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  • Research article
  • Open Access
  • Open Peer Review

Depression and glycaemic control among type 2 diabetes patients: a cross-sectional study in a tertiary healthcare facility in Ghana

BMC Psychiatry201818:357

https://doi.org/10.1186/s12888-018-1933-2

  • Received: 8 December 2017
  • Accepted: 19 October 2018
  • Published:
Open Peer Review reports

Abstract

Background

Diabetes and depression are both chronic debilitating conditions, and their coexistence has been associated with adverse outcomes. In this study, we investigated the association between glycaemic control and depression in type 2 diabetes (T2DM) patients attending a tertiary healthcare facility in Ghana.

Methodology

In a cross-sectional study design, Patient Health Questionnare-9 (PHQ-9) was used to assess depression in 400 T2DM, aged 30–65 years. Anthropometric characteristics and blood pressure were measured. Venous blood was collected to measure the levels of glycated haemoglobin (HbA1c).

Results

The prevalence of depression was 31.3% among T2DM patients. Female gender, being unmarried, frequent intake of alcohol, previous smoking status and insulin use were associated with increased odds of depression, whereas being educated above basic school level was associated with a decreased odds of depression. In a multivariable logistic regression model, being unmarried and poor glycaemic control were associated with an increase in odds of depression after adjusting for age, gender, and social factors. The association between depression and glycaemic control was attenuated when clinical factors were introduced into the model.

Conclusion

In our study population, we found that depression is common among Ghanaians with T2DM, and not associated with poor glycaemic control in a fully multivariable-adjusted model.

Keywords

  • Depression
  • Type 2 diabetes mellitus
  • Glycaemic control
  • Ghana

Background

The prevalence of diabetes mellitus has reached epidemic levels globally resulting in enormous human, economic and social cost worldwide. Currently, 415 million people are living with diabetes, 75% of whom live in low and middle-income countries; this number has been projected to increase to 642 million by 2040 [1]. Depression is also a chronic disease that affects about 340 million people at any given time worldwide [2]. The temporal relationship between diabetes and depression has been found to be bi-directional; patients with depression have an increased risk of 1.6 fold of developing diabetes, whereas diabetes patients are twice as likely to be depressed, compared to non-diabetes individuals [35].

The presence of depressive symptoms may affect T2DM patient’s adherence to diabetes self-care regimen, particularly diabetic medications, dietary modifications and exercise [6]. Compared to non-depressed T2DM patients, depressed T2DM patients have relatively higher glycated haemoglobin (HbA1c) and increased prevalence of microvascular and macrovascular complications [79]. Screening and treatment of depression in diabetes patients have been reported to favourably improve glycemic control, as well as prevent or delay diabetes-related complications [4]. However, the recognition and treatment of depression among patients with diabetes have been found to be less than optimum [10]. Moreover, in low and middle-income countries including Ghana, there is a dearth of data on the burden of depression among diabetes patients [11, 12].

In this study, we investigated the association between glycaemic control and depression among T2DM patients attending a tertiary care facility in Ghana. We hypothesized that compared to non-depressed diabetes patients, diabetes patients with depression will have poor glycaemic control.

Methodology

The study was a cross-sectional design, conducted at the National Diabetes Management and Research Centre, Korle Bu Teaching Hospital, Accra, Ghana. The minimum sample size was calculated based on the hypothetical prevalence rates of depression and glycaemic control to be 26% and 15% [13] respectively; 340 patients were required to achieve a power of 80% and α of 0.05. In all, 400 T2DM patients, aged between 30 and 65 years, were enrolled into the study by systematic random sampling of every fourth consenting patient visiting the clinic. Patients with type 1 diabetes, pregnant women and those aged less than 30 years at diagnosis or patients older than 65 years were excluded from the study. In addition, patients with other causes of depression such as loss of a close family member within past 4 months and medication/history of depression or manic/hypomanic episode were excluded from the study. The socio-demographic characteristics such as age, gender, education, employment, alcohol & smoking status, as well as clinical data like duration of diabetes, diabetes medication, hypertension and antihypertensive medication were collected using structured questionnaires.

