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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".