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Comparing COVID-19 Risk Factors in Brazil Using Machine Learning: The Importance of Socioeconomic, Demographic and Structural Factors

7 Pages Posted: 12 Mar 2021

See all articles by Pedro Baqui

Pedro Baqui

Espírito Santo Federal University - Núcleo de Astrofísica e Cosmologia (Cosmo-ufes)

Valerio Marra

Espírito Santo Federal University - Núcleo de Astrofísica e Cosmologia (Cosmo-ufes); Espírito Santo Federal University - Departamento de Física

Ahmed M. Alaa

University of California, Los Angeles (UCLA) - Department of Electrical and Computer Engineering

Ioana Bica

University of Oxford - Department of Engineering Science

Ari Ercole

University of Cambridge - Division of Anaesthesia

Mihaela van der Schaar

The Alan Turing Institute

More...

Abstract

Background: The COVID-19 pandemic continues to have a devastating impact on Brazil. Brazil's social, health and economic crises are aggravated by strong societal inequities and  persisting political disarray. This complex scenario motivates careful study of the clinical, socioeconomic, demographic and structural factors contributing to increased risk of mortality from SARS-CoV-2 in Brazil specifically.

Methods: We consider the Brazilian SIVEP-Gripe catalog, a very rich respiratory infection dataset which allows us to estimate the importance of several non-laboratorial and socio-geographic factors on COVID-19 mortality. We analyze the catalog using machine learning algorithms to account for likely complex interdependence between metrics.

Findings: The XGBoost algorithm achieved excellent performance, producing an AUC-ROC of 0.813 (95%CI 0.810-0.817), and outperforming logistic regression. Using our model we found that, in Brazil, socioeconomic, geographical and structural factors are more important than individual comorbidities. Particularly important factors were: The state of residence and its development index; the distance to the hospital (especially for rural and less developed areas); the level of education; hospital funding model and strain. Ethnicity is also confirmed to be more important than comorbidities but less than the aforementioned factors.

Interpretation: Socioeconomic and structural factors are as important as biological factors in determining the outcome of COVID-19. This has important consequences for policy making, especially on vaccination/non-pharmacological preventative measures, hospital management and healthcare network organization.

Funding: None.

Declaration of Interests: We declare no competing interests.

Suggested Citation

Baqui, Pedro and Marra, Valerio and Alaa, Ahmed M. and Bica, Ioana and Ercole, Ari and van der Schaar, Mihaela, Comparing COVID-19 Risk Factors in Brazil Using Machine Learning: The Importance of Socioeconomic, Demographic and Structural Factors. Available at SSRN: https://ssrn.com/abstract=3803367 or http://dx.doi.org/10.2139/ssrn.3803367

Pedro Baqui

Espírito Santo Federal University - Núcleo de Astrofísica e Cosmologia (Cosmo-ufes)

Vitória, 29075-910
Brazil

Valerio Marra (Contact Author)

Espírito Santo Federal University - Núcleo de Astrofísica e Cosmologia (Cosmo-ufes) ( email )

Vitória, 29075-910
Brazil

Espírito Santo Federal University - Departamento de Física ( email )

Vitória
Brazil

Ahmed M. Alaa

University of California, Los Angeles (UCLA) - Department of Electrical and Computer Engineering ( email )

405 Hilgard Avenue
Box 951361
Los Angeles, CA 90095
United States

Ioana Bica

University of Oxford - Department of Engineering Science

Mansfield Road
Oxford, Oxfordshire OX1 4AU
United Kingdom

Ari Ercole

University of Cambridge - Division of Anaesthesia ( email )

United Kingdom

Mihaela Van der Schaar

The Alan Turing Institute

British Library, 96 Euston Road
96 Euston Road
London, NW12DB
United Kingdom

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