Depression was screened using Patient Health Questionnaire-9 (PHQ-9). The PHQ-9 is an instrument that has been validated in sub-Saharan Africans [14, 15] and in diabetes patients [16] for the detection of general depressive symptoms. The PHQ-9 assesses how often the respondent has experienced specific symptoms over the past 2 weeks, assigning values of 0 to 3 points (0 - not at all, 1 - several days, 2 - more than half of the days, 3 - nearly every day) [17]. Major depressive disorder was defined as the presence of at least five symptoms, reported for more than half the days in the past 2 weeks, including depressed mood or anhedonia, as well as the thought of suicide or better dead [18].

Blood pressure was measured after 5 min rest using an automated digital blood pressure monitor (Omron 907XL pro, Healthcare, Inc., Vernon Hills, IL), with the patients seated comfortably with a back support and arm resting on a table. Body weight and height were measured with a Seca 740 scale and a stadiometer respectively, and the body mass index (BMI) computed using the formula: weight in kilogrammes divided by the height in metres squared.

Venous blood was drawn from each participant for the measurement of glycated haemoglobin (HbA1c) using the boronate affinity chromatography method on PDQ Plus HPLC autoanalyzer (Primus Diagnostics, Trinity Biotech, Ireland). Good glycaemic control was defined as HBA1c of less than 7% [19]. Ethical approval was sought and obtained from the Ethical and Protocol Review Committee of the College of Health Sciences, University of Ghana (Protocol ID MS-Et/M.6-P4.4/2012–13) and each patient provided written voluntary informed consent after the rationale and procedure of the study were thoroughly explained. Patients found to be depressed we referred to the Psychiatry department for further assessment and possible management.

Statistical analysis

Data was analysed using SPSS version 18. Data was presented as mean with standard deviation for continuous variables and as proportions for categorical variables. The study subjects were dichotomized based on the presence or absence of depression. Differences between the two groups with regards to their socio-demographic and clinical variables as well as HbA1c were assessed. The chi-squared (χ2) test was used for the comparison of categorical variables and Students t-test for continuous measures. Logistic regression models were performed to determine the change in odds of depression and glycaemic control after adjusting for confounding variables. The level of significance was set at p < 0.05.

Results

In our study population with a high representation of females (male: female = 1:4), 125 (31.3%) of the participants were found to have depression. Age, duration of diabetes and BMI were comparable for T2DM patients with or without depression (T-test, p-values> 0.05). Female gender, marital status, educational level, alcohol intake and smoking status were associated with depression (χ2, p-values< 0.05). T2DM patients with depression had higher systolic blood pressure and glycated haemoglobin level than T2DM patients without depression (T-test, p < 0.05; Table 1).
Table 1

General characteristics of participants by depression status

 

T2DM without depression

T2DM with depression

All Patients

Test statistic

P

N (%)

275 (68.7)

125 (31.3)

400

  

Age

52.9 ± 8.5

52.2 ± 9

52.7 ± 8.7

t (0.79)

0.48

Duration of diabetes, yrs

9.1 ± 7.3

9.5 ± 6.3

9.2 ± 7

t (0.56)

0.57

Females, n (%)

208 (75.6)

106 (84.6)

314 (78.5)

Χ2 (6.72)

0.01

Married n (%)

190 (69.1)

72 (57.6)

262 (65.5)

Χ2 (5.02)

0.02

Employed n (%)

194 (70.5)

83 (66.4)

277 (69.2)

Χ2 (0.69)

0.41

Education

   

Χ2 (5.04)

0.02

 Primary or less

68 (24.7)

41 (32.8)

109 (27.3)

  

 Junior grade

119 (43.3)

55 (44)

174 (43.5)

  

 High school

50 (18.2)

19 (15.2)

69 (17.3)

  

 Tertiary

38 (13.8)

10 (8)

48 (12)

  

Alcohol consumption

   

Χ2 (4.86)

0.035

 Doesn’t drink

212 (77.1)

92 (73.6)

304 (76)

  

 Occasional

61 (22.2)

21 (16.8)

