|Year : 2019 | Volume
| Issue : 2 | Page : 147-153
Depression and associated factors in type 2 diabetic patients: A community-based cross-sectional study from East Delhi
Kanika Singh, Anita Shankar Acharya, Sanjeev Kumar Rasania
Department of Community Medicine, Lady Hardinge Medical College, New Delhi, India
|Date of Submission||18-Jul-2019|
|Date of Decision||05-Sep-2019|
|Date of Acceptance||16-Oct-2019|
|Date of Web Publication||19-Dec-2019|
Anita Shankar Acharya
Department of Community Medicine, Lady Hardinge Medical College, New Delhi - 110 001
Source of Support: None, Conflict of Interest: None
Introduction: Diabetes is a chronic disease which has no cure and requires life-long management, frequent hospital visits, as well as monitoring of blood sugar levels, which can cause distress to the patient. Various risk factors have been identified that may influence the course of diabetes and its complication, of which depression has emerged as a significant factor. Diabetes mellitus doubles the likelihood of depression as compared to normal individuals. We conducted this study to find the prevalence of depression and its associated factors in diabetes mellitus patients in East Delhi.
Material and Methods: A community-based cross-sectional study was conducted among 250 diabetics aged above 30 years in East Delhi in 2017. They were screened for depression using Physical Health Questionnaire 9. Sociodemographic details, diabetic profile, and behavioral factors were assessed. Data were collected and entered in SPSS software version 23. Multivariate analysis was done for associated factors.
Results: Out of total 250 study patients who fulfilled the inclusion and exclusion criteria, 79 (31.6%) were males and 171 (68.4%) were females. Age, gender, marital status, type of family, diabetic profile, comorbidity, complications, blood pressure status, body mass index, physical activity, stress, and sleeping hours were the factors assessed for the association with depression.
Conclusion: Diabetes is a chronic illness requiring a variety of self-management behaviors. To improve patients' diabetes self-management behaviors, health-care providers should cultivate patient-centered relationships. Screening of all diabetics for depression is of paramount importance.
Keywords: Comorbidities, complications, depression, diabetes, Physical Health Questionnaire 9
|How to cite this article:|
Singh K, Acharya AS, Rasania SK. Depression and associated factors in type 2 diabetic patients: A community-based cross-sectional study from East Delhi. Indian J Community Fam Med 2019;5:147-53
|How to cite this URL:|
Singh K, Acharya AS, Rasania SK. Depression and associated factors in type 2 diabetic patients: A community-based cross-sectional study from East Delhi. Indian J Community Fam Med [serial online] 2019 [cited 2020 Jul 15];5:147-53. Available from: http://www.ijcfm.org/text.asp?2019/5/2/147/273529
| Introduction|| |
Diabetes mellitus is a major public health problem all over the world. By 2030, India is expected to have around 79.4 million people with diabetes, most of them with Type 2 diabetes mellitus (T2DM). Diabetes is a chronic disease which has no cure and requires life-long management, frequent hospital visits, as well as monitoring of blood sugar levels, which can cause distress to the patient. Various risk factors have been identified that may influence the course of diabetes and its complication, of which depression has emerged as a significant factor. Diabetes mellitus doubles the likelihood of depression as compared to normal individuals.
Patients with T2DM and depressive symptoms are reported to have worse glycemic control, amplification of symptoms, reduced adherence to dietary and drug recommendations, decreased physical activity, and increased microvascular and macrovascular complications. Depressed diabetics have poor self-management and moreover, depression in diabetics increases the risk of cardiovascular disease leading to higher mortality. Depression and other associated comorbidities can significantly worsen the health and economic burden of diabetes patients.
Untreated depression may further exacerbate the progression of diabetes. The importance of screening for and addressing depression in people with diabetes is recognized in the guidelines of several countries and also by the International Diabetes Federation. It is also important to identify the risk factors associated with diabetes in order to improve their quality of life.
| Material and Methods|| |
This is a community-based, cross-sectional study which was carried out from December 2016 to March 2018. Data were collected during the calendar year of 2017.
The study participants were recruited from the diabetic clinic of Lal Bahadur Shastri (LBS) Hospital, Kalyanpuri, which is a secondary-level care center with bed strength of 100 located in East Delhi. The diabetic clinic is scheduled twice a week, i.e., Monday and Wednesday afternoon from 2 to 4 p.m. A team of health professionals supervise the working of clinic, which includes medicine specialists, residents, nursing staff, and other staff. Besides outpatient department facilities, the hospital provides free-of-cost diabetes medicines and laboratory investigations including glycated hemoglobin (HbA1c). Diabetes was diagnosed using the guidelines of American Diabetes Association (ADA) with either blood sugar levels after fasting >126 mg/dl and postprandial >200 mg/d or HbA1c cutoff of >6.5.
