Boston University

OpenBU http://open.bu.edu

Theses & Dissertations Boston University Theses & Dissertations

2014

Medication adherence in diabetic

mellitus: a review of barriers and

interventions

Zhan, Senmiao

http://hdl.handle.net/2144/14654

Boston University

BOSTON UNIVERSITY

SCHOOL OF MEDICINE

Thesis

MEDICATION ADHERENCE IN DIABETIC MELLITUS: A REVIEW OF

BARRIERS AND INTERVENTIONS

by

SENMIAO ZHAN

B.S., Duke University, 2011

Submitted in partial fulfillment of the

requirements for the degree of

Masters of Science

2014

© 2014 by SENMIAO ZHAN All rights reserved

Approved by

First Reader Abdulmaged Traish, M.B.A., Ph.D. Professor of Biochemistry

Second Reader Elizabeth J. Rourke, M.D. Instructor in Medicine

iv

DEDICATION

I would like to dedicate this work to my fiancé Stuart and my parents.

v

ACKNOWLEDGMENTS

I would like to thank Dr. Rourke for the great opportunity to conduct research at

Boston Medical Center and reviewing my thesis in depth. I would also like to

thank Dr. Traish for reading my thesis as well.

vi

MEDICATION ADHERENCE IN DIABETIC MELLITUS: A REVIEW OF

BARRIERS AND INTERVENTIONS

SENMIAO ZHAN

ABSTRACT

Poor adherence is common in patients with diabetes mellitus and other

chronic diseases that require extensive self-management. This behavior has

been linked to increased complications, mortality rate, and health care costs.

Although much effort has been put into studying the barriers to adherence and

ways to improve both patient self-care and clinical outcomes, little success can

be observed in the long run. Literature review of studies related to medication

adherence in diabetes has shown a lack of uniformity in study parameters and

statistical analysis making the juxtaposition of studies difficult and unreliable.

Intervention studies in the field have shown general improvement in adherence

rate in a short period of time, but rarely making any significant differences in

clinical outcomes. Since diabetes mellitus is a chronic disease, it would be

important to design studies measuring long term effects of interventions in the

future.

vii

TABLE OF CONTENTS

TITLE……………………………………………………………………………………...i

COPYRIGHT PAGE………………………………………………………………….....ii

READER APPROVAL PAGE……………………………………………………...…..iii

DEDICATION .......................................................................................................iv

ACKNOWLEDGMENTS ....................................................................................... v

ABSTRACT ..........................................................................................................vi

LIST OF TABLES .................................................................................................ix

LIST OF FIGURES ............................................................................................... x

LIST OF ABBREVIATIONS ..................................................................................xi

INTRODUCTION .................................................................................................. 1

Medication adherence ....................................................................................... 5

Identify poor adherence .................................................................................. 12

Improvement of poor medication adherence ................................................... 13

PUBLISHED STUDIES ....................................................................................... 20

RESULTS ........................................................................................................... 21

Diabetic adherence studies without intervention ............................................. 21

Intervention ..................................................................................................... 29

Adherence studies on non-diabetic diseases .................................................. 38

viii

DISCUSSION ..................................................................................................... 40

Baseline adherence rate and HbA1c ................................................................ 40

Poor adherence and barriers to adherence .................................................... 41

Intervention ..................................................................................................... 43

CONCLUSION .................................................................................................... 47

REFERENCES ................................................................................................... 49

VITA ................................................................................................................... 57

ix

LIST OF TABLES

Table Title Page

1 Methods used in studying medication adherence 9

2 General overview of interventions to improve

adherence

14

3 Diabetic adherence studies 22-23

4 Average HbA1c 24

5a Average adherence rate (%) 25

5b Weighted average of adherence rate (%) 25

6 Factors associated with non-adherence 28

7 Overview of intervention studies 35-37

x

LIST OF FIGURES

Figure Title Page

1 Major complications from diabetes 2

2 Medical cost breakdown 16

xi

LIST OF ABBREVIATIONS

BGM ............................................................ Blood Glucose Monitoring Frequency

BID ...................................................................................................... Twice Daily

CDC ............................................................................ Centers for Disease Control

CMG ...................................................... Continuous measure of Medication Gaps

CVD ................................................................................. Cardiovascular Disease

DM .............................................................................................. Diabetes Mellitus

HbA1c ......................................................................................... Hemoglobin A1c

MARS ............................................................ Medication Adherence Report Scale

MDKT ............................................................. Michigan Diabetes Knowledge Test

MMAS .......................................................... Morisky Medication Adherence Scale

MPR ........................................................................ Medication Possession Ratio

OD ....................................................................................................... Once Daily

OHA ............................................................................. Oral Hypoglycemic Agents

PDC ............................................................................ Proportion of Days Covered

RAAS ....................................................... Renin Angiotensin Aldosterone System

SDSCA .................................................. Summary of Diabetes Self-Care Activities

T1DM .............................................................................. Type 1 Diabetes Mellitus

T2DM .............................................................................. Type 2 Diabetes Mellitus

1

INTRODUCTION

Diabetes Mellitus (DM), or diabetes, is a serious and complex metabolic

disease that disturbs the lives of millions globally (Guariguata et al., 2013).

According to the Centers for Disease Control (CDC), about 25.8 million people in

the United States alone were affected by diabetes in the year of 2010, and about

35% of adults above the age of 20 are prediabetic based on their hemoglobin

A1c (HbA1c) levels (CDC, 2011). Although much new information has been

gained through various scientific studies regarding the cause of diabetes

(Schulze & Hu, 2014), there is a fast growing trend in the prevalence of the

disease (Danaei et al., 2011; Guariguata et al., 2013). With more people being

diagnosed and treated for diabetes, there is also an increase in the economic

burden. In 2012, it has been estimated that the expenditure related to diabetes

was about $245 billion ($176 billion in direct medical cost and $69 billion in lost

productivity) in the US (Association, 2013). Due to the substantial costs and

detrimental effects of the disease, prevention and reduction of diabetes is of

great importance.

Generally speaking, DM is a disease where the insufficiency of insulin

and/or tissues’ lack of response to insulin leads to abnormal levels of glucose in

the blood. Although the diagnosis of DM is made primarily through the

concentration of plasma glucose (fasting plasma glucose > 126mg/dL for two

days) (Koeppen & Stanton, 2009), the pathological manifestation of the disease

can be observed through various systems in the body (Figure 1).

2

Cerebral vascular disease Retinopathy Blindness

Coronary heart disease

Nephropathy Renal Disease

Neuropathy (Peripheral nervous system) Ulceration and amputation of foot

Figure 1. Major complications from diabetes: The figure depicts some of the microvascular and macrovascular complications from diabetes

Peripheral vascular disease

3

Complications of prolonged hyperglycemia, or high blood sugar, are

categorized into microvascular and macrovascular. Microvascular complications

include retinopathy, neuropathy, and nephropathy. Retinopathies are retinal

abnormalities and are the leading cause of new onset blindness in the US (Fong

et al., 2004). It has been shown through various studies that highly regulated

glycemic control has protective effects against the development of retinopathy

(Fong et al., 2004). Peripheral nerve dysfunction (neuropathy) can lead to

problems such as sensory loss and paresthesia in patients with DM, particularly

in the lower extremities. In severe cases, foot ulceration and injuries can even

result in amputation. To disturb the body’s homeostasis even more,

hyperglycemia can damage the renal functioning in patients. Nephropathy as a

result of DM is the leading cause of renal failure in the US (Fowler, 2008).

Similar to retinopathy, it has been shown that glycemic control is strongly

associated with decreased incidence of both neuropathy and nephropathy as

well (Fowler, 2008; Gross et al., 2005).

The macrovascular complications of DM manifest themselves as an

accelerated rate of developing atherosclerosis. The thickening of the arterial wall

is likely the result of vascular injuries and chronic inflammation. Due to the

macrovascular complications, diabetic patients are more likely to develop

cardiovascular diseases (CVD), such as coronary artery disease, and have

increased chances of myocardial infarction and stroke (Brownlee, 2001; Fowler,

2008; Koeppen & Stanton, 2009). This chance of developing CVD is augmented

4

when the patient also has hypertension and/or dyslipidemia (Haffner, 1999;

Sowers, Epstein, & Frohlich, 2001).