90 (22.5)

  

 Always

2 (0.7)

3 (2.4)

5 (1.3)

  

Smoking status

   

Χ2 (5.48)

0.04

 Never

260 (94.5)

116 (92.8)

376 (94)

  

 Previous

14 (5.1)

8 (6.4)

22 (5.5)

  

 Current

1 (0.4)

1 (0.8)

2 (0.5)

  

BMI (mean ± SD)

30.2 ± 6.6

29.4 ± 5.6

29.9 ± 6.3

t (1.18)

0.24

Obesity classification

   

Χ2 (3.99)

0.076

 Normal

56 (20.4)

24 (19.2)

80 (20.1)

  

 Overweight

89 (32.4)

41 (32.8)

130 (32.7)

  

 Obese

128 (46.5)

60 (48)

188 (47.2)

  

Waist circumferece

99.9 ± 13

99.2 ± 11.7

99.7 ± 12.6

t (0.54)

0.58

WHR

0.93 ± 0.11

0.94 ± 0.09

0.94 ± 0.1

t (0.96)

0.34

Hypertension n (%)

219 (79.6)

99 (79.2)

318 (79.5)

 

0.39

Systolic BP, mmHg

126 ± 26

133 ± 21

132 ± 23

t (2.16)

0.03

Diastolic BP, mmHg

81 ± 12

80 ± 12

81 ± 12

t (0.77)

0.44

Heart rate, bpm

80 ± 15

81 ± 12

80 ± 14

t (0.71)

0.48

FPG, mol/l

9.2 ± 5.5

9.9 ± 4.3

9.4 ± 5.1

t (1.38)

0.17

HbA1c, %

9.4 ± 2.8

10.2 ± 3

9.9 ± 2.9

t (2.52)

0.013

WHR waist-hip ratio, BMI body mass index, BP blood pressure, FPG fasting plasma glucose, HbA1c glycated haemoglobin, t T-test statistic, Χ2 chi-square statistic

In unadjusted logistic regression models, being female (OR = 2.84), unmarried (OR = 1.63), regular alcohol consumer (OR = 5.47), former smoker (OR = 1.28), using insulin (OR = 1.3) and having poor glycaemic control (OR = 1.82) were associated with an increased odds of depression (Table 2). Multivariable logistic regression models were constructed to analyze the relationship between depression versus glycaemic control status and marital status, with sequential adjustments for social and clinical factors. Poor glycaemic control was associated with an increase in odds of depression, even after adjustment for age and gender (AOR = 1.4, p = 0.037), as well as social factors (AOR = 1.25, p = 0.047); the association was however not significant after the introduction of clinical factors into the model (AOR = 1.04, p = 0.286). Being unmarried was associated with increased odds of depression in the fully adjusted model (AOR = 1.47, p = 0.046; Table 3).
Table 2

Unadjusted logistic regression of depression with socio-demographic and clinical factors

 

OR (95% CI)

Wald’s

p

Female gender (Ref: Males)

2.84 (1.67–4.07)

4.59

< 0.001

Age (per 1 year change)

1.303 (0.84–1.68)

1.5

0.13

Duration of diabetes ≥10 years (Ref < 10 yrs)

1.05 (0.81–1.46)

0.32

0.76

Unmarried (Ref: Married)

1.63 (1.05–2.54)

2.17

0.003

Above basic school education (Ref: Basic school)

0.64 (0.4–0.93)

2.07

0.037

Alcohol status (Ref: Never)

 Occasional

1.1 (0.66–1.74)

0.39

0.711

 Always

5.47 (1.56–13.03)

3.14

0.002

Former Smokers (Ref: Non-smokers)

1.28 (1.05–3.84)

0.75

0.041

BMI (ref: Nornal)

 Overweight

1.07 (0.55–2.1)

0.2

0.851

 Obese

0.9 (0.47–1.76)

0.3

0.761

Hypertension (ref: Non-hypertensive)

1.13 (0.87–1.85)

0.63

0.536

Insulin use (Ref: Oral Hypoglycaemic drugs)

1.3 (1.18–1.62)

