https://orcid.org/0000-0002-7341-5536
Figures
Abstract
Background
Diabetes mellitus is a serious global public health problem that affects the whole life of people in terms of their biological, psychological, and social effects. Complications and death from diabetes occur from poorly controlled blood glucose levels. Thus, dealing with glycemic control is essential for controlling the development of devastating acute and chronic complications related to diabetes. Therefore, this study aims to assess factors associated with poor glycemic control among type2 diabetes patients in public hospitals of Gamo and Gofa zone southern, Ethiopia, 2021.
Methods
An institution-based unmatched case-control study was employed among 312 randomly selected participants using a pre-tested, interviewer-administered, and structured questionnaire. Bivariate and multivariable logistic regression analysis was conducted to identify factors associated with poor glycemic control using IBM SPSS version 25. The strength of association was assessed by using an Adjusted odds ratio (AOR) with a 95% confidence interval (CI).
Result
Factors associated with poor glycemic control based on multivariable analysis were, having comorbidity (AOR = 2.35, 95% CI (1.39–3.95)), adhering to dietary recommendations (AOR = 0.31, 95% CI (089–0.51)), poor social support (AOR = 3.31, 95% CI (1.59–6.85)), physical exercise (AOR = 1.86 95% CI (1.11–3.12)), and having poly-pharmacy (AOR = 2.83, 95% CI (1.39–5.74)).
Conclusion and recommendation
This study indicated a significant association of comorbidity, physical exercise, poly-pharmacy, low social support, and adherence to dietary recommendations with poor glycemic control. We suggest that the health care providers and concerned bodies encourage patients to have regular check-ups and work on providing necessary social support.
Citation: Dawite F, Girma M, Shibiru T, Kefelew E, Hailu T, Temesgen R, et al. (2023) Factors associated with poor glycemic control among adult patients with type 2 diabetes mellitus in Gamo and Gofa zone public hospitals, Southern Ethiopia: A case-control study. PLoS ONE 18(3): e0276678. https://doi.org/10.1371/journal.pone.0276678
Editor: Innocent Ijezie Chukwuonye, Federal Medical Centre Umuahia, NIGERIA
Received: November 22, 2021; Accepted: October 12, 2022; Published: March 10, 2023
Copyright: © 2023 Dawite et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: All relevant data are within the paper.
Funding: The author(s) received no specific funding for this work.
Competing interests: The authors have declared that no competing interests exist.
List of abbreviations: ADA, American Diabetes Association; AOR, Adjusted Odds Ratio; CI, Confidence Interval; DM, Diabetes Mellitus; DKA, Diabetic Ketoacidosis; DGT, Decreased Glucose; ETB, Ethiopian Birr; FPG, Fasting Plasma Glucose; FGC, Fasting Glucose Change Tolerance; GTT, Glucose Tolerance Test; GD, Gestational Diabetes; HC, Hip Circumference; IDF, International Diabetes Federation; MMS, Modified Morisky Scale; T1DM, Type 1 Diabetes Mellitus, T2D: Type 2 Diabetes; T2DM, Type 2 Diabetes Mellitus, SDCA: Self Diabetic Care Activity; SPSS, Statistical Package for the Social Sciences; NCD, Non-Communicable Diseases; OPD, Outpatient Department; WC, Waist Circumference; WHO, World Health Organization
Introduction
Non-communicable diseases (NCDs), such as cardiovascular diseases, cancers, diabetes, and chronic respiratory diseases, are now the leading cause of death in most regions of the world [1]. Diabetes is a serious, chronic disease that occurs either when the pancreas does not produce enough insulin (a hormone that regulates blood glucose), or when the body cannot effectively use the insulin it produces [2]. Type 2 diabetes mellitus (T2DM), previously referred to as “noninsulin-dependent diabetes” or “adult-onset diabetes,” accounts for 90–95% of all diabetes. This form encompasses individuals who have relative insulin deficiency and peripheral insulin resistance [3]. The global diabetes prevalence in 20–79-year-olds in 2021 was estimated to be 10.5% (536.6 million people), rising to 12.2% (783.2 million) in 2045 [4]. The burden of diabetes mellitus (DM) is higher in low and middle-income countries like Ethiopia where, the total unmet need for diabetes care is around 77.0%, and rural facilities, particularly in SSA lack access to proper monitoring of glucose like hemoglobin A1c and other key biologic tests [5, 6]. About half (46.2%) of the deaths attributable to diabetes occur in people under the age of 60 years [7]. The Africa Region has the highest (73.1%) proportion of deaths attributable to diabetes in people under the age of 60 years [7]. Furthermore, diabetes carries a significant “double burden” of infectious and chronic diseases in Africa [8]. Diabetes is not fatal if managed effectively, but untreated hyperglycemia results in various multi-organ complications that cause acute and chronic morbidity and death [9]. Poor glycemic control is associated with reduced life expectancy, significant morbidity due to specific diabetes-related microvascular complications, increased risk of macrovascular complications, and diminished quality of life [10].
