Abstract
Background: The purposes of this study are to determine (i) if neighbourhood socio-economic status (SES) is systematically related to the prevalence of overweight children and youth in Canada, (ii) if the factors accounting for the apparent relationship have face validity, and (iii) if neighbourhood SES has an independent influence on this distribution.
Methods: Cross-sectional data from Cycle 4 (2000/2001) of the National Longitudinal Survey of Children and Youth were used. Children and youth aged 5 to 17 were included. Overweight was established using age and sex cut-off points. Neighbourhood socio-economic data were obtained from the Statistics Canada 2001 Dissemination Area databases and SES quartiles constructed using a composite of socio-economic variables. Hierarchical non-linear modelling was used to test for independent neighbourhood effects.
Results: A gradient of increasing overweight prevalence by decreasing neighbourhood SES quartiles was observed (24% high SES, 30% mid-high SES, 33% mid-low SES, 35% low SES). Controlling for individual age, gender, family income and education hierarchical analysis found that a child’s odds of being overweight increases if living in a low versus a high SES neighbourhood (OR=1.29, 95% CI=1.14-1.46).
Interpretation: The prevalence of child and youth overweight in Canada is inversely and statistically significantly related to neighbourhood SES. Independent effects indicate that neighbourhood characteristics directly influence the odds of being overweight. This research suggests that consideration of opportunity structures that exist in different types of neighbourhoods is fundamentally important to health promotion and disease prevention strategies.
MeSH terms: Obesity, socioeconomic factors, population health, residence characteristics
Résumé
Contexte: Notre étude visait à déterminer i) si le statut socioéconomique (SSE) du quartier est systématiquement lié à la prévalence de l’embonpoint chez les enfants et les adolescents au Canada, ii) si les facteurs expliquant ce lien possible ont une validité apparente, et iii) si le SSE du quartier exerce une influence indépendante sur cette répartition.
Méthode: Nous avons utilisé des données transversales du 4e cycle (2000 2001) de l’Enquête longitudinale nationale sur les enfants et les jeunes, en incluant les enfants et les adolescents de 5 à 17 ans. L’embonpoint a été déterminé selon des points limites par âge et par sexe. Les données socioéconomiques des quartiers sont tirées des bases de données des aires de diffusion de Statistique Canada pour 2001, et les quartiles par SSE ont été calculés à l’aide d’une moyenne composée de variables socioéconomiques. Nous avons utilisé la modélisation hiérarchique non linéaire pour évaluer l’influence indépendante exercée par les quartiers.
Résultats: Nous avons observé une prévalence croissante de l’embonpoint inversement proportionnelle au quartile du statut socioéconomique du quartier (24 % d’embonpoint dans les quartiers ayant un SSE supérieur, 30 % pour un SSE moyen à supérieur, 33 % pour un SSE moyen à inférieur et 35 % pour un SSE inférieur). Après avoir apporté des ajustements pour tenir compte des effets de l’âge, du sexe, du revenu familial et de la scolarité, nous avons déterminé grâce à une analyse hiérarchique que la probabilité d’embonpoint augmente si l’enfant vit dans un quartier au SSE faible plutôt que dans un quartier au SSE élevé (RC=1,29, IC de 95 %=1,14-1,46).
Interprétation: La prévalence de l’embonpoint chez les enfants et les jeunes au Canada est inversement proportionnelle au SSE du quartier, et cette relation est statistiquement significative. D’après l’étude des effets indépendants, les caractéristiques du quartier influencent directement la probabilité de faire de l’embonpoint. Notre étude donne à penser qu’il est fondamental, dans les stratégies de promotion de la santé et de prévention des maladies, de tenir compte des structures de possibilités qui existent dans différents quartiers.
Footnotes
Disclaimer: This analysis was based on the Statistics Canada master file NLSCY (Cycle 4) which contains anonymized data collected in 2000/2001. All computations were prepared by Lisa Oliver and conducted at the British Columbia Interuniversity Research Data Centre, University of British Columbia, Vancouver, British Columbia, Canada. The responsibility for the use and interpretation of these data is solely that of the authors. The opinions expressed in this paper are those of the authors and do not represent the views of Statistics Canada.
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