3.24

0.001

Poor glycaemic control (Ref: HbA1c < 7%)

1.82 (1.32–2.48)

3.72

< 0.001

Table 3

Multivariable regression of depression with marital status, medication adherence and glycaemic control

 

Unmarried

Wald’s (p)

Poor glycaemic control

Wald’s (p)

Model 1

1.63 (1.15–2.54)

2.42 (0.016)

1.82 (1.32–2.48)

3.72 (< 0.001)

Model 2

1.54 (1.1–2.41)

2.16 (0.031)

1.4 (1.27–2.39)

2.09 (0.037)

Model 3

1.48 (1.09–2.33)

2.02 (0.042)

1.25 (1.17–2.04)

1.98 (0.047)

Model 4

1.46 (1.12–2.31)

2.05 (0.04)

1.04 (0.85–2.29)

0.56 (0.286)

Model 5

1.47 (1.11–2.37)

1.99 (0.046)

 

This Table represents multivariable logistic regression analyses with depression status as dependent variable, and either marital status, medication adherence or glycaemic control as an independent variable in separate models (Model 1). Further adjustments to the models were performed by introducing variables sequentially as indicated below:

Model 1: Unadjusted

Model 2: Model 1 + age & gender

Model 3: Model 2 + social factors (education, employment, alcohol & smoking status)

Model 4: Model 3 + clinical factors (duration of diabetes, diabetes medication & hypertension)

Model 5: Model 4 + Glycaemic control

Discussion

Our study has shown that depression is present in about one-third of T2DM patients after screening, and depression was associated with poor glycaemic control. Patients with diabetes have twice the odds of developing depression compared with those without diabetes [4, 5]. Depression in T2DM patients has been studied extensively in populations from high-income countries, however, data from lower-income countries such as Ghana are sparse [11, 12].

The prevalence of depression among T2DM patients in our study was similar to that reported in other studies. In a meta-analysis of 42 studies involving over 21,000 adult patients, clinically significant depression was diagnosed in 31% of T2DM patients [5]. Comparable results have also been reported among adult patients with diabetes in Greece (33.4%) [20], rural Bangladesh (30%) [21] and in the UK (25%) [22]. In contrast to our findings, relatively lower prevalence of depression was found in studies from rural Pakistan (14.7%) [23] and Brazil (18.6%) [24], with a much lower rate in the United States (8.3%) [25]. On the other hand, using different depression assessment tools, higher rates of depression were reported in urban centers in Iran (71.8%) and Pakistan (43.5%) [11, 26]. The wide variation in depression across various studies may be attributed to the difference in the socio-cultural background of the participants. In screening for depression in the various these studies, different assessment tools with varying sensitivity and specificity to detect depression were used [16].

Very few studies in Africa have evaluated the occurrence of depression and its effects among patients with diabetes [12]. However available studies from Nigeria reported the prevalence of depression among T2DM patients within the range of 19.4% to 30%. [2729]. Depression in the general population in Ghana was reported to be rare decades ago, [30] however, in recent years the prevalence has been shown to be comparable to that in western countries [30]. The WHO reported a population-based prevalence of mild depression to be 6.7% among Ghanaian adults > 50 years of age, far lower than the findings of the current study [31]. Indeed different studies have reported the prevalence of depression in different Ghanaian populations to be within the range of 3.8 to 9.9% [3133], and whereas 24.5% of patients referred to a psychiatry clinic were diagnosed with depression [34].

In general, the co-existence of diabetes and depression worldwide has been shown to vary by type of diabetes and the socio-economic status of populations studied [35]. Depression and other psychological problems tend to be more common in developing countries compared with developed countries [3638]. Possible explanations for this observation include higher level of gender inequalities, social insecurity, lower level of education, greater level of poverty and financial constraints among others [37, 38]. Female gender, unmarried status and a lower level of education, all of which have socio-economic implications, were associated with depression in this study. These results are supported by similar findings in both the general population [37, 38] as well as among people with diabetes [22, 39].

