Address correspondence to Brett O'Hara, Ph.D., Census Bureau, Data Integration Division, Small Area Estimates Branch, 6H122A, Washington, DC 20233-8500, e-mail: Brian.J.Ohara@census.gov
Experimental Health Insurance Estimates for Low-Income and Demographic Groups by State
Article first published online: 5 MAY 2008
No claim to original U.S. government works. © Health Research and Educational Trust
Health Services Research
Volume 43, Issue 5p1, pages 1693–1707, October 2008
How to Cite
O'Hara, B. (2008), Experimental Health Insurance Estimates for Low-Income and Demographic Groups by State. Health Services Research, 43: 1693–1707. doi: 10.1111/j.1475-6773.2008.00851.x
- Issue published online: 20 SEP 2008
- Article first published online: 5 MAY 2008
- small area analysis;
- health policy
Objective. To assess the quality of new modeled estimates of health insurance based on a federal survey.
Data Sources/Study Setting. The study uses data from the Annual Social and Economic Supplements to the Current Population Survey (CPS ASEC), calendar years 2001–2003. Health insurance estimates for low-income populations are analyzed.
Study Design. To assess a method for making estimates for uninsured low-income persons, survey estimates of low-income children are compared with modeled estimates. Inferences can be drawn from this comparison and the method is extended to account for demographic groups.
Data Collection. Data for 2001–2002 CPS ASEC were self-tabulated for low-income children aged 0–17. A special tabulation of the CPS ASEC was used to categorize the numbers of uninsured by age, race, sex, and Hispanic origin by low income at the state level. This special tabulation was the underlying data for the model.
Principal Findings. The modeled estimates reduce the variance and margin of error substantially compared with the survey estimates.
Conclusions. These health insurance estimates are credible and increase the precision for the low-income uninsured population. They have broad uses for policy makers and program administrators who focus on the uninsured in special populations.