Medication Adherence and its Predictors in Type 2 Diabetic Patients Referring to Urban Primary Health Care Centers in Kerman City, Southeastern Iran


avatar Lila Benrazavy ORCID 1 , avatar Ali Khalooei ORCID 1 , *

Social Determinants of Health Research Center, Institute for Futures Studies in Health, Kerman University of Medical Sciences, Kerman, Iran

how to cite: Benrazavy L, Khalooei A. Medication Adherence and its Predictors in Type 2 Diabetic Patients Referring to Urban Primary Health Care Centers in Kerman City, Southeastern Iran. Shiraz E-Med J.20(7):e84746. doi: 10.5812/semj.84746.



Medication adherence (MA) is one of the crucial aspects in the management of chronic diseases such as diabetes.


This study aimed to evaluate MA and its predictors in type 2 diabetic patients referring to urban primary health care centers.


This cross-sectional study was carried out among type 2 diabetic patients referring to urban primary health care centers in 2017. The data were collected by the Persian version of the eight-item Morisky MA scale. Demographic and disease-related data were also collected. The data were analyzed using SPSS version 22.


Of 589 patients under study, more than 70% used oral hypoglycemic agents as the medication regimen and 29.2% received insulin as monotherapy or in combination with oral antidiabetic agents. Over half of the diabetic patients (51.1%) had other comorbid chronic diseases; moreover, 51.3% of them had at least one of diabetes-related complications. The mean MA score was 6.27 ± 1.81. One-third (33.3%, n = 196) of the patients had a moderate level of MA while 35.4% (n = 208) and 31.3% (n = 184) showed low and high MA levels, respectively. Binary logistic regression analysis showed that education level, type of medication, age, and treatment duration were the predicting factors of MA.


MA was at a suboptimal level among diabetic patients referring to the urban primary health care centers. Poor medication adherence can have negative outcomes for diabetic patients. Thus, primary health care providers should consider self-care behaviors of patients and monitor their medication adherence, as well as other aspects of diabetes management.

1. Background

Diabetes mellitus (DM) is a serious progressive disease with an increasing trend globally. Based on the WHO 2014 report, 422 million adult population had diabetes giving the prevalence of 8.5% in the world (1, 2). Annually, 1.5 million people worldwide die due to DM and its complications such as nephropathy, neuropathy, and cardiovascular complications. Thus, the disease is the eighth leading cause of mortality in the world (2). In Iran, 4.6 million people are estimated to live with DM. The prevalence of DM and its social and economic consequences have increased in recent decades in Iran (3).

Comprehensive care for a DM patient includes regular blood glucose monitoring, exercise, dietary modification, and the use of antidiabetic drugs, which are necessary for disease control (4). Good glycemic control is the cornerstone of DM management and leads to the prevention or delayed onset of the DM-induced complications (5). Antidiabetic medication is one of the main components of blood glucose control and its subsequent positive effects (5). The proper control of DM and achieving optimal therapeutic outcomes require adherence to prescribed regimens, including regular and timely medication use. Therefore, medication adherence (MA) plays a crucial role in attaining optimal treatment results (2).

Haynes et al. defined adherence as the extent to which individuals follow their prescribed treatment instructions (6, 7). In diabetes management, there are factors that can decrease treatment adherence, including the complexity of treatment which requires drastic changes in different aspects of lifestyle such as diet, physical activity, and taking several medications or doses in a day. Moreover, many factors such as biological, sociodemographic, cognitive, and psychological factors can influence the MA (6).

Studies have shown wide variations in the status of MA among type 2 diabetic patients (8-12). The results of five studies in Gaza Strip, Korea, India, Botswana, and Singapore have indicated good MA with the rates of 58%, 61%, 16.6%, 41.8%, and 42.9%, respectively (8-12). A systematic review in 2017 in Iran reported good MA indices ranging from 37.2% to 87% (13).

2. Objectives

As far as we know, there is no study of MA among diabetic patients at the first level of health delivery system in Iran. As MA is an essential part of diabetes management, this study was carried out to evaluate the MA and its predicting factors in urban primary health care centers.

3. Methods

This cross-sectional study was carried out among type 2 diabetic patients between April and July 2017. The study population included type 2 diabetic patients aged over 18 years referring to 42 urban health care centers in Kerman city. Kerman is located in the southeast of Iran with a population of about one million. Three urban health care centers were randomly selected from each of the four zones of the city. Therefore, 12 out of the total 42 urban health care centers were selected. A convenience sampling method was used to select 50 patients from each of the selected centers; hence, the study sample included 600 participants. The inclusion criteria were a diagnosis of type 2 diabetes according to the WHO guidelines with at least one-year disease duration and at least one-year use of antidiabetic medications (oral antidiabetic agents or insulin).

