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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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.
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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38. Krass I, Schieback P, Dhippayom T. Adherence to diabetes medication: a systematic review. Diabetic Medicine. 2015;32(6):725-37.39. McGovern A, Hinton W, Calderara S, Munro N, Whyte M, de Lusignan S. A class comparison of medication persistence in people with type 2 diabetes: a retrospective observational study. Diabetes Therapy. 2018;9(1):229-42.40. Parada Jr H, Horton LA, Cherrington A, Ibarra L, Ayala GX. Correlates of medication nonadherence among Latinos with type 2 diabetes. The Diabetes Educator. 2012;38(4):552-61.41. Cohen HW, Shmukler C, Ullman R, Rivera CM, Walker EA. Measurements of medication adherence in diabetic patients with poorly controlled HbA1c. Diabetic Medicine. 2010;27(2):210-6.42. Wong M, Kong AP, So WY, Jiang JY, Chan JC, Griffiths SM. Adherence to oral hypoglycemic agents in 26 782 Chinese patients: a cohort study. The Journal of Clinical Pharmacology. 2011;51(10):1474-82.43. Chen C-C, Blank RH, Cheng S-H. Medication supply, healthcare outcomes and healthcare expenses: Longitudinal analyses of patients with type 2 diabetes and hypertension. Health policy. 2014;117(3):374-81.44. Thorpe CT, Johnson H, Dopp AL, Thorpe JM, Ronk K, Everett CM, et al. Medication oversupply in patients with diabetes. Res Social Adm Pharm. 2015;11(3):382-400. Epub 2014/10/08.45. 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):900-19.46. American Diabetes Association. Implications of the United Kingdom prospective diabetes study. Diabetes care. 2003;26(suppl 1):s28-s32.47. 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; 2009.48. 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):691-700.49. 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. Journal of diabetes. 2013;5(3):309-18.50. 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):e108145.51. 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;123:55-62.52. 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):13775.53. Improving Type 2 Diabetes Therapy Compliance and Persistence in the Kingdom of Saudi Arabia. IMS Institute for Healthcare Informatics, July 2016.54. Roebuck MC, Kaestner RJ, Dougherty JS. Impact of Medication Adherence on Health Services Utilization in Medicaid. Medical care. 2018;56(3):266-73.55. 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.
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56. Schectman JM, Nadkarni MM, Voss JD. The association between diabetes metabolic control and drug adherence in an indigent population. Diabetes care. 2002;25(6):1015-21.57. Rabba AK, Aljiris WS, Ahmed NJ, Alkharfy KM. Medication adherence in type 2 diabetic patients: a study in Saudi Arabia. 2017.58. 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.
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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
Article Type: Research
Date Submitted by the Author: 15-Apr-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
<b>Primary Subject Heading</b>: Diabetes and endocrinology
Secondary Subject Heading: General practice / Family practice
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: 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
*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: 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.
Setting: Large tertiary hospital in the central region of Saudi Arabia.
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,
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
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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.
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 medication adherence rates. 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 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.
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 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).
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 (15, 16), reduced risk of diabetes complications, and reduced economic
burden (17, 18). 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 (19). In Saudi Arabia, nearly 56% of patients have been shown to have low medication
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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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 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
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
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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
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 (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
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 (38). 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
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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) (39, 40). 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 (41). 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
means and standard deviations were calculated for continuous variables (age). Chi-square tests
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
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 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
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
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%) (Table 2).
OADs Adherence and Glycemic Control
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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
The adjusted odds ratios (AOR) and 95% confidence intervals (CI) from multinomial
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
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).
Medication Oversupply
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
There is compelling data to show that glycemic control is essential in the management of
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
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 (57). 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
(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.
Poor glycemic control due to non-adherence to OADs can lead higher microvascular
complications (i.e., nephropathy, retinopathy, and neuropathy) (63), higher macrovascular
complications (i.e., myocardial infarction and ischemic stroke) (64), and high risk of mortality
(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).
Strengths and limitations
Adherence can be measured by different methodological approaches. In the current study,
adherence was assessed using the mMPR method, which is a reliable method and 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 cost and also prevents recall bias, which is associated with patient
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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, 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
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 (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
The project was fully supported financially by the Vice Deanship of Research Chairs, King Saud
University Riyadh, Saudi Arabia.