All the diabetics aged >30 years residing in Kalyanpuri and attending the diabetic clinic of LBS in the calendar year of 2017 formed the study population. The inclusion criteria were permanent residents of Kalyanpuri (residing for >1 year), diagnosed cases of Type II diabetes attending the diabetic clinic (>1 year), and patients of both sexes >30 years. Patients with known psychiatric illness, those with serious illnesses, those on antidepressants, or pregnant patients were excluded from the study.
A sample size of 250 patients was calculated for this study taking prevalence of depression as 41% based on the study on Thour et al. (2015), Chandigarh, with 15% relative error.
The diabetic clinic was visited once a week. Four hundred and seventeen study patients who were registered at LBS were screened for eligibility in the study. Two hundred and ninety-seven study patients who fulfilled the inclusion and exclusion criteria were enrolled from the diabetic clinic. Once they were enrolled from the diabetic clinic at LBS, they were interviewed at their residence at Kalyanpuri, which is a purposive sampling. Data were recorded in the predesigned, pretested interview schedule.
Out of the 297 study participants, 27 (9.09%) could not be traced at given address. Ten (3.36%) were not available on three consecutive visits and hence were excluded. The remaining 120 cases did not fulfill the inclusion criteria as 110 were nonresidents of Kalyanpuri, 6 were on antidepressants, and 4 were pregnant. Finally, 250 study participants were enrolled in the study. An informed written consent was recorded from each study participant and for illiterate participants, thumb impression was taken in front of a witness. All the study participants who were found suffering from depression were referred to the Department of Psychiatry in a secondary-level hospital/Lady Hardinge Medical College for further workup.
The following instruments were used to assess the study participants:
- A pretested semi-structured interview schedule consisting of:
- Sociodemographic particulars
- A detailed history of diabetes, its duration, treatment, complications, and associated comorbidities was recorded
- Behavioral factors included hours of sleep, addiction habits regarding smoking, alcohol consumption, and smokeless tobacco use.
- Physical activity using the Global Physical Activity Questionnaire by the WHO
The tool was developed by the WHO for physical activity surveillance in countries. It collects information on physical activity participation in three settings (or domains) as well as sedentary behavior, comprising of 16 questions. The domains are activity at work, travel to and from places, and recreational activities, for which total score is calculated as total physical activity metabolic equivalent of task minutes/week. Physical activity cutoff value as per WHO recommendations, should be more than 600.
- Assessment of stress using the General Health Questionnaire 12
The General Health Questionnaire is widely used to measure mental health status especially in detection of emotional stress. Each item is accompanied by four responses, typically being “not at all,” “no more than usual,” “rather more than usual,” and “much more than usual;” the scores range from 0 to 3, respectively, with a score of 3 as cutoff for detecting stress.
- A questionnaire for Depression scale (Physical Health Questionnaire 9)
The Physical Health Questionnaire 9 (PHQ 9) is a screening questionnaire containing nine questions which assess major depressive disorders according to the Diagnostic Statistical Manual IV Edition (DSM IV) criteria. The questions are concerning fatigue, concentration, depressive complaints, thoughts of death, etc., on a 5-point Likert scale, as follows: not at all, various days, more than half the days, and almost every day; 0, 1, 2, and 3 points were scored, respectively, for these categories, and a sum score of the nine questions was calculated. Score was classified into different grades of severity using standard cutoff values: 5–9 was classified as mild depression, 10–14 as moderate depression, 15–19 as moderately severe depression, and 20–27 as severe depression. The time frame of questions was last fortnight [Annexure 1]. It is validated and available in Hindi and used in other studies,,, Institutional Ethical clearance was obtained for the study (Approval no. LHMC/ECHR/2016/54R1). Written informed consent was obtained from each patient.
