Open Access

Factors associated with glycemic control among adult patients with type 2 diabetes mellitus: a cross-sectional survey in Ethiopia

BMC Research Notes20169:78

https://doi.org/10.1186/s13104-016-1896-7

Received: 19 November 2015

Accepted: 31 January 2016

Published: 9 February 2016

Abstract

Background

Even though the prevalence of type 2 diabetes mellitus is swelling rapidly in Ethiopia, data regarding glycemic control, a key strategy for marked reduction of diabetes mellitus complications, is scant. We have assessed the status of glycemic control and its contributing factors among adult patients with type 2 diabetes mellitus.

Methods

This was a facility based cross-sectional survey of 325 adults with type 2 diabetes mellitus attending in Jimma University Teaching Hospital, South west Ethiopia. Data from all the patients were collected between February and April 2014. Glycemic level was assessed by using fasting blood glucose level, and ‘poor glycemic control’ was defined when fasting blood glucose level was above 130 mg/dL (7 mm/L). Analysis included both descriptive and inferential statistics, and SPSS version 20.0 was used for all analysis.

Results

309 respondents were included in the survey. More than two-third (70.9 %) of the patients had poor blood glycemic control. Patients who were illiterate (AOR = 3.46, 95 % CI 1.01–11.91) and farmer (AOR = 2.47, 95 % CI 1.13–5.39) had high odds of poor glycemic control. In addition, taking combination of insulin and oral medication (AOR = 4.59, 95 % CI 1.05–20.14) and poor medication adherence (AOR = 5.08 95 % CI 2.02–12.79) associated statistically with poor glycemic control.

Conclusion

Majority of patients had poor glycemic control. Patients with low level of education, being employed, on combinations of insulin and oral medication, and lower adherence to their medication were likely to have poor glycemic control. Education and awareness creation could be a cross cutting intervention for the significant factors.

Keywords

Glycemic control Fast blood glucose Cross-sectional Type 2 diabetic mellitus Ethiopia

Background

Diabetes mellitus (DM) [1] has been ravaging millions of people from all over the world. Globally, diabetes has killed 4.6 million people in 2013 alone [2]. More than 77 % of morbidity [3] and 88 % of mortality [4] due to DM occur in low- and middle-income countries. In Ethiopia, the prevalence of diabetes was 3.5 % in 2011 [5]. Type 2 diabetes mellitus (T2DM) is the most common form of DM, accounting for more than 90 % of cases [2]. Control of diabetes is more than just taking medicine; other aspects of self-management such as self-monitoring of blood glucose, dietary restrictions, regular foot care and ophthalmic examination have all been shown to markedly reduce the incidence and progression of diabetes complications [2]. Previous studies have reported that suboptimal glycemic control could cost diabetes patients more care requirement, associated health-care costs, and loom the complications [6, 7].

The prevalence of poor glycemic control is paramount. A study done in Malaysia [8], 2015 showed that 72 % of patients had poor glycemic control, and two third of DM patients in Ethiopia [9] also had poor control. Previous studies assured that poor glycemic control correlated with enlarged risk of visual impairment [10], enlarged risk of kidney failure [11], and enlarged risk of cardiovascular disease [12]. In addition, the possible reasons included lack of awareness, time constraint, lack of adequate human power, poor adherence, and most importantly lack of appropriate guidelines and diabetes education for both care givers and patients [9, 13]. Therefore, it is easy to understand that adequate glycemic control among T2DM patients prevents short-term complications, decrease the risk of long-term complications, and decrease health care resource use and costs [1416]. This figure indicted the need to give an attention for glycemic control. The Healthy People 2020 aimed a 10 % reduction in the proportion of DM patients with poor glycemic control as a target [17].

But despite the swift growth of prevalence of T2DM in Ethiopia, data regarding glycemic control, the key strategy for marked reduction of acute and chronic complications of DM, is scant. Such data are noteworthy for the overall diabetic health care delivery services. We have assessed the status of glycemic control and its contributing factors among adult patients with T2DM.

