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1.
Gesundheitswesen ; 69(1): 26-33, 2007 Jan.
Article in German | MEDLINE | ID: mdl-17347929

ABSTRACT

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. METHODOLOGY: 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.


Subject(s)
Cluster Analysis , Health Status Indicators , Infant Mortality , Life Expectancy , Proportional Hazards Models , Risk Assessment/methods , Urban Health/statistics & numerical data , Adolescent , Adult , Aged , Aged, 80 and over , Child , Child, Preschool , Female , Germany/epidemiology , Humans , Infant , Infant, Newborn , Male , Middle Aged , Mortality , Risk Factors
2.
Article in German | MEDLINE | ID: mdl-15340720

ABSTRACT

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.


Subject(s)
Data Collection/statistics & numerical data , Health Status Indicators , National Health Programs/statistics & numerical data , Public Health Informatics/statistics & numerical data , Aged , Aged, 80 and over , Germany , Humans , Life Expectancy , Mathematical Computing
3.
Soz Praventivmed ; 33(1): 51-5, 1988.
Article in German | MEDLINE | ID: mdl-3376580

ABSTRACT

The "Consensus-Conference" of the European Atherosclerosis Association held in June 1986, came to an agreement, that all adults with cholesterol levels above 200 mg/dl should receive medical attention. First representative investigations in the Federal Republic of Germany showed that levels below 200 mg/dl are found in less than 30% of persons aged 30 to 59 years. 76% of all patients who where examined didn't know their own cholesterol level. These data verify the urgency of intensive preventive actions. Cholesterol screening without simultaneous supply of information for behaviour modification (especially nutrition education), would be of little use. Therefore in the proposed screening-concept communicative procedures are of great significance. In addition, cooperation between physicians, psychologists, teachers and suitable organisations is necessary.


Subject(s)
Cholesterol/blood , Coronary Disease/prevention & control , Mass Screening , Adult , Germany, West , Humans , Hypercholesterolemia/prevention & control , Middle Aged , Risk Factors
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