Background
Econometric Framework
Estimation Results
Policy Implications
Conclusion and Future Direction
The Effect of Plan Design on Utilization: a
Comparison of Preventive Care in Traditional
and Consumer Driven Health Plans
Caroline Carlin, Department of Applied Economics
Steve Parente, PhD, Department of Finance
Robert Town, PhD, Department of Health Policy and
Management
University of Minnesota
June 29, 2009
Carlin, Parente & Town
Preventive Care
Background
Econometric Framework
Estimation Results
Policy Implications
Conclusion and Future Direction
Outline
Background
Econometric Framework
Estimation Results
Policy Implications
Conclusion and Future Direction
Carlin, Parente & Town
Preventive Care
Background
Econometric Framework
Estimation Results
Policy Implications
Conclusion and Future Direction
This is work in progress – comments are
welcome!
Carlin, Parente & Town
Preventive Care
Background
Econometric Framework
Estimation Results
Policy Implications
Conclusion and Future Direction
Background
I
Preventive care, particularly cancer screening, has been
shown to improve mortality
I
There have been significant increases in patient
copayments, especially through high-deductible
consumer-driven health plans (CDHPs).
There is concern that this patient cost sharing will
discourage care (Dixon et al 2008)
I
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Even when preventive care is fully covered in a CDHP,
there is concern that confusion on the part of the consumer
leads to reduced preventive care.
Carlin, Parente & Town
Preventive Care
Background
Econometric Framework
Estimation Results
Policy Implications
Conclusion and Future Direction
Background
A number of studies have compared a CDHP group with
traditional plan groups, where preventive services are fully
covered, or have a minimal copayment, and found little
difference in rates of preventive services.
I Busch, et al (2006), took advantage of a natural
experiment when Alcoa mandated high-deductible
coverage for a subset of employees.
I
I
I
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Controls were union groups – unobserved differences?
Rowe et al, 2008 found little difference in preventive care
utilization after the first year.
89% of the CDHP population had another plan option –
selection?
Wilson et al (2009) compare CDHP to CMM enrollees with
controls for observable characteristics.
I
No attempt to adjust for selection.
Carlin, Parente & Town
Preventive Care
Background
Econometric Framework
Estimation Results
Policy Implications
Conclusion and Future Direction
Background
Wharam, et al (2008), is one of the few studies that looks at the
impact of a CDHP plan on preventive care use in a controlled
setting.
I
Treatment group had employer-mandated CDHP coverage
I
Control group had HMO coverage with no CDHP option
I
Found little evidence of plan-induced change in rates of
cancer screening.
Carlin, Parente & Town
Preventive Care
Background
Econometric Framework
Estimation Results
Policy Implications
Conclusion and Future Direction
Background
Wharam, et al (2008), is one of the few studies that looks at the
impact of a CDHP plan on preventive care use in a controlled
setting.
I
Treatment group had employer-mandated CDHP coverage
I
Control group had HMO coverage with no CDHP option
I
Found little evidence of plan-induced change in rates of
cancer screening.
May still be some selection impact at the employer level, or to
coverage at the spouse’s employer. We add to the literature by
explicitly controlling for selection.
Carlin, Parente & Town
Preventive Care
Background
Econometric Framework
Estimation Results
Policy Implications
Conclusion and Future Direction
Study Setting
I
Our data come from a large Twin Cities-based employer.
I
Panel data include detailed claims and enrollments for
2002 through 2004.
9231 employees included
I
I
Dependents excluded to avoid interfamily correlations
I
Employees choose between an HMO, two POS plans, and
a CDHP.
I
Used CPT-4 code and ICD9 code information to identify
preventive care.
Carlin, Parente & Town
Preventive Care
Background
Econometric Framework
Estimation Results
Policy Implications
Conclusion and Future Direction
Study Setting
Ad hoc changes in employer’s contribution philosophy provided
exogenous variation in premiums across years, providing an
instrument for the selection correction.
