bmj openfor peer review only 25 16. lin l-k, sun y, heng bh, chew dek, chong p-n. medication...

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For peer review onlyOral Antidiabetic Medication Adherence and Glycemic

Control among Type 2 Diabetes Mellitus in Saudi Arabia

Journal: BMJ Open

Manuscript ID bmjopen-2019-029280

Article Type: Research

Date Submitted by the Author: 25-Jan-2019

Complete List of Authors: Balkhi, Bander; King Saud University, College of PharmacyAlwhaibi, Monira; King Saud University, College of PharmacyAlqahtani, Nasser ; Saudi Food and Drug Authority, Riyadh, Saudi ArabiaAlhawassi, Tariq ; Medication Safety Research Chair, College of Pharmacy, King Saud University, Riyadh, Saudi Arabia Alshammari, Thamir; Department of Clinical Pharmacy, College of Pharmacy, University of Hail, Hail, Saudi Arabia; 2 Medication Safety Research Chair, College of Pharmacy, King Saud University, Riyadh, Saudi Arabia Mahmoud, Mansour; 6Clinical and Hospital Pharmacy Department, College of Pharmacy, Taibah University, Al-Madinah Al-Munawara, Saudi ArabiaAlmetwazi, Mansour ; 1 Department of Clinical Pharmacy, College of Pharmacy, King Saud University, Riyadh, Saudi ArabiaAta, Sondus ; 5 Investigational Drugs and Research Unit, King Khalid University Hospital, Riyadh, Saudi Arabia Kamal, Khalid; Duquesne University School of Pharmacy, Division of Pharmaceutical, Administrative and Social Sciences

Keywords: Diabetes, oral antidiabetic drugs (OADs), adherence, electronic health records (EHR), medication possession ratio (MPR)

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Oral Antidiabetic Medication Adherence and Glycemic Control among Type 2 Diabetes Mellitus in Saudi ArabiaBander Balkhi, Ph.D1,2*, Monira Alwhaibi, Ph.D1,2, Nasser Alqahtani, Ph.D2,3, Tariq M. Alhawassi, Ph.D1,2,5, Thamir M. Alshammari, Ph.D2,3,4, Mansour A. Mahmoud, Ph.D6, Mansour Almetwazi, Ph.D1,2, Sondus Ata, MS5, Khalid M. Kamal7

1 Department of Clinical Pharmacy, College of Pharmacy, King Saud University, Riyadh, Saudi Arabia2 Medication Safety Research Chair, College of Pharmacy, King Saud University, Riyadh, Saudi Arabia 3 Saudi Food and Drug Authority, Riyadh, Saudi Arabia4 Department of Clinical Pharmacy, College of Pharmacy, University of Hail, Hail, Saudi Arabia5 Investigational Drugs and Research Unit, King Khalid University Hospital, Riyadh, Saudi Arabia 6Clinical and Hospital Pharmacy Department, College of Pharmacy, Taibah University,

Al-Madinah Al-Munawara, Saudi Arabia7 Division of Pharmaceutical, Administrative and Social Sciences, Duquesne University School of Pharmacy, Pittsburgh, PA, US

*Corresponding author: Bander Balkhi, Pharm.D, Ph.D.Assistant professor, College of Pharmacy, King Saud UniversityP. O. Box 2457 Riyadh 11451, Saudi ArabiaTel.: +966114691878 Email: [email protected]

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ABSTRACT

Objectives: To assess the impact of adherence rates of oral antidiabetic drugs (OADs) on

glycemic control in patients with T2DM and to examine the predictors associated with

nonadherence and medication oversupply.

Design: Cross sectional retrospective study

Setting: Large tertiary hospital in the central region of Saudi Arabia.

Participants: 5,457 patients aged 18 years and older with type 2 diabetes mellitus (T2DM)

during the period (January 1, 2016 to December 31, 2016)

Primary and secondary outcome measures: The Medication Possession Ratio (MPR) was

calculated as a proxy measure for adherence of OADs. The independent predictors of

nonadherence and medication oversupply were assessed using multinomial logistic regression

models. The secondary outcomes were to measure the association between OADs adherence and

glycemic control.

Results: Majority of the patients with T2DM were females (n=3,400, 62.3%). The average

glycated hemoglobin (HbA1c) was 8.2+1.67. The adherence levels for OADs ranged from 48%

(MPR>0.8, good adherence) to 42% (MPR<0.8, poor adherence) with 8.6% indicating

medication oversupply (MPR>1.2). In the multivariate analysis, women with T2DM were more

likely to have poor adherence (AOR=0.76, 95% CI=0.67, 0.86) compared to men. Also,

medication oversupply was more likely among patients with hyperpolypharmacy (AOR=1.88,

95% CI=1.36, 2.63), comorbid osteoarthritis (AOR=1.72, 95% CI=1.20, 02.45), and non-Saudi

patients (AOR=1.53, 95% CI=1.16, 2.01).

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Conclusion: The study findings support the growing concern of nonadherence to OADs among

patients with T2DM in Saudi Arabia. Decision makers have to invest in behavioral interventions

that will boost the adherence rates to medications. This is particularly important in patients with

polypharmacy and high burden of comorbid conditions.

Keywords: Diabetes; oral antidiabetic drugs (OADs); adherence; medication possession ratio

(MPR); electronic health records (EHR).

Strengths and Limitations of this Study

This study provides a real insight into the current status of medication adherence rates

among patient with T2DM in Saudi Arabia.

Using real-world data from more than 5,000 patients with T2DM in Saudi Arabia, the

study assesses the impact of adherence among different patient subgroups.

This study did not control for the severity of diabetes or diabetes complications which

may affect the rate of adherence to oral antidiabetic drugs (OADs)

This study indirectly measured adherence to OADs using patient electronic health

records, which may not reflect the actual adherence rate.

Findings from this study cannot be generalized to different populations and settings.

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INTRODUCTION

Type 2 diabetes mellitus (T2DM) is a highly prevalent chronic progressive disorder

characterized by high glucose levels in the blood (1). The estimated global prevalence in the

adult population in 2015 was 8.8% (2) and is projected to increase to 10.4% by 2040 (2). In

Saudi Arabia, the estimated prevalence of T2DM is 23% and is projected to rise to 24.5% by

2035 (3, 4). While DM will be the 7th leading cause of mortality and disability worldwide by

2030 (5), it is already the 6th leading cause of death in Saudi Arabia based on the World Health

Organization (WHO) report (6). Uncontrolled T2DM is associated with negative health

consequences such as blindness, kidney failure, lower limb amputation and other complications,

all of which result in poor quality of life in patients (7, 8). Also, T2DM is a well-established risk

factor for cardiovascular disease (CVD) and macrovascular complications including costly

conditions like coronary heart disease, stroke, nephropathy, retinopathy, and others (9, 10). A

meta-analysis concluded that the risk of CVD in patients with diabetes was three times compared

to those without diabetes (10).

The cost of T2DM on the health care system is enormous. A recent study estimated the

cost of DM worldwide in adult patients at US$ 1.31 trillion, which represents 1.8% of the global

gross domestic product (11). Two-thirds of this cost was direct medical cost, and the remaining

one-third was attributable to indirect cost such as loss in productivity (11). Even in the Middle

East and North Africa region, there will be a projected increase of 67% in the healthcare cost due

to diabetes by 2045. (3). Given the high prevalence of the disease in Saudi Arabia, the economic

burden is substantial with an estimated direct cost of 17 billion Saudi Riyals (US $4.5 billion) in

2014, which is expected to increase to 43 billion Saudi Riyals (US $ 11.4 billion) in the future

(12).

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Patient adherence to recommended treatment regimen is one of the key contributors to

quality health outcomes in T2DM. The benefits of drug therapy in terms of improved glycemic

control and subsequent reduction in microvascular, macrovascular complications, and morbidity

have been fairly demonstrated. Adherence to oral antidiabetic drugs (OADs) is associated with

better glycemic control (13, 14), reduced risk of diabetes complications, and reduced economic

burden (15, 16). Investigators in Spain reported that a change in glycated hemoglobin (HbA1c)

between the first and last patient visits was largely driven by OADs adherence in 13% of the

cases (17). In Saudi Arabia, nearly 56% of patients have been shown to have low medication

adherence (18).

Although poor adherence (medication underuse) is a well-recognized issue in any health

care setting, having excess supply of medications than needed (medication oversupply) may also

lead to negative health outcomes such as increase in toxicity risks, inefficient use of available

health resources, and increase in unnecessary health care costs (19). Prevalence of oversupply is

varied across different institutions, medication classes, and countries and ranged from 11% to

53% (20). Medication oversupply was common especially among T2DM patients which may

lead to increase the risk of hospitalization (21-24) and further, influence achieving the

recommended level of HbA1c (25).

With the prevalence of T2DM increasing at an alarming rate in Saudi Arabia, there is an

urgent need to address nonadherence to positive interventions, such as the OADs, so as to reign

in the rapid escalation of health care costs. Although several studies have measured the

adherence and oversupply rate in diabetic patients, their prevalence among patient with T2DM in

Saudi Arabia along with their impact on glycemic control is largely unknown. (26-29). Thus, the

goal of the study is to assess the impact of adherence rates of OADs on glycemic control in

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patients with T2DM and to examine the predictors associated with nonadherence and medication

oversupply.

Methods

Study Design and Setting

A retrospective study was conducted in King Saud University Medical City (KSUMC),

the largest tertiary teaching hospital located in the central region of Saudi Arabia. KSUMC is

equipped with more than 1,200 beds and provides a wide array of medical services. The patient

population is composed mainly of Saudi citizens who are predominantly residents of the capital

city Riyadh but the hospital also serves as a referral center for the whole country. The study was

approved by the Institutional review board (IRB) at KSUMC (IRB number: E-16-2203).

Data Sources

Patients with a recorded diagnosis of T2DM (using International Classifications of

Diseases – 9th edition, Clinical Modification (ICD-9-CM) codes) and receiving OADs at

outpatient clinics of KSUMC were retrospectively identified from the electronic health records

(EHRs) for the period January 1 to December 30, 2016. The extracted data included

demographic information (age, gender, marital status, nationality), laboratory data (HbA1c), and

prescription data (name of prescription filled; dispensing date; quantity of drug; the number of

days supplied and refills).

Study Population

Patients aged 18 years and older with T2DM who had received their treatment at

outpatient clinics at KSUMC during the study period and had at least two prescription fills for

one of the following OADs: sulfonylureas (glibenclamide); biguanides (metformin),

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thiazolidinediones (pioglitazone), meglitinide analogs (repaglinide), glucosidase inhibitor

(acarbose), glucagon-like peptide-1 receptor agonist (sitagliptin) and combination therapy were

included in the study. Patients on insulin or incretin mimetics (liraglutide injection) and those

without at least one HbA1C value were excluded from this study.

Patient and public involvement

Patients and public were not involved in the design or conduct of this study.

Outcome Measures

Primary Outcome - Adherence to OADs

The modified Medication Possession Ratio (MPRm) was used as a proxy to measure the

adherence rate in this study. MPRm in a standard tool that has been commonly reported in the

adherence literature using EHRs and administrative claims data (30, 31). MPRm was calculated

as the sum of the total days' supply for all OADs fills divided by the sum of the number of days

covered and the last refill days [18-19]. The date of the first prescription fill was designated as

the index date. The total days supplied for each OAD were calculated from the index date until

the end of 2016. Patients were considered adherent to their medication regimen if the estimated

MPRm ≥0.8. Otherwise, the patients were deemed as poorly adherent. The MPRm value greater

than one indicates that the patients filled their medications early before entirely consuming their

preceding stock of medications, and thus, had excessive medications with them than needed.

Medication oversupply was defined as MPRm >1.2; the cutoff point of 20% difference in supply

has been reported as an acceptable range in several studies (21, 23). In this study, adherence was

categorized into: poor adherence (MPRm < 0.8), good adherence (MPRm> 0.8) and oversupply

(MPRm >1.2) (32). The MPRm was calculated separately for each OADs prescribed during the

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12-month study period. Then, an average MPRm was calculated with equal weighting of each

drug class.

