Content area
Full Text
ABSTRACT
Objective: To discuss critically the contribution of using second-level residuals from multilevel analyses to further the understanding of how place relates to health and to visualize areas, in the province of Quebec (Canada), with above- and below-average levels of overweight.
Methods: Data on 20,449 individuals are from the Canadian Community Health Survey (CCHS Cycle 2.1) and were linked to 51 spatial units. Area-level residuals were computed from a multilevel analysis examining individual and area characteristics associated with the risk of overweight. Mapping the area-level residuals indicates geographic areas where the risk of overweight is higher or lower compared to the provincial adjusted prevalence.
Results: Controlling for socio-economic conditions and lifestyle, distinct spatial patterns of overweight were observed, indicating that the processes linking place to health may differ between men and women and between regional contexts. In some regions, the probability of overweight differed by 23% for men and 38% for women living in privileged conditions in comparison to the province's adjusted prevalence of overweight.
Conclusions: Analyzing and visualizing area-level residuals provides multi-scaled information that could enhance the understanding of the geographic distribution of overweight and has the potential to support more integrated and locally relevant interventions.
Key words: Overweight; obesity; Québec; medical geography; multilevel analysis; population health
La traduction du résumé se trouve à la fin de l'article. Can J Public Health 2010;101(2):133-37
Mots clés : excès de poids; obésité; Québec; géographie médicale; analyse multiniveau; santé publique
During the last decade, there has been a strong research interest in the influence of local context on health, and on overweight and obesity more specifically.1-6 Because health inequalities are never fully clarified by ordinary (single-level) statistical models, and part of these inequalities might be explained by the specific context in which one lives,7,8 multilevel models are increasingly used to investigate the social determinants of health.9-11 These models allow for the assessment of variation in health across small areas as a function of both composition (characteristics of the individuals) and context (characteristics of the group) of these areas. Consideration of second-level residuals obtained from multilevel models is particularly interesting because it allows for the identification of 'outliers',12-15 i.e., areas with better/worse than expected health outcomes. When individual and area-level variables do not entirely explain the spatial...