Medication Adherence in Chronically Ill 

Veterans: Copayments, Other Potential 

Barriers, and Health System Factors to 

Potentially Mitigate Cost Burdens

John E. Zeber, PhD MHA

Co-director, Health Outcomes Core

Center for Applied Health Research

Scott & White Healthcare (Temple, TX)

Investigator, Department of Veterans Affairs

Associate Professor, Texas A&M Health 

Science Center College of Medicine, 

Departments of Medicine & Psychiatry
Medication Adherence in Schizophrenia: 

Impact of Copayments

University of Michigan

Department of Psychiatry 

Zeber JE, Grazier KL, Valenstein M, Blow FC, Lantz PM (2007), American Journal of Managed 

Care, 13(6):335-46
Introduction

40-50% of patients with serious mental illness (SMI) 

are poorly adherent 

Severe ramifications & costs (symptom exacerbation, 

 risk of ER, re-admissions, treatment $$)

Widespread issue: Medicaid, managed care, 

Medicare, VA, other health systems

Intriguing measurement or definition issues
Figure 1: from Valenstein et al., Schizophrenia Bulletin (2004), 30(2): 255-64 

Frequency Distribution of MPR Values (N=49,003)

0

1000

2000

3000

4000

5000

6000

Medication Possession Ratio

“excess” MPR
Adherence and Rate of Psychiatric Admission 

23.4%

13.4%

18.5%

8.3%

28.4%

18.6%

28.1%

31.7%

29.5%

25.9%

17.0%

11.2%

9.6%

5%

10%

15%

20%

25%

30%

35%

<0.1 0.1 -0.2 0.2 -0.3 0.3 -0.4 0.4 -0.5 0.5 -0.6 0.6-0.7 0.7 -0.8 0.8 -0.9 0.9 -1.0 1.0 -1.1 1.1 -1.2 1.2 -1.3

MPR Range

Psychiatric Admission

Figure 2: from Valenstein et al., Medical Care (2002), 40(8):630-9
Study Background

Rising pharmacy costs (national & VA)

Medication restriction due to cost

Numerous other risk factors (aside from cost) 

Variety of medication copayment plans

17-second history of VA copayments

$2 in 1990  $7 in 2002 (now $8 or $9)
Health Services Utilization (outpatient, hospital, ER)

RAND, Group Health (psychiatric), others

Pharmacy Utilization (many studies – few targeting 

mental health: see Soumerai et al.)

reduced utilization

differential effects of copays / cost-sharing

ethnicity, elderly, sicker patients, lower SES

Effect of Copayments on Utilization 

(hint: mostly negative)
Study Design & Exclusions

Period 0 Period 1 Period 2 Period 3 Period 4

Copay increase

February 1, 2002

6 – 1 – 99 6 – 1 – 00 4 – 1 – 01 2 – 1 – 02 12 – 1 – 02 9 – 30 – 03

Study Period

“Pre” “Post”
Data Source & Study Groups

 Administrative DX of schizophrenia (T0) per the 

National Psychosis Registry (SMITREC)

 Patient groups

Groups 1 & 2 = “Copay” (non-service connected 0-

49%)

vs

Group 3 = “Exempt” (SC≥50%)

 Multivariable longitudinal random effects models
Primary Outcomes

Pharmacy utilization (30-day fills)

total, medical, psychiatric

Health Services Utilization 

total psychiatric days & total outpatient visits

Costs from VA perspective

total pharmacy
Results: Descriptive Statistics

 final sample = 80,668 ~ 50% “Exempt”

 overall means:

 age = 52.8; women = 5.3%; minority = 36.8%; # of 

comorbidites = 1.97

 bivariate analysis: (baseline)

 Groups 1 & 2 healthier, utilization, less VA tenure 

 Group 2 somewhat distinct from non-SC Group
Medical Drug Fills (mean #)

0

10

20

30

40

50

T1 T2 T3 T4

Group 1 Group 2 Group 3 (Exempt)

Copay increase
Psychiatric Drug Fills (mean #)

0

4

8

12

16

20

T1 T2 T3 T4

Group 1 Group 2 Group 3 (Exempt)
* among those patients with any admission

Psychiatric Inpatient Days (mean #) *

16

17

18

19

20

21

T1 T2 T3 T4

Group 1 Group 2 Group 3 (Exempt)
Pharmacy Costs (mean $)

0

400

800

1200

1600

2000

T1 T2 T3 T4

Group 1 Group 2 Group 3 (Exempt)
Discussion

 Hypotheses & Interpretation of Results

 Theoretical Context 

 Adherence / Utilization: Role of Health Beliefs

 Equity Issues & VA mission

 VA cost savings, generalizability, limitations

 Copayment increase: “Success”?

 cost-effectiveness vs. mission vs. philosophy

 other cost-sharing options? 

