Intensive COVID-19 testing associated with reduced mortality - an ecological analysis of 108 countries

  1. Chris Kenyon

Reviewed by

Read the full article

Discuss this preprint

Start a discussion What are Sciety discussions?

Listed in

Log in to save this article

Abstract

Background

Intensive screening and testing for COVID-19 could facilitate early detection and isolation of infected persons and thereby control the size of the epidemic. It could also facilitate earlier and more targeted therapy. These factors could plausibly reduce attributable mortality which was the hypothesis tested in this study.

Methods

Linear regression was used to assess the country-level association between COVID-19 attributable mortality per 100 000 inhabitants (mortality/capita) and COVID-19 tests/capita (number of tests/100 000 inhabitants) controlling for the cumulative number of COVID-19 infections/100 000 inhabitants (cases/capita), the age of the epidemic (number of days between first case reported and 8 April), national health expenditure per capita and WHO world region.

Results

The COVID-19 mortality rate varied between 0.3 and 3110 deaths/100 000 inhabitants (median 30, IQR 8–105). The intensity of testing per 100 000 also varied considerably (median 21,970, IQR 2,735–89,095) as did the number of COVID-19 cases per 100 000 (median 1,600, IQR 340–4,760 cases/100 000). In the multivariate model, the COVID-19 mortality rate was negatively associated with tests/capita (Coef. –0.036, 95% CI –0.047- –0.025) and positively associated with cases/capita (Coef. 0.093, 95% CI 0.819- 1.034).

Conclusions

The results are compatible with the hypothesis that intensive testing and isolation could play a role in reducing COVID-10 mortality rates.

Article activity feed

  1. ScreenIT

    SciScore for 10.1101/2020.05.28.20115691: (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
    The analyses were performed in STATA version 16 (Stata Corp, College Station, Tx).
    STATA
    suggested: (Stata, RRID:SCR_012763)

    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 10.1101/2020.05.28.20115691 on medRxiv