Methods

Study design, settings and participants

A facility based cross-sectional study was carried out in diabetic clinic at Jimma University Teaching Hospital (JUTH), Southwest Ethiopia from February 14 to April 9, 2014. The hospital serves the rural, urban and semi-urban areas. Drug Administration and Control Authority of Ethiopia Contents [18], a guideline similar with International Diabetes Federation clinical guideline [3] were followed for diagnosis and classification of DM. The study was conducted among T2DM adult patients (≥18 years) who were on active follow up for at least four visits. The required sample size was calculated via OpenEpi software using single population proportion calculation formula using the following assumptions: 58.2 % prevalence rate of poor glycemic control [19], 95 % confidence level, 5 % margin of error and 10 % non-response rate. Considering a correction formula, the total calculated sample yielded 325. Using sampling frame of DM records, simple random sampling technique was used to recruit the study participants (Fig. 1).
Fig. 1

Summary of flowchart record selection, 2014

Variables of the study and measurement

Glycemic level was the response variable that was coded as poor or good. Poor glycemic control was operationally defined if fasting blood glucose (FBG) level was above 130 mg/dl. Patients FBG reading for at least 4 months were recorded and computed the mean blood glucose level [20]. The explanatory variables included: socio-demographic and economic data (age, sex, level of education, marital status, occupation, income, ethnicity and religion), history of smoking, history of alcohol consumption, family history of DM, medication adherence, duration of therapy, body mass index (BMI) and number of diabetic medication.

Level of education was classified as illiterate (could not read and write their local language—‘Afan Oromo’ or ‘Amharic’), literate (could read and write but received no formal education), primary (received education up to class eight), secondary (received education class 9–12), and college/university (joined college or university). History of smoking and history of alcohol consumption has been assessed as during lifetime. Family history of DM was measured if any family member (mother or father) had DM. Morisky adherence score [21], eight-item yes/no questionnaire, was used to assess the self-reported measures of adherence to medications. For all questions, responses were coded 1 if patients responded ‘yes’ otherwise 0 if not, except one question that was coded reverse. The total score of adherence was classified into low adherence if the score was >2, medium adherence if between 1 and 2, and high adherence if 0.

Demographic and clinical characteristics were assessed via face-to-face interview and the average glycemic levels were reviewed from their chart. To ensure quality of data, the tool was developed in English, translated to local language (Amharic and Afan-Oromo) and back translated into English to check its consistency.

Statistical analysis

Descriptive statistics included mean, median, standard deviations, and range values for continuous data; percentage and frequency tables for categorical data. Bivariate logistic regression analysis was conducted to see the existence of crude association and select candidate variables (with P value below 0.25 were considered) to multivariable logistic regression. We checked multi-collinearity among selected independent variables via variance inflation factor (VIF) and none was found. P value ≤0.05 was considered as a cut point for statistical significance in the final model. Fitness of goodness of the final model was checked by Hosmer and Lemeshow and was found fit. The Data was summarized using odds ratio (OR) and 95 % confidence interval. The analysis was done in Statistical Package for the Social Sciences (SPSS) 20 version software [22].

Ethical considerations

Informed consent was obtained from study participants before the commencement of each interview, and no personal identification was registered. There was no any financial compensation or provision for the study participants. Permission was obtained from JUSH and the study was approved by institutional review board (IRB) of college of health sciences at Jimma University, Southwest Ethiopia.

Results

Socio-demographic and clinical characteristics of respondents

Three hundred and twenty-five DM patients were considered eligible, whereof 16 were excluded since their chart were not available (Fig. 1). In total, 309 patients included in the analysis making 95 % response rate. Table 1 shows demographic and economic characteristics of the respondents. Males were over-represented (61.8 %) and almost two-fifth (36.9 %) of the respondents represented the age group 51–60 years. Nearly half (46.6 %) of the respondents followed Muslim religion and four out of five (81.2 %) respondents were married. Two fifth (36.2 %) respondents attained grades (1–8), and 30.4 % of respondents were farmers.
Table 1

