Overweight And Obesity In Canada: Understanding The Individual and Socio-environmental Determinants
Notice bibliographique
Résumé
<p> This research examined the geographic variability as well as the individual-and neighbourhood-level determinants of overweight and obesity in Canada. Overweight and obesity represent a significant public health problem with grave implications for individuals as well as populations. Over the past two decades, the prevalence of overweight and obesity has reached epidemic proportions with the most substantial increases observed in economically developed countries. The World Health Organization indicated that globally 1.6 billion adults (age 15+) are overweight and at least 400 million adults were obese. In a Canadian context, recent data from Statistics Canada confirms that over the past twenty-five years, adult obesity rates in Canada have doubled (23% ), while childhood obesity rates have nearly tripled. </p> \n<p> Until recently, research has focused on biological and behavioural determinants of obesity, and currently there is a great deal of knowledge regarding the relationships between weight status and various risk factors at the individual-level (e.g. age, sex, socioeconomic deprivation, diet, physical activity). However, the majority of existing research has ignored the potential role played by the environment in the development of these conditions, despite a growing consensus that environmental and/or societal constraints may be major influences on increasing prevalence rates. </p> \n<p> Using data from the Canadian Community Health Surveys and the Desktop Mapping Information Technologies Incorporated spatial database, this research addressed the following objectives: (I) to examine sex-specific spatial patterns of overweight/obesity in Canada as well as investigate the presence of spatial clusters (2) to investigate the prevalence and determinants of overweight and obesity in Canada using spatial analysis and geographical information systems (GIS) and (3) to identify heterogeneities associated with the relationships between individual and socioenvironmental determinants and overweight and obesity at the individual-and community-levels. </p> \n<p> Results revealed marked geographical variation in overweight/obesity prevalence with higher values in the Northern and Atlantic health-regions and lower values in the Southern and Western health-regions of Canada. Significant positive spatial autocorrelation was found for both males and females, with significant clusters of high values or 'hot spots' of obesity in the Atlantic and Northern health-regions of Alberta, Saskatchewan, Manitoba and Ontario. Results also demonstrate the important role of the built-environment after adjustment demographic, socio-economic and behavioural characteristics. With regard to the built environment measures, landuse mix and residential density were found to be significantly associated with BMI. This study also demonstrated significant differences at the area-level of analysis, supporting related research that has suggested that individual-level factors alone cannot explain variation in obesity rates across space. In particular, average dwelling value was related to BMI independently of individual-level characteristics. Ultimately, this research has demonstrated that Canadian urban environments play a small but significant role in shaping the distribution of BMI. Yet, reversing current trends will require a multifaceted public health approach where interventions are developed from the individual-to the neighbourhood-level, specifically focusing on altering obesogenic environments. </p>
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».