Epidemiological Analysis of Hospitalized Cases of COVID-19 in Indigenous People in an Amazonian Region: Cross-Sectional Study with Data from the Surveillance of Acute and Severe Respiratory Syndromes in Brazil

  1. Ana Lucia da Silva Ferreira Daniele Melo Sardinha
  2. Claudia Ozela El-Husny Carmem Aliandra Freire de Sá
  3. Emilyn Costa Conceição Karla Valéria Batista Lima

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Abstract

Indigenous people are considered more vulnerable to new infectious agents. In view of the novel coronavirus causing COVID-19, health authorities are concerned about the possible impact of the pandemic on reaching vulnerable populations, such as the indigenous people of the Brazilian Amazon. Thus, we aimed to carry out an epidemiological analysis of serious cases and deaths from COVID-19 in indigenous population in the state of Pará, Brazil. The data was obtained from the public Ministry of Health platform. Data analysis was performed using the Statistical Package for the Social Sciences 20, Chi-square of adherence, the independence test and G test. For spatial distribution was used ArcGIS. We observed 123 COVID-19 cases: 46 deaths (37.40%), male gender (76-61.79%), age above 60 years (61- 49.6%), the most frequent risk factor was chronic cardiovascular disease (18- 14.63%). The predictors of death were: invasive ventilation has (6.8) more chances for the outcome death, those not vaccinated against influenza have (3.5) and age (1.4). COVID-19 occurrence was higher in municipalities that have villages with health care or commerce, or with migrants from the Warao ethnic group. Notifications should take into consideration the specific issues of indigenous people so that effective control measures can be defined.

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  1. Version published to 10.20546/ijcmas.2022.1102.045
  2. ScreenIT

    SciScore for 10.1101/2021.12.15.21267872: (What is this?)

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

    Table 1: Rigor

    Ethicsnot detected.
    Sex as a biological variablenot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    2.4 Data Collection and Analysis: The database was available in Microsoft Excel 2019 format, and the variables were selected based on the SIVEP-Gripe notification form (M. da S.
    Microsoft Excel
    suggested: (Microsoft Excel, RRID:SCR_016137)
    Data analysis was performed using the Statistical Package for the Social Sciences 20 (SPSS – https://www.ibm.com/analytics/spss-statistics-software).
    Statistical Package for the Social Sciences
    suggested: (SPSS, RRID:SCR_002865)
    SPSS
    suggested: (SPSS, RRID:SCR_002865)
    The spatial distribution was performed in ArcGIS software (https://www.arcgis.com/) using the number of cases by municipality of residence and the quartile to separate the classes, without cases (0) – (1-4) – (5-11) and (12-17).
    ArcGIS
    suggested: (ArcGIS for Desktop Basic, RRID:SCR_011081)

    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: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

    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.

    Results from scite Reference Check: We found no unreliable references.

    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.

  3. Version published to 10.1101/2021.12.15.21267872v1 on medRxiv