Socio-demographic characteristics of T2DM patients on follow up at JUTH, 2014

Socio-demographic characteristics (n = 309)

Categories

n (%)

Sex

Male

189 (61.8)

Female

120 (38.2)

Age

18–32

5 (1.6)

33–41

30 (9.7)

42–50

72 (23.3)

51–60

114 (36.9)

≥61

88 (28.5)

Marital status

Married

251 (81.2)

Single

9 (2.9)

Divorce

12 (3.9)

Widow/er

37 (12.0)

Ethnicity

Oromo

170 (55.1)

Amhara

78 (25.2)

Keficho

21 (6.8)

Gurage

10 (3.2)

Dawero

8 (2.6)

Yem

8 (2.6)

Othera

14 (4.5)

Educational level

Illiterate

87 (28.2)

Read & write

22 (7.1)

1–8

112 (36.2)

9–12

44 (14.3)

College/University

44 (14.2)

Religion of respondents

Muslim

144 (46.6)

Orthodox

138 (44.7)

Protestant

23 (7.4)

Othersb

4 (1.3)

Occupation

Farmer

94 (30.4)

No job

78 (25.2)

Employed

72 (23.3)

Merchant

29 (9.4)

Daily labor

36 (11.7)

aTigre, Wolayita

bCatholic, Jehovah witness

Table 2 shows clinical characteristics of the respondents. Mean BMI of the respondents was 24.4(±4.39) kg/m2, and 33 % of the respondents had overweight. Great majority of the respondents had not access for self-monitoring blood glucose (SMBG). About half of the respondents had not get social support, and one fourth (24.6 %) of the respondents had family history of DM. The prevalence of smoking and alcohol, respectively, was 3 and 5 %. DM patients were followed for an average of 7.2 (±5.8) years with a minimum of 4 months and a maximum of 40 years. Diabetic neuropathy was the most common DM complication accounting for 41.5 %. Regarding diabetic medications, 63.1 % of respondents were taking oral medication only followed by insulin (25.6 %). The respondents took a mean of 3.66 (±1.60) medications ranged between 1 and 7 medications.
Table 2

Clinical characteristics of T2DM patients on follow up at JUTH, 2014

Clinical characteristics (n = 309)

Category

n (%)

Body mass index

Under weight

17 (5.5)

Normal weight

161 (52.1)

Over weight

102 (33.0)

Obese

29 (9.4)

Family/social support

Yes

175 (56.6)

No

134 (43.4)

Family history

Yes

76 (24.6)

No

233 (75.4)

Habit of smoking

Smoker

9 (2.9)

Non-smoker

287 (92.9)

Ex-smoker

13 (4.2)

Alcohol drinking

Non-drinker

293 (94.8)

Drinker

16 (5.2)

Access for self-monitoring blood glucose

Yes

30 (9.7)

No

279 (90.3)

Duration of treatment

<5

145 (46.9)

5–10

105 (33.9)

>10

59 (19.2)

Anti-diabetics

Insulin

79 (25.6)

Oral medication

195 (63.1)

Insulin & oral medication

35 (11.3)

Diabetic complication (n = 352)

Neuropathy

146 (41.5)

Nephropathy

56 (15.9)

Retinopathy

94 (26.7)

Cardiac complications

56 (15.9)

No. of medications

<2 drugs

115 (37.2)

2–4 drugs

135 (43.7)

>4 drugs

59 (19.1)

Glycemic control level and its contributing factors among T2DM patients

Poor glycemic control was seen in 219 (70.9 %) respondents. Age, sex, education, occupation, BMI, anti-diabetics, number of medications and adherence to medication were the candidate variables for multiple logistic regression. The following factors were statistically significant in bivariate logistic analysis: being illiterate, being achieved grades 1–8, being employed, being farmer, being under weight, taking combinations of insulin and oral medication, taking 2–4 drugs and >4 drugs, medium medication adherence and low medication adherence. In multiple logistic regression, statistically significant difference was found in poor glycemic control to education, occupation, anti-diabetics and level of medication adherence.

