COVID-19 hospitalizations in Brazil’s Unified Health System (SUS)

  1. Carla Lourenço Tavares de Andrade
  2. Claudia Cristina de Aguiar Pereira
  3. Mônica Martins
  4. Sheyla Maria Lemos Lima
  5. Margareth Crisóstomo Portela

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Abstract

To study the profile of hospitalizations due to COVID-19 in the Unified Health System (SUS) in Brazil and to identify factors associated with in-hospital mortality related to the disease.

Methods

Cross-sectional study, based on secondary data on COVID-19 hospitalizations that occurred in the SUS between late February through June. Patients aged 18 years or older with primary or secondary diagnoses indicative of COVID-19 were included. Bivariate analyses were performed and generalized linear mixed models (GLMM) were estimated with random effects intercept. The modeling followed three steps, including: attributes of the patients; elements of the care process; and characteristics of the hospital and place of hospitalization.

Results

89,405 hospitalizations were observed, of which 24.4% resulted in death. COVID-19 patients hospitalized in the SUS were predominantly male (56.5%) with a mean age of 58.9 years. The length of stay ranged from less than 24 hours to 114 days, with a mean of 6.9 (±6.5) days. Of the total number of hospitalizations, 22.6% reported ICU use. The odds on in-hospital death were 16.8% higher among men than among women and increased with age. Black individuals had a higher likelihood of death. The behavior of the Charlson and Elixhauser indices was consistent with the hypothesis of a higher risk of death among patients with comorbidities, and obesity had an independent effect on increasing this risk. Some states, such as Amazonas and Rio de Janeiro, had a higher risk of in-hospital death from COVID-19. The odds on in-hospital death were 72.1% higher in municipalities with at least 100,000 inhabitants, though being hospitalized in the municipality of residence was a protective factor.

Conclusion

There was broad variation in COVID-19 in-hospital mortality in the SUS, associated with demographic and clinical factors, social inequality, and differences in the structure of services and quality of health care.

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  1. ScreenIT

    SciScore for 10.1101/2020.09.03.20187617: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variableThe variable ‘sex’ is binary as informed in the SIH, and we considered female as the reference category.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Data analysis was performed with the SAS statistical package.
    SAS
    suggested: (SASqPCR, RRID:SCR_003056)

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    Our study has limitations. The main gap refers to the source of information used. SIH covers only the SUS hospital network, which makes it impossible to carry out a more comprehensive analysis including healthcare received by those privately insured. Additionally, the data flow from providers to the system, and its subsequent consolidation, is slower than desirable to monitor the care provided in a pandemic context that requires fast decisions. The sufficiency and quality of the information recorded should be stressed, in particular the high underreporting of comorbidities and the variable ‘race/color’. Further, it was not possible to include cases treated in the emergency wards, and data on the evolution of cases (such as vital signs), and on the care process (professionals involved, use of invasive mechanical ventilation and laboratory tests, including tests for the detection of COVID-19) are absent from this source, which impedes more specific analysis. The study also does not cover deaths that occurred outside hospitals, that may have also played an important role in understanding the real pandemic morbidity and mortality scenario. It is important to remember that the pandemic has evolved dynamically throughout the country. This study addresses hospitalizations in the initial months. Faced with a scenario of cases’ spread in the interior of the country, it is likely that the same analyses in subsequent months provide another overview of how the states were affected. Despi...

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

  2. Version published to 10.1371/journal.pone.0243126
  3. Version published to 10.1101/2020.09.03.20187617v1 on medRxiv