Michel Boudreaux presented, "Changes to the Imputation Routine for Health Insurance in the CPS ASEC," at the Joint Statistical Meetings of the American Statistical Association, August 3, 2011 in Miami Beach, FL.

This reserach reflects work conducted for the U.S. Census Bureau.

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Joanna Turner presented "Modeling Health Insurance Coverage Estimates for Minnesota Counties," at the Joint Statistical Meetings of the American Statistical Association, August 1, 2011 in Miami Beach, FL.

In this paper SHADAC created a model to predict lack of insurance coverage for Minnesota counties using multiple data sources. While the results are exploratory in nature, the methodology allows for additional applications using the Bayesian framework developed.

This research was based on work conducted for the Minnesota Department of Health, Health Economics Program.

Download the Conference Proceedings paper.

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SHADAC Issue Brief #25 provides state-level estimates of health insurance coverage by age and income from the American Community Survey (ACS).  The ACS is a relatively new source of state-level health insurance coverage estimates, but its large sample size makes it a potentially powerful source of information for states. The references at the end of this brief provide background on the ACS and how it compares to other surveys that measure health insurance coverage.

The maps and tables included in this brief provide state-level estimates for the nonelderly population (age 0 to 64), children (age 0 to 18), and nonelderly adults (age 19 to 64). Within each age group, we also present separate estimates for the low-income population (people with family incomes below 200 percent of federal poverty guidelines).

Standard errors of the estimates are provided in the Appendix.

Objectives. We sought to determine whether aggregate national data for American Indians/Alaska Natives (AIANs) mask geographic variation and substantial subnational disparities in prenatal care utilization.

Methods. We used data for US births from 1995 to 1997 and from 2000 to 2002 to examine prenatal care utilization among AIAN and non-Hispanic White mothers. The indicators we studied were late entry into prenatal care and inadequate utilization of prenatal care. We calculated rates and disparities for each indicator at the national, regional, and state levels, and we examined whether estimates for regions and states differed significantly from national estimates. We then estimated state-specific changes in prevalence rates and disparity rates over time.

Results. Prenatal care utilization varied by region and state for AIANs and non-Hispanic Whites. In the 12 states with the largest AIAN birth populations, disparities varied dramatically. In addition, some states demonstrated substantial reductions in disparities over time, and other states showed significant increases in disparities.

Conclusions. Substantive conclusions about AIAN health care disparities should be geographically specific, and conclusions drawn at the national level may be unsuitable for policymaking and intervention at state and local levels. Efforts to accommodate the geographically specific data needs of AIAN health researchers and others interested in state-level comparisons are warranted.

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OBJECTIVES:

We examined whether 3 nationally representative data sources produce consistent estimates of disparities and rates of uninsurance among the American Indian/Alaska Native (AIAN) population and to demonstrate how choice of data source impacts study conclusions.

METHODS:

We estimated all-year and point-in-time uninsurance rates for AIANs and non-Hispanic Whites younger than 65 years using 3 surveys: Current Population Survey (CPS), National Health Interview Survey (NHIS), and Medical Expenditure Panel Survey (MEPS).

RESULTS:

Sociodemographic differences across surveys suggest that national samples produce differing estimates of the AIAN population. AIAN all-year uninsurance rates varied across surveys (3%-23% for children and 18%-35% for adults). Measures of disparity also differed by survey. For all-year uninsurance, the unadjusted rate for AIAN children was 2.9 times higher than the rate for White children with the CPS, but there were no significant disparities with the NHIS or MEPS. Compared with White adults, AIAN adults had unadjusted rate ratios of 2.5 with the CPS and 2.2 with the NHIS or MEPS.

CONCLUSIONS:

Different data sources produce substantially different estimates for the same population. Consequently, conclusions about health care disparities may be influenced by the data source used.

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