the uninsured in wisconsin uninsured in... · of residents had health insurance for only part of...

The Uninsured in Wisconsin: A Small Area Analysis Using the Wisconsin Family Health Survey

2002 – 2004

September 2006

For theWisconsin Department of Health and Family Services

State Planning Grant

Table of Contents I. INTRODUCTION..................................................................................................................................... 1

Table 1: County Weighted Population Estimates................................................................................. 2 II. HEALTH INSURANCE COVERAGE OVER THE PAST YEAR .................................................... 4

Table 2: Uninsured by Age Group and County.................................................................................... 5 Table 3: Uninsured by Age Group and DHFS Region......................................................................... 6 Table 4: Uninsured by Gender and County ......................................................................................... 7 Table 5: Uninsured by Gender and DHFS Region .............................................................................. 9 Table 6 and 7: Uninsured by Race and County; and Uninsured by Race and DHFS Region ............. 9 Table 8 and 9: Uninsured by Poverty Status and County; and Uninsured by Poverty Status and

DHFS Region ...................................................................................................................................... 11 Tables 10 and 11: Uninsured by Employment Status and County; and Uninsured by Employment

Status and DHFS Region .................................................................................................................... 13 III. PREDICTORS OF COUNTY-LEVEL UNINSURANCE RATES ................................................. 17

DATA SOURCE.......................................................................................................................................... 18 ANALYTIC STRATEGY .............................................................................................................................. 18 FINDINGS.................................................................................................................................................. 19

Table 12: Statistical Model ................................................................................................................ 20 IV. VARIOUS SOURCES OF THE UNINSURED BY COUNTY ........................................................ 21

Table 13: Uninsured Proportion by Region....................................................................................... 21 Table 14: Uninsured Proportion by County ...................................................................................... 22 Table 15: Small Area Estimate Analysis of Predicted Insurance Coverage by DHFS Region in

Wisconsin ............................................................................................................................................ 28 Table 16: Small Area Estimate Analysis of Predicted Insurance Coverage by County in Wisconsin 28

List of Tables TABLE 1: COUNTY WEIGHTED POPULATION ESTIMATES .................................................................. 2 TABLE 2: UNINSURED BY AGE GROUP AND COUNTY ......................................................................... 5 TABLE 3: UNINSURED BY AGE GROUP AND DHFS REGION ............................................................. 6 TABLE 4: UNINSURED BY GENDER AND COUNTY................................................................................ 7 TABLE 5: UNINSURED BY GENDER AND DHFS REGION .................................................................... 9 TABLE 6: UNINSURED BY RACE AND COUNTY ..................................................................................... 9 TABLE 7: UNINSURED BY RACE AND DHFS REGION ....................................................................... 11 TABLE 8: UNINSURED BY POVERTY STATUS AND COUNTY ............................................................ 11 TABLE 9: UNINSURED BY POVERTY STATUS AND DHFS REGION................................................. 13 TABLE 10: UNINSURED BY EMPLOYMENT STATUS AND COUNTY .................................................. 14 TABLE 11: UNINSURED BY EMPLOYMENT STATUS AND DHFS REGION ...................................... 15 TABLE 12: STATISTICAL MODEL ........................................................................................................... 20 TABLE 13: UNINSURED PROPORTION BY DHFS REGION ............................................................... 21 TABLE 14: UNINSURED PROPORTION BY COUNTY FROM FOUR SOURCES ................................. 22 TABLE 15: SMALL AREA ESTIMATE ANALYSIS OF PREDICTED INSURANCE COVERAGE BY

DHFS REGION IN WISCONSIN...................................................................................................... 28 TABLE 16: SMALL AREA ESTIMATE ANALYSIS OF PREDICTED INSURANCE COVERAGE BY

COUNTY IN WISCONSIN.................................................................................................................. 28

APS Healthcare 1 September 2006

I. Introduction The Family Health Survey (FHS) is conducted by the Wisconsin Department of Health and Family Services, Division of Public Health, Bureau of Health Information and Policy. The FHS report, “Wisconsin Health Insurance Coverage, 2004” (http://www.dhfs.state.wi.us/stats/pdf/fhs04ins.pdf) served as a model for this report. The data extract and analysis for this report was supported by the Wisconsin Department of Health and Family Services (DHFS), State Planning Grant Program with funds from the U.S. Department of Health and Human Services. This report provides information on the absence of health insurance coverage throughout Wisconsin. This information was compiled from the 2002, 2003 and 2004 Wisconsin FHS. The FHS sample is designed to represent five broad regional areas of the state rather than each county, so not every county is represented in the sample. By design, some are under-represented and others are over-represented. In order to achieve a large enough sample for this analysis, it was necessary to combine data from three years of the survey. As a result, the figures contained in this report are estimates and represent averages for the three years of the survey. The county estimates were derived through representative sample small areas (zip-code level) from which county-level data were inferred. By building county-level estimates up from small areas, we avoid the well-known statistical bias that can be caused by working with aggregate data, referred to in the statistical literature as “aggregation bias” or “the fallacy of ecological inference.” Background The Wisconsin FHS collects information about health insurance coverage, health status, health problems and the use of health care services among Wisconsin residents. The survey is conducted on a continuous basis, collecting information every month. A random sample of households is telephoned by trained interviewers who speak with the household member most knowledgeable about the health of all household members. This respondent provides information for all people living in the household at the time of the interview. Survey Design The FHS results are representative of Wisconsin household residents (approximately 97 percent of all persons residing in the state). Non-household residents, such as individuals living in nursing homes, dormitories, prisons and other institutions are not represented in the survey. For the years 2002-2004, there were 20,723 Wisconsin household residents interviewed for the FHS. Interpretive Results The tables included in this report represent estimates of Wisconsin residents by County and by the Wisconsin DHFS Regions. These estimates should not be considered as precise results because they were derived from sample data.


Table 1: County Weighted Population Estimates The estimated number of Wisconsin residents living in households presented in the tables are derived from a weighting formula used by the Wisconsin DHFS for the statewide FHS. The weighting formula generates a Wisconsin household population that is 5,305,653 as seen in Table 1. The DHFS Bureau of Health Information total Wisconsin population estimate for 2003 is 5,498,350. DHFS estimates that the State’s household population is 97 percent of its total population at any time. Again, please note that the estimated numbers of residents living in households represents an average of the three years (2002-2004) included in the analysis.