The data were collected through the Persian version of the eight-item Morisky Medication Adherence Scale (MMAS) (14). The MMAS is a general questionnaire with eight items for the assessment of medication-taking behavior. Seven items are yes/no questions whereas the last item is rated on a five-point Likert scale. The total MMAS score ranges from 0 to 8 and the adherence level could be categorized as high (score = 8), moderate (score = 6 to < 8), and low (score < 6) (14). Moreover, the original researchers suggested a cutoff point of six to categorize the MMAS scores and this method was applied by other researchers, as well (8, 15, 16). For data analysis, the patients were classified into two groups: non-adherent (MMAS score < 6) and adherent patients (MMAS score ≥ 6).

The validity and reliability of the MMAS were confirmed in Iran for chronic diseases such as hypertension (16, 17). Furthermore, we carried out a pilot study to assess the reliability and validity of the MMAS by applying it twice among 30 diabetic patients with an interval duration of 10 to 14 days. The result of this assessment indicated the Cronbach’s alpha and ICC values of 0.75 and 0.88, respectively.

Furthermore, we gathered the demographic data such as age, sex, marital status, education level, occupation, and income level, as well as disease-related characteristics including disease duration, type of medication, comorbidity with other chronic diseases, diabetes-related complications, and the number of follow-up visits for controlling diabetes by a general practitioner, internist, or endocrinologist during the previous year. The MMAS was completed by the participants. In the case of illiterate participants, a trained interviewer read the questions for the patients and completed the questionnaire. For each participant, the goals of the study and how to complete the questionnaire were explained. After obtaining written consent, the questionnaires were completed. Furthermore, the study was approved by the Ethics Committee of Kerman University of Medical Sciences (ethics code: IR.KMU.AH.REC.1396.1301).

The collected data were analyzed by SPSS version 22 software. The descriptive data were presented as frequency, percentage, mean, and standard deviation in tables. The chi-square, independent t-test, and ANOVA were also employed to determine differences between subgroups. Binary logistic regression analysis was performed to determine the predicting factors of MA.

4. Results

Out of the 600 gathered questionnaires, 11 were excluded due to incomplete data. Therefore, 589 questionnaires (response rate, 98.2%) were included in data analysis. The mean age of the participants was 56.40 years (SD = 11.97) whereas 72.3% (n = 426) of the patients were aged 64 years or younger. More than two-thirds (67.9%, n = 400) of the study group were female and 26.6% (n = 157) were widowed or single. The majority of the participants were housewives (55.7%).

The mean values of disease duration and treatment duration were 8.63 years (SD = 6.17, median = 7) and 7.84 years (SD = 5.65, median = 6), respectively. More than 70% of the patients took oral antidiabetic agents; the rest of them took insulin as monotherapy or in combination with oral antidiabetic drugs in the medication regimen. Over half of the diabetic patients (51.1%) had other comorbid chronic diseases. Moreover, more than half of them (51.3%) suffered from at least one diabetes-related complications (Table 1). The mean number of patients’ visits in the previous year by a general physician was 4.26 ± 3.51 with an interquartile range of 3 - 6 in primary health care centers. The mean number of visits in the previous year by a specialist (internist) or subspecialist (endocrinologist) was 2.44 with an interquartile of 2 - 4 (Table 2).

