Knowledge about socio-demographic differences in the health status of the population is particularly important for prevention measures in order to be able to react appropriately to health risks in districts and urban districts. The analysis shows that an intense regional accumulation of problems will have a negative influence on health status, an influence which is more significant than the positive influence of prosperous regions on the health status.

In May 2003, the third revised version of the indicator set for health reporting activities was confirmed by the health ministries of all German States (Bundesländer). Modeled on the restructured indicator set which has been annotated with meta-data descriptions, most Bundesländer have now started to collect data for their specific health reporting activities. Thanks to the support provided by national data holders and the Federal Statistical Office, it has been possible to further enlarge the database and for the first time also ensure access via the Federal Statistical Office. In this contribution the authors describe the methodological and statistical principles of the indicator set. Another aspect is the benefit of the indicator set for the health reporting activities in the German States.

BackgroundLife expectancy is of increasing prime interest for a variety of reasons. In many countries, life expectancy is growing linearly, without any indication of reaching a limit. The state of North Rhine–Westphalia (NRW) in Germany with its 54 districts is considered here where the above mentioned growth in life expectancy is occurring as well. However, there is also empirical evidence that life expectancy is not growing linearly at the same level for different regions.MethodsTo explore this situation further a likelihood-based cluster analysis is suggested and performed. The modelling uses a nonparametric mixture approach for the latent random effect. Maximum likelihood estimates are determined by means of the EM algorithm and the number of components in the mixture model are found on the basis of the Bayesian Information Criterion. Regions are classified into the mixture components (clusters) using the maximum posterior allocation rule.ResultsFor the data analyzed here, 7 components are found with a spatial concentration of lower life expectancy levels in a centre of NRW, formerly an enormous conglomerate of heavy industry, still the most densely populated area with Gelsenkirchen having the lowest level of life expectancy growth for both genders. The paper offers some explanations for this fact including demographic and socio-economic sources.ConclusionsThis case study shows that life expectancy growth is widely linear, but it might occur on different levels.

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