| Titre : | Geographic analysis of diabetes prevalence in an urban area. (2003) |
| Auteurs : | Chris GREEN ; J.F. Blanchard ; HOPPA (Robert-D) : CAN. Department of Anthropology. University of Manitoba. Winnipeg Man. ; YOUNG (Tkue) : CAN. Department of Community Health Sciences. University of Manitoba. Winnipeg Man. ; Epidemiology Unit. Manitoba Health. Winnipeg Man. CAN |
| Type de document : | Article |
| Dans : | Social science and medicine (vol. 57, n° 3, 2003) |
| Pagination : | 551-560 |
| Langues: | Anglais |
| Mots-clés : | Diabète ; Examen complémentaire ; Epidémiologie ; Prévalence ; Géographie ; Race ; Facteur sociodémographique ; Elément environnement ; Homme ; Canada ; Amérique ; Glande endocrine [pathologie] ; Amérique du Nord |
| Résumé : | [BDSP. Notice produite par INIST-CNRS DIBLMR0x. Diffusion soumise à autorisation]. The objective of this research is to identify the sociodemographic, environmental, and lifestyle factors associated with the geographic variability of Diabetes Mellitus (DM) prevalence in the City of Winnipeg, Manitoba in Canada. An ecological regression study design was employed for this purpose. The study population included all prevalent cases of DM in 1998 for Winnipeg. Predictor and outcome data were aggregated for analysis using two methods. First, the spatial scan statistic was used to aggregate study data into highly probable diabetes prevalence clusters. Secondly, predictor and outcome data were aggregated to existing administrative health areas. Analysis of variance and spatial and non-spatial linear regression techniques were used to explore the relationship between predictor and outcome variables. The results of the two methods of data aggregation on regression results were compared. Mapping and statistical analysis revealed substantial clustering and small-area variations in the prevalence of DM in the City of Winnipeg. The observed variations were associated with variations in socioeconomic, environmental and lifestyle characteristics of the population. The two methods of data aggregation used in the study generated very similar results in terms of identifying the geographic location of DM clusters and of the population characteristics ecologically correlated to those clusters. High rates of DM prevalence are strongly correlated with indicators of low socioeconomic status, poor environmental quality and poor lifestyles. This analysis further illustrates what a useful tool the spatial scan statistic can be when used in conjunction with ecological regression to explore the etiology of chronic disease. |
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