The cost-effectiveness of a cash-based transfer, specialised nutritious food, and social and behaviour change communication intervention package to prevent undernutrition among children 6–23 months in Pakistan: A cluster randomised controlled trial
Bibliographic record
Abstract
Background: There is mixed evidence on the cost-effectiveness of cash transfers, along with food supplements and behaviour change communication interventions in improving child nutrition outcomes. To add to existing literature, we examined the cost-effectiveness of medium-quantity lipid-based nutrient supplement (LNS) and social and behaviour change communication (SBCC) messaging, separately and combined, compared to an existing unconditional cash transfers (UCT) programme in children 6-23 months of age in the district Rahim Yar Khan, Pakistan. Methods: This was a four-arm, community-based cluster randomised controlled trial. The UCT provided a quarterly sum of USD 32, the medium-quantity LNS contained a daily ration of 50 g of LNS, and the SBCC included monthly and quarterly messaging on nutrition, health, and hygiene to eligible households. Cost data were collected from a provider perspective through the review of procurement invoices and budgets, as well as interviews with stakeholders. We examined cost-effectiveness via statistically significant differences between the intervention and control arms, and estimated as cost per case of stunting, and disability-adjusted life years (DALYs) averted at six and 18 months of intervention. Results: Costs were higher for SBCC intervention combinations (UCT + SBCC and UCT + LNS + SBCC) due to high training costs for lady health workers. UCT + LNS achieved a reduction in stunting at a per-case cost of USDS 278.74 at six months and USD 897.15 at 18 months. UCT + LNS + SBCC achieved a reduction in stunting at per case cost of USD 846.48 at six months and USD 2324.58 at 18 months. The cost per DALYs averted for preventing stunting was USD 234 to USD 557.42 at six months, and USD 787.73 to USD 1537 at 18 months without discounting and age-weights. Conclusions: Although the affordability of such interventions is arguable, combining UCTs with LNS appears to be very cost-effective for reducing undernutrition and averting DALYs, while combining cash transfers with LNS and SBCC showed limited cost-effectiveness when targeting stunting. Registration: Clinicaltrials.gov: NCT03299218.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".