Medication adherence, ethnicity, and the influence of...

Multiple Adherence Barriers

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Medication adherence, ethnicity, and the influence of multiple psychosocial and financial barriers

John E. Zeber, PhD1,2

Alexander L. Miller, MD2

Laurel A. Copeland, PhD1,2

John F. McCarthy, PhD3,4

Marcia Valenstein, MD3,4

Kara Zivin, PhD3,4

Devra Greenwald, MPH5

Amy M. Kilbourne, PhD3,4

1. Veterans Affairs HSR&D: South Texas Veterans Health Care System (VERDICT), San

Antonio, TX 2. University of Texas Health Science Center at San Antonio, Department of Psychiatry, San

Antonio, TX 3. Center for Health Equity Research and Promotion (CHERP), VA Pittsburgh Healthcare

System, Pittsburgh, PA 4. VA Ann Arbor Healthcare System, Serious Mental Illness Treatment Research and

Evaluation Center, Ann Arbor, MI 5. University of Michigan, Department of Psychiatry, Ann Arbor, MI Mailing address (contact author):

John E. Zeber, PhD South Texas Veterans Health Care System 7400 Merton Minter Boulevard (VERDICT 11c6) San Antonio, TX 78229-4404 telephone: (210) 617-5300, ext. 16666 fax: (210): 567-4423 email: [email protected]

running header: Multiple Adherence Barriers Word count: = ~2100 (Methods & Results only; should be 4000+ overall) Abstract: = 312

(need to prune a little)


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Abstract

Objectives: Patients with bipolar disorder are often poorly medication adherent, resulting in

deteriorating symptomology, higher admission rates, and diminished quality of life. Many factors

are strongly associated with adherence, including financial burdens and a variety of psychosocial

factors. However, analyses typically consider potential barriers independently rather than

conjointly from the patient’s perspective. Such approaches neglect the complex interplay of risk

factors, many of which are amenable to health policy or clinical interventions. This study uses

self-reported data to evaluate the differential and cumulative impact of nine barriers upon

medication adherence.

Methods: We recruited 435 patients from the Continuous Improvement for Veterans in Care -

Mood Disorders (CIVIC-MD, FY04-06) study which examined quality of care provided to

veterans with bipolar disorder. Surveys collected information on multiple financial and

psychosocial adherence barriers: medication copayments, foregoing treatment due to cost, binge

drinking, access difficulty, social support problems, poor therapeutic alliance, and low

medication insight. Medication adherence was measured by the validated Morisky scale.

Multivariable logistic regression modeled adherence as a function of perceived barriers upon

adherence, controlling for demographics, homelessness, and affective symptomology.

Results: Nearly half of the respondents reported adherence difficulty. Patients experienced an

average of 2.8 barriers, with 41 percent perceiving at least three. Minority veterans reported

poorer adherence than white patients (56 percent versus 40 percent, p=.01), while claiming more

overall barriers, particularly financial burden, binge drinking, and difficulty obtaining psychiatric

care when needed. Multivariable models revealed that the total number of barriers was

significantly associated with poor adherence (OR=1.24 per barrier). The most significant were

low medication insight, binge drinking, and difficulty accessing psychiatric care (ORs of 2.41,

1.95 and 1.73, respectively).

Discussion: Veterans with bipolar disorder experience multiple barriers to medication adherence,

a scenario possibly exacerbated by recent copayment increases. Besides the total number of


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barriers, certain psychosocial and financial obstacles proved especially pernicious in connection

to worse adherence.

Conclusion Recognizing multiple barriers can assist in developing tailored clinical interventions

to improve poor adherence by tailoring efforts towards reducing psychosocial risk factors.

Furthermore, the interaction with VA health benefit polices potentially contributes to burdens

faced by vulnerable veterans already experiencing adherence problems.

key words: medication adherence, psychosocial barriers, copayments, ethnicity, bipolar disorder,

veterans


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Introduction

Bipolar disorder is a chronic mental illness associated with substantial functional

impairment, tremendous health care costs, and premature mortality (Murray and Lopez 1996;

Bauer et al., 2002). Its significance, which the World Health Organization currently lists as #7 in

terms of overall disease burden, may even be considerably underestimated given the number of

attendant physical and mental health comorbidities, social and occupational instability, and

substantial care-taker burden (Manning, 2005). A psychiatric illness uniquely characterized by

alternating periods of mania, psychosis and depression, patients afflicted with bipolar disorder

require intensive pharmacological and psychosocial management (Gitlin et al., 1989; Keck et al.,

1997).

