Physiological and socioeconomic characteristics predict COVID-19 mortality and resource utilization in Brazil.
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ABSTRACT: Background
Given the severity and scope of the current COVID-19 pandemic, it is critical to determine predictive features of COVID-19 mortality and medical resource usage to effectively inform health, risk-based physical distancing, and work accommodation policies. Non-clinical sociodemographic features are important explanatory variables of COVID-19 outcomes, revealing existing disparities in large health care systems.Methods and findings
We use nation-wide multicenter data of COVID-19 patients in Brazil to predict mortality and ventilator usage. The dataset contains hospitalized patients who tested positive for COVID-19 and had either recovered or were deceased between March 1 and June 30, 2020. A total of 113,214 patients with 50,387 deceased, were included. Both interpretable (sparse versions of Logistic Regression and Support Vector Machines) and state-of-the-art non-interpretable (Gradient Boosted Decision Trees and Random Forest) classification methods are employed. Death from COVID-19 was strongly associated with demographics, socioeconomic factors, and comorbidities. Variables highly predictive of mortality included geographic location of the hospital (OR = 2.2 for Northeast region, OR = 2.1 for North region); renal (OR = 2.0) and liver (OR = 1.7) chronic disease; immunosuppression (OR = 1.7); obesity (OR = 1.7); neurological (OR = 1.6), cardiovascular (OR = 1.5), and hematologic (OR = 1.2) disease; diabetes (OR = 1.4); chronic pneumopathy (OR = 1.4); immunosuppression (OR = 1.3); respiratory symptoms, ranging from respiratory discomfort (OR = 1.4) and dyspnea (OR = 1.3) to oxygen saturation less than 95% (OR = 1.7); hospitalization in a public hospital (OR = 1.2); and self-reported patient illiteracy (OR = 1.1). Validation accuracies (AUC) for predicting mortality and ventilation need reach 79% and 70%, respectively, when using only pre-admission variables. Models that use post-admission disease progression information reach accuracies (AUC) of 86% and 87% for predicting mortality and ventilation use, respectively.Conclusions
The results highlight the predictive power of socioeconomic information in assessing COVID-19 mortality and medical resource allocation, and shed light on existing disparities in the Brazilian health care system during the COVID-19 pandemic.
SUBMITTER: Wollenstein-Betech S
PROVIDER: S-EPMC7556459 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundThe ongoing Coronavirus disease of 2019 (COVID-19) pandemic has hit Brazil hard in period of different dominant variants. Different COIVD-19 variants have swept through the region, resulting that the total number of cases in Brazil is the third highest in the world. This study is aimed at investigating the regional heterogeneity of in-hospital mortality of COVID-19 in Brazil and the effects of vaccination and social inequality.MethodsWe fitted a multivariate mixed-effects Cox model to a national database of inpatient data in Brazil who were admitted for COVID-19 from February 27, 2020 to March 15, 2022. The in-hospital mortality risks of vaccinated and unvaccinated patients were compared, with adjustment for age, state, ethnicity, education and comorbidities. And the effects of variables to in-hospital mortality were also compared. Stratified analysis was conducted across different age groups and vaccine types.ResultsBy fitting the multivariate mixed-effects Cox model, we concluded that age was the most important risk factor for death. With regards to educational level, illiterate patients (hazard ratio: 1.63, 95% CI: 1.56-1.70) had a higher risk than those with a university or college degree. Some common comorbidities were more dangerous for hospitalized patients, such as liver disease (HR: 1.46, 95% CI: 1.34-1.59) and immunosuppression (HR:1.32, 95% CI: 1.26-1.40). In addition, the states involving Sergipe (HR: 1.75, 95% CI: 1.46-2.11), Roraima (HR: 1.65, 95% CI: 1.43-1.92), Maranhão (HR: 1.57, 95% CI: 1.38-1.79), Acre (HR: 1.44, 95% CI: 1.12-1.86), and Rondônia (HR: 1.26, 95% CI: 1.10-1.44) in the north and the northeast region tended to have higher hazard ratios than other area. In terms of vaccine protection, vaccination did not significantly reduce mortality among hospitalized patients. Sinovac and AstraZeneca offered different protection in different regions, and no vaccine provided high protection in all regions.ConclusionThe study revealed the regional heterogeneity of in-hospital mortality of Covid-19 in Brazil and the effects of vaccination and social inequality. We found that ethnic concentrations were consistent with higher proportion of death cases relative to population size. White Brazilians had more frequent international travel opportunities. As race revealed the intersection of social connections, we speculated that uneven interactions with residential communities partially contribute to the spread of the epidemic. Additionally, the vaccine showed different protection in different regions. In the northern and northeastern regions, AstraZeneca was much more protective than Sinovac, while Sinovac was more protective for hospitalized patients with varying numbers of comorbidities in the Central-west, Southeast and South regions.
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