Dataset Information

[Spatio-temporal distribution of COVID-19 in Cologne and associated socio-economic factors in the period from February 2020 to October 2021].

ABSTRACT:

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.

SUBMITTER: Neuhann F

PROVIDER: S-EPMC9362610 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature

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Publications

[Spatio-temporal distribution of COVID-19 in Cologne and associated socio-economic factors in the period from February 2020 to October 2021].

Neuhann Florian F Ginzel Sebastian S Buess Michael M Wolff Anna A Kugler Sabine S Schlanstedt Günter G Kossow Annelene A Nießen Johannes J Rüping Stefan S

Bundesgesundheitsblatt, Gesundheitsforschung, Gesundheitsschutz 20220803 9

<h4>Background and goals</h4>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, G ...[more]

PMID: 35920847

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Project description:BackgroundHemorrhagic fever with renal syndrome (HFRS) occurs widely in Northeastern China, but the mechanism and interactions of meteorological and socio-economic factors on the transmission of HFRS are still largely unknown.ObjectiveWe explored the effects of socioeconomic-environmental factors on the spatio-temporal variation of HFRS incidence from 2001 to 2019 in Northeastern China. Specifically, the relative importance and contribution rates (CR) of determinants of HFRS were identified by boosted regression tree and variance partitioning analysis, respectively. Structural equation models (SEMs) were used to explain the roles of climatic and socio-economic factors in the transmission of HFRS. And a negative binomial regression was used to identify the risk effect between monthly meteorological variables and HFRS with 0-6 months lags in Northeastern China.ResultsOver the past decades, the high-risk areas of HFRS were mainly concentrated in the northern and eastern areas of Northeastern China. Additionally, HFRS mainly presented a decreasing trend from 2001 to 2019 in most areas of Northeastern China, but slightly increased in the cities of Daqing, Songyuan, Baicheng, and Tonghua. The temporal dynamics of the incidence of HFRS were primarily explained by the variations in population density (CR = 27.30%), climate (CR = 13.30%), and economic condition(CR = 1.90%). The spatial variations of HFRS were medicated by the climate (CR = 16.95%) and population density (CR = 9.45%) and medical health care (CR = 2.25%). The SEM models indicated that humid and warm climates were conducive to the incidence and increase of HFRS, but the improvement in education and an increase in population density reduced the transmission of HFRS.ConclusionClimate and population density appeared to mediate the spatio-temporal variation of HFRS in Northeastern China. These findings may provide valuable empirical evidence for the management of HFRS in endemic areas.

Project description:BackgroundThe disease burden caused by pulmonary tuberculosis (TB) in Sichuan province still persisted at a high level, and large spatial variances were presented across regional distribution disparities. The socio-economic factors were suspected to affect the population of TB notification, we aimed to describe TB case notification rate (CNR) and identify which factors influence TB epidemic are necessary for the prevention and control of the disease in Sichuan province.MethodsA retrospective cross-sectional study and an ecological spatial analysis was conducted to quantify the presence and location of spatial clusters of TB by the Moran's I index and examined these patterns with socio-economic risk factors by hierarchical Bayesian spatio-temporal model.ResultsA total of 630,009 pulmonary TB cases were notified from 2006 to 2015 in 181 counties of Sichuan province. The CNR decreased year by year since 2007, from 88.70 to 61.37 per 100,000 persons. The spatial heterogeneities of CNR were observed during the study periods. Global Moran's I index varied from 0.23 to 0.44 with all P-value < 0.001. The Bayesian spatio-temporal model with parametric spatio-temporal interactions was chosen as the best model according to the minimum of Deviance Information Criterion (DIC)(19,379.01), and in which the quadratic form of time was taken. The proportion of age group and education year were all associated with CNR after adjusting the spatial effect, temporal effect and spatio-temporal interactions. TB CNR increased by 10.2% [95% credible interval (CI): 6.7-13.7%] for every 1-standard-deviation increase in proportion of age group and decreased by 23% (95% CI: 13.7-32.7%) for every 1-standard-deviation increase in education year.ConclusionsThere were spatial clusters of TB notification rate in Sichuan province from 2006 to 2015, and heavy TB burden was mainly attributed to aging and low socioeconomic status including poor education. Thus, it is more important to pay more attention to the elderly population and improve socioeconomic status including promoting education level in Sichuan province to reduce the TB burden.

Dataset Information

[Spatio-temporal distribution of COVID-19 in Cologne and associated socio-economic factors in the period from February 2020 to October 2021].

Background and goals

Methods

Results

Conclusion

Publications

[Spatio-temporal distribution of COVID-19 in Cologne and associated socio-economic factors in the period from February 2020 to October 2021].

Similar Datasets

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