Table 1: County Weighted Population Estimates

County N Adams 27,215 Ashland 15,796 Barron 37,285 Bayfield 15,590 Brown 228,258 Buffalo 12,346 Burnett 17,246 Calumet 50,884 Chippewa 66,441 Clark 32,679 Columbia 51,221 Crawford 16,652 Dane 427,773 Dodge 100,067 Door 34,375 Douglas 38,927 Dunn 38,469 Eau Claire 90,164 Florence 10,938 Fond du Lac 97,388 Forest 9,787 Grant 44,565 Green 42,090 Green Lake 14,910 Iowa 19,023 Iron 6,290 Jackson 20,548 Jefferson 70,833 Juneau 26,055 Kenosha 147,581 Kewaunee 17,544 La Crosse 96,279 Lafayette 12,116 Langdale 20,953


Table 1: County Weighted Population Estimates

County N Lincoln 29,565 Manitowoc 72,509 Marathon 113,464 Marinette 47,715 Marquette 26,723 Menominee 6,628 Milwaukee 914,596 Monroe 55,260 Oconto 38,227 Oneida 42,421 Outagamie 159,852 Ozaukee 113,191 Pepin 6,887 Pierce 31,789 Polk 38,306 Portage 57,340 Price 12,167 Racine 179,250 Richland 23,007 Rock 124,360 Rusk 17,233 St. Croix 61,211 Sauk 59,190 Sawyer 14,688 Shawano 28,126 Sheboygan 109,495 Taylor 17,419 Trempealeau 33,329 Vernon 24,252 Vilas 28,929 Walworth 61,924 Washburn 9,310 Washington 143,844 Waukesha 349,656 Waupaca 45,177 Waushara 28,809 Winnebago 146,584 Wood 87,389 State of WI 5,305,653

Source: 2002-2004 FHS Weighted Average


II. Health Insurance Coverage Over the Past Year1 Over the three-year period included in this analysis, a relatively small number of Wisconsin residents had no health insurance for the entire year prior to the survey interview. Of the total combined sample population, only 4.3 percent reported having no health insurance for the twelve months prior to the survey interview. When looking at individuals of all ages who responded to having insurance coverage or not in the FHS for the prior twelve months to being interviewed, the top five counties demonstrating the highest estimated rates of uninsurance included Washburn (16.8), Shawano (16.7), Green (16.6), Bayfield (15.0) and Waushara (12.7). Among the DHFS regions, the Northeastern region had the largest rate of uninsurance (4.7) and the Southeastern region had the lowest rate (4.1) of no insurance among the residents for the twelve months prior to the survey interview. An estimated 89.5 percent of Wisconsin household residents reported having health insurance for the twelve months prior to the survey interview. An additional 5.7 percent of residents had health insurance for only part of the year. As seen in Table 2, residents between the ages of 18 and 64 were the least likely to be insured during the previous twelve months. Among all Wisconsin residents, 5.9 percent of individuals ages 18 through 64 reported having no insurance in the prior twelve months compared to 2.2 percent of individuals under the age of 18 and 1.1 percent of all residents age 65 and greater. Moreover, the number of residents aged 18 through 64 who had insurance coverage part of the year was 6.7 percent compared to 5.2 percent for residents under the age of 18 and 0.8 percent of residents age 65 and older. When reviewing the following results, several caveats should not be overlooked. Because these findings are based on sample data, results are not always precise. For example, in some counties all individuals sampled happened to have insurance coverage in the prior twelve months to being interviewed. Also, how the data are analyzed (by age, gender, race, poverty status or unemployment status) can also give the perception of full insurance coverage in a given county. Not all individuals who could be identified within the FHS as belonging to a particular group or having particular characteristics or circumstances were consistent across each subgroup analyses for each individual. Further, individuals aged 65 and older were excluded from the remainder of the reported results due to the large proportion of these individuals having some type of private or public insurance coverage. Additionally, many of these individuals are no longer working and their circumstances can bias findings.

1 Health Insurance Coverage Over the Past Year. This estimates three groups: the percentage of residents who were covered by any type of insurance over the entire 12 months preceding the telephone interview, the percentage who had coverage during part of the 12 months and had no insurance part of the time, and the percentage who had no health insurance at all during the preceding 12 months. Because FHS interviews were conducted throughout the year, the “preceding 12-month” period is variable.


Table 2: Uninsured by Age Group and County Among individuals 17 years of age and younger, Washburn county had the highest rate of uninsured individuals with 11.5 percent. Iron county ranked the highest for uninsured rate among 18 to 45 year olds with 16.7 percent. When observing 46 to 64 year olds, Waushara county had the largest rate of uninsured with 10.2 percent.

Table 2: Uninsured by Age Group and County County 17 and Under (%) 18 - 45 (%) 46 - 64 (%) Total

Adams 1.3 3.8 5.1 10.1 Ashland 5.2 12.1 1.7 19.0 Barron 0.8 7.3 1.6 9.7 Bayfield 3.8 9.6 3.8 17.3 Brown 1.0 4.7 1.2 6.9 Buffalo 0.0 0.0 0.0 0.0 Burnett 0.0 0.0 6.8 6.8 Calumet 1.2 7.0 1.2 9.4 Chippewa 2.9 6.3 1.5 10.7 Clark 1.1 5.6 3.3 10.0 Columbia 0.0 3.9 1.3 5.3 Crawford 9.8 3.9 2.0 15.7 Dane 0.9 3.6 0.6 5.2 Dodge 0.3 4.1 0.7 5.1 Door 0.8 2.4 4.8 8.1 Douglas 2.1 4.9 4.9 12.0 Dunn 2.5 4.1 1.6 8.2 Eau Claire 1.0 4.9 2.1 8.0 Florence 8.7 8.7 0.0 17.4 Fond du Lac 0.0 2.8 0.6 3.3 Forest 0.0 9.7 3.2 12.9 Grant 0.0 4.7 0.8 5.4 Green 3.0 6.7 3.7 13.4 Green Lake 2.0 7.8 5.9 15.7 Iowa 3.4 0.0 1.7 5.2 Iron 0.0 16.7 0.0 16.7 Jackson 1.5 1.5 4.6 7.7 Jefferson 0.9 1.9 0.0 2.8 Juneau 2.6 9.1 2.6 14.3 Kenosha 3.3 5.7 0.8 9.8 Kewaunee 0.0 1.8 3.6 5.4 La Crosse 0.7 3.3 0.3 4.3 Lafayette 0.0 2.9 2.9 5.9 Langdale 0.0 2.8 1.4 4.2 Lincoln 0.0 8.7 1.9 10.7 Manitowoc 2.4 3.7 1.6 7.8 Marathon 2.4 2.4 1.0 5.7 Marinette 2.0 4.7 2.7 9.5 Marquette 0.0 2.6 3.9 6.5 Menominee 0.0 0.0 0.0 0.0


Table 2: Uninsured by Age Group and County County 17 and Under (%) 18 - 45 (%) 46 - 64 (%) Total

Milwaukee 1.2 6.4 1.9 9.5 Monroe 1.6 5.4 2.2 9.1 Oconto 1.6 4.8 2.4 8.7 Oneida 0.7 3.6 2.9 7.2 Outagamie 0.0 1.9 0.7 2.6 Ozaukee 0.7 3.1 1.0 4.8 Pepin 0.0 0.0 0.0 0.0 Pierce 0.0 4.6 0.0 4.6 Polk 0.9 3.5 0.9 5.3 Portage 0.9 4.2 2.3 7.5 Price 0.0 0.0 0.0 0.0 Racine 0.2 5.7 2.0 7.9 Richland 4.1 5.5 0.0 9.6 Rock 0.7 2.4 1.7 4.8 Rusk 6.4 10.6 0.0 17.0 St. Croix 0.9 4.7 1.9 7.4 Sauk 0.0 4.8 2.7 7.5 Sawyer 2.4 2.4 9.8 14.6 Shawano 4.7 8.5 1.9 15.1 Sheboygan 1.0 3.4 1.3 5.8 Taylor 0.0 3.4 1.7 5.1 Trempealeau 0.0 1.7 0.0 1.7 Vernon 0.0 1.1 3.2 4.2 Vilas 1.2 3.5 4.7 9.3 Walworth 1.8 3.6 0.6 6.0 Washburn 11.5 7.7 3.8 23.1 Washington 0.5 3.5 0.0 4.1 Waukesha 0.2 1.9 1.3 3.4 Waupaca 3.2 9.0 4.5 16.7 Waushara 1.1 8.0 10.2 19.3 Winnebago 0.4 4.0 1.7 6.2 Wood 0.3 3.3 0.3 3.9

Source: 2002-2004 FHS data aggregated up to county by zip code Table 3: Uninsured by Age Group and DHFS Region When looking at the uninsured by DHFS region, the 18 to 45 year old group clearly had the largest percentage of individuals reporting not being insured in the prior twelve months. The Southeastern region had the largest overall uninsured proportion at 7.7 percent while the Southern region had the least at 6.2 percent.