[prune all this adherence background a little] Despite the dissemination, if not wide

implementation, of practice guidelines, bipolar disorder outcomes remain suboptimal, largely

due to poor treatment adherence. Lingam et al. reported non-adherence rates among bipolar

patients ranged from 20-66% across 22 studies, with a median of 41% (Lingam and Scott, 2002).

The problem is even less sanguine for antipsychotic drugs, increasingly prescribed for this

population; even accounting for the newer atypical medications, with arguably better side-effect

profiles, 48% of all veterans with bipolar disorder are poorly adherent (Sajatovic et al., 2006).

Numerous adverse clinical and behavioral outcomes are directly attributable to inadequate

adherence (Salloum et al., 2005). In addition to treatment disruption, ramifications such as

worsening symptoms, deteriorating functional status, higher treatment costs, and increased risks

for psychiatric admission and relapse are quite common (Post et al., 2003; Scott and Pope, 2002).

The documented risk factors include demographic characteristics (e.g., younger age and male

gender), lower cognitive functioning (Danion et al., 1987), manic episodes (Keck et al., 1996),

and poor illness insight (Adams and Scott, 2000). Comorbid substance abuse is especially

problematic, exacerbating symptoms in an already complex and challenging disorder (Swartz et

al., 1998). Despite the severity of this problem, patient and provider explanations as to the


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primary reasons for medication failure are weakly correlated (Pope and Scott, 2003), hindering

potential remedies for improving adherence.

[cut Alliance stuff way back, much of it from our prior paper] Maintaining a beneficial

therapeutic alliance between patients and providers is one effective strategy for improving

treatment retention, adherence, and subsequent outcomes. Proactive patients, autonomous

individuals who feel respected as a treatment partner and expressing confidence in their

healthcare environment, are hypothesized to fare better than patients lacking a voice in their own

treatment course. Investigations into the association between psychiatric disorders, therapeutic

alliance, and adherence can be traced back to at least 1970 (Howard et al., 1970). A rich body of

literature has since developed, comprising numerous descriptive studies, incorporating an array

of conceptual frameworks borrowed from medical sociology (Blackwell, 1997). The link

between patient-provider relationships and adherence is observed across several mental health

conditions, including substance abuse, schizophrenia, and depression outcomes (Meier et al.,

2005; Fenton et al., 1997; Klein et al., 2003).

However, despite the apparent conceptual applications, until recently comparatively few

studies connecting clinical relationships and adherence has been translated to bipolar disorder

research, at least in explicit quantitative efforts. The excellent qualitative study by Sajatovic and

colleagues recently explored one significant dimension of therapeutic alliance, the collaborative

care model. They found a consistently positive association between alliance and treatment

adherence as expressed through the Drug Attitudes Inventory (a multi-question survey of

medication beliefs), self-reported adherence, and primary care attendance (Sajatovic et al., 2005).

In another smaller study (n=61), Gaudiano and Miller observed that better alliance perceptions

improved treatment outcomes in patients with bipolar disorder, such as psychotherapy retention

(Gaudiano and Miller, 2006). Berk et al. conducted a comprehensive literature review exploring

the practical and theoretical issues surrounding the therapeutic environment. Psychosocial

interventions, including efforts to develop stronger clinical relationships, offered tremendous

optimism for sustained adherence (Berk et al., 2004). Patients entering treatment with greater


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therapy expectations and trust in their providers achieve better adherence. The beneficial nature

of appropriate clinical relationships has itself been described as a “mood stabilizer” (Havens and

Ghaemi, 2005).

Certain patients may garner particular advantages from a strong patient-provider alliance.

Demographics, health beliefs, and cultural values are frequently expressed in attitudes

surrounding adherence to psychotropic medications. Fleck et al. observed that African American

and white patients endorsed different sets of culturally-based values and treatment perceptions as

explanations for poor adherence (Fleck at al., 2005). While some studies revealed minimal

association between ethnicity and either the strength of clinical relationships or the influence of

alliance upon outcomes (Ricker et al., 1999), others indicate that minority patients might place

greater value on closer therapeutic relationships than white patients (Tonigan, 2003). The role of

ethnicity and healthcare environment perceptions are quite relevant, given consistent findings of

significantly worse adherence among minority patients with serious mental illness (Valenstein et

al., 2004; Opolka et al., 2003).

Then make sure to add a little here about Insight plus other “barriers” used in this study

& references: TX cost, copayments, social support, language, stigma medication beliefs, distance

& access (inc. MH specialist), substance abuse … try to keep brief & focus from lit searches Move some to Discussion, as appropriate …

• SMI patients face multiple potential burdens to appropriate adherence – yet

frequently these factors are studied independently with interventions targeting individual

barriers rather than the interaction between multiple ones.