Despite the availability of a wide range of effective glucose-lowering therapies, approximately half of patients with T2DM in the world do not achieve glycemic targets [11, 12]. A multicenter study conducted in Eastern Europe, Asia, and Latin America showed that 96.4% of study participants had poor glycemic control [13]. similarly, Ethiopia had a consistent and high prevalence of poor glycemic control among diabetic patients ranging from 62.5 in Tigray to 65.5 in the Oromo region [14].
In Ethiopia a cross-sectional study has been conducted on factors associated with poor glycemic control Self-monitoring blood glucose, presence of comorbidities, duration of diabetes mellitus, physical activity, total cholesterol of 200 mg/dl or more, waist-to-hip ratio or and types of anti-diabetic medication were identified as factors significantly associated with of poor glycemic control [15–18].
Diabetes management aims to prevent mortality and complications by optimizing the blood glucose level [19]. Clinical trials have shown that tight blood glucose control correlates with a reduction in those complications in a patient with T2DM [20, 21]. Each 1% reduction in the mean Glycated hemoglobin (HbA1c) is associated with a reduction in risk of 21% for deaths related to diabetes, 14% for myocardial infarction, and 37% for microvascular complications [22]. Even if several studies were conducted on factors associated with poor glycemic control in Ethiopia most of them are in the Amhara and Oromia regions which may not represent the southern region of the country very well, so, therefore, there is limited evidence on determinants of poor glycemic control in the southern region of the country. Additionally, the previous studies did not assess the influence of social support and poly-pharmacy on poor glycemic control very well. As well as this study is new in the research area. So then this study aimed to identify factors associated with poor glycemic control among type 2 diabetes mellitus patients in public hospitals of Gamo and Gofa zone, southern, Ethiopia; besides the association of perceived social support with glycemic control was also investigated.
Materials and methods
Study design, setting, and period
This study was conducted in selected three Hospitals in Gamo and Gofa Zones, Southern Ethiopia from March 18 to May 18, 2021. These two Zones are found within the South Nations, Nationalities, and Peoples’ Region (SNNPR) of Ethiopia The administrative center of the two zones, Arba Minch (Gamo zone) and Sawla (Gofa zone) are located at 434 km and 455 km respectively far south of Addis Ababa, the capital city of Ethiopia. The total population of the study area is 2,658,345 in the year 2017/18 as estimated from the 2007 Ethiopian census. There are two general and four primary hospitals providing curative, preventive, and rehabilitative services for the population in the two zones. The hospitals have a total of 2115 diabetic patients who are under follow up and from those 1091 T2DM have a follow-up in the three study hospitals.
Population
Cases were T2DM patients in the follow-up clinic that were classified as having poor glycemic control by using an average of three consecutive Fasting Plasma Glucose (FPG) levels and who had an average FPG >130mg/dl. Controls were T2DM in the follow-up clinic that were classified as having good glycemic control by using three consecutive average FPG levels and who had FPG 80–130 mg/dl.
The source population; is for cases of all patients in the follow-up clinic with poor glycemic control. To control all T2DM in the follow-up clinic with good glycemic control.
The study population; for cases all T2DM with poor glycemic control came to the follow-up clinic during data collection time. To control all T2DM patients with good glycemic control who came to the follow-up clinic during data collection time.
Sampling
The sample size was calculated by using Epi info7 software stat calc. The following assumptions were considered 95% confidence interval, 80% power, a 50% expected proportion of DM patients with adequate physical exercise from a study in northwest 2017 Ethiopia who had good glycemic control (control), and a 67.1% expected proportion of DM patients with inadequate physical exercise who had poor glycemic control (cases), and a case to control the ratio of 1:1. Based on the above assumptions, the sample size calculated was 312(156 cases and 156 controls). Among the variables considered, physical exercise was selected as an associated factor variable for poor glycemic control since it gave a maximum sample size. Both cases and controls were selected by employing a systematic random sampling technique with a proportional allocation of samples (26, 27, 29, 40, 67).
Data collection procedures and instruments
Structured interviewer-administered questionnaires were used after reviewing all the relevant literature, and recorded review and physical measurements were done. The questionnaire had four parts: socio-demographic information, clinical and anthropometric measure data related to behavioral factors, and a perceived social support scale.
