Persistence of HMO Performance: Are
Good HMOs Always Good and Bad HMOs
Always Bad?
Introduction
• Last 10-15 years have witnessed growth in
quality/performance measures in healthcare
– Hospitals, physicians, health insurance plans
Shailender Swaminathan University of Alabama at Birmingham
• Increase in demand for such measures from
payers, consumers, and regulators
Mike Chernew –
University of Michigan
– Concerns about cost, value, quality and safety
• Utility of these measures depends on the ability of
users to process multiple measures for purposes of
decision making and evaluation
Dennis Scanlon –
Penn State University
This research was funded by a grant from the Agency for
Healthcare Research & Quality [P01-HS10771]
Two Key Challenges for
Performance Measure Use
Typical Financial Product
Disclaimer
• There are often multiple indicators of quality for a given
provider (e.g., hospital or doctor) or health plan, making it
challenging to summarize overall performance
• “Past performance does not guarantee future
results”
– Should a consumer, purchaser/payer believe the
same thing for health plans and health
providers?
– Need to find a way to aggregate measures, assuming each provides
some signal about quality but also contains some noise
• Measures of performance are based on data gathered
before decisions are made, and the decisions themselves
generally do not take effect for some time until after the
decision date (e.g., 2003 data for 2005 plan enrollment that
takes effect in 2006)
– The utility of past data for predicting future performance depends
on the stability of plan performance
Unadjusted Transition Probabilities
Longitudinal Variation: MMR Rate
(Absolute Rankings based on mean in 2000 and +/- one std dev - % and N)
100
Probability of rating in top third in year (t+1)
percent
95
DTP Rate
MMR Rate
OPV Rate
HIB Rate
Hepatitis B
Rate
VZV Rate
Rating in t-1
Rating in t
Above Upper
threshold
Above Upper
threshold
0.75 (3)
0.88(24)
0.78 (23)
1.0 (3)
0.5 (2)
In middle range
Above Upper
threshold
0.78(9)
0.53 (19)
0.75 (6)
0.8 (10)
0.6 (5)
.
Below lower
threshold
Above Upper
threshold
0.0 (2)
.
0 (2)
0.0 (2)
0.0 (2)
0.0 (2)
ALL
Above Upper
threshold
0.67(15)
0.72(42)
0.73 (33)
0.73 (15)
0.44 (9)
0.0 (2)
In middle range
In middle range
0.03 (92)
0.11(99)
0.12 (93)
0.09 (99)
0.03 (87)
0.14 (7)
Below lower
threshold
In middle range
0.0 (20)
0.0(4)
0.0 (12)
0.0 (20)
0.04 (23)
0.0 (26)
ALL
In middle range
0.02 (121)
0.12 (114)
0.12 (117)
0.07 (129)
0.04 (112)
0.03 (33)
Below lower
threshold
Below lower
threshold
0.0 (15)
.
0 (2)
0.0 (5)
0 (23)
0 (121)
90
85
80
1998
1999
2000
y ear
MMR Rate
2001
2002
.
1
Research Objectives
• Develop a methodology to estimate
longitudinal “transition probabilities” when
multiple indicators of performance exist
• Estimate aggregate transition probabilities
using multiple HEDIS childhood
immunization measures
• Incorporate the effects of measured
covariates on the transition probabilities
Empirical Model
• There are 6 HEDIS childhood immunization measures
so the model is:
⎡( Chicken Pox (VZV) Rate )it
⎤
⎡ λ01 ⎤
⎡ λ11 ⎤
⎡u1it ⎤
⎢
⎥
⎢λ ⎥
⎢λ ⎥
⎢u ⎥
⎢( H Influenza Type B (HIB) Rate )it
⎥
⎢ 02 ⎥
⎢ 12 ⎥
⎢ 2 it ⎥
⎢( Measles, Mumps & Rubella (MMR) Rate )
⎥
⎢
⎥
⎢
⎥
⎢u3it ⎥
λ
λ
03
13
*
it
⎢
⎥ =Y jit = ⎢ ⎥ + γ ' Hybrid jit + ⎢ ⎥ * q it + ⎢ ⎥ (1)
⎢( Hepatitis B Rate )it
⎥
⎢ λ04 ⎥
⎢λ14 ⎥
⎢u4 it ⎥
⎢( Diphtheria, Tetanus & Pertussis (DTP) Rate ) ⎥
⎢ λ05 ⎥
⎢λ15 ⎥
⎢u5it ⎥
it
⎢
⎥
⎢ ⎥
⎢ ⎥
⎢ ⎥
⎢⎣ λ06 ⎥⎦
⎣⎢λ16 ⎦⎥
⎣⎢u6it ⎦⎥
⎣⎢( Polio (OPV) Rate )it
⎦⎥
{ }
• Latent quality is a function of both time invariant and
time varying covariates and is written as:
qit* = β 0 + β1 ' X it + β 2 ' Dt + δ i + ηi (t − t ) + ε it
(2)
Using Model Parameters to Estimate
Transition Probabilities
Empirical Model
• The MIMIC model relates the indicators to the
underlying latent variable but we assume that the
indicator specific errors (u’s) are uncorrelated both
contemporaneously and over time.
