An analysis of complementary products associated with unhealthy food purchases using household grocery sales data in Montréal, Canada
Notice bibliographique
Résumé
BackgroundConsumption of soft drinks and snack food contributes to the increasing global incidence of chronic illnesses such as cardiovascular diseases and type II diabetes. Previous studies addressing the purchasing patterns of such foods have emphasized the importance of complementary and co-occurring products, as both may undermine interventions seeking to limit the intake of unhealthy foods. However, research using household-level data to analyze such patterns in purchasing has traditionally been limited in terms of volume, objectivity, and representativeness. Moreover, few studies have searched explicitly for co-purchasing of soft drinks and snack foods in the same basket.Research objectiveThe objective of this study is to identify patterns in food categories purchased by households together with, or complementary to, soft drinks and snack foods as well as fresh fruits and vegetables.MethodsWe used longitudinal, household-level transaction data from 14,999 loyalty card members of a large grocery retailer in Montréal, Canada between February 2015 and September 2017 (1,522,501 transactions). Association rule mining was used to identify frequently co-purchased item categories for soft drinks, snack foods, juice, fruits, and vegetables.ResultsTransactions (baskets) containing snack foods and soft drinks were also likely to contain canned or highly-processed foods. For example, soft drinks were highly associated with salty snacks (confidence: 17%; odds ratio: 1.82 ± 0.02), bottled water (confidence: 16%, odds ratio: 1.77 ± 0.02), and frozen meals and sides (confidence: 16%; odds ratio: 1.78 ± 0.03). Conversely, purchases with qualitatively healthier foods were found to be associated with purchases of fruits and vegetables: purchases with vegetables were highly associated with fresh herbs (confidence: 84%; odds ratio: 1.90 ± 0.03) and packaged salads (confidence: 73%; odds ratio: 1.61 ± 0.01).ConclusionsThese empirical results quantify the extent to which healthy and unhealthy food-purchasing behaviours cluster within baskets. Public health practitioners seeking to design interventions that decrease the frequency of soft drink and snack food purchases in the grocery retail environment should consider the tendency for multiple unhealthy foods to be purchased concurrently. While loyalty card data do not capture the entirety of a household’s food purchasing behaviour, they represent objective and proximal outcomes to dietary patterns and should therefore be used alongside more traditional means of dietary assessment
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) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,001 | 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 ».