Cross-sectional study of depression and help-seeking in Uttarakhand, North India

Abstract

ObjectivesThis study sought to use a population-based cross-sectional survey to describe depression prevalence, healthcare seeking and associations with socioeconomic determinants in a district in North India.SettingThis study was conducted in Sahaspur and Raipur, administrative blocks of Dehradun district, Uttarakhand, in July 2014.ParticipantsA population-based sample of 960 people over the age of 18 years was selected in 30 randomised clusters after being stratified by rural:urban census ratios.Primary outcome measuresThe survey used a validated screening tool, Patient Health Questionnaire, to identify people with depression, and collected information regarding socioeconomic variables and help-seeking behaviours. Depression prevalence and health seeking behaviours were calculated, and multivariable logistic regression was used to assess associations between risk factors and depression.ResultsPrevalence of depression was 6% (58/960), with a further 3.9% (37/960) describing a depressive episode of over 2 weeks in the past 12 months. Statistically significant adjusted OR for depression of more than 2 were found for people who were illiterate, classified as Scheduled Caste/Tribe or Other Backward Castes, living in temporary material housing and who had recently taken a loan. While over three quarters of people with depression (79%) had attended a private or government general medical practitioner in the past 3 months, none had received talking therapy (100% treatment gap) and two people (3.3%) had been prescribed antidepressants.ConclusionsThere are clear associations between social, educational and economic disadvantage and depression in this population. Strategies that address the social determinants of depression, such as education, social exclusion, financial protection and affordable housing for all are indicated. To address the large treatment gap in Uttarakhand, we must ensure access to primary and secondary mental health providers who can recognise and appropriately manage depression.

Health care use and treatment-seeking for depression symptoms in rural India: A descriptive cross-sectional analysis

“…Adults with high levels of depression symptoms are likely to be in contact with health services, but their primary complaints are rarely the depression symptoms listed in the PHQ-9. This echoes previous findings from India that depressed individuals frequently present to health services with somatic symptoms (11,12,(40)(41)(42). Therefore, the most important challenge from a public health perspective appears not to be to persuade depressed individuals to visit services, but rather to enable health workers to recognise their mental health needs during consultations about other complaints.…”

Prevalence and associated risk factors of depression among housewives in rural Saharanpur, Uttar Pradesh, India

“…The prevalence of depression among women 18-59 years age was found to be 18% in the current study. A study by Poongothai [14].…”

“…A study by Mathias et al observed people in their middle years had a slightly higher risk of depression than those under 30 years and over 50 years of age [14]. [17][18][19][20].…”

“…A study by Urvashi et al also found an inverse relationship between the level of education and depression and observed education has an important influence on psychosocial characteristics such as efficacy and self-esteem both of which have a moderator effect on depression [13]. Research findings of Mathias et al suggested a dose-response relationship in educational status, with the risk of depression increases with decreasing years of completed schooling [14]. Similar findings were also observed by Deswal et al and Ali et al [15,20].…”

Prevalence and association of Depression with other chronic morbidities among adults in an urban area of Haryana

“…The prevalence observed in present study was higher (16.2%) compared to these studies, despite using the same study tool, this difference can be because Mathias et al and Maideen et al used a higher cut off for labelling depression (>10 point on PHQ-9) compared to present study where mild depression (≥5 points on PHQ-9) was also included. (10,24) A large urban population-based study from South India by Poongothai et al (2009) using PHQ-9and reported overall prevalence of depression to be 15.1%. This similarity with present study may be attributed to the similarity in the population type and the study tool used for the assessment of depression.…”