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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, TA, 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 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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Table 1Characteristics of the Study Population and Number and Row Percentage of Characteristics by Adherence level
among Adults with Diabetes
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
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 9.5
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Table 1Characteristics of the Study Population and Number and Row Percentage of Characteristics by Adherence level
among Adults with Diabetes
Total Poor AdherenceGood Adherence Medication Oversupply
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
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 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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Table 2Frequency and Percentage of OADs
And Comparison between Medication Possession Ratio, Modified (mMPR)
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 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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37. Hess LM, Raebel MA, Conner DA, Malone DC. Measurement of adherence in pharmacy administrative databases: a proposal for standard definitions and preferred measures. Annals of Pharmacotherapy. 2006;40(7-8):1280-8.38. Association AD. Standards of medical care in diabetes—2014. Diabetes care. 2014;37(Supplement 1):S14-S80.39. Teljeur C, Smith SM, Paul G, Kelly A, O’Dowd T. Multimorbidity in a cohort of patients with type 2 diabetes. The European journal of general practice. 2013;19(1):17-22.40. Iglay K, Hannachi H, Joseph Howie P, Xu J, Li X, Engel SS, et al. Prevalence and co-prevalence of comorbidities among patients with type 2 diabetes mellitus. Current medical research and opinion. 2016;32(7):1243-52.41. Monégat M, Sermet C, Perronnin M, Rococo E. Polypharmacy: definitions, measurement and stakes involved: review of the literature and measurement tests. Quest d’économie la santé. 2014;204:1-8.42. Cramer JA. A systematic review of adherence with medications for diabetes. Diabetes care. 2004;27(5):1218-24.43. Krass I, Schieback P, Dhippayom T. Adherence to diabetes medication: a systematic review. Diabetic Medicine. 2015;32(6):725-37.44. Hansen RA, Farley JF, Droege M, Maciejewski ML. A retrospective cohort study of economic outcomes and adherence to monotherapy with metformin, pioglitazone, or a sulfonylurea among patients with type 2 diabetes mellitus in the United States from 2003 to 2005. Clinical therapeutics. 2010;32(7):1308-19.45. McGovern A, Hinton W, Calderara S, Munro N, Whyte M, de Lusignan S. A class comparison of medication persistence in people with type 2 diabetes: a retrospective observational study. Diabetes Therapy. 2018;9(1):229-42.46. Parada Jr H, Horton LA, Cherrington A, Ibarra L, Ayala GX. Correlates of medication nonadherence among Latinos with type 2 diabetes. The Diabetes Educator. 2012;38(4):552-61.47. Cohen HW, Shmukler C, Ullman R, Rivera CM, Walker EA. Measurements of medication adherence in diabetic patients with poorly controlled HbA1c. Diabetic Medicine. 2010;27(2):210-6.48. Wong M, Kong AP, So WY, Jiang JY, Chan JC, Griffiths SM. Adherence to oral hypoglycemic agents in 26 782 Chinese patients: a cohort study. The Journal of Clinical Pharmacology. 2011;51(10):1474-82.49. Mahmoud MIH. Compliance with treatment of patients with hypertension in Almadinah Almunawwarah: A community-based study. Journal of Taibah University Medical Sciences. 2012;7(2):92-8.50. Khayyat SM, Khayyat SMS, Alhazmi RSH, Mohamed MM, Hadi MA. Predictors of medication adherence and blood pressure control among Saudi hypertensive patients attending primary care clinics: a cross-sectional study. PloS one. 2017;12(1):e0171255.51. Khalil SA, Elzubier AG. Drug compliance among hypertensive patients in Tabuk, Saudi Arabia. Journal of hypertension. 1997;15(5):561-5.52. Manteuffel M, Williams S, Chen W, Verbrugge RR, Pittman DG, Steinkellner A. Influence of patient sex and gender on medication use, adherence, and prescribing alignment with guidelines. Journal of women's health. 2014;23(2):112-9.53. Chen C-C, Blank RH, Cheng S-H. Medication supply, healthcare outcomes and healthcare expenses: Longitudinal analyses of patients with type 2 diabetes and hypertension. Health policy. 2014;117(3):374-81.54. Thorpe CT, Johnson H, Dopp AL, Thorpe JM, Ronk K, Everett CM, et al. Medication oversupply in patients with diabetes. Res Social Adm Pharm. 2015;11(3):382-400. Epub 2014/10/08.
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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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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 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
Article Type: Research
Date Submitted by the Author: 20-Jun-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
<b>Primary Subject Heading</b>: Diabetes and endocrinology
Secondary Subject Heading: General practice / Family practice
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 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
*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: 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.
Setting: Large tertiary hospital in the central region of Saudi Arabia.
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,
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
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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.
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 medication adherence rates. 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 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.
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 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).
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 (15, 16), reduced risk of diabetes complications, and reduced economic
burden (17, 18). 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 (19). In Saudi Arabia, nearly 56% of patients have been shown to have low medication
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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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 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
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
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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
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 (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
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 (38). 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
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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) (39, 40). 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 (41). 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
means and standard deviations were calculated for continuous variables (age). Chi-square tests
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
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 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
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
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%) (Table 2).
