[Travel time to memory clinics in Bavaria: A geographical analyses within the framework of digiDEM Bayern]. / Räumliche Erreichbarkeit von Gedächtnisambulanzen ­ Eine geographische Analyse im Rahmen von digiDEM Bayern.

BACKGROUND: Memory clinics can contribute significantly to a qualified diagnosis of dementia. Since the accessibility of medical facilities is an important predictor for their utilisation, the aim of this study was to determine the accessibility of memory clinics for persons with dementia in Bavaria. METHODS: We used a Geographic Information System (GIS) to determine travel times to the nearest memory clinic for all Bavarian municipalities based on OpenStreetMap road network data. RESULTS: The majority of the modelled persons with dementia in Bavaria (40%; n = 93,950) live in communities with an average travel time of 20 to 40 minutes to the nearest memory clinic. Almost 7,000 (3%) require more than one hour. Especially persons from rural communities have to travel significantly longer distances than people from urban areas. CONCLUSION: In view of demographic developments, there is an urgent need for memory clinics to be accessible throughout the country for all persons with dementia, regardless of where they live. The systematic development of memory clinics in areas with long travel times or the establishment of mobile diagnostic services could help to improve dementia care.

A multimethod approach for county-scale geospatial analysis of emerging infectious diseases: a cross-sectional case study of COVID-19 incidence in Germany.

BACKGROUND: As of 13 July 2020, 12.9 million COVID-19 cases have been reported worldwide. Prior studies have demonstrated that local socioeconomic and built environment characteristics may significantly contribute to viral transmission and incidence rates, thereby accounting for some of the spatial variation observed. Due to uncertainties, non-linearities, and multiple interaction effects observed in the associations between COVID-19 incidence and socioeconomic, infrastructural, and built environment characteristics, we present a structured multimethod approach for analysing cross-sectional incidence data within in an Exploratory Spatial Data Analysis (ESDA) framework at the NUTS3 (county) scale. METHODS: By sequentially conducting a geospatial analysis, an heuristic geographical interpretation, a Bayesian machine learning analysis, and parameterising a Generalised Additive Model (GAM), we assessed associations between incidence rates and 368 independent variables describing geographical patterns, socioeconomic risk factors, infrastructure, and features of the build environment. A spatial trend analysis and Local Indicators of Spatial Autocorrelation were used to characterise the geography of age-adjusted COVID-19 incidence rates across Germany, followed by iterative modelling using Bayesian Additive Regression Trees (BART) to identify and measure candidate explanatory variables. Partial dependence plots were derived to quantify and contextualise BART model results, followed by the parameterisation of a GAM to assess correlations. RESULTS: A strong south-to-north gradient of COVID-19 incidence was identified, facilitating an empirical classification of the study area into two epidemic subregions. All preliminary and final models indicated that location, densities of the built environment, and socioeconomic variables were important predictors of incidence rates in Germany. The top ten predictor variables' partial dependence exhibited multiple non-linearities in the relationships between key predictor variables and COVID-19 incidence rates. The BART, partial dependence, and GAM results indicate that the strongest predictors of COVID-19 incidence at the county scale were related to community interconnectedness, geographical location, transportation infrastructure, and labour market structure. CONCLUSIONS: The multimethod ESDA approach provided unique insights into spatial and aspatial non-stationarities of COVID-19 incidence in Germany. BART and GAM modelling indicated that geographical configuration, built environment densities, socioeconomic characteristics, and infrastructure all exhibit associations with COVID-19 incidence in Germany when assessed at the county scale. The results suggest that measures to implement social distancing and reduce unnecessary travel may be important methods for reducing contagion, and the authors call for further research to investigate the observed associations to inform prevention and control policy.