Objectives: The recovery rate is important to determine a country’s development towards controlling coronavirus. It is a function of myriad factors – death rate, cases requiring hospitalization, quality of care, and discharge policies, among others. India’s recovery rate is growing steadily from an earlier low of 10% to 11%. It is imperative to understand the determinants of recovery rate in a country to enable improvements in the same. Material and Methods: COVID-19 data have been compiled from several sources, including the Ministry of Health and Family Welfare, National Disaster Management Authority, and Indian Council of Medical Research and demographic and health data from Census of India, 2011, National Health Profile, 2019, and were used. The study uses linear regression to understand the relationship between recovery rate and development indicators in India. Results: Our analysis emphasizes the beneficial impacts of the health system and better economy on the recovery rate. Investment in health, urban stay, non-slum and non-poor population, and effective governance is instrumental in improving recovery rate. Conclusion: Scaling up health facilities and medical infrastructure, slum decongestion, focus on economically weaker sections, capacity building of health workers and ameliorating long-term investments in health, health research, and better quality of living are also essential to address recovery of COVID-19.

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Objective: To determine the prevalence and predictors of combined BMI-WC disease risk categories among Indian adults.Methods: The study utilizes data from Longitudinal Ageing Study in India (LASI Wave 1) with an eligible sample of 66, 859 individuals. Bivariate analysis was done to get the proportion of individuals in different BMI-WC risk categories. Multinomial logistic regression was used to identify the predictors of BMI-WC risk categories.Results: Poor self-rated health, female sex, urban place of residence, higher educational status, increasing MPCE quintile, and cardio-vascular disease increased with increasing BMI-WC disease risk level while increasing age, tobacco consumption, and engagement in physical activities was negatively associated with BMI-WC disease risk.Conclusion: Elderly persons in India have a considerable higher prevalence of BMI-WC disease risk categories which make them vulnerable to developing several disease. Findings emphasize the need of using combined BMI categories and waist circumference to assess the prevalence of obesity and associated disease risk. Finally, we recommend that intervention programs with an emphasis on urbanites wealthy women and those with a higher BMI-WC risk categories be implemented.

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