Background: COVID-19 mortality rates vary widely across regions and sex/gender. Understanding the reasons behind such variation could help in developing suitable management strategies. Methods: This paper presents a comprehensive analysis of incidence and mortality rates on 2,331,363 cases and 46,239 deaths over a cumulative period of approximately 6.5 months from February to August 2020 across 411 districts of India in the age group 15-49. Together with health data from government surveys, we identify risk and protective factors across regions, socio-economic status, literacy, and sex. To obtain common indicators, we apply both machine learning techniques and statistical tests on different health factors. We also identify positive and negative correlates at multiple population scales by dividing the cohort into sub-cohorts formed from two Indian states that were further segregated by sex. Results: We show that males and females differ in their risk factors for mortality. While obesity (lasso regression coefficient: KA=0.5083, TN=0.318) is the highest risk factor for males, anemia (KA=0.3048, TN=0.046) is the highest risk factor for females. Further, anemia (KA=-0.0958, TN=-0.2104) is a protective factor for males, while obesity (KA=-0.0223, TN=-0.3081) is a protective factor for females. Conclusion: Districts with a high prevalence of obesity pose a significantly greater risk of severe COVID-19 outcomes in males. On the other hand, in females, the prevalence of anemia in districts is notably associated with a higher risk of severe COVID-19 outcomes. It is important to consider sex-wise heterogeneity in health factors for better management of health resources.

Incidence and mortality rates due to COVID-19 have varied widely in different parts of the world and placed a huge strain on hospital resources. Understanding the underlying reasons behind such variation is crucial to developing population-specific or even individual-specific management strategies. This paper presents a comprehensive analysis of incidence and mortality rates from data collected over a cumulative period of approximately 6.5 months from February to August 2020 across 411 districts of India, totalling over 2 million individuals. We identify the health factors which have both positive as well as negative correlates with high mortality rates, using data obtained from district-wise aggregated COVID-19 incidence and mortality rates and health data obtained from National Family Health Survey (NFHS). To obtain robust indicators, we apply both machine learning techniques as well as classical statistical methods and show that the same factors are identified by both methods. We also identify positive and negative correlates at multiple population scales by dividing the cohort into sub-cohorts formed from two Indian states which were further segregated by gender. We show that there is a disparity of risk factors among males and females. While obesity is the highest risk factor for men, anaemia is the highest risk factor for women. Hence, to better manage the health of a specific group of people, it is important to consider gender-wise heterogeneity in health risk factors which could contribute to differing vulnerabilities