The Summary of diabetic self-care activity (SDCA) was used to measure behavioral factors such as adherence to diabetes-related exercise and self-monitoring of blood glucose levels. It was used in previous studies in evaluating adherence to diabetes medication and diabetes diet among DM patients [23, 24]. Moreover, 10 and 8 items Modified Morisky Scale (MMS) were used to measure other behavioral factors such as adherence to medication and diet [23, 24].
Measurement. A multidimensional scale of perceived social support (MSPSS) was used to measure perceived social support MSPSS. The scale is composed of 12 items in three groups, each of which is composed of four items regarding family (Items 3, 4, 8, and 11), friend (Items 6, 7, 9, and 12), and a special person (Items 1, 2, 5, and 10) [25]. Each item was graded using a 7-point scale. The subscale score is obtained by adding the scores of four items in each scale, and the total scale score is obtained by adding all the subscale scores. The lowest score from the subscales is 4, and the highest score is 28. Based on the mean score it was categorized as good social support if it is above the mean else poor social support [26].
Other DM-related variables that might influence values of glycemic control were taken from medical history records and these are duration with DM, the presence of comorbidity type of medication currently taken, and FPG level.
Anthropometric measurements.
Bodyweight and height were measured by a stadiometer (Seca Germany) a portable weight scale machine. Body weight was measured to an accuracy of 0.1 kg by using Seca Germany and Subjects were measured barefoot. Height was measured in, standing upright by a stadiometer Seca Germany to the nearest 0.1c.m. Body mass index (BMI) was calculated as the ratio of weight in kilograms (kg) to the square of height in meters (m2).
Waist-to-hip ratio.
Waist-to-hip ratio (WHR) was measured after the participants stood with arms at the sides, feet positioned close together, and weight evenly distributed across the feet, the waist circumference (WC) was measured to the nearest 1 cm three times at the approximate midpoint between the lower margin of the last palpable rib and the top of the iliac crest, at the end of a normal exhalation. The mean of the three measurements was calculated and taken at the end. Participants were told to relax and take a few deep, natural breaths before the actual measurement was done to minimize the inward pull of the abdominal contents during the waist measurement. The hip circumference (HC) of the patients was measured three times to the nearest centimeter at the largest circumference of the buttocks. Both hip and waist circumferences were measured with stretch-resistant tape that is wrapped snugly around the participants and the tape was kept level and parallel to the floor at the point of measurement. This protocol of measurement is per the world health organization’s (WHO) Stepwise approach to surveillance [27]. Six trained BSc nurses working in different areas of the study sites were recruited as data collectors and two of them were employed as supervisors for consecutive two months.
Data quality management
The structured questionnaire was developed after reviewing different kinds of literature. Then it was translated from English to Amharic and back-translated into English by the same individual to assure its consistency. Three days of training were given to data collectors and supervisors about the aim, procedure, tool, and ethics before data collection. Whereas pretest was done on patients (5%) on 8 cases and 8 controls at Grease primary hospital and necessary changes were done based on the result of the test. The collected data were reviewed and checked daily for completeness and consistency by the supervisor and principal investigator and ongoing supervision was made.
Data processing and analysis
Data were recorded in the mobile KOBO toolbox and exported to the SPSS version 25 software package for further management and analysis. Descriptive and analytic statistics were done. Descriptive statistics were used to describe the distribution of explanatory variables among case and control. The findings were presented in narration and tables proportions, and mean and standard deviation. Bi-variable and multivariable analyses using a logistic regression model with odds ratio and its corresponding (CI) 95% confidence interval were done. To identify the significant association variables (p≤0.25) were considered the candidate variables for the multivariable binary logistic regression analysis and finally the strength of association was measured by computing the AOR with a 95% CI. The statistical significance was declared at computing (p≤0.05). And all assumptions of binary logistic regression were checked. Multi-collinearity was assessed by using the variance inflation factor and tolerance. The VIF was <10 and the tolerance was >0.1. Multivariate outliers have been checked by cooked distance and it was < 1 since then there was no problem with a multivariate outlier. In addition assumptions of chi-square were assessed none of the cells has an expected frequency <5. Cross-tabulation was used to summarize descriptive statistics. An odds ratio with 95% CI was used for measuring the strength of the association. P-value < 0.05 was considered as statistically significant. The fitness of the model was checked by Hosmer and Lemeshow goodness of fit test and the p-value of the test were 0.321.
Ethical considerations
Ethical approval was obtained from the ethical review committee of Arba Minch University, College of Medicine and Health Sciences (IRB/1089/2021/reference). Following the approval, an official letter of co-operation was written to concerned bodies by the Department of Public Health of Arba Minch University. Letter of cooperation was obtained from the respective hospitals and written informed consent was obtained from the study participants after informing the purpose of the study.
