Matthew M. Davis, MD, MAPP Rachel M. Quinn, MPP, MPH

1. CHEAR Unit, Division of General Pediatrics, and the Gerald R. Ford School of Public Policy, University of Michigan Assessing the Potential Effect of Programmatic Changes in Medicaid and SCHIP on Children’s Uninsured Rates Matthew M. Davis, MD, MAPP Rachel M. Quinn, MPP, MPH With support from the Michigan Department of Community Health

2. How Low Can Child Uninsurance Rates Go? • Political opportunities • Fiscal realities • Programmatic options?

3. Focus on Individual-Level Determinants of Uninsurance • Clinician’s perspective • Causes • Effects • Anecdotally powerful

4. Focus on Individual-Level Determinants of Uninsurance • Clinician’s perspective • Causes • Effects • Anecdotally powerful • But what about programmatic opportunities at state and federal levels?

5. Research Question What are sociodemographic and programmatic factors at the state level associated with rates of uninsurance among children?

6. Program Opportunities in Context of Population Factors • Candidate sociodemographic and programmatic factors at the state level associated with rates of uninsurance among children • Sociodemographic • Race/ethnicity, immigration, median income, unemployment rates, employer insurance offer rates, population age balance • Programmatic • Medicaid and SCHIP income eligibility thresholds, asset tests, copays/premiums for SCHIP, SCHIP program type

7. State-to-State Comparison of Child Uninsurance Rates • Current Population Survey (CPS) • March (“Sociodemographic”) Supplement • Annual household survey • Nationally representative • Representative estimates for all states and DC • 2000 – 2004 (rates from 1999-2003)

8. State Data Regarding Candidate Uninsurance Factors • Census data • Bureau of Labor Statistics • Centers for Medicare and Medicaid Services • Foundation reports • Kaiser Family Foundation StateFacts • Center for Budget and Policy Priorities

9. Data Analysis • Time-series analysis • Generalized estimating equations • Within each state (1999-2003) • Between states • Bivariate analyses •  Multivariate analyses • Adjust for different state populations

10. Methodologic Options and Challenges • Outcomes • For all children • For low-income children • Collinearity of independent variables • e.g., Income eligibility levels for different child age groups within Medicaid • Necessitated “families” of models with interchanging collinear variables

11. Results: Uninsurance Rates for All Children • Variables significant in bivariate tests included: • Sociodemographic variables: • Median income • Proportion of state population who are Hispanic • Proportion of state population who are children • Programmatic variables: • Asset test • SCHIP income eligibility thresholds • Medicaid income eligibility thresholds

12. Models of Uninsurance Rates for All Children *P<.0001; ‡P<.05

13. Models of Uninsurance Rates for All Children *P<.0001; ‡P<.05

14. Models of Uninsurance Rates for All Children *P<.0001; ‡P<.05

15. Models of Uninsurance Rates for All Children *P<.0001; ‡P<.05

16. Models of Uninsurance Rates for All Children *P<.0001; ‡P<.05

17. Models of Uninsurance Rates for All Children *P<.0001; ‡P<.05

18. Models of Uninsurance Rates for Low-income Children *P<.0001; ‡P<.01; also adjusted for type of SCHIP program

19. Limitations • CPS data not equivalently accurate for all states • Larger states likely with better estimates • Much variation in child uninsurance rates remains unexplained by state-level variables • Opportunity for multi-level model of likelihood of uninsurance for a child, given individual, family, community, and state-level variables • Influence of state variables likely varies across states

20. Summary • State-level model consistent with individual-level factors associated with uninsurance • Income • Hispanic ethnicity • Consistent with hypothesized program effects • Eligibility thresholds • Asset test • New insight • Proportion of state population comprised by children

21. Implication: Eliminate the Asset Test

22. Implication: Eliminate the Asset Test • But only 6 states still have an asset test • CO, ID, MT, NV, TX, UT

23. Implication: Modify Medicaid Eligibility Thresholds If State X has Medicaid eligibility threshold of 100% FPL and a low-income child uninsurance rate of 10% …

24. Implication: Modify SCHIP Eligibility Thresholds If State Y has SCHIP eligibility threshold of 185% FPL and an overall child uninsurance rate of 10% …

25. Implication: Consider the State Proportion of Children • Range of states’ proportions of population comprised by children: • High • Low

26. Implication: Consider the State Proportion of Children • Range of states’ proportions of population comprised by children: • High • UT 32.6% • AK 30.1% • Low

27. Implication: Consider the State Proportion of Children • Range of states’ proportions of population comprised by children: • High • UT 32.6% • AK 30.1% • Low • ME 22.4% • DC 19.7%

28. Implication: Consider the State Proportion of Children • Range of states’ proportions of population comprised by children: • High Child uninsurance rate • UT 32.6% 9.0% • AK 30.1% 12.3% • Low • ME 22.4% 6.0% • DC 19.7% 11.4%

29. Conclusions • Value of considering child uninsurance within the state context • Opportunities to use models to inform legislators and policymakers about possible yields of program changes • New insights about possible factors for consideration in federal match rate