Abstract
Background
In recent years, India has witnessed a significant rise in rural-to-urban migration among its elderly population, posing distinct challenges, particularly concerning later-life health outcomes. Present study aims to examine the association between rural-urban migration in later life and mental health among elderly individuals currently residing in urban area.
Method
Using data from the Longitudinal Ageing Study (LASI) 2017-18 of India present study (n = 4529) applied bivariate analysis, logistic regression. Factors associated with depression is determine by logistic regression. However, Fairlie decomposition employed to show differences between depression between the rural-urban migrant elderly and non-migrant elderly populations.
Results
The findings reveal around a 22 percent of the study's participants, especially females [OR: 1.62, CI: 1.06–2.48] and wealthy people are migrated from rural to urban areas and continuously residing in urban areas since last ten years. Further, the study examines that migrating from rural to urban areas after the age of 50 and adapting to the new urban environment are linked with a higher likelihood of depression [OR:1.68, CI 0.93–2.17] among the elderly compared to their urban-native. Additionally, socio-demographic factors such as higher education and greater wealth significantly influence depressive symptoms. Notably, elderly individuals who co-reside with their spouse and children [OR: 0.62, CI: 0.34–1.13] tend to experience lower levels of depression, although this is intricately tied to decisions regarding their current place of residence.
Conclusion
Disparities in depression levels among elderly are accelerated by factors such as lower education, low wealth, living arrangements, and decisions regarding current residence when it collides with migration status. Consequently, there is a pressing need for governmental intervention aimed at bolstering economic security and establishing robust social support systems to bolster the mental well-being of India's elderly population.