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

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

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.

Suggested Citation

  • Salomón Wollenstein-Betech & Amanda A B Silva & Julia L Fleck & Christos G Cassandras & Ioannis Ch Paschalidis, 2020. "Physiological and socioeconomic characteristics predict COVID-19 mortality and resource utilization in Brazil," PLOS ONE, Public Library of Science, vol. 15(10), pages 1-15, October.
    • Handle: RePEc:plo:pone00:0240346
    DOI: 10.1371/journal.pone.0240346
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    References listed on IDEAS

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    1. Daron Acemoglu & Victor Chernozhukov & Ivàn Werning & Michael D. Whinston, 2020. "A Multi-Risk SIR Model with Optimally Targeted Lockdown," CeMMAP working papers CWP14/20, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
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    1. Soheil Shayegh & Javier Andreu-Perez & Caroline Akoth & Xavier Bosch-Capblanch & Shouro Dasgupta & Giacomo Falchetta & Simon Gregson & Ahmed T Hammad & Mark Herringer & Festus Kapkea & Alvaro Labella , 2023. "Prioritizing COVID-19 vaccine allocation in resource poor settings: Towards an Artificial Intelligence-enabled and Geospatial-assisted decision support framework," PLOS ONE, Public Library of Science, vol. 18(8), pages 1-11, August.
    2. Kiran Saqib & Afaf Saqib Qureshi & Zahid Ahmad Butt, 2023. "COVID-19, Mental Health, and Chronic Illnesses: A Syndemic Perspective," IJERPH, MDPI, vol. 20(4), pages 1-13, February.

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