2 research outputs found
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
Abstract 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
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