Food Fortification and Decline in the Prevalence of Neural Tube Defects: Does Public Intervention Reduce the Socioeconomic Gap in Prevalence?
Bibliographic record
Abstract
OBJECTIVE: A significant decline in the prevalence of neural tube defects (NTD) through food fortification has been reported. Questions remain, however, about the effectiveness of this intervention in reducing the gap in prevalence across socioeconomic status (SES). STUDY DESIGN: Using health number and through record linkage, children born in Ontario hospitals between 1994 and 2009 were followed for the diagnosis of congenital anomalies. SES quintiles were assigned to each child using census information at the time of birth. Adjusted rates and multivariate models were used to compare trends among children born in different SES groups. RESULTS: Children born in low SES areas had significantly higher rates of NTDs (RR = 1.25, CI: 1.14-1.37). Prevalence of NTDs among children born in low and high SES areas declined since food fortification began in 1999 although has started rising again since 2006. While the crude decline was greater in low SES areas, after adjustment for maternal age, the slope of decline and SES gap in prevalence rates remained unchanged overtime. CONCLUSIONS: While food fortification is successful in reducing the prevalence of NTDs, it was not associated with removing the gap between high and low SES groups.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".