• Need to cite some other specific burdens we address in this study (our own for

med beliefs and alliance, but more on substance abuse, cognitive limitations,

primary language (see Gilmer & Ojeda paper), acculturation, illness severity, # of

meds/ chronic DXs, travel distance, social support, etc.)

• Must limit info presented in INTRO and move more to Discussion


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• a couple recent papers by our VA colleagues in Little Rock, (Teresa Hudson, Jeff Pyne),

have tried to examine a range of barriers in veterans with SCH. So we’re extending that

concept here to BP …

• qualitative study on explanatory factors interviewed with only 26 patients, and

found 214 unique barriers (collapsed into 8 domains), with agreement between pts

and providers low re: disease etiology and adherence barriers.

• A 2nd paper by Hudson did something similar to our here, quantifying a few key

barriers (alliance, etc.)

• Somewhere, define “potential barriers which can be grouped as psychosocial factors”,

reflecting both the patient’s internal perspective but also their relationship with the health

system, providers, and social support networks

• under this admittedly wide umbrella the literature describes illness insight (make sure to

cite Kemp et al), strength of the therapeutic alliance, their living environment, personal

health beliefs, access to needed care, and other dimensions that could affect the ability

and desire to be medication adherent.

• Furthermore, certain more vulnerable patient subgroups might suffer a greater burden

from many of these potential barriers – older patients, those with several chronic

conditions, and ethnic minorities.

• Yet provider and system factors also play a role: TX decisions and polypharmacy,

awareness of the impact of side effects, cultural competence and appreciation of TX

preferences, organizational structures, questions of access … and, even health benefit

policies.


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• on top of all these psychosocial factors, add the additional burden of financial or

economic concerns, such as income, cost-related TX restrictions, and of course drug

copayments. (see Zeber copays paper, plus other recent VA ones for other conditions

• note that very little out there about financial barriers & ethnicity

• briefly mention theoretical frameworks often used to study TX adherence

• Becker’s such as the Health Belief Model (patient’s perception & acceptance of

his / her illness, the potential benefits of TX, and both internal and environmental

factors that may facilitate or hinder adherence); Theory of Planned Behavior tends

to view the patient as an informed, activated participant in their treatment;

Anderson behavioral model takes a health services perspective on factors that

influence decisions to seek & obtain care.

• also Donabedian 1976: role of health benefits as primary Equity issue …

distribute limited healthcare resources.

• Again, we have previously used the CIVIC-MD dataset to explore a few barriers

individually (also CAM use, access etc.) – see some in Methods section

• VA background: treated nearly 5 million patients last year, including about 90,000 each

for SCH and BP … many with limited resources, other health options, etc. - so major

patient and system implications

• Might briefly present VA medication copayments policy, or just leave for Methods

summary

then here more about patient and system / economic ramifications of poor adherence? -

relapse, deteriorating symptoms, psychiatric admissions (Valenstien & SCH papers), ER visits,

and TX costs … Medicaid could save $106 million in IP costs by reducing adherence gaps for


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SCH pts. (Marcus & Olfson, 2008), 25% of all ER visits (Olshaker) … also 2007 Sun et al. re:

$1.5 billion in Medicaid savings due to adherence.

Significance / WHY of this study here –

WHY is understanding effect of multiple barriers important: return to this in discussion, but

while we can’t do much about medication cost / economic barriers, many interventions have

proven successful for helping with psychosocial ones: CBT for poor insight, pharmacy blister-

paks for many drugs / cognition, CAT for chaotic environmental instability (see Velligan, Zeber

et al, 2008), alliance for minimizing cost issues (see Piette, plus Parchman / Zeber recent work

with diabetes), etc.

• So … understanding which barriers are more problematic, esp. for vulnerable

subgroups, we can target clinical leverage points and help us design tailored

interventions to improve adherence.

Objective:

Per the National Psychosis Registry (Blow et al., 2005), the Veterans Health

Administration (VA) provided care to 79,000 veterans diagnosed with bipolar disorder in 2004.

As the nation’s largest integrated healthcare system, the VA provides preferential care to its

more disadvantaged patients, prioritizing treatment to veterans who are poorer and more

disabled. In addition, access and cost barriers have historically been less restrictive than in other

health systems; this includes pharmacy benefits and drug costs, the latter a significant adherence

barrier (Zeber et al., 2007). As a result, the VA represents an excellent setting to examine our

primary objective, the association between patient perceptions of multiple adherence barriers and

medication adherence in patients diagnosed with bipolar disorder. Using a very rich primary

dataset of patient self-reported information, for this study we explore the relative and cumulative

impact of multiple financial and psychosocial barriers, along with potential added burdens faced

by minority veterans.


