ArticleBundesgesundheitsblatt, Gesundheitsforschung, Gesundheitsschutz2022
[Spatio-temporal distribution of COVID-19 in Cologne and associated socio-economic factors in the period from February 2020 to October 2021].
Article in Bundesgesundheitsblatt, Gesundheitsforschung, Gesundheitsschutz, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Socioeconomic determinants of COVID-19 incidence in a German rural area.BMC public health · 2026Article
- Results of the Cologne Corona Surveillance (CoCoS) study - a cross-sectional study: survey data on risk factors of SARS-CoV-2 infection in adults.BMC public health · 2023Article
- Involving trained community health mediators in COVID-19 prevention measures. A process evaluation from Bremen, Germany.Frontiers in digital health · 2023Article
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9 authors.
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Abstract
background and goalsEven 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.
methodsPseudonymized 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.
resultsThe 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.
conclusionThe 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.
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