COVID-19 trends in Colombian regions with the highest disease burden

  1. Pablo Chaparro
  2. Helmer Zapata
  3. Isabel Hurtado
  4. Alberto Alzate
  5. Maria Cristina Lesmes
  6. Sócrates Herrera

Reviewed by

Read the full article See related articles

Listed in

Log in to save this article

Abstract

Introduction

COVID-19 pandemic is currently the most significant global public health challenge, with more than 31 million cases reported to date. Colombia first reported COVID-19 cases in the country by early March 2020, and six months later it has reached ∼750,471 clinical cases, with significant regional differences in morbidity, mortality, and hospitalization rates.

Aims

Identify population characteristics and hospital capacity in the 13 municipalities with the highest disease notification and examine differences in cumulative reported cases, hospitalization, and mortality rates that may explain the regional differences.

Materials and methods

A multi-group ecological study was performed based on the information available from public databases. Notification of cases, hospitalization, and crude mortality and age-adjusted rates were calculated.

Results

The municipalities with the highest COVID-1 burden at different times during the study period displayed significant differences in population density and the proportion of elderly inhabitants, indigenous and afro descendants minorities; indices of unsatisfied basic needs and multidimensional poverty index, as well as the number of hospital beds. Likewise, essential variations in notification rates, hospitalization, and mortality were observed. The highest age-adjusted of reported cases (4,219 cases) and mortality (230.4 cases) rates were found in Leticia, the lowest general hospitalization rates in Buenaventura (37.5 cases) and the lowest ICU hospitalization rates (0) in Leticia and Tumaco due to a lack of these units in these municipalities.

Conclusion

The probability of getting sick, hospitalized, and dying from COVID-19 appeared closely related to socio-economic, ethnic, and cultural characteristics, and also to hospital bed capacity.

Article activity feed

  1. ScreenIT

    SciScore for 10.1101/2020.10.09.20210187: (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 variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Data analyses: The information obtained was processed in Microsoft Excel spreadsheets and analyzed with the Stata Version 12 program.
    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: 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.

    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 10.1101/2020.10.09.20210187v1 on medRxiv