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.

Figure 1

The XCOVID-BR machine learning model. XCOVID-BR takes as input a range of medical, socioeconomic and structural factors and returns as output the probability of death by COVID-19. XCOVID-BR can be applied to individuals, groups or whole sections of the Brazilian population.

Figure 2

Flowchart of SIVEP-Gripe data used in this study. SARS-CoV-2 stands for severe acute respiratory syndrome coronavirus 2. SIVEP-Gripe stands for Sistema de Informação de Vigilância Epidemiológica da Gripe.

Figure 3

Model calibration. The calibration analysis shows that mortality predicted by the XCOVID-BR model performs uniformly across bins of mortality.

Figure 4

Relative feature importance (median and IQR) for mortality risk to COVID-19. The coloring marks the categories listed in Fig. 1. Feature importance is estimated via the permutation method and a logarithmic scale is employed for clarity.

Figure 5

Feature importance for older and younger patients. Each point represents a feature in the SIVEP-Gripe dataset, and the axes show the relative importance for COVID-19 mortality prediction for older ($\ge$60 years, x axis) and younger ($<60$ years, y axis) hospitalized patients. Variables deviating from the dotted identity line suggests a different relative importance for the groups. The coloring marks the categories listed in Fig. 1.

Figure 6

Mortality rate for public and privately funded hospitals, stratified according to age. The bars are normalized by dividing the fatalities by the total number of cases for each type of hospital funding model.

Figure 7

Distribution of survival probability—ranging from 0 (death) to 1 (recovery)—as estimated by the XCOVID-BR model. We contrast typical publicly and privately funded hospitals from Pernambuco, an example of a region in the more socioeconomically challenged Northeast, with examples from the richer Paraná region in the South. Stratifying by age, the dominant clinical predictor of mortality, it is apparent that the probability distribution is skewed with lower mortality in the wealthier (Paraná) region and this is particularly apparent in younger patients and in privately-funded hospitals.