Capturing modelled and perceived spatial access to ambulatory health care services in rural and urban areas in Germany.

Studies on social and regional inequalities in access to health care often use spatial indicators such as physician density to measure access to health care. However, the concept of access is more complex, comprising, among others, patient perceptions. In this study, we evaluate the association between different spatial measures of access (i.e. physician density, distance to the nearest provider, and measures based on floating catchment area methods) and measures of perceived spatial access to ambulatory health care in rural and urban areas in Germany. Using correlation and regression analysis, we found that the significance and strength of the relation between perceived and modelled spatial access depends on the type of area and the physician group. The distance to the nearest physician is associated with perceived spatial access to GPs only in rural areas but not in urban areas. More sophisticated measures of spatial access seem not to explain perceived access better than the simpler indicators.

[Spatial Distribution of Type 2 Diabetes Mellitus in Berlin: Application of a Geographically Weighted Regression Analysis to Identify Location-Specific Risk Groups]. / Die räumliche Verbreitung des Typ 2 Diabetes Mellitus in Berlin ­ Die Anwendung einer geografisch gewichteten Regressionsanalyse zur Identifikation ortsspezifischer Risikogruppen.

Understanding which population groups in which locations are at higher risk for type 2 diabetes mellitus (T2DM) allows efficient and cost-effective interventions targeting these risk-populations in great need in specific locations. The goal of this study was to analyze the spatial distribution of T2DM and to identify the location-specific, population-based risk factors using global and local spatial regression models. To display the spatial heterogeneity of T2DM, bivariate kernel density estimation was applied. An ordinary least squares regression model (OLS) was applied to identify population-based risk factors of T2DM. A geographically weighted regression model (GWR) was then constructed to analyze the spatially varying association between the identified risk factors and T2DM. T2DM is especially concentrated in the east and outskirts of Berlin. The OLS model identified proportions of persons aged 80 and older, persons without migration background, long-term unemployment, households with children and a negative association with single-parenting households as socio-demographic risk groups. The results of the GWR model point out important local variations of the strength of association between the identified risk factors and T2DM. The risk factors for T2DM depend largely on the socio-demographic composition of the neighborhoods in Berlin and highlight that a one-size-fits-all approach is not appropriate for the prevention of T2DM. Future prevention strategies should be tailored to target location-specific risk-groups.