Cost-effectiveness analysis of implementing an integrated neonatal care kit to reduce neonatal infection in rural Pakistan
Bibliographic record
Abstract
OBJECTIVE: To evaluate the cost-effectiveness of distribution of the integrated neonatal care kit (iNCK) by community health workers from the healthcare payer perspective in Rahimyar Khan, Pakistan. SETTING: Rahimyar Khan, Pakistan. PARTICIPANTS: N/A. INTERVENTION: Cost-utility analysis using a Markov model based on cluster randomised controlled trial (cRCT: NCT02130856) data and a literature review. We compared distribution of the iNCK to pregnant mothers to local standard of care and followed infants over a lifetime horizon. PRIMARY AND SECONDARY OUTCOME MEASURES: The primary outcome was incremental net monetary benefit (INMB, at a cost-effectiveness threshold of US$15.50), discounted at 3%. Secondary outcomes were life years, disability-adjusted life years (DALYs) and costs. RESULTS: At a cost-effectiveness threshold of US$15.50, distribution of the iNCK resulted in lower expected DALYs (28.7 vs 29.6 years) at lower expected cost (US$52.50 vs 55.20), translating to an INMB of US$10.22 per iNCK distributed. These results were sensitive to the baseline risk of infection, cost of the iNCK and the estimated effect of the iNCK on the relative risk of infection. At relative risks of infection below 0.79 and iNCK costs below US$25.90, the iNCK remained cost-effective compared with current local standard of care. CONCLUSION: The distribution of the iNCK dominated the current local standard of care (ie, the iNCK is less costly and more effective than current care standards). Most of the cost-effectiveness of the iNCK was attributable to a reduction in neonatal infection.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".