Table 1. The Frequency Distribution of Patients at Various MA Levels Based on Demographic and Diabetes-Related Variables in the Study Samplea
Variable/CategoriesTotal SampleAdherentNon-AdherentP Value
Female400 (67.9)251 (62.7)149 (37.3)
Male189 (32.1)129 (68.6)59 (31.4)
Marital status0.442
With spouse423 (73.4)277 (64.3)154 (34.7)
Without spouse157 (26.6)103 (65.6)54 (34.4)
Education level0.060
Primary school or less224 (38.0)132 (58.9)92 (41.1)
Secondary or high school301 (51.1)202 (67.3)98 (32.7)
University64 (10.9)46 (71.9)18 (28.1)
Job category0.258
Housewife328 (55.7)203 (61.9)125 (38.1)
Government employee77 (13.1)52 (67.5)25 (32.5)
Non-government employee87 (14.7)55 (64.0)31 (36.0)
Retired97 (16.5)70 (72.2)27 (27.8)
Monthly income (US dollars), $0.004
< 250161 (27.3)89 (56.6)71 (44.4)
> 250428 (72.2)262 (68.2)122 (31.8)
Type of medication< 0.001
Insulinb172 (29.2)135 (78.5)37 (21.5)
Oral hypoglycemic agents417 (70.8)245 (58.9)171 (41.1)
Comorbidity of chronic diseases0.029
Yes301 (51.1)206 (68.4)95 (31.6)
No288 (48.9)174 (60.6)113 (39.4)
Diabetes complication0.003
Yes302 (51.3)211 (70.1)90 (29.9)
No287 (48.7)169 (58.9)118 (41.1)
Table 2. Comparison of the Mean Age, Disease Duration, Treatment Duration, and Number of Visits in the Health Care Centers by a Specialist/Subspecialist Between Adherent and Non-adherent Groupsa
VariableTotal SampleAdherentNon-AdherentP Value
Age, y56.40 ± 11.9756.93 ± 11.7655.42 ± 12.330.143
Disease duration, y8.63 ± 6.179.21 ± 6.217.57 ± 5.960.002
Treatment duration7.84 ± 5.668.42 ± 5.766.77 ± 5.330.001
Number of visits in health care center4.26 ± 3.524.22 ± 3.754.28 ± 3.400.824
Number of visits by specialist/subspecialist2.44 ± 1.932.65 ± 2.002.06 ± 1.72< 0.001

The mean MA score was 6.27 (SD = 1.81, median = 7, interquartile = 5.5 - 8). One-third (33.3%, n = 196) of the patients had a moderate level of MA, while 35.4% (n = 208) and 31.3% (n = 184) showed low and high MA levels, respectively. Considering the score of 6 as the cutoff point, 64.6% (n = 380) of the patients were categorized into the adherent group and 35.4% as the non-adherent group. Table 1 presents the frequencies of patients at different MA levels based on various variables. The frequency of adherent patients was significantly higher in the group with a monthly income of over 250 US dollars than the group with monthly income of 250 US dollars or less (68.2% vs. 56.6%, P = 0.004). However, there were no significant differences in the frequency of adherent patients based on sex, marital status, education level, and occupation (Table 1). The frequency of adherent patients was higher in the group taking insulin (as monotherapy or in combination with oral antidiabetic drugs) than in the group receiving oral antidiabetic medications (78.5% vs. 58.9%, P < 0.001). Comorbidity with another chronic disease (68.4% vs. 60.6%, P = 0.029) and having diabetes-related complications (70.1% vs. 58.9%, P = 0.003) were associated with more adherence.

The mean disease duration was 1.74 years higher in the adherent group than in the non-adherent group (P = 0.002). In addition, the mean treatment duration was 1.65 years higher in the adherent group than in the non-adherent group (P = 0.001). The mean age and the mean number of visits in health care centers did not show any significant difference between adherent and non-adherent groups (P = 0.824), while the mean number of follow-up visits by specialist/subspecialist was higher in the adherent group (P < 0.0001) (Table 2).

Based on the binary logistic regression analysis, education level, type of medication, age, and treatment duration were the predicting factors of adherence. The odds of adherence for patients with “secondary or high school” and university education levels were 2.43 (P < 0.0001) and 5.86 (P < 0.0001) times more than those of patients with primary school or illiterate patients, respectively. The odds of adherence for patients who took insulin as monotherapy or in combination with oral antidiabetic agents was 2.38 (P = 0.019) times more than those of patients who took oral antidiabetic drugs. By every unit increase in age and treatment duration (in years), the odds of adherence increased by 1.026 (P = 0.035) and 1.045 (P < 0.0001) times, respectively (Table 3). As a result, the education level and treatment type were the most important predicting factors of MA.

Table 3. Binary Logistic Regression for Determining the Predictors of MA in the Study Sample
PredictorBSEExp (B)95% CI for Exp (B)
Education level
Primary school or illiterateReference
Secondary or high school0.8910.2352.431.53 - 3.87
University1.7600.4915.862.24 - 15.32
Type of medication
Oral antidiabetic medicationReference
Insulin0.8700.2362.381.50 - 378
Treatment duration0.0440.0211.0451.002 - 1.089
Age0.0260.0211.0261.003 - 1.049

5. Discussion

In this study, the mean MA score was 6.27 out of 8 and 35.4% of the patients were non-adherent. Thus, MA was at a suboptimal level among patients referring to the first level of the health care system. Studies in other countries reported various MA scores ranging from 16% to 86% (8-12). Moreover, studies in Iran demonstrated that 37% - 87% of type 2 diabetic patients had good MA. In line with other studies, our results showed that MA was not satisfactory among diabetic patients. Non-adherence to medication can lead to negative consequences such as inadequate glycemic control, waste of medication, disease progression, increased morbidity and mortality, reduced functional abilities, and decreased quality of life (8, 10, 18). In addition, non-adherence to medication can result in the increased demand for outpatient care, complex health care services, emergency departments’ visits, hospitalization, and the use of medical resources, which all impose a significant financial burden on patients and the health care system (4, 19).