• X’s are measured covariates such as profit status and
MSA managed care penetration
• D are time period specific dummy variables
• The term δ i represents unmeasured heterogeneity in
quality levels while ηi represents unmeasured
heterogeneity in growth rates, and ε it is an
independently distributed error term
Data
• We simulate joint probabilities over multiple periods (e.g.,
the joint probability that plans were in the bottom third in
1998 and 1999 is:
⎡τ 1,98 - ( β 0 + β1 X 98 ) τ 1,99 - ( β 0 + β1 X 99 )
⎤
P ( q98 < τ 1,98 , q99 < τ 1,99 ) = Φ ⎢
,
| σ 12 ⎥
σ 11
σ 22
⎣⎢
⎦⎥
• Transition probabilities can be simulated for transitions
involving more than 2 periods (e.g., we can find the
probability that plans are in the upper third in 2000 given
that they were in the middle third in 1999 and in the
bottom third in 1998)
– We use the Geweke, Hajivassiliou and Keane (GHK) simulator (Geweke,
Keane and Runkle 1994).
Summary Statistics: Distribution of
Years of Data Per Measure (N=527)
• Data sources
– NCQA’s HEDIS (1998-2002)
• We use 6 indicators of childhood immunization
• We use data from both public reporting and nonpublicly reporting plans and we control for the data
collection method used
(5)
% of Plans
Childhood Immunization
Measures
DTP Rate
MMR Rate
OPV Rate
HIB Rate
Hepatitis B Rate
VZV Rate
1 year
2 years
3 years
4 years
5 years
30%
28%
17%
17%
13%
14%
15%
13%
25%
28%
32%
30%
30%
30%
12%
17%
17%
16%
12%
13%
13%
14%
16%
15%
15%
15%
28%
25%
25%
25%
– Interstudy MSA Profiler and Competitive Edge
• Plan and market characteristics
ƒ About 40 % of plans report at least 4 years of data for each
measure
ƒNeeded for identification of MIMIC Model
2
Results: Effect of Covariates on
Quality
Summary Statistics
Table 1: Descriptive Statistics for Quality Measures
Childhood Immunization
Measures
Childhood Immunizations
– DTP Rate
Childhood Immunizations
– MMR Rate
Childhood Immunizations
– OPV Rate
Childhood Immunizations
– HIB Rate
Childhood Immunizations
– Hepatitis B Rate
Childhood Immunizations
– VZV Rate
1998 (N=357)
Mean
Std Dev
75.93
14.93
1999 (N=334)
2000 (N=329)
Mean Std Mean Std Dev
Dev
78.87 12.95 80.79 12.07
2001 (N=305)
Mean
Std Dev
2002 (N=290)
Mean
Std Dev
81.68
10.77
80.38
85.59
9.87
87.09
8.70
88.77
6.66
89.57
6.41
90.24
5.91
81.62
14.10
82.83
11.79 84.65
11.22
85.56
9.68
86.35
10.97
11.91
78.14
13.77
80.82
11.67 82.95
10.29
83.45
9.84
83.52
10.67
71.32
17.28
75.34
14.29 78.04
13.81
80.19
11.82
82.17
12.63
52.06
11.72
64.01
10.85 70.76
9.43
75.29
9.21
82.37
7.94
Variable
Estimate
(Asymptotic standard errors)
1.0844 ***
(0.3500)
0.5279
(0.4121)
0.1338
(0.1273)
-4.2460 ***
(1.1837)
9.8481 **
(3.9232)
0.1427
(1.7316)
-20.4495 ***
(6.0307)
0.3207 **
(0.1508)
5.6873 **
(2.8589)
0.4975
(0.8536)
-0.0994 ***
(0.0360)
0 <= Linear Spline Plan Age <8
8 <= Linear Spline Plan Age <12
12> Linear Spline Plan Age
For profit
MSA HMO Penetration (weighted)
Herfindahl Index (weighted)
% of MSA Population Non-white
MSA Per Capita Income (weighted)
Staff Group Model
IPA Network Model
MSA Population
Time Dummies (Reference = 1998)
Dummy Variable Year = 1999
Dummy Variable Year = 2000
Dummy Variable Year = 2001
Dummy Variable Year = 2002
0.8994 *
3.4587 ***
4.1045 ***
4.6947 ***
(0.5413)
(0.8293)
(1.1153)
(1.3135)
Simulated Transition Probabilities
Results: Variance Components
Variance Components
