[Health indicator-based cluster analysis of districts and urban districts in North Rhine-Westphalia].
OBJECTIVE
North Rhine-Westphalia (NRW's) indicator set for health reporting activities comprises more than 70 regional health indicators, which means that these data are available for health reporting purposes for all 54 districts and urban districts. Morbidity and mortality indicators differ in part quite considerably and require further interpretation. With the help of selected indicators, the authors of the following article try to explain the relation between social status and health status.


METHODS
Ten years ago, NRW, as part of its health reporting activities, started to carry out multivariate analyses to classify socio-demographically different types of regions, leading to the establishment of six types of regions which can be linked to health-related data. Social structure indicators are part of a first step submitted to a main component analysis and grouped together by a small number of features and/or factors which clearly reflect differences in living conditions. As a result, two factors were extracted: an economic prosperity factor which is mainly determined by the disposable income and a so-called A-factor which mainly describes the fact that poorer, elderly, unemployed and foreign population groups live concentrated in regions with a declining population but high population density. These factors are, in a second step, used for a cluster analysis aimed at classifying the 54 districts and urban districts and at establishing different types of regions. In a subsequent step, the cluster method is used to explain regional variations of selected health indicators.


RESULTS
It is a proven fact that morbidity and mortality are influenced by social status. With the help of selected indicators, six clusters with a different socio-economic structure influencing the health status of the population can be established for NRW. Special attention should be paid to the cluster of the Ruhr area with its below-average social situation. With 90% NRW's population primarily living within the other 5 clusters which are differently structured but increasingly adjusting their living conditions to each other. The authors of this publication assign four health status indicators to predefined clusters and analyse the relation between the social and health status: female and male life expectancy, the proportion of underweight live births, infant mortality and avoidable deaths.In regions with high A-factor values (poverty pole), i. e., in several ways socially deprived regions, male and female average life expectancy is significantly lower than in regions with a clearly less pronounced accumulation of problems. Moreover, a significantly higher life expectancy for male live births can be observed in regions with a high disposable income. The model fails to establish a convincing correlation between social status and infant mortality and breast cancer.


CONCLUSIONS
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.
Strohmeier
K P
KP
Ruhr-Universität Bochum, Bochum, Germany.
Schultz
A
A
Bardehle
D
D
Annuss
R
R
Lenz
A
A
D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D002648:Child; D002675:Child, Preschool; D016000:Cluster Analysis; D005260:Female; D005858:Germany / Q000453:epidemiology; D006305:Health Status Indicators; D006801:Humans; D007223:Infant; D007226:Infant Mortality; D007231:Infant, Newborn; D008017:Life Expectancy; D008297:Male; D008875:Middle Aged; D009026:Mortality; D016016:Proportional Hazards Models; D018570:Risk Assessment / Q000379:methods*; D012307:Risk Factors; D014504:Urban Health / Q000706:statistics & numerical data*