Secondary Outcome - Association between OADs adherence and Glycaemic Control

The secondary outcome of glycemic control was based on the patient’s last HbA1c

reading. The American Diabetes Association (ADA) Standards of Care has recommended that

HbA1c <7% should be the glycemic goal for adults with T2DM (33). Using this threshold, the

HbA1c was classified into two categories: <7% indicating good glycemic control and >7%

indicating poor glycemic control. The last HbA1c reading was used to determine the relationship

between adherence and glycemic control.

Independent Variables

Independent variables included age groups, gender, marital status, nationality, and

diagnosed comorbid chronic conditions (hypertension, heart failure, ischemic heart disease,

dyslipidemia, cancer, chronic kidney disease, asthma, osteoarthritis, osteoporosis, depression and

anxiety) (34, 35). Additionally, polypharmacy was calculated for each patient in this study.

Although there is no consensus on the threshold regarding the number of medications used by

patients to be considered as polypharmacy, the most accepted definition of polypharmacy is the

use of five or more drugs (36). Based on this threshold, polypharmacy was categorized as hyper

polypharmacy ( 10 medications), major polypharmacy (5-9 medications), and minor ≥

polypharmacy (2-4 medications).

Statistical Analysis

Descriptive statistics (frequency and percentages) were used to summarize the categorical

variables (sex, marital status, nationality, polypharmacy and co-existing chronic conditions) and

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means and standard deviations were calculated for continuous variables (age). Chi-square tests

were utilized to determine the factors associated with adherence. The predictors of nonadherence

and medication oversupply were assessed using multinomial logistic regression models after

adjusting for independent variables (age, gender, nationality, marital status and co-existing

chronic conditions). All statistical analyses were conducted using the Statistical Analysis

Software, version 9.2 (SAS® 9.2) and an a priori significance level was set at p<0.05.

Results

A total of 5,457 patients with T2DM were identified for year 2016 with a majority of them being

females (n=3,400, 62.3%) and adults 60 years and older (n=2,358; 43.2%). More than half of the

sample had co-existing chronic conditions; hypertension (65.6%) and dyslipidemia (66.0%)

being the most common conditions. Around 59.8% of patients had major polypharmacy with

18.7% having hyperpolypharmacy. The average glycated hemoglobin (HbA1c) was 8.2+1.67.

Table 1 presents the demographic and clinical characteristics of the study population. Overall,

the vast majority (89.2 %) of adults with T2DM were using metformin followed by sitagliptin

(23.5%), glibenclamide (16.5%), and pioglitazone (12.1%) (Table 2).

OADs Adherence Rates

Among the study population, 48.6% had good adherence (MPRm>0.8) and 42.8% have

poor adherence (MPRm<0.8) to OADs (Table 1). Around 8.6% had medication oversupply

(MPRm>1.2). A significantly higher rate of poor adherence was reported among women as

compared to men (45.2% vs. 38.9%, p value=0.0001). OADs oversupply was significantly

higher among patients with heart failure (24.2%), ischemic heart disease (16.8%), chronic kidney

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disease (21.6%), osteoarthritis (12.3%), anxiety (14.4%), and depression (11.0%) as compared to

those without these comorbid conditions. Medication oversupply was also significantly higher

among patients with hyperpolypharmacy as compared to those without polypharmacy (13.5% vs.

7.8%, p value =0.0001). The medication oversupply rate was highest for acarbose (17.1%)

followed by pioglitazone (15.4%) and glibenclamide (13.9%) (Refer Table 2).

OADs Adherence and Glycemic Control

Results show that good adherence was highest among those used repaglinide (71.0%)

followed by pioglitazone (65.0%) and sitagliptin (59.0%). Poor adherence was more common in

patients on metformin (48.1%) followed by those on combination therapy (46.6%) and on

glibenclamide (29.2%) (Table 2). This study has also found an association between the use of

OADs and glycemic control (Table 3). Good glycemic control (HbA1C <7%) was observed

among patients on metformin (24.1%) followed by combination therapy (12.0%) and

pioglitazone (11.5%).

Predictors of OADs Adherence

The adjusted odds ratios (AOR) and 95% confidence intervals (CI) from multinomial

logistic regressions on adherence to OADs are presented in Table 4. Several predictors were

identified: gender, nationality, co-existing chronic conditions, and polypharmacy. Women with

T2DM were less likely to have good adherence (AOR=0.76, 95% CI=0.67, 0.86) compared to

men. Medication oversupply was more likely among T2DM patients with hyperpolypharmacy

(AOR=1.88, 95% CI=1.36, 2.63), comorbid osteoarthritis (AOR=1.72, 95% CI=1.20, 02.45, and

non-Saudi patients (AOR=1.53, 95% CI=1.16, 2.01).

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Discussion

Medication adherence

The study utilized patients’ refill data from EHRs to measure their overall adherence to

OADs. The study found that patients with T2DM had an overall mean MPRm of 48.6% and

good adherence varied widely across different medications with the highest adherence observed

in patients on repaglinide (71.0%) followed by pioglitazone users (65.0%). Several studies have

demonstrated that patients on pioglitazone and sulfonylureas have a greater adherence rate than

those on metformin (37). A systematic review found that the rate of adherence in T2DM ranged

from 38% to 93%, with a few studies reporting adherence rate greater than 80%. The variation in

adherence was affected by the adherence measurement tools, population or institution where the

studies were conducted and the medications included in the studies (38).

Overall, 42.8% of patients were poorly adherent to OADs and poor adherence was more

common in metformin. Metformin is associated with some side effects such as flatulence and

diarrhea, which possibly results in poor adherence rates among patients (39). Also, there is

evidence that patients tend to be more adherent to monotherapy compared to combination

medications. Women were less likely to be adherent as compared to men. This finding was not

consistent with previous studies, which demonstrated that women significantly had higher

adherence rates (40-42). However, there is no supporting evidence in the literature that explains

why Saudi woman have poor adherence to OADs compared to men, and this warrants further

investigation.

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Medication Oversupply

Patients with excessive (20% or more) supply of their medications were considered as

having medication oversupply. The definition for oversupply has been used previously as it

allows patients to cover some of their medication loss or refill delays. The study found that

around 8.6% of the patients had medication oversupply, in particular, among those with heart

failure and with chronic kidney disease. Besides, there was a significant difference in patients

with hyperpolypharmacy who had medication oversupply versus those without polypharmacy. In

addition to the significant economic burden associated with it, oversupply of diabetes

medications may end up in accidental medication errors, which can possibly lead to negative

outcomes (43).

In the global literature, the current study results are consistent with those by Thorpe C

and colleagues (2015), which used data from Medicare and a local private payer and reported

medication oversupply of around 6.7% (44). However, our study results are lower than

Dilokthornsakul and colleagues (2014), which reported a higher prevalence of medication

oversupply (13.4%). Some of their results were similar to our study, especially in patients with

hyperpolypharmacy who were at a higher risk of medication oversupply compared to patients

with minor polypharmacy (21, 44). Stroupe and colleagues (2000) reported a very high

percentage of medication oversupply (47%). Another study has found similar results of high rate

of medication oversupply (23).

In general, medication oversupply could be a result of a number of factors - sicker

patients, issue with medication management system, poor medication reconciliation, patients

visiting multiple clinicians, and miscommunication between the patients and their clinicians (23,

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44). The current study identified some predictors of medication oversupply such as having

polypharmacy, male gender, and comorbid osteoarthritis. Also, patients having a prescription of

pioglitazone or acarbose were more likely to have medication oversupply. However, compared to

other studies reported in the literature, this study reports lower prevalence of medication

oversupply which can be attributed to electronic prescribing and utilization of medication

counseling and/or medication reconciliation, which can identify and prevent early refills. In

addition, KSUMC utilizes a 90-day-supply for chronic disease, which has been previously

reported as being more convenient to the patient and can improve patient adherence. However, it

may also increase the chance of the patient having an excess amount of medication, which can

lead to oversupply. This is especially the case when the treatment regimen of a patient has been

switched or changed. (44, 45).

Relationship Between Adherence to OADs and Glycemic Control

There is compelling data to show that glycemic control is essential in the management of

T2DM and has been shown to dramatically reduce diabetes-related complications. In the UK

Prospective Diabetes Study (UKPDS) trial; the microvascular complication rate was reduced by

25% and myocardial infarction (MI) by 16% in the intensive treatment arm (46). A meta-analysis

of four diabetes trials; Action to Control Cardiovascular Risk in Diabetes (ACCORD), Action in

Diabetes and Vascular Disease: Preterax and Diamicron Modified Release Controlled Evaluation

(ADVANCE), UK Prospective Diabetes Study (UKPDS), Veterans Affairs Diabetes Trial

(VADT) showed a 15% reduction of relative risk in non-fatal MI with every 1% decrease in

HbA1c (47). A retrospective cohort study reported that medication adherence was associated

with better glycemic control by one and a half folds when compared to non-adherent patients

(48). A cross-sectional registry-based study concluded that the reason behind not achieving the

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target HbA1c in approximately 22% of diabetic patients was poor adherence to OADs (49). One

additional study, which recruited a large cohort of OADs users, demonstrated that nearly 75% of

diabetic patients had moderate (46.4%) to good (28.8%) level of adherence, which was inversely

associated with poor glycemic control (50).

Poor glycemic control due to non-adherence to OADs can lead higher microvascular

complications (i.e., nephropathy, retinopathy, and neuropathy) (51), higher macrovascular

complications (i.e., myocardial infarction and ischemic stroke) (52), and high risk of mortality

(52). Besides, poor glycemic control due to non-adherence to OADs can lead to an increased

diabetes care cost. In the US, non-adherence to diabetes medications cost $337 billion in 2013.

And improving adherence rate resulted in cost savings of $661 million annually (when MPR

≥0.6) to $1.16 billion annually (when MPR = 1) (15, 16). Lin and colleagues (2017) reported

that poor adherence was associated with increased hospitalization and emergency department

visits with an odds ratio of 2.6 and 2.4, respectively (14). In Saudi Arabia, it has been estimated

that the economic burden of T2DM compliance and persistence for patients who struggle to

achieve the optimal therapy is around 3.9 billion Saudi Riyals (US $1.04 billion) and the cost is

expected to significantly increase in the future (53). The value of adherence to pharmacologic

treatments in achieving and controlling diabetes has clearly been established. Good adherence

can mitigate the risk of hospitalization, emergency room visits (54), all-cause mortality (55), and

could improve the overall glycemic control. In one study, HbA1c was reduced by 0.16% with

every 10% increment in medication adherence (56). In another study, a reduction in HbA1c by

0.05% was estimated with every 25% increase in OADs regimen adherence (13, 14). Achieving

and maintaining an optimal blood glucose level minimize many of the diabetes complications.

This study reported that 77.4% of patients with T2DM failed to achieve adequate glycemic

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control (HbA1c <7%) which is consistent with another published study in Saudi Arabia (57), but

higher than the average global HbA1c levels reported in the literature (45%) (58). A good

glycemic control varied across OADs, with better glycemic control observed in patients taking

metformin, combination therapy, and pioglitazone. In our study, no significant association

between achieving HbA1c target level of 7% and adherence level was seen. Also, the adherence

was higher when patients on metformin and sulfonylurea achieved the target HbA1c goal

compared with those that did not achieve the target level.

Strengths and limitations

Adherence can be measured by different methodological approaches. In the current study,

adherence was assessed using the MPRm method, which is a reliable method that depends on real-

world data obtained from patient’s medication refill history. This method can be used for a large

population at a relatively low costs and also prevents recall bias, which is associated with patient

interview-based adherence methods. This study sheds some light into concomitant diseases that

were associated with OADs nonadherence. Although the current study provides real-world

information from more than 5,400 patients about adherence rate to different OADs and the

relationship of OADs and glycemic control, few limitations were observed. The study population

was mostly patients who attended the KSUMC in central Saudi Arabia; therefore, it is difficult to

extrapolate the study findings to the whole Saudi population residing in the other geographical

areas of the country. Nevertheless, patients included in this study represent almost all Saudi

citizens and expatriates’ groups living in Saudi Arabia. In addition, important information such as

health literacy and educational level, which might be contributing factors to medication

nonadherence, were not collected. It should be noted that the adherence in this study was based on

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pharmacy refills data, which only indicates the possession of the medication, but cannot ensure the

actual consumption of the medication by the patient.