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Methods

Study Population and Sample

Participants were recruited from the Continuous Improvement for Veterans in Care -

Mood Disorders (CIVIC-MD) population-based study of VA patients with bipolar disorder

(additional details regarding study objectives provided elsewhere, see Kilbourne et al., 2007).

Inclusion criteria included a current diagnosis of bipolar disorder (I, II, NOS), cyclothymia, or

schizoaffective disorder-bipolar subtype based upon chart review and a confirmatory diagnosis

from their provider. This naturalistic cohort study examined patient and provider factors

associated with treatment quality and outcomes, along with important mediators of these

outcomes (e.g., adherence). Eligible patients were currently receiving treatment for bipolar

disorder at a large urban VA mental health facility in Western Pennsylvania between July 2004

and July 2006. This medical center serves as the catchment area for the vast majority of VA

psychiatric care in the region. Upon providing written informed consent, individuals completed a

baseline survey with a trained interviewer. The primary exclusion criterion was having an

unstable medical condition or significant cognitive impairment precluding patients from

completing the surveys or providing informed consent.

In addition to survey date on demographics and other patient characteristics, the analysis

for this study utilized self-reported data on symptomatology, substance use, and perceptions of

multiple potential barriers associated with medication adherence. The study was reviewed and

approved by the medical center Institutional Review Board.

Measures


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Dependent variables

As the central outcome, we evaluated medication adherence using two complementary

self-completed patient assessments. The first measure utilized the validated Morisky scale

(Morisky et al., 1986), a four-item yes/no instrument frequently used for adherence research

across a variety of chronic medical and psychiatric conditions, including affective disorders

(Shalansky et al., 2004; George et al., 2000). This scale aims at understanding commonly

perceived intrapersonal barriers to adherence, with good adherence commonly defined as having

only 0 or 1 of the following self-reported obstacles: ever forget to take medications, careless at

times about taking medication, then either stop taking medications when feeling better or when

feeling worse. The second assessment of adherence asked how many days within the past four

the patient missed at least one medication dose for their bipolar disorder, a question strongly

correlated with good adherence based upon electronic bottle cap data (Kilbourne et al., 2005).

Independent variables – barriers to adherence

Nine different dynamics associated with poor adherence were identified in the CIVIC-

MD surveys, reflecting self-reported financial and psychosocial barriers. Each of the following

variables have been dichotomized as representing a potentially significant barrier based upon the

literature, our past work with these data and the cohort, and an examination of the continuous

distribution across the study population. Therefore, each patient could experience up to nine

unique adherence barriers. A sensitivity analysis altering the cut-points for the two continuous

barriers described below did not affect the primary results. In addition, the selected cut-points

generally reflect a fairly conservative definition of each barrier, as the influence of each factor

upon adherence might be underestimated versus its effect as a continuous variable. [move last

sentence to discussion]


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The first of three financial or economic barriers is a reported annual income below

$20,000, a figure just below the medium for this patient group. The second barrier is the first of

two items used here from the Cunningham survey on problems accessing care (Cunningham,

1995), asking patients if during the past year they restricted mental health treatment or

medication due to cost. Our prior work revealed substantial reported access problems for a

variety of reasons, even among these patients currently in VA care (Zeber, 2009). Given the

substantial evidence linking out-of-pocket pharmacy costs to adherence difficulties, the last

financial barrier reflected whether the patient incurs a VA medication copayment based upon

self-report of their service connection percentage. This measure is the extent to which a patient’s

disability is directly attributable to their military service; per VA policy, a service connection of

less than 50% means a prescription is subject to a copayment (currently $8).

Turning to information on the six psychosocial barriers collected in the CIVIC-MD

surveys, a second Cunningham access item inquired about difficulty seeing a mental health

specialist when needed, a scenario hypothesized as interfering with prescription receipt and

adherence delays. Perceptions of the therapeutic alliance were solicited from the Health Care

Climate Questionnaire, a 10-item instrument designed specifically for a bipolar population

(Ludman, 2002), measuring the degree of comfort that a patient expresses with mental health

treatment and their health care environment. Each question has a 0-6 Likert scale response option

(strongly disagree to strongly agree), for a total score of 60. Given a mean value of 39.4 in this

cohort and the positive association between the HCCQ and adherence (Zeber, 2008), the chosen

cut-point of ≤25 here signifies a rather negative view of their provider relationship. Medication

beliefs were defined by a scale reflecting perceptions of how effective psychotropic medications

are in achieving treatment goals such as relieving manic or depressive episodes, improving social


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or job functioning, minimizing side effects, or preventing relapse (Gallo, 2002). As Copeland et

al. described the association of this instrument with poor adherence, with a mean of 10.7 out of a

total score of 16 for this patient cohort (Copeland, 2008), our cut-point of ≤7 suggests a quite

low appreciation of medication benefits.

