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Inter- and intrastate variation in Medicaid expenditures

Rick Kronick, Ph.D. Todd Gilmer, Ph.D. 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?

Data

Medicaid Analytic eXtract (MAX) data for CY 20012005 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 person level Complete claims and eligibility data on approximately 280 million beneficiaries (not necessarily unique) over five years

Methods

Exclude partial benefits beneficiaries (SLMBs, QMBs, family planningonly, etc) Focus on Cash Assistance Medicaid-Only, fee for service 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 ­ R t i t to Medicaid-only ( li i t d l eligibles) t get a complete view Restrict t M di id l (eliminate dual li ibl ) to t l t i 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

Distribution of Medicaid Beneficiaries and Expenditures, 20012005

Beneficiaries 47.2 million

Total Expenditures $234.6 billion

Acute $149.2 billion

LTC $74.8 billion

Cash Assistance, Medicaidonly, Disabled (CAMOD

Other disabled

Aged

Adults

Children

Correlation Coefficients, State-level Expenditures per Beneficiary and Expenditures per CAMOD, 2001-2005 Standardized Expenditures per Beneficiary 1.00 0.86 0.81 0.81 Acute expenditures per CAMOD -- -- 1.00 0.81 LTC expenditures per CAMOD -- -- -- 1.00

Expenditures per CAMOD -- 1.00 0.96 0.93

ndardized expenditures per beneficiary enditures per CAMOD te expenditures per CAMOD C expenditures per CAMOD

urce: 2001-2005 MAX data. N=46 (excludes AL, AZ, DE, MD, and ND).

RESULTS

Distribution of Statelevel per Beneficiary Acute and LTC Spending on CAMODs, 2001­2005

Distribution of Statelevel per Beneficiary Acute Care Medicaid Spending on CAMODs, 2001­2005, by Type of Service

The relationship between Medicare and Medicaid utilization and spending

Distribution of statelevel 2004 Medicare spending per beneficiary, and 20012005 acute care Medicaid spending per CAMOD

2004 Medicare spending per beneficiary and 20012005 acute care Medicaid spending per CAMOD

2004 Medicare admissions/1,000 and 20012005 Medicaid admissions per CAMOD

2004 Medicare Part B spending, and 20012005 Medicaid 'Part B' spending

2004 Medicare spending per beneficiary and 20012005 acute care Medicaid spending per CAMOD, by HRR, selected states

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

HRR-level regressions on selected outcomes Ambulatory Care Sensitive Hospitalizations Diabetes COPD CHF Asthma n/a +++ +++ n/a +++ n/a +++ ++ n/a +++ +++

Medicaid Hospital Stays 30 Day Readmissions +++ n/a n/a +++

e Hospital Stays d Hospital Stays

are Beds / 1000

ysician Visits ++ +++

----+++

--+++ + ++ +++ ++

armacy Fills rugs ns per 1,000 of MDs in primary

+ +++

--0.39 0.53

-0.45

--0.21

-0.47

--0.68

ed

-

p < 0.01 p < 0.05 p < 0.10

essions are HRR-level regressions, and all Medicaid variables are measured on cash-assistance, Medicaid-only, FFS d, using MAX data from 2001-2005. Table entries show significance level and direction of the parameter estimates. spitalizations a e meas ed spitali ations are measured among beneficiaries with the diagnosis (diabetes, COPD, etc.) during a CY period. beneficia ies ith (diabetes COPD etc ) d ing pe iod

Conclusions

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, Mid Atlantic New England and the upper Midwest England, Inpatient utilization only partially follows the contours of acute care spending p g 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

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 i MD/OPD/Cli i i l ll hil i i in MD/OPD/Clinic has very little effect Inpatient spending varies approximately equally because p p g pp y q y of volume and price, while MH and drugs variation is driven almost entirely by volume

At the state level, Medicaid and Medicare spending are unrelated M di di l t d

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

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

Making sense of the MedicareMedicaid l ti M di id relationship hi

Virtually zero state-level correlation in spending, and very y p g y small correlation in inpatient admissions suggest that Medicaid policy variables mediate the supply-utilization relationship suggested by the Dartmouth Atlas p gg y 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

Is more better in Medicaid?

At the state level, some suggestion that more p y , gg 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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