Table 3: Uninsured by Age Group and DHFS Region

DHFS Region 17 and Under (%) 18 - 45 (%) 46 - 64 (%) Total

Southern 1.1 3.8 1.3 6.2 Southeastern 1.0 5.2 1.5 7.7 Northeastern 1.0 4.2 1.9 7.0


Table 3: Uninsured by Age Group and DHFS Region

DHFS Region 17 and Under (%) 18 - 45 (%) 46 - 64 (%) Total

Western 1.4 4.4 1.8 7.6 Northern 1.4 4.3 1.8 7.5

Source: 2002-2004 FHS using DHFS Region membership designation Table 4: Uninsured by Gender and County When looking at the uninsured by gender, Iron county had the highest rate among males at 16.7 percent while Florence county proved to be the highest among females at 13.0 percent. Across the state, 4 percent of males were uninsured in the year prior to being surveyed, while only 3.3 percent of women noted being uninsured during this same period.

Table 4: Uninsured by Gender and County

County Male (%) Female (%) Total (%)Adams 6.3 3.8 10.1 Ashland 12.1 6.9 19.0 Barron 5.6 4.0 9.7 Bayfield 7.7 9.6 17.3 Brown 3.9 3.0 6.9 Buffalo 0.0 0.0 0.0 Burnett 3.4 3.4 6.8 Calumet 4.7 4.7 9.4 Chippewa 6.3 4.4 10.7 Clark 7.8 2.2 10.0 Columbia 3.3 2.0 5.3 Crawford 3.9 11.8 15.7 Dane 3.2 2.0 5.2 Dodge 3.1 2.0 5.1 Door 3.2 4.8 8.1 Douglas 7.0 4.9 12.0 Dunn 3.3 4.9 8.2 Eau Claire 4.9 3.1 8.0 Florence 4.3 13.0 17.4 Fond du Lac 1.1 2.2 3.3 Forest 12.9 0.0 12.9 Grant 3.9 1.6 5.4 Green 5.2 8.2 13.4 Green Lake 9.8 5.9 15.7 Iowa 0.0 5.2 5.2 Iron 16.7 0.0 16.7 Jackson 4.6 3.1 7.7 Jefferson 1.4 1.4 2.8 Juneau 3.9 10.4 14.3 Kenosha 6.3 3.5 9.8


Table 4: Uninsured by Gender and County

County Male (%) Female (%) Total (%)Kewaunee 3.6 1.8 5.4 La Crosse 3.0 1.3 4.3 Lafayette 5.9 0.0 5.9 Langdale 2.8 1.4 4.2 Lincoln 6.8 3.9 10.7 Manitowoc 3.7 4.1 7.8 Marathon 2.9 2.9 5.7 Marinette 4.7 4.7 9.5 Marquette 3.9 2.6 6.5 Menominee 0.0 0.0 0.0 Milwaukee 5.1 4.4 9.5 Monroe 6.5 2.7 9.1 Oconto 5.6 3.2 8.7 Oneida 5.1 2.2 7.2 Outagamie 2.1 0.5 2.6 Ozaukee 2.4 2.4 4.8 Pepin 0.0 0.0 0.0 Pierce 3.7 0.9 4.6 Polk 3.5 1.8 5.3 Portage 4.2 3.3 7.5 Price 0.0 0.0 0.0 Racine 3.9 3.9 7.9 Richland 2.7 6.8 9.6 Rock 1.4 3.4 4.8 Rusk 6.4 10.6 17.0 St. Croix 3.7 3.7 7.4 Sauk 4.8 2.7 7.5 Sawyer 7.3 7.3 14.6 Shawano 8.5 6.6 15.1 Sheboygan 2.9 2.9 5.8 Taylor 1.7 3.4 5.1 Trempealeau 0.8 0.8 1.7 Vernon 1.1 3.2 4.2 Vilas 5.8 3.5 9.3 Walworth 4.2 1.8 6.0 Washburn 15.4 7.7 23.1 Washington 3.0 1.1 4.1 Waukesha 1.5 1.9 3.4 Waupaca 9.6 7.1 16.7 Waushara 8.0 11.4 19.3 Winnebago 3.8 2.3 6.2 Wood 2.3 1.6 3.9

Source: 2002-2004 FHS data aggregated up to county by zip code


Table 5: Uninsured by Gender and DHFS Region Among males, the Western DHFS region had the largest proportion of the uninsured at 4.5 percent while the women in the Southeastern region had the greatest percentage of uninsured (3.6).

Table 5: Uninsured by Gender and DHFS Region

DHFS Region Male (%) Female (%) Total (%)Southern 3.2 3.0 6.2 Southeastern 4.2 3.6 7.7 Northeastern 3.8 3.2 7.0 Western 4.5 3.1 7.6 Northern 4.3 3.1 7.5

Source: 2002-2004 FHS using DHFS Region membership designation Table 6 and 7: Uninsured by Race and County; and Uninsured by Race and DHFS Region The county with the highest rate of uninsured individuals for non-whites was Sawyer (9.8) and the Southeastern region had the highest proportion (3.6) for this same group. Among whites, Washburn county had the highest rate of uninsurance (23.1) and the Western DHFS region showed the largest proportion of uninsured (7.2) for this group. Statewide, 1.9 percent of non-whites indicated not being insured in the twelve months prior to being interviewed while 5.3 percent of whites were not insured at this time.

Table 6: Uninsured by Race and County County Non-White (%) White (%) Total (%)

Adams 3.8 5.1 9.0 Ashland 8.8 8.8 17.5 Barron 0.0 9.7 9.7 Bayfield 0.0 17.3 17.3 Brown 3.0 3.9 6.9 Buffalo 0.0 0.0 0.0 Burnett 0.0 6.8 6.8 Calumet 1.8 7.6 9.4 Chippewa 1.0 9.7 10.7 Clark 0.0 10.0 10.0 Columbia 0.0 5.4 5.4 Crawford 0.0 15.7 15.7 Dane 1.4 3.5 4.8 Dodge 0.0 5.1 5.1 Door 0.0 8.1 8.1 Douglas 2.1 9.9 12.0 Dunn 0.0 8.3 8.3 Eau Claire 0.7 7.3 8.0 Florence 0.0 17.4 17.4 Fond du Lac 0.3 3.0 3.3



