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 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.

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.