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

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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.

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Data Availability

The Python notebook and datasets used in this work are available at web repository https://github.com/Above02/Covid19_Analysis.

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Acknowledgements

We are grateful for the computer resources provided by the project “National High-Performance Computing Laboratory (LANCAD-6-2023) - Machine and deep learning”. Additionally, we thank the anonymous referees for their valuable feedback and suggestions.

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Author notes

  1. Alejandro Carvantes-Barrera and Lorena Díaz-González these authors contributed equally to this work.

Authors and Affiliations

  1. Maestría en Optimización y Cómputo Aplicado, Universidad Autónoma del Estado de Morelos, Cuernavaca, 62209, Morelos, México

    Alejandro Carvantes-Barrera

  2. Centro de Investigación en Ciencias, Universidad Autónoma del Estado de Morelos, Cuernavaca, 62209, Morelos, México

    Lorena Díaz-González

  3. Instituto de Biotecnología, Universidad Nacional Autónoma de México, Cuernavaca, Morelos, 62209, México

    Mauricio Rosales-Rivera

  4. Unidad de Innovación Clínica y Epidemiológica del Estado de Guerrero, Acapulco, Guerrero, 39715, México

    Luis A. Chávez-Almazán

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  1. Alejandro Carvantes-Barrera
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  2. Lorena Díaz-González
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  3. Mauricio Rosales-Rivera
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  4. Luis A. Chávez-Almazán
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Contributions

Alejandro Carvantes-Barrera: conceptualized, applied and evaluated the methodology, analyzed the results and prepared figures and tables. Lorena Díaz-González: conceptualized and supervised the study, analyzed the results, wrote and revised the manuscript. Mauricio Rosales-Rivera: conceptualized and supervised the study, analyzed the results and reviewed the manuscript. Luis A. Chávez: reviewed and edited the manuscript. All authors read and approved the manuscript.

Corresponding author

Correspondence to Lorena Díaz-González.

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Carvantes-Barrera, A., Díaz-González, L., Rosales-Rivera, M. et al. Risk Factors Associated with COVID-19 Lethality: A Machine Learning Approach Using Mexico Database. J Med Syst 47, 90 (2023). https://doi.org/10.1007/s10916-023-01979-4

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