Folic acid fortification and late‐onset colorectal cancer risk: A systematic assessment of the worldwide evidence
Bibliographic record
Abstract
Concerns have been raised about potential hazards associated with folic acid fortification. This study aimed to explore associations between diverse folic acid fortification policies (mandatory vs. no mandatory fortification) and global late-onset colorectal cancer (LOCRC) incidence rates. The study systematically assessed (i) folic acid fortification policies in 193 member states of the World Health Organization, and (ii) age-standardized LOCRC incidence rates by country. We examined the associations between folic acid fortification types and LOCRC incidence using an ecological study design. Incidence trends before and after fortification were analyzed using a log-linear joinpoint regression model, and the annual percent change and average annual percent change with 95% confidence interval were determined for representative countries with mandatory fortification (the United States [U.S.] and Canada). By September 2024, 69 countries enacted mandatory folic acid fortification legislation, while 124 had no fortification. The overall LOCRC incidence rates per 100,000 were 70.8 and 84.0 with mandatory and no mandatory fortification, respectively. The decreasing trends after implementing folic acid fortification were more rapid than in the pre-fortification period in the U.S. and Canada. These findings suggested an association between mandatory folic acid fortification policies and reduced LOCRC incidence. These global data provide a scientific basis for transitioning fortification policies and inform strategies for cancer prevention through the fortification with folic acid.
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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 | 0.017 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".