The majority of diabetic patients can be classified into two groups. Type 1

DM (T1DM), or insulin-dependent DM, accounts for a small portion of the diabetic

patient population. It is hallmarked by the destruction of beta cells in the

pancreas, often occurring through autoimmune mechanisms. Since beta cells

are the producers of insulin, T1DM patients have impaired insulin production;

therefore, exogenous insulin is an effective treatment to help prevent ketosis.

The vast majority of the T1DM patients are diagnosed in childhood, but the onset

can happen any time. Type 2 DM (T2DM), on the other hand, makes up about

90% of the diagnosed diabetic population. T2DM differs from T1DM in that

individuals are still able to produce insulin, either normally or at a diminished rate;

however, key organs are unable to sense the presence of insulin. Although the

mechanism varies from case to case, the underlying problem with T2DM seems

to be insulin resistance (Koeppen & Stanton, 2009).

Due to the complex nature of the disease, treatment for T2DM can be very

complex. In order to gain glycemic control, patients may need to take certain oral

treatments and/or insulin along with adhering to careful diet plans and exercise

regimes. Additionally, patients also have to monitor their blood glucose

regularly. With the progression of the disease, patients have to take additional

medications to manage the microvascular and macrovascular complications. In

5

other words, glycemic control is strictly dependent on the patients’ abilities to

self-manage.

Medication adherence

In order to measure how well patients follow their medication regime,

concepts such as persistence, adherence, and compliance have been used in

literature (Cramer, 2004; Kravitz & Melnikow, 2004; Osterberg & Blaschke,

2005). It should be noted that the definition of these terms are not uniformly

accepted. Some consider compliance and persistence to be synonymous with

adherence, where the terms are defined as “the extent to which a patient is

consistent in following medical or health advice (Dunbar-Jacob & Mortimer-

Stephens, 2001; Haynes, Ackloo, Sahota, McDonald, & Yao, 2008).” Others

consider persistence as the duration for which a patient is adherent or compliant

to certain medications (J. K. Lee, Grace, & Taylor, 2006). Some literature even

suggests that “adherence” is a better term because “compliance” is making the

assumption that the patient is passively following doctor’s orders (Osterberg &

Blaschke, 2005). Either way, the assessment of medication utilization behavior

can be critical to the outcome of the treatment due to the obvious reason that, if a

patient is not taking the medication as prescribed, the intended therapeutic

effects cannot occur. For the purpose of this paper, adherence and compliance

will be used interchangeably.

6

Clinical studies of medication adherence have shown varied results. In a

2004 quantitative review of adherence studies from the past five decades, it was

found that, within the 569 studies assessed, the rate of adherence ranged from

~5% to 100%, with an overall average of ~75% (DiMatteo, 2004). One of the

factors that could contribute to such large variability is the lack of a numerical

consensus on what is considered to be adequate adherence. Although

adherence is a continuous factor that could be expressed in a percentage scale

(100% adherence meaning taking all medications on time), most studies consider

adherence and non-adherence as dichotomous variables, where a cutoff is

established. In an effort to unify measures of adherence in recent publications,

the conventional “good adherence” has been established at greater than 80%

(Dunbar-Jacob & Mortimer-Stephens, 2001); however, it is not uncommon to see

studies using cutoffs higher or lower than 80% (Osterberg & Blaschke, 2005).

Another factor that could contribute to the large disparities in medication

adherence is the type of disease the medications are treating. In the

aforementioned review study, compliance rate was the highest in HIV and lowest

in sleep disorders (DiMatteo, 2004).

Study design can also play a key role in data variation. There are different

methods that studies can utilize to gather patient adherence information, each

with its own set of strengths and weaknesses. The methods can be generally

divided into three groups: direct, indirect-voluntary, and indirect-involuntary.

7

Table 1 provides a summary of each method and its advantages and

disadvantages.

The direct method consists of biochemical measurements, where samples

of body fluids provided by patients are assessed for medications (or the

metabolites), or by directly observing patients taking their medications. These

methods can be fairly impractical because they require constant or frequent

monitoring of the patients. Depending on how frequently the researchers

communicate with the patients, the extra attention could result in overestimation

of patients’ own ability to stay compliant to the medication. Although these types

of studies provide a very detailed and objective data set, it is generally costly and

cannot always be used due to the fact that not all medications have easily

measurable metabolites. The amount of effort required to gather data plus the

costliness of the study makes this method an inadequate way to assess

adherence in a large population (Vermeire, Hearnshaw, Van Royen, &

Denekens, 2001).

The indirect-voluntary measurements for adherence are methods

dependent on patients’ abilities to report information. These methods could

present themselves in the form of surveys with detailed questions regarding

patients’ medication taking behavior or as records kept by the patients in the form

of diaries. Questioning patients regarding their behaviors in the form of

questionnaires can be an inexpensive and simple way to gather data regarding

adherence; however, the study would be susceptible to response bias, where the

8

patients might answer questions based on how they expect the healthcare

providers to want them to answer. When designing studies that gather patient

responses, researchers also need to pay close attention to how each question is

worded because differences in the interpretation of questions can also result in

response biases. Since a healthcare provider’s intention is for the patients to

take their medication and follow the treatment regimes, there could be a gross

overestimation in the adherence rate reported by patients (Vermeire et al., 2001).

The indirect-involuntary group of methods consists of pill counts,

electronic monitoring, and the use of medication refill data. Pill counts are

usually conducted through the pharmacy where pharmacists will count the

remaining number of pills left in a patient’s bottle. When pill counts are

conducted through the patient, it becomes a type of patient self-report, and is

prone to all the biases discussed above in the voluntary section. However, even

when pill count is conducted through the pharmacy, patients still can easily alter

the outcome by discarding the pills or by switching them with other bottles in

order to appear to be following the treatment regimen. Although pill counting is

an attractive method due to its simplicity, it is by no means a good measure of

adherence due to its lack of reliability (Rudd et al., 1988).

9

Table 1. Methods used in studying medication adherence: This table compares the pros and cons of various methods used to measure adherence.

Method Pros Cons

Direct Biochemical measures (levels of drug or its metabolites in body fluids)

Highly accurate for drugs that does not metabolize easily.

Expensive The special attention given to the patient might overestimate adherence Does not work for every medication due to variation in metabolism of each patient.

Directly observe patient taking the medication

Highly accurate Patients could hide pills Overestimation of adherence due to special attention

Indirect voluntary

Patient self-report and surveys

Easily conducted with low cost. Patients could easily alter their answers and distort the data

Patient record/diaries Good longitudinal record Patients could easily alter their answers and distort the data

Indirect involuntary

Pill count Simple and inexpensive. Easily quantifiable data

Patients could easily alter the pill numbers

Electronic monitors Simple and easily quantifiable data

Monitoring devices are very expensive. Patients could out-smart the system and alter data

Pharmacy refill data Data is already entered through pharmacy. Easily obtainable

Prescription refill does not equal actual ingestion of medication

10

As technological advancements are made, electronic monitoring systems

are becoming more available for adherence studies. Electronic devices can

record time stamped data each time the patient opens a bottle or canister. The

newer devices can even transmit such data via internet, making the data

gathering process highly accurate and simplified (J. Hughes, Sterns,

Mastandrea, & Smith, 2013). Through the use of electronic monitoring systems,

phenomena such as “white-coat adherence” and “drug holidays” have been

discovered (Osterberg & Blaschke, 2005; Urquhart, 1997). “Drug holidays”

refers to lapses in medication adherence longer than three days. This happens

to about one sixth of the patient population monthly (Urquhart, 1994). “White-

coat adherence” on the other hand, refers to the phenomenon where patients are

more likely to improve their medication-taking behavior five days prior to and post

an appointment with their provider, and the improvement deteriorates until the

next appointment (FEINSTEIN, 1990). Although this approach to studying

adherence can provide highly precise information on the patients’ medication-

taking behavior, it cannot provide information on whether patients actually ingest

the medication or how much is ingested. The patient can also cheat the system

by taking out the medication without actually ingesting it, or take the wrong

dosage and thus invalidate the data. The high cost of the electronic monitoring

devices also makes it unfavorable to study large sample sizes.