Contrary to various study reports, we did not find significant association between poor glycaemic control and depression. Some studies have reported that depressed T2DM patients have higher HbA1c levels compared with non-depressed individuals [25, 40, 41]. The coexistence of depression with diabetes has been reported to be significantly associated with poor glycaemic control [42]. However, this association has not been demonstrated in all studies [43]. In systematic reviews and meta-analysis, depression was found to be weakly correlated with glycaemic status [44]. In type 1 diabetes patients, the association between depression and glycaemic control exist in younger patients, and not in adult patients [45]. The findings of our study indicate that, clinical factors like diabetes duration, medication and hypertension status are important factors that link depression to glycaemic status. It must be noted also that predictors of depression in non-diabetes population in Ghana has not been well studied. However, it is worth noting that there is evidence that remission of depression can result in the improvement of glycaemic control [7]. In addition, depression is said to impair good self-care practices resulting in poor adherence to medication, as well as other diabetes management regimens [46]. Diabetes patients with comorbid depression have been found to have fewer days adherence to dietary, exercise and self-monitoring recommendations, with a 2.3 fold increased odds of missing medication doses compared with those without depression [47].

The findings of this study indicate that, compared to unmarried participants, currently married participants had lower risk prevalence of depression. Studies have shown marriage is strongly and positively associated with psychological well-being for men and women. On average, the currently married report higher levels of psychological well-being (measured by lower rates of depression, substance abuse, and alcoholism) than never-married, divorced, widowed, or separated individuals [48, 49]. Spousal support is among the important sources of psychological fortitude during chronic illness like diabetes, although there is a possibility of disruptions in the marital relationship during chronic illness [48]. The importance of marital support in diabetes management might stem from the other spouse helping the ailing spouse to adhere to diabetes medication and self-management practices. Even with married patients, Katz [50] reported that the self-management behavior of husbands with diabetes often deteriorates when conflict exists with their wives. In addition, the belief of a spouse in the importance of glycemic control predicts such control better than the patient’s beliefs [49]. However, in this study, we did not investigate in the quality of intimacy in married participants, as well as the beliefs of the spouse on diabetes management practices.

Limitation of the study

The major limitation of our study is that the results may not reflect the true burden of depression in the general T2DM population since patients were selected from a specialized tertiary hospital in urban Ghana. Possible replication of this study in a primary health care setting in suburban or rural communities to confirm these findings will be vital. Our study did not include non-diabetes controls for comparison, limiting the interpretation of the possible role of diabetes in depression. Also, being a cross-sectional study by design, we cannot deduce the ‘cause-effect’ relationship between diabetes and depression from our findings; a longitudinal study design will be needed to investigate this relationship.

Conclusions

The findings of our study have shown that depression is quite common among Ghanaian patients with T2DM. Poor glycaemic control was not associated with depression among T2DM patients in our study. Marriage was found to be protective against depression. Future studies may investigate the role of quality of marital intimacy and spousal beliefs on depression.

Abbreviations

HbA1c: 

Glycated haemoglobin

MMAS: 

Morisky Medication Adherence Scale

PHQ-9: 

Patient Health Questionnaire-9

T2DM: 

Type 2 diabetes mellitus

WHO: 

World Health Organization

Declarations

Acknowledgements

The authors wish to acknowledge Prof Albert G.B, Amoah, Prof Richard Adanu and Prof Olugbenga Ogedegbe and his team for their tremendous support before, during and after this project. We also wish to acknowledge the contributions of Margaret Reynolds and Naa in the acquisition of data.

Funding

No funding was available for the study.

Availability of data and materials

Data supporting the conclusion of this study is available for systematic review and meta-analysis upon request.

Authors’ contributions

JA was involved in the conception, design, acquisition of data, interpretation of data and initial drafting of the manuscript. EY participated in conception, design, acquisition of data and interpretation of data. KY was involved in the conception, design, analysis and interpretation of data. JAQ participated in the conception, design, acquisition of data and interpretation of data. WB participated in the conception, design and interpretation of data. All authors critically reviewed the manuscript for important intellectual content and agreed on the version to be published.

