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Abstract B008: Neighborhood fast food, alcohol, and fruit/vegetable expenditure and early-onset colorectal cancer: A statewide spatio-ecological analysis in Alabama

2025· article· en· W4417201107 on OpenAlexaboutno aff
R. Blake Buchalter, S. M. Qasim Hussaini, Mahak Bhargava, Geetanjali Saini, Nashira I. Brown, Mackenzie E. Fowler, Ritu Aneja

Bibliographic record

VenueClinical Cancer Research · 2025
Typearticle
Languageen
FieldMedicine
TopicCardiovascular Health and Risk Factors
Canadian institutionsnot available
Fundersnot available
KeywordsColorectal cancerCancer registryIncidence (geometry)CancerCancer incidenceCovariateWeightingConsumption (sociology)

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.383
Threshold uncertainty score0.761

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.091
GPT teacher head0.470
Teacher spread0.379 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2025
Admission routes1
Has abstractyes

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