Table 3 presents the multiple logistic regression analysis with demographic and clinical characteristics, and poor glycemic control. The association of poor glycemic control among illiterate respondents was three times (AOR = 3.46, 95 % CI 1.01–11.92) greater than among those who were in college/university. The relative probability of poor glycemic control among farmers was higher than (AOR = 2.47, 95 % CI 1.14–5.39) unemployed ones. Compared to respondents who took oral medication, respondents who took combinations of insulin and oral medication were five times (AOR = 4.59, 95 % CI 1.05–20.14) more likely to have poor glycemic control. On the other hand, the odds of poor glycemic control among patients who had medium and low adherence to their medication were three (AOR = 3.49, 95 % CI 1.72–7.09) and five times (AOR = 5.08, 95 % CI 2.02–12.79) more than patients who had high adherence to their medication respectively.
Table 3

Factors independently associated with poor glycemic level among T2DM patients JUTH, 2014

Variables (n = 309)

Glycemic level

Crude OR (95 % CI)

Adjusted OR (95 % CI)

P value

Good, n (%)

Poor, n (%)

Education

 Illiterate

18 (20.7 %)

69 (79.3 %)

2.65 (1.20–5.87)a

3.46 (1.01–11.91)

0.049a

 Read & write

10 (45.5 %)

12 (54.5 %)

0.83 (0.29–2.33)

0.81 (0.20–3.26)

0.766

 1–8

31 (27.7 %)

81 (72.3 %)

1.81 (0.87–3.75)

2.45 (0.85–7.03)

0.095

 9–12

13 (29.5 %)

31 (70.5 %)

1.65 (0.68–3.99)

1.97 (0.69–5.55)

0.202

College/University

18 (40.9 %)

26 (59.1 %)

1

1

 

 Occupation

 No job

31 (39.7 %)

47 (60.3 %)

1

1

 

 Employed

21 (29.2 %)

51 (70.8 %)

1.60 (0.81–3.16)

2.65 (0.96–7.24)

0.048a

 Merchant

6 (20.7 %)

23 (79.3 %)

2.53 (0.92–6.91)

2.69 (0.86–8.37)

0.086

 Farmer

19 (20.2 %)

75 (79.8 %)

2.60 (1.32–5.12)a

2.47 (1.13–5.39)

0.023a

 Daily labor

13 (36.1 %)

23 (63.9 %)

1.17 (0.51–2.64)

2.22 (0.80–6.11)

0.124

Anti-diabetics

 Insulin

21 (26.6 %)

58 (73.4 %)

1.38 (0.77–2.47)

1.77 (0.60–5.19)

0.298

 Oral medication

65 (33.3 %)

130 (66.7 %)

1

1

 

 Insulin and oral medication

4 (11.4 %)

31 (88.6 %)

3.88 (1.31–11.45)a

4.59 (1.05–20.14)

0.043a

Medication adherence

 High

52 (45.2 %)

63 (54.8 %)

1

1

 

 Medium

25 (21.4 %)

92 (78.6 %)

3.04 (1.71–5.43)a

3.49 (1.72–7.09)

0.001a

 Low

13 (16.9 %)

64 (83.1 %)

4.06 (2.02–8.18)a

5.08 (2.02–12.79)

0.001a

aStatistically significant at P value <0.05

Discussion

The main goal of diabetes management is to ensure optimal glycemic control. We have assessed the magnitude of poor glycemic control and associated factors among T2DM patients. Results of this study showed that nearly three fourth of patients with T2DM had poor glycemic control. This result was comparable to those obtained in an earlier study that reported 65 % [23] and 81.9 % [24] of respondents had poor glycemic control. This significant proportion of poor glycemic control in the country underpins the need to work more on self-management strategies of DM patients. The current finding is far higher than from developed countries such as 12.9 % in United States [7]. This variation could be due to knowledge difference of respondents between developing and developed countries, absence of uniform guidelines in assessing glycemic control for physicians to set the score cutoff, and the presence health insurance and the difference in health insurance coverage and access to primary care [7, 25, 26].

