COVID-19 death risk predictors in Brazil using survival tree analysis: a retrospective cohort from 2020 to 2022

• Published in: International journal for equity in health 2024-02, Vol.23 (1), p.33-33, Article 33
• Main Authors: Martins Neto, Carlos, Branco, Maria Dos Remédios Freitas Carvalho, Dos Santos, Alcione Miranda, de Oliveira, Bruno Luciano Carneiro Alves
• Format: Article
• Language: English
• Subjects: Adult; Brazil; Brazil - epidemiology; COVID-19; Death; Diabetes; Epidemiology; Fatalities; Health aspects; Hospital patients; Hospitalization; Humans; Influenza; Information systems; Intensive care; Interaction; Kidney diseases; Longitudinal Studies; Mechanical ventilation; Medical research; Medicine, Experimental; Mortality; Mortality risk; Pandemics; Patients; Public health; Retrospective Studies; Risk factors; Risk groups; SARS-CoV-2; Severe acute respiratory syndrome; Sociodemographics; Subgroups; Survival; Survival analysis; Survival tree analysis; Variables; Ventilators; Viral diseases; Womens health
• ISSN: 1475-9276; 1475-9276
• DOI: 10.1186/s12939-024-02101-x

This study analyses the survival of hospitalized patients with Severe Acute Respiratory Syndrome (SARS) due to COVID-19 and identifies the risk groups for death due to COVID-19 from the identification of potential interactions between its predictors. This was a retrospective longitudinal study with data from 1,756,917 patients reported in the Influenza Epidemiological Surveillance Information System from 26 February 2020 to 31 December 2022. In this study, all adult and older (≥ 20 years) patients were hospitalized with SARS due to COVID-19, with death as the outcome. Survival tree analysis was used to identify potential interactions between the predictors. A model was built for each year of study. Hospital lethalitywas 33.2%. The worst survival curve was observed among those who underwent invasive mechanical ventilation and were aged 80 years or older in the three years of the pandemic. Black and brown race/color were predictors of deaths in the years 2020 and 2021 when there was greater demand from the health system due to the greater number of cases. By applying survival tree analysis we identified several numbers of homogeneous subgroups with different risks for mortality from COVID-19. These findings show the effects of wide inequalities of access by the population, requiring effective policies for the reduction and adequate management of the disease.