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.

Advancing tools to promote health equity across European Union regions: the EURO-HEALTHY project.

BACKGROUND: Population health measurements are recognised as appropriate tools to support public health monitoring. Yet, there is still a lack of tools that offer a basis for policy appraisal and for foreseeing impacts on health equity. In the context of persistent regional inequalities, it is critical to ascertain which regions are performing best, which factors might shape future health outcomes and where there is room for improvement. METHODS: Under the EURO-HEALTHY project, tools combining the technical elements of multi-criteria value models and the social elements of participatory processes were developed to measure health in multiple dimensions and to inform policies. The flagship tool is the Population Health Index (PHI), a multidimensional measure that evaluates health from the lens of equity in health determinants and health outcomes, further divided into sub-indices. Foresight tools for policy analysis were also developed, namely: (1) scenarios of future patterns of population health in Europe in 2030, combining group elicitation with the Extreme-World method and (2) a multi-criteria evaluation framework informing policy appraisal (case study of Lisbon). Finally, a WebGIS was built to map and communicate the results to wider audiences. RESULTS: The Population Health Index was applied to all European Union (EU) regions, indicating which regions are lagging behind and where investments are most needed to close the health gap. Three scenarios for 2030 were produced - (1) the 'Failing Europe' scenario (worst case/increasing inequalities), (2) the 'Sustainable Prosperity' scenario (best case/decreasing inequalities) and (3) the 'Being Stuck' scenario (the EU and Member States maintain the status quo). Finally, the policy appraisal exercise conducted in Lisbon illustrates which policies have higher potential to improve health and how their feasibility can change according to different scenarios. CONCLUSIONS: The article makes a theoretical and practical contribution to the field of population health. Theoretically, it contributes to the conceptualisation of health in a broader sense by advancing a model able to integrate multiple aspects of health, including health outcomes and multisectoral determinants. Empirically, the model and tools are closely tied to what is measurable when using the EU context but offering opportunities to be upscaled to other settings.

Population Health Inequalities Across and Within European Metropolitan Areas through the Lens of the EURO-HEALTHY Population Health Index.

The different geographical contexts seen in European metropolitan areas are reflected in the uneven distribution of health risk factors for the population. Accumulating evidence on multiple health determinants point to the importance of individual, social, economic, physical and built environment features, which can be shaped by the local authorities. The complexity of measuring health, which at the same time underscores the level of intra-urban inequalities, calls for integrated and multidimensional approaches. The aim of this study is to analyse inequalities in health determinants and health outcomes across and within nine metropolitan areas: Athens, Barcelona, Berlin-Brandenburg, Brussels, Lisbon, London, Prague, Stockholm and Turin. We use the EURO-HEALTHY Population Health Index (PHI), a tool that measures health in two components: Health Determinants and Health Outcomes. The application of this tool revealed important inequalities between metropolitan areas: Better scores were found in Northern cities when compared with their Southern and Eastern counterparts in both components. The analysis of geographical patterns within metropolitan areas showed that there are intra-urban inequalities, and, in most cities, they appear to form spatial clusters. Identifying which urban areas are measurably worse off, in either Health Determinants or Health Outcomes, or both, provides a basis for redirecting local action and for ongoing comparisons with other metropolitan areas.

Who is where at risk for Chronic Obstructive Pulmonary Disease? A spatial epidemiological analysis of health insurance claims for COPD in Northeastern Germany.

BACKGROUND: Chronic obstructive pulmonary disease (COPD) has a high prevalence rate in Germany and a further increase is expected within the next years. Although risk factors on an individual level are widely understood, only little is known about the spatial heterogeneity and population-based risk factors of COPD. Background knowledge about broader, population-based processes could help to plan the future provision of healthcare and prevention strategies more aligned to the expected demand. The aim of this study is to analyze how the prevalence of COPD varies across northeastern Germany on the smallest spatial-scale possible and to identify the location-specific population-based risk factors using health insurance claims of the AOK Nordost. METHODS: To visualize the spatial distribution of COPD prevalence at the level of municipalities and urban districts, we used the conditional autoregressive Besag-York-Mollié (BYM) model. Geographically weighted regression modelling (GWR) was applied to analyze the location-specific ecological risk factors for COPD. RESULTS: The sex- and age-adjusted prevalence of COPD was 6.5% in 2012 and varied widely across northeastern Germany. Population-based risk factors consist of the proportions of insurants aged 65 and older, insurants with migration background, household size and area deprivation. The results of the GWR model revealed that the population at risk for COPD varies considerably across northeastern Germany. CONCLUSION: Area deprivation has a direct and an indirect influence on the prevalence of COPD. Persons ageing in socially disadvantaged areas have a higher chance of developing COPD, even when they are not necessarily directly affected by deprivation on an individual level. This underlines the importance of considering the impact of area deprivation on health for planning of healthcare. Additionally, our results reveal that in some parts of the study area, insurants with migration background and persons living in multi-persons households are at elevated risk of COPD.

