Obesity and the Incidence of Upper Gastrointestinal Cancers: An Ecological Approach to Examine Differences across Age and Sex
Bibliographic record
Abstract
BACKGROUND: Esophageal and gastric cancers differ in their epidemiology but have several risk factors in common. The aim of this study was to assess age and sex differences in the burden of esophageal and gastric cancers in the context of the global obesity epidemic. METHODS: Data from 50 countries were obtained from Cancer Incidence in Five Continents Volume X and GLOBOCAN 2012. Age-specific and age-standardized incidence rates of esophageal adenocarcinoma and squamous cell carcinoma (ESCC), as well as cardia (CGC) and noncardia (NCGC) gastric cancer, were estimated. Countries were grouped and analyzed according to their obesity prevalence. RESULTS: A gradient across quartiles of obesity prevalence was found for esophageal adenocarcinoma, with the highest incidence rates in high prevalence countries (ASR 3.0 vs. 0.8 per 100,000 in highest vs. lowest obesity quartiles, males). In contrast, for ESCC as well as for CGC and NCGC the reverse was true, with the highest rates observed in countries with the lowest obesity prevalence (ESCC, 2.2 vs. 11.5; CGC, 2.8 vs. 7.8; NCGC, 3.9 vs. 17.4 in highest vs. lowest obesity quartiles, males). Although for esophageal adenocarcinoma, sex and age differences in incidence were most pronounced in countries with a high prevalence of obesity, these differences were much smaller for the other cancer sites assessed. CONCLUSIONS: Variation in obesity prevalence may partly explain age and sex differences in the incidence of esophageal adenocarcinomas. IMPACT: Ecologic studies can help assess relationships between risk factors and cancer, and generate new hypotheses that may be pursued through more directed research.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".