RESEARCH ARTICLE
Sex-disaggregated Analysis of Risk Factors of COVID-19 Mortality Rates in India
Anush V. Kini1, Harish P.B.1, Monica Anand2, Uma Ranjan3, *
Article Information
Identifiers and Pagination:
Year: 2023Volume: 16
E-location ID: e187494452305082
Publisher ID: e187494452305082
DOI: 10.2174/18749445-v16-e230517-2022-210
Article History:
Received Date: 06/11/2022Revision Received Date: 22/03/2023
Acceptance Date: 12/04/2023
Electronic publication date: 23/06/2023
Collection year: 2023
open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: https://creativecommons.org/licenses/by/4.0/legalcode. This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Abstract
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.
