1. THE EFFECT OF MEDICAID RATE ON POTENTIALLY PREVENTABLE HOSPITALIZATIONS FROM NURSING HOME* Orna Intrator with V. Mor, N. Wu, D. Grabowski †, D. Gifford and Z. Feng Brown University and † UAB * Funded by NIA RO1 AG20557

2. Objective Medicaid payment rates are reflected in the availability of the clinical and managerial infrastructure necessary to manage nursing home residents’ medical conditions. Over 60% of all nursing home residents are Medicaid recipients Differences in reimbursement rates and other Medicaid reimbursement policies likely to contribute to observed inter-state differences in hospitalization rates.

3. Hospitalization STATE Policies MARKET Context NURSING HOME Context RESIDENT Characteristic s CONCEPTUAL MODEL Direct and indirect effects

4. Hypotheses: direct effects Nursing home residents in states with Higher Medicaid rates experience fewer potentially preventable hospitalizations. Bedhold policies will be more likely to be hospitalized With casemix reimbursement will be more likely to be hospitalized because a hospitalization would result in change in per-diem rate

5. Hypotheses: Indirect effects Higher Medicaid rates  More NP/PAs  Less hospitalizations More RNs in nursing home nursing force  Less hospitalizations More investment in physicians  Less hospitalizations

6. Data and Cohort Minimum Data Set (MDS) to identify long-stay residents or urban free standing nursing homes in 48 contiguous states in 2000 (N=575,188 in 9124 facilities) Facility data from Centers for Medicare and Medicaid Services’ Online Survey Certification and Reporting (OSCAR) system. Medicare claims of all hospitalizations within 5 months of baseline MDS that were initiated from baseline nursing home (N=101,105) Area Resource File for information on counties as NH markets

7. Survey of State Medicaid Policies* 48 continguous states contacted Information on: Method of calculation Casemix method and updating schedule Average per-diem payment rate and ancillary payments Bedhold rate and durations CON and moratorium *Forthcoming article in Health Affairs Web Exclusive June 18, 2004

8. State Policy Measures Average per diem rate: Total payments divided by total bed days Free standing and hospital based Annually, 1999-2002 Used 2000 data in this study Bedhold policies* Proportion of NH rate paid Maximum number days in period Minimum occupancy requirements *Poster at 6pm tonight

9. State Policy Measures Casemix reimbursement: Type of system (RUG based, other) How frequently updated (annually, quarterly) Based on resident, facility, or both Four category variable: No casemix (N=17) Not resident specific only updated annually (N=9) Facility specific quarterly or semi-annually (N=14) Most responsive: Resident specific quarterly or semi- annually or both and quarterly (N=8)

10. Outcome Definition Hierarchical outcome: Any potentially preventable hospitalization (using ambulatory care sensitive diagnoses) Any other hospitalization Death Remaining in the facility. Distribution of outcome: Any potentially preventable …………… 7.4% Other Hosp ……………………………..……. 12.6% Died ……………………………………….……….. 9.2%

12. Model and Estimation Multinomial response (4 categories) Multilevel: Resident Facility County State Estimation using MLWiN for binomial response: Outcome vs. remain in NH

13. Results: Direct Medicaid Policy Effects

14. Results: Indirect Medicaid Policy Effects

15. Policy Implications Highlights competing motivation of Medicaid and Medicare: Higher Medicaid rates  lower Medicare expenditures from less hospitalizations  higher Medicare expenditures from increased LOS Higher bedhold rates  higher Medicare expenditures More bedhold days  better quality of life What is “optimal” policy For Medicare? For Medicaid? For Residents?