Spatial Aspects of Health-Developing a Conceptual Framework.

Numerous studies and models address the determinants of health. However, in existing models, the spatial aspects of the determinants are not or only marginally taken into account and a theoretical discussion of the association between space and the determinants of health is missing. The aim of this paper is to generate a framework that can be used to place the determinants of health in a spatial context. A screening of the current first serves to identify the relevant determinants and describes the current state of knowledge. In addition, spatial scales that are important for the spatial consideration of health were developed and discussed. Based on these two steps, the conceptual framework on the spatial determinants of health was derived and subsequently discussed. The results show a variety of determinants that are associated with health from a spatial point of view. The overarching categories are global driving forces, policy and governance, living and physical environment, socio-demographic and economic conditions, healthcare services and cultural and working conditions. Three spatial scales (macro, meso and micro) are further subdivided into six levels, such as global (e.g., continents), regional (e.g., council areas) or neighbourhood (e.g., communities). The combination of the determinants and spatial scales are presented within a conceptual framework as a result of this work. Operating mechanisms and pathways between the spatial levels were added schematically. This is the first conceptual framework that links the determinants of health with the spatial perspective. It can form the working basis for future analyses in which spatial aspects of health are taken into account.

[Mapping Regional Differences in Infection Rates for the Coronavirus (COVID-19): Results of a Bayesian Approach to Administrative Districts of Bavaria]. / Kartografische Darstellung regionaler Unterschiede der Infektionsraten beim Coronavirus (COVID-19): Ergebnisse einer bayesianischen Glättung über Landkreise Bayerns.

BACKGROUND: Since the beginning of the COVID-19 pandemic, thematic maps showing the spread of the disease have been of great public interest. From the perspective of risk communication, those maps can be problematic, since random variation or extreme values may occur and cover up the actual regional patterns. One potential solution is applying spatial smoothing methods. The aim of this study was to show changes in incidence ratios over time in Bavarian districts using spatially smoothed maps. METHODS: Data on SARS-CoV-2 were provided by the Bavarian Health and Food Safety Authority on 29.10.2021 and 17.02.2022. The demographic data per district are derived from the Statistical Report of the Bavarian State Office for Statistics for 2019. Four age groups per sex (<18, 18-29, 30-64,>64 years) divided into 16 time periods (01/28/2020 to 12/31/2021) were included. Maps show standardized incidence ratios (SIR) spatially smoothed by Bayesian hierarchical modelling. RESULTS: The SIR varied remarkably between districts. Variations occurred for each time period, showing changing regional patterns over time. CONCLUSION: Smoothed health maps are suitable for showing trends in incidence ratios over time for COVID-19 in Bavaria and offer the advantage over traditional maps in giving more realistic estimates by including neighborhood relationships. The methodological approach can be seen as a first step to explain the regional heterogeneity in the pandemic, and to support improved risk communication.