Buck Christoph, Koller Daniela, Kibele Eva, Schulze Katharina, Augustin Jobst
Leibniz Institut für Präventionsforschung und Epidemiologie - BIPS, Bremen, Deutschland.
Institut für Medizinische Informationsverarbeitung, Biometrie und Epidemiologie, Ludwig-Maximilians-Universität München (LMU), München, Deutschland.
Bundesgesundheitsblatt Gesundheitsforschung Gesundheitsschutz. 2025 Sep 4. doi: 10.1007/s00103-025-04125-2.
Geography is, among other things, the study of spatial and temporal changes in structures and processes. Health geography applies the methods, models, and paradigms of geography to health-related issues. The example of the COVID-19 pandemic in Bremen is used to illustrate the geographical perspective on health and its benefits.
The study is based on spatio-temporal data of new COVID-19 infections by calendar week at the district level in the city of Bremen between March 2020 and May 2022. In addition to the number of cases, selected indicators of the socio-demographic situation (e.g., household structure, social status, and migration status) were taken into account. Spatio-temporal analyses were performed descriptively and using linear regression models.
The first wave of the pandemic shows clear local differences and high incidence respectively period prevalence in individual city districts. For the later waves, a clustering with high case numbers in predominantly deprived city districts was identified. For example, in wave 2 there was an association between the number of cases and the number of persons per household (β = 1.099, p < 0.001) and in wave 4 with the SGBII rate (β = 0.056, p = 0.004).
The results show spatial differences in COVID-19 case numbers and a greater burden in deprived city districts. The study has shown the great benefit of a spatio-temporal perspective using the example of the COVID-19 pandemic in Bremen. This applies not only to the analysis of the dynamics of the pandemic, but also from a public health perspective to the identification of vulnerable populations and the implementation of targeted prevention measures.
地理学除其他方面外，还研究结构和过程的时空变化。健康地理学将地理学的方法、模型和范式应用于与健康相关的问题。以不来梅市的新冠疫情为例，来说明地理学对健康的视角及其益处。
该研究基于2020年3月至2022年5月不来梅市各行政区按日历周统计的新冠新增感染病例的时空数据。除病例数外，还考虑了社会人口状况的选定指标（如家庭结构、社会地位和移民状况）。进行了描述性时空分析，并使用线性回归模型。
疫情的第一波显示出明显的局部差异，个别城市行政区的发病率和期间患病率较高。对于后来的几波疫情，发现在主要贫困的城市行政区病例数聚集。例如，在第二波疫情中，病例数与每户人数之间存在关联（β = 1.099，p < 0.001），在第四波疫情中与法定健康保险第二类费率存在关联（β = 0.056，p = 0.004）。
结果显示了新冠病例数的空间差异以及贫困城市行政区负担更重。该研究以不来梅市的新冠疫情为例，展示了时空视角的巨大益处。这不仅适用于分析疫情动态，从公共卫生角度来看，也适用于识别弱势群体和实施有针对性的预防措施。
