A Counterfactual Graphical Model Reveals Economic and Sociodemographic Variables as Key Determinants of Country-Wise COVID-19 Burden
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 <jats:ext-link xmlns:xlink="http://www.w3.org/1999/xlink" ext-link-type="uri" xlink:href="https://www.worldometers.info/coronavirus/">https://www.worldometers.info/coronavirus/</jats:ext-link> on 10th April and 12th 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 10th April, days between 100th and 1000th 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..
| Medienart: |
|
|---|
Preprint| Erscheinungsjahr: |
2021 |
|---|---|
| Erschienen: |
2021 |
| Enthalten in: |
bioRxiv.org - (2021) vom: 05. Jan. Zur Gesamtaufnahme - year:2021 |
|---|
| Sprache: |
Englisch |
|---|
| Beteiligte Personen: |
Sethi, Tavpritesh [VerfasserIn] |
|---|
| Links: |
Volltext [kostenfrei] |
|---|
| doi: |
10.1101/2020.06.16.20132563 |
|---|
| funding: |
|
|---|---|
| Förderinstitution / Projekttitel: |
|
| PPN (Katalog-ID): |
XBI018178057 |
|---|
| LEADER | 01000caa a22002652 4500 | ||
|---|---|---|---|
| 001 | XBI018178057 | ||
| 003 | DE-627 | ||
| 005 | 20230429100621.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 200622s2021 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1101/2020.06.16.20132563 |2 doi | |
| 035 | |a (DE-627)XBI018178057 | ||
| 035 | |a (DE-599)biorXiv10.1101/2020.06.16.20132563 | ||
| 035 | |a (biorXiv)10.1101/2020.06.16.20132563 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 082 | 0 | |a 570 |q DE-84 | |
| 100 | 1 | |a Sethi, Tavpritesh |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a A Counterfactual Graphical Model Reveals Economic and Sociodemographic Variables as Key Determinants of Country-Wise COVID-19 Burden |
| 264 | 1 | |c 2021 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a Computermedien |b c |2 rdamedia | ||
| 338 | |a Online-Ressource |b cr |2 rdacarrier | ||
| 520 | |a 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 <jats:ext-link xmlns:xlink="http://www.w3.org/1999/xlink" ext-link-type="uri" xlink:href="https://www.worldometers.info/coronavirus/">https://www.worldometers.info/coronavirus/</jats:ext-link> on 10th April and 12th 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 10th April, days between 100th and 1000th 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. | ||
| 700 | 1 | |a Kedia, Saurabh |e verfasserin |4 aut | |
| 700 | 1 | |a Awasthi, Raghav |e verfasserin |4 aut | |
| 700 | 1 | |a Lodha, Rakesh |e verfasserin |4 aut | |
| 700 | 1 | |a Ahuja, Vineet |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t bioRxiv.org |g (2021) vom: 05. Jan. |
| 773 | 1 | 8 | |g year:2021 |g day:05 |g month:01 |
| 856 | 4 | 0 | |u http://dx.doi.org/10.1101/2020.06.16.20132563 |z kostenfrei |3 Volltext |
| 912 | |a GBV_XBI | ||
| 912 | |a SSG-OLC-PHA | ||
| 951 | |a AR | ||
| 952 | |j 2021 |b 05 |c 01 | ||
| 953 | |2 045F |a 570 | ||