2002
2003
Annual
Annual
Share
Share
Premium
Premium
2004
Annual
Share
Premium
2005
Annual
Share
Premium
.622
$0
.688
$0
.688
$382
.686
$408
Tier 1
.077
$0
.044
$21
.040
$741
.047
$770
POS 1 Tier 2
.096
$242
.082
$247
.080
$1,064
.072
$1,074
Tier 3
.073
$522
.095
$512
.050
$1,474
.031
$1,721
POS 2
.090
$1,346
.060
$1,258
.064
$1,463
.086
$783
CDHP
.041
$329
.061
$194
.074
$670
.073
$793
HMO
Carlin, Parente & Town
Preventive Care
Background
Econometric Framework
Estimation Results
Policy Implications
Conclusion and Future Direction
Study Setting
I
All patients were assumed to be eligible for general
preventive care visits
I
I
preventive care visit (41% of 9231)
Patients with prior history or ineligible age/gender were
excluded for
I
I
I
Mammography (49% of 3447)
Pap smear (18% of 4459)
Colonoscopy (7.5% of 6428)
Carlin, Parente & Town
Preventive Care
Background
Econometric Framework
Estimation Results
Policy Implications
Conclusion and Future Direction
Study Setting
Predictive variables in the model include
I
Demographic information: age, gender, income
I
Diagnosis-based risk measure developed from the Johns
Hopkins ACG software, a "concurrent weight" (CW)
capturing health status as a relative measure of expected
claims in the current year.
I
The office visit copay level (waived for preventive care)
Carlin, Parente & Town
Preventive Care
Background
Econometric Framework
Estimation Results
Policy Implications
Conclusion and Future Direction
Selection Correction
I
I
We estimated a joint choice and utilization model to
facilitate adjustment for selection effects.
Extending the methods used in Deb et al (2006) to a
multinomial setting, we included the unobserved
heterogeneity from the choice model utility equations as a
covariate in the probit utilization equations.
I
This term was significant only in the cervical cancer
screening equation.
Carlin, Parente & Town
Preventive Care
Background
Econometric Framework
Estimation Results
Policy Implications
Conclusion and Future Direction
Choice Model Introduction
The analysis uses a multinomial probit framework, with
temporally correlated errors.
Wijt = Uijt − Ui6t = xijt0 β + ijt
for
individual i = 1, ..., 3578
choice j = 1, ..., 5, and
time t = 1, ..., 3
Carlin, Parente & Town
Preventive Care
Background
Econometric Framework
Estimation Results
Policy Implications
Conclusion and Future Direction
Choice Model Introduction
The analysis uses a multinomial probit framework, with
temporally correlated errors.
Wijt = Uijt − Ui6t = xijt0 β + ijt
for
individual i = 1, ..., 3578
choice j = 1, ..., 5, and
time t = 1, ..., 3
Where it = i1t . . . i5t ∼ MVN (0, Σ)
Carlin, Parente & Town
Preventive Care
Background
Econometric Framework
Estimation Results
Policy Implications
Conclusion and Future Direction
Choice Model Introduction
The analysis uses a multinomial probit framework, with
temporally correlated errors.
Wijt = Uijt − Ui6t = xijt0 β + ijt
for
individual i = 1, ..., 3578
choice j = 1, ..., 5, and
time t = 1, ..., 3
Where it = i1t . . . i5t ∼ MVN (0, Σ)
and AR(1) errors:
ijt = ρj ij,t−1 + ηijt , with ηijt ∼ N(0, τj2 )
Carlin, Parente & Town
Preventive Care
Background
Econometric Framework
Estimation Results
Policy Implications
Conclusion and Future Direction
Choice Model
The complexity of the model required estimation with Bayesian
methods, rather than classical maximum likelihood methods.
I
A key component of Bayesian estimation of discrete choice
models is the data augmentation process (Tanner & Wong
1987)
I
The latent utility is sampled from its posterior distribution,
constrained by the observed choice pattern
I
This allows us to "observe" the latent utility, and thus
compute the unobserved heterogeneity for inclusion in the
preventive care model
Carlin, Parente & Town
Preventive Care
Background
Econometric Framework
Estimation Results
Policy Implications
Conclusion and Future Direction
Preventive Care Utilization Model
Probit equations were estimated for the four types of preventive
services, with individual intercepts used to control for temporal
correlation and capture correlation across equations. The latent
variables for person i at time t enrolled in plan j had the form
Vitjp = zitj0 βjp + bip + γ p itj + ωitjp
Vitjcc = zitj0 βjcc + bicc + γ cc itj + ωitjcc
Vitjco = zitj0 βjco + bico + γ co itj + ωitjco
Vitjm = zitj0 βjm + bim + γ m itj + ωitjm
The vector of individual intercepts, bi , is assumed to be N(0, D).
The errors ωitj were assumed to be iid N(0, 1).