There is an increasing trend in adherence studies to utilize pharmaceutical

refill databases. Although there are many flaws, the refill data for medication has

11

proven to be a good alternative to direct measurements of compliance (Steiner &

Prochazka, 1997). The use of databases is subject to the same type of problems

as the electronic devices and pill counts, where patient might refill medications

without actually ingesting them. Additionally, medication is usually filled on a

monthly basis, making it difficult to assess the patients’ medication-taking

behavior within the month. Regardless, due to the efficiency of current pharmacy

record systems, near complete records are available for a large group of

patients. This means that using the database would give researchers a relatively

cost-effective way to study the behavior of a great sum of patients.

Other factors such as selection bias, small sample size, and

experimenter’s bias can also be attributed to the variations in adherence data.

There is no one perfect strategy to study adherence. Even though the flaws of

experimental designs are known, it is difficult to eliminate the biases due to

limited resources and the nature of the study population.

The rate of medication adherence in T2DM patients is quite low, ranging

from 67-85% for oral hypoglycemic agents (OHAs) to 62-64% for insulin (Cramer,

2004). Poor adherence not only reduces the overall efficacy of medications, but

is also associated with increased complications and higher mortality rates (Ho et

al., 2006). Moreover, with the growing number of patients suffering from

complications and a great number of resources being wasted each year from

medication omission, non-adherence is becoming a substantial economic

burden. All in all, the cost of poor compliance in the United States has been

12

estimated to be around $100 billion each year (D. A. Hughes, Bagust, Haycox, &

Walley, 2001).

Improving medication adherence should be one of the main focuses of

health care providers. It has been shown that patients who maintain a high rate

of medication compliance have significantly reduced rates of hospitalization, thus

costing less in healthcare (Sokol, McGuigan, Verbrugge, & Epstein, 2005).

There are also other losses such as reduced worker efficiency, death, and

treatment for other complications that can perhaps be reduced if a better

adherence rate can be achieved.

Identify poor adherence

The first step towards improving medication compliance is to recognize

poor adherence, especially in T2DM patients where signs of non-adherence

might not be immediately apparent. In the clinical setting, HbA1c is often used as

a tool to measure a patient’s long-term glycemic control. HbA1c is hemoglobin

that has been glycated by exposure to plasma glucose. Since the life-span of

hemoglobin is usually 120 days, once the hemoglobin goes through glycation, it

will stay glycated for the remainder of its life time. This feature makes HbA1c a

great marker because elevated levels of glycated hemoglobin would indicate

prolonged hyperglycemia. Patients who are compliant to medication had

generally lower HbA1c than those who are not. One study was able to utilize a

set of questionnaires called the Morisky score in conjunction with HbA1c to

13

identify poorly adherent patients (Krapek et al., 2004). There are other factors

associated with patients at a higher risk for medication non-adherence such as

age, race, and socio-economic status that will be expanded upon later.

Improvement of poor medication adherence

Once the identification of non-adherence is achieved, there needs to be

interventions to improve medication compliance. The types of interventions for

medication adherence can generally be categorized into five groups: cost based,

convenience based, reward based, communication based, and finally a

combination of intervention groups. Table 2 shows an overview of the groups of

interventions and some examples of how the interventions could be utilized.

Although not all of these interventions have been studied with diabetic patients,

there are similar studies on other chronic diseases such as hypertension and

cardiovascular diseases that diabetes is closely related to. The specific data and

effectiveness of the interventions will be discussed later on.

14

Table 2. General overview of interventions to improve adherence: Five types of interventions with summaries and examples of each.

Type of intervention

Summary Example

Cost based Cost based intervention usually is achieved through changing the cost of medication directly or indirectly leading to either increased or decreased drug acquisition cost.

Decrease copayments

Increase insurance coverage

Convenience based

These interventions usually involve the increase in convenience for the patients to either acquire the medication or to use the medication.

Mail-order pharmacy

Getting easier access to the pharmacy/medication

Simplified dosage regimen

Simplified packaging

Alternative route of medication administration

Reward based Patients in this type of intervention group either receive rewards as a mean of achieving certain clinical goals, receive rewards in order to help them improve their adherence, or the reward is given at a random chance.

Financial incentives as a reward for achieving certain goals.

Glucometer or blood pressure cuffs awarded to the patients as a mean to increase self-checkups.

A lottery system

Communication based

Increased communication between healthcare provider/patient, patient family/patient, patient/patient in order to educate or remind patients regarding their conditions

Care programs

Counseling through various means

Educational materials

Increased home visits or phone calls

Small group sessions

Combination Two or more of the above interventions used together

Using care programs in conjunction with decreased copayments

15

The cost-based interventions stem from the idea that cost is one of the

main players in causing medication non-adherence. Even if the cost of

medication is covered by insurance, the bills can stack up from the copayments.

In fact, many studies have shown that lack of drug coverage is strongly

associated with decreased use of prescription drugs (Soumerai et al., 2006).

Additionally, this cost-related non-adherence is most prominent in elderly and in

disabled patient populations (Soumerai et al., 2006). There are many sources of

cost regulations that can be considered when designing cost-based interventions

to increase medication adherence. Since penalizing unhealthy behavior by

increasing cost is unethical and has definitive adverse effect on medication

adherence (Doshi, Zhu, Lee, Kimmel, & Volpp, 2009), it should be avoided at all

cost. Figure 2 displays the general breakdown of medical costs based on patient

and health insurance. Several sources of cost contributing to the out-of-pocket

cost from patients can be observed. From an intervention stand point, the

easiest way to reduce expenses is to perhaps reduce copayment, since it is

usually a flat rate. Other approaches would be to reduce the patient portion of

coinsurance, decrease the deductible limit, increase coverage for medication, or

reduce premiums.

16

Total Cost

Copayment

Coinsurance

Insurance Patient

Deductible

Travel and other

expenses

Premiums

Exclusions

Figure 2. Medical cost breakdown: This figure shows the general cost breakdown of medical expenses for a patient that has health insurance.

17

Convenience based interventions focus on alleviating any inconveniences

in the process of acquiring or administering the medication for the patients. One

example of such an intervention is to give patients easier access to their

medication by using mail-order pharmacies instead of traveling to their local

pharmacies since the medication possession rate is higher in patients who use

mail-order pharmacies (Duru et al., 2010).

From adherence studies from the past decades, researchers have found

that frequency of dosing and complexity of treatment regimen is highly correlated

with reduced adherence (Dezii, Kawabata, & Tran, 2002; Donnan, MacDonald, &

Morris, 2002; Girvin, McDermott, & Johnston, 1999; Ingersoll & Cohen, 2008).

Dosage simplification and specialized packaging are both examples of

interventions based on adding convenience to taking medication.

Reward based interventions have been used in many studies to reinforce

certain behaviors such as smoking cessation, weight loss, and medication

adherence (Burns, 2007; Volpp, John, et al., 2008; Volpp et al., 2009). The way

in which rewards are given can vary between studies. Although the type of

rewards are usually financial incentives such as cash payments and shopping

vouchers, there are also examples of shortening jail sentences as a reward to

improve psychiatric treatment adherence (Monahan et al., 2005). In another

study based on medication use (Volpp, Loewenstein, et al., 2008), the

researchers used a lottery system in conjunction with electronic monitoring

devices.

