Medicaid Expenditures Todd Gilmer, PhD Rick Kronick, PhD - - PowerPoint PPT Presentation

Inter- and Intrastate Variation in Medicaid Expenditures

Todd Gilmer, PhD Rick Kronick, PhD

University of California, San Diego

SUPPORTED BY A GRANT FROM HCFO/RWJ

Research Questions

• Does interstate variation in Medicaid spending

result primarily from variation in the volume of services or in the price per unit of service?

• How do inter- and intrastate variation in Medicaid

utilization and spending compare to variation in Medicare spending and utilization?

• Is more better for Medicaid beneficiaries?

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Data

• Medicaid Analytic eXtract (MAX) data for CY 2001-

2005 for all 50 states and DC

• MAX data starts with data from the Medicaid

Statistical Information System (MSIS), and is then massaged by CMS to create person-level analytic files

• Complete claims and eligibility data on

approximately 280 million beneficiaries (not necessarily unique) over five years

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Methods

• Exclude partial benefits beneficiaries (SLMBs, QMBs,

family planning-only, etc)

• Focus on Cash-Assistance, Medicaid-Only, fee-for-service,

beneficiaries with Disabilities (CAMODs)

• Restrict to cash disabled because uniform national eligibility

standard for SSI increases comparability of the analysis sample across states

• Restrict to Medicaid-only (eliminate dual eligibles) to get a

complete view of utilization and expenditures

• Restrict to FFS because encounter data are incomplete for

beneficiaries in managed care

• In analyses of spending on CAMODs, exclude five states

(AL, AZ, DE, MD, and ND) because managed care penetration is too high or other data anomalies

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0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Beneficiaries Total Expenditures Acute LTC 47.2 million $234.6 billion $149.2 billion $74.8 billion

Distribution of Medicaid Beneficiaries and Expenditures, 2001-2005

Cash Assistance, Medicaid-only, Disabled (CAMOD Other disabled Aged Adults Children

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Correlation Coefficients, State-level Expenditures per Beneficiary and Expenditures per CAMOD, 2001-2005 Standardized Acute LTC Expenditures per Expenditures expenditures expenditures Beneficiary per CAMOD per CAMOD per CAMOD Standardized expenditures per beneficiary 1.00 — — — Expenditures per CAMOD 0.86 1.00 — — Acute expenditures per CAMOD 0.81 0.96 1.00 — LTC expenditures per CAMOD 0.81 0.93 0.81 1.00 Source: 2001-2005 MAX data. N=46 (excludes AL, AZ, DE, MD, and ND).

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Measuring Volume and Price

• Inpatient (PH & MH)
• Volume = inpatient days
• Price = expenditure per day
• Outpatient (PH & MH)
• Volume = unique days
• Price = expenditure per day
• Pharmacy
• Volume = number of fills
• Volume = average expenditure per fill at mean prices
• Price = Ratio of actual expenditure to predicted

expenditure at mean prices

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Calculating Incremental Effects

• Decomposition of price and volume effects
• Incremental volume is the difference between

actual and mean volume multiplied by mean price

• Vinc = (Vi – Vm) * Pm
• Incremental Price is the difference between actual

and mean price multiplied by actual volume

• Pinc = (Pi – Pm) * Vi
• Combined incremental effect is the product
• Inc Effect = Vinc + Pinc

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Results

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Distribution of State-level per Beneficiary Acute and LTC Spending on CAMODs, 2001–2005

Source: 2001-2005 MAX data. Note: Data exclude AL, AZ, DE, MD, and ND.

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Robust Effect of Primary Care

• Multiple indicators of access to primary care are

related to reduced admissions:

• Supply of primary care physicians
• Average number of outpatient visits
• Average Price Per Visit

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The relationship between Medicare and Medicaid utilization and spending

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Distribution of state-level 2004 Medicare spending per beneficiary, and 2001-2005 acute care Medicaid spending per CAMOD

Source: Medicare, Dartmouth Atlas; Medicaid, MAX data, 2001-2005 AL, AZ, DE, MD, and ND are excluded.

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2004 Medicare spending per beneficiary and 2001-2005 acute care Medicaid spending per CAMOD

Source: Medicare, Dartmouth Atlas; Medicaid, MAX data, 2001-2005 AL, AZ, DE, MD, and ND are excluded.

