Cost-effectiveness of universal iron supplementation and iron-containing micronutrient powders for anemia among young children in rural Bangladesh: analysis of a randomized, placebo-controlled trial
Bibliographic record
Abstract
BACKGROUND: Universal provision of iron supplements or iron-containing multiple micronutrient powders (MNPs) is widely used to prevent anemia in young children in low- and middle-income countries. The BRISC (Benefits and Risks of Iron Interventions in Children) trial compared iron supplements and MNPs with placebo in children <2 y old in rural Bangladesh. OBJECTIVES: We aimed to assess the cost-effectiveness of iron supplements or iron-containing MNPs among young children in rural Bangladesh. METHODS: We did a cost-effectiveness analysis of MNPs and iron supplements using the BRISC trial outcomes and resource use data, and programmatic data from the literature. Health care costs were assessed from a health system perspective. We calculated incremental cost-effectiveness ratios (ICERs) in terms of US$ per disability-adjusted life-year (DALY) averted. To explore uncertainty, we constructed cost-effectiveness acceptability curves using bootstrapped data over a range of cost-effectiveness thresholds. One- and 2-way sensitivity analyses tested the impact of varying key parameter values on our results. RESULTS: Provision of MNPs was estimated to avert 0.0031 (95% CI: 0.0022, 0.0041) DALYs/child, whereas iron supplements averted 0.0039 (95% CI: 0.0030, 0.0048) DALYs/child, over 1 y compared with no intervention. Incremental mean costs were $0.75 (95% CI: 0.73, 0.77) for MNPs compared with no intervention and $0.64 ($0.62, $0.67) for iron supplements compared with no intervention. Iron supplementation dominated MNPs because it was cheaper and averted more DALYs. Iron supplementation had an ICER of $1645 ($1333, $2153) per DALY averted compared with no intervention, and had a 0% probability of being the optimal strategy at cost-effectiveness thresholds of $200 (reflecting health opportunity costs in Bangladesh) and $985 [half of gross domestic product (GDP) per capita] per DALY averted. Scenario and sensitivity analyses supported the base case findings. CONCLUSIONS: These findings do not support universal iron supplementation or micronutrient powders as a cost-effective intervention for young children in rural Bangladesh. This trial was registered at anzctr.org.au as ACTRN1261700066038 and trialsearch.who.int as U1111-1196-1125.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.010 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| 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".