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
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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 Statement not detected. Randomization not detected. Blinding not detected. Power Analysis not detected. Sex as a biological variable not detected. Table 2: Resources
Software and Algorithms Sentences Resources 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. Maysuggested: NoneResults 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…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 Statement not detected. Randomization not detected. Blinding not detected. Power Analysis not detected. Sex as a biological variable not detected. Table 2: Resources
Software and Algorithms Sentences Resources 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. Maysuggested: NoneResults 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, there are certain limitations. We could not calculate exact R0 due to lack of information on susceptible population and rates of recovery. We did not include other variables such as international travel, and population age, though we believe that there are other evidences supporting the associations of these variables with COVID-19 burden. Our study provides strong evidence for the role of countries’ economy and factors such as testing rates as major determinants of COVID-19 burden. We also find that other factors in line with the hygiene hypothesis may have some role in COVID-19 burden. Finally, our approach can form the basis of further studies using a similar integrate-and-dissect analytical framework for learning granular determinants and policy to effectively mitigate the pandemic.
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.
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