Forest 0.0 12.9 12.9 Grant 0.0 5.4 5.4 Green 4.5 9.0 13.4 Green Lake 0.0 15.7 15.7 Iowa 0.0 5.2 5.2 Iron 5.6 11.1 16.7 Jackson 3.1 4.6 7.7 Jefferson 0.0 2.8 2.8 Juneau 3.9 10.5 14.5 Kenosha 2.2 7.5 9.7 Kewaunee 0.0 5.4 5.4 La Crosse 0.3 4.0 4.3 Lafayette 0.0 5.9 5.9 Langdale 0.0 4.2 4.2 Lincoln 1.0 9.7 10.7 Manitowoc 0.0 7.8 7.8 Marathon 0.7 5.1 5.8 Marinette 0.7 8.8 9.5 Marquette 1.3 5.2 6.5 Menominee 0.0 0.0 0.0 Milwaukee 5.7 3.9 9.5 Monroe 0.0 9.1 9.1 Oconto 1.6 7.1 8.7 Oneida 0.0 6.6 6.6 Outagamie 0.2 2.2 2.4 Ozaukee 0.0 4.8 4.8 Pepin 0.0 0.0 0.0 Pierce 0.0 4.6 4.6 Polk 0.0 5.3 5.3 Portage 0.0 7.5 7.5 Price 0.0 0.0 0.0 Racine 2.7 5.2 7.9 Richland 0.0 9.6 9.6 Rock 0.2 4.6 4.8 Rusk 0.0 17.0 17.0 St. Croix 0.0 7.5 7.5 Sauk 0.0 7.5 7.5 Sawyer 9.8 4.9 14.6 Shawano 0.0 16.0 16.0 Sheboygan 1.3 4.5 5.8 Taylor 0.0 5.1 5.1 Trempealeau 0.0 1.7 1.7 Vernon 0.0 4.2 4.2 Vilas 2.3 7.0 9.3 Walworth 0.0 6.0 6.0



Washburn 0.0 23.1 23.1 Washington 0.3 2.8 3.0 Waukesha 0.1 3.3 3.4 Waupaca 1.9 14.7 16.7 Waushara 2.3 17.4 19.8 Winnebago 0.2 5.4 5.6 Wood 0.0 3.9 3.9


Table 7: Uninsured by Race and DHFS RegionDHFS Region Non-White (%) White (%) Total (%)

Southern 1.0 5.0 6.1 Southeastern 3.6 4.1 7.7 Northeastern 1.1 5.8 7.0 Western 0.4 7.2 7.6 Northern 1.0 6.4 7.4

Source: 2002-2004 FHS using DHFS Region membership designation Table 8 and 9: Uninsured by Poverty Status and County; and Uninsured by Poverty Status and DHFS Region2 Among poor individuals, Green Lake county had the largest percent (9.8) of uninsured individuals while the Northeastern region had the largest percent of uninsured people (1.8) surveyed. For those individuals categorized as near-poor, Washburn county had the largest amount of uninsured (23.1) and the Northern region had the biggest proportion of uninsured for this group (2.9). Finally, among non-poor respondents, 13.9 percent in Shawano county was the largest showing of uninsured individuals for this group and the Western region (4.0) had the most uninsured people. Among poor respondents across Wisconsin, 1.5 percent indicated having no insurance while 2.3 and 3.3 percent of near-poor and not poor respondents, respectively, did as well.

Table 8: Uninsured by Poverty Status and County County Poor (%) Near-Poor (%) Not Poor (%) Total (%)

Adams 3.9 6.5 0.0 10.4 Ashland 1.7 6.9 10.3 19.0 Barron 0.8 0.8 8.3 9.9 Bayfield 0.0 12.0 2.0 14.0

2 Poverty Status. The relationship between the number of people in a household and the annual income of that household determines the poverty status. The Family Health Survey asked several questions about total household income during the calendar year prior to the survey, and used current household size to determine whether a household’s income was below the federal poverty guideline. A household was considered poor if the total income was less than 100 percent of the federal poverty guideline for a household its particular size. The “near-poor” category used in this report includes all people in households where the income was greater than the poverty guideline (100 percent) but less than twice the guideline (200 percent). Not poor households were defined as having an income 200 or more percent of the federal poverty guideline for a household of its size.



Brown 2.5 1.6 1.9 5.9 Buffalo 0.0 0.0 0.0 0.0 Burnett 1.9 5.7 0.0 7.5 Calumet 0.6 1.8 5.5 7.9 Chippewa 2.0 1.0 8.1 11.1 Clark 2.3 5.8 0.0 8.1 Columbia 0.0 2.6 2.6 5.3

Crawford 0.0 14.0 2.0 16.0 Dane 1.7 1.2 2.1 5.0 Dodge 1.0 0.7 3.5 5.2 Door 0.8 0.0 7.3 8.1 Douglas 2.2 2.2 6.6 11.0 Dunn 0.0 4.2 1.7 5.9 Eau Claire 1.1 2.5 4.3 7.8 Florence 0.0 17.4 0.0 17.4 Fond du Lac 0.3 0.8 2.0 3.1 Forest 0.0 6.5 6.5 12.9 Grant 2.3 0.8 2.3 5.5 Green 3.8 0.8 9.1 13.6 Green Lake 9.8 5.9 0.0 15.7 Iowa 0.0 5.4 0.0 5.4 Iron 0.0 11.1 5.6 16.7 Jackson 0.0 3.2 4.8 8.1 Jefferson 0.5 1.9 0.5 2.8 Juneau 0.0 5.3 9.3 14.7 Kenosha 1.4 2.0 5.5 8.9 Kewaunee 1.8 3.6 0.0 5.4 La Crosse 0.7 2.0 1.7 4.4 Lafayette 0.0 6.1 0.0 6.1 Langdale 0.0 0.0 4.2 4.2 Lincoln 2.0 3.0 5.9 10.9 Manitowoc 0.9 3.9 2.6 7.3 Marathon 0.5 2.0 0.8 3.3 Marinette 1.4 1.4 7.1 10.0 Marquette 2.6 0.0 3.9 6.5 Menominee 0.0 0.0 0.0 0.0 Milwaukee 2.3 3.0 4.1 9.5 Monroe 0.5 2.7 6.0 9.2 Oconto 1.6 0.8 6.4 8.8 Oneida 0.8 3.1 3.8 7.6 Outagamie 0.7 0.5 1.4 2.6 Ozaukee 0.7 2.1 2.1 4.8 Pepin 0.0 0.0 0.0 0.0 Pierce 0.0 0.0 4.6 4.6



Polk 0.9 2.6 1.8 5.3 Portage 0.0 2.4 5.3 7.7 Price 0.0 0.0 0.0 0.0 Racine 2.3 3.2 2.3 7.9 Richland 0.0 6.8 2.7 9.6 Rock 0.7 2.2 2.0 4.9 Rusk 7.0 0.0 11.6 18.6 St. Croix 1.4 1.9 4.3 7.6 Sauk 1.1 1.1 4.9 7.0 Sawyer 7.7 0.0 7.7 15.4 Shawano 1.0 1.0 13.9 15.8 Sheboygan 1.4 1.6 3.0 6.0 Taylor 1.7 0.0 3.4 5.1 Trempealeau 0.0 0.8 0.8 1.7 Vernon 3.2 0.0 1.1 4.2 Vilas 4.8 3.6 1.2 9.5 Walworth 1.2 3.1 1.8 6.1 Washburn 0.0 23.1 0.0 23.1 Washington 0.3 2.2 1.6 4.1 Waukesha 0.3 0.7 2.3 3.4 Waupaca 5.3 7.2 3.9 16.4 Waushara 8.5 4.9 7.3 20.7 Winnebago 1.5 1.3 3.2 6.0 Wood 0.0 2.0 1.7 3.6


Table 9: Uninsured by Poverty Status and DHFS Region DHFS Region Poor (%) Near-Poor (%) Not Poor (%) Total (%)

Southern 1.5 2.0 2.8 6.2 Southeastern 1.7 2.5 3.4 7.7 Northeastern 1.8 1.7 3.3 6.8 Western 1.1 2.3 4.0 7.5 Northern 0.9 2.9 3.0 6.8

Source: 2002-2004 FHS using DHFS Region membership designation Tables 10 and 11: Uninsured by Employment Status and County; and Uninsured by Employment Status and DHFS Region3 For individuals categorized as part-time workers, Forest County had the largest percentage of uninsured (25.0) and the Southern region had the most uninsured (4.3) for 3 For purposes of this report, employment status is classified in the following manner: those working full-time for an employer at the time of the survey interview are grouped together; some in this group also were self-employed. Among those not working full-time for an employer, those who were self-employed full-time are grouped together, as are those who were working part-time. The remaining adults ages 18-64 include homemakers, the retired, full-time students, persons laid off, the unemployed (either looking or not looking for work), and those disabled persons who are unable to work. These adults were not grouped together, as they are too disparate.


these workers as well. Waushara and Washburn counties had the greatest proportion of uninsured (14.3) among full-time workers and the Western region had the greatest percentage for this same group when looking at DHFS region (5.2). Among full-time workers throughout Wisconsin, 3.6 percent indicated having no insurance while 3.7 percent did not for part-time workers.

