An Empirical Study on Diabetes Depression over Distress Evaluation Using Diagnosis Statistical Manual and Chi-Square Method

Sohail M. Noman, Jehangir Arshad, Muhammad Zeeshan, Ateeq Ur Rehman, Amir Haider, Shahzada Khurram, Omar Cheikhrouhou, Habib Hamam and Muhammad Shafiq
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Sohail M. Noman: Department of Cell Biology and Genetics, Shantou University Medical College, Shantou 515041, China
Jehangir Arshad: Department of Electrical & Computer Engineering, COMSATS University Islamabad, Lahore Campus, Lahore 54000, Pakistan
Muhammad Zeeshan: Department of Medicine and Surgery, Al-Nafees Medical College and Hospital, Isra University, Islamabad 44000, Pakistan
Ateeq Ur Rehman: Department of Electrical Engineering, Government College University, Lahore 54000, Pakistan
Amir Haider: Department of Intelligent Mechatronics Engineering, Sejong University, Seoul 05006, Korea
Shahzada Khurram: Faculty of Computing, The Islamia University of Bahawalpur, Bahawalpur 63100, Pakistan
Omar Cheikhrouhou: College of CIT, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia
Habib Hamam: Faculty of Engineering, Moncton University, Moncton, NB E1A3E9, Canada
Muhammad Shafiq: Department of Information and Communication Engineering, Yeungnam University, Gyeongsan 38541, Korea

IJERPH, 2021, vol. 18, issue 7, 1-11

Abstract: Diabetes distress is an alternative disorder that is often associated with depression syndromes. Psychosocial distress is an alternative disorder that acts as a resistance to diabetes self-care management and compromises diabetes control. Yet, in Nigeria, the focus of healthcare centers is largely inclined toward the medical aspect of diabetes that neglects psychosocial care. In this retrospective study, specific distress was measured by the Diabetes Distress Screening (DDS) scale, and depression was analyzed by the Beck Depression Inventory (BDI) and Diagnosis Statistics Manual (DSM) criteria in type 2 diabetes mellitus (T2DM) patients of Northwestern Nigeria. Additionally, we applied the Chi-square test and linear regression to measure the forecast prevalence ratio and evaluate the link between the respective factors that further determine the odd ratios and coefficient correlations in five nonintrusive variables, namely age, gender, physical exercise, diabetes history, and smoking. In total, 712 sample patients were taken, with 51.68% male and 47.31% female patients. The mean age and body mass index (BMI) was 48.6 years ± 12.8 and 45.6 years ± 8.3. Based on the BDI prediction, 90.15% of patients were found depressed according to the DSM parameters, and depression prevalence was recorded around 22.06%. Overall, 88.20% of patients had DDS-dependent diabetes-specific distress with a prevalence ratio of 24.08%, of whom 45.86% were moderate and 54.14% serious. In sharp contrast, emotion-related distress of 28.96% was found compared to interpersonal (23.61%), followed by physician (16.42%) and regimen (13.21%) distress. The BDI-based matching of depression signs was also statistically significant with p < 0.001 in severe distress patients. However, 10.11% of patients were considered not to be depressed by DSM guidelines. The statistical evidence indicates that depression and distress are closely correlated with age, sex, diabetes history, physical exercise, and smoking influences. The facts and findings in this work show that emotional distress was found more prevalent. This study is significant because it considered several sociocultural and religious differences between Nigeria and large, undeveloped, populated countries with low socioeconomic status and excessive epidemiological risk. Finally, it is important for the clinical implications of T2DM patients on their initial screenings.

Keywords: type 2 diabetes mellitus forecasting; diabetes distress; depression; prevalence; clinical implications; regression correlation factors (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2021
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