Analyzing State Health Markets: Lessons from State and Federal Data

1. Using State and Federal Data to Analyze and Model State Health Markets: Examples and Lessons Learned Scott Leitz Director, Health Economics Program Minnesota Department of Health November 10, 2004

2. Overview • Some background on state and federal data sources for analysis and modeling • A few examples of Minnesota modeling exercises • Lessons learned and things to consider

3. State versus Federal data sources for analysis and modeling • State legislators generally believe their state is unique • Not having state data can be a reason not to do something, therefore collection of state-specific information is critical • But: not every question asked by state policymakers can be answered with state-specific data • Even when it can, the estimates can sometimes differ • Example: CPS versus state-specific surveys

4. State versus Federal data sources for analysis and modeling (II) • Even where state data may not be available, or is limited, national data can be used and adjustments made • Assumptions are important • National data is a good crosscheck to state data

5. Example 1 How much uncompensated care might result from a proposal to eliminate a state health insurance program for very low income people and reduce income eligibility for a Medicaid population?

6. The Challenge • Turning estimates of enrollment loss into hospital-specific estimates of uncompensated care • Multiple steps involved: • How many will end up without coverage? • How many services will this population seek? • How will that care get paid for? • How will behavior change? • Need for using both state and national data to answer these questions

7. A brief overview of methodology • Estimated number of people who would lose coverage under Governor’s proposal, adjusted for take-up (crowd out studies) • Adjust result to account for differences in expenditures between the uninsured and the insured: • Uninsured spend approximately half of what the insured spend on health care. (MEPS, Hadley & Holahan 2003, Long & Marquis 1994). • Adjustment to reflect that public program enrollees are sicker in general than the uninsured (2001 MN Health Access Survey, Holahan 2001). • Result: estimate uninsured spend 61% of what they would have spent if enrolled in a public program.

8. Methodology (II) • Resulting figure is the estimated use of services by the additional uninsured (“uninsured costs”). • Uninsured costs can be “paid” for in two ways: • Out of pocket payments by the uninsured • Uncompensated care • Research shows that the uninsured pay around a third of their health care costs • Surprisingly consistent across income levels • (MEPS, Hadley & Holahan 2003). • Remaining is uncompensated care

9. Methodology (III) • This uncompensated care figure is divided between hospital-based uncompensated care and clinic-based uncompensated care. • UC allocated 34% to clinics and 66% to hospitals (Hadley & Holahan 2003, 2000 Minnesota-specific analysis of uncompensated care).

10. Results: Estimated Impact on the Uninsurance Rate • Percentage of Minnesotans without health coverage increases by the following relative to current levels, assuming all other things remain constant: • Baseline, 2002: 5.4% • 2004: 6.0% • 2005: 6.4% • 2006: 6.5% • 2007: 6.6% • Additional of approximately 63,000 additonal uninsured Minnesotans

11. How Do These Estimated Increases in Uncompensated Care at Hospitals Compare to Current Levels? +88% +80% +63% +34%

12. Lessons learned • Using state-specific data is important, but it likely can’t answer every question • State-specific: UC baseline data, uninsured characteristics • Federal/national: MEPS, national studies • Can use both credibly, as long as their respective roles are appropriate • Use national data as crosscheck for state-specific data

13. Example 2 How will an aging population affect use of health care services and hospital bed capacity over the next 10, 20, and 30 years?

14. Very Brief Background on Example 2 • Minnesota has operated under a hospital inpatient bed construction moratorium since 1984 • Bed capacity essentially static for 20 years • Question: how will population demographics affect use of services and how will that compare to bed capacity?

15. Again: The need for both state and federal data • State: Demographic trends and projections, average length of stay • Federal: Hospitalization rates by age, average length of stay crosscheck

16. Projected Minnesota Population Growth,by Age Group Source: Minnesota State Demographic Center

17. In Sheer Numbers, How Much Will Minnesota’s Elderly Population Increase? Source: Miinnesota State Demographic Center

18. How Does Use of Health Care Services Vary by Age? Hospitals Hospitalization Rates by Age (2000 data) Baby boomers Sources: National Center for Health Statistics (2000 National Hospital Discharge Survey); U.S. Bureau of the Census

19. Projected Growth in Minnesota Hospital Utilization Source: Minnesota Department of Health, Health Economics Program

20. Sources of Growth in Projected Minnesota Hospital UtilizationExample: Inpatient Days Source: Minnesota Department of Health, Health Economics Program

21. Projections of Capacity Utilization (as % of total available MN hospital beds) Source: Minnesota Department of Health, Health Economics Program

22. Lessons learned • Questions are sometimes less complicated than they seem • Relatively simple projections can give you estimates that are likely as accurate as expensive, sophisticated modeling • Tradeoff: timeliness and cost versus perceived sophistication and credibility

23. Overall lessons learned and things to consider • Know what you can answer with state-specific data and what you can’t, and be prepared to support your decision • Know what to prepare for • CPS versus state-specific survey findings • How sophisticated does the analysis need to be? • Is it important it be an econometric model or does simple projection get you just as close? • Cost/Timeliness/model understanding critical

24. Overall lessons learned and things to consider • Contracting with experts versus doing your own modeling/projection • Credibility? • There’s nothing magic or mystical about modeling; understand assumptions and how the detail was arrived at • Use technical assistance • SHADAC, SCI, others • National data can be a critical and important crosscheck to state data