Comparison of Published Estimates of the National Prevalence of Iron, Vitamin A, and Zinc Deficiency and Sources of Inconsistencies
Bibliographic record
Abstract
Micronutrient deficiencies result in a broad range of adverse health and functional consequences, but the true prevalence of specific deficiencies remains uncertain because limited information is available from nationally representative surveys using recommended biomarkers. The present review compares various reported national deficiency prevalence estimates for nutrients and years where the estimates overlap for individual countries that conducted nationally representative surveys and explores possible reasons for any discrepancies discovered. Nationally representative micronutrient status surveys that were conducted since 2000 among preschool-aged children or women of reproductive age and included assessment of iron, vitamin A, or zinc status based on recognized biomarkers were considered eligible for inclusion, along with any modeled deficiency prevalence estimates for these same countries and years. There was considerable variation across different published prevalence estimates, with larger inconsistencies when the prevalence estimate was based on proxies, such as hemoglobin for iron deficiency and dietary zinc availability for zinc deficiency. Numerous additional methodological issues affected the prevalence estimates, such as which biomarker and what cutoff was used to define deficiency, whether the biomarker was adjusted for inflammation, and what adjustment method was used. For some country-years, the various approaches resulted in fairly consistent prevalence estimates. For other country-years, however, the results differed markedly and changed the conclusions regarding the existence and severity of the micronutrient deficiency as a public health concern. In conclusion, to determine micronutrient status, we consider the assessment of one of the recommended biomarkers in a population representative survey as the best available information. If indicated, results should be adjusted for inflammation and generally acceptable cutoffs should be applied to facilitate comparisons, although individual countries may also apply nationally defined cutoffs to determine when and where to intervene. Global consensus is needed on best practices for presenting survey results and defining the prevalence of deficiency.
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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 | 0.102 | 0.353 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.025 | 0.022 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".