Comparing COVID-19 risk factors in Brazil using machine learning: the importance of socioeconomic, demographic and structural factors

Abstract 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 s...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Pedro Baqui, Valerio Marra, Ahmed M. Alaa, Ioana Bica, Ari Ercole, Mihaela van der Schaar
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/646d5ac4d71847f6b5c57ef46251141a
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:646d5ac4d71847f6b5c57ef46251141a
record_format	dspace
spelling	oai:doaj.org-article:646d5ac4d71847f6b5c57ef46251141a2021-12-02T17:06:32ZComparing COVID-19 risk factors in Brazil using machine learning: the importance of socioeconomic, demographic and structural factors10.1038/s41598-021-95004-82045-2322https://doaj.org/article/646d5ac4d71847f6b5c57ef46251141a2021-08-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-95004-8https://doaj.org/toc/2045-2322Abstract 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. 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. 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. In conclusion, 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.Pedro BaquiValerio MarraAhmed M. AlaaIoana BicaAri ErcoleMihaela van der SchaarNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-10 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Pedro Baqui Valerio Marra Ahmed M. Alaa Ioana Bica Ari Ercole Mihaela van der Schaar Comparing COVID-19 risk factors in Brazil using machine learning: the importance of socioeconomic, demographic and structural factors
description	Abstract 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. 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. 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. In conclusion, 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.
format	article
author	Pedro Baqui Valerio Marra Ahmed M. Alaa Ioana Bica Ari Ercole Mihaela van der Schaar
author_facet	Pedro Baqui Valerio Marra Ahmed M. Alaa Ioana Bica Ari Ercole Mihaela van der Schaar
author_sort	Pedro Baqui
title	Comparing COVID-19 risk factors in Brazil using machine learning: the importance of socioeconomic, demographic and structural factors
title_short	Comparing COVID-19 risk factors in Brazil using machine learning: the importance of socioeconomic, demographic and structural factors
title_full	Comparing COVID-19 risk factors in Brazil using machine learning: the importance of socioeconomic, demographic and structural factors
title_fullStr	Comparing COVID-19 risk factors in Brazil using machine learning: the importance of socioeconomic, demographic and structural factors
title_full_unstemmed	Comparing COVID-19 risk factors in Brazil using machine learning: the importance of socioeconomic, demographic and structural factors
title_sort	comparing covid-19 risk factors in brazil using machine learning: the importance of socioeconomic, demographic and structural factors
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/646d5ac4d71847f6b5c57ef46251141a
work_keys_str_mv	AT pedrobaqui comparingcovid19riskfactorsinbrazilusingmachinelearningtheimportanceofsocioeconomicdemographicandstructuralfactors AT valeriomarra comparingcovid19riskfactorsinbrazilusingmachinelearningtheimportanceofsocioeconomicdemographicandstructuralfactors AT ahmedmalaa comparingcovid19riskfactorsinbrazilusingmachinelearningtheimportanceofsocioeconomicdemographicandstructuralfactors AT ioanabica comparingcovid19riskfactorsinbrazilusingmachinelearningtheimportanceofsocioeconomicdemographicandstructuralfactors AT ariercole comparingcovid19riskfactorsinbrazilusingmachinelearningtheimportanceofsocioeconomicdemographicandstructuralfactors AT mihaelavanderschaar comparingcovid19riskfactorsinbrazilusingmachinelearningtheimportanceofsocioeconomicdemographicandstructuralfactors
_version_	1718381556018446336

Comparing COVID-19 risk factors in Brazil using machine learning: the importance of socioeconomic, demographic and structural factors

Ejemplares similares