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Die zeitlich-räumliche Verteilung von COVID-19 in Köln und beeinflussende soziale Faktoren im Zeitraum Februar 2020 bis Oktober 2021

BACKGROUND AND GOALS: Even in the early phase of the COVID-19 pandemic, which took a very different course globally, there were indications that socio-economic factors influenced the dynamics of disease spread, which from the second phase (September 2020) onwards particularly affected people with a ...

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Detalles Bibliográficos
Autores principales: Neuhann, Florian, Ginzel, Sebastian, Buess, Michael, Wolff, Anna, Kugler, Sabine, Schlanstedt, Günter, Kossow, Annelene, Nießen, Johannes, Rüping, Stefan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9362610/
https://www.ncbi.nlm.nih.gov/pubmed/35920847
http://dx.doi.org/10.1007/s00103-022-03573-4
Descripción
Sumario:BACKGROUND AND GOALS: Even in the early phase of the COVID-19 pandemic, which took a very different course globally, there were indications that socio-economic factors influenced the dynamics of disease spread, which from the second phase (September 2020) onwards particularly affected people with a lower socio-economic status. Such effects can also be seen within a large city. The present study visualizes and examines the spatio-temporal spread of all COVID-19 cases reported in Cologne, Germany (February 2020–October 2021) at district level and their possible association with socio-economic factors. METHODS: Pseudonymized data of all COVID-19 cases reported in Cologne were geo-coded and their distribution was mapped in an age-standardized way at district level over four periods and compared with the distribution of social factors. The possible influence of the selected factors was also examined in a regression analysis in a model with case growth rates. RESULTS: The small-scale local infection process changed during the pandemic. Neighborhoods with weaker socio-economic indices showed higher incidence over a large part of the pandemic course, with a positive correlation between poverty risk factors and age-standardized incidence. The strength of this correlation changed over time. CONCLUSION: The timely observation and analysis of the local spread dynamics reveals the positive correlation of disadvantaging socio-economic factors on the incidence rate of COVID-19 at the level of a large city and can help steer local containment measures in a targeted manner.