Since hazardous drinking rather than a more inclusive list of other illicit drugs was

associated with poor adherence in our cohort, for this study substance abuse was defined as a

binge drinking episode within the past month. This was assessed using one question from the

Alcohol Use Disorders Identification Test (AUDIT), which inquired about having 5 or more

drinks on a single occasion (Saunders, 1993). A proxy for social support was incorporated for

patients who claimed to live alone, a variable highly correlated with VA administrative data.

Finally, patients who traveled over 50 miles to VA care experienced this last distance barrier, a

significant factor influencing treatment access and utilization for veterans with schizophrenia

(McCarthy, 2007).

Covariates included the demographics of ethnicity, age (per decade), and homelessness

within the past month. Ethnicity was categorized into three categories, per patient report: White,

African-American (the predominate minority group in the study area), and Hispanic.

Recognizing the important association between mood states and adherence, we controlled for the

presence of current affective symptomology via the Internal State Scale (ISS), validated for the

identification of manic, depressive, mixed, and euthymic mood states in patients with bipolar

disorder (Glick et al., 2003). The ISS generates a summary score from 15 current symptoms

experienced during the past 4 weeks. Finally, we wished to control for the possibility that

veterans might receive some medication outside the system, so a broad marker of any self-


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reported non-VA utilization (inpatient, outpatient, or ER) was initially included in the models.

This variable proved highly insignificant and was dropped from the final models.

Study Design and Analysis

The study used a cross-sectional prospective design from sequential patients seeking care

and meeting inclusion criteria. Despite the large sample size for a primary data study, recognition

of statistical power issues urged some caution in determining the final models, so we first

examined the bivariate association of each potential barrier with adherence. Then, in

combination with the conceptual relevance, five of the more significant barriers were entered

into the final analysis. Multivariable logistic regression models predicted the probability of good

medication adherence per both outcomes (i.e., no missed doses and only 0 or 1 Morisky

barriers). For each adherence outcome, two separate models were conducted to examine different

aspects of the relationship between the selected barriers and adherence: 1) the total number of

barriers per patient as the primary predictor, plus covariates, to gauge the total level of adherence

burden; 2) each of the primary five barrier variables separately to generate unique coefficients,

along with the same covariates. All analyses were performed with SAS, version 9.0 (SAS

Institute, Cary, NC).

Results

The descriptive and bivariate characteristics of the study population (N=435) are

provided in Table 1. The mean age was 49.4 years (sd=10.6), with 14% women and 24% ethnic

minorities, split fairly evenly between African-Americans and Hispanics. This profile is well

representative of all veterans diagnosed with bipolar disorder, with an average age of 51.4,

13.0% women, and 9.7% African-American (Blow et al., 2005). Over 2/3 of these patients had

some post-high school education and 17% completed a college degree. Recent mania was noted


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in nearly 30% of this sample, while 55% experienced some affective disorder and 12% reported

being homeless. In addition to their primary psychiatric diagnosis, medical comorbidities were

also quite prevalent, particularly hypertension (34%) and obesity (25%).

Medication Adherence and Self-reported Barriers:

Overall, nearly half (46%) of patients in the study population reported having problems

with medication adherence per the Morisky variable, with 27% admitting they missed at least

one recent prescribed dose. Ethnicity was significantly associated with poor adherence on both

outcomes: 58% of African-Africans and 63% of Hispanics reported “yes” on more than one

Morisky item, compared to 42% of white veterans. Similarly, a greater percent of individuals

from these two minority groups acknowledged missing some recent doses, 38% and 40%,

respectively, than the 23% of white patients (p<.01 on both adherence measures). Concerning

other patient characteristics, no significant differences associated with either adherence outcome

were found for age, education, income, marital status, smoking, or use of non-VA services.

In terms of perceived problems remaining adherent, the mean overall number of barriers

experienced was 2.8; however, 20% of patients claimed at least four with 14% facing five or

more different adherence barriers. A significant ethnic difference also existed in total barriers,

with minorities reporting an average of 3.5 versus 2.6 for white veterans; once again, Hispanics

were slightly higher than the other groups on this outcome, with a mean of 3.7. Table 2 presents

this information, along with bivariate group differences across the nine individual adherence

barriers. Beginning with the three potential financial obstacles, 58% of the cohort reported an

annual income below $20,000, and this was the only economic variable where minorities

experienced a significantly greater burden. However, African-American did acknowledge

restricting some treatment due to cost more frequently than other patients, and both ethnic groups


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faced medication copayments at a higher rate than white patients (67% versus 55%, not

statistically significant). Perceived psychosocial barriers were also quite common, ranging from

14% of patients expressing low beliefs of medication effectiveness to poor social support in 36%

of individuals who lived alone. Approximately 20% also reported problems were their

therapeutic relationship, limited access to a mental health specialist, and binge drinking.

