Starting with 2008 SAHIE, we model single-year health insurance coverage as measured by the American Community Survey (ACS). The ACS is an ongoing survey that provides vital information on a yearly basis about our nation and its people. Information from the survey generates data that help determine how more than $675 billion in federal and state funds are distributed each year. The full-production ACS has a sample size of roughly 3.5 million addresses, and the sample is selected from all counties and county equivalents in the United States, and from all municipios in Puerto Rico (PR).

For 2007 SAHIE and prior, the Annual Social and Economic Supplement (ASEC) of the Current Population Survey (CPS) was used. While the CPS ASEC is the official source for national estimates of health insurance coverage, the ACS has more precise state-level and sub-state estimates.  Starting with 2008 ACS, the health insurance question was added to the ACS to enable the U.S. Department of Health and Human Services and other federal agencies to more accurately distribute resources and better understand state and local health insurance needs.  This health insurance question had been part of the ACS Content Test administered in 2006.

The SAHIE modeling uses single-year ACS direct survey estimates from all counties and states regardless of population size. Single-year ACS estimates are published for counties and other places with population size 65,000 or larger, and five-year ACS estimates are available for all counties, school districts, and other small geographic areas (e.g., census tracts or block groups). Since collection for the health insurance question started with 2008 ACS, the first five-year ACS health insurance estimates were released in December 2013.

The ACS health insurance question asks respondents whether they are currently covered at the time of interview, based on interviews conducted throughout the year.  Respondents are considered insured if they are covered by any type of health insurance coverage, and they are considered uninsured if they are not covered by any type of health insurance. People with no coverage other than access to Indian Health Service are also considered uninsured. For more information about the concept measured in ACS compared to that measured in CPS ASEC or Survey of Income and Program Participation, see the About Health Insurance page. 

The ACS health-insurance estimates are typically reported for the civilian noninstitutionalized population, which excludes active-duty military personnel and excludes persons in correctional institutions, juvenile institutions, nursing homes, skilled nursing facilities, psychiatric hospitals, and facilities for the disabled. However, the SAHIE data include only those for whom income is reported in the ACS (i.e., the "poverty universe"), which is a slightly more restrictive subset.

SAHIE also uses income data from the ACS in order to estimate the numbers of people in specific income-to-poverty ratio (IPR) categories.  IPR is defined as total income of the family divided by the poverty threshold for that family size.  For example, the SAHIE 200% IPR data provide estimates of the insured and uninsured who have income at or below 200% of the poverty threshold.

Starting with 2010 SAHIE, the following three SAHIE predictors are created from five-year ACS: educational-attainment estimates, citizenship-status estimates, income-to-poverty ratio (IPR) estimates. For 2009 SAHIE and prior, these three predictors were created from the Census 2000 long-form sample. The SAHIE modeling by education, citizenship and IPR is described in the methodology documents available at Methodology. Importantly, we lag the five-year ACS predictors back by one year in order to avoid any overlap and/or correlation between the estimation years and the predictor years. For instance, for 2011 SAHIE, we model 2011 single-year ACS in conjunction with 2006-2010 five-year ACS predictors.

For more information about health insurance coverage see the health insurance main page.

For more information about characteristics of the ACS data see the ACS main page.