Controlling for Medicare Patient Selection into Hospital-based versus Freestanding Nursing Homes

1. Controlling for Medicare Patient Selection into Hospital-based versus Freestanding Nursing Homes Sally Stearns, Kathleen Dalton, Mark Holmes Cecil G. Sheps Center for Health Services Research, UNC-Chapel Hill Susanne Seagrave Centers for Medicare & Medicaid Services AcademyHealth Annual Research Meeting, June28 2005 Funding source: Medicare Payment Advisory Commission

2. Industry Background • Skilled nursing facilities (SNFs) • About 14,000 certified for Medicare in 2000 • 85% freestanding (FS) • 75% are for-profit • 15% hospital-based (HB) • 17% are for-profit • Hospital-based facilities are different: • Medicare length of stay half as long • Cost per day 50% to 100% higher • Better staffing ratios • Patients thought to be different • 1998: Cost-based -> Prospective Payment

3. Research Issues • Study question: • For clinically similar patients, do HB and FS settings differ in: • Medicare covered length of stay • rate of hospital readmission • rate of discharge to home within 30 days? • Research Challenge: • Controlling for selection • Standard regression models often unsatisfactory in removing bias from unmeasured case mix differences

4. Possible Analytic Approaches • Use regression analysis of outcome variable with many explanatory measures tominimize selection bias • Propensity score approach (PS) • Construct a prediction model for HB = f (X) • Group observations according to predicted p(HB) • Model outcome within group • Analogous to adding more measures (i.e., lots of interactions) • Instrumental variables (IV) • Being addressed elsewhere

5. Data Source • Nursing home stay-level data set • Claim from initial qualifying hospitalization • Claim from Medicare-covered nursing home stay • Detailed patient status data from 5, 14, 30, 60- and 90-day MDS nursing home assessments • Claim from any re-hospitalization • Date of death (followed through 2002) • We added facility & market-level variables • Very large data set (648,306 stay observations from 7/2000 – 6/2001 after exclusions)

6. Unadjusted Statistics

7. Driving Force for Referral Decision • Single strongest predictor of HB referral is coming from a hospital that operates its own SNF. • Referral decision appears to be very different when coming from a hospital that does not have operate its own SNF

8. The probability of HB referral is different in a hospital with a SNF versus one without. It appears to be a different choice process.

9. SNF Patient Characteristics

10. Outcome Regression Model • Where HB is a dummy and X is: • Patient characteristics • Sociodemographics • Clinical info from hospital stay and MDS data • Variables capturing prognosis • SNF characteristics • Market area characteristics

11. Stratified Propensity Model • Referral Equation: • Use only observations from hospitals with SNFs • Outcome Equation • For J=1,…,5 groups of observations based on predicted probability of being referred to a hospital-based SNF

12. Modified Propensity Approach • Usual propensity score approach problematic • Balancing achieved with respect to p(HB) but not with respect to all covariates. • Continued need to control for covariates in outcome models. • Simplified approach: divide p(HB) into 5 strata • Groups may not be as clinically similar as would be desirable • But should provide a rough approximation of formal propensity groups to demonstrate impact

13. HB Referral Equation Results • Used only patients from hospitals with SNFS • Outcome estimations used full sample • Good candidates for fast recovery and discharge home are preferentially selected into HB SNFs • Most important factors related to prognosis, rather than specific diagnoses, procedures or RUG groups

14. Estimated Outcome Differences(HB versus FS)

16. Summary of Results/Issues • Substantial differences in unadjusted outcomes greatly reduced by controlling for observed variables. • Propensity stratification approach provided modest reduction in some groups • Useful to identify heterogeneity in treatment effects • Limitations • Potential endogeneity of “short stay” assessment • Other unobserved variables

17. Conclusions • Selection of certain types of patients into HB versus FS facilities occurs • Differences in outcome remain: • May be attributable to care or institutional differences in treatment • May be explained by other unobserved factors