Significant difference of poor glycemic control was observed among illiterates than college/university graduates. Consistent with this, studies from Jordan [27] and China [28] reported the correlation of lower level of education and poor glycemic control. This could indicate illiterate patients had low diabetes knowledge, low self-management behaviors, lower self-efficacy and lower continuity of care. Thus, we are recommending investment on getting rid of illiteracy as it has a significant impact on the reduction of diabetic morbidity and mortality [29, 30]. Poor glycemic control appeared to be greater among farmers compared to unemployed respondents. This, however, might be due to allocating less time for self-management. Majority of the farmers in this study were also illiterate compared to unemployed respondents implying that farmers might have poor awareness of self-care of DM.

It was also likely that the difference in glycemic control might result from differences in anti-diabetic treatment. Compared to respondents on oral medication, respondents who were on combinations of insulin and oral medication were five times more likely to have poor glycemic control. This association was corroborated by study done in Malaysia [31]. The poor control among patients receiving a combination of insulin and oral anti-diabetic drugs shows that multi therapy might challenge satisfactory glycemic control. There was a strong inverse association between adherence level and poor glycemic control as supported by the previous studies [32]. Showing very poor outcomes among respondents who fail to comply with the prescribed clinical regimen is not surprising [33]. Accordingly, counseling and improving adherence rather than changing medication or altering the dose has been suggested [33]. It is plausible to have an endeavor to tackle non-adherence by designing strategies encompassing cost, health belief, dosing frequency, personality disorders and patient provider relationship.

Worth noting limitations should have noted in this study. The use of FBS over HbA1c my under estimate the prevalence of poor glycemic control even though FBS was found more reliable than HbA1c [34]. The institutional based nature of the study might not infer for other diabetic patients. The nature of cross-sectional study design does not show temporal relationship or causality. Recall bias might also be there during measuring self-report of medication to adherence.

Conclusion

In summary, the findings from the current study agree in many points with the findings of previous publications. Significant number of DM patients did not achieve the recommended glucose level. This was affected by education, occupation, anti-diabetic treatment and adherence status of the patients. Education and awareness creation could be a cross cutting intervention for the significant factors. We recommend further population based research.

Abbreviations

BMI: 

body mass index

DM: 

diabetes mellitus

FBG: 

fasting blood glucose

HbA1c: 

glycosylated hemoglobin

IDFA: 

International Diabetes Federation Atlas

JUSH: 

Jimma University Specialized Hospital

OHA: 

oral hypoglycemic agent

SMBG: 

self-monitoring blood glucose

SDSCA: 

summary of diabetes self-care activities

T2DM: 

type-2 diabetes mellitus

Declarations

Authors’ contributions

TK involved in designing of the study, data collection, data analysis, drafting and critically reviewing the manuscript. Likewise, TE and HG involved in designing of the study, analysis of the data and critically reviewing the manuscript. All authors read and approved the final manuscript.

Acknowledgements

The authors are grateful to the data collectors who gathered the data. This research was funded by Jimma University.

Competing interests

The authors declare that they have no competing interest. This research was funded by Jimma University. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
Dilchora Hospital
(2)
Department of Clinical Pharmacy, College of Health Sciences, Jimma University
(3)
Department of Epidemiology, College of Health Sciences, Jimma University
(4)
Discipline of Public Health, Faculty of Medicine, Nursing and Health Sciences, Flinders University

