Temporal analysis of the clinical evolution of confirmed cases of COVID-19 in the state of Mato Grosso do Sul - Brazil

  1. Carolina Mariano Pompeo
  2. Marcos Antonio Ferreira
  3. Andréia Insabralde de Queiroz Cardoso
  4. Luciana Scarlazzari Costa
  5. Mercy da Costa Souza
  6. Felipe Machado Mota
  7. Maria Lúcia Ivo

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Abstract

The objective was to analyze the evolution of confirmed cases of COVID-19 in the first four months of the pandemic in Mato Grosso do Sul, a state in the Center-West region of Brazil, as well as the factors related to the prevalence of deaths. This was an observational study with a cross-sectional and time series design based on data from the information system of the State Department of Health of Mato Grosso do Sul, Brazil. The microdata from the epidemiological bulletin is open and in the public domain; consultation was carried out from March to July 2020. The incidences were stratified per 100,000 inhabitants. The cross-section study was conducted to describe COVID-19 cases, and the trend analysis was performed using polynomial regression models for time series, with R-Studio software and a significance level of 5%. There was a predominance of women among the cases, and of men in terms of deaths. The presence of comorbidities was statistically related to mortality, particularly lung disease and diabetes, and the mean age of the deaths was 67.7 years. Even though the macro-region of the state capital, Campo Grande, had a higher number of cases, the most fatalities were in the macro-region of Corumbá. The trend curve demonstrated discreet growth in the incidence of cases between epidemiological weeks 11 and 19, with a significant increase in week 20 throughout the state. The trend for COVID-19 in the state of Mato Grosso do Sul was upward and regular, but there was an important and alarming exponential increase. The health authorities should adopt the necessary measures to enforce health precautions and encourage social distancing of the population so that health services will be able to care for those afflicted by the disease, especially older people, those with comorbidities, and vulnerable sectors of the population.

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  1. ScreenIT

    SciScore for 10.1101/2020.09.21.20198812: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementIRB: Both databases are in the public domain and do not require previous consideration or authorization by a research ethics committee.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    The data was collected between March and July 2020 and was tabulated on spreadsheets using Microsoft Excel 2010®.
    Microsoft Excel
    suggested: (Microsoft Excel, RRID:SCR_016137)

    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:
    One of the limitations of this study, similar to others with cross-sectional designs that use secondary data, is related to the time of the outcomes with regard to COVID-19 in the population, since there may be underestimation of real data due to underreporting and the presence of non-symptomatic carriers, in addition to the reality of the organization of local health services.

    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 10.1101/2020.09.21.20198812v1 on medRxiv