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Counting Uninsurance and Means-Tested Coverage in the American Community Survey: A Comparison to the Current Population Survey

Authors

  • Michel Boudreaux,

    1. University of Minnesota, 2221 University Ave. SE, Suite 345, Minneapolis, MN 55414
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    • Address correspondence to Michel Boudreaux, M.S., University of Minnesota, 2221 University Ave. SE, Suite 345, Minneapolis, MN 55414; e-mail: boudr019@umn.edu. Jeanette Y. Ziegenfuss, Ph.D, is with the Mayo Clinic, Rochester, MN. Peter Graven, B.S., and Lynn A. Blewett are with the Division of Health Policy and Management, School of Public Health, at the University of Minnesota, Minneapolis, MN. Michael Davern, Ph.D., is with the National Opinion Research Center at the University of Chicago, Chicago, IL.

  • Jeanette Y. Ziegenfuss,

    1. Mayo Clinic, Rochester, MN
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  • Peter Graven,

    1. Division of Health Policy and Management, School of Public Health, at the University of Minnesota, Minneapolis, MN
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  • Michael Davern,

    1. National Opinion Research Center at the University of Chicago, Chicago, IL.
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  • Lynn A. Blewett

    1. Division of Health Policy and Management, School of Public Health, at the University of Minnesota, Minneapolis, MN
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Abstract

Objective. To compare health insurance coverage estimates from the American Community Survey (ACS) to the Current Population Survey (CPS-ASEC).

Data Sources/Study Setting. The 2008 ACS and CPS-ASEC, 2009.

Study Design. We compare age-specific national rates for all coverage types and state-level rates of uninsurance and means-tested coverage. We assess differences using t-tests and p-values, which are reported at <.05, <.01, and <.001. An F-test determines whether differences significantly varied by state.

Principal Findings. Despite substantial design differences, we find only modest differences in coverage estimates between the surveys. National direct purchase and state-level means-tested coverage levels for children show the largest differences.

Conclusions. We suggest that the ACS is well poised to become a useful tool to health services researchers and policy analysts, but that further study is needed to identify sources of error and to quantify its bias.

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