Determinants of multimorbidity among elderly population in maharashtra, India: Logistic regression analysis.

Background: Population aging is an emerging global trend. Because of decreasing fertility rates and improved healthcare, the lifespan of elderly population increased. Consequently, proportion of elderly population is increasing at an alarming rate. This is accompanied by an increased recognition of the occurrence of multimorbidity and associated mortality risks. So, the purpose of this study was to determine the prevalence and predictors of multimorbidity among elderly population in Maharashtra with its variation among socio-demographic spectrum, functional health and health behaviors.

Materials and methods: Sample of elderly population aged > 60 years were selected to examine multimorbidity and its associated risk factors. Statistical methods such as Chi-square test were used to show the association between multimorbidity and other covariates. Binary logistic regression analysis was used to understand the effects of predictor variables on multimorbidity. Receiver Operating Characteristic (ROC) Curve Analysis was carried out to improve the performance of the classification model by using a modified cut-off probability value. Z scores were calculated to compare model performance in training data and test data.

Results: The prevalence of multimorbidity in Maharashtra in training data and test data was found to be 32.8% and 32.9%. Residence, living arrangement, MPCE Quintile, marital status, work status, education, tobacco consumption, physical activity, Instrumental Activities of Daily Living (IADL), Activities of Daily Living (ADL) and self-rated health of elderly population were important determinants that exert a significant adverse effect on multimorbidity.

Conclusion: Prediction percentages indicate that appropriate actions should be undertaken to ensure good quality of life for all the elderly in Maharashtra.