OADs Adherence and Glycemic Control
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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
The adjusted odds ratios (AOR) and 95% confidence intervals (CI) from multinomial
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
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).
Medication Oversupply
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
There is compelling data to show that glycemic control is essential in the management of
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
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 (61). 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
(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.
Poor glycemic control due to non-adherence to OADs can lead higher microvascular
complications (i.e., nephropathy, retinopathy, and neuropathy) (67), higher macrovascular
complications (i.e., myocardial infarction and ischemic stroke) (68), and high risk of mortality
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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).
Strengths and limitations
Adherence can be measured by different methodological approaches. In the current study,
adherence was assessed using the mMPR method, which is a reliable method and 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 cost 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, 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
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 (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
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, TA, 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 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.
Table 1Characteristics of the Study Population and Number and Row Percentage of Characteristics by Adherence level
among Adults with Diabetes
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
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 16.8
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Table 1Characteristics of the Study Population and Number and Row Percentage of Characteristics by Adherence level
among Adults with Diabetes
Total Poor AdherenceGood Adherence Medication Oversupply
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
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
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 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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Table 2Frequency and Percentage of OADs
And Comparison between Medication Possession Ratio, Modified (mMPR)
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 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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53. Mahmoud MIH. Compliance with treatment of patients with hypertension in Almadinah Almunawwarah: A community-based study. Journal of Taibah University Medical Sciences. 2012;7(2):92-8.54. Khayyat SM, Khayyat SMS, Alhazmi RSH, Mohamed MM, Hadi MA. Predictors of medication adherence and blood pressure control among Saudi hypertensive patients attending primary care clinics: a cross-sectional study. PloS one. 2017;12(1):e0171255.55. Khalil SA, Elzubier AG. Drug compliance among hypertensive patients in Tabuk, Saudi Arabia. Journal of hypertension. 1997;15(5):561-5.56. Manteuffel M, Williams S, Chen W, Verbrugge RR, Pittman DG, Steinkellner A. Influence of patient sex and gender on medication use, adherence, and prescribing alignment with guidelines. Journal of women's health. 2014;23(2):112-9.57. Chen C-C, Blank RH, Cheng S-H. Medication supply, healthcare outcomes and healthcare expenses: Longitudinal analyses of patients with type 2 diabetes and hypertension. Health policy. 2014;117(3):374-81.58. Thorpe CT, Johnson H, Dopp AL, Thorpe JM, Ronk K, Everett CM, et al. Medication oversupply in patients with diabetes. Res Social Adm Pharm. 2015;11(3):382-400. Epub 2014/10/08.59. 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):900-19.60. Association AD. Implications of the United Kingdom prospective diabetes study. Diabetes care. 2003;26(suppl 1):s28-s32.61. 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; 2009.62. 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):691-700.63. 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. Journal of diabetes. 2013;5(3):309-18.64. 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):e108145.65. Rabba AK, Aljiris WS, Ahmed NJ, Alkharfy KM. Medication adherence in type 2 diabetic patients: a study in Saudi Arabia. 2017.66. 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;10:1299.67. 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;123:55-62.68. 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):13775.69. Improving Type 2 Diabetes Therapy Compliance and Persistence in the Kingdom of Saudi Arabia. IMS Institute for Healthcare Informatics, July 2016.70. Roebuck MC, Kaestner RJ, Dougherty JS. Impact of Medication Adherence on Health Services Utilization in Medicaid. Medical care. 2018;56(3):266-73.
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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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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
2
IntroductionBackground/rationale 2 Explain the scientific background and rationale for the investigation
being reported4
Objectives 3 State specific objectives, including any prespecified hypotheses 6
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
Participants 6 (a) Give the eligibility criteria, and the sources and methods of selection of participants
7
Variables 7 Clearly define all outcomes, exposures, predictors, potential confounders, and effect modifiers. Give diagnostic criteria, if applicable
7-8
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
6-7
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
Statistical methods 12
(e) Describe any sensitivity analyses Not Applicable
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
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
10Descriptive data 14*
(b) Indicate number of participants with missing data for each variable of interest
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
Other analyses 17 Report other analyses done—eg analyses of subgroups and interactions, and sensitivity analyses
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
Interpretation 20 Give a cautious overall interpretation of results considering objectives, limitations, multiplicity of analyses, results from similar studies, and other relevant evidence
11-12
Generalisability 21 Discuss the generalisability (external validity) of the study results 14
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
*Give information separately for exposed and unexposed groups.
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