Investigating spatial variability in COVID-19 pandemic severity across 19 geographic areas, Spain, 2020
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..
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Preprint| Erscheinungsjahr: |
2022 |
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| Erschienen: |
2022 |
| Enthalten in: |
bioRxiv.org - (2022) vom: 21. Okt. Zur Gesamtaufnahme - year:2022 |
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| Sprache: |
Englisch |
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| Beteiligte Personen: |
Dahal, Sushma [VerfasserIn] |
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| Links: |
Volltext [kostenfrei] |
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| doi: |
10.1101/2020.04.14.20065524 |
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| PPN (Katalog-ID): |
XBI017598575 |
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| 520 | |a 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. | ||
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