Results
Socio-demographic characteristics of study participants
Three hundred twelve study participants (156 cases and 156 controls) were included in the study with a 100% response rate. The majority of controls and cases 98(56.4%) and 88(62.8%) were females. The mean age of the respondents was 59±14 (years ±SD) and 57±17(years ±SD) for cases and controls respectively. More than one-third of both cases and controls were in the age group >64 years”. Eighty-nine percent (140) of cases and 137(87.8%) of control were married. Regarding educational background 30(19.2%) of cases and controls had no formal education. Likewise, 89(57.1%) of cases and 95(60.9%) of controls live in an urban area (Table 1).
Anti-hyperglycemic medications and poly-pharmacy
Regarding anti-diabetic medications, 77(49.4%) of cases and 73(46.8%) of control were prescribed with Metformin and Glibenclamide followed by metformin alone 28(17.9%) for both cases and controls. Additionally, 35(22.4%) for control and 26(10.7%) for cases take insulin alone. Regarding poly-pharmacy, twenty-one percent of 34(21.8%) of cases and 16(10.3%) of control were taking greater than or equal to five medications including medication for blood glucose control. Regarding the duration of diabetes, the duration of diabetes was greater than 10 years in 32(23.1%) of the cases and 31(19.2%) of the controls. Correspondingly seventy percent of 42(26.9%) of the cases and 60(38.5%) of the controls had abnormal WHR of ≥ 0.9 for males or ≥ 0.85 for females. Regarding comorbidity 77(49.4%) of cases and 46(29.5%) of controls had comorbidity (Table 2).
Diabetes self-care activities and behavioral factors
The proportions of T2DM diabetic patients who adhere to dietary recommendations were 47 (30.7%) among cases and 90(57.7%) among controls who had good adherence to the dietary plan. In the assessment of medication adherence, 81(51.9) of cases and 60(38.5%) of controls had no adherence to medication. Of the study participants who did adequate exercise 49(31.4%) percent of the cases and 77(49.4%) of the controls have been involved in, at least 30 min of, exercise for 4 and above- days during the last seven days preceding the study. Moreover, the majority of both cases and controls did not do blood glucose self-monitoring. In addition majority of the study participants never smoked a cigarette and never chew khat in both cases and controls. Regarding alcohol drinking 17(10.9%) of cases and 9(5.8%) of controls were current drinkers (Table 3).
Factors associated with poor glycemic control
In bi-variable logistic regression analysis, educational level, blood glucose self-monitoring, being a member of DM association, social support, comorbidity, dietary adherence, medication adherence, poly-pharmacy, sex, physical exercise, and alcohol drinking were found to have p-value <0.25 and entered into the multivariate logistic analysis. In multivariable binary logistic analysis, poly-pharmacy, dietary adherence, comorbidity, physical exercise, and social support were found to be significantly associated with poor blood sugar control (Table 4).
The odds of poor glycemic control were 2.35 times higher among T2DM with comorbidity compared to those who do not have comorbidity (AOR = 2.35, 95% CI (1.39–3.95)). The odds of poor glycemic control were lower for those T2DM diabetic patients who adhere to a dietary recommendation by 69% compared to those T2DM diabetic patients who do not adhere to a dietary recommendation (AOR = 0.31, 95% CI (.19-.51)) (Table 4).
The odds of poor glycemic control were 2.83 times higher among T2DM with poly-pharmacy as compared to participants who had no ploy pharmacy (AOR = 2.83, 95% CI (1.37–5.85)). The odds of poor glycemic control were1.86 times (AOR = 1.86 95% CI (1.11–3.12)) higher among T2DM patients involved in physical activity only for less than three days as compared to participants doing regular physical activity for more than three days.
The odds of poor glycemic control were 2.35 times higher among T2DM with comorbidity compared to those who do not have comorbidity (AOR = 2.35, 95% CI (1.39–3.95)). The odds of poor glycemic control were lower for those T2DM diabetic patients who adhere to a dietary recommendation by 69% compared to those T2DM diabetic patients who do not adhere to a dietary recommendation (AOR = 0.31, 95% CI (.19-.51)) (Table 4).
Discussion
This study assessed factors associated with poor glycemic control among T2DM patients in Gamo and Gofa zone public hospitals. The results show that comorbidity, poly-pharmacy, social support, dietary adherence, and physical exercise are factors associated with poor glycemic control.
