Association between Activity Space Exposure to Food Establishments and Individual Risk of Overweight
Notice bibliographique
Résumé
OBJECTIVE: Environmental exposure to food sources may underpin area level differences in individual risk for overweight. Place of residence is generally used to assess neighbourhood exposure. Yet, because people are mobile, multiple exposures should be accounted for to assess the relation between food environments and overweight. Unfortunately, mobility data is often missing from health surveys. We hereby test the feasibility of linking travel survey data with food listings to derive food store exposure predictors of overweight among health survey participants. METHODS: Food environment exposure measures accounting for non-residential activity places (activity spaces) were computed and modelled in Montreal and Quebec City, Canada, using travel surveys and food store listings. Models were then used to predict activity space food exposures for 5,578 participants of the Canadian Community Health Survey. These food exposure estimates, accounting for daily mobility, were used to model self-reported overweight in a multilevel framework. Median Odd Ratios were used to assess the proportion of between-neighborhood variance explained by such food exposure predictors. RESULTS: Estimates of food environment exposure accounting for both residential and non-residential destinations were significantly and more strongly associated with overweight than residential-only measures of exposure for men. For women, residential exposures were more strongly associated with overweight than non-residential exposures. In Montreal, adjusted models showed men in the highest quartile of exposure to food stores were at lesser risk of being overweight considering exposure to restaurants (OR = 0.36 [0.21-0.62]), fast food outlets (0.48 [0.30-0.79]), or corner stores (0.52 [0.35-0.78]). Conversely, men experiencing the highest proportion of restaurants being fast-food outlets were at higher risk of being overweight (2.07 [1.25-3.42]). Women experiencing higher residential exposures were at lower risk of overweight. CONCLUSION: Using residential neighbourhood food exposure measures may underestimate true exposure and observed associations. Using mobility data offers potential for deriving activity space exposure estimates in epidemiological models.
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 | 0,001 | 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 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 ».