Vitamin A Supplementation Programs and Country-Level Evidence of Vitamin A Deficiency
Bibliographic record
Abstract
Vitamin A supplementation (VAS) programs targeted at children aged 6-59 months are implemented in many countries. By improving immune function, vitamin A (VA) reduces mortality associated with measles, diarrhea, and other illnesses. There is currently a debate regarding the relevance of VAS, but amidst the debate, researchers acknowledge that the majority of nationally-representative data on VA status is outdated. To address this data gap and contribute to the debate, we examined data from 82 countries implementing VAS programs, identified other VA programs, and assessed the recentness of national VA deficiency (VAD) data. We found that two-thirds of the countries explored either have no VAD data or data that were >10 years old (i.e., measured before 2006), which included twenty countries with VAS coverage ≥70%. Fifty-one VAS programs were implemented in parallel with at least one other VA intervention, and of these, 27 countries either had no VAD data or data collected in 2005 or earlier. To fill these gaps in VAD data, countries implementing VAS and other VA interventions should measure VA status in children at least every 10 years. At the same time, the coverage of VA interventions can also be measured. We identified three countries that have scaled down VAS, but given the lack of VA deficiency data, this would be a premature undertaking in most countries without appropriate status assessment. While the global debate about VAS is important, more attention should be directed towards individual countries where programmatic decisions are made.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.022 | 0.072 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".