Loneliness in Myanmar’s older population: A mixed-methods investigation

Abstract

Objectives Little is known about loneliness in lower- and middle-income countries. This study investigates loneliness in the older population of Myanmar using a mixed-methods approach. Methods To identify predictors of loneliness, hierarchical regression models were used to analyze data from the Myanmar Aging Survey 2012 (N = 3,618, 57% women). In a mixed-methods sequential explanatory design, quantitative data were integrated with qualitative data from semi-structured interviews with older adults in Myanmar in 2019. Results The prevalence of loneliness varied by between-person characteristics. Health impairments, lower income, being widowed, not having children, and living with fewer household members were each associated with loneliness. Qualitative findings suggested that the physical presence of family members was especially protective against loneliness. Religion had mixed associations with loneliness, depending on the type of religious practice, demographic characteristics, health status, and community engagement. Discussion The findings contribute to a better understanding of individuals’ experiences of loneliness and may inform the design of interventions to prevent loneliness in Myanmar and globally.

Differences in Loneliness and Social Isolation among Community-Dwelling Older Adults by Household Type: A Nationwide Survey in Japan

“…The results of the multiple regression analysis showed that ST had significant associations with UCLA for educational level, subjective economic status, subjective health status, and GSES, while MT had significant associations with UCLA for subjective economic status, subjective health status, and GSES. Studies conducted before the coronavirus pandemic revealed that health problems (multiple diseases, poor health) [55,56], subjective health status [35], low income [35,55], and education (years) [34] were associated with loneliness, which is consistent with the results of the present study. Therefore, variables such as educational level, subjective economic status, and subjective health status may be important variables for predicting loneliness in ST and MT individuals during the coronavirus pandemic.…”