Ethics approval and consent to participate

Ethical approval was obtained from the Ethical and Protocol Review Committee of the College of Health Sciences, University of Ghana (Protocol ID MS-Et/M.6-P4.4/2012–13) and each patient provided written voluntary informed consent after the rationale and procedure of the study were thoroughly explained.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

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Authors’ Affiliations

(1)
Department of Medicine & Therapeutics, School of Medicine & Dentistry, University of Ghana, Accra, Ghana
(2)
Department of Dietetics, School of Biomedical and Allied Health Sciences, University of Ghana, Accra, Ghana
(3)
College of Medicine, University of Ibadan, Ibadan, Nigeria
(4)
Department of Physiology, School of Biomedical and Allied Health Sciences, University of Ghana, Accra, Ghana

References

  1. IDF Diabetes atlas: Seventh edition. www.diabetesatlas.org. Assessed 12 Aug 2016.
  2. Greden JF. Physical symptoms of depression: unmet needs. J Clin Psychiatry. 2003;64(Suppl. 7):5–11.PubMedGoogle Scholar
  3. Mezuk B, Eaton WW, Albrecht S, Golden SH. Depression and type 2 diabetes over the lifespan. Diabetes Care. 2008;31:2383–90.View ArticleGoogle Scholar
  4. Ali S, Stone MA, Peters JL, Davies MJ, Khunti K. The prevalence of co-morbid depression in adults with type 2 diabetes: a systematic review and meta-analysis. Diabet Med. 2006;23:1165–73.View ArticleGoogle Scholar
  5. Anderson RJ, Freedland KE, Clouse RE, Lustman PJ. The prevalence of comorbid depression in adults with diabetes: a meta-analysis. Diabetes Care. 2001;24:1069–78.View ArticleGoogle Scholar
  6. Gonzalez JS, Peyrot M, McCarl LA, Collins EM, et al. Depression and diabetes treatment nonadherence: a meta-analysis. Diabetes Care. 2008;31:2398–403.View ArticleGoogle Scholar
  7. Lustman PJ, Anderson RJ, Freedland KE, De Groot M. Depression and poor glycemic control: a meta-analytic review of the literature. Diabetes Care. 2000;23:934–42.View ArticleGoogle Scholar
  8. De Groot M, Anderson R, Freedland KE, Clouse RE, Lustman PJ. Association of depression and diabetes complications: a meta-analysis. Psychosom Med. 2001;63:619–30.View ArticleGoogle Scholar
  9. Roy T, Lloyd CE, Parvin M, Mohiuddin GB, Rahman M. Prevalence of co-morbid depression in out-patients with type 2 diabetes mellitus in Bangladesh. BMC Psychiatry. 2012;12:123.View ArticleGoogle Scholar
  10. Katon WJ, Simon G, Russo J, et al. Quality of depression care in a population-based sample of patients with diabetes and major depression. Med Care. 2004;42:1222–9.View ArticleGoogle Scholar
  11. Khuwaja AK, Lalani S, Dhanani R, Azam IS, et al. Anxiety and depression among outpatients with type 2 diabetes: a multi-Centre study of prevalence and associated factors. Diabetology Metab Syndr. 2010;2(72):1–7.Google Scholar
  12. Patel V, Kim Y. Contribution of low- and middle-income countries to research published in leading general psychiatry journals, 2002-2004. Br J Psychiatry. 2007;190:77–8.View ArticleGoogle Scholar
  13. Heckbert SR, et al. Depression in relation to long-term control of glycemia, blood pressure, and lipids in patients with diabetes. J Gen Intern Med. 2010;25:524–9.View ArticleGoogle Scholar
  14. Weobong B, Akpalu B, Doku V, Owusu-Agyei S, et al. The comparative validity of screening scales for postnatal common mental disorder in Kintampo, Ghana. J Affect Disord. 2009;113:109–17.View ArticleGoogle Scholar
  15. Adewuya AO, Ola BA, Afolabi OO. Validity of the patient health questionnaire (PHQ-9) as a screening tool for depression amongst Nigerian university students. J Affect Disord. 2006;96:89–93.View ArticleGoogle Scholar