Testing of a more complex covariance structure resulted in
covariances that were statistically significant, but not
meaningfully large.
Carlin, Parente & Town
Preventive Care
Background
Econometric Framework
Estimation Results
Policy Implications
Conclusion and Future Direction
Choice Results - Mean parameters
The parameters predicting the means of the plan utilities
followed a logical pattern:
I
Those who are female and older prefer plans that have
less tightly managed care
I
Typically-unobservable health status, measured by the
CW, was also significant, with sicker individuals preferring
less tightly managed plans
I
The impact of increasing premiums and the office visit
copay was negative and highly significant
Carlin, Parente & Town
Preventive Care
Background
Econometric Framework
Estimation Results
Policy Implications
Conclusion and Future Direction
Choice Results - Covariance Structure
The estimated AR(1) coefficients are quite high, indicating
significant "stickiness" in plan elections. The AR(1) coefficient
for the HMO is 0.70, and greater than 0.90 for all other plans.
We find a variance-covariance matrix with off-diagonal
elements significantly different from zero, and strong evidence
of heteroskedasticity.
HMO
POS 1, Tier 1
POS 1, Tier 2
POS 1, Tier 3
POS 2
HMO
1.0000
Across-Plan Variance-Covariance Matrix (std dev)
POS 1, Tier 1 POS 1, Tier 2 POS 1, Tier 3
POS 2
0.2035 *** 0.2164 ***
(0.0325)
(0.0306)
-0.0510 ** 0.0780 *** 0.1969 ***
(0.0225)
(0.0215)
(0.0324)
-0.0583 ** 0.0387
*
0.1407 *** 0.2509 ***
(0.0317)
(0.0225)
(0.0299)
(0.0402)
0.1817 *** -0.0061
-0.0007
0.0645 *** 0.2614 ***
(0.0382)
(0.0184)
(0.0227)
(0.0293)
(0.0478)
* 90% Bayesian Confidence Interval excludes zero
** 95% Bayesian Confidence Interval excludes zero
*** 99% Bayesian Confidence Interval excludes zero
Carlin, Parente & Town
Preventive Care
POS 1, Tier 1
0.2035 *** 0.2164 ***
Background
(0.0325)
(0.0306)
Econometric Framework
Estimation
POS 1, Tier 2
-0.0510
** Results
0.0780 *** 0.1969 ***
Policy Implications
(0.0225)
(0.0215)
(0.0324)
Conclusion
and Future Direction
POS 1, Tier 3
-0.0583 ** 0.0387
*
0.1407 *** 0.2509 ***
(0.0317)
(0.0225)
(0.0299)
(0.0402)
POS 2
0.1817 *** -0.0061
-0.0007
0.0645 *** 0.2614 *
(0.0382)
(0.0184)
(0.0227)
(0.0293)
(0.0478)
Results - Covariance Structure
90% Bayesian Confidence Interval excludes zero
It is***instructive
to construct the implied correlations coefficients,
95% Bayesian Confidence Interval excludes zero
several
which
exceed
*** 99%of
Bayesian
Confidence
Interval25%.
excludesThis
zero is strong evidence that the
IIA assumption is not appropriate in this employer’s population.
Implied Across-Plan Correlation Coefficients
HMO
POS 1, Tier 1 POS 1, Tier 2 POS 1, Tier 3
POS 1, Tier 1 0.4375
POS 1, Tier 2 -0.1150
0.3780
POS 1, Tier 3 -0.1164
0.1663
0.6331
POS 2
0.3555
-0.0256
-0.0032
0.2518
Carlin, Parente & Town
Preventive Care
Background
Econometric Framework
Estimation Results
Policy Implications
Conclusion and Future Direction
Preventive Care Results
I
The parameters on the utility errors were statistically
significant only for the cervical cancer screening.
I
The Preventive Care model reproduced the actual visit
patterns well.
I
A traditional random-effect probit model with the same
covariate structure (excluding utility errors) was also
estimated.
I
An intercept-only model was also estimated.
Carlin, Parente & Town
Preventive Care
Background
Econometric Framework
Estimation Results
Policy Implications
Conclusion and Future Direction
Preventive Care Results
We see better accuracy in prediction within the proposed
model.
