preprints.org > business, economics and management > econometrics and statistics > doi: 10.20944/preprints202012.0599.v1

Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Version 1 : Received: 22 December 2020 / Approved: 23 December 2020 / Online: 23 December 2020 (16:29:18 CET)
Version 2 : Received: 13 January 2021 / Approved: 15 January 2021 / Online: 15 January 2021 (12:09:12 CET)

Disadvantaged groups around the world have suffered and endured higher mortality during the current COVID-19 pandemic. This contrast disparity suggests that socioeconomic and health-related factors may drive inequality in disease outcome. To identify these factors correlated with COVID-19 outcome, country aggregate data provided by the Lancet COVID-19 Commission subjected to correlation analysis. Socioeconomic and health-related variables were used to predict mortality in the top 5 most affected countries using ridge regression and extreme gradient boosting (XGBoost) models. Our data reveal that predictors related to demographics and social disadvantage correlate with COVID-19 mortality per million and that XGBoost performed better than ridge regression. Taken together, our findings suggest that the health consequence of the current pandemic is not just confined to indiscriminate impact of a viral infection but that these preventable effects are amplified based on pre-existing health and socioeconomic inequalities.

COVID-19; mortality; socioeconomic disparity; disadvantaged groups

Business, Economics and Management, Econometrics and Statistics

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