[Spatial turn : Opportunity, challenge and methodological momentum for geographical health research]. / "Spatial turn" : Chance, Herausforderung und Methodenimpuls für die geographische Gesundheitsforschung.

The (re)-discovery of the spatial dimension in many sciences has been guided for some time under the designation "spatial turn". Immense progress in geographic information sciences (GIS), global positioning systems (GPS), remote sensing and computer-aided cartography, in addition to geostatistical methods such as spatial distribution analysis and trend analysis, multi-level analysis, spatial data-mining and agent-based modelling, has created entirely new opportunities for spatial analysis and the modelling of spatial, health-relevant processes. These methods are increasingly being employed in epidemiology, public health and healthcare research.In the fields of cultural and social sciences, "spatial turn" refers to a paradigm shift that recognizes that geographical space also has a social and cultural meaning. This spatial conception considers space not only as an empty container, but also as a result of social processes. The Euclidean space is extended by socially and culturally shaped spatial perceptions and constructions. The "spatial turn" as a paradigm shift is not limited to the fact that space itself becomes an object of advanced investigation methods. It is instead about approaching objects of research with spatial categories.In light of the "spatial turn", geographical health research is currently facing great opportunities, but also a double challenge: on the one hand, recognizing, mediating and making meaningful use of the new methodological possibilities. On the other hand, and in line with its self-conception as a part of the medical humanities, it is challenged to implement the "spatial turn" in its social and cultural-scientific dimension, to go beyond stereotypical reception and to meet the paradigmatic significance of "spatial turn".

Do the risk factors for type 2 diabetes mellitus vary by location? A spatial analysis of health insurance claims in Northeastern Germany using kernel density estimation and geographically weighted regression.

BACKGROUND: The provision of general practitioners (GPs) in Germany still relies mainly on the ratio of inhabitants to GPs at relatively large scales and barely accounts for an increased prevalence of chronic diseases among the elderly and socially underprivileged populations. Type 2 Diabetes Mellitus (T2DM) is one of the major cost-intensive diseases with high rates of potentially preventable complications. Provision of healthcare and access to preventive measures is necessary to reduce the burden of T2DM. However, current studies on the spatial variation of T2DM in Germany are mostly based on survey data, which do not only underestimate the true prevalence of T2DM, but are also only available on large spatial scales. The aim of this study is therefore to analyse the spatial distribution of T2DM at fine geographic scales and to assess location-specific risk factors based on data of the AOK health insurance. METHODS: To display the spatial heterogeneity of T2DM, a bivariate, adaptive kernel density estimation (KDE) was applied. The spatial scan statistic (SaTScan) was used to detect areas of high risk. Global and local spatial regression models were then constructed to analyze socio-demographic risk factors of T2DM. RESULTS: T2DM is especially concentrated in rural areas surrounding Berlin. The risk factors for T2DM consist of proportions of 65-79 year olds, 80 + year olds, unemployment rate among the 55-65 year olds, proportion of employees covered by mandatory social security insurance, mean income tax, and proportion of non-married couples. However, the strength of the association between T2DM and the examined socio-demographic variables displayed strong regional variations. CONCLUSION: The prevalence of T2DM varies at the very local level. Analyzing point data on T2DM of northeastern Germany's largest health insurance provider thus allows very detailed, location-specific knowledge about increased medical needs. Risk factors associated with T2DM depend largely on the place of residence of the respective person. Future allocation of GPs and current prevention strategies should therefore reflect the location-specific higher healthcare demand among the elderly and socially underprivileged populations.