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Publication
State-Level Health Insurance Coverage Estimates from the 2009 American Community Survey
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.
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Publication
Immigrant children's access to health care: differences by global region of birth
We use data from the National Health Interview Survey (2000-2006) to examine the social determinants of health insurance coverage and access to care for immigrant children by 10 global regions of birth. We find dramatic differences in the social and economic characteristics of immigrant children by region of birth. Children from Mexico and Latin America fare worse than immigrant children born in the U.S. with significantly lower incomes and little or no education. These social determinants, along with U.S. public health policies regarding new immigrants, create significant barriers to access to health insurance coverage, and increase delayed or foregone care. Uninsured immigrant children had 6.5 times higher odds of delayed care compared with insured immigrant children.
Publication
American Indian/Alaska Native uninsurance disparities: A comparison of three surveys.
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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Publication
Disparities in Public Use Data Availability for Race, Ethnic, and Immigrant Groups: National Surveys for Healthcare Disparities Research
Background: Disparities in healthcare coverage and access have a prominent place on the national health policy agenda. It is, therefore, essential to understand strengths and limitations of national surveys that provide annual or periodic data for population-based healthcare disparities research and monitoring. Importantly, publicly available data on healthcare coverage and access are needed for disparities populations defined by race, ethnicity, or immigrant group (REI).
Objective: To document public use data availability for REI groups, insurance coverage, and access to care measures in selected national surveys used for healthcare disparities research.
Design: We examined public use data for general population surveys that collect information on healthcare coverage and access on an annual or periodic basis for the nation. Data sources examined include the following: Current Population Survey, Survey of Income and Program Participation, National Health Interview Survey (NHIS), National Health and Nutrition Examining Survey, National Survey of Children's Health, Behavioral Risk Factor Surveillance System, and Medical Expenditure Panel Survey-Household Component.
Results: Although each survey has strengths for healthcare disparities research, there is no single survey that has detailed REI group identifiers, comprehensive measures of coverage and access, and geographic identifiers.
Conclusions: Current Population Survey and NHIS have detailed REI identifiers. NHIS and Medical Expenditure Panel Survey-Household Component have comprehensive measures of coverage and access but are limited by smaller samples and no geography. Findings summarized in this article will assist with identifying existing data to examine healthcare coverage and access disparities and highlight areas for improvement in public use data availability.
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Publication
Counting Uninsurance and Means-Tested Coverage in the American Community Survey: A Comparison to the Current Population Survey
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.
