Decomposing rural-urban differences in depression prevalence: a cross-sectional analysis of two community-based southern Indian cohorts

Abstract

Introduction: Depression is a growing public health concern in India but its prevalence is uneven across the country, possibly influenced by several sociodemographic factors. We aimed to assess the rural-urban disparity in the prevalence of depression and their associated sociodemographic and lifestyle-related factors.

Methods: Participants were middle-aged and older adults (≥45 years) from two parallel, prospective cohorts from rural (CBR-SANSCOG, n=4493) and urban (CBR-TLSA, n=972) southern India. We used cross-sectional data from the baseline clinical and biochemical assessments of the above two cohorts. The Geriatric Depression Scale (GDS-30) was used to screen for depression (cut-off ≥10). Logistic regression was used to assess the relationship between place of residence (rural vs urban) and prevalence of depression, adjusting for age, sex, education, income, marital status, Body Mass Index (BMI), alcohol use, tobacco use and number of comorbidities. The Fairlie decomposition analysis was used to decompose the rural-urban disparity.

Results: We found that the prevalence of depression was significantly higher in rural than in urban participants (14.49% vs 8.23%, p<0.001). The fully adjusted binary logistic regression model showed that rural-dwelling individuals were 1.57 times more likely to have depression than urban residents (AOR: 1.57, 95% CI: 1.03, 2.39). In the decomposition analysis, the variables included in this model (age, sex, education, income, marital status, BMI, alcohol use, tobacco use and number of comorbidities) explained 35.21% of the rural-urban disparity in the prevalence of depression, with sex and marital status being the significant contributors.

Conclusion: Participants in our rural cohort had significantly higher odds for depression as compared to their urban counterparts, with sociodemographic factors playing a key role in this disparity. This underscores the need for scaling up mental health services in the rural communities of India including training primary healthcare providers to promptly identify and manage depression.

Keywords: Community Health; Prevalence; Public Health; Sociodemographic Factors.

Figure 1. This figure is a forest plot depicting the associations of the place of residence and other sociodemographic and lifestyle-related factors with the prevalence of depression.