The Fallacy of Using Administrative Data in Assessing the Effectiveness of Food Fortification. Comment on: “Folic Acid Fortification and Neural Tube Defect Risk: Analysis of the Food Fortification Initiative Dataset. Nutrients 2020, 12, 247”
Bibliographic record
Abstract
Our objective in this comment is to highlight several limitations in an ecological research study that was published in Nutrients by Murphy and Westmark (2020) in January 2020. The study used data from the Food Fortification Initiative (FFI) website, and applying an ecological study design, made an error of “ecologic fallacy” in concluding that “national fortification with folic acid is not associated with a significant decrease in the prevalence of neural tube defects (NTDs) at the population level”. We list study limitations that led to their erroneous conclusions, stemming from incorrect considerations regarding NTD prevalence, the average grain availability for a country, the fortification coverage in a country, the population reach of fortified foods within a country, and the absence of the consideration of fortification type (voluntary vs. mandatory), country-specific policies on elective terminations for NTD-affected pregnancies, stillbirth proportions among those with NTDs, and fortification implementation. FFI data are derived from many sources and intended for fortification advocacy, not for hypothesis testing. The flawed study by Murphy & Westmark (2020) in Nutrients promotes a confusing and incorrect message to stakeholders, misguides policy makers, and hinders progress in global NTD prevention through a cost-effective, safe, and effective intervention: the mandatory large-scale folic acid fortification of staple foods.
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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 | 0.101 | 0.417 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.008 | 0.004 |
| Research integrity | 0.017 | 0.021 |
| Insufficient payload (model declined to judge) | 0.006 | 0.005 |
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".