18

Communication based interventions have been used widely through

medication adherence studies because it can take various forms. This category

of intervention methods can be further divided based on the benefits they

provide. Instructional interventions provide patients with more knowledge

regarding their disease, potential complications, and self-management plans. In

addition to oral communication, these can take the form of written materials and

learning programs. Another method of communication based intervention works

through reinforcement of compliance to medication schedules. These can

include automated telephone calls, electronic monitoring systems, or casual

reminders from health care providers, either through phone calls, home visits, or

office visits. A third method of communicative intervention works through

structured counseling programs. These programs could take the form of group

or individual sessions, where patients can discuss social, economic, and

emotional factors that might otherwise deter them from adherence.

Combined interventions usually mix a communication based intervention

with some other intervention category. An example of a combined intervention

would be providing financial incentives to patients while giving them educational

pamphlets detailing the disease’s complications and self-management plans.

The ultimate goal of adherence interventions is not only to alter the

patients’ behaviors, but also to improve health conditions. For diabetic patients,

this means reduced HbA1c levels, rates of microvascular and macrovascular

complications, inpatient and outpatient hospital visits, and mortality rates. This

19

means that interventions must have a persisting effect, lasting long after the

intervention period ends. One of the goals this paper is to compare various

intervention methods and assess their relative effectiveness in both increasing

medication adherence and improving clinical outcomes.

20

PUBLISHED STUDIES

Literature searches were conducted through Google Scholar, PubMed,

and Web of Knowledge, inputting keywords that generally relate to medication

adherence in diabetes (eg. adherence, compliance, non-adherence, intervention,

diabetes, and diabetes mellitus). Instead of conducting an exhaustive review that

encompasses all of the literature published about the topic in the past two

decades, a limited number of reviews and studies were selected due to study

design and relevancy to this paper. Relevant review articles were further

reviewed and screened for citation of studies that fit the search criteria.

Initially, articles were separated into two groups: medication adherence

studies for diabetes, and adherence studies for non-diabetic diseases. The

diabetic articles were further divided into “measures of behavior and/or HbA1c

only”, “intervention with measures of behavioral outcomes”, and “intervention with

measure of both behavioral and clinical outcomes”. The “measures of behavior

and/or HbA1c only” articles will be used to distinguish factors that help identify

non-adherence, while the intervention articles will be assessed for effectiveness

of the said interventions. Each article will be reviewed for study design, standard

of adherence, outcomes, strengths, and weaknesses.

21

RESULTS

Diabetic adherence studies without intervention

An overview of the studies reviewed with measurements of adherence

behavior and associated HbA1c are presented in Table 3. Out of the fourteen

studies in this category, seven are based on self-reported adherence data

(including Morisky scales), six on pharmacy refill data, and one on electronic

monitoring device data. Although many studies included both T1DM patients and

T2DM patients, the majority of the patients have T2DM due to the prevalence in

the population and age restrictions. Nine studies gathered adherence data for

general prescription medications of the patients and did not exclude non-diabetic

medication. Nine studies provided some kind of HbA1c data, three of which

defined certain levels of HbA1c as categorical values and reported the percentage

of population that falls under these categories.

22

Table 3. Diabetic adherence studies: This is an overview of the diabetic adherence studies that contain measurements with no intervention.

Reference T2 / T1

Duration of Study

n Age (years) Medication Adherence Method

Adherence Rate

HbA1c (%)

(Grant, Devita, Singer, & Meigs, 2003)

T2 -- 128 66 ± 12 4.1 ± 1.9 types Self-report (out of 7days)

6.7 ± 1.1 days 7.7 ± 1.5

(Ciechanowski, Katon, & Russo, 2000)

Both 120 days 367 61.3 ± 11.7* OHA (Oral Hypoglycemic Agents)

Refill data (days in OHA interruption)

Depression Low 7.1 ± 12 Med 9.3 ± 15.5 High 14.9 ± 20.0

--

(Broadbent, Donkin, & Stroh, 2011)

Both -- T1 49 T2 108

43.2 ± 20.6 T1, 58.0 ± 11.3 T2

Mixed Self-report Insulin 86%

--

(Donnelly, Morris, & Evans, 2007)

T2 6.75 yrs 1099 61.5 ± 11.9 Insulin Database (>80% MPR cutoff)

71 ± 18 % 8.5 ± 1.3

(Lin et al., 2004) Both 2 yrs 4385 63.3 ± 13.4 OHA Pharmacy data (days not covered)

80.5 %* 7.8 ± 1.6

(Pladevall et al., 2004)

Both 3 years 677 63.9 ± 10.6 OHA; 2.1 types

Refill data gaps >20% non-adherent.

Metformin 57%*

8.0 ± 1.4

(Bailey et al., 2012) Both -- 59 50.4 ± 10.3 Mixed. >50% taking 3 or more.

Morisky Scale (0-6 = non-adherent 7-8 = adherent)

44.1% --

(Al-Qazaz et al., 2011)

T2 -- 505 58.2 ± 9.2 Mixed. Median 4.0

Morisky Scale Median 6.5 Good (≤6.5) 20.8%, Poor (≥6.5)79.2%

* indicates an adherence number calculated by subtracting non-adherence data.

23

Table 3 continued. Diabetic Adherence Studies: This is an overview of the diabetic adherence studies that contain measurements with no intervention.

Reference T2 / T1

Duration of Study

n Age (years) Medication

Adherence Method

Adherence Rate

HbA1c (%)

(Stuart et al., 2011)

Both 3 years Statin 1139 RAAS 1766

~85% > 65 y.o. Mixed Logs and Pharmacy Receipts (Diaries). Medication Possession Ratios (MPR)

Median MPR RAAS = 0.88 Statins = 0.77. % adherence RAAS >50% Statin <50%

--

(Ngo-Metzger, Sorkin, Billimek, Greenfield, & Kaplan, 2012)

T2 2 yrs 1135

White 61±10.8, 68.7±8.9 Viet, 55.6±10.9 Mex

Mixed Self-Report White 72.8% Vietnamese 72.4% Mexican 46.8%

Poor Glucose Control = A1C >/= 8%. White: Poor = 15.6%, Viet: Poor = 10%, Mex: Poor = 45.2%, p<0.001

(Osborn et al., 2011)

Both -- 383 54.4±13.0 Mixed Self-Report, SDSCA Scale Score (out of 7 days)

Average 6.7 ± 1.0

Average 7.6 ± 1.7, White 7.4±1.5, Black 7.9 ± 1.9, p=0.005

(Aikens & Piette, 2013)

T2 -- 287 56.4±8.7 Mixed 4 Item Morisky Scale

51% 59% of participants had HbA1c>7%

(Nagrebetsky et al., 2012)

T2 1-2yrs 1yr 161 Non 50

1yr 63.5 ± 10.4 non 62.1 ± 11.7

1yr 5.0 ± 2.5 Non 4.6 ± 2.8

Electronic Monitoring system (TrackCap) Medication Adherence Report Scale (MARS, scale ranged from 5-25)

TrackCap 1yr 75.5 ± 28.3 non 68.2 ± 28.8 MARS: (avg) 1yr 24 Non 25

1yr 8.2 ± 1.1 non 8.6 ± 1.4

(Virdi, Daskiran, Nigam, Kozma, & Raja, 2012)

T2 365 days 5172 51.4 ± 8.4 Non-insulin

Refill Data MPR (>80%)

44.4% --

24

Table 4. Average HbA1c: The mean and standard deviation of HbA1c from seven studies

Reference n Mean HbA1c (%) Standard Deviation

(Grant et al., 2003) 128 7.7 1.5

(Donnelly et al., 2007) 1099 8.5 1.3

(Lin et al., 2004) 4385 7.8 1.6

(Pladevall et al., 2004) 677 8.0 1.4

(Osborn et al., 2011) 383 7.6 1.7

(Nagrebetsky et al., 2012)

161 8.2 1.1

50 8.6 1.4

Weighted Mean and pooled SD: 7.9 1.5

Average percentages of HbA1c were given by six studies. They ranged

from 7.6% to 8.6% with a weighted average of 7.9% and a pooled standard

deviation of 1.5% (Table 4). The Nagrebetsky data was divided into two groups

and thus accounted for two separate samples when the weighted average was

calculated (Nagrebetsky et al., 2012). Percentages of adherence were

presented by nine studies (Table 5a) and ranged from 44.1% to 86%. The

weighted average for all nine studies came out to be 61.4 ± 15.8% (Table 5b). A

comparison of the weighted average of adherence rate for six self-report studies

and four database studies was carried out in Table 5b. A p-value was produced

as a result of a t-test for the null hypothesis that the self-report average was

different from the database average (H0: μself-report ≠ μdatabase). Since p= 0.47, the

analysis was not statistically significant and the null is rejected, indicating that the

average adherence from self-reported data was not significantly different from

the database average.