 Veterans as vulnerable population

unique patients with schizophrenia

 other complex medical or psychiatric conditions
Ethnicity, Copayments, and Differential 

Cost-Related Burdens

Department of Psychiatry

Zeber JE, Copeland LA, Miller AL, Kilbourne AM, Velligan DI. [abstract presented 

at 2008 ISPOR meeting, Toronto]
 Sub-analysis and enhanced study design approach 

(4 ethnic groups, 22 time periods) 

 Findings: all groups restricted psychotropics as 

before (16-22%)

 However, African-Americans and Hispanics 

experienced far greater ramifications (e.g., IP 

days, ER visits)

 Summary: differential burden of medication cost, 

equity issues

Study Summary
A Cost-Benefit Analysis of Changing 

Pharmacy Benefit Policy

Zeber JE, Leykum L, Valenstein M, 

Copeland LA, Miller AL. [abstract to be 

presented at the 2009 HSRD meeting and 

2011 Mental Health Economic Policy 

meeting, Venice, Italy]
Introduction & Objective

little work done in mental health 

increasing HSR&D research into the adverse effect 

of medication copayments 

Wang, 2011; Maciejewsky, 2010; Doshi, 2009; Stroup, 2007

* From the VA’s perspective, what are the costoffset policy implications of  copayments, 

balancing additional copayment revenue with 

extra treatment costs?
Methods

all Copayment veterans with SCH in FY99 (N=33,431)

Apr 1999 – Sept 2005 with 22 quarterly time points

pharmacy fills, psychiatric IP and total ER utilization, 

along with total costs

additional copayment revenue

+

cost-related  psychotropic fills

vs.  Inpatient and ER costs
Analysis:

 Microsoft OfficeTM statistical package & sophisticated 

program coding: + (“plus”, etc.), -, /, ... Σ

 primary analysis focused on POST-policy utilization & costs

 $$ adjusted to 1999 medical CPI

Sensitivity: ER $$ estimation, Δs attributable to policy 

T1 T2 T3 T4 T5 T6 T7 T8 T9 T10 T11 T12 T13 T14 T15 T16 T17 T18 T19 T20 T21 T22

Copay increase

actual utilization

predicted utilization
Results

Cost-offset calculation:

 additional copay revenue: + $15.62 million

 reduced psychotropic pharmacy costs: + $ 2.94 million

 higher IP costs: - $18.85 million

 higher ER costs: - $1.83 million

 TOTAL: - $2.12 million 

~$771,000 annualized loss
Discussion

2002 benefit Δ = clinical & budgetary implications 

Current study reflects only 0.6% of all VA patients

Study period does not include 2006 or future 

copayment increases

Other economic or resource costs? 

Concerns about “silo mentality” in cost savings
Medication Adherence, Ethnicity, and Multiple 

Psychosocial & Financial Barriers in Veterans 

with Bipolar Disorder

Zeber JE, Miller AL, Copeland LA, McCarthy JF, 

Zivin K, Valenstein M, Greenwald D, Kilbourne AM. 

Administration & Policy in Mental Health / Mental 

Health Services Research (2011).

subtitle: “A young(ish) 

researcher’s slow but inexorable 

journey towards self-realization”
Patients face multiple barriers to adherence, yet the 

cumulative effect and interaction often not examined

Psychosocial factors: personal, environmental, & 

cultural context

Burden of financial barriers: income, copayments

Involves complex interactions across diverse population

Certain individuals experience inequitable burdens of 

these barriers: elderly, multiple conditions, minorities

Introduction
Psychosocial Barriers

Diverse matrix of health beliefs, TX preferences & careseeking, social or environmental support, perceptions

Fortunately many interventions have proven successful:

 cognitive behavioral therapy 

(low insight)

 blister-paks (M Valenstein)

 cognitive adaptive training 

(environmental instability) 

 family sessions or motivational 

therapy

 patient-centered care / CCM

 health benefit policies
Our prior work with this dataset & population:

Therapeutic alliance 1-3

Medication Beliefs 4

Access to care 5-6

Complementary and Alternative Medicine (CAM) 7

Objective: Explore panoply of potential barriers, 

reconcile TX preferences, provider goals,  design 

tailored interventions

1 - Zeber JE et al. (2008), Jour Affec Disord; 2 - Perron BE et al. (2009), JNMD; 3 - Ilgen MA et al. 

