223517 County as a Fundamental Unit of Analysis of Health Insurance Coverage in the United States

Tuesday, November 9, 2010 : 3:30 PM - 3:45 PM

Tennille Marley, MPH , Department of Sociology, Robert Wood Johnson Foundation Center for Health Policy, University of New Mexico, Albuquerque, NM
Lisa Cacari-Stone, PhD , Department of Family & Community Medicine, Robert Wood Johnson Foundation Center for Health, University of New Mexico Health Sciences Center, Albuquerque, NM
Sonia Bettez, MSW , Department of Sociology, Robert Wood Johnson Foundation Center for Health Policy, University of New Mexico, Albuquerque, NM
Blake Boursaw, MS , Robert Wood Johnson Foundation Center for Health Policy, University of New Mexico, Albuquerquea, NM
Howard Waitzkin, MD, PhD , Departments of Sociology and Robert Wood Johnson Foundation Center for Health Policy, University of New Mexico, Albuquerque, NM
As the debate about health care reform continues, counties remain an important locus of access and barriers to access for the uninsured, the underinsured, and those covered under medically indigent adult programs. National- and state-level proposals for improving health equity rarely consider current policies and potential interventions at the county level. This study examined inter-county variability in insurance coverage, as well as county characteristics as predictors of uninsurance. We merged and analyzed data for 3,079 counties from public use data sets (Area Resource File, USA Counties, and the Small Area Health Insurance Estimates program). Multiple regression analysis, including semi-partial correlation coefficients, assessed the relative importance of local structural capacities (tax base, income, and health professional availability), political ideology (votes cast for President by political party), and county demographic characteristics in predicting health insurance coverage. We found major inter-county variability in uninsurance ranging from 4% to 38%, a much wider range than revealed by state-level aggregated data. In the multiple regression analysis, variables strongly predicting county-level uninsurance rates included percentage of persons living in poverty, percentage Hispanic, and percentage voting Republican. County-level regression analysis was able to explain 84% of the variance in uninsurance rates.

Findings from our study provide insights for public health leaders, researchers, and policy makers on the extent of inequities in access to health care and potential solutions for improving coverage. As one example, our findings show that the problem of uninsurance is highly concentrated in a limited number of counties throughout the country. In contrast, some counties are capable of mobilizing local leadership and resources to support policy changes that enhance access. The responsibility for financing, delivery, and regulation of health care in the United States is shared jointly among federal, tribal, state, and county governments. Although this inter-governmental authority has comprised a cornerstone of our nation's health care safety net, the details of these relationships remain surprisingly varied. Strategies for achieving health care reform should embrace inter-governmental solutions that consider the role of counties in providing health and public health services, increasing insurance coverage, and leveraging resources to assure adequate local infrastructure for delivery of services at the county level.

Learning Areas:
Public health or related public policy

Learning Objectives:
Explain the predictors of uninsurance at the county-level. Explain why national- and state-level proposals for improving health should consider the county as a fundamental unit of health insurance coverage. Discuss how neglecting county-level information serves to mask health inequities for disadvantaged populations.

Keywords: Health Insurance, Public Policy

Presenting author's disclosure statement:

Qualified on the content I am responsible for because: I am qualified to present because I am the third author and I contributed to this study by helping with acquisition of data, analysis and interpretation of data, critical revision of the manuscript for important intellectual content, statistical analysis, and administrative, technical or material support.
Any relevant financial relationships? No

I agree to comply with the American Public Health Association Conflict of Interest and Commercial Support Guidelines, and to disclose to the participants any off-label or experimental uses of a commercial product or service discussed in my presentation.