Data analysis was done by coding the data and entering it in Statistical Package for the Social Sciences version 23. Mean and standard deviation were used for quantitative data. Chi-square test, ANOVA, and t-test were applied wherever necessary. Bivariate analysis and multivariate analysis were done. Odds ratio (OR) with 95% confidence interval (CI) was calculated for categorical comparisons. All tests were two tailed, with P < 0.05 considered statistically significant.
| Results|| |
Out of the total 250 study participants who fulfilled the inclusion and exclusion criteria, 79 (31.6%) were males and 171 (68.4%) were females. The overall mean age of the study participants was 55.17 ± 11 years, with a range from 31 to 82 years [Table 1].
|Table 1: Distribution of the study participants according to sociodemographic characteristics (n=250)|
Click here to view
Half of the study participants with nearly equal proportion of males (51.8%) and females (49.1%) were diagnosed with diabetes mellitus in the last 5 years. About one-fourth (25.3%) of the males and almost one-third (31.6%) of the females had a family history of diabetes. Approximately two-fifths (43.6%) of the study participants had uncontrolled diabetes (as per the standard guidelines of ADA) and approximately one-third (34.4%) of the study participants were not aware of their current diabetic status (blood sugar reports were not available at the time of interview). Majority of the study participants (80%) had low adherence to diabetic medication (as per Morisky Medication Adherence Score 8) [Annexure 2]. Males were more adherent to the medication as compared to females. The difference was found to be statistically highly significant (P < 0.01). More than one-third (35.2%) of the study participants had one or more complication of diabetes. Approximately three-fourth (75%) of the study participants were having tingling, burning sensation, or numbness, whereas nearly two-fifth (47%) were having blurring of vision as a complication of diabetes. Other complications such as shortness of breath, chest pain, syncope, palpitation, and burning micturition were also reported.
Prevalence of depression
The prevalence of depression was found to be 17.6% (95% CI, 13.6–22) as per the PHQ 9. Mild depression was seen in 22 (8.8%) study participants, moderate depression in 12 (4.8%) study participants, and moderately severe and severe depression in 6 (2.4%) and 4 (1.6%) participants, respectively. Majority (82.4%) of the study participants did not have depression.
Factors associated with depression
Factors which were studied for the association with depression were age, gender, marital status, type of family, diabetic profile, comorbidity, complications, blood pressure status, body mass index (BMI), physical activity, stress, and sleeping hours.
On bivariate analysis, age, type of family, comorbidities, complications, physical activity, stress, and sleeping hours were significantly associated with depression. Age more than 50 years was associated with depression with an OR of 3.42 (95% CI, 1.51–7.72) as compared to 31–50 years' age group. Participants living in a joint family showed twice more risk of having depression (OR: 2.20, 95% CI, 1.09–4.45) as compared to nuclear family. Participants who were under stress were at more risk of depression (OR: 46.5, 95% CI, 18.45–117.44) as compared to those who were not stressed. Odds of having depression was less in those who were physically active as compared to those who were physically inactive (OR: 0.033, 95% CI, 0.16–0.64). Participants having sleep >7 h had decreased odds of having depression compared to those who were sleeping <7 hours (OR: 0.14, 95% CI, 0.07–0.28,). Diabetics with comorbidities were associated with depression with an OR of 3.30 (95% CI, 1.61–6.77) as compared to diabetics with no comorbidities. Diabetics with complications were associated with depression with an OR of 6.02 (95% CI, 2.08–17.38) [Table 2].
|Table 2: Bivariate logistic regression analysis of factors associated with depression|
Click here to view
On bivariate analysis, age, type of family, physical activity, stress, comorbidities, complications, and sleeping hours were found to be associated with depression. However, in a multivariate analysis, only family type, stress, and sleeping hours were found to be associated with depression [Table 3]. Depression was seen three times more in joint family as compared to nuclear family (OR: 3.64, 95% CI, 1.22–10.8). Presence of stress leads to fifty times more chances of depression compared to the study participants who were not having stress (OR: 50.8, 95% CI, 16.6–152.4). Sleeping hours <7 h have more odds of having depression compared to the study participants sleeping <7 h (OR: 7.04, 95% CI, 3.4–14.3).
|Table 3: Multivariate logistic regression analysis of factors associated with depression (Physical Health Questionnaire-9 score)|
Click here to view
| Discussion|| |
This was a community-based, cross-sectional study to determine the factors of depression in patients of T2DM. Out of the total 250 study participants, 79 (31.6%) were males and 171 (68.4%) were females. As the diabetic clinic was functional between 2 and 4 p.m. on working days, there was a preponderance of females (68.4%) as compared to males (31.6%); as males are employed during that time, they could not attend the diabetic clinic.
Overall, the mean age was 55.17± 11 years with a range from 31 to 82 years. Almost half (50.8%) of them were in the age group of 41–60 years. The age distribution of the study participants in the present study is in conformity to studies conducted by Bahety et al. (mean age: 56.09 ± 5.92 years) and Chew et al. (mean age: 56.94 years). In other studies, 7, ,,,,, also, similar age distribution was seen.