(2009), Jour Affec Disord; 4 - Copeland LA et al. (2008), JNMD; 5 - McCarthy JF et al. (2010), 

Psych Serv; 6 - Zeber JE et al. (2009), AJPH; 7- Kilbourne AM, et al. (2007), Psychopharm Bull
All variables and survey data from CIVIC-MD study (PI –

Kilbourne)

Large population-based study examining quality of care 

provided to veterans with bipolar disorder (N=435)

Self-reported measures of medication adherence and 

perceived barriers

Primary outcome: two definitions of adherence

Morisky scale – intrapersonal barriers (2+ = non-adherence)

No Missed Days, in past 4 

Methods
Financial

Income (<$20,000)

Ever restricted treatment due to cost

Medication copayment (service connection <50%)

Psychosocial

Difficulty accessing a mental health specialist

Poor therapeutic alliance (HCCQ ≤ 25)

Low medication insight (med-perspective ≤ 7)

Binge drinking

Live alone

Travel 50+ miles to VA care

Adherence Barriers

* Side effects? sadly not available ...

[Zeber JE et al. (2010), Ann Pharmacother]
Bivariate analysis examined association between the 

9 barriers adherence; 5 selected for final models

Logistic regression predicted poor adherence

Covariates = ethnicity, age, some college, homeless, 

any affective symptoms

two separate models

1) adherence = total # of barriers + covariates

2) adherence = barrier1 + ...barrier5 + covariates 

Analysis
Descriptive & Bivariate Results

 Non-adherence rates:

Morisky = 46%; No missed days = 27%

mean # of barriers = 2.8; 20% experienced >4 

 Specific barriers (“yes”):

low income = 58%; forego TX = 13%; copay = 59%

access to specialist = 18%; poor alliance = 18%; low 

medication insight = 14%; binge drinking = 22%; 

live alone = 35%; >50+ to TX = 16%

 Ethnic differences: low income, access to MH specialist, 

binge drinking
Multivariable Models

Model #1:

OR=1.29 per Morisky barrier 

key covariates: affective symptoms (1.95), other race (2.25)

Model #2:

insight (2.41), binge (1.95), specialist access (1.73)

covariates = affective symptoms (1.76)

Ethnicity * barrier interaction models
Discussion

Patients experience numerous barriers, with # and type 

associated with medication adherence problems

Significant barriers include insight, substance abuse, 

and access, but also affective disorder symptoms

Ethnic differences were observed here - more work 

needed to understand importance & interaction effects

Financial barriers (e.g., copayments) not as significant

Results support designing tailored interventions to 

improve adherence, recognizing patient-level burdens
Medication Adherence in Patients with 

Chronic Illnesses: The Role of Provider 

and Organizational Factors
Chronic Care Model  A1c / CAD risk (Parchman – PI)

NIH / NIDDK Grants #R34 DK067300 and R18 DK075692

Pilot study (5 clinics) and larger project (40 clinics)

Goal: facilitate delivery of diabetes care to improve 

intermediate clinical outcomes

Education efforts directly targeting outcomes 

often less successful

However, elements of the Chronic Care Model (CCM) 

offers potential benefits

Project(s) Summary
ABCs: a conceptual model

↑ Activation / 

Self-engagement

Leadership Communication

Learning

Implementation of 

CCM / PCMH

Facilitation efforts & 

“Toolkit” of Strategies 

↑ Medication 

Adherence

Improved A1c 

control and other 

clinical outcomes
Risk of Coronary Artery Disease in Type 2 Diabetes and the 

Delivery of Care Consistent With the Chronic Care Model in 

Primary Care Settings

Participatory Decision Making, Patient Activation, 

Medication Adherence, and Intermediate Clinical Outcomes 

in Type 2 Diabetes

ABCs Pilot Study (n=157, 5 clinics)

Parchman ML, JE Zeber, Romero R, Pugh JA 

(2007), Medical Care, 45(12):1129-34

Parchman ML, Zeber JE, Palmer R (2010), 

Annals of Family Medicine, 8(5):410-7
Participatory Decision Making, Therapeutic Alliance, 

Perceived Drug Costs & Clinical Outcomes in Diabetes

Therapeutic Alliance and Adherence

Zeber JE at al. [abstract presented 

at 2009 HSR&D meeting] 

Objective: Examine association 

between dimensions of the 

therapeutic alliance, 

perceived drug costs, and 

medication adherence
Measures: 

 patient engagement / self-activation (Lorig); physician level of 

patient-centered care (Kaplan); cost-related medication 

restrictions (Piette) 

