Lynn A. Blewett, Ph.D. State Health Access Data Assistance Center

1. SCI Modeling Meeting Use of State and National Data forState Simulations of Coverage Expansion Lynn A. Blewett, Ph.D. State Health Access Data Assistance Center University of Minnesota, School of Public Health November 10, 2004

2. Overview of Presentation • Key elements of modeling • Use of state and national data sources • Some words of advice • State examples • Minnesota • Massachusetts

3. Key Variables in Modeling Expansions • Whose eligible? • Family structure • Insurance unit • Income based on unit • Current insurance status • Average program costs • Other Behavioral Assumptions • Take up rates of eligibles • Crowd out • Premium elasticities Data Source: Household Surveys

4. Estimates Depend on Data Source • Primary source of key variables is state representative household survey data • Current Population Survey (CPS) • State Surveys • Measures of key variables WILL differ based on data source used • Other data sources and surveys to help with modeling assumptions

5. Key Differences: CPS and State Surveys • Insurance Unit • CPS will have more information on individuals • Income • CPS has more detail than most state surveys • Insurance Status • CPS estimates of uninsurance are likely to be higher than estimates from state surveys

6. Eligibility • Income eligibility thresholds • Composition of the family • Age of the children • How income is counted and disregards • Family/Filing Unit • Do the incomes of all people living in the household count? Or just head of household? Need specific information on individuals, family, and program characteristics

7. Determining the Filing Unit • Census Family - CPS • All people in household related by blood or by marriage • Broader definition but most readily available in the CPS • Insurance Family – Filing Unit • Spouse, children up to 18 or to age 23 if in school • More in line with public program filing unit • Need to develop using available CPS data • State surveys may have less data on individuals in the Household and relationships

8. Some data will be missing Both in CPS and State Surveys • Legal status of immigrants • Details on assets • Detailed information on income and poverty level Will need to consider estimating in your model…..

9. Problems with Income Measurement • Omnibus Questions • Many state surveys use omnibus • Likely to overstate eligibility • Does not allow for subtracting income disregards likely overstates eligibility • Detailed Questions • Asks for specific income or categories of income • Asks for information on assets • Need information on what income sources are included or disregarded • Can understate income • Does it wash out?

10. Problems with Insurance Measurement • Point-in-time vs. Full-year • CPS Measures full-year uninsured • Although many people think it actually resembles point-in-time • State Surveys Measure Point-in-time and Full-year • Better to get both measures • Offers more flexibility for modeling as a result

11. Other Issues to Consider 1. Imputation • What to plug in for missing values • CPS and State surveys will need to impute data CPS Experience • Income: 17% missing values • Health Insurance: 11% missing values -How you do the imputation can affect your estimate -We estimate some bias with the methods used by CPS -State of residence is not used in imputation procedure for Health insurance coverage in CPS…..

12. Other Issues to Consider 2.Weighting • Adjustments to the data to make sample represent the population Adjustments for: • Non-Responders • Cell Phones • No Phones • Call Screening Be award that these will impact your estimates. Be aware of what you know and don’t know…

13. Other Issues to Consider 3. Measurement Error in All Surveys • Medicaid undercount • Indian Health Service classification of Uninsured • Edits in system (e.g. CPS TANF Kids) • People response may not accurately reflect their actual insurance • Measurement of insurance status. Surveys vary on approach, order of question, and estimate….

14. Advantages of State Surveys • Typically more sample than CPS • Ability to drill down to subpopulations • Children • Geographic Units • Race/ethnicity • Analysts have data in hand • Better knowledge of data • Ability to do analysis in-house

15. Some Problems with State Surveys • Most are telephone surveys • Adjust for non-telephone households, cell phones, response rates • But may underestimate low-income households • Documentation of Survey • Some long-standing surveys documentation is good, for some one-time state surveys difficult to know problems/changes • Because of costs, variables are limited • May not have key variable of interest • May cost too much to get that key variable • Lack of detail of all people in HH

16. Other Select State Data Sources • State Public Program enrollment files • Current estimate of program participation of eligible population • State Public Program claims paid files • Average cost per program participant in different categories • BRFSS • Health outcomes and behavior

17. Other Select National Data • SIPP: National & Regional Estimates Only • Estimates of churning • Program participation rates • Better wealth and income data • MEPS-Household: National & Regional Estimates Only • Premium. OOP costs, health spending • Decennial Census Data • Population estimates of income, poverty, age distribution and race/ethnicity at county level and lower

18. Use of National Data • Benchmark Comparisons • Is your estimate of different parameters consistent with national estimate? • Results might direct you to look more closely on areas of refinement • Develop estimates for use in your model • Public program participation rates from NSAF analysis • Average utilization of specific service • Hospital utilization of those 18-65 (MEPS-HH)

19. Borrow Assumptions from Other Experts • Congressional Budget Office • Modeling from national legislative proposals • Borrow assumptions and sources from their documentation • Gruber, Holahan, Chollet and others • Behavioral assumptions for employers • Take up rates, crowd out, premium price elasticities … • Consulting Groups (e.g. Lewin, Deloitte etc) • Be careful of assumptions and who is paying • e.g. range of cost estimates of Kerry health plan

20. New Small-Area Models Coming • Census Model-Based Estimates for Uninsurance at State and County level • Using multiple sources of data to get better estimates for local use • Being reviewed in January • AHRQ Initiative in developing Model-based estimates at local level • Both for MEPS-IC and more recent interest in MEPS-HH Potential New Source of Data for State Modeling

21. Some Advice (1) • Do several estimates using both the CPS and State Survey sources of data to estimate model • Provide ranges of estimates with low and high end to cover your bases • Then, when forced to pick one, pick in the middle. • Document your assumptions and data sources and rationale • Be brave – modeling is tricky business but have to start somewhere • Be flexible – and willing to change or modify assumptions when you get better information • Be careful who you share the methodology with as it can come back to haunt you in committee hearings

22. Some Advice (2) • Use of your State survey data helps to build constituency and support for additional rounds of data collection • Policymakers become familiar with seeing data • Familiar with seeing differences between state survey data and CPS • Become more confident in state estimates using state data

23. Some Advice (3) • Contracting Out Estimates (if you have the resources.) • Be involved in the process • Learn all you can • Make sure they are using local data when feasible and understanding local nuances • Ask for the sophisticated model and then a simpler model you can use next year when you don’t have the experts available to you

24. State Panel • Scott Leitz, Minnesota Department of Health, Health Economics Program • Amy Lischko, Massachusetts Division of Health Care Finance and Policy • Both have long-standing state surveys • Experience in reporting results, modeling costs, and working with the experts Part Art and Part Science: You only learn by doing.