Table 10: Uninsured by Employment Status and County

County Part-Time (%) Full-Time (%) Total (%) Adams 0.0 0.0 0.0 Ashland 8.7 0.0 8.7 Barron 0.0 7.7 7.7 Bayfield 13.3 13.3 26.7 Brown 3.4 3.8 7.2 Buffalo 0.0 0.0 0.0 Burnett 0.0 0.0 0.0 Calumet 1.8 1.8 3.6 Chippewa 5.6 6.9 12.5 Clark 0.0 8.0 8.0 Columbia 3.4 3.4 6.8 Crawford 0.0 6.7 6.7 Dane 4.2 2.1 6.3 Dodge 1.0 3.0 4.0 Door 7.9 0.0 7.9 Douglas 7.1 7.1 14.3 Dunn 2.8 2.8 5.6 Eau Claire 4.6 3.7 8.3 Florence 0.0 0.0 0.0 Fond du Lac 3.6 0.7 4.3 Forest 25.0 0.0 25.0 Grant 7.0 4.7 11.6 Green 5.0 7.5 12.5 Green Lake 0.0 0.0 0.0 Iowa 6.3 0.0 6.3 Iron 0.0 12.5 12.5 Jackson 0.0 4.5 4.5 Jefferson 0.0 1.2 1.2 Juneau 12.5 0.0 12.5 Kenosha 2.1 5.6 7.7 Kewaunee 0.0 0.0 0.0 La Crosse 1.9 3.8 5.7 Lafayette 0.0 0.0 0.0 Langdale 0.0 7.4 7.4 Lincoln 6.0 2.0 8.0 Manitowoc 0.0 3.6 3.6 Marathon 0.7 2.8 3.4 Marinette 7.9 5.3 13.2


Table 10: Uninsured by Employment Status and County

County Part-Time (%) Full-Time (%) Total (%) Marquette 10.0 0.0 10.0 Menominee 0.0 0.0 0.0 Milwaukee 4.9 4.7 9.6 Monroe 4.8 6.5 11.3 Oconto 2.0 4.0 6.0 Oneida 4.2 6.3 10.4 Outagamie 3.6 0.0 3.6 Ozaukee 1.0 5.1 6.1 Pepin 0.0 0.0 0.0 Pierce 2.9 8.6 11.4 Polk 0.0 7.5 7.5 Portage 2.7 6.8 9.5 Price 0.0 0.0 0.0 Racine 3.7 6.9 10.6 Richland 9.1 0.0 9.1 Rock 5.5 0.8 6.3 Rusk 9.1 9.1 18.2 St. Croix 3.0 6.1 9.1 Sauk 5.6 4.2 9.7 Sawyer 11.8 5.9 17.6 Shawano 0.0 3.2 3.2 Sheboygan 4.1 0.7 4.7 Taylor 0.0 0.0 0.0 Trempealeau 0.0 2.1 2.1 Vernon 3.0 3.0 6.1 Vilas 4.3 4.3 8.7 Walworth 3.2 0.0 3.2 Washburn 0.0 14.3 14.3 Washington 2.9 1.4 4.3 Waukesha 1.8 1.8 3.5 Waupaca 7.8 11.8 19.6 Waushara 10.7 14.3 25.0 Winnebago 3.6 2.4 6.0 Wood 1.0 5.0 6.0


Table 11: Uninsured by Employment Status and DHFS Region

DHFS Region Part-Time (%) Full-Time (%) Total (%) Southern 4.3 2.5 6.8 Southeastern 3.7 4.1 7.8 Northeastern 3.7 2.6 6.3 Western 3.0 5.2 8.2


Table 11: Uninsured by Employment Status and DHFS Region

DHFS Region Part-Time (%) Full-Time (%) Total (%) Northern 3.1 4.3 7.4

Source: 2002-2004 FHS using DHFS Region membership designation


III. Predictors of County-Level Uninsurance Rates The availability and affordability of health insurance is a major policy concern in the U.S. About one-fifth of the U.S. population between the ages of 18 and 64 lacks health insurance and the size of this population is expected to rise as health insurance becomes more costly relative to income for many people (DeNavas-Walt, Proctor, and Mills 2004; Rowland 2004). According to the U.S. Census Bureau, the overall number of Americans without health insurance is up nearly seven million since 2000, to 46.6 million in 2005, the share of uninsured Americans has increased from 14.2% to 15.9%. In another example of the difficulties facing working Americans, the greatest declines in health insurance coverage occurred in employment-based insurance, which has dropped every year since 2000. In 2000, 63.6% of the population had employment-based coverage. By 2005, this rate had dropped to 59.5%. There was a significant increase in the number of uninsured workers between 2004 and 2005. In 2005, 18.7% of all workers were uninsured. Most strikingly, however, is the fact that full-time workers experienced lower rates of coverage in 2005, while part-time workers did not. There were nearly one million more uninsured full-time workers in 2005 than in 2004, with a significant increase of 2 percentage points in the share of uninsured workers since 2000. For most of the last decade, yearly job-based health insurance premium increases were higher than increases in both earnings and inflation (Gabel et al. 2001). If the premiums for health insurance continue to increase faster than personal income, recent estimates suggest that the percentage of uninsured workers could increase by more than 50 percent by 2009 (Gilmer and Kronick 2001). Although there is some empirical evidence that uninsured adults – particularly those with low income levels (e.g., Cunningham 1999) – have greater difficulties accessing healthcare in communities with high rates of uninsurance, it is not known whether any negative access-related effects extend to the insured population (e.g., Cunningham and Kemper 1998; Cunningham 2004). Moreover, extant research has not taken into account the complex contextual relationship between individual-level access to healthcare and community-level uninsurance rates after accounting for both individual and community effects. The purpose of this analysis was to analyze the relationship between community uninsurance rates and individual healthcare for uninsured individuals in Wisconsin. Using household survey data from the 2002-2004 Wisconsin Family Health Survey (FHS), we estimate multilevel logistic regression models for uninsurance and how this is related to individual and community level attributes. We hypothesize that uninsured individuals residing in communities with relatively high proportions of unemployment and inequality would be more likely to report that they did not have insurance coverage within the last year than if they lived in communities with lower proportions of uninsured individuals.