Although minorities generally reported higher levels of these barriers, only two reached

statistical significance, mental health specialist access and binge drinking, the latter reported by

Hispanics at nearly twice the prevalence as white patients.

Multivariable Analysis Findings:

Table 3 presents findings from the multivariable analyses. The first logistic regression

model examined the cumulative role of the number of different barriers upon adherence, with

each patient receiving a value between 0 and 9. For the Morisky outcome, the odds ratio (OR)

was 1.29 per unique barrier [95% CI 1.05 – 1.41], indicating that for each additional barrier a

patient experienced, their likelihood of poor adherence increased by about 30%. Total barriers

for the other adherence definition, missed doses in the past 4 days, did not reach statistical

significance. Important covariates, first for the Morisky outcome, included the presence of

current affective disorder symptomology, which increased the likelihood of poor adherence

about 2-fold (OR = 1.95 CI 1.24 – 2.97). Hispanic ethnicity was also significant, with an OR of

2.25 (CI 1.16 – 3.80). For adherence as defined by no missed days, African-American was the

only significant covariate (OR = 1.94 CI 1.16 – 3.10), although older age and current affective

symptoms proved moderately protective at a trend significance level.

Although the raw prevalence rates for financial barriers generally exceeded the

psychosocial ones, an examination of the association between each barrier and medication


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adherence supported the inclusion of the following five barriers into the second multivariable

model examining unique barriers. All of these variables originated from the psychosocial

category: access to mental health specialist, therapeutic alliance, medication beliefs, binge

drinking, and travel distance for VA care. In the second regression model and Morisky outcome,

the variable most influential in terms of poor adherence was negative beliefs in medication

efficacy with an OR of 2.41 (CI 1.17 – 3.91). This barrier was followed by followed by binge

drinking (OR = 1.95 CI 1.04 – 2.93) and limited access to mental health specialist (OR = 1.73 CI

1.08 – 2.69). The strength of the therapeutic alliance and distance traveled for VA care were both

nearly significant though only moderately influential with ORs near 1.5. Once again, a current

affective disorder episode was one of the strongest and significant covariates (OR = 1.76 CI 1.13

– 2.79). Results for the other adherence outcome were quite similar although slightly attenuated

in comparison to the Morisky findings.

Since some ethnic differences were observed in the bivariate barriers analysis, some

exploratory interaction models, limited by statistical power, did suggest that certain barriers

might be more influential of promoting adherence among minority patients. For example, the

association between the therapeutic alliance and adherence was slightly stronger for African-

Americans, while both facing a medication copayment and living alone were more detrimental to

appropriate adherence for Hispanic patients.

[since power weak here, should we even report any interaction results? – maybe reserve

for discussion the need for additional work?]

Discussion

Nearly 30% of patients acknowledged medication adherence difficulties, while close to

half expressed significant barriers to taking their medications appropriately … as anticipated,


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veterans with BP had many different barriers, with both the overall # and type associated with

adherence … more influential barriers appear to be feelings regarding medication benefits,

substance abuse, and perceived access problems, though current affective disorder episodes also

problematic … ethnic difference perhaps small but given adh gap etc. Also observed some ethnic

difference in the burden of potential adherence barriers, though ongoing work on interaction

models will continue this investigation. (more later on)

[move this paragraph down & shorten] Care-seeking / TX priorities: this issue is not

limited to VA or BP: recent NCS publications (Kessler et al, 2001) found that of all SMI people,

40% did not receive stable TX. Furthermore, over ½ really didn’t think they needed it; of those

who did, 52% = situational barriers, 46% = financial, and low med / therapy beliefs = 45%. …

also see Gonzalez, Zeber et al., in press? – importance of health beliefs & care-seeking behavior

differs by ethnic group: For African Americans, compared to non-Latino whites, a perception of

greater benefits of mental health treatment improved the likelihood of past-year specialty use.

