Risk Factors Associated with COVID-19 Lethality: A Machine Learning Approach Using Mexico Database

Abstract

Identifying risk factors associated with COVID-19 lethality is crucial in combating the ongoing pandemic. In this study, we developed lethality predictive models for each epidemiological wave and for the overall dataset using the Extreme Gradient Boosting technique and analyzed them using Shapley values to determine the contribution levels of various features, including demographics, comorbidities, medical units, and recent medical information from confirmed COVID-19 cases in Mexico between February 23, 2020, and April 15, 2022. The results showed that pneumonia and advanced age were the most important factors predicting patient death in all cohorts. Additionally, the medical unit where the patient received care acted as a risk or protective factor. IMSS medical units were identified as high-risk factors in all cohorts, except in wave four, while SSA medical units generally were moderate protective factors. We also found that intubation was a high-risk factor in the first epidemiological wave and a moderate-risk factor in the following waves. Female gender was a protective factor of moderate-high importance in all cohorts, while being between 18 and 29 years old was a moderate protective factor and being between 50 and 59 years old was a moderate risk factor. Additionally, diabetes (all cohorts), obesity (third wave), and hypertension (fourth wave) were identified as moderate risk factors. Finally, residing in municipalities with the lowest Human Development Index level represented a moderate risk factor. In conclusion, this study identified several significant risk factors associated with COVID-19 lethality in Mexico, which could aid policymakers in developing targeted interventions to reduce mortality rates.

Keywords: Extreme Gradient Boosting (XGBoost); Human Development Index; Point Biserial Correlation; Protective factor; Risk factor; SHapley additive exPlanation (SHAP).