The absence of public or private health insurance could have health, economic and social effects on individuals, families and communities by influencing different aspects of the healthcare system (IOM 2004; Thorpe 2004). An initial issue is what determines the proportion of uninsured and their access to care. Then we could simply relate the variation in uninsured to the rate of the insured. If instead, different communities respond by providing more or less access to the uninsured, that access may itself affect the number of uninsured (Herring 2005) and, in turn, the levels of provision of services may positively depend on the number of uninsured, so that the levels of provision of services absorb some of the exogenous effect of various numbers of uninsured. Data Source We used data from the 2002-2004 Wisconsin Family Health Survey (N=20,723) to predict uninsurance. The FHS collects information about health insurance coverage, health status, health problems and the use of health care services among Wisconsin residents. The FHS is representative of households throughout Wisconsin. A random sample of households is telephoned by trained interviewers who speak with the household member most knowledgeable about the health of all household members. This respondent provides information for all people living in the household at the time of the interview. Additionally, U.S. Census data at the zip code level were utilized. Analytic Strategy For this study, a multilevel logistic regression model was used to analyze which individual and community-level variables help predict rates of no insurance across Wisconsin’s seventy-two counties. Further, this model provided the predicted county level estimates of uninsurance rates for the counties across the state. The outcome analyzed from the FHS was whether or not an individual had no insurance in the last year or not and was determined through three groups: the percentage of residents who were covered by any type of insurance over the entire 12 months preceding the telephone interview, the percentage who had coverage during part of the 12 months and had no insurance part of the time, and the percentage who had no health insurance at all during the preceding 12 months. The independent variable of greatest interest to this analysis was the estimated percentage uninsured in the community of residence (see Section IV). The empirical specification assumed that access and healthcare utilization were determined by the perceived need for services (self-reported health status), individual predisposing characteristics (age, gender, educational attainment, racial/ethnic background4, poverty status, part-time or full-time self-employment, marital status), and factors at the community/contextual level (percentage unemployed and income inequality in the community of residence). These factors were grouped and included in the model based on previous research suggesting that healthcare utilization and access vary substantially across these dimensions (Andersen and Davidson 2001; Andersen et al. 2002). 4 Other racial and ethnic backgrounds were studied, but determined to not possess the predictive power as that of those individuals categorized as being of Hispanic origin.


Multilevel models are appropriate for this study because it is interested in individual-level dependent variable but the available data afforded both community- and individual-level variables as predictors. This provides the ability to assess how community-level variables were related to individual outcomes after controlling for individual-level effects (Diez Roux, 2001). The models included random effects to account for dependence in the variation in community effects within each of the 72 counties. Findings The findings from this study should be considered by the State as it works toward overall expansion of publicly funded insurance coverage throughout Wisconsin. The results can provide insight to each of the communities across the seventy-two counties for better understanding individual and community level circumstances that help predict health insurance coverage. At the individual level, Table 12 shows the results from the multilevel logistic model of uninsurance and that individuals self-reporting fair to poor health had a positive relationship with not having insurance. The results show that being married is negatively related to the likelihood of not having insurance. Age is negatively related to individuals reporting not having insurance. Gender is negatively related to not having insurance meaning that females are significantly more likely to have insurance than males. Ones educational attainment is also important. The less education one has, the greater the likelihood for not having insurance. Individuals designated as poor or near-poor were positively related to not having insurance. Individuals of Hispanic origin are positively associated with not having insurance. Finally, when observing self-employed workers, those who are part-time are significantly and positively related to not having insurance while those self-employed full-time are negatively related to not having insurance. Looking at community-level variables, the unemployement percent of the community is positively related to not having insurance. The Gini Coefficient illustrates the level of income inequality in a community and the greater it is, the less likely people are to not be insured5. Among these variables, some clearly have moderate predictive power while others have substantial predictive qualities for community levels of uninsurance. Those individual-level variables that indicate strong predictive power include ones educational attainment (have less than a high school diploma or GED), being of Hispanic origin, self-reported fair to poor health, and being self-employed part-time. The level of unemployment in the community was extremely predictive of one being uninsured.

5 The Gini coefficient is a summary measure of income inequality index that can range from zero (perfectly equal distribution of income) to one (all income in the community goes to one person or household). Thus, higher values of the Gini coefficient indicate higher income inequality.


Table 12: Statistical Model

Table 12: Statistical Model Variable B S.E. Sig. Exp(B)

Level 1 (Individual) Constant 0.792 .373 0.034 0.453 Age -0.014 0.178 0.000 0.361 Gender (1=Female) -0.268 0.116 0.021 0.765 Educational Attainment (1= Less Than HS Grad or GED)

1.225 0.149 0.000 3.404

Hispanic (1=Yes) 0.885 0.242 0.000 2.423 Self Reported Fair to Poor Health 0.433 0.194 0.026 1.542

Poverty Status (Poor or Near Poor) 1.019 0.178 0.000 0.361

Part-time Self Employment 1.479 0.615 0.016 4.387

Full-time Self Employment -1.931 1.006 0.055 0.145

Married (1=Yes) -1.593 0.136 0.000 0.203 Level 2 (Community/Zip Code)

Gini Coefficient -21.842 7.499 0.004 0.000 Unemployment % (Zip Code Level) 6.939 2.387 0.004 1031.448

Variance explained of community (zip code) random effect .202 Log likelihood 2351.979 The demographic and socioeconomic differences between the insured and uninsured populations were large and statistically significant. On average, uninsured individuals had poorer health, were younger, had fewer years of education, were disproportionately of Hispanic origin, were less likely to be married, were male, tended to be self-employed part-time, were poorer and lived in poorer communities with higher rates of unemployment.


IV. Various Sources of the Uninsured by County Two specific sources of data provided further insight for gleaning information about the uninsured throughout Wisconsin. As discussed in the previous section of this report, the combined FHS for years 2002-2004 served as one source. The U.S. Census Bureau also provided small area estimates by county on uninsurance rates6. This information was then utilized to construct further estimates of insurance rates by county. One was developed by creating an average between the FHS and Census Bureau small area estimates. The final category was a predictive multilevel logistic model using FHS small area estimates to predict uninsurance rates for each of Wisconsin’s seventy-two counties. This model is described in detail in the next section. These estimates can be seen in Tables 13 and 14. Although these various sources of data provide similar or relatively close information in many cases, there is clear variation between the sources when looking at the extremes. For example, among DHFS regions, the FHS (6.9) and the average between the FHS-Census estimates (7.4) for the uninsured were greatest in the Western region. The Census Bureau estimates (10.2) were largest in the Northern region and the predictive model (7.5) had the greatest proportion of uninsured individuals in the Southern region. Similarly, when viewing the largest rates of uninsurance by county for each source, Washburn county was the greatest for the FHS (23.0) and the FHS-Census average (17.5), but Menominee county had the biggest proportion of uninsured (25.2) among the Census Bureau estimates as did Green Lake county (26.3) for the predictive model. Table 13: Uninsured Proportion by Region

Table 13: Uninsured Proportion by DHFS Region

By DHFS Region FHS Census

FHS-Census Average Predicted

Southern 6.1% 9.9% 6.5% 7.5% Southeastern 5.8% 8.6% 5.8% 5.3% Northeastern 6.2% 9.2% 7.1% 6.8% Western 6.9% 9.8% 7.4% 6.2% Northern 6.8% 10.2% 6.4% 6.8%

6 Census Bureau Small Area Estimates Health Insurance Coverage Definition – Current Population Survey (CPS) Based Defintion - A person was considered covered by health insurance at some time during the year if he or she was covered by at least one of the following types of coverages: Employer/union, Privately purchased (not related to employment), Medicare, Medicaid, Military health care (military, CHAMPUS, CHAMPVA, VA, Indian Health Services), Someone outside the household, Other. An individual can have more than one type of coverage during the year.


