Comorbidity of depression and diabetes: an application of biopsychosocial model

Abstract

BACKGROUND: Type 2 diabetes (T2D) is one of the most psychologically demanding chronic medical illness in adult. Comorbidity between diabetes and depression is quite common, but most studies were based on developed country sample. Limited data exists to document biopsychosocial predictors of depressive symptoms in Ethiopian patients. Therefore, the aim of the study was to describe the association of depressive symptoms and T2D and explore the potential underlying associated biopsychosocial risk factors. METHODS: Institution based cross-sectional study was conducted on 276 patient with T2D at diabetic clinic, Black Lion General Specialized Hospital in Ethiopia. Patients were selected using systematic random sampling technique. Depressive symptoms score, which constructed from a validated nine-item Patient Health Questionnaire (PHQ-9), was an outcome variable. Finally, significant associated factors were identified using multiple linear regression analysis with backward elimination procedure. Statistical Package for Social Science (SPSS) version 22.0 (IBM SPSS Corp.) was used to perform all analysis. RESULTS: Total of 264 patient data was analyzed with 95.7% response rate. Patients mean (SD) current age and age at diagnosis was 55.9 (10.9) and 43.9 (10.9) years, respectively. Patients waist circumference (mean ± SD) was 98.9 ± 11.1 cm. The average PHQ-9 score was 4.9 (SD 4.1) and fasting blood glucose was 166.4 (SD 73.2). Marital status (divorced), occupation (housewife), diabetic complication (nephropathy), negative life event in the last six months, and poor social support significantly associated with increased mean PHQ-9 score after adjustment for covariates. Whereas not fearing diabetic-related complication and death significantly lower mean PHQ-9 score. CONCLUSION: Biopsychosocial variables including marital status, negative life event in the last 6 months, occupation, diabetic complication, and poor social support significantly increase average depressive symptoms score. Evidence-based intervention focusing on these identified biopsychosocial factors are necessary to prevent the development of depressive symptoms.