U.S. Department of Commerce

Small Area Health Insurance Estimates (SAHIE)

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Frequently Asked Questions about SAHIE


  1. What is SAHIE?
  2. How are the SAHIE program state and county estimates constructed?
  3. What is the definition of insured?
  4. What is the definition of income-to-poverty rates (IPRs) less than or equal to 138, 200, 250, 400 and 138 to 400 percent?
  5. Why don't you have data for ages 65 and over?
  6. What does MOE stand for? What does it mean?
  7. Why was a new IPR added for the 2012 SAHIE release?
  8. Why did SAHIE switch from using data from the Current Population Survey (CPS) to the American Community Survey (ACS)?
  9. If I have further questions, whom should I contact?
  10. How can one make statistical comparisons using SAHIE data?
  11. Why were the SAHIE 2009 county-level data revised?
  1. What is SAHIE?

    SAHIE is the Small Area Health Insurance Estimates program of the U.S. Census Bureau. SAHIE produces and disseminates model-based estimates of health insurance coverage for counties and states.

    The SAHIE program recently released 2012 COUNTY estimates of people with and without health insurance coverage by:

    • ages 0-64, 18-64, 40-64, and 50-64;
    • female, male, and both sexes;
    • all incomes and income-to-poverty ratios (IPR) less than or equal to 138, 200, 250, 400, and 138 to 400 percent of the poverty threshold;
    • a special estimate for the 0-18 age group for 'both sexes,' and 'all incomes,' less than or equal to 138, 200, 250, or 400 percent of the poverty threshold; and
    • measures of uncertainty of the estimates.

    Also recently released are the 2012 STATE estimates of people with and without health insurance coverage by:

    • ages 0-64, 18-64, 40-64 and 50-64;
    • female, male, and both sexes;
    • all Races, White alone (not Hispanic), Black alone (not Hispanic), and Hispanic (any race);
    • all incomes and income-to-poverty ratios (IPR) less than or equal to 138, 200, 250, 400, and 138 to 400 percent of the poverty threshold;
    • a special estimate for the 0-18 age group for ‘both sexes,’ and ‘all incomes,’ less than or equal to 138, 200, 250, 400 percent of the poverty threshold; and
    • measures of uncertainty of the estimates.

    For more information, please see our About SAHIE page.

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  2. How are the SAHIE program state and county estimates constructed?

    The SAHIE program models health insurance coverage by combining survey data with population estimates and administrative records. SAHIE estimates are based on data from the following sources:

    • American Community Survey (ACS);
    • demographic population estimates;
    • aggregated federal tax returns;
    • participation records for the Supplemental Nutrition Assistance Program (SNAP), formerly known as the Food Stamp program;
    • County Business Patterns;
    • Medicaid and Children's Health Insurance Program (CHIP) participation records; and
    • Census 2010.

    More information is available at Methodology.

    For further information on these data sources, see information about data inputs.

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  3. What is the definition of insured?

    SAHIE uses the American Community Survey (ACS) definition of insured: Is this person CURRENTLY covered by any of the following types of health insurance or health coverage plans?

    • Insurance through a current or former employer or union (of this person or another family member)
    • Insurance purchased directly from an insurance company (by this person or another family member)
    • Medicare, for people 65 and older, or people with certain disabilities (Note: SAHIE does not report insurance rates for people over 65 since over 98% of people over the age of 65 are insured)
    • Medicaid, Medical Assistance, or any kind of government-assistance plan for those with low incomes or a disability
    • TRICARE or other military health care
    • Indian Health Services*
    • VA (including those who have ever used or enrolled for VA health care)
    • Any other type of health insurance or health coverage plan (user specified)
    • *People whose only health coverage is Indian Health Service are uninsured as IHS is not considered comprehensive coverage.

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  4. What is the definition of income-to-poverty rates (IPRs) less than or equal to 138, 200, 250, 400 and 138 to 400 percent?

    These categories are defined by the ratio of family income to the federal poverty threshold. A lower ratio indicates lower income. Less than or equal to 138 percent of poverty indicates people in families with total money income less than or equal to 138 percent of the federal poverty threshold applicable to that family. The same reasoning holds for the additional IPRs listed.

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  5. Why don't you have data for ages 65 and over?

    Most people ages 65 and over are covered by Medicare or Supplemental Security Income (SSI). According to recent CPS ASEC data, less than 2 percent of the 65+ population were uninsured nation-wide.

