Journal Article

Physiological and socioeconomic characteristics predict COVID-19 mortality and resource utilization in Brazil

Paschalidis, Ioannis Ch, Silva, Amanda A. B., Fleck, Julia L., Cassandras, Christos G., Wollenstein-Betech, Salomón

2020

Overview

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. 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. 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.

• Publisher: Public Library of Science,Public Library of Science (PLoS)
• Subject: Brazil / Chronic illnesses / Comorbidity / Coronavirus Infections - epidemiology / Coronavirus Infections - mortality / COVID-19 / Cultural differences / Decision trees / Demographic aspects / Demographic variables / Demography / Demography - statistics & numerical data / Diabetes mellitus / Dyspnea / Economic aspects / Engineering and Technology / Facilities and Services Utilization - statistics & numerical data / Geographical locations / Health care / Health risks / Healthcare Disparities - statistics & numerical data / Humans / Immunosuppression / Mechanical ventilation / Medical care utilization / Medicine and Health Sciences / Models, Statistical / Mortality / Oxygen / Oxygen content / Pandemics / Patient outcomes / Patients / People and places / Physiological aspects / Pneumonia, Viral - epidemiology / Pneumonia, Viral - mortality / Resource allocation / Resource utilization / Resources utilization / Respiration / Saturation / Signs and symptoms / Social factors / Socio-economic aspects / Socioeconomic data / Socioeconomic Factors / Socioeconomics / Support vector machines / Underserved populations / Ventilation