Table 14: Uninsured Proportion by County

Table 14: Uninsured Proportion by County from Four Sources

By County FHS Census FHS-Census

Average Predicted Adams 8.8% 13.5% 11.1% 8.1% Ashland 19.8% 12.8% 16.3% 2.7% Barron 5.3% 9.0% 7.2% 10.3% Bayfield 14.1% 12.7% 13.4% 22.1% Brown 6.8% 8.8% 7.8% 4.3% Buffalo 0.0% 8.7% 4.4% 4.5% Burnett 5.0% 11.2% 8.1% 3.9% Calumet 10.3% 6.5% 8.4% 10.3% Chippewa 9.0% 7.8% 8.4% 10.0% Clark 3.8% 15.4% 9.6% 8.6% Columbia 3.6% 7.0% 5.3% 2.6% Crawford 14.9% 11.4% 13.1% 25.2% Dane 5.5% 8.1% 6.8% 2.1% Dodge 4.4% 7.7% 6.0% 6.0% Door 3.3% 7.8% 5.5% 8.1% Douglas 12.5% 10.1% 11.3% 9.5% Dunn 8.2% 9.1% 8.6% 8.9% Eau Claire 6.4% 8.7% 7.5% 11.3% Florence 0.0% 10.2% 5.1% 17.4% Fond du Lac 4.7% 7.5% 6.1% 2.6% Forest 5.9% 12.0% 9.0% 17.0% Grant 3.3% 9.6% 6.4% 3.2% Green 4.4% 7.5% 6.0% 17.5% Green Lake 14.7% 9.6% 12.1% 26.3% Iowa 0.0% 6.8% 3.4% 2.9% Iron 10.8% 12.7% 11.8% 20.5% Jackson 6.4% 13.3% 9.8% 10.1% Jefferson 3.2% 8.1% 5.6% 0.4% Juneau 13.9% 12.3% 13.1% 18.0% Kenosha 12.7% 10.9% 11.8% 9.4% Kewaunee 6.7% 6.8% 6.7% 1.3% La Crosse 3.5% 7.3% 5.4% 1.4% Lafayette 0.0% 11.7% 5.9% 4.1% Langdale 3.0% 9.5% 6.3% 1.8% Lincoln 7.4% 7.3% 7.3% 17.6% Manitowoc 7.8% 7.6% 7.7% 7.4% Marathon 6.0% 7.4% 6.7% 5.0% Marinette 7.3% 9.0% 8.1% 8.8% Marquette 3.2% 9.8% 6.5% 4.4% Menominee 0.0% 25.2% 12.6% 5.9% Milwaukee 7.0% 13.9% 10.5% 8.4% Monroe 10.2% 12.0% 11.1% 6.5% Oconto 5.9% 8.0% 7.0% 6.5% Oneida 7.4% 8.2% 7.8% 4.0% Outagamie 2.4% 6.7% 4.5% 1.5% Ozaukee 4.9% 3.8% 4.3% 2.4% Pepin 0.0% 9.5% 4.8% 2.8%


Table 14: Uninsured Proportion by County from Four Sources

By County FHS Census FHS-Census

Average Predicted Pierce 5.5% 6.9% 6.2% 2.4% Polk 6.0% 7.8% 6.9% 3.0% Portage 7.1% 8.5% 7.8% 8.8% Price 0.0% 9.6% 4.8% 1.4% Racine 7.1% 10.8% 8.9% 11.4% Richland 15.5% 10.7% 13.1% 11.5% Rock 5.6% 10.2% 7.9% 15.0% Rusk 17.6% 12.6% 15.1% 15.5% St. Croix 6.5% 5.4% 6.0% 4.4% Sauk 8.1% 8.6% 8.4% 9.5% Sawyer 3.2% 14.9% 9.1% 18.1% Shawano 6.3% 9.8% 8.1% 16.3% Sheboygan 5.3% 7.6% 6.5% 3.3% Taylor 2.9% 9.4% 6.1% 0.4% Trempealeau 0.0% 7.2% 3.6% 0.7% Vernon 3.1% 13.7% 8.4% 4.4% Vilas 5.9% 10.8% 8.3% 3.5% Walworth 6.3% 10.6% 8.5% 12.0% Washburn 23.0% 11.9% 17.5% 2.0% Washington 5.0% 5.8% 5.4% 0.6% Waukesha 2.7% 6.5% 4.6% 8.7% Waupaca 17.5% 7.5% 12.5% 17.6% Waushara 14.4% 11.5% 12.9% 12.1% Winnebago 4.7% 6.9% 5.8% 3.7% Wood 2.7% 6.5% 4.6% 3.6% Wisconsin Average: 6.8% 9.6% 8.2% 8.1% Results from these four sources by county are illustrated in the following maps which geocode the percent of uninsured for each of the four uninsurance estimates. Clearly, the FHS and predictive model results illustrate much broader variation when viewing the uninsured rates within various percentage bands. This does make sense given the sampling approach (described in the first section of this report) and the FHS data are utilized with various Census Bureau measures for purposes of the predictive model. Tables 15 and 16 provide the small area estimates used for the predictive modeling.

Levels of Uninsurance by County from the Family Health Survey 2002-2004


Table 15: Small Area Estimate Analysis of Predicted Insurance Coverage by DHFS Region in Wisconsin

Table 15: Small Area Estimate Analysis of Predicted Insurance Coverage by DHFS Region in Wisconsin

By DHFS Region

Insurance Coverage Female

HS Diploma or

Greater Hispanic7

Self Reported

Fair or Poor

Health County

UnemploymentPoverty Percent Gini Index

Southern 92.5% 51.1% 89.7% 2.9% 9.8% 7.3% 8.0% 0.020 Southeastern 94.7% 52.9% 86.8% 5.1% 11.4% 8.9% 11.9% 0.026 Northeastern 93.2% 50.5% 86.6% 2.1% 11.2% 7.1% 7.2% 0.021 Western 93.8% 51.1% 88.2% 0.9% 10.3% 7.8% 8.9% 0.012 Northern 93.2% 49.7% 85.1% 1.3% 10.8% 7.3% 8.3% 0.016

Table 16: Small Area Estimate Analysis of Predicted Insurance Coverage by County in Wisconsin


By County Insurance Coverage Female

HS Diploma or

Greater Hispanic8

Self Reported

Fair or Poor

Health County


Adams 91.9% 50.0% 87.2% 2.5% 9.6% 6.1% 6.9% 0.022 Ashland 97.3% 41.7% 70.0% 0.0% 19.4% 9.0% 10.0% 0.001 Barron 89.7% 47.3% 82.5% 0.0% 7.2% 6.4% 9.5% 0.009 Bayfield 77.9% 54.9% 83.1% 7.2% 9.2% 6.0% 6.7% 0.019 Brown 95.7% 48.3% 81.0% 0.0% 21.7% 12.1% 7.8% 0.015 Buffalo 95.5% 52.1% 79.3% 0.0% 8.2% 3.4% 7.1% 0.015 Burnett 96.1% 36.4% 80.0% 0.0% 4.5% 11.8% 9.6% 0.005 Calumet 89.7% 46.7% 84.6% 2.7% 12.0% 9.8% 9.5% 0.014 Chippewa 90.0% 51.3% 92.5% 0.4% 5.6% 5.6% 5.7% 0.021

