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Unpacking Self-Rated Health and Quality of Life in Older Adults and Elderly in India: A Structural Equation Modelling Approach
Authors:Siddhivinayak Hirve  Johan H L Oud  Somnath Sambhudas  Sanjay Juvekar  Yulia Blomstedt  Stephen Tollman  Stig Wall  Nawi Ng
Institution:1. Vadu Rural Health Program, KEM Hospital Research Center, Pune, India
2. Division of Epidemiology and Global Health, Department of Public Health and Clinical Medicine, Ume? Centre for Global Health Research, Ume? University, Ume?, Sweden
3. Behavioural Science Institute, Radboud University, Nijmegen, The Netherlands
4. Health and Population Division, School of Public Health, University of Witwatersrand, Johannesburg, South Africa
Abstract:The Study on global AGEing and adult health (SAGE) aims at improving empirical understanding of the health and well-being of older adults in low- and middle-income countries. A total of 321 adults aged 50 years and older were interviewed in rural Pune district, India, in 2007. We used Structural Equation Modelling (SEM) to examine the pathways through which social factors, functional disability, risk behaviours, and chronic disease experience influence self-rated health (SRH) and quality of life (QOL) amongst older adults in India. Both SRH and QOL worsened with increased age (indirect effect) and limitations in functional ability (direct effect). QOL, socio-economic status (SES), and social networking had no significant effect on SRH. Smoking was associated with the presence of at least one chronic illness, but this did not have a statistically significant effect on SRH. Higher social networking was seen amongst the better educated and those with regular income, which in turn positively affected the QOL rating. QOL had a direct, but statistically not significant, effect on SRH. In conclusion, the indirect effects of age on SRH mediated through functional ability, and the effects of SES on QOL mediated through social networking, provide new understanding of how age and socio-economic status affect SRH and QOL. By allowing for measurement errors, solving for collinearity in predictor variables by integrating them into measurement models, and specifying causal dependencies between the underlying latent constructs, SEM provides a strong link between theory and empirics.
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