In this study, the odds of poor glycemic control were 3.05 times higher among T2DM patients with poor social support compared to those with good social support (AOR = 3.05, 95% CI (1.64–5.65)). In line with this study finding a study in China, Ghana, New work also revealed that Social support had significant correlations with glycemic [35–37]. The possible justification can be diabetic patients who have low social support have low self-care practice which indirectly leads to poor glycemic control [38]. Increased social support is directly associated with reduced HbA1c, but it is also indirectly associated with HbA1c through various mechanisms including diet and medication adherence. Social support is important in helping patients with diabetes cope with the disease and improves treatment adherence. In addition results in turkey indicate that social support and empowerment are important for nurses to consider when planning interventions that increase the self-care behavior of individuals with type 2 diabetes and for improving glycemic control [26]. A study was done in turkey also revealed that Social support was a statistically significant predictor of, blood glucose monitoring (42). In contrast, a study done in South Africa revealed that there was no association between social support and HbA1c this might be because the population might be different in way of life and culture [39].
In this study, the odds of poor glycemic control were 2.83 times higher among T2DM patients with poly-pharmacy as compared to participants who had no ploy pharmacy (AOR = 2.83, 95% CI (1.39–5.74)). A possible justification can be Poly-pharmacy increases the probability of adverse drug events [40, 41]. Therefore poly-pharmacy might decrease compliance to anti-diabetic medications and leads to suboptimal glycemic control [42, 43]. A study done in Romania revealed that T2DM patients who received more drugs than their non-diabetes counterparts were exposed to more drug-drug and food-drug interactions [43]. And another possible justification can be low medication adherence to prescribed medications.
The odds of poor glycemic control were 1.86 times higher among T2DM patients with involvement in physical activity only for less than three days as compared to participants doing regular physical activity for more than three days (AOR = 1.86 95% CI (1.11–3.12)). This is in line with the study done in Jimma, Nekemte, Saudi Arabia, Jordan, and Thailand [15, 44–47]. This might be because, in people with type 2 diabetes, exercise can improve peripheral insulin sensitivity as well as enhance insulin binding. Exercise also decreases abdominal fat, reduces free fatty acids, and increases insulin-sensitive skeletal muscle, which may result in improved glycemic control [48, 49].
In this study, the odds of poor glycemic control were 2.35 times higher among T2DM patients with comorbidity when compared to those T2DM patients who do not have comorbidity (AOR = 2.35, 95% CI (1.39–3.95)). This finding is consistent with studies done in Mekelle town, Jimma, India, and the Netherlands [15, 50–52]. The possible justification might be because the presence of comorbid illness aggravates disease processes and reduces their quality of life [53]. Another reason might be comorbidity increases poly-pharmacy which increases pill burden and adverse drug reactions. In contrast with this, a study at a police referral hospital in Addis Ababa Ethiopia 2018 revealed that the presence of comorbidity showed a high likelihood of FPG target achievement among the patients this might be because the study participants might have good adherence to medication than comorbid T2DM diabetic study participants in this study [54].
Finally, In this study, the odds of poor glycemic control were lower for those T2DM patients who adhere to a dietary recommendation by 69% compared to those type 2 diabetic patients who do not adhere to dietary recommendations (AOR = .31, 95% CI (.19-.51)). This finding is consistent with the studies conducted at Suhul Hospital, Haromaya, Tigray, Debre-Tabour, Ethiopia, which determined that good dietary adherence is significantly associated with having good glycemic control [16, 23, 54, 55]. A possible justification could be avoiding a high-fat diet, especially one high in saturated fats, which has been linked to T2DM and insulin resistance. It appears that saturated fatty acids (but not unsaturated fats) activate immune cells, which produce an inflammatory protein, which in turn then makes cells more insulin resistant so then avoiding a high-fat diet decreases the occurrence of insulin resistance. And also Adherence to diet facilitate weight loss, improving glucose control and lipid profiles in patients with T2DM [56].
Conclusion and recommendation
In this study, the presence of comorbidity, physical exercise, poly-pharmacy, low social support, and adherence to dietary recommendations were factors associated with poor glycemic control. Therefore, concerned health authorities and health professionals should give special attention to regular follow-up and control of blood glucose levels. Healthcare providers should take aware of psychosocial factors in the treatment regime of the patient. Family members and society should be educated about diabetes, the importance of controlling blood glucose levels consciously, and the long-term complications of the disease. A cohort study is recommended for future researchers to infer substantial evidence of causality.
Limitations of the study
None of the patients had HbA1c determination due to the unavailability of the laboratory service for the HbA1c determination in the study hospitals. As some parts of the questionnaire depended on the memory of respondents may have resulted in recall bias.
Acknowledgments
Our special thanks go to Arba Minch General hospital, Sawla general hospital, Chencha primary hospital, and their respective staff for their support while undertaking this study. We would like to acknowledge all study subjects who voluntarily participated in this study.
