk Limited English Proficiency is a Risk factor for Readmissionfactor for Readmission

• Retrospective review of registry of 7023 hospitalized patients 2001-2003

• 8% Chinese, 4% Spanish, 4% Russian• Similar LOS, cost, mortality• LEP patients had higher adjusted odds of

readmission: OR = 1.3 (1.0 - 1.7)• Chinese and Spanish speaking LEP

patients had increased odds (1.7 and 1.5) of readmission

Karliner L, et al. J Hosp Med 2010; 5: 276-282

Use of Interpreters in Language Use of Interpreters in Language Discordant EncountersDiscordant Encounters

• Patients using interpreters ask less, say less, answer less (professional)

• Encounters take twice as long or do half as much –– cost and time

• Who translates matters: professional interpreter should be required for all important interactions: better accuracy

• Technology can help: dual head set telephones, Video conferencing, new technology?

VMI Studies of Interpretation VMI Studies of Interpretation Experiences in Clinical SettingsExperiences in Clinical Settings

• Survey of clinicians’ experience with Survey of clinicians’ experience with non-Spanish interpreter-mediated non-Spanish interpreter-mediated visitsvisits

• Hospitalized patients (Spanish, Hospitalized patients (Spanish, Chinese) interviewed admission & Chinese) interviewed admission & post- dischargepost- discharge

• Survey of professional interpreters Survey of professional interpreters working at 3 medical centersworking at 3 medical centers

Characteristics of 283 LEP Visits Characteristics of 283 LEP Visits Primary Care Community ClinicsPrimary Care Community Clinics

• Mode of Interpretation– In-person 114 (40%) – VMI 107 (38%) – Ad hoc 62 (22%)

• 20 different languages• Half were with women, Mean age 56 y• 25% first visit with clinician• No differences in demographics of

patients by interpretation mode

Clinician Ratings by Interpreter ModeClinician Ratings by Interpreter Mode

In-PersonIn-Person VMIVMI Ad hocAd hoc

High quality interpretation High quality interpretation 89 89 9393 81 81(Good/V.Good/Excellent)(Good/V.Good/Excellent)

High quality communication High quality communication 77 77 8989 66* 66* (Good/V.Good/Excellent) (Good/V.Good/Excellent)

High patient engagementHigh patient engagement 95 95 9797 94 94(Fairly well, Well, Very well)(Fairly well, Well, Very well)

*p<0.01*p<0.01

Higher Quality of Interpretation or Higher Quality of Interpretation or CommunicationCommunication

In-Person In-Person VMI vs. VMI vs. Ad hoc vs. Ad hoc

Interpretation 1.93 6.926.92 3.59(95% CI) (0.90, 4.14) (1.88, 25.5) (1.30, 9.91)

Communication 1.96 3.18 1.62(95% CI) (0.90, 4.14) (1.88, 25.5) (1.30, 9.91)

Adjusted for site, pt’s language, pt’s sex, pt’s health status, pt’s emotional distress, clinician’s age and sexNapoles AM, et al, J Health Care Poor and Underserved 2010; 21: 301-317

Use of Interpreters in 234 Use of Interpreters in 234 Hospitalized PatientsHospitalized Patients

With MD at With MD at AdmissionAdmission

With MD With MD during stayduring stay

With RNWith RN

TotalTotal 57%57% 60%60% 37%37%

≥ ≥ 65 Years65 Years 78%78% 81%81% 51%51%

Predictors of UsePredictors of Use10 yrs age 1.4 (1.1-1.8) 1.2 (0.9-1.4) 1.2 (1.0-1.5)

< HS Grad _______ 1.4 (0.7-2.8) 2.2 (1.0-4.9)

Chinese 1.5 (0.5-4.6) 1.8 (0.6-5.1) 3.3 (1.2-9.3)

Interpreters’ satisfaction with communication by modality

Does VMI represent a significant Does VMI represent a significant improvement?improvement?

Scenario% responding

telephonic interpretation is

at least adequate

Odds ratio for responding VMI

is at least adequate

(vs telephonic)

Family meeting 26 3.4 (1.5-7.7)

Physical Therapy 47 4.5 (1.6-12.1)

Inpatient RN Teaching 60 3.0 (1.2-7.9)

MD Evaluation in ED 64 1.4 (0.8-2.5)

Consent for Procedure 70 5.0 (1.6-15.8)

Hospital Discharge 70 3.2 (1.1-9.2)

Case Man/ Social work 70 2.5 (1.2-5.4)

Neighborhood and PlaceNeighborhood and Place

Physical and Social Environment

What is neighborhood?What is neighborhood?

