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Assessing the Potential Effect of Programmatic Changes in Medicaid and SCHIP on Childrens Uninsured Rates Matthew M. Davis, MD, MAPP Rachel M. Quinn, MPP,

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Presentation on theme: "Assessing the Potential Effect of Programmatic Changes in Medicaid and SCHIP on Childrens Uninsured Rates Matthew M. Davis, MD, MAPP Rachel M. Quinn, MPP,"— Presentation transcript:

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

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

3 Focus on Individual-Level Determinants of Uninsurance Clinicians perspective –Causes –Effects Anecdotally powerful

4 Focus on Individual-Level Determinants of Uninsurance Clinicians 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 Model 1Model 2Model 3Model 4Model 5 Median income Prop. Hispanic Prop. children No asset test SCHIP elig thresh Mcaid elig 0-1 Mcaid elig 2-5 Mcaid elig 6-16 Mcaid elig 17-19 *P<.0001; P<.05

13 Models of Uninsurance Rates for All Children Model 1Model 2Model 3Model 4Model 5 Median income-.0002* Prop. Hispanic.261* Prop. children.390* No asset test SCHIP elig thresh Mcaid elig 0-1 Mcaid elig 2-5 Mcaid elig 6-16 Mcaid elig 17-19 *P<.0001; P<.05

14 Models of Uninsurance Rates for All Children Model 1Model 2Model 3Model 4Model 5 Median income-.0002*-.0001* Prop. Hispanic.261*.243* Prop. children.390*.347* No asset test-.644 SCHIP elig thresh-.012 Mcaid elig 0-1-.012 Mcaid elig 2-5 Mcaid elig 6-16 Mcaid elig 17-19 *P<.0001; P<.05

15 Models of Uninsurance Rates for All Children Model 1Model 2Model 3Model 4Model 5 Median income-.0002*-.0001*-.0002* Prop. Hispanic.261*.243*.248* Prop. children.390*.347*.353* No asset test-.644-.744 SCHIP elig thresh-.012 -.013 Mcaid elig 0-1-.012 Mcaid elig 2-5-.009 Mcaid elig 6-16 Mcaid elig 17-19 *P<.0001; P<.05

16 Models of Uninsurance Rates for All Children Model 1Model 2Model 3Model 4Model 5 Median income-.0002*-.0001*-.0002*-.0001* Prop. Hispanic.261*.243*.248*.247* Prop. children.390*.347*.353*.352* No asset test-.644-.744-.697 SCHIP elig thresh-.012 -.013 -.012 Mcaid elig 0-1-.012 Mcaid elig 2-5-.009 Mcaid elig 6-16-.009 Mcaid elig 17-19 *P<.0001; P<.05

17 Models of Uninsurance Rates for All Children Model 1Model 2Model 3Model 4Model 5 Median income-.0002*-.0001*-.0002*-.0001* Prop. Hispanic.261*.243*.248*.247*.245* Prop. children.390*.347*.353*.352*.348* No asset test-.644-.744-.697-.580 SCHIP elig thresh-.012 -.013 -.012 -.011 Mcaid elig 0-1-.012 Mcaid elig 2-5-.009 Mcaid elig 6-16-.009 Mcaid elig 17-19-.010 *P<.0001; P<.05

18 Models of Uninsurance Rates for Low-income Children Model 1Model 2Model 3Model 4Model 5 Prop. Hispanic.349*.320*.329*.321* Prop. children.642.225.285.274.293 No asset test-2.58-2.90-2.83-2.50 SCHIP elig thresh-.010-.012-.010-.006 Mcaid elig 0-1-.036 Mcaid elig 2-5-.032 Mcaid elig 6-16-.030 Mcaid elig 17-19-.033 *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 But only 6 states still have an asset test –CO, ID, MT, NV, TX, UT

23 Implication: Modify Medicaid Eligibility Thresholds If raise Medicaid eligibility threshold to: Estimated child uninsurance rate 133%8.7% - 9.1% 150%8.1% - 8.7% 185%6.7% - 7.8% 200%6.2% - 7.4% 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 raise SCHIP eligibility threshold to: Estimated child uninsurance rate 200%9.8% - 9.9% 235%9.2% - 9.3% 250%9.0% - 9.2% 300%8.3% - 8.5% 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 UT32.6% AK30.1% –Low

27 Implication: Consider the State Proportion of Children Range of states proportions of population comprised by children: –High UT32.6% AK30.1% –Low ME22.4% DC19.7%

28 Implication: Consider the State Proportion of Children Range of states proportions of population comprised by children: –HighChild uninsurance rate UT32.6%9.0% AK30.1%12.3% –Low ME22.4%6.0% DC19.7%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

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