Article activity feed

  1. SciScore for 10.1101/2020.06.02.20120147: (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

    No key resources detected.


    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:
    However, this analysis has several limitations. First, national critical care estimates may, in some instances, over- or under-represent the true critical care capacity of a country (i.e. estimates from government published reports may only reflect critical care capacity at public sector health facilities designated to care for COVID-19 patients, excluding those from the private sector). In addition, since information on critical care components were collected from different sources and represented estimates from different time points, the number of hospital beds may include a proportion of available ICU beds. Peak estimates are generated from an SECIR model which assumes populations are well-mixed and this can lead to overestimation of the peak need. We assume that all hospital beds, ICU beds, and ventilators are functioning and available exclusively for people with COVID-19 infections, an assumption which is unlikely to reflect reality. Anecdotally, healthcare workers from multiple African countries report that health facilities are seeing significantly fewer patients compared to pre-pandemic months as patients face increased challenges accessing health services or fear becoming infected with the virus at a health facility. Data on hospital bed, ICU bed, and ventilator distribution were unavailable, and this analysis assumes that they are evenly distributed across populations; in reality, healthcare infrastructure is typically clustered in capital cities and other urban cen...

    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.

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  2. Our take

    In this study, available as a preprint and thus not yet peer reviewed, authors projected the total number of severe COVID-19 cases and estimated national capacity to meet hospital bed-, ICU bed-, and ventilator-needs assuming different levels of care seeking in 52 African countries. Overall, countries were ill-equipped to handle the number of projected hospitalizations at the peak of the outbreak, even assuming care-seeking as low as 30% among severe cases. Model assumptions likely resulted in the overestimation of average national capacity, but this study provides useful estimates for surge capacity preparation in countries in which the outbreak has not yet peaked.

    Study design

    modeling-simulation

    Study population and setting

    Using data on current case numbers and interventions, authors modified a Susceptible-Exposed-Contagious-Infected-Recovered (SECIR) compartmental model to predict the total number of severe COVID-19 cases for 52 African countries. Then, using these projections, authors estimated the number of hospital beds, ICU beds, and ventilators needed at the peak of the outbreak per country assuming four levels of care-seeking behavior (if 30, 50, 70, or 100% of patients with severe COVID-19 symptoms sought care). Authors assumed that interventions would decrease transmission by 25% while implemented, but would return to 90% of the pre-intervention value after being lifted. Although they did not explicitly account for age and comorbidities in the model, authors did include these parameters as a function of the projected number of people who would develop severe disease.

    Summary of main findings

    The average number of total projected severe cases per country was 138 per 100,000 (this ranged from 102 per 100,000 to 145 per 100,000). Assuming the most burdensome care volume (i.e., 100% of persons with severe infection sought care), an average of 131.7 per 100,000 hospital beds, 6.5 per 100,000 ICU beds, and 3.18 per 100,000 ventilators would be needed. Authors estimated that 62% of countries do not have enough hospital beds to meet this need and that some countries would require up to 10,000 additional beds. Authors estimated that over 87% of countries do not have sufficient ICU beds to handle the projected number of severe cases and that countries would require 5,000 to 12,000 additional ICU beds. Authors estimated that over 91% of countries do not have enough ventilators to meet national needs. Assuming the least burdensome care volume (i.e., 30% seek care), authors still estimated that 20%, 71%, and 76% of countries could not meet hospital bed, ICU bed, and ventilator needs.

    Study strengths

    Authors used current case numbers and specific interventions per country, so estimates are country-specific.

    Limitations

    Authors assumed that all resources (hospital beds, ICU beds, and ventilators) would be functional and immediately available for first use by COVID-19 patients. This discounts the consumption of these resources by other patients hospitalized for non-COVID-19 related reasons and likely overestimates each country’s true capacity. Authors also assumed hospital beds, ICU beds, and ventilators were distributed evenly throughout each country’s population; these resources are often clustered in urban areas and this overestimates the availability of these resources to populations in more rural areas, which also likely overestimates national capacity. Authors used case severity data from China, Europe, and the US; studies that have since been released suggest that case severity may differ considerably in African countries, which have younger age distributions and different contact patterns and potential exposures.

    Value added

    As confirmed COVID-19 cases and deaths continue to increase across the African continent, critical care capacity, gaps, and needs should be assessed to inform pandemic planning and preparation. This study is among the first to provide a comprehensive assessment of these parameters across Africa.

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