People – composition, % poverty level, average incomes, % unemployed, %LEP

Social environment: People relationships, Collective efficacy, Social support

Physical or Built environment:

• Green space / parks,

• Roads and walkways

• Housing

What is neighborhood – part IIWhat is neighborhood – part IIPublic services

• Public transportation

• Police and safetyCommercial services

• grocery

• Retail stores

Neighborhood-Health ConnectionsNeighborhood-Health Connections

Main outcomes of interest:

Self-reported health

Chronic disease

Injury, alcohol, violence

Mortality

Health behaviors: diet, physical activity, tobacco

Neighborhood Environment Neighborhood Environment Health DisparitiesHealth Disparities

Policies that create diversity Policies that create diversity

in neighborhoods: in neighborhoods:

public investmentpublic investment

zoningzoning Characteristics of neighborhoodsCharacteristics of neighborhoods

Composition: poverty, segregationComposition: poverty, segregation

““built environment”: stores, parks, built environment”: stores, parks, treestrees

Social environment: threats, social Social environment: threats, social cohesioncohesion

Neighborhood - DefinitionsNeighborhood - Definitions

• Census boundariesCensus boundaries

• Zip codeZip code

• Half-mile around homeHalf-mile around home

• Perceived – no Perceived – no

concrete definitionconcrete definition

Neighborhood - MeasuresNeighborhood - Measures

Administrative Administrative data: census, data: census, city planning city planning zoning zoning

perceptions

Direct Direct observationobservation

Geospatial data

Administrative Data:Administrative Data:Census VariablesCensus Variables

• Family income - Median income for all households

• Poverty - Proportion of persons whose annual income falls at or below 175% of the poverty

line• Education - Proportion of persons 25 years

and older with less than a high school education

• Housing value - Median value of owner-occupied housing units

• Limited English Proficiency – % households where language other than English spoken

Alameda County StudyAlameda County Study

• • Is there an Is there an association of association of neighborhood environment with neighborhood environment with mortality?mortality?

•1983 data - 50% sample (1,799)

• Alameda County residents in 1983 (1,129)

• Neighborhood defined as census tract

Yen IH, Kaplan GA. American Journal of Epidemiology 1999

Is neighborhood environment associated with mortality?

Community SES

• Per capita income

• % white collar employees

• crowding

Environment/Housing

• population of census tract

• Area of tract

• % renters

• % single family dwellings

Commercial Services

• Supermarkets

• Laundromats / Dry cleaners

• Beauty Parlor / Barber Ships

• Pharmacies

Yen IH, Kaplan GA. American Journal of Epidemiology 1999

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Low social environmental quality and 11-yearLow social environmental quality and 11-year mortality risk: mortality risk: 2-level logistic regression; Alameda County Study 2-level logistic regression; Alameda County Study

1983 (n=996)1983 (n=996)

Yen IH, Kaplan GA. American Journal of Epidemiology 1999

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Commercial services and 11-year Commercial services and 11-year mortality risk: ACSmortality risk: ACS

Yen IH, Kaplan GA. American Journal of Epidemiology 1999

Community SES & 11-yr Mortality Risk

By individual income level

0

1

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Low income

Lowest community

SES

Highest community

SES

Yen IH, Kaplan GA. American Journal of Epidemiology 1999

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Yen IH, Syme SL. Annual Review of Public Health 1999

City Center

Transition

Workers’ homes

Residential Commuter

Chicago School of SociologyChicago School of Sociology

Survey Assessment of Perceived Neighborhood

What happens when we measure perceived neighborhood environment?

Self-administered questions over-ride limitation of census boundaries

• Began in 1992

• Sample of patients with asthma from northern California physicians

•RDD sample added in 1999

• Ages 18 to 50 at baseline

• Wave 5 (Feb 2000 – May 2001); n = 439

UCSF Asthma and Rhinitis PanelUCSF Asthma and Rhinitis Panel

Yen IH, et al. Am J Public Health 2006.

“Thinking about your neighborhood as a whole, how much of a problem do you feel each of the following is in your neighborhood?” [on a scale of 0 to 5, 0 is not a problem, 5 is a serious problem]

Too much traffic

Excessive noise

Trash and litter

Smells or odors from factories or farms

Smoke from fires or burning

Assessing neighborhood environment

-5-4-3-2-101

Quartile 1 Quartile 2 Quartile 3 Quartile 4

Neighborhood problems and Neighborhood problems and physical functioningphysical functioning

* *

Yen IH, et al. Am J Public Health 2006.

adjusted for age, sex, income, education, ethnicity, & asthma severity

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Direct Observation in Collecting Direct Observation in Collecting Data on NeighborhoodsData on Neighborhoods

Kaiser CYGNET Study: Kaiser CYGNET Study: 444 7-year old girls

Follow for 5 years –– pubertal transition

Collect data on diet, physical activity, height, and weight

Live in Alameda, San Francisco, Marin, and Contra Costa Counties

Neighborhood Observations in Neighborhood Observations in CYGNETCYGNET

Select half of the 7-year old girls

Send trained observer to girls’ neighborhood. Walk around to collect information about presence/absence of food stores, fast food and other restaurants, recreation opportunities, and walkability and bikeability.

