Regional variation in cost of neonatal intensive care for extremely preterm infants
Bibliographic record
Abstract
Abstract Background Regional variation in cost of neonatal intensive care for extremely preterm infant is not documented. We sought to evaluate regional variation that may lead to benchmarking and cost saving. Methods An analysis of a Canadian national costing data from the payor perspective. We included all liveborn 23–28-week preterm infants in 2011–2015. We calculated variation in costs between provinces using non-parametric tests and a generalized linear model to evaluate cost variation after adjustment for gestational age, survival, and length of stay. Results We analysed 6932 infant records. The median total cost for all infants was $66,668 (Inter-Quartile Range (IQR): $4920–$125,551). Medians for the regions varied more than two-fold and ranged from $48,144 in Ontario to $122,526 in Saskatchewan. Median cost for infants who survived the first 3 days of life was $91,000 (IQR: $56,500–$188,757). Median daily cost for all infants was $1940 (IQR: $1518–$2619). Regional variation was significant after adjusting for survival more than 3 days, length of stay, gestational age, and year (pseudo-R2 = 0.9,p < 0.01). Applying the model on the second lowest-cost region to the rest of the regions resulted in a total savings of $71,768,361(95%CI: $65,527,634–$81,129,451) over the 5-year period ($14,353,672 annually), or over 11% savings for the total program cost of $643,837,303 over the study period. Conclusion Costs of neonatal intensive care are high. There is large regional variation that persists after adjustment for length of stay and survival. Our results can be used for benchmarking and as a target for focused cost optimization, savings, and investment in healthcare.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".