References

  1. Islam SM, Niessen LW, Seissler J, Ferrari U, Biswas T, Islam A, Lechner A. Diabetes knowledge and glycemic control among patients with type 2 diabetes in Bangladesh. SpringerPlus. 2015;4:284PubMed CentralView ArticlePubMedGoogle Scholar
  2. Aschner P, Beck-Nielsen H, Bennett P, Boulton A, Colagiuri R. Diabetes and impaired glucose tolerance. 5th ed. Brussels: IDF Diabetes Atlas; 2012.Google Scholar
  3. IDF. Diabetes and impaired glucose tolerance. 6th ed. Brussels: Diabetes Atlas; 2013.Google Scholar
  4. Mathers CD, Loncar D. Projections of global mortality and burden of disease from 2002 to 2030. PLoS Med. 2006;3(11):e442.PubMed CentralView ArticlePubMedGoogle Scholar
  5. Whiting DR, Guariguata L, Weil C, Shaw J. IDF diabetes atlas: global estimates of the prevalence of diabetes for 2011 and 2030. Diabetes Res Clin Pract. 2011;94(3):311–21.View ArticlePubMedGoogle Scholar
  6. Harris MI. Racial and ethnic differences in health care access and health outcomes for adults with type 2 diabetes. Diabet Care. 2001;24(3):454–9.View ArticleGoogle Scholar
  7. Ali MK, Bullard KM, Imperatore G, Barker L, Gregg EW. Characteristics associated with poor glycemic control among adults with self-reported diagnosed diabetes—National Health and Nutrition Examination Survey, United States, 2007–2010. MMWR Morb Mortal Wkly Rep. 2012;61(2):32–7.PubMedGoogle Scholar
  8. Firouzi S, Barakatun-Nisak MY, Azmi KN. Nutritional status, glycemic control and its associated risk factors among a sample of type 2 diabetic individuals, a pilot study. J Res Med Sci. 2015;20(1):40–6.PubMed CentralPubMedGoogle Scholar
  9. Abebe SM, Berhane Y, Worku A, Alemu S, Mesfin N. Level of sustained glycemic control and associated factors among patients with diabetes mellitus in Ethiopia: a hospital-based cross-sectional study. Diabet Metab Synd Obes. 2015;8:65–71.View ArticleGoogle Scholar
  10. Colagiuri S, Lee CM, Wong TY, Balkau B, Shaw JE, Borch-Johnsen K. Glycemic thresholds for diabetes-specific retinopathy: implications for diagnostic criteria for diabetes. Diabet Care. 2011;34(1):145–50.View ArticleGoogle Scholar
  11. Lachin JM, Genuth S, Nathan DM, Zinman B, Rutledge BN. Effect of glycemic exposure on the risk of microvascular complications in the diabetes control and complications trial–revisited. Diabetes. 2008;57(4):995–1001.View ArticlePubMedGoogle Scholar
  12. Selvin E, Marinopoulos S, Berkenblit G, Rami T, Brancati FL, Powe NR, Golden SH. Meta-analysis: glycosylated hemoglobin and cardiovascular disease in diabetes mellitus. Ann Intern Med. 2004;141(6):421–31.View ArticlePubMedGoogle Scholar
  13. Gudina EK, Amade ST, Tesfamichael FA, Ram R. Assessment of quality of care given to diabetic patients at Jimma University Specialized Hospital diabetes follow-up clinic, Jimma. Ethiopia. BMC Endocr Disord. 2011;11:19.View ArticlePubMedGoogle Scholar
  14. Fiallo-Scharer R. Eight-point glucose testing versus the continuous glucose monitoring system in evaluation of glycemic control in type 1 diabetes. J Clin Endocrinol Metab. 2005;90(6):3387–91.View ArticlePubMedGoogle Scholar
  15. JDRF. Randomized clinical trial to assess the efficacy of real-time continuous glucose monitoring in the management of type 1 diabetes: research design and methods. Diabet Technol Ther 2008; 10(4):310–21.Google Scholar
  16. Tamborlane WV, Beck RW, Bode BW, Buckingham B, Chase HP, Clemons R, Fiallo-Scharer R, Fox LA, Gilliam LK, Hirsch IB, et al. Continuous glucose monitoring and intensive treatment of type 1 diabetes. New Engl J Med. 2008;359(14):1464–76.View ArticlePubMedGoogle Scholar
