Prediction of fall-risk factors specific to the old-age Indian population

Abstract

AbstractBackground/ObjectivesThe health care infrastructure of India, designed to treat acute problems, can benefit from preventive medicine-based policies that address chronic and non-communicable issues of relevance to India’s growing elderly population. Unintentional fall related injuries are one such issue whose economic burden can be streamlined with proper preventative public health policies. It is imperative that fall-risk factors specific to the Indian population be identified and analyzed for use in geriatric falls-risk assessment. We aim to determine factors predictive of falls in the aging Indian population in this study using Wave 1 data from the World Health Organization Study on Global Ageing and Adult Health (WHO SAGE) in India.MethodsCross-sectional analysis of results from WHO SAGE Wave 1 was conducted. Multivariate analysis was used to determine risk factors of falls specific to the Indian population in adults over the age of 50. Prediction models were created and evaluated using these risk factors and their performances were evaluated. SAGE Wave 1 India was implemented in six states that together provided nationally representative samples. Multistage stratified sampling was used to select these states and systematic sampling was used to select households from villages and urban districts within these states. Data from all individuals over the age of 50 in selected households was compiled for analysis.Findings34 fall risk factors specific to the Indian population were determined. The model that did not weigh the factors was determined as the best model for possible use in clinically assessing old-age adults at risk for falling. Furthermore, 6 risk factors in the Indian census were used to identify falls risks hotspots on a district-level map of India.ConclusionThis analysis can be used in public health policy recommendations and can form a basis for assessing and addressing falls-risk issues in India.