Economic Analysis of Exclusive Human Milk Diets for High-Risk Neonates, a Canadian Hospital Perspective
Bibliographic record
Abstract
Background: There is increasing evidence that premature newborns and infants with low birth weight can benefit substantially from an exclusive human milk-based diet (EHMD), consisting of human milk supplemented with a pasteurized donor human milk-derived fortifier. However, compared with the standard infant diet, EHMD also represents a significant added cost to the hospital and/or health system, thereby raising important questions about the economic feasibility of incorporating EHMD into newborn care. Design: We conducted a cost analysis and estimated the potential cost savings to a Canadian tertiary hospital based on the attributable complications averted from EHMD among low-weight neonates. A meta-analysis was performed to derive input parameters. A probabilistic analysis was conducted to determine the probability that EHMD is cost saving and 95% confidence interval (CI) around our estimates. Results: Our findings show that providing EHMD to preterm infants under 750 g at birth and at the highest risk of developing major complications is likely to be cost saving in the amount of $107,567 (95% CI: −145,229 to 360,362) per year. Extending EHMD to higher weight classes may be economically feasible depending on the pricing of the human milk-derived fortifier and the baseline risk of complications in the hospital setting. Conclusions: This comprehensive study provides critical insight for hospital-based decision makers to evaluate the potential gains and uncertainties associated with improved nutritional care for neonatal patients.
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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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".