  16. van Steenbergen-Weijenburg KM, de Vroege L, Ploeger RR, et al. Validation of the PHQ-9 as a screening instrument for depression in diabetes patients in specialized outpatient clinics. BMC Health Serv Res. 2010;10:235–41.View ArticleGoogle Scholar
  17. Kroenke K, Spitzer RL, Williams JB. The PHQ-9: validity of a brief depression severity measure. J Gen Intern Med. 2001;16:606–13.View ArticleGoogle Scholar
  18. Manea L, Gilbody S, McMillan D. Optimal cut-off score for diagnosing depression with the patient health questionnaire (PHQ-9): a meta-analysis. Can Med Assoc J. 2012;184:E191–6.View ArticleGoogle Scholar
  19. American Diabetes Association. Standards of Medical Care in Diabetes 2014: Position statement of the American Diabetes Association. Diabetes Care. 2014;37:S14–80.Google Scholar
  20. Sotiropoulou A, Papazatiropoulou A, Apostolou O, Kokolaki A, Gikas A, Pappas S. Prevalence of depressive symptoms among non-insulin treated Greek type 2 diabetes subjects. BMC Res Notes. 2008;1:101.View ArticleGoogle Scholar
  21. Asghar S, Hussain A, Sm A, Khan AK, Magnusssson A. Prevalence of depression and diabetes: a population-based study from rural Bangladesh. Diab Med. 2008;24:124–7.Google Scholar
  22. Collins MM, Corcorant P, Perry IJ. Anxiety and depression symptoms in patients with diabetes. Diabet Med. 2009;26:153–61.View ArticleGoogle Scholar
  23. Zahid N, Asghar S, Claussen B, Hussain A. Depression and diabetes in rural community in Pakistan. Diabetes Res Clin Pract. 2008;29:124–7.View ArticleGoogle Scholar
  24. Papelbaum M, Moreira RO, Coutinho W, Kupfer R, Zagury L, Freitas S, Appolinário JC. Depression, glycemic control and type 2 diabetes Marcelo. Diabetol Metab Syndr. 2011;3:26.View ArticleGoogle Scholar
  25. Li C, Ford ES, Strine TW, Mokdad AH. Prevalence of depression among US adults with diabetes: findings from 2006 behavioral risk factor surveillance system. Diabetes Care. 2008;31:105–7.View ArticleGoogle Scholar
  26. Khamseh ME, Baradaran HR, Rajabali H. Depression and diabetes in Iranian patients: a comparative study. Int J Psychiatry Med. 2007;37:81–6.View ArticleGoogle Scholar
  27. James BO, Morakinyo O, Eze GO, Lawani AO, Omoaregba JO. Depression and subjective quality of life among outpatients with diabetes mellitus at a teaching hospital in Nigeria. Ment Health Fam Med. 2010;7:179.PubMedPubMed CentralGoogle Scholar
  28. Akinlade K, Ohaeri J, Suberu M. The psychological condition of a cohort of Nigerian diabetic subjects. Afr J Med Med Sci. 1996;25:61.PubMedGoogle Scholar
  29. Agbir T, Audu M, Adebowale T, Goar S. Depression among medical outpatients with diabetes: a cross-sectional study at Jos University teaching hospital, Jos, Nigeria. Ann Afr Med. 2010;9(1):5-10.View ArticleGoogle Scholar
  30. Read UM, Doku VCK. Mental Health Research in Ghana: a literature review. Ghana Med J. 2012;46(2 Suppl):29–38.PubMedPubMed CentralGoogle Scholar
  31. Thapa SB, Martinez P, Clausen T. Depression and its correlates in South Africa and Ghana among people aged 50 and above: findings from the WHO study on global ageing and adult health. J Psychiatry. 2014;17:1–6.View ArticleGoogle Scholar
  32. Weobong B, Asbbroek AHA, Soremekun S, Danso S, et al. Determination of postnatal depression in rural Ghana: finding from the DON population-based cohort study. Depress Anxiety. 2015;32:108–19.View ArticleGoogle Scholar
