The Impact of Kidney Transplant Report Cards
on Consumer Choice
David H Howard
Emory University
Bruce Kaplan
University of Florida
Funding provided by NIDDK/NIH R21DK67611
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Report cards in transplantation
“…The OPTN should use rapidly advancing Internet
technology to make information swiftly, conveniently,
and inexpensively available throughout the nation”
--DHHS Final Rule April 1998
2
Center-specific survival report: UAB
Graft survival at 1 Year
Adult (Age 18+)
Transplants
692
Actual graft survival (%)
94.6
Expected graft survival (%)
91.5
Ratio of Observed to Expected Failures
0.59
95% Confidence Interval:
Lower Bound
0.41
Upper Bound
0.82
P-value (2-sided)
From: www.ustransplant.org.
Coverage 07/01/2000 to 12/31/2002
<0.01
3
Outcomes report cards by UNOS and SRTR
January 1, 2000
← data →
October 31, 2002
4
Outcomes report cards by UNOS and SRTR
SEP
1999
January 1, 2000
← data →
October 31, 2002
5
Outcomes report cards by UNOS and SRTR
SEP
2000
SEP
1999
January 1, 2000
← data →
October 31, 2002
6
Outcomes report cards by UNOS and SRTR
JAN
2002
← data →
JUL
2002
JUL
2001
SEP
2000
SEP
1999
January 1, 2000
October 31, 2002
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Outcomes report cards by UNOS and SRTR
JUL
2002
JAN
2002
January 1, 2000
2
JUL
2001
SEP
2000
SEP
1999
1
3
4
5
← data →
October 31, 2002
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Empirical strategy in words
Do centers whose outcomes improved from one
report card to the next experience a
proportionately larger increase in registrations
compared to other centers?
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Data
National, patient-level registration data from Jan 1, 2000 to
October 31, 2002
Use to calculate number of registrations at each center
between each report card release
Keep centers with >10 registrations in all five periods
Exclude VA hospitals, childrens hospitals
N = 102 centers, 5 periods, 510 center/period observations
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Regression models
Poisson fixed effects
registrationsit = αqualityit + xitβ + δi + ηt + εit
OLS fixed effects
ln(registrationsit) = αqualityit + xitβ + δi + ηt + εit
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Quality measures: graft failure at 1 year post-tx
Continuous
1. Quality = exp. rate – act. rate
2. Weighted quality = exp. rate – act. rate
SE
Dichotomous
3. Quality > median quality
4. Weighted quality > median weighted quality
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Mean SE
Registrations
Quality measures
Expected - Adjusted
Weighted Expected - Adjusted
65
61
Min
Max
11
522
0.00 0.05 -0.52
-0.04 1.86 -20.69
0.11
4.03
Control variables
Volume
Total registrants, 0 to 100 miles
Total registrants, 100 to 200 miles
169 112
229 216
38 75
N
510
5 921
16 1,419
0 706
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Correlation matrix, quality
SEP99 SEP00 JUL01 JAN02 JUL02
SEP99
SEP00
JUL01
JAN02
JUL02
1.00
0.42
0.30
0.24
0.26
1.00
0.65
0.37
0.34
1.00
0.55
0.53
1.00
0.63
1.00
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Number of periods in top half, quality-wise
Periods Centers
Prct
5
4
3
2
1
0
20
11
12
24
19
16
20%
11%
12%
24%
19%
16%
N
102
100%
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Coefficients on quality variables from OLS
OLS
Coeff. T-stat
random effects
Coeff. T-stat
fixed effects
Coeff. T-stat
Quality
0.85 1.86
0.62 1.41
0.25 0.57
Wgt quality
0.02 1.75
0.02 1.33
0.01 0.53
Top half, quality
0.12 2.67
0.09 1.97
0.04 0.93
Top half, wgt quality
0.13 2.93
0.11 2.46
0.08 1.67
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Coefficients on quality variables from Poisson
OLS
Coeff. T-stat
random effects
Coeff. T-stat
fixed effects
Coeff. T-stat
Quality
1.74 12.46
0.12
0.77
0.06 0.37
Wgt quality
0.05 14.31
0.00
0.61
0.00 0.21
Top half, quality
0.21 17.33
0.05
2.85
0.04 2.53
Top half, wgt quality
0.21 17.44
0.09
5.38
0.08 5.19
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Conclusion
• In cross section quality matters
– Mixed logit model indicates that one SD increase in graft
failure rates leads to a 5 percent decline in patient
registrations
• Results on impact of report cards depend on specification
– Try additional specifications
• Quality can affect choice and patient flows even if report
cards do not (e.g. “centers of excellence” programs)
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