Future Implications

Health care professionals have to pay special attention to diabetic patients with

polypharmacy and concomitant diseases as this will increase the risk for nonadherence to OADs

medication. Although we have few patients with medication oversupply in our study, it is

worthwhile for health care providers and policymakers to be aware of this important issue and to

plan for a strategy to prevent both under and over-supply of medications. Further investigations

are needed to explore the oversupply across different populations and chronic diseases as the rate

could vary significantly. This will allow the estimation of financial loss due to medication

oversupply and can help implement effective strategies to prevent medication oversupply.

Conclusions

The study findings support the growing concern of nonadherence to OADs among patients with

T2DM in Saudi Arabia. Both providers and pharmacists, in their interaction with the patients,

should stress the importance of adherence and its impact on clinical, economic, and humanistic

outcomes and also focus on preventive measures such as lifestyle modifications. Decision

makers have to invest in behavioral interventions that will boost the adherence rates to

medications and reduce medication oversupply. This is particularly important in patients with

polypharmacy or hyperpolypharmacy and high burden of comorbid conditions.

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Acknowledgment

The project was fully supported financially by the Vice Deanship of Research Chairs, King Saud

University Riyadh, Saudi Arabia.

Contributors

BB, MA, NA, and TA participated in designing the study. SA, and MAM reviewed the proposal

and obtained ethical approval. BB and MA did the statistical analysis. BB, MA, TMT, and KMK

contributed to the interpretation of the results. BB, MA, NA, TMA, TA, MAM, MA, SA, KMK

drafted the manuscript, revised and approved the final version of this manuscript.

Funding This research received no specific grant from any funding agency in the public, commercial or not-for-profit sectors.

Competing Interests

None declared.

Patient consent

Not required.

Ethical Approval

The study was approved by the Institutional Review Board (IRB) protocol number (E-16-2203)

Data Sharing StatementNo additional data are available.

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Table 1Characteristics of the Study Population and Number and Row Percentage of Characteristics by Adherence level

among Adults with Diabetes

TotalPoor Adherence

Good Adherence Medication Oversupply

MPRm < 0.8 MPRm 0.8 <1.2 MPRm >1.2N % N % N % N % P value Sign.

Total 5,457 100.0 2,337 42.8 2,652 48.6 468.0 8.6Age Mean (SD) 58.2 (10.8) 57.8 (11.0) 58.5 (10.6) 58.3 (10.8)Age groups 0.089

18-29 39 0.7 15 38.5 20 51.3 4.0 10.330-39 220 4.0 107 48.6 96 43.6 17.0 7.740-49 759 13.9 362 47.7 338 44.5 59.0 7.850-59 2,081 38.1 884 42.5 1,017 48.9 180.0 8.6=>60 2,358 43.2 969 41.1 1,181 50.1 208.0 8.8

Marital Status 0.099Single 480 9.4 186 38.8 244 50.8 50.0 10.4Married 4,604 90.6 1,981 43.0 2,242 48.7 381.0 8.3

Gender <0.0001 ***Male 2,057 37.7 801 38.9 1,066 51.8 190.0 9.2Female 3,400 62.3 1,536 45.2 1,586 46.6 278.0 8.2

Nationality <0.0001 ***Saudi 4,830 88.7 2,105 43.6 2,335 48.3 390.0 8.1Non-Saudi 613 11.3 227 37.0 308 50.2 78.0 12.7

Obesity 0.542Yes 437 8.0 180 41.2 223 51.0 34.0 7.8No 5,020 92.0 2,157 43.0 2,429 48.4 434.0 8.6

Hypertension 0.340Yes 3,581 65.6 1,513 42.3 1,766 49.3 302.0 8.4No 1,876 34.4 824 43.9 886 47.2 166.0 8.8

Dyslipidemia 0.030 *Yes 3,603 66.0 1,556 43.2 1,764 49.0 283.0 7.9No 1,854 34.0 781 42.1 888 47.9 185.0 10.0

Heart Failure 0.005 **Yes 33 0.6 13 39.4 12 36.4 8.0 24.2No 5,424 99.4 2,324 42.8 2,640 48.7 460.0 8.5

Ischemic Heart disease <0.0001 ***Yes 161 3.0 67 41.6 67 41.6 27.0 16.8No 5,296 97.0 2,270 42.9 2,585 48.8 441.0 8.3

Chronic kidney disease 0.017 *Yes 37 0.7 14 37.8 15 40.5 8.0 21.6No 5,420 99.3 2,323 42.9 2,637 48.7 460.0 8.5

Cancer 0.961Yes 63 1.2 27 42.9 30 47.6 6.0 9.5No 5,394 98.8 2,310 42.8 2,622 48.6 462.0 8.6

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TotalPoor Adherence

Good Adherence Medication Oversupply

MPRm < 0.8 MPRm 0.8 <1.2 MPRm >1.2N % N % N % N % P value Sign.

Asthma 0.949Yes 571 10.5 244 42.7 276 48.3 51.0 8.9No 4,886 89.5 2,093 42.8 2,376 48.6 417.0 8.5

Osteoarthritis 0.009 **Yes 373 6.8 140 37.5 187 50.1 46.0 12.3No 5,084 93.2 2,197 43.2 2,465 48.5 422.0 8.3

Osteoporosis 0.189Yes 199 3.6 97 48.7 89 44.7 13.0 6.5No 5,258 96.4 2,240 42.6 2,563 48.7 455.0 8.7

Anxiety 0.001 **Yes 284 5.2 114 40.1 129 45.4 41.0 14.4No 5,173 94.8 2,223 43.0 2,523 48.8 427.0 8.3

Depression 0.640Yes 100 1.8 40 40.0 49 49.0 11.0 11.0No 5,357 98.2 2,297 42.9 2,603 48.6 457.0 8.5

Polypharmacy <0.0001 ***Hyperpolypharmacy 1,019 18.7 387 38.0 494 48.5 138.0 13.5Major polypharmacy 3,266 59.8 1,444 44.2 1,584 48.5 238.0 7.3Minor polypharmacy (Ref.) 1,172 21.5 506 43.2 574 49.0 92.0 7.8

Average HbA1C Mean (SD) 8.2 (1.67) 8.1 (1.67) 8.2 (1.66) 8.3 (1.66)Glycemic control

Good Glycemic Control 1,231 22.6Poor Glycemic Control 4,224 77.4

* Study Population Comprised of 5,457 Adults with diabetesN:Number; Sign: SignificanceAsterisks (*) represent significant differences based on MPRm from chi-sequare tests ***P< .001

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Table 2Frequency and Percentage of OADs

And Comparison between Medication Possession Ratio, Modified (MPRm )

N %Mean

Adherence SDPoor

AdherenceGood

AdherenceOversupply

rateAcarbose 205 3.8 0.97 0.31 24.9% 58.1% 17.1%Metformin 4,869 89.2 0.79 0.31 48.1% 43.3% 8.6%Glibenclamide 901 16.5 0.93 0.29 29.5% 56.6% 13.9%Sitagliptin 1,285 23.5 0.92 0.27 28.9% 59.9% 11.2%Repaglinide 46 0.8 0.97 0.21 17.5% 71.7% 10.8%Pioglitazone 662 12.1 0.97 0.27 19.6% 65.0% 15.4%Combination 410 7.5 0.84 0.32 46.6% 43.9% 9.5%

Note: Study Population Comprised of 5,457 Adults with diabetesN:Number; SD: Standard Deviation

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Table 3Association between OADs and Glycemic Control

Good Glycemic Control <7%

Poor Glycemic Control >7%

N % N %Chi square

value P value SigAcarbose 23.1 0.000 ***

Yes 18 8.8 187 91.2No 1,213 23.1 4,039 76.9

Metformin 60.8 0.000 ***Yes 1,173 24.1 3,696 75.9No 58 9.9 530 90.1

Glibenclamide 97.6 0.000 ***Yes 90 10.0 811 90.0No 1,141 25.0 3,415 75.0

Sitagliptin 172.1 0.000 ***Yes 118 9.2 1,167 90.8No 1,113 26.7 3,059 73.3

Repaglinide 5.1 0.024 *Yes 4 8.7 42 91.3No 1,227 22.7 4,184 77.3

Pioglitazone 52.9 0.000 ***Yes 76 11.5 586 88.5No 1,155 24.1 3,640 75.9

Combination Therapy 28.6 0.000 ***Yes 49 12.0 361 88.0No 1,182 23.4 3,865 76.6

Note: Study Population Comprised of 5,457 Adults with diabetes

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Table 4Adjusted Odds Ratios and 95% Confidence Intervals

From Multinomial Logistic Regression on Adherence among Adults with DiabetesGood Adherence OversupplyAOR 95% CI Sig. AOR 95% CI Sig.

Last HbA1CGood <7% 0.81 [0.70, 1.10] 0.90 [0.69, 1.17]Poor >7%

GenderFemale 0.76 [0.67, 0.86] ** 0.64 [ 0.49 , 0.83] **Male (Ref.)

NationalityNon-Saudi 1.15 [ 0.93 , 1.43] 1.53 [ 1.16 , 2.01] **Saudi (Ref.)

Ischemic Heart diseaseYes 0.78 [0.48 , 1.28] 1.37 [ 0.99 , 2.80] No (Ref.)

Heart FailureYes 0.78 [0.34 , 1.71] 1.77 [ 0.79 , 4.47]No (Ref.)

DyslipidemiaYes 1.06 [0.94 , 1.21] 0.83 [ 0.69 , 1.02]No (Ref.)

CKDYes 0.78 [0.42 , 1.81] 1.97 [ 0.79 , 4.48]No (Ref.)

OsteoarthritisYes 1.15 [0.92 , 1.81] 1.72 [ 1.20 , 2.45] **No (Ref.)

AnxietyYes 0.89 [ 0.64 , 1.25] 1.41 [ 0.95, 2.08]No (Ref.)

PolypharmacyHyperpolypharmacy 1.11 [ 0.86 , 1.41] 1.88 [ 1.36 , 2.63] *Major polypharmacy 1.04 [ 0.87 , 1.23] 1.27 [ 0.98 , 1.64]Minor polypharmacy (Ref.)

Note: Study Population Comprised of 5,457 Adults with diabetesRef: Reference GroupAsterisks (*) represent significant differences based on Adherence from multinomial logistic regressions with Poor adherence as the reference group ***P< .001

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STROBE Statement—Checklist of items that should be included in reports of cross-sectional studies

Item No Recommendation

PageNo

(a) Indicate the study’s design with a commonly used term in the title or the abstract

1Title and abstract 1

(b) Provide in the abstract an informative and balanced summary of what was done and what was found

1-2

IntroductionBackground/rationale 2 Explain the scientific background and rationale for the investigation being

reported3

Objectives 3 State specific objectives, including any prespecified hypotheses 5

MethodsStudy design 4 Present key elements of study design early in the paper 5Setting 5 Describe the setting, locations, and relevant dates, including periods of

recruitment, exposure, follow-up, and data collection5

Participants 6 (a) Give the eligibility criteria, and the sources and methods of selection of participants

5

Variables 7 Clearly define all outcomes, exposures, predictors, potential confounders, and effect modifiers. Give diagnostic criteria, if applicable

7

Data sources/ measurement

8* For each variable of interest, give sources of data and details of methods of assessment (measurement). Describe comparability of assessment methods if there is more than one group

6

Bias 9 Describe any efforts to address potential sources of biasStudy size 10 Explain how the study size was arrived atQuantitative variables 11 Explain how quantitative variables were handled in the analyses. If

applicable, describe which groupings were chosen and why(a) Describe all statistical methods, including those used to control for confounding

7-8

(b) Describe any methods used to examine subgroups and interactions 8(c) Explain how missing data were addressed 8(d) If applicable, describe analytical methods taking account of sampling strategy

8

Statistical methods 12

(e) Describe any sensitivity analyses

Results(a) Report numbers of individuals at each stage of study—eg numbers potentially eligible, examined for eligibility, confirmed eligible, included in the study, completing follow-up, and analysed

8

(b) Give reasons for non-participation at each stage

Participants 13*

(c) Consider use of a flow diagram(a) Give characteristics of study participants (eg demographic, clinical, social) and information on exposures and potential confounders

9Descriptive data 14*

(b) Indicate number of participants with missing data for each variable of interest

Outcome data 15* Report numbers of outcome events or summary measures 9Main results 16 (a) Give unadjusted estimates and, if applicable, confounder-adjusted

estimates and their precision (eg, 95% confidence interval). Make clear which confounders were adjusted for and why they were included

9

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(b) Report category boundaries when continuous variables were categorized(c) If relevant, consider translating estimates of relative risk into absolute risk for a meaningful time period

Other analyses 17 Report other analyses done—eg analyses of subgroups and interactions, and sensitivity analyses

DiscussionKey results 18 Summarise key results with reference to study objectives 10Limitations 19 Discuss limitations of the study, taking into account sources of potential

bias or imprecision. Discuss both direction and magnitude of any potential bias

14

Interpretation 20 Give a cautious overall interpretation of results considering objectives, limitations, multiplicity of analyses, results from similar studies, and other relevant evidence

10-12

Generalisability 21 Discuss the generalisability (external validity) of the study results 13

Other informationFunding 22 Give the source of funding and the role of the funders for the present study

and, if applicable, for the original study on which the present article is based

*Give information separately for exposed and unexposed groups.

