Abstract B008: Neighborhood fast food, alcohol, and fruit/vegetable expenditure and early-onset colorectal cancer: A statewide spatio-ecological analysis in Alabama
Notice bibliographique
Résumé
Abstract Introduction: U.S. incidence of early-onset colorectal cancer (eoCRC) has increased by 50% since the mid-1990s, yet underlying causes remain poorly understood. Shifts in food consumption patterns and westernized diets have been postulated as contributors. This study utilizes unique neighborhood-level food expenditure data and cancer registry data to examine relationships between fast food, alcohol, and fruit/vegetable expenditures, and eoCRC incidence. Methods: Colorectal cancer incidence data from 2010-2019 was obtained from the Alabama Statewide Cancer Registry. Annual average fast food, alcohol, fruit/vegetable, and total food/beverage expenditure data by block group were sourced from 2025 Esri data, based on U.S. Bureau of Labor Statistics consumer expenditure surveys. Model covariates included area deprivation index, percentages of non-Hispanic Black, Hispanic, female, and uninsured residents (2019 American Community Survey 5-year estimates), and 2010 USDA rural-urban commuting area codes. The primary outcome was the ratio of eoCRC cases (<50 years at diagnosis) to average-onset colorectal cancer (aoCRC) cases (50+ years at diagnosis) across 3,357 block groups in Alabama. Primary predictors (fast food, alcohol, fruit/vegetable expenditure) were converted to proportions of the total food/beverage expenditure per block group. A hierarchical Bayesian spatial hurdle model was fit, where inverse-variance weighting was employed to synthesize fixed effects. The same analyses were performed for late-stage CRC (regional/distant SEER Summary Stages). Results: Of 24,926 new CRC cases in Alabama from 2010-2019, 12,727 were late-stage cases, 2,607 were eoCRC, and 1,596 were late-stage eoCRC. 61.2% of eoCRC cases were diagnosed at late stages, while 49.8% of aoCRC cases were late stage. Across Alabama block groups, an annual mean of 17.3% of food/beverage expenditures were allocated to fast food, 10.8% to fruits/vegetables, and 5.2% to alcohol. A 1% increase in mean fast food expenditure per block group was associated with a 14.25% increase (all stage; 95% Credible Interval: 9.72% to 18.75%) and 5.23% increase (late stage; 95% CrI: 3.03% to 11.16), respectively, in the proportion of cases that were eoCRC rather than aoCRC. In contrast, a 1% increase in mean fruit/vegetable expenditure corresponded to a 17.72% decrease (all stage; 95% CrI: -31.20% to -4.33%) and 33.77% decrease (late stage; 95% CrI: -50.95% to -16.58%), respectively, in eoCRC cases. Alcohol expenditure was not significantly associated with an increased or decreased share of all- or late-stage eoCRC cases. Conclusions: Higher fast food expenditure at the neighborhood level was strongly associated with a greater proportion of both all-stage and late-stage eoCRC in Alabama, while higher fruit/vegetable spending appeared strongly protective. These findings provide preliminary ecological evidence that westernized dietary patterns may contribute to increased eoCRC risk. Further individual-level research is warranted to better understand how specific food exposures influence eoCRC risk. Citation Format: R. Blake. Buchalter, S.M. Qasim. Hussaini, Mahak Bhargava, Geetanjali Saini, Nashira I. Brown, Mackenzie E. Fowler, Ritu Aneja. Neighborhood fast food, alcohol, and fruit/vegetable expenditure and early-onset colorectal cancer: A statewide spatio-ecological analysis in Alabama [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: The Rise in Early-Onset Cancers—Knowledge Gaps and Research Opportunities; 2025 Dec 10-13; Montreal, QC, Canada. Philadelphia (PA): AACR; Clin Cancer Res 2025;31(23_Suppl):Abstract nr B008.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 source (Gemma direct ou Codex distillé), 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 ».