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Background: The COVID-19 pandemic has affected various urban population groups in different ways. Earlier studies have shown that severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) disproportionally impacts nursing home residents by increasing morbidity and mortality following viral exposure. However, little is known about the epidemiology of this disease in detail. Therefore, the objective of this study is to analyze the development of the COVID-19 pandemic in 14 nursing homes across Berlin-Neukölln, Germany, during pandemic waves 1 to 5 (Feb 2020 - May 2022)., Methods: Reporting data to the Neukölln Department of Public Health on COVID-19 cases in connection with nursing homes were extracted from the SORMAS database. The case fatality rates (CFRs) and odds ratios (ORs) of demographic parameters, prevalent variants of concern (VOCs) and vaccine availability were calculated. In addition, the temporal course in waves 1-5 in Neukölln and the relevant government measures were examined., Results: Data collected from nursing homes providing age-dependent physical care revealed that 1.9 % of the total 108,600 cases registered in Berlin-Neukölln during the study period were related one of the 14 facilities. Compared to the general population in Neukölln, nursing homes exhibited a 20-fold increase in the CFR. Notably, nursing homes with higher bed capacities displayed a greater CFR than did smaller nursing homes. Similarly, elderly residents living in nursing homes faced a much greater mortality rate than did their counterparts living outside of medical settings (OR = 3.5). The original wild-type SARS-CoV-2 strain had the most severe direct impact, with a CFR of 16.7 %, compared to the alpha (CFR = 6.9 %), delta (CFR = 10.2 %) and omicron (CFR = 2.8 %) variants in nursing homes. Interestingly, the number of infections increased following vaccination campaigns, but this trend was accompanied by a decrease in the number of deaths from 2.6 to 1.1 per week. As a result, the CFR significantly decreased from 18.4 to 5.5, while still exceeding the mean CFR compared to that of the general population of Neukölln., Conclusions: Our findings reveal the changing patterns of outbreak frequency and severity across the five pandemic waves. They highlight the crucial role of rapid vaccination programs for residents, staff, visitors, and third-party services in safeguarding nursing homes. Additionally, improvements in containment and cluster strategies are essential in prevaccination scenarios to prevent future infection traps for elderly individuals in long-term care facilities. The presented data highlight the importance of tailored protection measures for one of the most vulnerable populations in our society., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Identifying areas with high and low infection rates can provide important etiological clues. Usually, areas with high and low infection rates are identified by aggregating epidemiological data into geographical units, such as administrative areas. This assumes that the distribution of population numbers, infection rates, and resulting risks is constant across space. This assumption is, however, often false and is commonly known as the modifiable area unit problem. This article develops a spatial relative risk surface by using kernel density estimation to identify statistically significant areas of high risk by comparing the spatial distribution of address-level COVID-19 cases and the underlying population at risk in Berlin-Neukölln. Our findings show that there are varying areas of statistically significant high and low risk that straddle administrative boundaries. The findings of this exploratory analysis further highlight topics such as, e.g., Why were mostly affluent areas affected during the first wave? What lessons can be learned from areas with low infection rates? How important are built structures as drivers of COVID-19? How large is the effect of the socio-economic situation on COVID-19 infections? We conclude that it is of great importance to provide access to and analyse fine-resolution data to be able to understand the spread of the disease and address tailored health measures in urban settings.

