Abstract
OBJECTIVES: Regional public health policy-makers frequently adopt obesity programs and objectives that have been established at global, provincial/state or national levels. However, the presence of substantial inter-regional disparities could render this practice inefficient. Studies that collectively assess obesity prevalence, temporal trends and their heterogeneity at the region level are rare, though they could be used to support better regional surveillance and planning. To address this gap, our study projected obesity prevalence time series to 2023 for 16 health regions in Quebec. We also compared the extent to which yearly rates of increase (or slope) versus cross-sectional prevalence drove regional heterogeneity and correlated with obesity-related socio-demographic and behavioural characteristics.
METHODS: Projections were done using weighted compositional regression to fit and extrapolate obesity prevalence time series (1987–2012). Heterogeneity in obesity prevalence as a function of time and obesity slope were characterized using standard deviation. The correlation of region-level obesity prevalence and slope with 14 area-level obesity-related characteristics was assessed.
RESULTS: Obesity prevalence is projected to increase in all regions. Region-level heterogeneity in prevalence in 2012 (σ = 2.2%) is projected to increase to (σ = 3.1%) by 2023. The increase in prevalence heterogeneity appeared to be driven by region-level heterogeneity in slope (β = 0.22%–0.51%/year). Obesity-related characteristics were found to be more strongly correlated with slope than with prevalence.
CONCLUSION: Large area obesity trends mask substantial and increasing region-level disparities. Obesity slope appears to drive region-level heterogeneity and correlate strongly with explanatory factors, and may represent a pertinent metric for public health monitoring.
Key Words: Obesity, epidemiology, public health
Résumé
OBJECTIFS: Les décideurs de politiques de santé publique régionaux appliquent souvent des programmes et objectifs relatifs à l’obésité établis à l’échelle provinciale ou nationale. Cependant, d’importantes disparités régionales suggèrent que cette pratique serait inefficace. Les études évaluant collectivement la prévalence, les tendances temporelles et leur hétérogénéité à l’échelle régionale pourraient soutenir les décideurs, mais elles sont peu nombreuses. Afin de répondre à cette lacune, nous avons effectué des projections jusqu’à 2023 sur des séries temporelles de la prévalence de l’obésité pour 16 régions sociosanitaires du Québec. Nous avons aussi comparé l’hétérogénéité régionale pour la pente reliant les prévalences annuelles à celle pour la prévalence annuelle considérée transversalement, ainsi que la corrélation entre chacune de ces mesures et les caractéristiques sociodémographiques et comportementales associées à l’obésité.
MÉTHODES: Les projections ont été faites grâce à la régression compositionnelle pondérée pour ajuster et extrapoler les séries temporelles de la prévalence de 1987–2012. L’ampleur de l’hétérogénéité régionale dans la pente des prévalences annuelles et dans la prévalence annuelle transversale est estimée par l’écart-type. La corrélation entre chacune des mesures de prévalence à l’échelle régionale avec 14 caractéristiques liées avec l’obésité ont été estimées par la méthode de Spearman.
RÉSULTATS: Les analyses de projections suggèrent une augmentation de la prévalence de l’obésité dans toutes les régions étudiées. On projette que l’hétérogénéité régionale de la prévalence augmentera entre 2012 (σ = 2,2 %) et 2023 (σ = 3,1 %). Cette augmentation dans l’hétérogénéité de la prévalence provient de l’hétérogénéité régionale observée avec la pente (β = 0,22 %–0,51 %/année). La corrélation entre les caractéristiques associées à l’obésité était également plus importante avec la pente.
CONCLUSION: Les tendances de l’obésité à grande échelle masquent des disparités importantes et grandissantes à l’échelle régionale. La pente des prévalences annuelles semble révéler le mieux l’hétérogénéité régionale et est fortement corrélée aux caractéristiques associées à l’obésité. Cette mesure pourrait être un indicateur pertinent pour la surveillance et la planification sociosanitaire.
Mots Clés: obésité, épidémiologie, santé publique
Footnotes
Conflict of Interest: None to declare.
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