25

Table 5a. Average adherence rate (%): Percent of patient adherent in ten studies

Reference Method n Adherence rate (%)

(Broadbent et al., 2011) Self-report 157 86

(Ngo-Metzger et al., 2012) Self-report (Non-hispanic white)

249 72.8

(Ngo-Metzger et al., 2012) Self-report (Vietnamese)

194 72.4

(Ngo-Metzger et al., 2012) Self-report (Mexican)

533 46.8

(Bailey et al., 2012) Self-report Morisky scale 59 44.1

(Aikens & Piette, 2013) Self-report Morisky Scale 287 51

(Donnelly et al., 2007) Refill data (>80% MPR) 1099 71

(Lin et al., 2004) Refill data (>80% MPR) 4385 80.5

(Pladevall et al., 2004) Refill data (<20% CMG*) 677 57

(Virdi et al., 2012) Refill data (>80% MPR) 5172 44.4

*CMG = continuous measure of medication gaps

Table 5b. Weighted average of adherence rate (%): Calculated average adherence rate of studies in Table 5a.

Total Self-report Refill data p-value

61.4 ± 15.8% 59.4 ± 17.4% 61.7 ± 15.9% p= 0.47

26

Several studies have noted the significant association between increased

medication adherence and improved clinical outcomes, especially in long term

metabolic control (Aikens & Piette, 2013; Al-Qazaz et al., 2011; Donnelly et al.,

2007; Pladevall et al., 2004). One study noted that there is no significant

association between glycemic control and age, gender, or social deprivation

(Donnelly et al., 2007). Other studies attempted to control for various factors

that might have affected the outcome of the adherence data (Table 6).

Grant et al hypothesized that the number of prescription medications a

patient has to take can contribute to poor adherence (Grant et al., 2003). They

utilized correlation studies and found the Spearman’s correlation coefficient for

number of medications taken and adherence rate to be 0.07 (p= 0.4). The

finding was not statistically significant.

Two studies focused on the effect of depression on compliance to diabetic

medication regimen (Ciechanowski et al., 2000; Lin et al., 2004). Both studies

only focused on OHAs and found that there is significant evidence (p<0.05,

p<0.005 respectively) that patients with major depressive symptoms tend to have

poorer adherence to diabetic medication.

The study by Bailey et al concluded that the significant (p<0.05) factors

that are associated with medication non-adherence are “cost, no refills, poor

health status, fewer disease states, and any reason at all to not take medication

(Bailey et al., 2012).” When diabetic knowledge was assessed, Al-Qazaz et al

found that patients with better glycemic control (HbA1c ≤ 6.5) had higher

27

adherence rates (p<0.001) and also more knowledge about diabetes (p<0.001)

(Al-Qazaz et al., 2011).

When adherence rate was assessed by race, it was clear that Mexican

Americans and African Americans had the lowest adherence rates compared to

Whites in the US (Ngo-Metzger et al., 2012; Osborn et al., 2011). Ngo-Metzger

et al found that more Mexican Americans reported to have financial barriers and

lower overall income compared to Whites and Vietnamese Americans (Ngo-

Metzger et al., 2012). Osborn et al. found that while African Americans had

significantly lower adherence rate (p<0.05), they also had lower health literacy

(Osborn et al., 2011). When numeracy was assessed, no significant results were

found.

28

Table 6. Factors associated with non-adherence: This shows some studies that tried to control for factors that associate with non-adherence

Reference Factor studied Results Significance Conclusion

(Grant et al., 2003)

Number of medication taken by patient

Spearman’s correlation coefficient for number of medication vs. adherence = 0.07

p=0.4 Not significant

Virtually no correlation between total number of medication and average medication adherence

(Ciechanowski et al., 2000)

Depression Depression & Adherence (% days in OHA interruptions) Low dep. 7.1 ± 12 Med dep. 9.3 ± 15.5 High dep.14.9 ± 20.0

p<0.05 Significant

Depression severity is significantly associated with poor adherence

(Lin et al., 2004)

Depression Major dep. 24.5% non-adherence No Major dep 18.8% non-adherence

p<0.005 Significant

Depression is significantly associated with non-adherence

(Bailey et al., 2012)

Access, barriers, medication use, and demographics

Adherence data for specific categories were not given

P<0.05 for Cost, no refills, poor health status, fewer disease states, and any reason

Cost, no refills, poor health status, fewer disease states, and having any reason at all are significantly associated with non-adherence.

(Al-Qazaz et al., 2011)

Diabetes knowledge

A1c ≤ 6.5 10 MDKT* 8 MMAS † MDKT*

p<0.001 MMAS

†

p<0.001 More knowledge about diabetes is correlated with better adherence rate and better glycemic control.

A1c > 6.5 7 MDKT* 5.8 MMAS †

(Ngo-Metzger et al., 2012)

Financial barriers and race

Adherence White 72.8%

Viet 72.4%

Mexican 46.8%

p<0.001 for all measures between white and Mexican or Viet and Mexican

Mexicans had lower adherence rate and poor glycemic control. Mexicans also reported more financial barrier.

A1c≥8% 15.6% 10.0% 45.2%

(Osborn et al., 2011)

Race, Diabetes literacy, and numeracy

White Adherence 6.8 ± 0.8 SDSCA

‡

Black Adherence 7.9 ± 1.9 SDSCA

‡

p<0.05 African Americans had lower adherence, but this could be due to health literacy. Health literacy vs. Adherence r=-0.1 p<0.02

* MDKT = Michigan Diabetes Knowledge Test (0-14). Higher score indicates more knowledge. † MMAS = Morisky Medication Adherence Scale (0-8). Higher score indicates better adherence.

‡ SDSCA = Summary of Diabetes Self-Care Activities. This is the number of days being adherent to medication in the past 7 days.

29

Intervention

Twelve studies were reviewed regarding interventions to improve

medication adherence in diabetes (Table 7). Although many studies measured

MPR as a means of adherence, due to the differences in measurement methods,

the results are difficult to compare across studies.

One study (Maciejewski, Farley, Parker, & Wansink, 2010) utilized a cost-

based intervention in which the researchers studied enrollees in a value-based

insurance design program, from which the sample of the intervention group was

drafted. These patients received significant copayment reductions based on the

type of medication prescribed and whether or not the medication was from a

name brand. The control group consisted of patients who were not enrolled in

the value program. Using a pre-post quasi-experimental study design,

Maciejewski et al. assessed changing MPR percentages for a series of

medications. For the purposes of this paper, the data for metformin was

specifically examined. The study showed that there was a significant decrease in

adherence for patients who were not enrolled in the copayment reduction

program (p<0.001). No clinical outcomes were assessed.

Four of the twelve studies can be classified as convenience-based

interventions. Of these four, two studied a change in method of insulin

administration (vial/syringe to pen) in order to increase adherence. Xie et al.

conducted a pre-post study with two groups of patients (Xie et al., 2013). The

intervention group switched to pens, while the control group continued to use

30

vials and syringes. Lee et al., on the other hand, studied the adherence rate of

one group of patients who had switched to pens (W. C. Lee, Balu, Cobden,

Joshi, & Pashos, 2006). Xie et al. demonstrated that there was a significant

difference between the intervention group and the control group in adjusted

MPRs after the switch (p<0.05). They also showed that persistence was

significantly longer for patients who had switched to the insulin pen (p<0.0001).

