Turning the Tide?
Medication Adherence
After Copayment
Reductions
Matthew L. Maciejewski, PhD
Durham VA & Duke University
AcademyHealth Methods Workshop
June 29, 2009
Acknowledgements

Collaborators
– Joel Farley, PhD RPh: UNC
– Evelyn Reyes-Harvey, MS: BCBSNC
– Daryl Wansink, PhD: BCBSNC

Funding and data
– BlueCross Blue Shield of North Carolina
Medication Cost-Sharing Faced
by Covered Workers, 2000-2008
2000
27%
2001*
49%
41%
2002*
41%
55%
2003*
‡
‡
5%
2007
7%
2008*
7%
0%
30%
69%
16%
8%
40%
50%
*Distribution is statistically different from distribution for the previous year
shown (p<.05).
‡No statistical tests are conducted between 2003 and 2004 or between
2006 and 2007 due to the addition of a new category.
Note: Fourth-tier drug cost sharing information was not obtained prior to
2004.
Source: Kaiser/HRET Survey of Employer-Sponsored Health Benefits,
2000-2008.
60%
70%
80%
2%
2%
8%
15%
1%
1%
15%
16%
1%
10%
70%
70%
20%
13%
20%
68%
10%
13%
23%
65%
2005* 4%
2%
18%
30%
63%
2004 3%
2006
22%
2%
2%
6%
1%
3%
1%
4%
90%
100%
Four or More Tiers
Three Tiers
Two Tiers
Payment is the same regardless of type of drug
No cost sharing after deductible is met
Other
Medication Copay Trends for
Workers with 3+ Tiers, 2000-2008
1st Tier
$80
2nd Tier
3rd Tier
4th Tier
$70
$60
$50
$40
$30
$20
$10
$0
2000
2001
2002
2003
2004 2005
2006
2007
2008
2000-2008 Kaiser/HRET Survey of Employer-Sponsored Health Benefits
Increased Out-of-Pocket Costs
Reduce Medication Adherence
Change in Drug Days Supplied (%
0
-5
Diabetes
LipidLowering
Anti-HTNs
Anti-Asthma
AntiDepressants
-10
-15
-20
-25
-30
-35
-40
Goldman 2004 JAMA; figure from Chernew & Fendrick, 2009 SOA
Research Questions

Did reductions in medication copayments
impact adherence?
– CHF, diabetes, HTN, hyperlipidemic medications

Do results change when we reduce covariate
imbalance via propensity scores?
Value-Based Insurance Design as
Alternative to Increasing Copays

Copayment level and formulary placement
driven by acquisition cost
– Some linkage to value or effectiveness via tiers

VBID: Link copayment level to clinical
effectiveness and value
– Highly effective medications have lowest copays
Limited Information on VBID

Simulation studies of Medicare beneficiaries
– Free ACEIs is cost saving (Rosen, 2005 AIM)
– Free aspirin, beta-blockers, ACEIs/ARBs & statins postAMI is cost-effective (Choudry, 2008 Circ.)

Empirical studies
– Copay reduction (to $0 for generics, 50% for brand) &
disease management reduced non-adherence 7-14%
(Chernew, 2008 Health Affairs)
– Pitney-Bowes dropped copays and improved diabetes
adherence (Mahoney, 2005 AJMC)
– Copay drop to $0 for anti-HTNs & behavioral intervention
didn’t improve adherence for patients w/ HTN (Volpp)
Policy Change of Interest

On 1/1/08, BCBS of North Carolina…
– Brand-name copays reduced (from Tier 3 to
Tier 2) in selected classes for all enrollees
– Generic copays eliminated for 747,300
enrollees of employers who opted in
– Generic copays ($10-12) unchanged for
652,161 enrollees of employers who opted out

8 Rx classes: Diuretics, ACEIs, ARBs, betablockers, Calcium channel blockers, statins,
oral hypoglycemics, cholesterol absorption
inhibitors
Study Design and Data
 Study
Design
– Retrospective pre-post design with nonequivalent cohort
– Jan-Dec 2007 (pre), Nov 07-Oct 08 (post)
 Administrative
data
– Outcomes: Medication adherence (MPR),
pooled for generic and brand-name meds
– Covariates: Demographics, casemix
– Census: Median income in zip code (2000)
Analytic Problem

