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
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
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
Possible
Analytic Approaches
• Use regression analysis of outcome variable with many
explanatory measures to minimize 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
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)
Unadjusted Statistics
N
Average SNF LOS
Mean values
All
HB
FS
(excludes deaths & readmits)
494,934
24.8
Percent discharged
home within 30 days
648,306 36.2%
Percent with preventable
608,213 10.9%
readmission
13.5
30.3
61.9%
25.8%
7.0%
12.6%
Driving Force for Referral Decision
By Qualifying
Hospital Stay
All
Hospital
has SNF
Percent referred from a
hospital with its own SNF unit
47.7
Percent in a hospital-based
SNF
28.8
By SNF setting:
No SNF
Hospitalbased
Freestanding
100
0
84.6
32.7
51.1
8.5
100
0
• 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
The probability of HB referral is different in a hospital with a SNF versus one without.
It appears to be a different choice process.
10
5
0
density function
15
predicted probablity of HB referral
0
.2
.4
.6
.8
p(HB)
hospital has HBunit
does not have HBunit
1
SNF Patient Characteristics
By Qualifying Hospital
By SNF setting:
Has SNF
No SNF
HB
Freestanding
33.2
36.4
27.9
37.8
Joint replacement
9.2
8.0
12.9
6.9
Other joint procedures
6.1
6.3
6.3
6.2
Pneumonia/COPD
10.6
10.8
9.2
11.3
Cardiac
17.6
17.5
17.6
17.5
Stroke/TIA
6.3
6.7
6.0
6.7
Rehab
75.1
77.8
75.3
77.0
Extensive Services
14.7
12.3
15.1
12.8
Special & Med Complex
9.0
8.4
8.9
8.6
53.8
31.5
85.6
24.5
Percent age 85+
% by admit reason:
% by initial RUG:
Nurse assess: likely short stay
Outcome Regression Model
ln( SNFLOS )i   HB HBi  X  u
• 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
Stratified Propensity Model
• Referral Equation:
HBi  f ( X  ei )
– Use only observations from hospitals with SNFs
• Outcome Equation
ln( SNFLOS )ij   HB j HB ji  X j  uij
– For J=1,…,5 groups of observations based on
predicted probability of being referred to a
hospital-based SNF
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
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
Estimated Outcome Differences
(HB versus FS)
Unadjusted
Adjusted
Unstratified
Unstratified
stratified
weighted
average
-55.4%
-17.3%
-16.7%
Probability of discharge home
in 30 days
0.361
0.076
0.077
Probability of preventable
readmission
-0.056
-0.025
-0.023
Average SNF length of stay
Point difference attributable to HB
setting
Probability of being discharged home or to community within 30
days of SNF admission
0.140
0.120
0.100
0.080
0.060
0.040
Unstratified
By Strata with 95% CI
0.020
0.000
Strata 1: Lowest
p(HB)
Strata 2
Strata 3
Strata 4
Strata 5: Highest
p(HB)
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
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