A Counterfactual Graphical Model Reveals Economic and Sociodemographic Variables as Key Determinants of Country-Wise COVID-19 Burden

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Abstract

Importance

Insights into the country-wise differences in COVID-19 burden can impact the policies being developed to control disease spread.

Objective

Present study evaluated the possible socio-economic and health related factors (and their temporal consistency) determining the disease burden of COVID-19.

Design

A retrospective analysis for identifying associations of COVID-19 burden.

Setting

Data on COVID-19 statistics (number of cases, tests and deaths per million) was extracted from the website https://www.worldometers.info/coronavirus/ on 10 th April and 12 th May. Variables obtained to estimate the possible determinants for COVID-19 burden included economic-gross domestic product; socio-demographic-Sustainable Development Goals, SDGs indicators related to health systems, percentage Chinese diaspora; and COVID-19 trajectory-date of first case in each country, days between first reported case and 10 th April, days between 100 th and 1000 th case, and government response stringency index (GRSI).

Main outcomes and Measures

COVID-19 burden was modeled using economic and socio-demographic determinants. Consistency of inferences for two time points at three levels of increasing statistical rigor using (i) Spearman correlations, (ii) Bayesian probabilistic graphical model, and (iii) counterfactual impact was evaluated.

Results

Countries’ economy (reflected by GDP), mainly through the testing rates, was the major and temporally consistent determinant of COVID-19 burden in the model. Reproduction number of COVID-19 was lower where mortality due to water, sanitation, and hygiene (WaSH) was higher, thus strengthening the hygiene hypothesis. There was no association between vaccination status or tuberculosis incidence and COVID burden, refuting the claims over BCG vaccination as a possible factor against COVID-19 trajectory.

Conclusion and Relevance

Countries’ economy, through testing power, was the major determinant of COVID-19 burden. There was weak evidence for hygiene hypothesis as a protective factor against COVID-19.

Article activity feed

  1. SciScore for 10.1101/2020.06.16.20132563: (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
    Trajectory indicators-date of first case, days between first reported case and 10th April, days between 100th and 1000th case (as per 12th May)[15], Government response stringency index (GRSI)[16] and country-wise R0 estimates obtained through an exponential fit model were included.
    May
    suggested: None

    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 Limita…