The clinical profile of the study participants was assessed according to the duration of diabetes since diagnosis, family history of diabetes, their diabetic status as per standards of ADA, adherence status as per the Morisky Medication Adherence Scale 8, complications of the diabetes, and comorbidities.
Half of the study participants were diagnosed with diabetes mellitus within the last 5 years. Similar finding was seen in studies conducted by Chung et al. (57.1%) and Zhang et al. (46.1%). Approximately one-third (29.6%) of the study participants had a family history of diabetes mellitus, but higher percentage was seen in other studies.,, This difference may be due to more than half of the study participants being illiterate and may not be aware of the family history.
Oral hypoglycemic drugs were the most common medication used by majority (89%) of the study participants, which is similar to a study done by Chew et al. (91.1%). About one-third (35.2%) of the study participants had one or more complications of diabetes, which is in concordance with other studies., The present study showed that nearly half of the study participants (49.6%) had at least one comorbidity which was also reported by Vankar et al. (54%) and Roy et al. (46%).
In our study, age, family type, stress, physical activity, comorbidity, complications, and sleeping hours came out to be significantly associated with depression on univariate or bivariate analysis.
There was a highly statistically significant association between depression and presence of comorbidities in the study participants, which was similar to studies done by other authors.,,,
In our study, there was a highly statistically significant association between depression and presence of complications in the study participants, which was similar to studies done by various authors,,,, although on multivariate analysis of these factors, family type, stress, and sleeping hours were found to be significant.
Patients staying within a joint family showed more signs of depression than who stayed in a nuclear family, which was also seen in a study done by Niraula et al.
Presence of stress increases the chances of depression up to fifty times. However, in a study done by Wang et al., it was seen that patients who had stress had an average of 4.49 times more depression than those without stress. Hence, all efforts should be made to reduce stress levels in diabetic patients. They can indulge in more of recreational activities, meditation, or hobbies.
Participants sleeping <7 h were found to be at risk of depression in the present study. The study conducted by Wang et al. showed that participants who slept <7 h had 2.52 times more depression compared to nondepressive patients. Yu et al. showed that with longer sleep duration, participants were less likely to have depression (OR: 0.45, P = 0.02). Thereby, it should be emphasized through counseling/health education to have an adequate sleep of 7 h or more in patients with T2DM.
Diabetes is a chronic illness requiring a variety of self-management behaviors and a patient-centered model of care for providing a more skillful approach to improving diabetes self-care behaviors. Depression in diabetics was mainly associated with patient-initiated behaviors that are difficult to maintain (e.g., exercise, diet, and medication adherence) but not with preventive services for diabetes. Depression was found to be associated with various clinical and behavioral factors and physical activity, so the community should be educated on healthy behavioral practices. Family support should be reinforced with counseling sessions specifically targeting the depressed diabetics. Capacity building of health-care providers at primary care level for identification of depression in diabetics needs to be undertaken.
| Conclusion|| |
According to PHQ 9 score, depression was found to be present in 44 (17.6%) study participants. Mild depression was seen in 22 (8.8%) study participants. Majority (82.4%) of the study participants did not have depression. There was a statistically significant association between depression with age, family type, comorbidity, complications, sleep hours, physical activity, and stress. There was no statistically significant association of depression with marital status, occupation, socioeconomic status, family history of diabetes, duration of diabetes, adherence status, and BMI.
Nearly every fifth diabetic is associated with depression. Hence, screening of all the diabetics for depression is of paramount importance. Further studies testing the effectiveness and cost-effectiveness of enhanced models of care of diabetic patients with depression are needed. Use of depression scoring can prove to be a cost-effective tool for screening of depression in diabetes, which would be of immense public health significance.
Financial support and sponsorship
Conflicts of interest
There are no conflicts of interest.
| References|| |
Mushtaque A, Gulati R, Hossain MM, Azmi SA. Prevalence of depression in patients of type 2 diabetes mellitus: A cross sectional study in a tertiary care centre. Diabetes Metab Syndr 2016;10:238-41.
Asghar S, Hussain A, Ali SM, Khan AK, Magnusson A. Prevalence of depression and diabetes: A population-based study from rural Bangladesh. Diabet Med 2007;24:872-7.
Ferreira MC, Piaia C, Cadore AC, Antoniolli MA, Gamborgi GP, Oliveira PP. Clinical variables associated with depression in patients with type 2 diabetes. Rev Assoc Med Bras (1992) 2015;61:336-40.
Singh H, Raju MS, Dubey V, Kurrey R, Bansal S, Malik M. A study of sociodemographic clinical and glycemic control factors associated with co-morbid depression in type 2 diabetes mellitus. Ind Psychiatry J 2014;23:134-42.
