Measuring the variation in health outcomes, for example, mortality, morbidity, hospitalization, across small areas is an accepted way of screening large amounts of routinely-collected data.
Although simple measures of variation, for example, the extremal quotient, are intuitively appealing, they have poor statistical properties.
More sophisticated measures, based on hierarchical models, have better statistical properties, but are in a form that is foreign to most public health officials.
The analyses in this paper converted the small-area variance obtained from a hierarchical model into three new measures : the ratio of high versus low rates across small areas, and the percentage and number of adverse events, such as deaths, that might be avoidable if the causes of the variation between areas could be removed.
The approach was applied to mortality data from New South Wales, Australia.
The three new measures can help public health officials make judgements about whether to proceed with more detailed (and expensive) studies without having to rely on the statistical significance of an obscure index.
Mots-clés Pascal : Variabilité, Analyse donnée, Aire superficielle, Structure hiérarchisée, Modélisation, Analyse statistique, Mortalité, Morbidité
Mots-clés Pascal anglais : Variability, Data analysis, Surface area, Hierarchized structure, Modeling, Statistical analysis, Mortality, Morbidity
Notice produite par :
Inist-CNRS - Institut de l'Information Scientifique et Technique
Cote : 99-0004191
Code Inist : 002B28F. Création : 31/05/1999.