For non-Latino whites, but not African Americans, greater comfort level talking to a professional

was associated with more past year specialty service use.

paragraph on FINANCIAL barriers first - despite strong evidence from past work,

including our own, surprised that copays and other financial barriers were not nearly as

influential as the others classified as psychosocial barriers … however, some possible

explanations: 1) maybe collinearity between income and copay status, if not cost-related TX

restriction. This is not prefect since copayment is also related to service connection and other

factors, but definitely something to consider more carefully; 2) we relied upon patient self-report

for their own SC% - our link with admin data should provide another accurate source for this

variable … and even though only 13% actually restricted care due to cost, this occurred within a VA


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system with relatively lower economic barriers. . and that policies such as medication copayments

can inequitably hurt these patients (Zeber et al., 2008), interventions to narrow the adherence gap

represent an important priority to the VA and other systems … so despite findings here, cannot

disregard cost and economic burden

next, paragraph(s) on PSYCHOSOCIAL barriers - summarize main findings and briefly

disucss … some evidence suggests that attitudes and health beliefs are better predictors of non-

adherence (Scott & Pope, 2002), we found major barriers to be … MED BELIEFS … it is

interesting to note that African-Americans had better medication beliefs and fewer minorities

were exempt from copayments … also ALLIANCE [cut this part way back]: The results of this

quantitative study confirm and extend previous findings linking better clinical relationships with

improved adherence, a critical goal in light of documented medication problems with this

population. While a slightly favorable perception of trust and satisfaction with bipolar treatment

currently exists in this study group, the quality of the therapeutic alliance is a mutable factor that

can be addressed through ongoing provider training and other interventions (Weiden and Rao,

2005). HCCQ a good instrument …Providers who “conveyed confidence” in patients’ ability to

participate in treatment and advocated “keeping in regular contact” proved to be highly

influential upon good adherence. The latter reflects a standard concern over treatment

consistency and retention, necessary elements for an optimistic therapeutic course of bipolar

care. Not surprisingly, the perception of having a provider who “regularly reviews treatment plan

progress” likewise supported an environment more conducive to better medication adherence.

Notwithstanding the more proximate objective of targeting medication adherence, Swann

believes there is tremendous hope for using solid therapeutic alliances to improve long-term

outcomes for bipolar disorder (Swann, 2005). Psychosocial interventions, which include


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assisting the development of enhanced patient-provider relationship, have been found to be cost-

effective in reducing overall disease burden (Chisholm et al., 2005). This fact is intriguing, since

the overall cost-effectiveness of medication adherence interventions has proven more difficult to

assess (Elliott et al., 2005). Enhanced alliance or trust in providers has even help mediate

adherence difficulty due to medication costs (Piette et al., 2005), not a trivial finding.

then, quickly highlight OTHER barriers / COVARIATES … mostly role of affective

symptoms (add citations) …some about age etc. … before returning again to ethnicity

Overall issue significant since adherence problems a major problem in ethnic minorities,

with worse ramifications … Furthermore, individuals encountering these problems were more

likely to be minorities, individuals suffering from mood disorders, the homeless, those with

substance abuse problems or lower perceived access to care – arguably more vulnerable patients.

In addition to hazardous drinking, ethnicity was strongly correlated with adherence, and offers a

particularly intriguing discussion area. Although no differences in overall perceptions of the

treatment environment existed across ethnic groups, nor were the interaction models significant,

ethnicity is highly associated with poor adherence and other related factors. Interaction with

Binge & Alliance … For example, in other bivariate analyses not reported here, minorities

reported much higher rates of hazardous drinking (white=19%, African-American=28%,

other=37%), which in turn was directly related to lower alliance. Minorities were also far more

likely to be homeless, use drugs, have lower income, and report access problems. Efforts to

develop stronger therapeutic relationships and culturally competent care, recognizing ethnic

differences in health beliefs and treatment preferences, should work towards better engaging

minorities and other vulnerable bipolar disorder patients in their own treatment.


21

Main message and room for optimism (several clinicians told me this at my 2008 APA

presentation) – despite many barriers and ramifications, we hope that by such exploratory

attempts to quantify the influence of many barriers, along with excellent qualitative studies, we

can advice possible tailored interventions: e.g., this might include using blisters for older

patients with many medications, CAT for individuals experiencing chaotic lifestyles non-

condusive to good adherence, or even revisiting VA copayment policies in the future … granted

the latter is a difficult issue, though an awareness of cost-related poor adherence can mitigate

cost-related restriction by improving pt-provider relationships (see Piette) … many interventions

to improve adherence, even if successful, are possible but not usually CE (add citation);

however, good chance this is due to fact these interventions are not tailored?

As Balkrishnan summarizes (reduce to a sentence: Balkrishnan, 2005) … In the past,

efforts to improve patient adherence to medication regimens have been primarily unidimensional

in nature. Many of these interventions such as education in self-medication management,

pharmacy management programs, health professional interventions, patient counseling, and

behavioral interventions have been shown to be effective in improving medication adherence

rates, but they were used alone. To implement strategies aimed at markedly improving

medication adherence, all the dimensions such as social and economic factors, therapy related

factors, condition-related factors, and patient-related factors need to be considered. A single-

factor approach might not provide maximum results because the factors determining adherence

intermingle and potentially influence each other. The most effective approaches have been

shown to be multilevel— targeting more than one factor with more than one intervention.

