Development of a tool to prioritize the monitoring of COVID-19 patients by public health teams

  1. Andres I. Vecino-Ortiz
  2. Nicolás Guzman-Tordecilla
  3. Yenny Fernanda Guzmán Ruiz
  4. Rolando Enrique Peñaloza-Quintero
  5. Julián A. Fernández-Niño
  6. Fernando Ruiz Gomez
  7. Antonio J. Trujillo

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Abstract

Background

In the context of the COVID-19 pandemic, public health teams have struggled to conduct monitoring for confirmed or suspicious COVID-19 patients. However, monitoring these patients is critical to improving the chances of survival, and therefore, a prioritization strategy for these patients is warranted. This study developed a monitoring algorithm for COVID-19 patients for the Colombian Ministry of Health and Social Protection (MOH).

Methods

This work included 1) a literature review, 2) consultations with MOH and National Institute of Health officials, and 3) data analysis of all positive COVID-19 cases and their outcomes. We used clinical and socioeconomic variables to develop a set of risk categories to identify severe cases of COVID-19.

Results

This tool provided four different risk categories for COVID-19 patients. As soon as the time of diagnosis, this tool can identify 91% of all severe and fatal COVID-19 cases within the first two risk categories.

Conclusion

This tool is a low-cost strategy to prioritize patients at higher risk of experiencing severe COVID-19. This tool was developed so public health teams can focus their scarce monitoring resources on individuals at higher mortality risk. This tool can be easily adapted to the context of other lower and middle-income countries. Policymakers would benefit from this low-cost strategy to reduce COVID-19 mortality, particularly during outbreaks.

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  1. ScreenIT

    SciScore for 10.1101/2021.04.08.21254922: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementIRB: This study was approved by the Institutional Review Board of the Johns Hopkins Bloomberg School of Public Health and deemed not human subjects research (IRB number: 14144).
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    We conducted a MEDLINE and Google Scholar review of papers in English and Spanish, in both preprint and peer-reviewed journals, and published before October 1, 2020.
    MEDLINE
    suggested: (MEDLINE, RRID:SCR_002185)
    Google Scholar
    suggested: (Google Scholar, RRID:SCR_008878)

    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:
    This study has some limitations. First, we conducted a literature review that might have overlooked some important variables that can potentially be included. Second, this work relies on the accuracy of the two data systems used for this study. Other studies have shown that the Colombian registries for COVID-19 cases are of good quality, and there are no reasons to believe that a significant number of critical cases or deaths have been underreported 2,9. This tool could be easily adapted to the context of other LMICs as data requirements are often available. This tool’s advantage is its low administrative cost, low organizational upfront investment, and flexibility to be improved over time as the demand for more effective monitoring changes. Implementing this tool will complement traditional public health strategies such as contact tracing, social distancing, lockdowns, etc. Finally, implementation needs to be a priority in the planning process. This tool will require the development of monitoring systems that will provide benefits in future pandemics. When monitoring is outside the realm of public institutions and involves negotiations with the private sector for its implementation, this might lead to additional implementation barriers. Appropriate monitoring needs concrete interventions to make it effective, including the provision of oximeters, home-based health care services, and the implementation of call centers. This study is a fundamental tool to improve public health...

    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 10.1101/2021.04.08.21254922v1 on medRxiv