Small Area Health Insurance Estimates (SAHIE) Program

1. Small Area Health Insurance Estimates (SAHIE) Program Joanna Turner, Robin Fisher, David Waddington, and Rick Denby U.S. Census Bureau October 6, 2004

2. Motivation for Estimates of the Uninsured • Broad interest in health insurance coverage issues • Not a question on the decennial census or on the American Community Survey (ACS) • State Children’s Health Insurance Program (SCHIP)

3. Small Area Income and Poverty Estimates (SAIPE) • Model-based estimates of median household income and poverty for states, counties, and school districts • Uses survey data, administrative records, and decennial census data • Estimates evaluated favorably by National Academy of Sciences (NAS) panel • Estimates used by Department of Education and by Department of Health and Human Services

4. SAIPE (2) • School district estimates are now produced annually • This fall will release estimates for 2001 and 2002, taking a full year off the lag time

5. Small Area Health Insurance Estimates (SAHIE) • Extend SAIPE knowledge and methodologies to the area of health insurance coverage • Estimates for all states and counties • Estimates for various age groups • Estimates of mean squared error

6. Experimental Estimates • State • Number of uninsured children ages 0-18 in households with income 200% of the federal poverty threshold in 1999 (SCHIP) • County • Total number of uninsured in 1999 and 2000

7. Survey Estimates • Current Population Survey’s (CPS) Annual Social and Economic Supplement (ASEC)

8. Covariates • Tax data • Medicaid Statistical Information System (MSIS): enrollment • Food Stamp Program: number of participants • Census 2000 and Demographic Population Estimates

9. IRS proportions between multiples of federal poverty threshold: (100%, 130%) (200%, 300%) Proportion enrolled in Medicaid: Children ages 0-18 Adults ages 35-64 Hispanics Blacks Variable SelectionCounty Total Insured

10. Census 2000 and Demographic Population Estimates Total population Proportion Hispanic Food Stamps Number of participants Variable Selection(2)County Total Insured

11. Role for Population Estimates • Covariates • Multiply by our rates to get numbers

12. Review and Advice • Census Advisory Committee • State Health Access Data Assistance Center (SHADAC) • Will help with validation study using states’ surveys • Federal State Cooperative Program for Local Population Estimates (FSCPE) • State Data Centers (SDC)

13. Census Advisory Committee ’02on the State Plan • Inclusion of Race/Ethnicity • Explore further the relationship between Hispanic ethnicity and insurance status • Bayesian methods ok? What priors? • Be explicit about modeling assumptions and do research regarding robustness to the assumptions

14. Census Advisory Committee ’02on the State Plan(2) • Can we use data of uneven availability/reliability? • Yes, unless the jurisdictions have incentive to not report.

15. Further Reading • “Health Insurance Estimates for States” (2002) by Robin Fisher and Jennifer Campbell • “Health Insurance Estimates for Counties” (2003) by Robin Fisher and Joanna Turner

16. Further Reading (2) • “Small Area Estimation of Health Insurance Coverage from the Current Population Survey’s Annual Social and Economic Supplement and the Survey of Income and Program Participation” (2004) by Robin Fisher and Joanna Turner • Available in November, 2004

17. SAIPE website • http://www.census.gov/hhes/www/saipe.html • SAHIE website coming in spring 2005

18. Statistical Models • Models • Find a simple relationship between covariates and the variable we want to predict • Random effects regression • Still subject to improvement

19. Model • True log insured rate • Could do ordinary least squares regression if we could observe Yi

20. Model (2) • CPS ASEC log insured • We want to estimate Yi

21. Insured rates Rather than numbers Take advantage of the correlation between CPS ASEC insured and universe Rather than uninsured Few counties have zero insured Some predictors measure aspect of insured Notes on Log Insured Rate

22. Logs Make variances more homogeneous Not essential, but makes the estimation less sensitive to variances Can be important for places with low insurance rates Notes on Log Insured Rate (2)

23. Bayesian Analysis • End result is the distribution of the model variables given the data • Estimates: means under this distribution • Minimizes mean squared error

24. Bayesian Analysis (2) • Benefits • Can calculate variances of the estimates exactly • Can calculate means on the linear scale exactly • Easy to interpret results • Easy to build in constraints • Insured rates must be in [0,1]

25. Model Fitting • Use regression methods for exploratory analysis • Bayesian methods • Posterior Predictive P-values (PPP-values) • Bayesian residuals

26. Results • Overall fit is good (PPP-values for mean, variance, and goodness of fit) • Variance model needs work • Mean posterior coefficient of variation for CPS ASEC uninsured: 7.0%

27. CPS ASEC Model Based Estimates Uninsured Rates: 1999 Lightest Gray < 8% to Darkest Gray > 20% Source U.S. Census Bureau

28. Validation • Other data • From states • Surveys • Model-based estimates • Other national surveys • National Health Interview Survey (NHIS) • Survey of Income and Program Participation (SIPP)

29. Validation (2) • Problems • Different definitions of insured • Unknown standard errors of other estimates • How do we know if we are validated?

30. Other Research • Why is Hispanicity so important? • Different approaches to Medicaid • Other surveys, e.g. SIPP • Variance models • Improvement of regression model

31. Questions for FSCPE • Would you like a chance to comment on the estimates before we move to producing them on a regular basis? • Are you aware of data appropriate for validation of these estimates?

32. Questions for FSCPE (2) • What if there are administrative records available for some areas but not others? • Is the inclusion of proportion Hispanic problematic?

33. Contact Information • (301) 763-3193 • hhes.sahie@census.gov