COVID-19 Mortality among Hospitalized Patients: Survival, Associated Factors, and Spatial Distribution in a City in São Paulo, Brazil, 2020

Marília Jesus Batista (), Carolina Matteussi Lino, Carla Fabiana Tenani, Adriano Pires Barbosa, Maria do Rosário Dias de Oliveira Latorre and Evaldo Marchi
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Marília Jesus Batista: Department of Public Health, Jundiaí Medical School, Jundiaí 13202-550, SP, Brazil
Carolina Matteussi Lino: Department of Health Sciences and Child Dentistry, Faculty of Odontology of Piracicaba, University of Campinas, Piracicaba 13414-903, SP, Brazil
Carla Fabiana Tenani: Department of Public Health, Jundiaí Medical School, Jundiaí 13202-550, SP, Brazil
Adriano Pires Barbosa: Department of Public Health, Jundiaí Medical School, Jundiaí 13202-550, SP, Brazil
Maria do Rosário Dias de Oliveira Latorre: Department of Epidemiology, Faculty of Public Health, University of São Paulo, São Paulo 01246-904, SP, Brazil
Evaldo Marchi: Department of Surgery, Jundiaí Medical School, Jundiaí 13202-550, SP, Brazil

IJERPH, 2024, vol. 21, issue 9, 1-14

Abstract: The aims of this study were to analyze patient survival, identify the prognostic factors for patients with COVID-19 deaths considering the length of hospital stay, and evaluate the spatial distribution of these deaths in the city of Jundiaí, São Paulo, Brazil. We examined prognostic variables and survival rates of COVID-19 patients hospitalized at a reference hospital in Jundiaí, Brazil. A retrospective cohort of hospitalized cases from April to July of 2020 was included. Descriptive analysis, Kaplan–Meier curves, univariate and multivariate Cox regression, and binary logistic regression models were used. Among the 902 reported and confirmed cases, there were 311 deaths (34.5%). The median survival was 27 days, and the mean for those discharged was 46 days. Regardless of the length of hospital stay, desaturation, immunosuppression, age over 60, kidney disease, hypertension, lung disease, and hypertension were found to be independent predictors of death in both Cox and logistic regression models.

Keywords: epidemiology; public health; SARS-CoV-2; regression analysis; mortality (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2024
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