1. 1 Safety Net Data Collection Strategies AHRQ User Liaison Program Washington, D.C. September 24, 2003 Supported by a grant from The Robert Wood Johnson Foundation Lynn A. Blewett, Ph.D. State Health Access Data Assistance Center School of Public Health, University of Minnesota blewe001@umn.edu www.shadac.org

2. 2 Estimating the Number of Uninsured Estimates of demand based on state and local estimates of the number of uninsured Most estimates come from household surveys that ask about health insurance coverage Also used in combination with employer surveys that ask about health insurance coverage

3. 3 Estimating the Size of the Uninsured Populations at the Local Level 1.Do your own survey - To directly measure health insurance coverage and rates of uninsurance 2.Use existing available data to: -Develop proxy measures of uninsurance -Develop statistical model-based estimates

4. 4 Direct Measures: National Data Three surveys provide state-level estimates of health insurance coverage. –Current Population Survey (CPS): Census –Medical Expenditure Panel (Employer) Survey – Insurance Component (MEPS-IC): Agency for Healthcare Research and Quality –State and Local Area Integrated Telephone Survey (SLAITS): National Center for Health Statistics

5. 5 Direct Measures: State Data 37 States have developed and fielded their own state household survey to estimate levels of health insurance coverage –Many funded by the federal Health Resources Services and Administration State Planning Grant Program –Typically have larger sample size and some regional/county estimates of coverage

6. 6 Other National Surveys of Note National Survey of America’s Families (NSAF): Urban Institute –Uninsurance estimates available for 13 states –Public use files available to states for analysis Community Tracking Study: Center for Health System Change –Uninsurance estimates can be made for the 12 communities in the CTS sample

7. 7 Proxy Measures Use an available measure to serve as a proxy for health insurance coverage Example: self-pay variable from hospital administrative records to estimate local levels of uninsurance

8. 8 Model-Based Approach Predicts health insurance coverage using one or more variables correlated with health insurance coverage Example: correlation between state unemployment and uninsurance and applying this at local level

9. 9 National Model-Based Estimates Small-Area Estimation Census and AHRQ are both working on sophisticated models to provide state and local area estimates –CPS: Uninsurance –MEPS-IC: Employment offer and take up rates

10. 10 Approach Principal Strength Principal Weakness Direct Estimation- New Survey PrecisionCost Proxy Measure CostBias Model-Based Predictive Accuracy Complexity Overview of Approaches for Estimating Number of Uninsured At the Local Level

11. 11 Conclusions Be knowledgeable and aware of different data sources available for your community Use existing resources and data and build on existing state and local survey activities Use multiple approaches to maximize information Be flexible as new data become available

12. 12 www.shadac.org 2221 University Avenue, Suite 345 Minneapolis Minnesota 55414 (612) 624-4802 Principal Investigator: Lynn Blewett, Ph.D. blewe001@umn.edu blewe001@umn.edu Co-Principal Investigator: Kathleen Call, Ph.D. callx001@umn.edu callx001@umn.edu Center Director: Kelli Johnson, M.B.A. johns706@umn.edu johns706@umn.edu Senior Research Associate: Timothy Beebe, Ph.D. beebe002@umn.edu beebe002@umn.edu Research Associate: Michael Davern, Ph.D. daver004@umn.edu daver004@umn.edu Contact Information