Main Outcome / Analysis: medication adherence (Morisky) 

with structural equation model 

Results:

 SEM model: direct inverse relationship between cost burden 

adherence; patient-centeredness associated with selfactivation, which then positively influenced medication 

adherence; self-activation mediated cost issues

Efforts to improve the therapeutic alliance can improve 

adherence & clinical outcomes
Perceived 

Drug Costs

Self-Activation 

(patient)

Patient 

Centeredness

(of provider) 

Patient Demographics

(age, gender, 

education, income)

Adherence

(baseline) 

Medication 

Adherence

(follow-up)

p=.001

p=.001

p=.02

p=.01
Numerous projects, including a couple on adherence

“Impact of the chronic care model on medication adherence 

when patients perceive cost as a barrier” 

Mackey K, Parchman MP, Leykum LK, Lanham HJ, Noel PH, Zeber JE. 

in press, Primary Care Diabetes

 40 clinics, n = 1,823 with a chronic health condition

 nested random effects models

 patient perceptions of chronic care delivery associated with costrelated adherence problems

 patients with intermediate adherence befitted most from  CCM

ABCs Full Study (n=2400, 40 clinics)
Cost-Related Medication Adherence and 

Patients’ Experience with the Chronic 

Care Model

Zeber JE et al. [abstract presented at 2010 Academy 

Health meeting; manuscript in preparation]

“The communication and coordination of 

scattered fragments of knowledge is 

perhaps the basic problem of any 

society.” - Thomas Sowell
Objectives / Methods

Aims:

1) Examine association between patients’ experience of 

the CCM and reported cost-related adherence burden

ABCs project: Foster CCM / PCMH implementation 

in small community clinics and  risk factors for 

diabetes complications

A = A1c B = Blood pressure C = Cholesterol

 
Population Studied: Patients with chronic medical illness at 40 

primary care offices in South Texas

Initial intervention group (20) and delayed control group (20)

Complex study: observations, facilitation sessions, provider and 

staff interviews, patient surveys, chart reviews, dissemination

For this study, we utilized patient survey data only (n=60 per clinic)

Measures & Analysis:

Cost-related adherence burden (CRAB) was measured with a 5-item 

scale, higher scores reflect more medication restrictions 

Patient Assessment of Chronic Illness Care (PACIC) - 20-item 

instrument assessing perceptions of primary care treatment; higher 

values = care more consistent with CCM 

Random effects models controlled patient nesting, demographics
Results

To date, 1368 patients completed baseline surveys 

Patient characteristics: age = 50.1 years; 65% women, ~50% 

Hispanic; overall self-reported health status good

poor adherence = 45% and ~30% with cost-related problems 

CRAB mean =1.50 (sd 0.8), total PACIC mean = 3.02 (sd 1.2)

Multivariable models

CRAB was inversely associated with total PACIC score (OR = 1.17) 

also, higher subscales scores for:

patient activation (OR = 1.28), problem solving (OR = 1.16), and 

practice design (OR = 1.26) 
Figure 1: Multivariable Model Predicting No CRAB 

Odds Ratio (OR) – per point change in PACIC score 

0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0

PACIC Total (OR=1.17)

Patient Activation (OR=1.28)

Problem Solving (OR=1.16)

Goal Setting (OR=1.04)

Care Coordination (OR=1.05)

Practice Design (OR=1.26)

* models controlled for age, gender, ethnicity, and education
Discussion

Patients experiencing care more consistent with the CCM 

had lower cost-related burden 

Being actively involved in clinical decisions and provided 

information about their care → added benefits 

** Efforts to develop highly activated, involved patients 

can help mitigate ramifications of financial pressures 

Community providers should better recognize and 

discuss medication cost burdens while focusing efforts 

in accordance with chronic care treatment delivery
adherence interventions are often not cost effective

[Elliott RA, Barber N, Horne R. (2005) Ann Pharmacother 39 (3), 508–515]

however, room for optimism and CCM efforts fit nicely 

into VA patient-centered goals (PACT)

Next steps: 

HSRD 2012 meeting abstract (adherence instability)

sub-group analysis re: CCM effects

merit grant of modern technologies (cell phones)

data from Learn & Relate study (J Pugh – PI)

potential use of HMORN data for cross-system analysis

Other Thoughts and Next Directions
Contact Information

John E Zeber, PhD

(254) 215-9877

jzeber@swmail.sw.org

john.zeber@va.gov