| Article Access Statistics|
| Viewed||128 |
| Printed||0 |
| Emailed||0 |
| PDF Downloaded||21 |
| Comments ||[Add] |
Click on image for details.
BRIEF RESEARCH COMMUNICATION
|Year : 2020
: 62 | Issue : 5 | Page
|Depression prevalence, its psychosocial and clinical predictors, in diabetes mellitus patients attending two health institutions of north India catering rural population
Jyoti Gupta1, Dheeraj Kapoor2, Vivek Sood2, Sukhjit Singh3, Neeraj Sharma3, Pankaj Kanwar4
1 Department of Psychiatry, Dr. YS Parmar Government Medical College, Nahan, Himachal Pradesh, India
2 Department Medicine, Dr. RPGMC, Kangra at Tanda, Chamba, Himachal Pradesh, India
3 Pt. JL Nehru Government Medical College, Chamba, Himachal Pradesh, India
4 Psychiatry, Dr. RPGMC, Kangra at Tanda, Chamba, Himachal Pradesh, India
Click here for correspondence address and
|Date of Submission||19-Mar-2019|
|Date of Decision||14-May-2019|
|Date of Acceptance||10-Jun-2020|
|Date of Web Publication||10-Oct-2020|
| Abstract|| |
Background: Diabetes mellitus (DM) poses a greater risk of depression and a poor quality of life (QoL). There is a limited data regarding relationship of depression to QoL in patients from rural health care settings of North India.
Aim: To know the prevalence and predictors of depression in patients of DM among various sociodemographic, clinical and QoL variables.
Settings and Design: This cross-sectional study was conducted in two hospitals of North India mostly catering rural population from 2014 to 2018.
Materials and Methods: Sociodemographic and clinical data of DM patients was collected. They were applied Hindi translation of QoL Instrument for Indian Diabetes Patients and Patient Health Questionnaire-9. Analyses were done by Statistical Package for Social Sciences (Version 17.0, USA).
Results: Among 300 patients, 25.6% had clinical depression. Illiteracy, the affect on general, emotional/mental health and role limitation by diabetes predicted risk of depression.
Conclusion: Education of patients regarding self-management in DM to assure good health should be emphasised.
Keywords: Depression, diabetes, quality of life
|How to cite this article:|
Gupta J, Kapoor D, Sood V, Singh S, Sharma N, Kanwar P. Depression prevalence, its psychosocial and clinical predictors, in diabetes mellitus patients attending two health institutions of north India catering rural population. Indian J Psychiatry 2020;62:566-71
|How to cite this URL:|
Gupta J, Kapoor D, Sood V, Singh S, Sharma N, Kanwar P. Depression prevalence, its psychosocial and clinical predictors, in diabetes mellitus patients attending two health institutions of north India catering rural population. Indian J Psychiatry [serial online] 2020 [cited 2020 Oct 24];62:566-71. Available from: https://www.indianjpsychiatry.org/text.asp?2020/62/5/566/297749
| Introduction|| |
Diabetes mellitus (DM) is India's fastest growing disease, where more than 72 million cases recorded in 2017, are expected to increase to about 134.3 million by 2045. Diabetes has been recognized as a “depressogenic” condition. One of the latest systematic review and meta-analysis reports it as 28% in the world and 32% in Asia. In India various authors have reported widely variable (2%–84%) prevalence of depression in DM.
A review of Indian studies reported female gender, elevated fasting blood sugar (FBS) level, physical disability and lack of physician's advice regarding lifestyle modifications as the predictors of depression in DM. Other risk factors were younger age, not having a spouse, poor social support, lower education, low socioeconomic status, medical comorbidity, being on insulin, longer duration of diabetes, presence of complications,, a low levels of daily activities and previous history of depression. Also, in Type 2 DM impairments in daily activities and lower health related quality of life (HRQOL) also predicted depressive symptomatology.
There is a limited literature data about prevalence of depression in rural health care settings of North India. So, this study was conducted to know the prevalence of depression in patients with DM and it's predictors among various sociodemographic, clinical and quality of life (QoL) variables.
| Materials and Methods|| |
This was a cross-sectional study conducted from 2014 to 2018 after taking approval from Institute's Ethics Committee and an informed consent from the DM patients capable of independent communication. They were enrolled from a weekly diabetic clinic of a tertiary care centre and medicine outpatient department and inpatient department of a secondary care centre of Northern India largely catering rural populations by a purposive sampling technique.