Preventive
Cerv Cancer
Colonoscopy
Mammography
% Correctly Predicted
Correlated
Probit with
Selection & Panel Probit Intercept
Ind'l Effects w/ Rand Eff Only Model
81.9%
67.7%
51.5%
87.6%
78.6%
71.1%
93.0%
87.9%
86.0%
77.2%
63.0%
50.0%
Carlin, Parente & Town
Preventive Care
Background
Econometric Framework
Estimation Results
Policy Implications
Conclusion and Future Direction
Preventive Care Results
The proposed model reproduced the visit rates well within
plan-visit type cells. The traditional model under-predicts pap
smear rates.
Actual Visit Rate
Preventive
Cerv Cancer
Colonoscopy
Mammography
hmo
39.3%
8.9%
6.0%
49.1%
pos
46.0%
33.7%
9.9%
51.4%
cdhp
42.7%
24.7%
9.8%
51.2%
total
41.3%
17.2%
7.4%
50.0%
Preventive
Cerv Cancer
Colonoscopy
Mammography
Expected Visit Rate -- Bayesian Model
hmo
pos
cdhp
total
38.8%
45.1%
40.6%
40.6%
7.6%
34.4%
21.2%
16.3%
4.6%
8.2%
7.4%
5.9%
50.0%
50.5%
46.8%
50.0%
Preventive
Cerv Cancer
Colonoscopy
Mammography
Expected Visit Rate -- Traditional Model
hmo
pos
cdhp
total
38.2%
45.2%
41.8%
40.4%
2.2%
22.5%
14.1%
8.9%
4.4%
8.2%
7.8%
5.8%
48.9%
50.3%
51.2%
49.5%
Carlin, Parente & Town
Preventive Care
Background
Econometric Framework
Estimation Results
Policy Implications
Conclusion and Future Direction
Predicted Visit Rates by Age
We see very little differences in rates by age among the health
plan types in three of the visit types, but we do see a difference
in the cervical cancer screening rates.
General Preventive Care Visit by Age
Colonoscopy by Age
0.7
0.16
0.14
0.5
HMO
0.4
POS
0.3
CDHP
0.2
Prob(Visit)
Prob(Visit)
0.6
0.1
0.12
0.1
0.08
HMO
POS
0.06
0.04
0.02
0
CDHP
0
25
30
35
40
45
50
55
60
65
25
30
35
40
Age
50
55
60
65
Mammography by Age
0.45
0.4
0.35
0.3
0.25
0.2
0.15
0.1
0.05
0
0.7
0.6
HMO
POS
CDHP
Prob(Visit)
Prob(Visit)
Cervical Cancer Screen by Age
45
Age
0.5
HMO
0.4
POS
0.3
CDHP
0
2
0.2
0.1
0
25
30
35
40
45
50
55
60
65
25
30
35
Age
40
45
Age
Carlin, Parente & Town
Preventive Care
50
55
60
65
Background
Econometric Framework
Estimation Results
Policy Implications
Conclusion and Future Direction
Predicted Visit Rates by Health Status
We see similar differences by health status.
Colonoscopy by Health Status
0.16
0.14
0.12
HMO
POS
CDHP
Prob(Visit)
Prob(Visit)
General Preventive Care Visit by Health Status
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
0.1
HMO
0.08
POS
0.06
CDHP
0.04
0.02
0
0
0.2
0.4
0.6
0.8
1
1.2
0
0.2
0.4
log(current health status)
0.8
1
1.2
Mammography by Health Status
Cervical Cancer Screen by Health Status
0.7
0.45
0.4
0.35
0.3
0.25
0.2
0.15
0.1
0.05
0
0.6
HMO
POS
CDHP
Prob(Visit)
ob(Visit)
Prob(Visit)
ob(Visit)
0.6
log(current health status)
0.5
HMO
0.4
POS
0.3
CDHP
0.2
0.1
0
0
0.2
0.4
0.6
0.8
log(current health status)
1
1.2
Carlin, Parente & Town
0
0.2
Preventive Care
0.4
0.6
0.8
log(current health status)
1
1.2
Background
Econometric Framework
Estimation Results
Policy Implications
Conclusion and Future Direction
Conclusion and Future Direction
I
We see that methods do matter – there is evidence of
selection in at least one visit type, with a corresponding
difference in predicted rates by method.
I
Leveling the financial playing field for preventive care, even
within high-deductible plans, appears to be primarily a
successful strategy – we need to look more closely at rates
of cervical cancer screening.
I
We will look at drug compliance for those with chronic
illnesses, where the deductibles impact the cost sharing,
applying the same selection correction process.
Carlin, Parente & Town
Preventive Care