Is Acute Rehabilitation
More Cost-Effective
Than Subacute Rehabilitation?
Bruce Vogel, PhD
This research was funded by the Department of Veterans Affairs, Health Services
Research and Development Service (HSR&D), Stroke Quality Enhancement Research
Initiative (QUERI), RRP 06-184, through the VA Rehabilitation Outcomes Research
Center (RORC), North FL/South GA Veterans Health System, Gainesville, FL.
This presentation does not necessarily represent the opinions or beliefs of the
Department of Veterans Affairs, VISN 8, or the RORC.
Collaborators

Tracey Barnett, Ph.D.
 Dean Reker, Ph.D.
 Xinping Wang, Ph.D.
Today’s Objectives



To describe important changes in the structure of
VA inpatient rehabilitation over the past 15 years
To estimate differences in VA costs and outcomes
for VA stroke patients in acute versus subacute
rehabilitation units
To draw some preliminary conclusions about the
cost-effectiveness of alternative rehabilitation sites
from the VA perspective to inform policy
Restructuring VA Rehab

Between 1995 and 2003, the VA closed 28
of its 59 acute hospital-based inpatient
rehabilitation units (ARBUs), or 47% of its
original complement of such units
 Over the same time, the VA established 24
new subacute nursing home-based
inpatient rehabilitation units (SRBUs).
Central Question
What has been the impact
of this major restructuring on VA
rehabilitation costs and outcomes?
Data Sources

Demographic and clinical data from chart
abstractions in previous study of the Stroke Impact
Scale
 483 post-acute stroke patients from 27 acute and
subacute units at 23 different VA Medical Centers
in FY2002-FY2005
 VA cost data for index rehab stay and 24 months
post-stay from VA Decision Support System
National Data Extracts (DSS NDE)
Dependent Variables

Cost data divided into index stay, shortterm (0-3 months post-stay), long-term (4-24
months post-stay) and total 24 month costs
 Outcomes measured by admission and
discharge total, motor, and cognitive
Functional Independence MeasureTM (FIMTM)
scores
Independent Variables

Both cost and functional status models
controlled for:






ARBU vs. SRBU
type of stroke
time from onset to admission
admission motor and cognitive FIMTM scores
demographics (age, martial status, and race)
VA medical center
Methods