Note: An Explanation and Elaboration article discusses each checklist item and gives methodological background and published examples of transparent reporting. The STROBE checklist is best used in conjunction with this article (freely available on the Web sites of PLoS Medicine at http://www.plosmedicine.org/, Annals of Internal Medicine at http://www.annals.org/, and Epidemiology at http://www.epidem.com/). Information on the STROBE Initiative is available at www.strobe-statement.org.

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Control among Type 2 Diabetes Mellitus: A Cross-Sectional Retrospective Study in a Tertiary Hospital in Saudi Arabia

Journal: BMJ Open

Manuscript ID bmjopen-2019-029280.R1


Date Submitted by the Author: 15-Apr-2019


<b>Primary Subject Heading</b>: Diabetes and endocrinology

Secondary Subject Heading: General practice / Family practice



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Oral Antidiabetic Medication Adherence and Glycemic Control among Type 2 Diabetes Mellitus: A Cross-Sectional Retrospective Study in a Tertiary Hospital in Saudi Arabia Bander Balkhi, Ph.D1,2*, Monira Alwhaibi, Ph.D1,2, Nasser Alqahtani, Ph.D2,3, Tariq M. Alhawassi, Ph.D1,2,5, Thamir M. Alshammari, Ph.D2,3,4, Mansour A. Mahmoud, Ph.D6, Mansour Almetwazi, Ph.D1,2, Sondus Ata, MS5, Khalid M. Kamal, M. Pharm., Ph.D7

1 Department of Clinical Pharmacy, College of Pharmacy, King Saud University, Riyadh, Saudi Arabia2 Medication Safety Research Chair, College of Pharmacy, King Saud University, Riyadh, Saudi Arabia 3 Saudi Food and Drug Authority, Riyadh, Saudi Arabia4 Department of Clinical Pharmacy, College of Pharmacy, University of Hail, Hail, Saudi Arabia5 Investigational Drugs and Research Unit, King Khalid University Hospital, Riyadh, Saudi Arabia 6Clinical and Hospital Pharmacy Department, College of Pharmacy, Taibah University,

Al-Madinah Al-Munawara, Saudi Arabia7 Division of Pharmaceutical, Administrative and Social Sciences, Duquesne University School of Pharmacy, Pittsburgh, PA, USA


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ABSTRACT

Objectives: The purpose of this study is to measure the adherence rates of oral antidiabetic drugs

(OADs) in patients with type 2 diabetes mellitus (T2DM) and assess the relationship of glycemic

control and adherence to OADs after controlling for other associated factors.

Design: Cross sectional retrospective study.


Participants: 5,457 patients aged 18 years and older diagnosed with T2DM during the period

(January 1, 2016 to December 31, 2016).

Primary and secondary outcome measures: The modified Medication Possession Ratio

(mMPR) was calculated as a proxy measure for adherence of OADs. The factors associated with

OADs nonadherence and medication oversupply were assessed using multinomial logistic

regression models. The secondary outcomes were to measure the association between OADs

adherence and glycemic control.

Results: Majority of patients with T2DM were females (n=3,400, 62.3%). The average glycated

hemoglobin (HbA1c) was 8.2+1.67. Among the study population, 48.6% had good adherence

(mMPR>0.8) and 8.6% had a medication oversupply (mMPR>1.2). Good adherence was highest

among those using repaglinide (71.0%) followed by pioglitazone (65.0%) and sitagliptin

(59.0%). In the multivariate analysis, women with T2DM were more likely to have poor

adherence (adjusted odds ratios [AOR]=0.76, 95% CI=0.67, 0.86) compared to men. Also,



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patients (AOR=1.53, 95% CI=1.16, 2.01). However, no association was found between glycemic

control and adherence to OADs.



that will boost medication adherence rates. This is particularly important in patients with







Using real-world data of more than 5,000 patients with T2DM in Saudi Arabia, the study

assesses the impact of adherence among different patient subgroups.

This study did not control the severity of diabetes or diabetes complications, which may

affect the rate of adherence to OADs.




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INTRODUCTION



adult population was 8.8% in 2015 (2) and is projected to increase to 10.4% by 2040 (2). In

Saudi Arabia, the estimated prevalence of T2DM is approximately 23% (3, 4) with another

25.5% of the population (30 years and older) classified as prediabetes. By 2035, the prevalence

of T2DM is Saudi Arabia is projected to double and is expected to reach an estimated 7.5 million

cases (5). While diabetes is projected be the 7th leading cause of mortality and disability

worldwide by 2030 (6), it is already the 6th leading cause of death in Saudi Arabia based on the

World Health Organization (WHO) report (7). It is well established that uncontrolled T2DM is

associated with negative health consequences such as blindness, kidney failure, lower limb

amputation and other complications, all of which result in poor quality of life in patients (8, 9).

Also, T2DM is a well-established risk factor for cardiovascular disease (CVD) and

macrovascular complications including costly conditions like coronary heart disease, stroke,

nephropathy, retinopathy, and others (10, 11). A meta-analysis concluded that the risk of CVD in

patients with diabetes was three times compared to those without diabetes (11).

Worldwide, diabetes imposes a large economic burden on the individuals and national

healthcare systems. A recent study estimated the global cost of diabetes in adult patients at US$

1.31 trillion, which represents 1.8% of the global gross domestic product (12). Two-third of this

cost was direct medical cost while the remaining one-third was attributable to indirect cost such

as loss in productivity (12). In the Middle East and North Africa regions, the rising prevalence of

diabetes is projected to increase the healthcare cost by 67% by 2045. (3). Similar trends are being

observed in Saudi Arabia as well and the medical healthcare expenditure for people with diabetes

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is 10 times higher ($3,686 vs. $380) than those without the condition (13). Diabetes and its

complications also result in high indirect costs, such as costs related to absenteeism, loss of

productivity, disability and premature mortality. The overall economic burden of diabetes in

Saudi Arabia is substantial with an estimated direct cost of 17 billion Saudi Riyals (US $4.5

billion) in 2014, which is expected to increase to 43 billion Saudi Riyals (US $ 11.4 billion) in

the future (14).









adherence (20).

Although poor adherence (medication underuse) is a well-recognized issue in any

healthcare setting, having excess supply of medications than needed (medication oversupply)

may also lead to negative health outcomes such as increase in toxicity risks, inefficient use of

available healthcare resources, and increase in unnecessary healthcare costs (21). Prevalence of

oversupply has been shown to vary across different institutions, medication classes, and

countries and ranges from 11% to 53% (22). Medication oversupply was common, especially

among T2DM patients, which may increase the risk of hospitalization (23-26) and further,

influence achieving the recommended level of HbA1c (27).

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urgent need to address nonadherence to positive interventions, such as OADs, so as to reign in

the rapid escalation of healthcare costs and improve patient outcomes. Although several studies

have measured the adherence and oversupply rate in patients with diabetes, their prevalence

among patients with T2DM in Saudi Arabia along with their impact on glycemic control is

largely unknown. (28-31). Thus, the purpose of this study is to measure the adherence rates of

OADs in patients with T2DM, examine factors associated with OADs adherence and oversupply

and assess the relationship of glycemic control and adherence to OADs after controlling for other

factors.

Methods








Data Sources






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prescription data (name of prescription filled; dispensing date; quantity of drug; number of days

supplied and refills). All the data was extracted from the EHRs and there was no direct

involvement of any patient in the study. Patient consent was therefore not required and all study

variables were collected retrospectively and anonymously (de identified data) from EHR.

Study Population

Patients aged 18 years and older with T2DM who had received their treatment at outpatient

clinics at KSUMC during the study period and had at least two prescription fills for one of the

following OADs: sulfonylureas (glibenclamide); biguanides (metformin), thiazolidinediones

(pioglitazone), meglitinide analogs (repaglinide), glucosidase inhibitor (acarbose), oral

dipeptidyl peptidase–4 (DPP-4) inhibitor (sitagliptin) and combination therapy were included in

the study. Patients on insulin or incretin mimetics (liraglutide injection) and those without at

least one HbA1C value were excluded from this study.

Patient and Public Involvement


Outcome Measures


The modified Medication Possession Ratio (mMPR) was used as a proxy to measure the

adherence rate in this study. The mMPR was chosen as it is one of the most commonly reported

adherence assessment method using EHRs and administrative claims data in the literature (24,

32-35). Also, as one of the aims of this study is to estimate the oversupply rate, mMPR allows

for calculation of medication oversupply over 100% while in other assessment methods such as

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the proportion of days covered (PDC), any medication oversupply (over 100%) is truncated (36).

The mMPR was calculated as the sum of the total days' supply for all OADs fills divided by the

sum of the number of days covered and the last refill days [18-19]. The date of the first

prescription fill was designated as the index date. The total days supplied for each OAD was

calculated from the index date until the end of 2016. Patients were considered adherent to their

medication regimen if the estimated mMPR ≥0.8. Otherwise, the patients were deemed as poorly

adherent. A mMPR value greater than one indicated that the patients filled their medications

early before entirely consuming their preceding stock of medications, thus they had excessive

medications than needed. Medication oversupply was defined as mMPR >1.2; the cutoff point of

20% difference in supply has been reported as an acceptable range in several studies (23, 25). In

this study, adherence was categorized into: poor adherence (mMPR < 0.8), good adherence

(mMPR > 0.8 to <1.2) and oversupply (mMPR >1.2) (37). The mMPR was calculated separately

for each OADs prescribed during the 12-month study period. Then, an average mMPR was

calculated with equal weighting of each drug class.

Secondary Outcome - Association between OADs adherence and Glycemic Control








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use of five or more drugs (41). Based on this threshold, polypharmacy was categorized as hyper-







were utilized to determine the factors associated with adherence and only significant factors were

used in the regression models. The factors associated with nonadherence and medication

oversupply were assessed using multinomial logistic regression models after adjusting for

independent variables (age, gender, nationality, marital status and co-existing chronic

conditions). The multinomial logistic regression is an extension of binomial logistic regression to

allow for a dependent variable with more than two categories of the outcome measure (e.g., good

adherence, poor adherence, and oversupply). All statistical analyses were conducted using the

Statistical Analysis Software, version 9.2 (SAS® 9.2) and an a priori significance level was set

at p<0.05.

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Results





18.7% having hyperpolypharmacy. The average HbA1c was 8.2+1.67. Table 1 presents the

demographic and clinical characteristics of the study population. Overall, the vast majority (89.2

%) of adults with T2DM were using metformin followed by sitagliptin (23.5%), glibenclamide

(16.5%), and pioglitazone (12.1%) (Table 2).

OADs Adherence

Among the study population, 48.6% had good adherence (mMPR>0.8 to <1.2) and 42.8%

have poor adherence (mMPR<0.8) to OADs (Table 1). Around 8.6% had medication oversupply

(mMPR>1.2). A significantly higher rate of poor adherence was reported among women as







followed by pioglitazone (15.4%) and glibenclamide (13.9%) (Table 2).