The results of the current study showed that education level was the most important predicting factor of adherence to medication. The odds of adherence in patients with university education and high school education were 5.86 and 2.43 times more than those of patients with primary or lower education levels, respectively. Consistent with this finding, a study in the USA and another study in Turkey showed that education level was associated with compliance to treatment so that diabetic patients with higher education levels had better compliance with medication (20, 21). On the other hand, in contrast to our results, several studies have not found any association between patients’ education level and adherence to medication (22-24). One explanation for better adherence rate in patients with higher education level can be that the more educated patients usually have more knowledge about the importance and positive effects of medication to attain glycemic control and prevent diabetes complications (25).

The results of the present study showed that age was a predictor of medication adherence. However, there was no significant difference between male and female patients in medication adherence. In accordance with our results, studies in Malaysia, Singapore, USA, and France showed that older patients had better MA status (18, 26-28). In contrast, some studies reported that the age of patients was not a determining factor for MA (8, 29). Overall, we can conclude that older patients have higher MA in any chronic conditions including diabetes (8). Better MA in older people could be attributed to their greater awareness of the disease and its complications, more positive attitudes toward treatment, higher frequency of diabetes complications, and comorbidity with other chronic diseases, as well as less concern about drug side effects (8).

The findings of this study disclosed that MA was associated with disease-related factors including disease and treatment duration, the presence of insulin in antidiabetic regimen, suffering from diabetes complications, the presence of other comorbid chronic diseases, and the number of annual follow-up visits by specialists or subspecialists. Treatment duration and the presence of insulin in antidiabetic regimen were the predictors of MA in the binary logistic regression model. Patients with longer treatment duration usually have longer diagnosis duration; hence, such patients are more aware of the disease and positive effects of treatment; they are also more likely to have diabetes complications; so, they have better attitudes toward the need for treatment, leading to better MA (30, 31).

Our findings showed that having insulin in antidiabetic regimen was associated with higher MA. In general, due to pain and fear of injection, as well as difficulties of injection preparation, poor MA is expected in insulin-injecting patients (32). Insulin is commonly used in patients with the more severe and prolonged disease and those who do not have satisfactory blood glucose control by oral antidiabetic agents; thus, in these advanced stages of the disease, the recommendation and prescribed drugs by health care providers will comply better (32). Several studies did not report such associations; however, consistent with our results, a study reported better MA in insulin-receiving diabetic patients (8, 9, 28, 33). In contrast to these results, a study in India showed that patients taking oral antidiabetic drugs had more MA (34). The contradictory results of various studies could be attributed to the differences in the study populations, study settings, and patterns of insulin prescription.

We noted that the mean number of visits by a specialist or subspecialist was significantly higher in the adherent group, while the mean number of visits in primary health care centers did not make such differences. In a study in the USA, there was no difference in MA between patients who referred to primary health care centers and those referring to endocrinologists (20). Another study demonstrated that being less engaged with a physician or other health care professionals was associated with poor MA. Furthermore, MA is better when patients report a sense of trust in their physician (35). Physicians can help diabetic patients improve their self-care behaviors by scheduling frequent follow-up visits and discussing self-care challenges with their patients (36). It is expected that primary health care providers play a significant role in the management of diabetes and its various aspects such as MA, but our results did not show this relationship.

This study faced two limitations. First, we enrolled patients attending primary health care centers. These patients may have different patterns of behaviors, including treatment adherence, compared to those who did not refer to primary health care centers. Moreover, measuring human behaviors via self-report methods usually results in underestimation than the actual status.

5.1. Conclusions

This study showed that medication adherence was at a suboptimal level among diabetic patients referring to primary health care centers. We found that more than half of the patients had at least one diabetes complication and 51% had other comorbid chronic diseases; thus, diabetes management was not satisfactory in urban primary health care centers. Moreover, the study showed that age, having insulin in antidiabetic regimen, treatment duration, higher education level, and the number of follow-up visits by a specialist/subspecialist were the predictors of MA. Primary health care centers are the first level of the health delivery system and the majority of diabetic patients receive health care in these centers. Therefore, improving patients’ knowledge of the disease and their self-care behaviors by a trained health care provider is necessary for better diabetes self-management and enhancement of MA.



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