Outcome equation
MPRit=β0+β1·Txi+β2·Postt+β3·Txi·Postt+β4·Xi+εit

Estimation Approach
– GLM with binary distribution, inverse link function

Estimation Problem
– If Xi balanced btn. treatment & control groups
due to randomization (or luck), then unbiased β3
– In this example, Xi not balanced
Baseline Descriptives of
Patients Taking Diuretics
Controls
N=8,823
Age in years
Treatment
N=15,417
Mean
SD
Mean
SD
Std.
Difference
52
7.8
52
7.9
4.4%
-15.5%
Male
37%
44%
Comorbidity (ETG)
2.48
2.57
2.47
2.55
0.2%
Medication Count
4.96
3.16
5.10
3.30
-4.1%
Ave Generic Copay ($)
10.74
5.41
10.02
3.11
16.3%
1+ Ninety Day Fills
23%
15%
19.9%
Prescriptions filled
that were generic
75%
75%
-2.7%
Trends in
Unadjusted Adherence
90%
Diuretics
Cases
Controls
MPR
85%
80%
75%
70%
December 2007
September 2008
Adjusted Adherence Difference
Associated with VBID Program
Drug Class
Number of
Unmatched Subjects
% Point Change in
MPR Due to Program
Diuretics
24,240
2.7%***
ACE Inhibitors
21,273
2.4%***
ARBs (only brand-name)
11,388
0.1%
Beta-blockers
17,262
2.1%***
Calcium Channel Blockers
10,600
1.1%*
Cholesterol Absorption
Inhibitors
5,885
-3.5%*
Biguanides
7,243
3.2%***
26,985
1.3%**
Statins
Note: *** p<0.001, ** p<0.01, * p<0.05 based on a generalized linear models, controlling for age, gender, Episode
Risk Groups, count of unique medications filled, generic dispensing rate, the average generic copay, and whether an
enrollee filled one or more prescriptions with a 90-day supply.
Propensity Score Matching

Step 1: Model Pr(Tx group)
– Pr(Tx group) = β0+β1·Xi+εit

Steps 2/3: Assess balance in covariates
– Assess reduction in standardized differences

Step 4: Rerun adherence analysis on
matched subset
– MPRit=β0 + β1·Txi + β2·Postt + β3·Txi·Postt + εit
– MPRit=β0+β1·Txi+β2·Postt+β3·Txi·Postt+β4·Xi+εi
Propensity Score Overlap Before
and After Matching
Distribution of Propensity Score Before Matching
Distribution of Propensity Score After Matching
20.0
15.0
17.5
12.5
0
12.5
10.0
Percent
0
Percent
15.0
10.0
7.5
7.5
5.0
5.0
2.5
2.5
0
0
20.0
15.0
15.0
12.5
12.5
10.0
1
10.0
7.5
Percent
1
Percent
17.5
7.5
5.0
5.0
2.5
2.5
0
0.21 0.25 0.29 0.33 0.37 0.41 0.45 0.49 0.53 0.57 0.61 0.65 0.69 0.73 0.77 0.81
Estimated Probability
0
0.3525 0.3825 0.4125 0.4425 0.4725 0.5025 0.5325 0.5625 0.5925 0.6225 0.6525 0.6825 0.7125 0.7425 0.7725 0.8025
prop
'
Improvement in Covariate
Balance After PS Matching
Standardized Differences
Unmatched Sample
Matched Sample
4.4%
0.34%
-15.5%
-8.1%
Comorbidity (ETG)
0.2%
0.08%
Medication Count
-4.1%
-1.9%
Ave Generic Copay ($)
16.3%
0.4%
1+ Ninety Day Fills
19.9%
2.5%
Prescriptions filled
that were generic
-2.7%
0.09%
Number in Treatment
Number in Control
15,417
8,823
8,298
8,298
Age in years
Male
Adjusted Adherence
Before/After PS Matching
PS Matched Results
Original
Results
(% Pt MPR ∆)
Without
Covariates
With
Covariates
Diuretics
2.7%***
2.0***
1.9***
ACE Inhibitors
2.4%***
1.8%***
1.7%***
ARBs (only brand-name)
0.1%
0.1%
0.1%
Beta-blockers
2.1%***
1.8%***
1.5%***
Calcium Channel Blockers
1.1%*
1.3%**
0.9%**
Cholesterol Absorption
Inhibitors
-3.5%*
-2.1%
-1.8%**
Biguanides
3.2%***
2.1%***
2.0%***
Statins
1.3%**
1.1%***
0.9%**
Limitations

Contamination: Post-period adherence
overlaps with pre-period by 2 months

Copays for brand-name meds dropped in
both groups & pooled brand & generic MPR
– ARBs provide strong “anti-test”

Limited covariate adjustment
– No instruments
Unobserved Confounding

Propensity scores can’t address this issue

Two possibilities
– Regression & propensity score results could both
be biased
– Results could be unbiased

Depends on satisfying the ignorability
assumption
Policy Conclusion

Generic copayment policy had a significant
protective effect against non-adherence
– Propensity score results consistent w/ others

Policy change had impact because baseline
adherence rates were 75-85%
– Consistent with Chernew 2008 Health Affairs

Important to assess if short-term results
hold in longer-term
Questions?