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Results show that good adherence was highest among those who used repaglinide

(71.0%) followed by pioglitazone (65.0%) and sitagliptin (59.0%) (Table 2). This study has also

found an association between the use of OADs and glycemic control (Table 3). A higher rate of

good glycemic control (HbA1C <7%) was observed among patients on metformin (24.1%)

followed by combination therapy (12.0%) and pioglitazone (11.5%).

Factors Associated with OADs Adherence


logistic regressions on adherence to OADs are presented in Table 4. Several factors associated

with OADs adherence were identified: gender, nationality, co-existing chronic conditions, and

polypharmacy. Women with T2DM were less likely to have good adherence (AOR=0.76, 95%

CI=0.67, 0.86) compared to men. Medication oversupply was more likely among T2DM patients

with hyperpolypharmacy (AOR=1.88, 95% CI=1.36, 2.63), comorbid osteoarthritis (AOR=1.72,

95% CI=1.20, 02.45, and non-Saudi patients (AOR=1.53, 95% CI=1.16, 2.01).

Discussion


The study utilized patients’ refill data from EHRs of a tertiary hospital to measure their

overall adherence to OADs. The study found that almost half of the study population with T2DM

had good adherence (mMPR > 0.8). In the published literature, good adherence has been shown

to vary widely and results from a systematic review of 20 retrospective studies demonstrated that

adherence to OADs therapy ranged from 36 to 93% in patients with diabetes (42). These

variations in adherence were affected by several factors including the adherence measurement

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tools, population or institution where the studies were conducted, and the medications included

in the studies (43).

The study findings also revealed that adherence varied widely across different

medications with the highest adherence rates observed in patients on repaglinide followed by

pioglitazone users and was lowest among metformin users. Several studies have demonstrated

that patients on pioglitazone and sulfonylureas have a greater adherence rate than those on

metformin (44). The side effects of metformin such as flatulence and diarrhea, can possibly be

the cause of poor adherence rates among patients (45). In this study, women were less likely to

be adherent as compared to men, which was not consistent with previous studies that showed

women having significantly higher adherence rates than men (46-48). However, few studies has

demonstrated similar surprising findings in which women have low level of adherence (49-51).

Some reason reported in the literature indicated that women often prescribed more medication,

have a complex medications regimen and experience more side effects which could contribute to

lower rates of adherence (52). Although, the actual reason behind these differences in adherence

level between men and women warrants further investigation, these differences should be

considered in personalized treatment plan in order to improve adherence rate and clinical

outcomes (52).


Another interesting study finding was related to medication oversupply; around 8.6% of

the patients had medication oversupply, in particular, among those with chronic kidney disease

and patients with hyper-polypharmacy. In addition to the significant economic burden associated

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with excessive supply of medications, oversupply of diabetes medications may result in

accidental medication errors, which can possibly lead to negative outcomes (53). The current

study results are consistent with the published studies that reported medication oversupply range

from 6.7% to 13.4% (23, 54). Medication oversupply could be a result of a number of factors

such as sicker patients, medication management system issues, poor medication reconciliation,

patients visiting multiple clinicians, and miscommunication between the patients and their

clinicians (25, 54). It should be noted that KSUMC utilizes a 90-day-supply for chronic disease,

which has been previously reported as being more convenient to the patient and can improve

patient adherence. However, it can also increase the chance of the patient having an excess

amount of medication, which can lead to oversupply and wastage. This is especially the case

when the treatment regimen of a patient has been switched or changed (54, 55).

Relationship between Adherence to OADs and Glycemic Control


T2DM and it dramatically reduces diabetes-related complications. In the UK Prospective

Diabetes Study (UKPDS) trial; the microvascular complication rate was reduced by 25% and

myocardial infarction (MI) by 16% in the intensive treatment arm (56). A meta-analysis of the

four diabetes trials; Action to Control Cardiovascular Risk in Diabetes (ACCORD), Action in



(VADT); showed a 15% reduction of relative risk in non-fatal MI with every 1% decrease in



(58). Not surprisingly, poor adherence to OADs was associated with poor glycemic control as

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documented by many published studies (59, 60). This study reported that around two-thirds of

patients with T2DM failed to achieve adequate glycemic control, which is consistent with

another published study in Saudi Arabia (61), but had higher than the average global HbA1c

levels reported in the literature (45%) (62). However, no significant association between

achieving glycemic control and adherence level was seen in this study.




(64), hospitalization and emergency department visits (16). Besides, poor glycemic control due

to non-adherence to OADs can lead to an increased diabetes care cost. In the US, non-adherence

to diabetes medications cost $337 billion in 2013 (17, 18). In Saudi Arabia, the economic burden

of T2DM compliance and persistence for patients who struggle to achieve optimal therapy is

estimated around 3.9 billion Saudi Riyals (US $1.04 billion) and the cost is expected to

significantly increase in the future (65). The value of adherence to pharmacologic treatments in

achieving and controlling diabetes has clearly been established. Good adherence can mitigate the

risk of hospitalization, emergency room visits (66), all-cause mortality (67), and could improve

the overall glycemic control (15, 16, 68).



adherence was assessed using the mMPR method, which is a reliable method and depends on real-


population at a relatively low cost and also prevents recall bias, which is associated with patient

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health literacy, educational level, economic status, marital status, knowledge about disease and

medications which might be contributing factors to medication nonadherence was not available

from the EHR. It should be noted that the adherence in this study was based on pharmacy refills

data, which only indicates the possession of the medication, but cannot ensure the actual

consumption of the medication by the patient.

Future Implications

Healthcare professionals have to pay special attention to patients with diabetes, especially

those with polypharmacy and concomitant diseases, as this will increase the risk for nonadherence

to OADs medication. Although we have few patients with medication oversupply in our study, it

is worthwhile for healthcare providers and policymakers to be aware of this important issue and to




oversupply (e.g., wastage) and can help implement effective strategies to prevent medication

oversupply. Additionally, our findings indicated a surprising result regarding the adherence in

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female with OADs in patient with T2DM, thereby identifying a need to investigate the factors

affecting adherence to OADs among females.

Clinical Practice Implications

Healthcare providers must remain vigilant when evaluating adherence to OADs

medications, by counseling patients at each visit and properly assessing medication adherence as

a behavior. Improving patient-clinician relationships, providing information to guide self-

management to support patients with diabetes can provide a clinically significant improvement in

glycemic control for some patients and improve other health outcomes. As medication

oversupply was in issue in our study especially among those with comorbid chronic conditions

and those with multiple medications, pharmacy services such as reconciliation and medication

therapy management is needed to optimize medication use.

Conclusions

The study findings support the growing concern of nonadherence to OADs among

patients with T2DM in Saudi Arabia. Both providers and pharmacists, in their interaction with

the patients, should stress the importance of adherence and its impact on clinical, economic, and

humanistic outcomes and also focus on preventive measures such as lifestyle modifications.

Decision makers have to invest in behavioral interventions that will boost the adherence rates to

medications and reduce medication oversupply. This is particularly important in patients with

polypharmacy or hyperpolypharmacy and high burden of comorbid conditions.

Acknowledgment



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Contributors


and obtained ethical approval. BB and MA did the statistical analysis. BB, MA, TA, and KMK



Funding

This research received no specific grant from any funding agency in the public, commercial or

not-for-profit sectors.

Competing Interests

None declared.

Patient consent

Not required.

Ethical Approval


Data Sharing Statement

EHRs data were requested for this study and used after the approval by IRB office (IRB number:

E-16-2203). To maintain the confidentiality of data throughout the research process, encrypted

identification numbers were assigned to each participant and extracted data were stored in a

secure, password protected, and limited accessed computers, which were only accessible to the

members of the research team (BB, TA, SA, MA). Therefore, based on the policy of the institute

no further data can be shared.

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Total Poor AdherenceGood Adherence Medication Oversupply

mMPR < 0.8mMPR > 0.8 -<1.2 mMPR >1.2

N % N % N % N % P value Sign.Total 5,457 100.0 2,337 42.8 2,652 48.6 468.0 8.6Age Mean (SD) 58.2 (10.8) 57.8 (11.0) 58.5 (10.6) 58.3 (10.8)Age groups 0.089

18-29 39 0.7 15 38.5 20 51.3 4.0 10.330-39 220 4.0 107 48.6 96 43.6 17.0 7.740-49 759 13.9 362 47.7 338 44.5 59.0 7.850-59 2,081 38.1 884 42.5 1,017 48.9 180.0 8.6=>60 2,358 43.2 969 41.1 1,181 50.1 208.0 8.8




Obesity 0.542Yes 437 8.0 180 41.2 223 51.0 34.0 7.8No 5,020 92.0 2,157 43.0 2,429 48.4 434.0 8.6




Ischemic Heart disease <0.0001 ***Yes 161 3.0 67 41.6 67 41.6 27.0 16.8No 5,296 97.0 2,270 42.9 2,585 48.8 441.0 8.3


Cancer 0.961Yes 63 1.2 27 42.9 30 47.6 6.0 9.5

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mMPR < 0.8mMPR > 0.8 -<1.2 mMPR >1.2

N % N % N % N % P value Sign.No 5,394 98.8 2,310 42.8 2,622 48.6 462.0 8.6

Asthma 0.949Yes 571 10.5 244 42.7 276 48.3 51.0 8.9No 4,886 89.5 2,093 42.8 2,376 48.6 417.0 8.5



Anxiety 0.001 **Yes 284 5.2 114 40.1 129 45.4 41.0 14.4No 5,173 94.8 2,223 43.0 2,523 48.8 427.0 8.3


Polypharmacy <0.0001 ***Hyperpolypharmacy 1,019 18.7 387 38.0 494 48.5 138.0 13.5Major polypharmacy 3,266 59.8 1,444 44.2 1,584 48.5 238.0 7.3Minor polypharmacy 1,172 21.5 506 43.2 574 49.0 92.0 7.8

Average HbA1C Mean (SD) 8.2 (1.67) 8.1 (1.67) 8.2 (1.66) 8.3 (1.66)

Glycemic control 0.005 **

Good Glycemic Control 1,231 698 698 46.9 671 45.1 119 8.0Poor Glycemic Control 4,224 1,076 1076 41.9 1,290 50.2 204 7.9

* Study Population Comprised of 5,457 Adults with type 2 diabetes mellitusN:Number; Sign: SignificanceAsterisks (*) represent significant differences based on mMPR from chi-square tests *** p < .001; **.001 ≤ p < .01; *.01 ≤ p < .05

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And Comparison between Medication Possession Ratio, Modified (mMPR)

N %Mean

Adherence SDPoor

AdherenceGood

AdherenceOversupply


Note: Study Population Comprised of 5,457 Adults with type 2 diabetes mellitusN:Number; SD: Standard Deviation; OADs: oral antidiabetic drugs

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Table 3Association between adherence to OADs and Glycemic Control

Good Glycemic Control

Poor GlycemicControl

N % N % Chi squareP value Sig

Adherence to Diabetic MedicationsAcarbose 0.912 0.634

Poor adherence 7 20 28 80Good adherence 15 17.6 70 82.4Oversupply 3 11.1 24 88.9

Metformin 0.978 0.613Poor adherence 698 39.2 1084 60.8Good adherence 601 38 980 62Oversupply 113 40.8 164 59.2

Glibenclamide 0.151 0.927Poor adherence 35 15.4 193 84.6Good adherence 68 15.7 366 84.3Oversupply 15 14.2 91 85.8

Sitaglibtine 0.188 0.91Poor adherence 50 19.5 206 80.5Good adherence 97 18.3 433 81.7Oversupply 18 18.2 81 81.8

Repaglinide 0.625 0.732Poor adherence 2 25 6 75Good adherence 5 20 20 80Oversupply 0 0 2 100

Pioglitazone 0.52 0.771Poor adherence 19 17.4 90 82.6Good adherence 70 20.3 275 79.7Oversupply 14 21.2 52 78.8

Combination Therapy 5.928 0.052Poor adherence 22 15.8 117 84.2Good adherence 30 23.6 97 76.4Oversupply 8 36.4 14 63.6

* Study Population Comprised of 5,457 Adults with type 2 diabetes mellitusSign: Significance; OADs: oral antidiabetic drugs

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Table 4Odds Ratios and 95% Confidence Intervals

From Multinomial Logistic Regression on Adherence among Adults with Type 2 Diabetes Mellitus

Good Adherence Medication OversupplyOR 95% CI P value Sig. OR 95% CI P value Sig.