Example of a map given to a Example of a map given to a street observerstreet observer

Results: Results: Kaiser CYGNET StudyKaiser CYGNET Study

Street observation data for 213 girls

Observed 3 to 32 street blocks per address; total of 2,301 street blocks

Created combination variables for items in audit form, e.g. food stores, public services (e.g. library, post office), walkability/bikeability (e.g. sidewalks, cross walks, speed bumps)

Used factor analysis to see how variables cluster together

Social context of PregnancySocial context of Pregnancy39-item survey representing four categories of neighborhood attributes: •neighborhood physical conditions; •social interactions; •nonresidential land use (commercial property); •public, residential and nonresidential space.

Laraia BA, et al. Int J Health Geographics 2006.

Examples of items recordedExamples of items recordedAdult activity (Code all that apply)If no adults present ……………………… Walking …………………………………… Socializing (Talking with neighbors) ……Socializing in mixed racial groups ………Home repair, landscaping, or car care …Sitting/standing on porch or stoop ………Supervising children ………………………Patronizing business establishments ……Standing on the sidewalk …………………Sitting/standing at the bus stop …………Getting into or out of vehicles …………… Walking a dog ……………………………. Conducting home-based vending ……… Recreational activity (e.g., jogging) ……

Laraia BA, et al. Int J Health Geographics 2006.

Practical Definition of neighborhood concepts – physical incivilities

Combination of physical disorder and poor housing conditions: theorized to communicate decreased local social control and may contribute to crime and further neighborhood deterioration. Items: •fences•hedges•Physical and symbolic signs that demarcate residential property

Definition of Neighborhoods Territoriality

Ownership and social control leading to protective effects against crime and other adverse events

Items : Items : • condition of housing, yards, commercial condition of housing, yards, commercial

and public spaces,and public spaces,• vacant or burned property vacant or burned property • litter and graffitilitter and graffiti

“Our Space” GIS database: UCSF, UC Berkeley, and Kaiser DOR

• census data• retail store data (InfoUSA or Dunn &Bradstreet)• pollution• traffic• green space / parks

OUR SPACEOUR SPACEFood store and eating establishmentsFood store and eating establishments

• Supermarkets• Produce vendors• Small groceries• Convenience stores• Specialty food service (e.g.

bagel, deli)• Restaurants• Fast food

Examples of buffer differencesExamples of buffer differences

Marin County Alameda County

Food stores/eating places by county, race/ethnicity, & household income

• San Francisco has more food stores and eating establishments than the other counties

• Racial/Ethnic differences in proximity to food stores and eating establishments

• Where there are county and racial/ethnic differences, not always household income differences

Access to Markets with Healthy Foods Access to Markets with Healthy Foods for Diabetics in New York Cityfor Diabetics in New York City

• Food targets: Fruit, vegetables, 1% fat milk, diet Food targets: Fruit, vegetables, 1% fat milk, diet drinks, high fiber breaddrinks, high fiber bread

• 173 stores in East Harlem and 152 stores in 173 stores in East Harlem and 152 stores in Upper East SideUpper East Side

• Had all 5 categories: 9% vs. 48%Had all 5 categories: 9% vs. 48%• More likely to live on a block with no store selling More likely to live on a block with no store selling

foods in E Harlem–50% vs. 24%foods in E Harlem–50% vs. 24%• Example of disparities in environmental justice Example of disparities in environmental justice

issues complicating behaviorissues complicating behaviorAJPH 2004; 94: 1549-54AJPH 2004; 94: 1549-54

52Stephen A. Matthews (Pennsylvania State University) - Multiple Activity Spaces (and Temporal Rhythms)