  33. Weobong B, Asbbroek AHA, Soremekun S, Kirkwood BR. Prevalence and determinants of antenatal depression among pregnant women in a predominantly rural population in Ghana: the Don population-based study. J Affect Disord. 2014;165:1–7.View ArticleGoogle Scholar
  34. Turkson SN. Psychiatric diagnosis among referred patients in Ghana. East Afr Med J. 1998;75(6):336–8.PubMedGoogle Scholar
  35. Egede LE, Ellis C. Diabetes and depression: global perspectives. Diab Res Clin Pract. 2010;87:302–12.View ArticleGoogle Scholar
  36. Stein DJ, Gureje O. Depression and anxiety in the developing world: is it time to medicalize their suffering. Lancet. 2004;364:233–4.View ArticleGoogle Scholar
  37. Mirza I, Jenkins R. Risk factors, prevalence, and treatment of anxiety and depressive disorders in Pakistan: systemic review. BMJ. 2004;328:794.View ArticleGoogle Scholar
  38. Khuwaja AK, Kadir MM. Gender differences and clustering patterns of behavioral risk factors for chronic non-communicable diseases: community-based study from a developing country. Chronic Illn. 2010;6:163–70.View ArticleGoogle Scholar
  39. Egede LE, Zheng D, Simpson K. Comorbid depression is associated with increased health care use and expenditures in individuals with diabetes. Diabetes Care. 2002;25:464–70.View ArticleGoogle Scholar
  40. Richardson LK, Egede LE, Mueller M, Echols CL, Gebregziabher M. Longitudinal effects of depression on glycemic control in veterans with type 2 diabetes. Gen Hosp Psychiatry. 2008;30:509–14.View ArticleGoogle Scholar
  41. Wagner JA, Abbot GL, Heapy A, Yong I. Depressive symptoms and diabetes control in African Americans. J Immigr Minor Health. 2009;11:66–70.View ArticleGoogle Scholar
  42. Lustman PJ, Clouse RE. Depression in diabetic patients: the relationship between mood and glycemic control. Journal of Diabetes and its Complications. 2005;19(2):113-22.PubMedGoogle Scholar
  43. Kaulgud RS, NekarMS SKJ, Joshi RR, Vijayalakshmi PB. Study of depression in patients with diabetes compared to non-diabetics among elderly population and its association with blood sugar, HbA1c values. Int J Bio Res. 2013;04:55–61.Google Scholar
  44. Ismail K, Winkley K, Rabe-Hesketh S. Systematic review and meta-analysis of randomised controlled trials of psychological interventions to improve glycaemic control in patients with type 2 diabetes. Lancet. 2004;363(9421):1589–97.View ArticleGoogle Scholar
  45. Winkley K, Landau S, Eisler I, Ismail K. Psychological interventions to improve glycaemic control in patients with type 1 diabetes: systematic review and meta-analysis of randomised controlled trials. BMJ. 2006;333(7558):65.View ArticleGoogle Scholar
  46. Egede LE. Effects of depression on self-management behaviours and health outcomes in adults with type 2 diabetes. Curr Diabetes Rev. 2005;1:235–43.View ArticleGoogle Scholar
  47. Gonzalez S, Sa S, Cagliero E, Wexler DJ, et al. Depression, self-care and medication adherence in type 2 diabetes: relationships across the full range of symptoms severity. Diabetes Care. 2007;30:2222–7.View ArticleGoogle Scholar
  48. Frech A, Williams K. Depression and the psychological benefits of entering marriage. J Health Soc Behav. 2007;48(2):149–63.View ArticleGoogle Scholar
  49. Trief PM, Sandberg J, Greenberg RP, Graff K, Castronova N, Yoon M, Weinstock RS. Describing support: a qualitative study of couples living with diabetes. Fam Syst Health. 2003;21(1):57.View ArticleGoogle Scholar
  50. Trief PM, Ploutz-Snyder R, Britton KD, Weinstock RS. The relationship between marital quality and adherence to the diabetes care regimen. Ann Behav Med. 2004;27(3):148–54.View ArticleGoogle Scholar

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