7 Hispanic of any race. 8 Hispanic of any race.




HS Diploma or

Greater Hispanic8

Self Reported

Fair or Poor

Health County


Clark 91.4% 47.9% 60.6% 1.1% 25.5% 13.1% 9.6% 0.002 Columbia 97.4% 52.1% 89.4% 6.1% 11.9% 11.8% 8.8% 0.021 Crawford 74.8% 47.8% 90.0% 0.0% 8.7% 6.7% 5.6% 0.012 Dane 97.9% 53.9% 93.4% 0.6% 7.0% 5.4% 9.7% 0.018 Dodge 94.0% 46.0% 78.3% 0.0% 16.0% 5.9% 8.6% 0.002 Door 91.9% 47.7% 74.1% 4.5% 18.2% 6.3% 10.2% 0.007 Douglas 90.5% 47.6% 78.0% 0.0% 14.3% 7.3% 7.2% 0.010 Dunn 91.1% 50.7% 83.1% 2.5% 10.1% 7.7% 7.0% 0.017 Eau Claire 88.7% 50.6% 83.3% 1.1% 6.3% 6.7% 7.2% 0.022 Florence 82.6% 50.3% 78.0% 3.7% 18.8% 8.1% 8.9% 0.012 Fond du Lac 97.4% 59.3% 76.7% 2.8% 13.9% 6.5% 8.4% 0.003 Forest 83.0% 62.5% 87.3% 0.0% 12.5% 12.6% 19.3% 0.000 Grant 96.8% 54.0% 83.4% 6.1% 13.2% 10.5% 16.2% 0.026 Green 82.5% 50.7% 81.9% 1.0% 14.4% 11.1% 10.2% 0.006 Green Lake 73.7% 49.0% 85.5% 0.7% 13.1% 7.0% 7.0% 0.011 Iowa 97.1% 52.7% 86.6% 1.2% 11.5% 7.8% 8.1% 0.019 Iron 79.5% 50.1% 92.0% 0.1% 8.1% 4.5% 5.6% 0.024 Jackson 89.9% 51.2% 95.6% 0.3% 7.0% 4.3% 3.6% 0.030 Jefferson 99.6% 41.2% 100.0% 0.0% 0.0% 4.8% 8.1% 0.010 Juneau 82.0% 50.9% 93.9% 3.4% 12.1% 6.2% 6.4% 0.017 Kenosha 90.6% 50.4% 92.2% 2.9% 15.8% 6.9% 7.2% 0.009 Kewaunee 98.7% 51.6% 88.9% 0.0% 10.1% 7.8% 8.4% 0.016 La Crosse 98.6% 53.1% 95.5% 0.0% 10.2% 4.8% 9.0% 0.010 Lafayette 95.9% 52.4% 83.2% 9.0% 14.3% 8.9% 9.3% 0.026 Langdale 98.2% 52.5% 83.3% 0.0% 16.3% 13.1% 9.6% 0.007 Lincoln 82.4% 53.3% 87.5% 3.9% 12.4% 7.9% 8.3% 0.018 Manitowoc 92.6% 58.5% 73.5% 1.5% 15.4% 15.1% 11.0% 0.000 Marathon 95.0% 50.8% 94.2% 0.0% 10.0% 6.0% 4.2% 0.025 Marinette 91.2% 49.8% 89.3% 3.8% 8.0% 8.4% 7.6% 0.020 Marquette 95.6% 50.0% 83.9% 0.0% 11.3% 9.1% 11.2% 0.008




HS Diploma or

Greater Hispanic8

Self Reported

Fair or Poor

Health County


Menominee 94.1% 47.1% 62.2% 0.0% 12.7% 8.1% 8.2% 0.008 Milwaukee 91.6% 50.3% 85.6% 3.3% 10.6% 6.5% 6.6% 0.025 Monroe 93.5% 49.3% 62.9% 0.0% 14.1% 6.1% 8.8% 0.006 Oconto 93.5% 48.9% 86.8% 0.7% 8.9% 3.6% 7.9% 0.012 Oneida 96.0% 52.0% 84.3% 0.0% 6.9% 8.4% 10.9% 0.001 Outagamie 98.5% 46.8% 89.8% 0.0% 21.6% 8.3% 8.0% 0.013 Ozaukee 97.6% 49.0% 78.8% 4.1% 6.2% 8.5% 8.0% 0.017 Pepin 97.2% 50.0% 83.3% 0.0% 17.6% 17.5% 9.7% 0.004 Pierce 97.6% 51.9% 87.4% 4.0% 7.8% 5.4% 4.6% 0.028 Polk 97.0% 50.8% 97.0% 1.0% 6.6% 4.6% 4.1% 0.029 Portage 91.2% 47.0% 76.0% 0.6% 17.1% 12.5% 7.4% 0.014 Price 98.6% 53.2% 85.7% 0.0% 16.5% 12.9% 9.6% 0.001 Racine 88.6% 50.4% 88.5% 0.7% 11.2% 5.8% 7.5% 0.022 Richland 88.5% 48.7% 91.1% 0.9% 10.6% 4.6% 7.8% 0.023 Rock 85.0% 53.3% 87.5% 3.9% 12.4% 7.9% 8.3% 0.018 Rusk 84.5% 58.5% 73.5% 1.5% 15.4% 15.1% 11.0% 0.000 St. Croix 95.6% 50.8% 94.2% 0.0% 10.0% 6.0% 4.2% 0.025 Sauk 90.5% 49.8% 89.3% 3.8% 8.0% 8.4% 7.6% 0.020 Sawyer 81.9% 50.0% 83.9% 0.0% 11.3% 9.1% 11.2% 0.008 Shawano 83.7% 47.1% 62.2% 0.0% 12.7% 8.1% 8.2% 0.008 Sheboygan 96.7% 50.3% 85.6% 3.3% 10.6% 6.5% 6.6% 0.025 Taylor 99.6% 49.3% 62.9% 0.0% 14.1% 6.1% 8.8% 0.006 Trempealeau 99.3% 48.9% 86.8% 0.7% 8.9% 3.6% 7.9% 0.012 Vernon 95.6% 52.0% 84.3% 0.0% 6.9% 8.4% 10.9% 0.001 Vilas 96.5% 46.8% 89.8% 0.0% 21.6% 8.3% 8.0% 0.013 Walworth 88.0% 49.0% 78.8% 4.1% 6.2% 8.5% 8.0% 0.017 Washburn 98.0% 50.0% 83.3% 0.0% 17.6% 17.5% 9.7% 0.004 Washington 99.4% 51.9% 87.4% 4.0% 7.8% 5.4% 4.6% 0.028 Waukesha 91.3% 50.8% 97.0% 1.0% 6.6% 4.6% 4.1% 0.029 Waupaca 82.4% 47.0% 76.0% 0.6% 17.1% 12.5% 7.4% 0.014




HS Diploma or

Greater Hispanic8

Self Reported

Fair or Poor

Health County


Waushara 87.9% 53.2% 85.7% 0.0% 16.5% 12.9% 9.6% 0.001 Winnebago 96.3% 50.4% 88.5% 0.7% 11.2% 5.8% 7.5% 0.022 Wood 96.4% 48.7% 91.1% 0.9% 10.6% 4.6% 7.8% 0.023

the uninsured in wisconsin uninsured in... · of residents had health insurance for only part of...

Documents