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  6. What does MOE stand for? What does it mean?

    MOE stands for "margin of error" or the difference between an estimate and its upper or lower confidence bounds. Confidence bounds can be created by adding the margin of error to the estimate (for an upper bound) and subtracting the margin of error from the estimate (for a lower bound). All published margins of error for the SAHIE program are based at the 90 percent significance level.

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  7. Why was a new IPR added for the 2012 SAHIE release?

    SAHIE includes IPRs used by the Patient Protection and Affordable Care Act. Families in the income range of "greater than or equal to 138 percent IPR but less than or equal to 400 percent IPR" are eligible for subsidies to purchase health insurance through the Health Insurance Marketplace. In states where Medicaid expansion has not occured, the population eligible for health insurance subsidies is "greater than 100 percent IPR but less than or equal to 400 percent IPR".

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  8. Why did SAHIE switch from using data from the Current Population Survey (CPS) to the American Community Survey (ACS)?

    The full-production ACS has a sample size of roughly 3 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). Single-year direct survey estimates are published for counties and other places with a population size of 65,000 or larger, and three-year estimates are published for counties and other places with population sizes of 20,000 or larger.

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  9. If I have further questions, whom should I contact?

    You can ask questions or send feedback by e-mailing the Small Area Estimates Branch or calling the Demographic Call Center Staff at 301-763-2422 or 1-866-758-1060 (toll free).

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  10. How can one make statistical comparisons using SAHIE data?

    If one would like to make comparisons between estimates of uninsured rates (or other SAHIE concepts) for different areas or demographic groups in a given year, or between estimates for the same area and demographic group for any two years after 2007, a reasonable approximation is available. First, construct an approximate upper bound for the margin of error (MOE) of the difference between the two estimates chosen for comparison. This approximate upper bound on the MOE is constructed as the square root of the sum of the squared MOEs for each estimate (see example below).

    If this constructed MOE for the difference is smaller than the absolute value of the calculated difference between the two point estimates then one can conclude that the two estimates are significantly different at the 90 percent confidence level or greater. If the MOE for the difference is larger than the calculated difference between the two point estimates then the comparison is inconclusive as to whether or not there is a statistically significant difference.

    For example, say the SAHIE estimate for percent uninsured within the population ages 18-64 at all income levels, were 15.1 percent for county A, with a MOE of 1.4 percent, and 18.2 percent in county B, with a MOE of 1.5 percent. Then the calculation is: The MOE for the difference between the two estimates = square-root (1.4 x 1.4 + 1.5 x 1.5) = 2.1 percent. The absolute value of the difference between the two estimates = 18.2 - 15.1 = 3.1 percent. Since the MOE for the difference, 2.1 percent, is less than the difference between the two estimates, 3.1 percent, one can conclude that the two estimates are significantly different at least at the 90 percent confidence level. In contrast, if county A's uninsured rate were instead, say 16.3 percent, and all other values as above then the difference between the two estimates, 1.9 percent, is less than the MOE of the difference, 2.1 percent, and the test would be inconclusive.

    Note that this method produces a reasonable upper bound approximation to the 90% confidence MOE for the difference and not an exact MOE. The reason for this caveat is that we do not have the estimated correlations among domains. Preliminary research on SAHIE estimates suggests that correlations between estimates within a year for different counties or states, or for different demographic groups, are mostly positive or small. Thus, for two estimates within a year, the MOE for the difference would be smaller than or of similar magnitude to that calculated above. Research from the Small Area Income and Poverty Estimates program has suggested that correlations between years for a given domain are mostly positive, meaning that the MOE would be smaller in general than the one calculated in the steps above, yielding a smaller confidence interval for the difference.

    For year-to-year comparisons, we recommend only comparing years after 2007 due to a substantial methodological change implemented between the 2007 and 2008 estimates, as described in our methodology documentation, resulting in a difference in how the two concepts measure health insurance coverage.


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  11. Why were the SAHIE 2009 county-level data revised?

    The SAHIE 2009 county-level estimates have been revised and re-released on November 1, 2012 due to a processing error present in the original October 2011 release.

    The poverty universe used during the original 2009 processing was incorrect. The appropriate parts of the group quarters population were not excluded from the universe. This revision most affected county estimates where a large portion of the county population resides in group quarters, such as prisons, barracks, and dorms.

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Source: U.S. Census Bureau | Small Area Health Insurance Estimates |  Last Revised: March 13, 2014