References
- 1.
Curbing N. Noncommunicable diseases in africa: youth are key to curbing the epidemic and achieving sustainable development. Washington: Population Reference Bureau. 2015.
- 2.
World Health Organization. Global report on diabetes: World Health Organization. Geneva S. 2016.
- 3. Association AD. 2. Classification and diagnosis of diabetes: standards of medical care in diabetes—2020. Diabetes care. 2020;43(Supplement 1):S14–S31. pmid:31862745
- 4. Sun H, Saeedi P, Karuranga S, Pinkepank M, Ogurtsova K, Duncan BB, et al. IDF Diabetes Atlas: Global, regional and country-level diabetes prevalence estimates for 2021 and projections for 2045. Diabetes research and clinical practice. 2022;183:109119. pmid:34879977
- 5. Manne-Goehler J, Geldsetzer P, Agoudavi K, Andall-Brereton G, Aryal KK, Bicaba BW, et al. Health system performance for people with diabetes in 28 low-and middle-income countries: a cross-sectional study of nationally representative surveys. PLoS medicine. 2019;16(3):e1002751. pmid:30822339
- 6. Park PH, Pastakia SD. Access to Hemoglobin A1c in Rural Africa: A Difficult Reality with Severe Consequences. Journal of Diabetes Research. 2018;2018:6093595. pmid:29682580
- 7. Saeedi P, Salpea P, Karuranga S, Petersohn I, Malanda B, Gregg EW, et al. Mortality attributable to diabetes in 20–79 years old adults, 2019 estimates: Results from the International Diabetes Federation Diabetes Atlas. Diabetes research and clinical practice. 2020;162:108086.
- 8. Aikins Ad-G, Unwin N, Agyemang C, Allotey P, Campbell C, Arhinful D. Tackling Africa’s chronic disease burden: from the local to the global. Globalization and health. 2010;6(1):1–7.
- 9. Wang C-Y, Neil D, Home P. 2020 vision—An overview of prospects for diabetes management and prevention in the next decade. Diabetes research and clinical practice. 2018;143.
- 10.
Consultation RoaWI. definition and diagnosis of diabetes mellitus and intermediate hyperglycemia. 2006.
- 11. Braga MF, Casanova A, Teoh H, Dawson KG, Gerstein HC, Fitchett DH, et al. Treatment gaps in the management of cardiovascular risk factors in patients with type 2 diabetes in Canada. Canadian Journal of Cardiology. 2010;26(6):297–302. pmid:20548975
- 12. Hoerger TJ, Segel JE, Gregg EW, Saaddine JB. Is glycemic control improving in US adults? Diabetes care. 2008;31(1):81–6.
- 13. Chan JC, Gagliardino JJ, Baik SH, Chantelot J-M, Ferreira SR, Hancu N, et al. Multifaceted determinants for achieving glycemic control: the International Diabetes Management Practice Study (IDMPS). Diabetes care. 2009;32(2):227–33. pmid:19033410
- 14. Teklehaimanot BF, kidanemariam Berhe A, Welearegawi GG. Poor glycemic control and associated factors among diabetic patients in Ethiopia; A Systemic review and meta-analysis. medRxiv. 2019:19004986.
- 15. Mamo Y, Bekele F, Nigussie T, Zewudie A. Determinants of poor glycemic control among adult patients with type 2 diabetes mellitus in Jimma University Medical Center, Jimma zone, south west Ethiopia: a case control study. BMC endocrine disorders. 2019;19(1):1–11, 91.
- 16. Fseha B. Glycemic control and it’s associated factors in type 2 diabetic patients in Suhul hospital, northwest tigray, Ethiopia. J Diabetes Metab. 2017;8(3):729.
- 17. Yigazu DM, Desse TA. Glycemic control and associated factors among type 2 diabetic patients at Shanan Gibe Hospital, Southwest Ethiopia. BMC research notes. 2017;10(1):1–6.
- 18. Tekalegn Y, Addissie A, Kebede T, Ayele W. Magnitude of glycemic control and its associated factors among patients with type 2 diabetes at Tikur Anbessa Specialized Hospital, Addis Ababa, Ethiopia. PloS one. 2018;13(3):e0193442–e. pmid:29505602
- 19. Association. AD. Standards of medical care in Diabetes—2018. 2018;41(Suppl 1):S13–28. Diabetes Care J Clin Appl Res Educ. 2018;41:s13-28.
- 20. Intensive blood-glucose control with sulphonylureas or insulin compared with conventional treatment and risk of complications in patients with type 2 diabetes (UKPDS 33). The Lancet. 1998;352(9131):837–53.