Last HbA1CGood <7% 0.81 [0.70, 1.10] 0.004 0.90 [0.69, 1.17] 0.434Poor >7%

GenderFemale 0.76 [0.67, 0.86] <.0001 ** 0.64 [ 0.49 , 0.83] 0.001 **Male (Ref.)

NationalityNon-Saudi 1.15 [ 0.93 , 1.43] 0.218 1.53 [ 1.16 , 2.01] 0.023 **Saudi (Ref.)

Ischemic Heart diseaseYes 0.78 [0.48 , 1.28] 0.215 1.37 [ 0.99 , 2.80] 0.287 No (Ref.)

Heart FailureYes 0.78 [0.34 , 1.71] 0.446 1.77 [ 0.79 , 4.47] 0.343No (Ref.)

DyslipidemiaYes 1.06 [0.94 , 1.21] 0.683 0.83 [ 0.69 , 1.02] 0.270No (Ref.)

CKDYes 0.78 [0.42 , 1.81] 0.756 1.97 [ 0.79 , 4.48] 0.089No (Ref.)

OsteoarthritisYes 1.15 [0.92 , 1.81] 0.260 1.72 [ 1.20 , 2.45] 0.008 **No (Ref.)

AnxietyYes 0.89 [ 0.64 , 1.25] 0.888 1.41 [ 0.95, 2.08] 0.072No (Ref.)

PolypharmacyHyperpolypharmacy 1.11 [ 0.86 , 1.41] 0.176 1.88 [ 1.36 , 2.63] 0.006 *Major polypharmacy 1.04 [ 0.87 , 1.23] 0.459 1.27 [ 0.98 , 1.64] 0.871Minor polypharmacy (Ref.)

Note: Study Population Comprised of 5,457 Adults with type 2 diabetes mellitusRef: Reference GroupAsterisks (*) represent significant differences based on Adherence from multinomial logistic regressions with Poor adherence as the reference group

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55. Domino ME, Martin BC, Wiley-Exley E, Richards S, Henson A, Carey TS, et al. Increasing time costs and copayments for prescription drugs: an analysis of policy changes in a complex environment. Health services research. 2011;46(3):900-19.56. Association AD. Implications of the United Kingdom prospective diabetes study. Diabetes care. 2003;26(suppl 1):s28-s32.57. Turnbull F, Abraira C, Anderson R, Byington R, Chalmers J, Duckworth W, et al. Intensive glucose control and macrovascular outcomes in type 2 diabetes. Springer; 2009.58. McAdam-Marx C, Bellows BK, Unni S, Wygant G, Mukherjee J, Ye X, et al. Impact of adherence and weight loss on glycemic control in patients with type 2 diabetes: cohort analyses of integrated medical record, pharmacy claims, and patient-reported data. Journal of Managed Care Pharmacy. 2014;20(7):691-700.59. Vichayanrat A, Matawaran BJ, Wibudi A, Ferdous HS, Aamir AH, Aggarwal SK, et al. Assessment of baseline characteristics, glycemic control and oral antidiabetic treatment in Asian patients with diabetes: The Registry for Assessing OAD Usage in Diabetes Management (REASON) Asia study (在亚洲

糖尿病患者中评估基线特征、血糖控制情况以及口服降糖药物使用情况：使用登记表评估糖尿病

治疗中口服药使用情况（ REASON）的亚洲研究). Journal of diabetes. 2013;5(3):309-18.60. Feldman BS, Cohen-Stavi CJ, Leibowitz M, Hoshen MB, Singer SR, Bitterman H, et al. Defining the role of medication adherence in poor glycemic control among a general adult population with diabetes. PLoS One. 2014;9(9):e108145.61. Rabba AK, Aljiris WS, Ahmed NJ, Alkharfy KM. Medication adherence in type 2 diabetic patients: a study in Saudi Arabia. 2017.62. Polonsky WH, Henry RR. Poor medication adherence in type 2 diabetes: recognizing the scope of the problem and its key contributors. Patient preference and adherence. 2016;10:1299.63. Fukuda H, Mizobe M. Impact of nonadherence on complication risks and healthcare costs in patients newly-diagnosed with diabetes. Diabetes research and clinical practice. 2017;123:55-62.64. Gatwood JD, Chisholm-Burns M, Davis R, Thomas F, Potukuchi P, Hung A, et al. Differences in health outcomes associated with initial adherence to oral antidiabetes medications among veterans with uncomplicated Type 2 diabetes: a 5-year survival analysis. Diabet Med. 2018;6(10):13775.65. Improving Type 2 Diabetes Therapy Compliance and Persistence in the Kingdom of Saudi Arabia. IMS Institute for Healthcare Informatics, July 2016.66. Roebuck MC, Kaestner RJ, Dougherty JS. Impact of Medication Adherence on Health Services Utilization in Medicaid. Medical care. 2018;56(3):266-73.67. Simard P, Presse N, Roy L, Dorais M, White-Guay B, Räkel A, et al. Association Between Metformin Adherence and All-Cause Mortality Among New Users of Metformin: A Nested Case-Control Study. Annals of Pharmacotherapy. 2018;52(4):305-13.68. Schectman JM, Nadkarni MM, Voss JD. The association between diabetes metabolic control and drug adherence in an indigent population. Diabetes care. 2002;25(6):1015-21.

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IntroductionBackground/rationale 2 Explain the scientific background and rationale for the investigation being

reported3


MethodsStudy design 4 Present key elements of study design early in the paper 5Setting 5 Describe the setting, locations, and relevant dates, including periods of

recruitment, exposure, follow-up, and data collection5


5


7



6

Bias 9 Describe any efforts to address potential sources of biasStudy size 10 Explain how the study size was arrived atQuantitative variables 11 Explain how quantitative variables were handled in the analyses. If

applicable, describe which groupings were chosen and why(a) Describe all statistical methods, including those used to control for confounding

7-8

(b) Describe any methods used to examine subgroups and interactions 8(c) Explain how missing data were addressed 8(d) If applicable, describe analytical methods taking account of sampling strategy

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(e) Describe any sensitivity analyses


8

(b) Give reasons for non-participation at each stage

Participants 13*

(c) Consider use of a flow diagram(a) Give characteristics of study participants (eg demographic, clinical, social) and information on exposures and potential confounders



Outcome data 15* Report numbers of outcome events or summary measures 9Main results 16 (a) Give unadjusted estimates and, if applicable, confounder-adjusted

estimates and their precision (eg, 95% confidence interval). Make clear which confounders were adjusted for and why they were included

9

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(b) Report category boundaries when continuous variables were categorized(c) If relevant, consider translating estimates of relative risk into absolute risk for a meaningful time period


DiscussionKey results 18 Summarise key results with reference to study objectives 10Limitations 19 Discuss limitations of the study, taking into account sources of potential

bias or imprecision. Discuss both direction and magnitude of any potential bias

14


10-12


Other informationFunding 22 Give the source of funding and the role of the funders for the present study

and, if applicable, for the original study on which the present article is based


Note: An Explanation and Elaboration article discusses each checklist item and gives methodological background and published examples of transparent reporting. The STROBE checklist is best used in conjunction with this article (freely available on the Web sites of PLoS Medicine at http://www.plosmedicine.org/, Annals of Internal Medicine at http://www.annals.org/, and Epidemiology at http://www.epidem.com/). Information on the STROBE Initiative is available at www.strobe-statement.org.

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Control among Patients with Type 2 Diabetes Mellitus: A Cross-Sectional Retrospective Study in a Tertiary Hospital in

Saudi Arabia

Journal: BMJ Open

Manuscript ID bmjopen-2019-029280.R2


Date Submitted by the Author: 20-Jun-2019


<b>Primary Subject Heading</b>: Diabetes and endocrinology

Secondary Subject Heading: General practice / Family practice



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Oral Antidiabetic Medication Adherence and Glycemic Control among Patients with Type 2 Diabetes Mellitus: A Cross-Sectional Retrospective Study in a Tertiary Hospital in Saudi Arabia Bander Balkhi, Ph.D1,2*, Monira Alwhaibi, Ph.D1,2, Nasser Alqahtani, Ph.D2,3, Tariq M. Alhawassi, Ph.D1,2,5, Thamir M. Alshammari, Ph.D2,3,4, Mansour A. Mahmoud, Ph.D6, Mansour Almetwazi, Ph.D1,2, Sondus Ata, MS5, Khalid M. Kamal, M. Pharm., Ph.D7

1 Department of Clinical Pharmacy, College of Pharmacy, King Saud University, Riyadh, Saudi Arabia2 Medication Safety Research Chair, College of Pharmacy, King Saud University, Riyadh, Saudi Arabia 3 Saudi Food and Drug Authority, Riyadh, Saudi Arabia4 Department of Clinical Pharmacy, College of Pharmacy, University of Hail, Hail, Saudi Arabia5 Investigational Drugs and Research Unit, King Khalid University Hospital, Riyadh, Saudi Arabia 6 Clinical and Hospital Pharmacy Department, College of Pharmacy, Taibah University,

Al-Madinah Al-Munawara, Saudi Arabia7 Division of Pharmaceutical, Administrative and Social Sciences, Duquesne University School of Pharmacy, Pittsburgh, PA, USA


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ABSTRACT

Objectives: The purpose of this study is to measure the adherence rates of oral antidiabetic drugs

(OADs) in patients with type 2 diabetes mellitus (T2DM) and assess the relationship of glycemic

control and adherence to OADs after controlling for other associated factors.

Design: Cross sectional retrospective study.


Participants: 5,457 patients aged 18 years and older diagnosed with T2DM during the period

January 1, 2016 to December 31, 2016.

Primary and secondary outcome measures: The modified Medication Possession Ratio

(mMPR) was calculated as a proxy measure for adherence of OADs. The factors associated with

OADs nonadherence and medication oversupply were assessed using multinomial logistic

regression models. The secondary outcomes were to measure the association between OADs

adherence and glycemic control.

Results: Majority of patients with T2DM were females (n=3,400, 62.3%). The average glycated

hemoglobin (HbA1c) was 8.2+1.67. Among the study population, 48.6% had good adherence

(mMPR>0.8) and 8.6% had a medication oversupply (mMPR>1.2). Good adherence was highest

among those using repaglinide (71.0%) followed by pioglitazone (65.0%) and sitagliptin

(59.0%). In the multivariate analysis, women with T2DM were more likely to have poor

adherence (adjusted odds ratios [AOR]=0.76, 95% CI=0.67, 0.86) compared to men. Also,



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patients (AOR=1.53, 95% CI=1.16, 2.01). However, no association was found between glycemic

control and adherence to OADs.



that will boost medication adherence rates. This is particularly important in patients with







Using real-world data of more than 5,000 patients with T2DM in Saudi Arabia, the study

assesses the impact of adherence among different patient subgroups.

This study did not control the severity of diabetes or diabetes complications, which may

affect the rate of adherence to OADs.




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INTRODUCTION



adult population was 8.8% in 2015 (2) and is projected to increase to 10.4% by 2040 (2). In

Saudi Arabia, the estimated prevalence of T2DM is approximately 23% (3, 4) with another

25.5% of the population (30 years and older) classified as prediabetes. By 2035, the prevalence

of T2DM is Saudi Arabia is projected to double and is expected to reach an estimated 7.5 million

cases (5). While diabetes is projected be the 7th leading cause of mortality and disability

worldwide by 2030 (6), it is already the 6th leading cause of death in Saudi Arabia based on the

World Health Organization (WHO) report (7). It is well established that uncontrolled T2DM is

associated with negative health consequences such as blindness, kidney failure, lower limb

amputation and other complications, all of which result in poor quality of life in patients (8, 9).

Also, T2DM is a well-established risk factor for cardiovascular disease (CVD) and

macrovascular complications including costly conditions like coronary heart disease, stroke,

nephropathy, retinopathy, and others (10, 11). A meta-analysis concluded that the risk of CVD in

patients with diabetes was three times compared to those without diabetes (11).

