Investigating spatial variability in COVID-19 pandemic severity across 19 geographic areas, Spain, 2020
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Abstract
Introduction
Spain has been disproportionately affected by the COVID-19 pandemic, ranking fifth in the world in terms of both total cases and total deaths due to COVID-19 as of May 20, 2020. Here we derived estimates of pandemic severity and assessed its relationship with socio-demographic and healthcare factors.
Methods
We retrieved the daily cumulative numbers of laboratory-confirmed COVID-19 cases and deaths in Spain from February 20, 2020 to May 20, 2020. We used statistical methods to estimate the time-delay adjusted case fatality risk (aCFR) for 17 autonomous communities and 2 autonomous cities of Spain. We then assessed how transmission and sociodemographic variables were associated with the aCFR across areas using multivariate regression analysis.
Results
We estimated the highest aCFR for Madrid (25.9%) and the average aCFR in Spain (18.2%). Our multivariate regression analysis revealed three statistically significant predictor variables: population size, population density, and the unemployment rate.
Conclusions
The estimated aCFR for 10 autonomous communities/cities in Spain are significantly higher than those previously estimated for other geographic regions including China and Korea. Our results suggest that public health interventions focused on densely populated areas and low socioeconomic groups can ameliorate the mortality burden of the COVID-19 pandemic in Spain.
Article activity feed
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SciScore for 10.1101/2020.04.14.20065524: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.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:Our study has some limitations. The preferential ascertainment of severe cases bias in COVID-19 may have spuriously increased our estimate of CFR [5], which is a frequent caveat in this type of studies [45, 46]. Similarly, given the long infection-death time for COVID-19 which ranges between 2 to 8 weeks [30], our estimate may have been …
SciScore for 10.1101/2020.04.14.20065524: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.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:Our study has some limitations. The preferential ascertainment of severe cases bias in COVID-19 may have spuriously increased our estimate of CFR [5], which is a frequent caveat in this type of studies [45, 46]. Similarly, given the long infection-death time for COVID-19 which ranges between 2 to 8 weeks [30], our estimate may have been affected by delayed reporting bias [5, 7]. Similarly, in our data, the date of report reflects the date of reporting and not the date of onset of illness. Finally, we assumed infant mortality and poverty risk rate as a proxy for areas with low socio-economic groups.
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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