Those patients with any other diagnosed comorbid chronic severe physical illnesses (except hypertension, coronary artery diseases or any other complications of DM), individuals under treatment for depression or other psychiatric illness and those suffering from psychoactive substance dependence (except tobacco) were excluded from the study.
All sociodemographical and clinical information of the patients was recorded in an ethically approved predesigned proforma. Latest laboratory investigation reports related to DM i.e., glycated haemoglobin or HbA1c (within last one month) or FBS within past 1 week (if HbA1c not available or done) were reviewed.
For assessment of depression Hindi version of Patient Health Questionnaire-9 (PHQ-9) was used. For defining diagnosis of depression, a PHQ-9 score of 8–9, 10–14 and >15 with one of the two cardinal symptoms (either depressed mood or anhedonia) were defined as minor, moderate and definite major depression respectively.
A 34 items scale QoL Instrument for Indian Diabetes Patients (QOLID) developed and validated by Nagpal et al. was used to assess QoL. It consists of eight domains covering all aspects of QoL, namely, role limitations due physical health, physical endurance, general health, treatment satisfaction, symptom frequency, financial worries, mental health (MH), and diet advice satisfaction. It uses a standard Likert scale across all questions. A Hindi translation was used in the study.
Socio-demographic and clinical data were reported as mean ± standard deviation/median or percentages. The differences in characteristics between variables were found using Independent sample t-test for continuous variables and Chi-square test for categorical variables. Logistic regression analysis was used as a statistical method for multivariate analysis to study predictors of depression in DM patients. The dependent variable was presence (PHQ-9 score >7) or absence of depression (PHQ-9 score ≤7) and various sociodemographic, clinical variables as well as QoL total and domain wise scores were entered as independent variables. All statistical analyses were carried out using Statistical Package for Social Sciences (Version 17.0, SPSS Inc., Chicago: USA).
| Results|| |
Among 300 patients evaluated in the study, 83 (27.7%) and 217 (72.3%) were from secondary and tertiary care centre respectively. Majority i.e., 216 (72%) patients were hailing from rural area. The mean age of patients was 55.52 ± 9.94 years and that of duration of DM was 8.02 ± 6.72 years (median 6). The mean of monthly cost of the treatment was 1266.12 ± 1000 INR (median 1000 INR).
Males were more educated (11.28 ± 4.05 years) than females (7.61 ± 4.77 years), (χ2 = 68; P < 0.001) and better compliant with treatment (P < 0.001). Abdominal obesity (waist circumference >90 cm in males and >80 cm in females) was more common in females (88.3% vs. 54.5%; P < 0.001) and more females were taking medicines from the hospital supply (16.8% vs. 9.9%; P < 0.001).
The mean PHQ-9 score was 5.22 ± 6.16 (median 3). The scores were higher (t = 3.85, P < 0.001) in females (6.32 ± 6.31) than in males (3.60 ± 5.56). A total of 77 (25.6%) patients reported clinical depression (PHQ-9 >7), 33% were females and 14.9% were males. Among them, minor depression was present in 5% (15), 6.1% females and 3.3% males; moderate depression in 10.3% (31), 14.5% females and 4.1% males; and definite major depression in 10.3% (31), 12.3% females and 7.4% males. All these gender differences were significant at P < 0.05.
The comparison of sociodemographic and clinical profile of DM patients with or without depression is shown in [Table 1] and [Table 2] respectively. A statistically significant difference in QoL total score and most of the domain scores (P < 0.001) among male and females was seen in our study, except role limitation and dietary satisfaction, with females having poorer scores. Males were less bothered due to symptoms of diabetes (P < 0.05). The comparison of QoL domains in patients with and without depression is also shown in [Table 2].