Death Rate by Race/Ethnicity, US 2000Death Rate by Race/Ethnicity, US 2000

WW B B L L A/PIA/PIHeart DiseaseHeart Disease 130130 191 89 191 89 72 72

StrokeStroke 25 25 44 20 44 20 24 24

DiabetesDiabetes 12 12 29 29 19 19 9 9

Age-adjusted per 100,000 NCHS

Causes of Death, US 2001Causes of Death, US 2001

LatinosLatinos %%

Heart DiseaseHeart Disease 23.923.9

CancerCancer 19.719.7

InjuryInjury 8.48.4

StrokeStroke 5.75.7

DiabetesDiabetes 5.05.0

HomicideHomicide 2.92.9

Liver DiseaseLiver Disease 2.92.9

WhitesWhites %%

Heart DiseaseHeart Disease 29.729.7

CancerCancer 23.323.3

StrokeStroke 6.86.8

COPD+COPD+ 5.65.6

InjuryInjury 3.93.9

Flu/pneumoniaFlu/pneumonia 2.62.6

DiabetesDiabetes 2.62.6

Causes of Death, Latinos and Whites, Causes of Death, Latinos and Whites, 65 y and over, US 200365 y and over, US 2003

LatinosLatinos raterate

Heart DiseaseHeart Disease 10361036

CancerCancer 682682

StrokeStroke 240240

DiabetesDiabetes 203203

Alzheimer’sAlzheimer’s 8787

ESRDESRD 7474

COPD plusCOPD plus 128128

WhitesWhites raterate

Heart DiseaseHeart Disease 15871587

CancerCancer 10891089

StrokeStroke 385385

DiabetesDiabetes 143143

Alzheimer’sAlzheimer’s 185185

ESRDESRD 93 93

COPD plusCOPD plus 323323

Cancer Mortality and Poverty in Cancer Mortality and Poverty in Latino Women, Latino Women, 1990-94/1995-001990-94/1995-00

SiteSite All All (rates)(rates)

< 10% < 10% povertypoverty

> 20% > 20% povertypoverty

LungLung 14.214.2

14.914.9

12.712.7

13.613.6

15.215.2

16.316.3

BreastBreast 18.618.6

18.018.0

16.316.3

15.015.0

20.020.0

20.820.8

ColonColon 11.611.6

11.411.4

9.49.4

9.99.9

11.311.3

11.611.6Chu K, et al. JNMA 2007; 1092-1104

Health Related Quality of Life by Health Related Quality of Life by Ethnicity - Los Angeles 1999Ethnicity - Los Angeles 1999

Poor and Unhealthy Activity N Fair Health Days Limitation D

White 3376 13.1% 7.1 2.7

Latino 3267 35.6% 6.3 2.4

AA 835 21.2% 8.3 3.5

API 716 15.3% 4.7 1.7MMWR 2001; 50:556-9

Screening for Colon CancerScreening for Colon Canceradults age 50-74, BRFSS, 2008adults age 50-74, BRFSS, 2008

Percent TestedWhite 64Black 62Asian / PI 56AI / AN 54Latino 50

< High School 46High School / GED 58Some College / Tech 64College Graduate 71

MMWR 2010; 59: 810

Screening Mammography US 2008Screening Mammography US 2008Women 50 – 74, BRFSSWomen 50 – 74, BRFSS

PercentPercentWhiteWhite 8181BlackBlack 8282Asian / PIAsian / PI 8080AI / ANAI / AN 7070LatinaLatina 8181

< High School< High School 7373High School / GEDHigh School / GED 7979Some College / TechSome College / Tech 8181College GraduateCollege Graduate 8686

MMWR 2010; 59: 814MMWR 2010; 59: 814

Prevalence of Elevated LDL-C, Prevalence of Elevated LDL-C, Treatment, and Control, Treatment, and Control, NHANES, 2005-8NHANES, 2005-8

PercentPercentHighHigh RxRx ContCont

WhiteWhite 3535 5050 3535BlackBlack 3030 4545 2626Mexican AmMexican Am 2828 3434 2020

< High School< High School 4141 4646 2828High SchoolHigh School 4242 5252 3636Some CollegeSome College 3636 4747 3232College GradCollege Grad 2929 4949 3939

MMWR 2011; 60: 109-112MMWR 2011; 60: 109-112

Breast Cancer Death Rates Among Breast Cancer Death Rates Among Women Aged 45-64 Years, by RaceWomen Aged 45-64 Years, by Race

United States, 1990-2007United States, 1990-2007

41% decrease in White and 24% in Black 41% decrease in White and 24% in Black womenwomen. MMWR 2010; 59: 29

Conceptual Framework: Multi-level Conceptual Framework: Multi-level Determinants of Health DisparitiesDeterminants of Health Disparities

Psychosocial - beliefs, attitudes, adherence, coping, personality

Behavior - exercise, diet, alcohol, smoking, sexual behavior, substance use

Health care system

Demographics - age, gender, race, ethnicity, education, income

Physical environment

Social environment

HealthHealth& health care& health care

disparitiesdisparities

Biological - genetics,stress, allostatic load, opiate receptors, metabolism, telomeres

Contextual Individual-level

Technical aspects of health care

Communication with clinicians

Economic resources