] [Full text]
Thour A, Das S, Sehrawat T, Gupta Y. Depression among patients with diabetes mellitus in North India evaluated using patient health questionnaire-9. Indian J Endocrinol Metab 2015;19:252-5.
Bahety P, Agarwal G, Khandelwal D, Dutta D, Kalra S, Taparia P, et al
. Occurrence and predictors of depression and poor quality of life among patients with Type-2 Diabetes: A Northern India Perspective. Indian J Endocrinol Metab 2017;21:564-9.
Rajkumar AP, Thangadurai P, Senthilkumar P, Gayathri K, Prince M, Jacob KS. Nature, prevalence and factors associated with depression among the elderly in a rural south Indian community. Int Psychogeriatr 2009;21:372-8.
Chew BH, Vos R, Mohd-Sidik S, Rutten GE. Diabetes-related distress, depression and distress-depression among adults with type 2 diabetes mellitus in Malaysia. PLoS One 2016;11:e0152095.
Mathew CS, Dominic M, Isaac R, Jacob JJ. Prevalence of depression in consecutive patients with type 2 diabetes mellitus of 5-year duration and its impact on glycemic control. Indian J Endocrinol Metab 2012;16:764-8.
Eker S. Prevalence of depression symptoms in diabetes mellitus. Open Access Maced J Med Sci 2018;6:340-3.
Zhang P, Lou P, Chang G, Chen P, Zhang L, Li T, et al
. Combined effects of sleep quality and depression on quality of life in patients with type 2 diabetes. BMC Fam Pract 2016;17:40.
Akena D, Kadama P, Ashaba S, Akello C, Kwesiga B, Rejani L, et al
. The association between depression, quality of life, and the health care expenditure of patients with diabetes mellitus in Uganda. J Affect Disord 2015;174:7-12.
El Mahalli AA. Prevalence and predictors of depression among Type 2 diabetes mellitus outpatients in Eastern Province, Saudi Arabia. Int J Health Sci (Qassim) 2015;9:119-26.
Mikailiūkštienė A, Juozulynas A, Narkauskaitė L, Žagminas K, Sąlyga J, Stukas R. Quality of life in relation to social and disease factors in patients with type 2 diabetes in Lithuania. Med Sci Monit 2013;19:165-74.
Chung JO, Cho DH, Chung DJ, Chung MY. Assessment of factors associated with the quality of life in Korean type 2 diabetic patients. Intern Med 2013;52:179-85.
Zhang W, Xu H, Zhao S, Yin S, Wang X, Guo J, et al
. Prevalence and influencing factors of co-morbid depression in patients with type 2 diabetes mellitus: A general hospital based study. Diabetol Metab Syndr 2015;7:60.
Wang L, Song R, Chen Z, Wang J, Ling F. Prevalence of depressive symptoms and factors associated with it in type 2 diabetic patients: A cross-sectional study in China. BMC Public Health 2015;15:188.
Vankar GK, Parikh V, Banwari G, Parikh M. Depression in diabetes mellitus. Arch Indian Psychiatry 2007;9:38-42.
Roy T, Lloyd CE, Parvin M, Mohiuddin KG, Rahman M. Prevalence of co-morbid depression in out-patients with type 2 diabetes mellitus in Bangladesh. BMC Psychiatry 2012;12:123.
Alonso-Morán E, Satylganova A, Orueta JF, Nuño-Solinis R. Prevalence of depression in adults with type 2 diabetes in the Basque country: Relationship with glycaemic control and health care costs. BMC Public Health 2014;14:769.
Sweileh WM, Abu-Hadeed HM, Al-Jabi SW, Zyoud SH. Prevalence of depression among people with type 2 diabetes mellitus: A cross sectional study in Palestine. BMC Public Health 2014;14:163.
Majdan M, Krajcovicova L, Pekarcikova J, Chereches R, O'Mullane M. Predictors of depression symptoms in patients with diabetes in Slovakia. Int J Psychiatry Med 2012;44:351-66.
Niraula K, Kohrt BA, Flora MS, Thapa N, Mumu SJ, Pathak R, et al
. Prevalence of depression and associated risk factors among persons with type-2 diabetes mellitus without a prior psychiatric history: A cross-sectional study in clinical settings in urban Nepal. BMC Psychiatry 2013;13:309.
Yu S, Yang H, Guo X, Zheng L, Sun Y. Prevalence of depression among rural residents with diabetes mellitus: A cross-sectional study from Northeast China. Int J Environ Res Public Health 2016;13. pii: E542.
[Table 1], [Table 2], [Table 3]