Global evaluation of the impact of food fortification with folic acid on rates of schizophrenia
Bibliographic record
Abstract
BACKGROUND: Low folate status is one of the multiple factors thought to contribute to the development of schizophrenia. As of 2023, over 70 countries have implemented mandatory fortification of foods with folic acid, a public health measure aimed at reducing neural tube defects; however, the impact of such policy on schizophrenia has not been comprehensively investigated. METHOD: We assessed the impact of mandatory folic acid fortification on changes in the schizophrenia rates in 194 jurisdictions between 1990 and 2019 using publicly available data. We used weighted regression models adjusted for sociodemographic and sociopolitical factors, experience of natural disasters, and baseline schizophrenia rate. RESULTS: Age-adjusted prevalence and incidence of schizophrenia increased marginally between 1990 and 2019. In all geographic regions, schizophrenia prevalence and incidence per 100,000 positively correlated with countries' sociodemographic index and were lower with fortification. Schizophrenia burdens were higher among males compared to females. Lower prevalence and incidence of schizophrenia were associated with having mandatory fortification with modest magnitudes. Duration of fortification or the fortification dose did not appear to have a strong impact. However, in the 15-39 year age-group, both mandatory fortification (β = -13·14 (-22·60, -3·68)) and duration of fortification (β = -0·82 (-1·40, -0·23)) were significantly associated with lower schizophrenia with larger magnitude in both sexes. The highest dose tertile was reported to have the lowest incidence and the smallest increase in prevalence in this age-group. CONCLUSION: Folic acid fortification may be a beneficial intervention in lowering schizophrenia among adolescents and young adults.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".