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2004 Medicare admissions/1,000 and 2001-2005 Medicaid admissions per CAMOD

Source: Medicare, Dartmouth Atlas; Medicaid, MAX data, 2001-2005AL, AZ, DE, MD, and ND are excluded. Admissions to psychiatric hospitals and admissions to acute care hospitals with a primary mental health diagnosis are excluded from the Medicaid data.

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Source: Medicare, Dartmouth Atlas; Medicaid, MAX data, 2001-2005AL, AZ, DE, MD, and ND are excluded. Medicaid 'Part B' spending includes MD/OPD/Clinic spending, and expenditures for laboratory and radiology services.

2004 Medicare Part B spending, and 2001-2005 Medicaid 'Part B' spending

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2004 Medicare Part B spending, and 2001-2005 Medicaid 'Part B' spending Source: Medicare, Dartmouth Atlas; Medicaid, MAX data, 2001-2005AL, AZ, DE, MD, and ND are excluded.

2004 Medicare spending per beneficiary and 2001-2005 acute care Medicaid spending per CAMOD, by HRR, selected states

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2004 Medicare spending per beneficiary and 2001-2005 acute care Medicaid spending per CAMOD, by HRR, California

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• 2000
• 1000

1000 2000 3000 4000 5000 6000

• 4000
• 3000
• 2000
• 1000

1000 2000 3000 4000 Medicare Medicaid

Exhibit 3. 2001–2005 Medicare and Medicaid Acute Care Spending per Beneficiary by Hospital Referral Region, Deviations from State Means

Source ce: Medicare data from the Dartmouth Atlas of Health care. Medicaid from MAX data. Note: Medicaid data limited to non-dually eligible FFS disabled beneficiaries receiving cash assistance. Excludes AL, AZ, DE, MD, ND. Each data point calculated as the difference from the state mean.

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Conclusions

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There is wide variation across states in spending per Medicaid beneficiary

• For example, NY spends more than twice as much

per beneficiary than CA on acute care

• Spending is generally lower in the South, and higher

in the Mid-Atlantic, New England, and the upper Midwest

• Inpatient utilization only partially follows the

contours of acute care spending

• Low in New England; high in FL, LA, TX, and OK
• There is much more interstate variation in mental

health and ‘other acute’ spending than in inpatient, MD/OPD/Clinics, or Rx, and much more variation in LTC than in acute spending

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Volume of services drives relative positioning, unit price is secondary

• High-spending and low-spending states are

different from the national average primarily because of volume (2/3), and only secondarily because of price (1/3)

• Inpatient, mental health, and other spending contribute

approximately equally, while variation in MD/OPD/Clinic has very little effect

• Inpatient spending varies approximately equally

because of volume and price, while MH and drugs variation is driven almost entirely by volume

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At the state level, Medicaid and Medicare spending are unrelated

• There is a weak relationship between Medicare

and Medicaid inpatient admissions/1,000

• There is no relationship between Medicare Part B

and Medicaid outpatient spending

• Inpatient hospital spending is a much larger

component of Medicare spending than of Medicaid spending

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Within most states, Medicaid and Medicare spending are strongly related at the HRR level

• Inpatient admissions strongly related
• Outpatient spending very weakly related
• California a notable exception, with no relationship

between Medicare and Medicaid within state

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Making sense of the Medicare-Medicaid relationship

• Virtually zero state-level correlation in spending, and

very small correlation in inpatient admissions suggest that Medicaid policy variables mediate the supply-utilization relationship suggested by the Dartmouth Atlas

• Relatively strong within-state relationship at the

HRR level for inpatient admissions suggests that, holding Medicaid policy constant, supply of resources affects Medicare and Medicaid utilization similarly

• Very weak within-state relationship on outpatient

spending requires more investigation

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Is more better in Medicaid?

• At the state level, some suggestion that more

physician visits are associated with lower readmission and lower ACS rates

• Strong association between larger fraction of

primary care physicians and lower hospitalization, and ACS rates

• At the state level, little indication that a higher

volume of mental health services or more prescription drug fills are associated with lower hospitalization rates

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