Several programs have demonstrated good results using multilevel team approaches. Well-

defined multidisciplinary patient interventions, which include explaining use of medications,


22

continuing education, monitoring of the treatment regimen, minimizing clinical side effects,

tailoring patient education, reminding and motivating patients to adhere with the regimen, and

finally better communication between physicians with patients may be the key to the highest

patient medication adherence.

Possible study limitations include the fact this was an observational study conducted in a

naturalistic setting, with patients recruited from one urban clinic. Generalizability extending

outside the VA might also be difficult, since veterans are frequently considered sicker than other

patients, plus predominantly older and male. However, this group is quite similar to other

indigent patient populations such as Medicaid recipients, except regarding gender, while

medication adherence and the observed barriers transcend multiple patient groups. In addition,

while memory issues and other subjectivity problems with self-disclosure are recognized, this

rich approach to primary data collection offers unique insights into patient perspectives and is a

practical method well suited to naturalistic settings. We also recognize that numerous other

potential adherence barriers were not incorporated here, including some examined in the

Sajatovic and Hudson studies. A partial list includes the influence of language problems or

acculturation, further issues surrounding health beliefs and treatment preferences (i.e., patient

and provider decisions concerning treatment efficacy versus medication side effects), or the

significance of cultural values regarding mental health services, stigma, and social acceptance.

[cite any other here not discussed in INTRO] Prior work has clearly suggested that side effects

are as one of the most significant reasons for drug treatment failure in patients with bipolar

disorder (Matson et al., 2006; Johnson et al., 2007), although the influence of many other factors

cannot be underestimated. Finally, a variety of clinician, environmental, or healthcare system

dynamics were also not included in these analyses. Nevertheless, insights gained from our


23

focused models shed further light the detrimental effect of an array of perceived barriers and

their collective burden upon adherence.

In conclusion, … understanding how veterans with debilitating conditions such as bipolar

disorder perceive barriers to adherence will permit tailored interventions … will enable the VA

or other organizations to better target clinical relationships towards improving medication

adherence, quality of life, and outcomes. … Recognizing multiple barriers can assist in

developing tailored clinical interventions to improve poor adherence by tailoring efforts towards

reducing psychosocial risk factors. Furthermore, the interaction with VA health benefit polices

potentially contributes to burdens faced by vulnerable veterans already experiencing adherence

problems.


24

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Table 1: Study Population – Descriptive Statistics (N=435)

Patient Characteristic N mean (sd) / %

Age, mean 435 49.4 (10.6)

% women 62 14.3%

Ethnicity

White

African-American

Hispanic

336

58

41

77.3%

13.3%

9.4%

Education: college graduate 75 17.3%

Income

< $10,000

$10,000 - $20,000

$20,000 - $30,000

$30,000 - $40,000

> $40,000

134

120

74

52

44

31.6%

28.3%

17.5%

12.3%

10.4%

Medical comorbidities

% diabetes

% hypertension

% obesity

113

149

109

26.2%

34.3%

25.1%

Self-reported health status

mean SF-12 score, physical

mean SF-12 score, mental health

435

435

37.9 (26.3)

31.8 (22.2)

% homeless, past 4 weeks 53 12.2%

% married 131 30.2%

% manic episode, past 4 weeks 126 29.1%

% any affective disorder, past 4 weeks 240 55.2%

% any illicit drug use, past year 123 28.3%

% current smoking 267 61.4%

% with some non-VA use 212 49.0%


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Table 2: Medication Adherence and Perceived Barriers (% yes), by Ethnic Group

Adherence Measure / Barrier All Patients (n=435)

White (n=336)

African-American

(n=58)

Hispanic (n=41)

Poor Medication Adherence Morisky (yes to 2+ items) * Any missed doses with past 4 days *

46.3% 26.7%

42.2% 23.0%

58.1% 38.1%

62.8% 40.9%

# of perceived barriers, mean 2.82 2.61 3.43 3.68 Income < $20,000 per year * 58.4% 55.5% 72.1% 62.9% Restricted treatment due to cost, past year 12.8% 12.1% 18.0% 11.4% Medication copayment 57.2% 54.5% 66.1% 66.7% Limited access to mental health specialist * 17.4% 14.6% 27.9% 25.7% Poor therapeutic alliance 17.7% 17.0% 18.3% 22.5% Low medication beliefs 13.9% 14.2% 10.2% 17.1% Binge drinking, past month * 21.6% 18.6% 27.9% 37.1% Live alone 35.5% 36.0% 31.2% 37.1% Travel 50+ miles for VA care 15.3% 15.5% 10.2% 20.6%

* p<.05

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