De toepassing van ruimtelijke analysetechnieken in milieu-epidemiologisch onderzoek. Rapportage in het kader van het SAVIAH-project
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Type
Report
Language
nl
Date
1997-02-28
Research Projects
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Title
De toepassing van ruimtelijke analysetechnieken in milieu-epidemiologisch onderzoek. Rapportage in het kader van het SAVIAH-project
Translated Title
Spatial analysis techniques in environmental epidemiological studies ; a summary of methods used in the Saviah project
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Abstract
Dit onderzoek naar de toepassing van ruimtelijke modellen voor betere prevalentieschattingen in geografische gebieden, maakt deel uit van het SAVIAH project. Dat is een samenwerkingsverband van centra in vier landen, waarbij de samenhang tussen luchtverontreiniging en het voorkomen van CARA bij kinderen bestudeerd wordt. Modellen kunnen de prevalentieschatting verbeteren, doordat de invloed van 'random fluctuaties' op de schatting verkleind wordt. Hierdoor krijgt men beter zicht op de werkelijke 'onderliggende' risico's op ziekte. Als richtlijn bij de modelkeuze werden eerst testen gedaan om te onderzoeken of er sprake was van heterogeniteit of ruimtelijke autocorrelatie. In het SAVIAH onderzoeksgebied in Amsterdam was geen sprake van heterogeniteit of ruimtelijke autocorrelatie bij de onderzochte CARA-prevalenties. Daarom zijn relatief eenvoudige modellen toegepast, die naar een algemeen gemiddelde 'smoothen'. De verschillen tussen de modellen worden besproken.<br>
This study on the application of Generalized Linear Mixed Model (GLMM) models to estimate disease prevalence in geographic regions is part of the SAVIAH project. The SAVIAH project (Small Area Variation in Air quality and Health) is a cooperation of centres in four countries, financed by the EEC and set up to study the relation between air pollution and the prevalence of CARA in children. The advantage of models over 'crude' estimates of prevalence is that they are able to reduce the random fluctuations in the outcome, thereby providing a clearer view on the true 'underlying' pattern of risk of disease. To make appropriate choices in the modelling process, first tests on the existence of heterogeneity and spatial autocorrelation were carried out. In the SAVIAH study area in Amsterdam heterogeneity and spatial autocorrelation of CARA prevalences in neighbourhoods were not present. Therefore relatively simple models were applied that 'smooth' values to a general mean. The differences between the models are discussed.<br>
This study on the application of Generalized Linear Mixed Model (GLMM) models to estimate disease prevalence in geographic regions is part of the SAVIAH project. The SAVIAH project (Small Area Variation in Air quality and Health) is a cooperation of centres in four countries, financed by the EEC and set up to study the relation between air pollution and the prevalence of CARA in children. The advantage of models over 'crude' estimates of prevalence is that they are able to reduce the random fluctuations in the outcome, thereby providing a clearer view on the true 'underlying' pattern of risk of disease. To make appropriate choices in the modelling process, first tests on the existence of heterogeneity and spatial autocorrelation were carried out. In the SAVIAH study area in Amsterdam heterogeneity and spatial autocorrelation of CARA prevalences in neighbourhoods were not present. Therefore relatively simple models were applied that 'smooth' values to a general mean. The differences between the models are discussed.<br>
Description
Publisher
Rijksinstituut voor Volksgezondheid en Milieu RIVM
Sponsors
IGZ