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? +34% +80% +88% +63%

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 Sources: National Center for Health Statistics (2000 National Hospital Discharge Survey); U.S. Bureau of the Census Baby boomers Hospitalization Rates by Age (2000 data)

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

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

21. Projections of Capacity Utilization (as % of total available MN hospital beds) Baseline 15% increase 15% decrease 200057% 201066%69%62% 202077%85%69% 203091%105%78% 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