[Regionalization of health indicators. Results from the GEDA-Study 2009]

Abstract

The representative health surveys conducted by the Department of Epidemiology and Health Reporting weren't used before to provide estimates for the spatial distribution of health outcomes. We are discussing the possibilities of providing these outcomes using methods for 'Small-Area-Estimation'. In the study we are using data of the "German Health Update 2009" (GEDA) to analyze regional inequalities for self-assessed health status, smoking and obesity on the district level in Germany. The small area estimates are provided by multilevel logistic regression models using additional regional statistical data from the official INKAR 2009 database of regional indicators for Germany. We are mapping the results of our analysis for the district level (NUTS-3) using simple thematic maps. Afterwards we compared the results of our small area models with conventional estimates that were based on the official German small scale census. The results showed that our estimates are in line with the prevalences of the census. Overall the results suggest that Small-Area-Estimation methods have a big potential to provide regionalized health indicators for the health reporting in Germany.