|Table 1: Comparison of socio-demographic profile of diabetes mellitus patients with or without depression|
Click here to view
|Table 2: Comparison of clinical profile and quality of life total and domain wise scores of diabetes mellitus patients with or without depression|
Click here to view
In logistic regression analysis, illiteracy (odds ratio [OR] = 0.13 [0.02–0.93] P < 0.05) was found as risk factors for depression in our study. Lower scores of affect on general health by diabetes domain (32.54 [6.47–163.93] P < 0.001), affect on emotional and MH domain (OR = 35.73 [6.86–186.050 P < 0.001]) and role limitation in social life, work and travel domain (OR = 4.99 [1.06–23.43], P < 0.05) of QoL also predicted depression in a best fitted model.
| Discussion|| |
A recent meta-analysis of 43 Indian studies has found the pooled prevalence of depression in Type-2 DM to be 38%. It has been found to be more prevalent in northern states of India than in southern states. In 8 Indian studies using PHQ-9, prevalence of depression was found ranging from 35.4% to 63%. But most of the sample was from urban population in these studies.,,, Other Indian studies have reported variable prevalence ranging from 2% to 84%. A study using Montgomery-Asberg Depression Rating Scale as assessment tool from a rural Indian setting, has found prevalence of depression as 11.6%. Similar to our study, previous studies have found rates of depression to be higher in females,,,, illiterates or those with low education and unemployed diabetics. Also, rural area residents and those coming from low socio-economic background, had higher rates of depression as seen in our study. This may also explain the finding of higher rates of clinical depression in patients taking free medicines from hospital supply, probably because of their low socioeconomic backgound. Furthermore, unmarried, divorced or separated, widows and widowers had higher rates as was also seen in a study by Rajangam et al. This can be explained by a lack of support of the spouse in the self-management required in diabetes.
Higher rates reported in various previous studies in patients on insulin for glycaemic control,, those with poor treatment compliance and poor glycaemic control,,, was also seen in the current study. Poor adherence to prescribed regime may lead to inadequate glycaemic control which enhances the risk of complications. As in the current study, presence of complications due to diabetes, including neuropathy has been found to be associated with depression.,, The incidence of depression in DM has been found to be associated with obesity,, physical inactivity and sedentary life in the literature. The greater abdominal obesity despite lower body mass index has been shown to make Asian Indian phenotype more prone to diabetes. In the current study, females were less educated and had higher rates of abdominal obesity, this may explain the higher rates of depression in them to some extent.
Significant inverse correlation has also been found between depression severity and QoL Compared to other ethnicities, Indians were found to be most likely to report poorer HRQOL in the domain of MH of SF-36 An updated systematic review and Meta-analysis by Khaledi et al, mentions a study (by Sidhu et al, 2017) with a similar strong correlation between emotional burden and depression using PHQ-9. In a study, DM and its complications affected negatively all of the domains of the WHOQOL-BREF; with strongest effects for the physical health and psychological domains. In a prospective cohort survey of elderly patients with Type 2 DM that impairments in daily activities and lower HRQOL were predictors of depressive symptomatology. Thus in the current study, the QOLID domains of affect on general health, role limitation in social life, work and travel and affect on emotional and MH by DM are replication of the results of the previous studies. An educated DM patient has lower risk of depression similar to the findings of previous studies. Educated patients are able to understand and comply with the advice regarding medicines, life style and dietary modifications in a better way. This may prevent deterioration in their glycaemic control, preventing development of complications and a resultant decline in QoL.
| Conclusion|| |
Depression is a matter of great concern in patients with DM. The health care professionals at primary care level should be sensitised regarding screening of depression in DM. An education regarding chronic nature of DM, compliance, an adequate glycaemic control, a possibility of complications, role of physical activity and dietary control to maintain desired anthropometric parameters and good general health should be incorporated in the management of DM and reinforced in every visit of the patient. Such interactions may also provide the patient with a platform to ventilate their emotions which could itself be therapeutic in certain cases. Thus there is a need for an integrated care for both depression and diabetes. Special public health initiatives are needed to create awareness at community level.
However, our study has few limitations of being a hospital based cross-sectional study having a small sample size. An assessment of depression through self-report questionnaire might have led to over-estimation of depression. The Hindi translation of the QOLID has not been validated. Also, all patients did not have their HbA1c test reports and glycaemic control in them was assessed from single value of FBS. These two may not be comparable as a measure of glycaemic control.