Worldwide, diabetes imposes a large economic burden on the individuals and national

healthcare systems. A recent study estimated the global cost of diabetes in adult patients at US$

1.31 trillion, which represents 1.8% of the global gross domestic product (12). Two-third of this

cost was direct medical cost while the remaining one-third was attributable to indirect cost such

as loss in productivity (12). In the Middle East and North Africa regions, the rising prevalence of

diabetes is projected to increase the healthcare cost by 67% by 2045. (3). Similar trends are being

observed in Saudi Arabia as well and the medical healthcare expenditure for people with diabetes

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is 10 times higher ($3,686 vs. $380) than those without the condition (13). Diabetes and its

complications also result in high indirect costs, such as costs related to absenteeism, loss of

productivity, disability and premature mortality. The overall economic burden of diabetes in

Saudi Arabia is substantial with an estimated direct cost of 17 billion Saudi Riyals (US $4.5

billion) in 2014, which is expected to increase to 43 billion Saudi Riyals (US $ 11.4 billion) in

the future (14).









adherence (20).

Although poor adherence (medication underuse) is a well-recognized issue in any

healthcare setting, having excess supply of medications than needed (medication oversupply)

may also lead to negative health outcomes such as increase in toxicity risks, inefficient use of

available healthcare resources, and increase in unnecessary healthcare costs (21). Prevalence of

oversupply has been shown to vary across different institutions, medication classes, and

countries and ranges from 11% to 53% (22). Medication oversupply was common, especially

among T2DM patients, which may increase the risk of hospitalization (23-26) and further,

influence achieving the recommended level of HbA1c (27).

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urgent need to address nonadherence to positive interventions, such as OADs, so as to reign in

the rapid escalation of healthcare costs and improve patient outcomes. Although several studies

have measured the adherence and oversupply rate in patients with diabetes, their prevalence

among patients with T2DM in Saudi Arabia along with their impact on glycemic control is

largely unknown (28-31). The purpose of the current study was to evaluate adherence to OADs

and to explore the variables associated with OADs non-adherence in patients with type 2

diabetes. The association between glycemic control and adherence to OADs will also be

examined.

Methods








Data Sources






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prescription data (name of prescription filled; dispensing date; quantity of drug; number of days

supplied and refills). All the data was extracted from the EHRs and there was no direct

involvement of any patient in the study. Patient consent was therefore not required and all study

variables were collected retrospectively and anonymously (de identified data) from EHR.

Study Population

Patients aged 18 years and older with T2DM who had received their treatment at outpatient

clinics at KSUMC during the study period and had at least two prescription fills for one of the

following OADs: sulfonylureas (glibenclamide); biguanides (metformin), thiazolidinediones

(pioglitazone), meglitinide analogs (repaglinide), glucosidase inhibitor (acarbose), oral

dipeptidyl peptidase–4 (DPP-4) inhibitor (sitagliptin) and combination therapy were included in

the study. Patients on insulin or incretin mimetics (liraglutide injection) and those without at

least one HbA1C value were excluded from this study.

Patient and Public Involvement


Outcome Measures


The modified Medication Possession Ratio (mMPR) was used as a proxy to measure the

adherence rate in this study. The mMPR was chosen as it is one of the most commonly reported

adherence assessment method using EHRs and administrative claims data in the literature (24,

32-35). Also, as one of the aims of this study is to estimate the oversupply rate, mMPR allows

for calculation of medication oversupply over 100% while in other assessment methods such as

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the proportion of days covered (PDC), any medication oversupply (over 100%) is truncated (36).

The mMPR was calculated as the sum of the total days' supply for all OADs fills divided by the

sum of the number of days covered and the last refill days [18-19]. The date of the first

prescription fill was designated as the index date. The total days supplied for each OAD was

calculated from the index date until the end of 2016. Patients were considered adherent to their

medication regimen if the estimated mMPR ≥0.8. Otherwise, the patients were deemed as poorly

adherent. A mMPR value greater than one indicated that the patients filled their medications

early before entirely consuming their preceding stock of medications, thus they had excessive

medications than needed. Medication oversupply was defined as mMPR >1.2; the cutoff point of

20% difference in supply has been reported as an acceptable range in several studies (23, 25). In

this study, adherence was categorized into: poor adherence (mMPR < 0.8), good adherence

(mMPR > 0.8 to <1.2) and oversupply (mMPR >1.2) (37). The mMPR was calculated separately

for each OADs prescribed during the 12-month study period. Then, an average mMPR was

calculated with equal weighting of each drug class.

Secondary Outcome - Association between OADs adherence and Glycemic Control








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use of five or more drugs (41). Based on this threshold, polypharmacy was categorized as hyper-







were utilized to determine the factors associated with adherence and only significant factors were

used in the regression models. The factors associated with nonadherence and medication

oversupply were assessed using multinomial logistic regression models after adjusting for

independent variables (age, gender, nationality, marital status and co-existing chronic

conditions). The multinomial logistic regression is an extension of binomial logistic regression to

allow for a dependent variable with more than two categories of the outcome measure (e.g., good

adherence, poor adherence, and oversupply). All statistical analyses were conducted using the

Statistical Analysis Software, version 9.2 (SAS® 9.2) and an a priori significance level was set

at p<0.05.

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Results





18.7% having hyperpolypharmacy. The average HbA1c was 8.2+1.67. Table 1 presents the

demographic and clinical characteristics of the study population. Overall, the vast majority (89.2

%) of adults with T2DM were using metformin followed by sitagliptin (23.5%), glibenclamide

(16.5%), and pioglitazone (12.1%) (Table 2).

OADs Adherence

Among the study population, 48.6% had good adherence to OAD (mMPR>0.8 to <1.2),

42.8% had poor adherence (mMPR<0.8) and 8.6% had medication oversupply (mMPR>1.2)

(Table 1). A significantly higher rate of poor adherence was reported among women as







followed by pioglitazone (15.4%) and glibenclamide (13.9%) (Table 2).


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Results show that good adherence was highest among those who used repaglinide

(71.0%) followed by pioglitazone (65.0%) and sitagliptin (59.0%) (Table 2). Also, this study

found no significant association between medication adherence with any OADs and glycemic

control (Table 3). However, a higher rate of good glycemic control (HbA1C <7%) was observed

among patients on metformin (24.1%) followed by combination therapy (12.0%) and

pioglitazone (11.5%).

Factors Associated with OADs Adherence


logistic regressions on adherence to OADs are presented in Table 4. Several factors associated

with OADs adherence were identified: gender, nationality, co-existing chronic conditions, and

polypharmacy. Women with T2DM were less likely to have good adherence (AOR=0.76, 95%

CI=0.67, 0.86) compared to men. Medication oversupply was more likely among T2DM patients

with hyperpolypharmacy (AOR=1.88, 95% CI=1.36, 2.63), comorbid osteoarthritis (AOR=1.72,

95% CI=1.20, 02.45, and non-Saudi patients (AOR=1.53, 95% CI=1.16, 2.01).

Discussion


Medication adherence is critical in the treatment of patients with diabetes. Failure to adhere

to prescribed medication regimen is recognized as a serious issue resulting in negative

consequences to the patient and the health care system as well. Thus, there is a need to identify

specific barriers that will help in adapting appropriate tools to overcome and improve medication

adherence.(35, 42) The heterogeneity, complexity and importance of this issue has been

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extensively studied and well recognized in the literature.(35, 43, 44) Several barriers and

facilitators of adherence have been identified including patient-related factors ( belief and

knowledge, cognitive function, health literacy), physician related-factors (communication with

patient), medication related-factors (adverse drug reaction, drug regimen complexity, cost), and

system-based factors ( lack of medication review, lack of patient follow-up).(43, 45) However,

factors associated with non-adherence were not comprehensive and are reported to vary depending

on the nature of the disease (e.g., severity), patients’ characteristics (e.g., demographic and

socioeconomic status) (46). To explore the issue of medication non-adherence in patients with

T2DM in Saudi Arabia, this study utilized patients’ refill data from EHRs of a tertiary hospital

and estimated patients’ overall adherence to OADs using mMPR values. The study found that

almost half of the study population with T2DM had good adherence (mMPR > 0.8). In the

published literature, good adherence has been shown to vary widely and results from a systematic

review of 20 retrospective studies demonstrated that adherence to OADs therapy ranged from 36%

to 93% in patients with diabetes (47). These variations in adherence were affected by several

factors including the adherence measurement tools, population or institution where the studies

were conducted, and the medications included in the studies (44).

The study findings also revealed that adherence varied widely across different

medications with the highest adherence rates observed in patients on repaglinide followed by

pioglitazone users and was lowest among metformin users. Several studies have demonstrated

that patients on pioglitazone and sulfonylureas have a greater adherence rate than those on

metformin (48). The side effects of metformin such as flatulence and diarrhea, can possibly be

the cause of poor adherence rates among patients (49). In this study, women were less likely to

be adherent as compared to men, which was not consistent with previous studies that showed

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women having significantly higher adherence rates than men (50-52). However, few studies has

demonstrated similar surprising findings in which women have low level of adherence (53-55).

Some reason reported in the literature indicated that women often prescribed more medication,

have a complex medications regimen and experience more side effects which could contribute to

lower rates of adherence (56). Although, the actual reason behind these differences in adherence

level between men and women warrants further investigation, these differences should be

considered in personalized treatment plan in order to improve adherence rate and clinical

outcomes (56).


Another interesting study finding was related to medication oversupply; around 8.6% of

the patients had medication oversupply, in particular, among those with chronic kidney disease

and patients with hyper-polypharmacy. In addition to the significant economic burden associated

with excessive supply of medications, oversupply of diabetes medications may result in

accidental medication errors, which can possibly lead to negative outcomes (57). The current

study results are consistent with the published studies that reported medication oversupply range

from 6.7% to 13.4% (23, 58). Medication oversupply could be a result of a number of factors

such as sicker patients, medication management system issues, poor medication reconciliation,

patients visiting multiple clinicians, and miscommunication between the patients and their

clinicians (25, 58). It should be noted that KSUMC utilizes a 90-day-supply for chronic disease,

which has been previously reported as being more convenient to the patient and can improve

patient adherence. However, it can also increase the chance of the patient having an excess

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amount of medication, which can lead to oversupply and wastage. This is especially the case

when the treatment regimen of a patient has been switched or changed (58, 59).

Relationship between Adherence to OADs and Glycemic Control


T2DM and it dramatically reduces diabetes-related complications. In the UK Prospective

Diabetes Study (UKPDS) trial; the microvascular complication rate was reduced by 25% and

myocardial infarction (MI) by 16% in the intensive treatment arm (60). A meta-analysis of the

four diabetes trials; Action to Control Cardiovascular Risk in Diabetes (ACCORD), Action in



(VADT); showed a 15% reduction of relative risk in non-fatal MI with every 1% decrease in



(62). Not surprisingly, poor adherence to OADs was associated with poor glycemic control as

documented by many published studies (63, 64). This study reported that around two-thirds of

patients with T2DM failed to achieve adequate glycemic control, which is consistent with

another published study in Saudi Arabia (65), but had higher than the average global HbA1c

levels reported in the literature (45%) (66). However, no significant association between

achieving glycemic control and adherence level was seen in this study.




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(68), hospitalization and emergency department visits (16). Besides, poor glycemic control due

to non-adherence to OADs can lead to an increased diabetes care cost. In the US, non-adherence

to diabetes medications cost $337 billion in 2013 (17, 18). In Saudi Arabia, the economic burden

of T2DM compliance and persistence for patients who struggle to achieve optimal therapy is

estimated around 3.9 billion Saudi Riyals (US $1.04 billion) and the cost is expected to

significantly increase in the future (69). The value of adherence to pharmacologic treatments in

achieving and controlling diabetes has clearly been established. Good adherence can mitigate the

risk of hospitalization, emergency room visits (70), all-cause mortality (71), and could improve

the overall glycemic control (15, 16, 72).



adherence was assessed using the mMPR method, which is a reliable method and depends on real-


population at a relatively low cost and also prevents recall bias, which is associated with patient









health literacy, educational level, economic status, marital status, knowledge about disease and

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medications which might be contributing factors to medication nonadherence was not available

from the EHR. It should be noted that the adherence in this study was based on pharmacy refills

data, which only indicates the possession of the medication, but does not capture the actual

consumption of the medication by the patient. This is one of the limitations in calculating

adherence using pharmacy refills data as it assumes that the medication was actually taken by the

patient, which may not necessarily be the case. The main advantage of using the EHRs is the

availability of large sample size which can provide precise estimates and availability of dispensing

date and quantity dispensed, which is hard to collect from the patients using the patient-reported

measures. Other tools that potentially provide more accurate estimation about the actual

consumption and adherence include direct measures such as measurement of drug of metabolite in

body fluids or testing for biomarkers. However, these are expensive and intrusive to patients and

therefore, difficult to implement.