The COVID-19 pandemic represents a worldwide threat to health. Since its onset in 2019, the pandemic has proceeded in different phases, which have been shaped by a complex set of influencing factors, including public health and social measures, the emergence of new virus variants, and seasonality. Understanding the development of COVID-19 incidence and its spatiotemporal patterns at a neighborhood level is crucial for local health authorities to identify high-risk areas and develop tailored mitigation strategies. However, analyses at the neighborhood level are scarce and mostly limited to specific phases of the pandemic. The aim of this study was to explore the development of COVID-19 incidence and spatiotemporal patterns of incidence at a neighborhood scale in an intra-urban setting over several pandemic phases (March 2020-December 2021). We used reported COVID-19 case data from the health department of the district Berlin-Neukölln, Germany, additional socio-demographic data, and text documents and materials on implemented public health and social measures. We examined incidence over time in the context of the measures and other influencing factors, with a particular focus on age groups. We used incidence maps and spatial scan statistics to reveal changing spatiotemporal patterns. Our results show that several factors may have influenced the development of COVID-19 incidence. In particular, the far-reaching measures for contact reduction showed a substantial impact on incidence in Neukölln. We observed several age group-specific effects: school closures had an effect on incidence in the younger population (< 18 years), whereas the start of the vaccination campaign had an impact primarily on incidence among the elderly (> 65  years). The spatial analysis revealed that high-risk areas were heterogeneously distributed across the district. The location of high-risk areas also changed across the pandemic phases. In this study, existing intra-urban studies were supplemented by our investigation of the course of the pandemic and the underlying processes at a small scale over a long period of time. Our findings provide new insights for public health authorities, community planners, and policymakers about the spatiotemporal development of the COVID-19 pandemic at the neighborhood level. These insights are crucial for guiding decision-makers in implementing mitigation strategies., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Schmitz, Lakes, Manafa, Lambio, Butler, Roth and Savaskan.)

Background: Noise annoyance is associated with adverse health-related conditions and reduced wellbeing. Thereby, subjective noise annoyance depends on the objective noise exposure and is modified by personal and regional factors., Objective: How many participants of the German National Cohort Study (GNC; NAKO Gesundheitsstudie) were annoyed by transportation noise during nighttime and what factors were associated with noise annoyance?, Materials and Methods: This cross-sectional analysis included 86,080 participants from 18 study centers, examined from 2014 to 2017. We used multinomial logistic regression to investigate associations of personal and regional factors to noise annoyance (slightly/moderately or strongly/extremely annoyed vs. not annoyed) mutually adjusting for all factors in the model., Results: Two thirds of participants were not annoyed by transportation noise during nighttime and one in ten reported strong/extreme annoyance with highest percentages for the study centers Berlin-Mitte and Leipzig. The strongest associations were seen for factors related to the individual housing situation like the bedroom being positioned towards a major road (OR of being slightly/moderately annoyed: 4.26 [95% CI: 4.01;4.52]; OR of being strongly/extremely annoyed: 13.36 [95% CI: 12.47;14.32]) compared to a garden/inner courtyard. Participants aged 40-60 years and those in low- and medium-income groups reported greater noise annoyance compared to younger or older ones and those in the high-income group., Conclusion: In this study from Germany, transportation noise annoyance during nighttime varied by personal and regional factors.

Background: In many German cities and counties, sustainable mobility concepts that strengthen pedestrian and cyclist traffic are promoted. From the perspectives of urban development, traffic planning and public healthcare, a spatially differentiated analysis of traffic accident data is decisive., Objectives: 1) The identification of spatial and temporal patterns of the distribution of accidents involving cyclists and pedestrians, 2) the identification of hotspots and exploration of possible underlying causes and 3) the critical discussion of benefits and challenges of the results and the derivation of conclusions., Material and Methods: Spatio-temporal distributions of data from accident statistics in Berlin involving pedestrians and cyclists from 2011 to 2015 were analysed with geographic information systems (GIS)., Results: While the total number of accidents remains relatively stable for pedestrian and cyclist accidents, the spatial distribution analysis shows, however, that there are significant spatial clusters (hotspots) of traffic accidents with a strong concentration in the inner city area., Conclusions: In a critical discussion, the benefits of geographic concepts are identified, such as spatially explicit health data (in this case traffic accident data), the importance of the integration of other data sources for the evaluation of the health impact of areas (traffic accident statistics of the police), and the possibilities and limitations of spatial-temporal data analysis (spatial point-density analyses) for the derivation of decision-supported recommendations and for the evaluation of policy measures of health prevention and of health-relevant urban development.