Using State and Federal Data to Analyze and Model State Health Markets: Examples and Lessons Learned...
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Transcript of Using State and Federal Data to Analyze and Model State Health Markets: Examples and Lessons Learned...
Using State and Federal Data to Analyze and Model State Health Markets: Examples and Lessons Learned
Scott LeitzDirector, Health Economics ProgramMinnesota Department of Health
November 10, 2004
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
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
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
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?
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
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.
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
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).
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
$0
$40,000,000
$80,000,000
$120,000,000
$160,000,000
$200,000,000
$240,000,000
$280,000,000
2004 2005 2006 2007
Baseline UC (assuming 10% annual growth in UC)Estimated UC, including additional UC from proposed changes
How Do These Estimated Increases in Uncompensated Care at Hospitals Compare to Current Levels?
+34%
+80%+88%
+63%
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
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?
Very Brief Background on Example 2
Minnesota has operated under a hospital inpatient bed construction moratorium since 1984
Bed capacity essentially static for 20 yearsQuestion: how will population demographics
affect use of services and how will that compare to bed capacity?
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
Projected Minnesota Population Growth,by Age Group
0% 20% 40% 60% 80% 100% 120%
2000-2030
2000-2020
2000-2010
60+40 to 5920 to 39Under 20
Source: Minnesota State Demographic Center
In Sheer Numbers, How Much Will Minnesota’s Elderly Population Increase?
594,266680,000
951,700
1,290,800
0
200,000
400,000
600,000
800,000
1,000,000
1,200,000
1,400,000
2000 2010 2020 2030
Source: Miinnesota State Demographic Center
How Does Use of Health Care Services Vary by Age? Hospitals
05
101520253035404550
<5yrs 5-14 15-24 25-34 35-44 45-54 55-64 65-74 75yrs+ Allages
# ho
spita
lizat
ions
per
100
pop
ulat
ion
Sources: National Center for Health Statistics (2000 National HospitalDischarge Survey); U.S. Bureau of the Census
Baby boomers
Hospitalization Rates by Age (2000 data)
Projected Growth in Minnesota Hospital Utilization
11%
20%27%
16%
36%
60%
0%
10%
20%
30%
40%
50%
60%
70%
2000-2010 2000-2020 2000-2030
MN Population Inpatient Days
Source: Minnesota Department of Health, Health Economics Program
Sources of Growth in Projected Minnesota Hospital Utilization
Example: Inpatient Days
69.4%56.0%
45.4%
30.6%44.0%
54.6%
0%
20%
40%
60%
80%
100%
2000-2010 2000-2020 2000-2030
Population Growth Changing Age Distribution
Source: Minnesota Department of Health, Health Economics Program
Projections of Capacity Utilization (as % of total available MN hospital beds)
Baseline15%
increase15%
decrease
2000 57% 57% 57%
2010 66% 69% 62%
2020 77% 85% 69%
2030 91% 105% 78%
Source: Minnesota Department of Health, Health Economics Program
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
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
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