Future Implications

Healthcare professionals have to pay special attention to patients with diabetes, especially

those with polypharmacy and concomitant diseases, as this will increase the risk for nonadherence

to OADs medication. Although we have few patients with medication oversupply in our study, it

is worthwhile for healthcare providers and policymakers to be aware of this important issue and to




oversupply (e.g., wastage) and can help implement effective strategies to prevent medication

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oversupply. Additionally, our findings indicated a surprising result regarding the adherence in

female with OADs in patient with T2DM, thereby identifying a need to investigate the factors

affecting adherence to OADs among females.

Clinical Practice Implications

Healthcare providers must remain vigilant when evaluating adherence to OADs

medications, by counseling patients at each visit and properly assessing medication adherence as

a behavior. Improving patient-clinician relationships, providing information to guide self-

management to support patients with diabetes can provide a clinically significant improvement in

glycemic control for some patients and improve other health outcomes. As medication

oversupply was in issue in our study especially among those with comorbid chronic conditions

and those with multiple medications, pharmacy services such as reconciliation and medication

therapy management is needed to optimize medication use.

Conclusions

The study findings support the growing concern of nonadherence to OADs among

patients with T2DM in Saudi Arabia. Both providers and pharmacists, in their interaction with

the patients, should stress the importance of adherence and its impact on clinical, economic, and

humanistic outcomes and also focus on preventive measures such as lifestyle modifications.

Decision makers have to invest in behavioral interventions such as motivational interviewing,

planned behavior education and problem-solving training which can boost the adherence rates to

medications and reduce medication oversupply.(73) This is particularly important in patients

with polypharmacy or hyperpolypharmacy and high burden of comorbid conditions.

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Acknowledgment



Contributors


and obtained ethical approval. BB and MA did the statistical analysis. BB, MA, TA, and KMK



Funding

This research received no specific grant from any funding agency in the public, commercial or

not-for-profit sectors.

Competing Interests

None declared.

Patient consent

Not required.

Ethical Approval


Data Sharing Statement

EHRs data were requested for this study and used after the approval by IRB office (IRB number:

E-16-2203). To maintain the confidentiality of data throughout the research process, encrypted

identification numbers were assigned to each participant and extracted data were stored in a

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secure, password protected, and limited accessed computers, which were only accessible to the

members of the research team (BB, TA, SA, MA). Therefore, based on the policy of the institute

no further data can be shared.




mMPR < 0.8mMPR > 0.8 -<1.2 mMPR >1.2

N % N % N % N % P value Sign.Total 5,457 100.0 2,337 42.8 2,652 48.6 468.0 8.6Age Mean (SD) 58.2 (10.8) 57.8 (11.0) 58.5 (10.6) 58.3 (10.8)Age groups 0.089

18-29 39 0.7 15 38.5 20 51.3 4.0 10.330-39 220 4.0 107 48.6 96 43.6 17.0 7.740-49 759 13.9 362 47.7 338 44.5 59.0 7.850-59 2,081 38.1 884 42.5 1,017 48.9 180.0 8.6=>60 2,358 43.2 969 41.1 1,181 50.1 208.0 8.8




Obesity 0.542Yes 437 8.0 180 41.2 223 51.0 34.0 7.8No 5,020 92.0 2,157 43.0 2,429 48.4 434.0 8.6




Ischemic Heart disease <0.0001 ***Yes 161 3.0 67 41.6 67 41.6 27.0 16.8

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mMPR < 0.8mMPR > 0.8 -<1.2 mMPR >1.2

N % N % N % N % P value Sign.No 5,296 97.0 2,270 42.9 2,585 48.8 441.0 8.3


Cancer 0.961Yes 63 1.2 27 42.9 30 47.6 6.0 9.5No 5,394 98.8 2,310 42.8 2,622 48.6 462.0 8.6

Asthma 0.949Yes 571 10.5 244 42.7 276 48.3 51.0 8.9No 4,886 89.5 2,093 42.8 2,376 48.6 417.0 8.5



Anxiety 0.001 **Yes 284 5.2 114 40.1 129 45.4 41.0 14.4No 5,173 94.8 2,223 43.0 2,523 48.8 427.0 8.3


Polypharmacy <0.0001 ***Hyperpolypharmacy 1,019 18.7 387 38.0 494 48.5 138.0 13.5Major polypharmacy 3,266 59.8 1,444 44.2 1,584 48.5 238.0 7.3Minor polypharmacy 1,172 21.5 506 43.2 574 49.0 92.0 7.8

Average HbA1C Mean (SD) 8.2 (1.67) 8.1 (1.67) 8.2 (1.66) 8.3 (1.66)

Glycemic control 0.005 **

Good Glycemic Control 1,231 698 698 46.9 671 45.1 119 8.0Poor Glycemic Control 4,224 1,076 1076 41.9 1,290 50.2 204 7.9

* Study Population Comprised of 5,457 Adults with type 2 diabetes mellitusN:Number; Sign: SignificanceAsterisks (*) represent significant differences based on mMPR from chi-square tests *** p < .001; **.001 ≤ p < .01; *.01 ≤ p < .05

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And Comparison between Medication Possession Ratio, Modified (mMPR)

N %Mean

Adherence SDPoor

AdherenceGood

AdherenceOversupply


Note: Study Population Comprised of 5,457 Adults with type 2 diabetes mellitusN:Number; SD: Standard Deviation; OADs: oral antidiabetic drugs

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Table 3Association between adherence to OADs and Glycemic Control

Good Glycemic Control

Poor GlycemicControl

N % N % Chi squareP value Sig

Adherence to Diabetic MedicationsAcarbose 0.912 0.634

Poor adherence 7 20 28 80Good adherence 15 17.6 70 82.4Oversupply 3 11.1 24 88.9

Metformin 0.978 0.613Poor adherence 698 39.2 1084 60.8Good adherence 601 38 980 62Oversupply 113 40.8 164 59.2

Glibenclamide 0.151 0.927Poor adherence 35 15.4 193 84.6Good adherence 68 15.7 366 84.3Oversupply 15 14.2 91 85.8

Sitaglibtine 0.188 0.91Poor adherence 50 19.5 206 80.5Good adherence 97 18.3 433 81.7Oversupply 18 18.2 81 81.8

Repaglinide 0.625 0.732Poor adherence 2 25 6 75Good adherence 5 20 20 80Oversupply 0 0 2 100

Pioglitazone 0.52 0.771Poor adherence 19 17.4 90 82.6Good adherence 70 20.3 275 79.7Oversupply 14 21.2 52 78.8

Combination Therapy 5.928 0.052Poor adherence 22 15.8 117 84.2Good adherence 30 23.6 97 76.4Oversupply 8 36.4 14 63.6

* Study Population Comprised of 5,457 Adults with type 2 diabetes mellitusSign: Significance; OADs: oral antidiabetic drugs

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Table 4Odds Ratios and 95% Confidence Intervals

From Multinomial Logistic Regression on Adherence among Adults with Type 2 Diabetes Mellitus

Good Adherence Medication OversupplyOR 95% CI P value Sig. OR 95% CI P value Sig.

Last HbA1CGood <7% 0.81 [0.70, 1.10] 0.004 0.90 [0.69, 1.17] 0.434Poor >7%

GenderFemale 0.76 [0.67, 0.86] <.0001 ** 0.64 [ 0.49 , 0.83] 0.001 **Male (Ref.)

NationalityNon-Saudi 1.15 [ 0.93 , 1.43] 0.218 1.53 [ 1.16 , 2.01] 0.023 **Saudi (Ref.)

Ischemic Heart diseaseYes 0.78 [0.48 , 1.28] 0.215 1.37 [ 0.99 , 2.80] 0.287 No (Ref.)

Heart FailureYes 0.78 [0.34 , 1.71] 0.446 1.77 [ 0.79 , 4.47] 0.343No (Ref.)

DyslipidemiaYes 1.06 [0.94 , 1.21] 0.683 0.83 [ 0.69 , 1.02] 0.270No (Ref.)

CKDYes 0.78 [0.42 , 1.81] 0.756 1.97 [ 0.79 , 4.48] 0.089No (Ref.)

OsteoarthritisYes 1.15 [0.92 , 1.81] 0.260 1.72 [ 1.20 , 2.45] 0.008 **No (Ref.)

AnxietyYes 0.89 [ 0.64 , 1.25] 0.888 1.41 [ 0.95, 2.08] 0.072No (Ref.)

PolypharmacyHyperpolypharmacy 1.11 [ 0.86 , 1.41] 0.176 1.88 [ 1.36 , 2.63] 0.006 *Major polypharmacy 1.04 [ 0.87 , 1.23] 0.459 1.27 [ 0.98 , 1.64] 0.871Minor polypharmacy (Ref.)

Note: Study Population Comprised of 5,457 Adults with type 2 diabetes mellitus

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Ref: Reference GroupAsterisks (*) represent significant differences based on Adherence from multinomial logistic regressions with Poor adherence as the reference group

References

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71. Simard P, Presse N, Roy L, Dorais M, White-Guay B, Räkel A, et al. Association Between Metformin Adherence and All-Cause Mortality Among New Users of Metformin: A Nested Case-Control Study. Annals of Pharmacotherapy. 2018;52(4):305-13.72. Schectman JM, Nadkarni MM, Voss JD. The association between diabetes metabolic control and drug adherence in an indigent population. Diabetes care. 2002;25(6):1015-21.73. Kini V, Ho PM. Interventions to improve medication adherence: a review. Jama. 2018;320(23):2461-73.

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PageNo




2

IntroductionBackground/rationale 2 Explain the scientific background and rationale for the investigation

being reported4


MethodsStudy design 4 Present key elements of study design early in the paper 6Setting 5 Describe the setting, locations, and relevant dates, including periods

of recruitment, exposure, follow-up, and data collection6


7


7-8



6-7

Bias 9 Describe any efforts to address potential sources of bias Not Applicable

Study size 10 Explain how the study size was arrived at Not Applicable

Quantitative variables 11 Explain how quantitative variables were handled in the analyses. If applicable, describe which groupings were chosen and why

Not Applicable

(a) Describe all statistical methods, including those used to control for confounding

9

(b) Describe any methods used to examine subgroups and interactions

9

(c) Explain how missing data were addressed 9(d) If applicable, describe analytical methods taking account of sampling strategy

9


(e) Describe any sensitivity analyses Not Applicable


10

(b) Give reasons for non-participation at each stage Not Applicable

Participants 13*

(c) Consider use of a flow diagram Not Applicable

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(a) Give characteristics of study participants (eg demographic, clinical, social) and information on exposures and potential confounders



Not Applicable

Outcome data 15* Report numbers of outcome events or summary measures 10(a) Give unadjusted estimates and, if applicable, confounder-adjusted estimates and their precision (eg, 95% confidence interval). Make clear which confounders were adjusted for and why they were included

10-11

(b) Report category boundaries when continuous variables were categorized

Not Applicable

Main results 16

(c) If relevant, consider translating estimates of relative risk into absolute risk for a meaningful time period

Not Applicable


Not Applicable

DiscussionKey results 18 Summarise key results with reference to study objectives 11Limitations 19 Discuss limitations of the study, taking into account sources of

potential bias or imprecision. Discuss both direction and magnitude of any potential bias

14


11-12


Other informationFunding 22 Give the source of funding and the role of the funders for the

present study and, if applicable, for the original study on which the present article is based

Not Applicable


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