The other study showed that the percentage of patients who met the adherence

cutoff (MPR > 80%) increased significantly after switching to insulin pens

(p<0.01). While Lee et al. did not conduct studies on clinical outcome, Xie et al.

observed the change in patients’ HbA1c and found no significant difference

between the intervention group and the control group (p=0.09).

One of the other convenience-based intervention studies examined the

difference in adherence between once-daily and twice-daily dosages for Glipizide

(Dezii et al., 2002). The study demonstrated that adherence was significantly

greater in once-daily dosage patients (p<0.05). The last convenience-based

intervention study looked at differences in prescription medication acquisition

(Zhang et al., 2011). The intervention group used mail-order pharmacy services,

where prescription medications can be delivered to the patients’ residences

directly, while the control group utilized retail pharmacies. PDC (proportion of

days covered) was assessed, and it was found that the intervention group had a

significantly higher adherence rate (p<0.001). No clinical data was presented by

either of these two studies.

31

Five of the twelve articles fall under the category of communication-based

interventions. Patel et al. and Vervloet et al. conducted their studies with

electronic monitoring devices that could send reminders to patients during the

time in which they needed to take their daily medications (Patel et al., 2013;

Vervloet et al., 2012). Patel et al. performed a pre-post analysis and compared

the PDC ratio of patients before activation of the mobile application and during

the 30days in which the patient utilized the application. The difference between

the pre-activation adherence and the post-activation adherence was not

significant; however, it approached significance (p=0.057). They also had a

seven months follow-up where the adherence rate fell below the pre-activation

period. Vervloet et al. differed from Patel et al.’s study in that the former used a

control group that did not receive a reminder system. They measured “days

without dosing” and found that there was no significant difference between the

intervention and control groups (p=0.283). However, due to the functionality of

the electronic monitoring system, they were able to obtain time stamped

information regarding how readily the patient took the dosage within the allotted

time slot (ranged from 15 – 45 minutes). The difference between the percentage

of doses taken within this time slot between the intervention group and control

group was significant (I: 56.7± 23.8% vs. C: 43.2± 26.2% p=0.003). No clinical

data was obtained through these two studies.

The remaining three of the five communication-based interventions

articles focused on education and emotional support from healthcare providers.

32

Walker et al. examined the effect of telephone calls to patients from health

educators on medication adherence (Walker et al., 2011). The telephone calls

were made to the intervention group and provided information regarding

medication adherence and life style changes. The control group did not receive

telephone calls; however, both groups received printed educational materials.

MPR was calculated from refill data. Although specific numerical data was not

found on the article, the authors reported that there was no significant difference

between those who received phone calls and those who did not.

Shah et al. studied the effects of pharmacist discharge counseling on

adherence (Shah et al., 2013). The intervention group received counseling

regarding their disease status and medication regimen from pharmacists before

being discharged from the hospital, while the control group was not counseled.

The PDC calculated from refill data for the intervention group was significantly

higher than that of the control group (p<0.005). Clinical outcomes were taken

into consideration in the form of long-term glycemic control. The average HbA1c

for the intervention group turned out to be significantly lower that of the control

group (p<0.005).

Stanger et al. was the only T1DM specific study in the intervention study

pool reviewed by this paper (Stanger et al., 2013). This 14-week pre-post study

involved providing adolescents with T1DM motivational counseling in conjunction

with contingency management in order to improve blood glucose monitoring

frequency (BGM). Free glucometers and test strips were also provided to the

33

subjects. This study assessed adherence through self-reported daily frequency

of BGM as part of the treatment regimen. A baseline BGM was established prior

to the introduction of the intervention. It was found that the daily BGM frequency

increased significantly as a result of the intervention (p<0.001). As for the clinical

outcomes, HbA1c decreased significantly over the course of the study (p<0.0001).

Furthermore, a three-month follow-up showed no significant difference in HbA1c

compared to the measurements obtained right after the intervention (p=0.42).

Of the featured articles, two fit within the reward-based intervention

category. Both studies utilized financial incentives as the reward. Austin & Wolfe

studied patient populations that had not had HbA1c or LDL screenings in the past

year (Austin & Wolfe, 2011). Adherence to regular clinical screenings was

assessed through lab results on file. The intervention group received a $6 gas

station gift card every time a lab result was sent to the health-care provider, while

the control group did not receive anything. Although numerical data was not

presented in the article, the authors reported that the intervention group had

significantly higher average number of screenings. The clinical outcome had a

positive trend in HbA1c reduction associated with number of screenings but the

result bore no significance.

Long et al. conducted their research using two different interventions

(Long, Jahnle, Richardson, Loewenstein, & Volpp, 2012). Intervention group 1

was received telephone peer mentoring and the mentors were provided $20

incentive to talk with the subjects every month for six months. Intervention group

34

2 was a reward-based group where they received $100 for achieving 1%

reduction in HbA1c at the end of the six months and $200 for 2-6% reduction. A

control group was also established receiving regular care with the lack of peer

mentoring and financial incentives. Adherence data was obtained through

patient self-report. Intervention group 1 achieved a 79% adherence rate, and

intervention group 2 had an 80% adherence rate compared to the 67% achieved

by the control group. Although a p-value was not presented for the difference in

adherence, it is clear that the numbers are quite different between the

intervention groups and the control group. HbA1c was measured before and after

the study in an effort to assess clinical outcomes. Change in HbA1c was

presented in the article. Although there is no significant change in HbA1c for any

of the groups before and after the study (p>0.05), intervention group 1 had

achieved significantly higher reduction in HbA1c than the control group (p<0.01).

35

Table 7. Overview of intervention studies: Findings from the intervention studies including adherence and clinical outcomes

Reference N Intervention Control Adherence measure and results

Sig. Clinical outcomes

Sig.

(Maciejewski et al., 2010)

nI = 2201 nc= 2201

Enrollment in program for reducing copayment based on need, drug type (band vs. non-brand)

Not enrolled in the program. Normal copayment

MPR %decrease (Metformin) I: ∆ ~0% C: ∆ ~-3.8%

p<0.001 for the difference significant

-- --

(Xie et al., 2013)

nI = 603 nc= 603

Switching from vials/syringe to pens for insulin administration

Continued use of vials/syringe

MPR (adjusted for the difference b/w pen and vial) Intervention: Pre 0.86 ± 0.18 Post 0.79 Persistence: 65.3% Control: Pre 0.86 ± 0.17 Post 0.76 Persistence: 49.8%

MPR post p=0.017 Significant Persistence p<0.0001 significant

∆ HbA1c% I: -0.04% C: -0.24%

p=0.09 Not significant

(W. C. Lee et al., 2006)

n=1156 Pre-post analysis

After switching from vial/syringe to pens for insulin administration

Before switching to pen

MPR (>80% as adherent) I: 54.6% C: 36.1%

p<0.01 significant

-- --

(Dezii et al., 2002)

nOD =746 nBID = 246

Once-daily dosage for Glipizide (OD)

Twice-daily dosage for Glipizide (BID)

Adherence Index (similar to MPR) OD: 60.5% BID: 52%

p=0.027 significant

-- --

I = intervention group C = control group

36

Table 7 continued. Overview of intervention studies

Reference N Intervention Control Adherence measure and results

Sig. Clinical outcomes

Sig.

(Zhang et al., 2011)

nI=1361 nC=1361

Mail-order pharmacy Retail pharmacy

PDC* (same as MPR) I: 49.7% C: 42.8%

p<0.001

-- --

(Patel et al., 2013)

n=50 Pre-post study

I1: During the activation of Pill Phone (Mobile application for medication reminder) I2: Post activation of Pill Phone

Pre-activation of Pill Phone

PDC (same as MPR) I1: 0.58 I2: 0.46 C: 0.54

C vs. I1 p=0.057 approaches significance -- --

(Vervloet et al., 2012)

nI=56 nC=48

Real time medication monitoring system that has customized SMS medication reminder system built in.

Regular care without the real time medication monitoring system

Days without dosing I: 11.9 ± 18.8 C: 13.8 ± 14.5 Dose taken within time period (range from 15min to 45min) I: 56.7% ± 23.8% C: 43.2% ± 26.2%

p=0.283 not significant p=0.003 significant

-- --

(Walker et al., 2011)

nI=262 nc=264

Telephone calls from health educators about medication adherence and life style changes.

