A Shared Destiny Community Effects of Uninsurance (2003) / Chapter Skim
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Appendix B: Geographic Differences in Uninsured Rates
Appendix B: Geographic Differences in Uninsured Rates
Pages 180-188
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From page 180... ...
for care, the relationship between these two measures, and the response of health care institutions and service providers to these factors. Given the limitations of existing data and studies, virtually all studies to date of community effects and this report rely for the most part on the uninsured rate as a measure of the likely relative impact of uninsurance .
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From page 181... ...
There is considerable geographic variation in the distribution of uninsured persons regionally and from state to state; see Figures B.1 and B.2 and Tables C.1, C.2, and C.3 in Appendix C Using CPS-derived estimates of uninsured rates to compare geographically defined communities involves acknowledging some important methodological limitations related to the sample sizes of the CPS survey and the period of time over which coverage status is measured.
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From page 182... ...
; persons reporting uninsured status at any time in the previous 12 months (average 21 percent uninsured rate, 6.2 million people) ; and persons uninsured for the entire 12 months preceding the survey interview (12 percent, or 3.6 million people)
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From page 183... ...
Both unemployment rates and average weekly income are strong predictors of uninsured rates in Ohio. · Southwestern border: For Texas, federal CPS data give the highest uninsured rate estimates for the border counties, although 75 percent of uninsured persons 2See the Committee report, Coverage Matters: Insurance and Health Care (IOM, 2001a)
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From page 184... ...
Modeling each county's uninsured rate as a function of the local economy, population demographic characteristics, and characteristics of the health services market, the authors find that the local unemployment rate is a key predictor of a county's uninsured rate, tempered by relative economic fortunes (e.g., whether in a recession or an upturn in the business cycle) (Marsteller et al., 1998~.
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From page 185... ...
For example, in Florida, rural, ethnically diverse counties with smaller populations have the highest uninsured rates, as does urban Dade county, while the lowest uninsured rates are in counties with smaller cities and suburban populations and relatively low proportions of African American and Hispanic residents (Lazarus et al., 2000~. Within a locality or health services market, the presence of a diversity of culturally or linguistically defined communities also has implications for health, health services delivery, and community effects.
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From page 186... ...
NATIONAL COMPARISONS Two national studies use different data sets to compare uninsured rates among sites around the nation. The first uses estimates from the CPS in a survey of the 85 largest metropolitan areas in the United States (1998)
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From page 187... ...
Making this adjusted comparison nonetheless substantially revises estimates for some regions. For example, the four southwestern border states, all with uninsured rates 5 to 12 percentage points above the national average of 17.5 percent, would have much lower uninsured rates if the measured characteristics of their states' populations matched those of the nation overall.
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From page 188... ...
188 A SHARED DESTINY: COMMUNITY EFFECTS OF UNINSURANCE Because the statistical adjustments in the analysis that the Committee originally performed mask the distinctiveness of local factors that are of policy interest, this report focuses on a number of these factors themselves, as they appear together within localities.
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