Followed standard econometric practice in
estimating cost models (Manning and Mullahy,
1998) by choosing between OLS and generalized
linear models based on residual kurtosis and
heteroskedasticity.
Estimated a distributed lag model of functional
outcomes where discharge FIMTM is modeled as a
function of admission FIMTM
Examined full and parsimonious models (p<.20)
Descriptive Statistics
Variable
Frequency (%)
Acute (N=315)
Frequency (%)
Subacute (N=168)
Mean (SD)
96.34 (21.43)
22214 (13818)
Mean (SD)
91.97 (25.01)
24861 (17819)
N (%)
N (%)
187 (64.71)
124 (65.26)
102 (35.29)
66 (34.74)
300 (64.66)
11 (73.33)
164 (35.34)
4 (26.67)
159 (66.81)
152 (63.07)
79 (33.19)
89 (36.93)
133 (65.84)
135 (65.85)
16 (80.0)
8 (61.54)
19 (48.72)
69 (34.16)
70 (34.15)
4 (20.0)
5 (38.46)
20 (51.28)
Mean (SD)
68.45 (11.25)
Mean (SD)
68.85 (10.89)
71.08 (21.33)
24.21 (7.57)
46.88 (16.27)
26.29 (103.83)
65.38 (24.47)
21.30 (8.36)
44.07 (18.96)
45.71 (308.83)
27.63 (5.97)
68.70 (17.14)
25.33 (7.21)
66.64 (20.22)
Dependent Variables
Discharge FIM total score*
Total index cost*
Independent Variables
Race
White
Other
Gender
Male
Female
Marital
Married
Not Married
Impairment Code
1.1
1.2
1.3
1.4
1.9
Age
Admission Score
FIM Total*
FIM Cognitive
FIM Motor*
Onset to Admission
Descriptive
Discharge Score
FIM Cognitive*
FIM Motor*
* p <.05
Cost and FIMTM Model Runs
Variable
Acute RBU
Impairment Code 1.1
Impairment Code 1.2
Impairment Code 1.3
Impairment Code 1.4
Impairment Code 1.9
Onset to Admit
FIM Motor at Admission
FIM Cognitive at Admission
Age
Married
White
Site 516
523
528
546
549
552
553
578
583
598
600
605
618
630
635
642
648
671
672
673
678
688
691
*p < .05; †p < .10
Full
Total Index
Stay Costs
2
R = .41
-5979.90*
765.07
1866.22
-2693.24
-183.92
reference
-.27
-451.23*
-75.75
-204.07*
41.99
1390.77
-7382.23†
2029.84
8397.23†
-5887.32
8993.35*
-4486.92
-6194.42
4128.98
12709.93*
7304.03*
-890.91
-6843.06
2855.75
6627.13
-7677.20†
-4266.90
-2015.00
-1291.84
-790.60
-2017.43
-4072.49
2628.07
reference
Parsimonious Total
Index
Stay Costs
2
R = .40
-5850.79*
-475.41*
-191.82*
-6105.27
1252.22
9106.78*
-5975.21
9848.49*
-3496.23
-6406.22
4223.46
13071.03*
7487.36*
-550.78
-6053.16
3695.13
6926.72
-7466.79†
-4502.05
-896.68
-546.89
-1013.02
-1110.00
-3580.50
2907.72
Full Total FIM
2
R = .68
8.63*
.38
3.68
-4.21
-1.68
reference
0.002
.71*
1.03*
-.32*
.65
-1.80
12.28*
2.16
-2.05
8.75*
-.88
1.72
9.06
-2.48
3.93
-.74
-1.96
15.40*
10.41*
9.04†
-4.66
-4.97
13.09*
-.30
-3.27
.75
16.18*
8.59†
reference
Parsimonious
Total FIM
2
R = .68
8.57*
.29
3.63
-3.90
-1.84
reference
.71*
1.03*
-.31*
11.50*
1.33
-2.49
8.67*
-1.08
1.50
9.60†
-2.82
3.53
-1.21
-2.05
15.13*
9.54*
9.37†
-5.05
-4.44
12.18*
-.78
-2.27
.26
15.63*
9.11*
reference
Significant Descriptives


ARBU patients had
higher admission and
discharge FIMTM
scores than SRBU
patients (71.1 vs. 65.4
at admission; 96.3 vs.
92.0 at discharge)
No differences in total,
motor, or cognitive
FIMTM gains were
significant
100
90
80
70
60
50
40
30
20
10
0
ARBU
SRBU
Admit FIM Dischg
FIM
Significant Descriptives

Average index stay
total cost was lower for
ARBU patients
($22,214 vs. $24,861)
25000
24500
24000
23500
23000
22500
22000
21500
21000
20500
ARBU
SRBU
Index Stay Costs
Moving On . . .
What happens to these results
when we control for the
independent variables listed above?
Significant Model Results

We find a considerably larger statistically
significant difference in total index costs of
approximately $6,000 (~25% of the index stay
cost)
 After regression adjustment, we find
statistically significantly higher total discharge
FIMTM scores in ARBUs than in SRBUs (~8.6
FIMTM points)
Cost Effectiveness?
When outcomes improve and costs
either decrease or stay the same,
cost-effectiveness is a given.
Implications

While moving patients out of the hospital can
often be cost effective, this may not always be
the case
 VA policymakers may want to reconsider the
trend of replacing acute, hospital-based rehab
units with subacute, nursing home-based units
Caveats

Failed to find significant differences for
short-term, long-term, and total two-year
costs – implications?
 Observational data – Could unobserved
selection bias be driving these results?
 Only examined VA cost – “business case”
 Only examined stroke patients – Do these
results carry over to all rehab patients?