Regular care with printed educational materials (intervention group also received printed materials)

MPR Numerical data not presented

p=0.23 for insulin users p=0.39 for non-insulin users Not significant

-- --

*PDC = Proportion of days covered

37

Table 7 continued. Overview of intervention studies

Reference N Intervention Control Adherence measure and results

Sig. Clinical outcomes

Sig.

(Shah et al., 2013)

nI = 64 nc = 64

Pharmacist discharge counseling

Regular care

PDC (same as MPR) I: 55.2% ± 42.0% C: 34.8% ± 37.9%

p=0.004 significant

HbA1c (%) I: 7.8±1.6 C: 9.5±2.9

p=0.003

(Stanger et al., 2013)

n=17 Pre-post study with a follow-up Adolescents with T1DM

Motivational interview and contingency management that targeted increased Blood Glucose Monitoring (BGM). Free glucometer and test strips were provided

Regular care before intervention

Self and parent-report of BGM Average frequency of BGM per day Pre: 4.1±1.9 Post: 6.3±2.0

p<0.001 HbA1c (%) Pre: 11.6 ± 2.5 Post: 9.1 ± 0.9 3m Follow-up: 9.8 ± 1.4

Pre vs. Post p<0.0001 Significant Post vs. Follow-up p=0.42 Not significant

(Austin & Wolfe, 2011)

nI = 464 nc = 2101 Patients lacking HbA1c screening

$6 gas gift card incentive for each lab result received by doctors.

Regular care

Avg number of HbA1c screenings I: 3.34 C: 2.69

Significant difference, p value not given

Positive trend

No significance

(Long et al., 2012)

n1 = 38 n2 = 40 nc = 39 African American veterans with poor diabetes control

I1: Telephone peer mentoring. I2: At the end of six months, $100 for 1% decrease in HbA1c and $200 for 2-6% decrease in HbA1c

Regular care

% of patient with self-reported good adherence I1: 79% I2: 80% C: 67%

Not given ∆HbA1c(%) from baseline I1: -1.08 I2: -0.46 C: -0.01 ∆from C I1: -1.07 I2: -0.45

p>0.05 for all ∆HbA1c from baseline. p<0.01 for I1 vs. C p=0.25 for I2 vs. C

38

Adherence studies on non-diabetic diseases

Many articles were found of adherence studies on non-diabetic disease.

Some of the studies on diseases most relevant to diabetes such as hypertension

and cardiovascular diseases were briefly reviewed.

Irvine et al. examined the difference in adherence and mortality between a

placebo group and a group using amiodarone therapy. Subjects are mainly

acute myocardial infarction survivors. Adherence was measured through pill

count and patients who have an average-count less than the 20th percentile were

considered to be poorly-adherent. The study found that those who are poorly-

adherent in both the amiodarone group and the placebo group had a significantly

increased risk for sudden cardiac death (p<0.01, p<0.05 respectively), total

cardiac mortality (p<0.01, p<0.02), and all-cause mortality (p<0.004, p<0.001).

The study identified that age above 70 years old was a predictor of poor

adherence to placebo (p<0.03). They have also found that the lack of social

activities was also correlated with reduced adherence (p<0.05) (Irvine et al.,

1999).

In a study for hypertension, Friedman et al. conducted an experiment to

assess the effect of a telecommunications system that monitored and counseled

patients on adherence to antihypertensive medications as well as blood pressure

control (Friedman et al., 1996). The control group received usual care with no

telephone systems. Adherence data was evaluated through pill count of

antihypertensive medications. Mean adherence improved significantly for the

39

intervention groups compared to the controls (p<0.05). Blood pressure was

assessed as a mean for clinical outcomes. The mean diastolic blood pressure

decreased significantly for intervention group compared to the controls as well

(p<0.05).

40

DISCUSSION

Baseline adherence rate and HbA1c

The first part of this review was focused on nonintervention diabetic

adherence studies that either helped to establish a baseline adherence rate or

provided insight to certain factors that interfere with adherence. One of the

biggest problems with the studies provided is the lack of standardization in

adherence measurement and analytic strategies. The self-report method

presented the greatest variety in evaluation of adherence. In the seven self-

reported studies, three utilized the Morisky scale of adherence. Although the use

of Morisky scales has provided these three studies with some standardization,

the presentation of results was completely different amongst the studies. Bailey

et al. classified a score of 0-6 as non-adherent and 7-8 as adherent, whereas Al-

Qazaz et al. only provided a median score with no classification.

Studies that used refill data as a source of adherence measurement

showed more uniformity in analysis strategies. With the exception of one, all of

these studies calculated some form of the medication possession ratio and set

the cut-off for whether a subject is considered adherent or not at >80%.

Out of the fourteen articles reviewed in this category, ten articles

evaluated adherence as categorical variables in the form of self-reported status,

Morisky scale scores, or dichotomous variable with a percentage cutoff. These

studies were able to provide percentages of subjects that were adherent during

the study and thus the data was compared. The total average of the proportion

41

of subjects who were considered to be adherent across the ten studies is 61.4 ±

15.8%. This number is slightly lower than the previously reported average

adherence rate of 70% (DiMatteo, 2004); however, this could be due to the fact

that only a limited number of articles were reviewed in this paper.

Since patient self-report has a tendency to overestimate adherence rate

(McDonald, Garg, & Haynes, 2002; Vermeire et al., 2001), the average

adherence rate from the self-reported studies were compared to the average

calculated using refill data. The two averages were not significantly different,

indicating that self-report was just as effective at assessing adherence rate as

the refill data. However, due to the limited number of studies reviewed, this

result lacks power in determining the true difference.

Nine studies out of the twelve provided data on HbA1c. Variation in data

analysis and presentation has once again limited the number of studies that can

be compared. The weighted mean HbA1c of subjects in six studies is 7.9 ± 1.5%

and the data ranged from 7.6% to 8.6%. This number is feasible considering that

the subjects studied are diagnosed with DM (>6.5% HbA1c).

Poor adherence and barriers to adherence

Four studies have confirmed that increased medication adherence rate is

associated with improved long term metabolic control (Aikens & Piette, 2013; Al-

Qazaz et al., 2011; Donnelly et al., 2007; Pladevall et al., 2004); however, it is

unclear whether medication adherence is the main factor contributing to the

42

change in glycemic control. Donnelly et al. has found no significant association

between glycemic control with age, gender, or social deprivation. Since there are

previous reports that old age (>70yrs) is a predictor of poor-adherence (Irvine et

al., 1999), the discrepancy might be due to the study design and patient

population.

Some predictors of poor-adherence are identified through the studies

presented. Identification of patients with poor-adherence would be helpful to

healthcare providers in order to provide interventions. Depression and race

(African American and Mexican American) are shown to be significantly

associated with poor-adherence (Ciechanowski et al., 2000; Lin et al., 2004;

Ngo-Metzger et al., 2012; Osborn et al., 2011); however, these factors are

merely associated with decreased adherence with no demonstrated causation

relationship. For example, Ngo-Metzger et al. found that Mexican Americans

had significantly lower adherence rates that correlated with their overall lower

household income and higher reported financial barriers. In the same study, they

found that financial barriers are associated with lower adherence and higher

HbA1c (p<0.001). Other studies also found that higher healthcare cost is a barrier

to adherence along with other factors such as lack of refills, poor health status,

and poor diet (Bailey et al., 2012; Ciechanowski et al., 2000).

In the case of Osborn et al.’s study, where African Americans had lower

adherence rate compared to whites, they hypothesized that diabetes literacy and

numeracy might be the barrier causing the difference in adherence. The

43

research found significant evidence that low health literacy in African Americans

was one of the reasons explaining the lowered adherence rate, but no clinical

evidence was examined.

