The COVID-19 pandemic: socioeconomic and health disparities
This study highlights how pre-existing health and socioeconomic inequalities amplify the health consequences of the COVID-19 pandemic for disadvantaged groups.
This paper investigates the correlation between socioeconomic and health-related factors and COVID-19 mortality using country aggregate data. It found that demographic and social disadvantage predictors correlate with COVID-19 mortality per million, with XGBoost outperforming ridge regression in prediction.
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.