Cost-effectiveness of specialized trauma care: A systematic review
Bibliographic record
Abstract
ObjectivesSeveral meta-analyses have shown the effectiveness of specialized trauma care, but evidence on cost and cost-effectiveness remains unestablished. We aimed to systematically review evidence on the cost or cost-effectiveness of hospitals specialized in advanced trauma care compared to non or less-specialized hospitals.MethodsWe conducted a systematic review following Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines. We searched PubMed, EMBASE, Cochrane Library, Web-of-Science, EconLit, and grey literature up until June 2024. Two reviewers independently assessed eligibility and extracted relevant data. Reporting quality was assessed using the Consolidated Health Economic Evaluation Reporting Standards (CHEERS) 2022 checklist. Per Cochrane recommendations, findings were synthesized qualitatively.ResultsWe identified 4 cost-effectiveness, 3 cost-consequence, and 3 cost-analysis studies, mostly US-based retrospective cohorts. Reporting quality was rated high for 4 studies. All cost-effectiveness studies found specialized trauma centres to be more costly but more effective than non-specialized centres, with incremental cost-effectiveness ratios ranging from 655 to 46,175 Int.$2022 (2022 international dollars) per quality-adjusted life-year (QALY) gained, 43,208 to 999,912 Int.$2022 per life-saved, and 48,567 Int.$2022 per life-year gained. Among cost-consequence studies, two found specialized trauma centres to be costlier and less effective, while one found the opposite. All cost analyses indicated higher costs at specialized trauma centres.ConclusionsFull economic evaluations identified in this review suggest that specialized trauma care may be cost-effective according to a threshold of $50,000 per QALY. However, our ability to draw conclusions is hampered by the low number of studies, high heterogeneity in study populations and settings, and lack of consideration of trauma systems and of costs beyond the acute phase. Results highlight a critical gap in evidence to guide policymakers in the development of cost-efficient trauma systems.
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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 | 0.015 | 0.066 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.014 | 0.021 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".