- 21. Whiteford HA, Degenhardt L, Rehm J, Baxter AJ, Ferrari AJ, Erskine HE, et al. Global burden of disease attributable to mental and substance use disorders: findings from the Global Burden of Disease Study 2010. The lancet. 2013;382(9904):1575–86. pmid:23993280
- 22. Kibirige GPA D., Kampiire L., Kiggundu D. S., and Lumu W.,. “Frequency and predictors of suboptimal glycemic control in an African diabetic population,”. International Journal of General Medicine,. 2017;10.:33–8.
- 23. Mohammed AS, Adem F, Tadiwos Y, Woldekidan NA, Degu A. Level of Adherence to the Dietary Recommendation and Glycemic Control Among Patients with Type 2 Diabetes Mellitus in Eastern Ethiopia: A Cross-Sectional Study. Diabetes Metab Syndr Obes. 2020;13:2605–12. pmid:32801812
- 24. Ali M, Alemu T, Sada O. Medication adherence and its associated factors among diabetic patients at Zewditu Memorial Hospital, Addis Ababa, Ethiopia. BMC research notes. 2017;10(1):676–. pmid:29202857
- 25. Zimet GD, Dahlem NW, Zimet SG, Farley GK. The multidimensional scale of perceived social support. Journal of personality assessment. 1988;52(1):30–41.
- 26. Arda Sürücü H, Büyükkaya Besen D, Erbil EY. Empowerment and social support as predictors of self-care behaviors and glycemic control in individuals with type 2 diabetes. Clinical nursing research. 2018;27(4):395–413. pmid:28132513
- 27.
Organization WH. STEPwise approach to surveillance (STEPS). 2008.
- 28. Freeman MC, Ogden S, Jacobson J, Abbott D, Addiss DG, Amnie AG, et al. Integration of water, sanitation, and hygiene for the prevention and control of neglected tropical diseases: a rationale for inter-sectoral collaboration. PLoS neglected tropical diseases. 2013;7(9):e2439. pmid:24086781
- 29. DEBORAH J. TOOBERT PSEH, PHD RUSSELL E. GLASGOW, PHD. The Summary of Diabetes Self-Care Activities Measurer esults from 7 studies and a revised scale. DIabets care. 2000;23(7).
- 30. Berhe et al. DIABETES SELF CARE PRACTICES AND ASSOCIATED FACTORS AMONG TYPE 2 DIABETIC PATIENTS IN TIKUR ANBESSA SPECIALIZED HOSPITAL, ADDIS ABABA, ETHIOPIA- A CROSS SECTIONAL STUDY. IJPSR,. 2012;3(11):4219–29.
- 31. Mariya T, Girmay A, Tasew H, Teklay G, Ayele E, Gerensea H, et al. Determinants of hypertension among diabetic patients in Public Hospitals of the Central Zone, Tigray, Ethiopia 2018: unmatched case-control study. Pan African Medical Journal. 2019;33:100. pmid:31489078
- 32. Yimam M, Desse TA, Hebo HJ. Glycemic control among ambulatory type 2 diabetes patients with hypertension Co-morbidity in a developing country: A cross sectional study. Heliyon. 2020;6(12):e05671–e.
- 33. Bjerrum LR, Hallas J. U., Kragstrup J, J. Methods for estimating the occurrence of polypharmacy by means of a prescription database. European journal of clinical pharmacology. 1997;53(1):7–11. pmid:9349923
- 34.
Organization WH. Obesity: preventing and managing the global epidemic. 2000.
- 35. Shao Y, Liang L, Shi L, Wan C, Yu S. The Effect of Social Support on Glycemic Control in Patients with Type 2 Diabetes Mellitus: The Mediating Roles of Self-Efficacy and Adherence. Journal of Diabetes Research. 2017;2017:2804178. pmid:28626769
- 36. Botchway M, Davis RE, Merchant AT, Appiah LT, Moore S. Diabetes-Related Stigma and its Influence on Social Networks, Social Support, and HbA1c in Ghana. Ethnicity & Disease. 2021;31(1):57–66. pmid:33519156
- 37. Maki KG. Social support, strain, and glycemic control: A path analysis. Personal Relationships. 2020;27(3):592–612.