Financial support and sponsorship
Conflicts of interest
There are no conflicts of interest.
| References|| |
Pan A, Lucas M, Sun Q, van Dam RM, Franco OH, Manson JE, et al
. Bidirectional association between depression and type 2 diabetes mellitus in women. Arch Intern Med 2010;170:1884-91.
Khaledi M, Haghighatdoost F, Feizi A, Aminorroaya A. The prevalence of comorbid depression in patients with type 2 diabetes: An updated systematic review and meta-analysis on huge number of observational studies. Acta Diabetol 2019;56:631-50.
Naskar S, Victor R, Nath K. Depresson in diabetes mellitus-A comprehensive systematic review of literature from an Indian perspective. Asian J Psychiatry 2017;27:85-100.
Thakur M. Diabetes and depression: A review with special focus on India. GJMPH 2015;4:1-5.
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.
Ravishankar SN, Madhuvan HS, Reddy VT, Prabha V, Dinesh SR. A study of depression in diabetes mellitus: Analysis from rural hospital, India. Arch Med 2014;6:1-4.
Poongothai S, Anjana RM, Pradeepa R, Ganesan A, Unnikrishnan R, Rema M, et al
. Association of depression with complications of type 2 diabetes – The Chennai Urban Rural Epidemiology Study (CURES- 102). J Assoc Physicians India 2011;59:644-8.
Pawaskar MD, Anderson RT, Balkrishnan R. Self-reported predictors of depressive symptomatology in an elderly population with type 2 diabetes mellitus: a prospective cohort study. Health Qual Life Outcomes 2007;5:50.
Katon W, Russo J, Lin EH, Heckbert SR, Karter AJ, Williams LH, et al
. Diabetes and poor disease control: Is comorbid depression associated with poor medication adherence or lack of treatment intensification? Psychosom Med 2009;71:965-72.
Kochhar PH, Rajadhyaksha SS, Suvarna VR. Translation and validation of brief patient health questionnaire against DSM IV as a tool to diagnose major depressive disorder in Indian patients. J Postgrad Med 2007;53:102-7.
] [Full text]
Nagpal J, Kumar A, Kakar S, Bhartia. The development of 'Quality of Life Instrument for Indian Diabetes Patients (QOLID): A validation and reliability study in middle and higher income groups. J Assoc Physicians India 2010;58:295-304.
Hussain S, Habib A, Singh A, Akhtar M, Najmi AK. Prevalence of depression among type 2 diabetes mellitus patients in India: A meta-analysis. Psychiatry Res 2018;270:264-73.
Taranum A, Angadi N, Shakeel M. A study of frequency and factors associated with depression among adult diabetics in urban areas of Davangere, Karnataka. Natl J Community Med 2016;7:111-5.
Eashwar VA, Gopalakrishnan SM, Umadevi R. Prevalence of depression in patients with type 2 diabetes mellitus and its association with fasting blood sugar levels, in an urban area of Kancheepuram district, Tamil Nadu. Int J Med Sci Public Health 2017;4:3399-406.
Khan P, Qayyum N, Malik F, Khan T, Khan M, Tahir A. Incidence of anxiety and depression among patients with type 2 diabetes and the predicting factors. Cureus 2019;11:e4254.
Rajangam T, Priyashini RM, Manickam S. A study of prevalence of depression in type 2 diabetic patients in a tertiary care centre. IJCMR 2018;5:113-5.
Bajaj HS, Pereira MA, Anjana RM, Deepa R, Mohan V, Mueller NT, et al
. Comparison of relative waist circumference between Asian Indian and US adults. J Obest 2014;2014:461956.
Verma SK, Luo N, Subramaniam M, Sum CF, Stahl D. Impact of depression on health related quality of life in patients with diabetes. Ann Acad Med Singapore 2010;39:913-9.
John R, Pise S, Chaudhari L, Deshpande PR. Evaluation of quality of life in type 2 diabetes mellitus patients using quality of life instrument for Indian diabetic patients: A cross-sectional study. J Mid Life Health 2019;10:81-8.
Department of Psychiatry, Dr. YS Parmar Governmenrt Medical College, Nahan, Sirmour, Himachal Pradesh
Source of Support: None, Conflict of Interest: None
[Table 1], [Table 2]