Factors that were studied but presented no significant correlation to

adherence are polypharmacy number greater than 1 (number of prescribed

medications is greater than 1), diabetes numeracy, and the utilization of

electronic medication monitoring devices (Grant et al., 2003; Nagrebetsky et al.,

2012; Osborn et al., 2011). Although all three studies had sample sizes (n)

greater than 60, the study could still present biases and confounding variables

that make the factors seem irrelevant even if it is.

Intervention

Twelve intervention studies were reviewed that included at least one of

each of the intervention types discussed in the introduction. Five of the studies

presented both medication adherence and clinical outcomes in the form of HbA1c.

Two studies showed no significant change to the adherence and two other

studies showed no significant change to the long-term glycemic control after

subjects had received the intervention. Three studies used pre-post study

designs and assessed the difference before the intervention and after the

intervention with the same group of subjects. These studies could be

underestimating the effects of the intervention due to the lack of control

44

comparison and the additional effects that time could have on the adherence

rate.

There are several problems with intervention studies as a whole. First of

all, the subject selection process is flawed. Although many studies randomly

select a list of patients who qualify for their study, only the patients who are

voluntarily choosing to participate are recruited. This lack of true randomization

means the intervention studies might fail to evaluate the patient population that

are unwilling to seek care. It would be better to design a study where patients

who are overdue for medical attention be contacted and given information

regarding the study and intervention so that more patients of that type could be

consented into participating in the study.

Secondly, adherence data are often gathered during the intervention

process or a short amount of time following the intervention. It is rare to see any

studies address the long-term benefits of the intervention, or even present data

on what happens when the intervention is terminated. Most follow-up periods are

around three months to six months and one can already observe a decrease in

the previously increasing adherence rate (Patel et al., 2013; Stanger et al.,

2013). Since diabetes is a chronic disease, an intervention should focus on

having a long lasting effect. This means that both interventions and follow-ups

have to last sufficiently long in order to gather meaningful results regarding

adherence.

45

In addition, a number of studies had sample sizes that are too small. A

study with small sample size would lack the power to distinguish differences in

the data. One can calculate the required sample size through tables or statistical

methods. Without an adequately large sample size, it would be difficult to

interpret the data and extrapolate the findings.

It was surprising to see that both studies that focused on electronic

reminder devices failed to make significant differences in adherence rate (Patel

et al., 2013; Vervloet et al., 2012); however, upon closer examination, it seems

that the devices were able to produce significant increase in doses taken by the

patients within the recommended time period. This means that, although the

device did not help increase long term adherence or persistence, it was able to

help patients take their medication at the right time during the day. This could be

very useful for complex medication regimens that require multiple dosages within

a day.

The study done by Long et al. showed rather interesting results. One of

the intervention groups received peer counseling and the mentors received

monthly financial incentives to carry out the peer counseling, while the other

intervention group received one incentive at the very end of the study for

reducing HbA1c. Both groups had significantly higher adherence rate compared

to the control group at the end of the study. However, the clinical outcomes did

not quite match the adherence data. The group that received peer counseling

had significantly lower HbA1c than the control group, but the average HbA1c of the

46

group that received a final incentive was not significantly different from that of the

control. Although this outcome can be interpreted in many ways, it could suggest

that while the second intervention helped increasing adherence rate, some other

factor might have more influence on improving glycemic control, such as the

emotional aspect of interacting with another human being through counseling.

However, the lack of monthly financial incentives given to the patients provides

no assessment of whether financial incentives could work the same way as the

monthly peer mentoring if provided in a shorter period in between.

This idea that adherence rate is not the only factor affecting clinical

outcomes can be further demonstrated by the study conducted by Irvine et al.,

where poor adherence in both the placebo group and the medication group

increased mortality rate significantly. This result suggests that poor adherence to

medication is only part of the problem; there is a greater underlying behavioral

factor that is hindering the effectiveness of treatments.

47

CONCLUSION

Poor adherence is common in patients with DM and other chronic

diseases that require extensive self-management. This behavior has been linked

to increased complications, mortality rate, and health care costs. Although old

age, race, and symptoms of depression are predictors of poor adherence, high

health care costs, poor health status, and poor diet act as barriers preventing

patients from being compliant with their medication regimens.

Much effort has been put into finding ways to improve adherence, and

these interventions have shown varied results in altering medication possession

rates. One of the biggest problems with studies in this field is the lack of

standardized methods in measuring true adherence. The use of pharmacy and

patient self-report data might be more cost-effective, but they provide no

assessment on whether a patient has actually taken the medication. It would be

critical for future studies to incorporate more direct methods such as blood and

urine tests to monitor the actual adherence rate of subjects.

The lack of a meaningful measurement of true adherence also renders the

studies on clinical outcomes less conclusive. From the reviewed intervention

literatures, only three have shown significant improvement in HbA1c. All three

studies used communication based interventions that focused on changing the

subjects’ behaviors as a whole. Although both adherence rate and glycemic

control have improved in these cases, there is no indication that the improved

adherence rate was the cause of these positive clinical outcomes. Furthermore,

48

while some interventions can provide a short-term improvement, there is no

evidence that these interventions can maintain the positive effects consistently

for a long period of time. Since diabetes mellitus is a chronic disease, it would be

important to design interventions that could maintain long-term effects.

The most important question to consider is whether changing medication

adherence alone is enough to solve the problems posed by poor patient self-

care. Perhaps the focus should be shifted towards identifying factors that could

provide enough motivation to the patients to change their behavior and ultimately

improve their health.

49

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57

VITA

SENMIAO ZHAN

Address: 5915 Lyon farm drive Durham, NC 27713 Telephone: (919) 452-3661 Email: sz16@bu.edu Year of Birth: 1988 Education: Duke University, Durham, NC

Bachelor in Sciences in Chemistry with a concentration in Pharmacology; May 15, 2011 with Distinction Boston University School of Medicine, Boston, MA Masters of Science in Medical Sciences; May, 2014 Curriculum Highlights Histology, Physiology, Organic Chemistry, Analytical Chemistry, Physical Chemistry, Mammalian Toxicology, Biochemistry, Pathology

Research Experience

09/2013 – present Boston Medical Center Dr. Elizabeth Rourke, MD Massachusetts

Served as a research assistant in a diabetic medication adherence study

08/2009 – 05/2011 Duke University Medical Center Dr. Hiroaki Matsunami North Carolina

Conducted independent study on odorant receptors in mice / Specific skills include: Polymer Chain Reaction, Electrophoresis, DNA mini-prep, Gene sequencing, Cell culture, Cell surface staining, Florescence Activated Cell Sorting, Calcium Imaging, Western Blot, Protein purification

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Clinical Experiences

09/2012 – 05/2013 Volunteer Boston Health Care for the Homeless Program Boston, Massachusetts

Volunteered as a front desk assistant taking phone calls and conducting patient satisfaction surveys

05/2011 – 06/2011 Volunteer 2nd Affiliated Hospital of Shandong University Shandong, China

Volunteered in the pediatrics department conducting simple tasks such as measuring weights and heights of patients

03/2011 – 03/2011 Shadowing Triad Neurological Associates Winston-Salem, North Carolina

Observed Dr. Giles F. Crowell as he examined patients

Work Experiences

09/2007 – 07/2012 Duke University Duke Center for Science Education Durham, North Carolina

Served as a work study student at first, then as a full time Science Education Intern to work on educational materials for high school students

Publications

(2013) Dey, Sandeepa, Zhan, Senmiao, & Matsunami, Hiroaki. (2013). A Protocol for Heterologous Expression and Functional Assay for Mouse Pheromone Receptors Pheromone Signaling (pp. 121-131): Springer.

(2013) Mainland, Joel D, Keller, Andreas, Li, Yun R, Zhou,

Ting, Trimmer, Casey, Snyder, Lindsey L, . . . Zhan, Senmiao… (2013). The missense of smell: functional

59

variability in the human odorant receptor repertoire. Nature neuroscience.

(2011) Dey, S., Zhan, S., & Matsunami, H. (2011). Assaying

surface expression of chemosensory receptors in heterologous cells. J Vis Exp(48). doi: 10.3791/2405