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 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.

Risk Factors Associated with Mortality in Hospitalized Patients with COVID-19 during the Omicron Wave in Brazil

“…For example, from Table 1 , among 17,640 patients who died, 5757 (32.6%) were not admitted to the ICU, likely due to a lack of access to appropriate hospital beds, thus reflecting regional economic disparities even within the same region. Despite the influence of socioeconomic factors on mortality, such as the location of residence, human development index, distance to the hospitals, and the level of education [ 64 ], in our study, these were not considered as many of them are hard to measure and correlate in a large multicultural country such as Brazil.…”

Machine learning and comorbidity network analysis for hospitalized patients with COVID-19 in a city in Southern Brazil

“…The large amount of COVID-19-related data enabled the application of Machine Learning (ML) algorithms to predict or identify people most likely susceptible to the disease ( Aktar et al, 2021 ; Baqui et al, 2021 ; De Souza et al, 2021 ; Khedr et al, 2020 ; Mason et al, 2021 ). ML is a branch of artificial intelligence that concentrates on prediction by finding generalizable predictive patterns.…”

Regional heterogeneity of in-hospital mortality of COVID-19 in Brazil

“…It is also essential to note the unequal access to education. Education level was also mentioned as a risk factor in a scientific report ( Baqui et al, 2021 ). Unlike the multivariate mixed-effects Cox model, the authors used machine learning prediction algorithms to explain the complex interdependencies that may exist between indicators.…”