Iron deficiency screening for children at 18 months: a cost-utility analysis
Bibliographic record
Abstract
BACKGROUND: The peak prevalence of iron deficiency is in children 6 months to 3 years of age, a sensitive period for neurodevelopment. Our study objective was to examine the cost-utility of a proposed iron deficiency screening program for 18-month-old children. METHODS: We used a decision tree model to estimate the costs in 2019 Canadian dollars and quality-adjusted life years (QALYs) associated with 3 iron deficiency screening strategies: no screening, universal screening and targeted screening for a high-risk population. We used a societal perspective and assessed lifetime QALY gains. We derived outcomes from the literature and prospectively collected data. We performed one-way and probabilistic sensitivity analyses to assess parameter uncertainty. RESULTS: The incremental costs to society of universal and targeted screening programs compared to no screening were $2286.06/QALY and $1676.94/QALY, respectively. With a willingness-to-pay threshold of $50 000/QALY, both programs were cost-effective. Compared to a targeted screening program, a universal screening program would cost an additional $2965.96 to gain 1 QALY, which renders it a cost-effective option. The study findings were robust to extensive sensitivity analyses. INTERPRETATION: A proposed universal screening program for iron deficiency would be cost-effective over the lifespan compared to both no screening (current standard of care) and a targeted screening program for children at high risk. Policy-makers and physicians may consider expanding the recommended 18-month enhanced well-baby visit to include screening for iron 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 | 0.008 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".