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.
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.



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.



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.



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.
Department of Medical Care, AOK Nordost - Die Gesundheitskasse, Berlin, Germany. boris.kauhl@nordost.aok.de.|Department III, Civil Engineering and Geoinformatics, Beuth University of Applied Sciences, Berlin, Germany. boris.kauhl@nordost.aok.de.|Department of Health, Ethics and Society, School of Public Health and Primary Care (CAPHRI), Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, The Netherlands. boris.kauhl@nordost.aok.de.
Boris
B
Kauhl
Department III, Civil Engineering and Geoinformatics, Beuth University of Applied Sciences, Berlin, Germany.
Jürgen
J
Schweikart
Department of Health, Ethics and Society, School of Public Health and Primary Care (CAPHRI), Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, The Netherlands.
Thomas
T
Krafft
Department of Medical Care, AOK Nordost - Die Gesundheitskasse, Berlin, Germany.
Andrea
A
Keste
Department of Medical Care, AOK Nordost - Die Gesundheitskasse, Berlin, Germany.
Marita
M
Moskwyn
D017677:Age Distribution; D000368:Aged; D000369:Aged, 80 and over; D001499:Bayes Theorem; D003924:Diabetes Mellitus, Type 2 / Q000453:epidemiology*; D005260:Female; D062305:Geographic Mapping; D005858:Germany / Q000453:epidemiology; D006301:Health Services Needs and Demand; D006801:Humans; D007345:Insurance Claim Review; D008297:Male; D008875:Middle Aged; D012111:Residence Characteristics / Q000706:statistics & numerical data*; D012307:Risk Factors; D017678:Sex Distribution; D012959:Socioeconomic Factors; D062206:Spatial Analysis
Geographically weighted regression; Germany; Healthcare; Kernel Density Estimation; SaTScan; Spatial analysis; Street-level; Type 2 diabetes mellitus; big data