- 38. Yehualashet FA, Tegegne ET, Ayele AD, Takele WW. Do adult patients with diabetes mellitus living in rural part of Ethiopia and having poor social support have disproportionately poor self-care practice? A systematic review and meta-analysis. Primary Care Diabetes. 2021. pmid:33674220
- 39. Ramkisson S, Pillay BJ, Sibanda W. Social support and coping in adults with type 2 diabetes. African Journal of Primary Health Care and Family Medicine. 2017;9(1):1–8. pmid:28828879
- 40. Ahmed B, Nanji K, Mujeeb R, Patel MJ. Effects of polypharmacy on adverse drug reactions among geriatric outpatients at a tertiary care hospital in Karachi: a prospective cohort study. PloS one. 2014;9(11):e112133. pmid:25402452
- 41. Nguyen JK, Fouts MM, Kotabe SE, Lo E. Polypharmacy as a risk factor for adverse drug reactions in geriatric nursing home residents. The American journal of geriatric pharmacotherapy. 2006;4(1):36–41. pmid:16730619
- 42. Bailey C, Kodack M. Patient adherence to medication requirements for therapy of type 2 diabetes. International journal of clinical practice. 2011;65(3):314–22. pmid:21314869
- 43. Dobrică E-C, Găman M-A, Cozma M-A, Bratu OG, Pantea Stoian A, Diaconu CC. Polypharmacy in type 2 diabetes mellitus: insights from an internal medicine department. Medicina. 2019;55(8):436. pmid:31382651
- 44. Khattab M, Khader YS, Al-Khawaldeh A, Ajlouni K. Factors associated with poor glycemic control among patients with type 2 diabetes. Journal of Diabetes and its Complications. 2010;24(2):84–9. pmid:19282203
- 45. Howteerakul N, Suwannapong N, Rittichu C, Rawdaree P. Adherence to regimens and glycemic control of patients with type 2 diabetes attending a tertiary hospital clinic. Asia Pacific Journal of Public Health. 2007;19(1):43–9. pmid:17784659
- 46. Alzaheb RA, Altemani AH. The prevalence and determinants of poor glycemic control among adults with type 2 diabetes mellitus in Saudi Arabia. Diabetes, metabolic syndrome and obesity: targets and therapy. 2018;11:15. pmid:29430192
- 47. Fekadu G, Bula K, Bayisa G, Turi E, Tolossa T, Kasaye HK. Challenges and factors associated with poor glycemic control among type 2 diabetes mellitus patients at Nekemte Referral Hospital, Western Ethiopia. Journal of multidisciplinary healthcare. 2019;12:963. pmid:31819470
- 48. Park J-H, Lee Y-E. Effects of exercise on glycemic control in type 2 diabetes mellitus in Koreans: the fifth Korea National Health and Nutrition Examination Survey (KNHANES V). Journal of physical therapy science. 2015;27(11):3559–64. pmid:26696738
- 49. Nayak S, Maiya A, Hande M. Influence of aerobic treadmill exercise on blood glucose homeostasis in noninsulin dependent diabetes mellitus patients. Indian journal of clinical biochemistry. 2005;20(1):47–51. pmid:23105493
- 50. Eticha T, Mulu A, Gebretsadik H, Kahsay G, Ali D. Factors associated with poor glycemic control in type 2 diabetic patients investigated at Ayder Referral Hospital, Mekelle, Ethiopia. Ijppr Human. 2016;6(3):160–71.
- 51. Sanal T, Nair N, Adhikari P. Factors associated with poor control of type 2 diabetes mellitus: a systematic review and meta-analysis. Journal of diabetology. 2011;3(1):1–10.
- 52. Hilde Luijks MB, Hans Bor, van Weel Chris, Lagro-Janssen Toine,de Grauw Wim, Schermer Tjard. The Effect of Comorbidity on Glycemic Control and Systolic Blood Pressure in Type 2 Diabetes: A Cohort Study with 5 Year Follow Up in Primary Care,netherland. plosone. 2015;13(71).
- 53. Zinab B, Debalke R. Acute glycemic level and its association with physical activity recommendation among type 2 diabetic patients in Illubabor zone oromiya, Ethiopia. International Journal of Nutrition and Metabolism. 2019;11(1):1–10.
- 54. Shimels T, Abebaw M, Bilal AI, Tesfaye T. Treatment Pattern and Factors Associated with Blood Pressure and Fasting Plasma Glucose Control among Patients with Type 2 Diabetes Mellitus in Police Referral Hospital in Ethiopia. Ethiop J Health Sci. 2018;28(4):461–72. pmid:30607059
- 55. Gebrie A, Tesfaye B, Sisay M. Evaluation of glycemic control status and its associated factors among diabetes patients on follow-up at referral hospitals of Northwest Ethiopia: A cross-sectional study, 2020. Heliyon. 2020;6(12):e05655–e.
- 56. Wen H, Gris D, Lei Y, Jha S, Zhang L, Huang MT-H, et al. Fatty acid–induced NLRP3-ASC inflammasome activation interferes with insulin signaling. Nature immunology. 2011;12(5):408–15. pmid:21478880