Plan specific growth component (standard deviation)
Plan specific level component (standard deviation)
Correlation in level and growth components
Autocorrelation parameter
Transitory error (standard deviation)
Log Likelihood for Full Model
Estimate (Asymptotic
standard errors)
1.7297 ***
(0.5516)
6.8656 ***
(2.4185)
-0.6580 *
(0.3930)
0.7135 ***
(0.1546)
5.5070 ***
(0.3640)
-25812.62
• Significant variance components for both levels and
growth
• Negative correlation between level and growth
components
• Significant AR(1)
(Absolute Rankings with thresholds based on mean in 2000 and +/- one std dev - %)
Probability of being above
upper threshold in year:
t+1 (2000)
N/A
Above Upper
threshold
In middle range
Below lower
threshold
N/A
N/A
0.66
0.53
0.12
0.00
0.13
0.00
Above Upper
threshold
Above Upper
threshold
In middle range
0.74
0.58
0.60
0.47
0.02
0.04
Below lower
threshold
0.00
0.00
RESULTS USING 2 YEARs OF PAST
PERFORMANCE
Rating in t-1 (1998) Rating in t
Above Upper
threshold
In middle range
Below lower
threshold
Below lower
threshold
Simulated Transition Probabilities
Effect of Covariates on
Transition Probabilities
(Relative Rankings with thresholds based on top, bottom and middle tercile)
Probability of being above
upper threshold in Year:
t+1 (2000)
t+2 (2001)
RESULTS USING ONLY 1 YEAR OF
PAST PERFORMANCE
Rating in t (1999)
t+2 (2001)
Effect on HMO Penetration and Profit Status on Transition Probabilities
RESULTS USING ONLY 1 YEAR OF
PAST PERFORMANCE
Rating in t
ALL
ALL
ALL
Above Upper
threshold
In middle range
Below lower
threshold
Probability of being above the upper threshold in
t+2
0.80
0.69
0.16
0.01
0.17
0.00
RESULTS USING 2 YEARS OF PAST
PERFORMANCE
Rating in t-1
Rating in t
Above Upper
threshold
In middle range
Below lower
threshold
Below lower
threshold
Above Upper
threshold
Above Upper
threshold
Above Upper
threshold
Below lower
threshold
0.75
0.75
0.60
0.57
0.54
0.49
0.03
0.03
Results from base
model
Rating in t-1
Above Upper
threshold
In middle range
Below lower
threshold
ALL
Below lower
threshold
ALL
Above Upper
threshold
Rating in t
Above Upper
threshold
Above Upper
threshold
Above Upper
threshold
Above Upper
threshold
In middle range
In middle range
Below lower
threshold
Increase in HMO
penetration 1 SD
from mean
Profit
Not for profit
0.58
0.65
0.44
0.65
0.47
0.53
0.45
0.56
0.04
0.05
0.00
0.05
0.53
0.59
0.50
0.61
0.04
0.07
0.02
0.07
0.13
0.00
0.16
0.00
0.10
0.00
0.17
0.01
3
Results Summary
• High plan performance in the past suggests, but does
not guarantee, high performance in the future
– High plan performance over multiple years is a stronger
predictor of high performance in future
• Plan level covariates can affect the transition
probabilities but sizable unmeasured heterogeneity
and autocorrelation induces an element of persistence
in plan performance
Policy/Practice Relevance
• Many public reporting and incentive payment
efforts implicitly assume that performance is
stable or increasing, which may not always be the
case
• Consumer directed health care approaches rely on
information being available, but also being useful
in terms of correctly forecasting future
performance
• A simulation approach such as the one used here
may be a useful way of forecasting future
performance for purposes of informing the
decisions of purchasers and consumers
4