  17. Healthy People 2020, 2015. http://www.healthypeople.gov/2020/topicsobjectives2020/objectiveslist.aspx?topicId=8.
  18. DACA. Standard treatment guideline for primary hospitals. 2nd ed. Addis Ababa; 2010.Google Scholar
  19. Wabe NT, Angamo MT, Hussein S. Medication adherence in diabetes mellitus and self management practices among type-2 diabetics in Ethiopia. NAm J Med Sci. 2011;3(9):418–23.Google Scholar
  20. ADA. Standards of medical care in diabetes—2013. Diabet Care 2013, 36(1): 11–66.Google Scholar
  21. Morisky DE, Green LW, Levine DM. Concurrent and predictive validity of a self-reported measure of medication adherence. Med Care. 1986;24(1):67–74.View ArticlePubMedGoogle Scholar
  22. IBM Corp. Released 2011. IBM SPSS Statistics for Windows, Version 20.0. Armonk, NY: IBM Corp.Google Scholar
  23. Gebrehiwot T, Jemal H, Dawit T. Non-adherence and Associated Factors among type 2 diabetic patients at Jimma University Specialized Hospital, Southwest Ethiopia. J Med Sci. 2013;13:578–84.View ArticleGoogle Scholar
  24. Hailu E, Mariam WH, Belachew T, Birhanu Z. Self-care practice and glycaemic control amongst adults with diabetes at the Jimma University Specialized Hospital in south-west Ethiopia: A cross-sectional study. Afr J Prim Health Care Fam Med. 2012;4(1):311–6.PubMed CentralView ArticleGoogle Scholar
  25. Islam SM, Niessen LW, Seissler J, Ferrari U, Biswas T, Islam A, Lechner A. Diabetes knowledge and glycemic control among patients with type 2 diabetes in Bangladesh. SpringerPlus. 2015;4:284.PubMed CentralView ArticlePubMedGoogle Scholar
  26. Wee HL, Ho HK, Li SC. Public awareness of diabetes mellitus in Singapore. Singap Med J. 2002;43(3):128–34.Google Scholar
  27. Al-Akour NA, Khader YS, Alaoui AM. Glycemic control and its determinants among patients with type 2 diabetes mellitus attending a teaching hospital. J Diabet Metab. 2011;2:129.Google Scholar
  28. Yin J, Yeung R, Luk A, Tutino G, Zhang Y, Kong A, Chung H, Wong R, Ozaki R, Ma R et al. Gender, diabetes education, and psychosocial factors are associated with persistent poor glycemic control in patients with type 2 diabetes in the Joint Asia Diabetes Evaluation (JADE) program. J Diabet. 2015;8(1):109–19. doi:https://doi.org/10.1111/1753-0407.12262 View ArticleGoogle Scholar
  29. Gautam A, Bhatta DN, Aryal UR. Diabetes related health knowledge, attitude and practice among diabetic patients in Nepal. BMC Endocr Disord. 2015;15:25.PubMed CentralView ArticlePubMedGoogle Scholar
  30. Rani PK, Raman R, Subramani S, Perumal G, Kumaramanickavel G, Sharma T. Knowledge of diabetes and diabetic retinopathy among rural populations in India, and the influence of knowledge of diabetic retinopathy on attitude and practice. Rural Remote Health. 2008;8(3):838.PubMedGoogle Scholar
  31. Huri HZ, Lim LP, Lim SK. Glycemic control and antidiabetic drugs in type 2 diabetes mellitus patients with renal complications. Drug Des Dev Ther. 2015;9:4355–71.View ArticleGoogle Scholar
  32. Khattab M, Khader YS, Al-Khawaldeh A, Ajlouni K. Factors associated with poor glycemic control among patients with type 2 diabetes. J Diabet Complicat. 2010;24(2):84–9.View ArticleGoogle Scholar
  33. Leichter S. Making outpatient care of diabetes more efficient: analyzing noncompliance. Clin Diabet. 2005;23:187–90.View ArticleGoogle Scholar
  34. Ghazanfari Z, Haghdoost AA, Alizadeh SM, Atapour J, Zolala F. A Comparison of HbA1c and Fasting Blood Sugar Tests in General Population. Int J Prev Med. 2010;1(3):187–94.PubMed CentralPubMedGoogle Scholar

Copyright

© Kassahun et al. 2016

Advertisement