Cost-effectiveness and Clinical Outcomes of Early Anti–Tumor Necrosis Factor–α Intervention in Pediatric Crohn’s Disease
Bibliographic record
Abstract
BACKGROUND: Anti-tumor necrosis factor-α (anti-TNF-α) treatments are increasingly used to treat pediatric Crohn's disease, even without a prior trial of immunomodulators, but the cost-effectiveness of such treatment algorithms has not been formally examined. Drug plan decision-makers require evidence of cost-effectiveness to inform funding decisions. The objective was to assess the incremental cost-effectiveness of early intervention with anti-TNF-α treatment vs a conventional step-up strategy per steroid-free remission-week gained from public health care and societal payer perspectives over 3 years. METHODS: A probabilistic microsimulation model was constructed for children with newly diagnosed moderate to severe Crohn's disease receiving anti-TNF-α treatment and concomitant treatments within the first 3 months of diagnosis compared with children receiving standard care consisting of steroids and/or immunomodulators with the possibility of anti-TNF-α treatment after 3 months of diagnosis. A North American multicenter observational study with 360 patients provided input into clinical outcomes and health care resource use. RESULTS: Early intervention with anti-TNF-α treatment was more costly, with an incremental cost of CAD$31,112 (95% confidence interval [CI], $2939-$91,715), and more effective, with 11.3 more weeks in steroid-free remission (95% CI, 10.6-11.6) compared with standard care, resulting in an incremental cost per steroid-free remission-week gained of CAD$2756 from an Ontario public health care perspective and CAD$2968 from a societal perspective. The incremental cost-effectiveness ratio was sensitive to the price of infliximab. CONCLUSIONS: The results suggest that although early anti-TNF-α was not cost-effective, it was clinically beneficial. These findings, along with other randomized controlled trial evidence, may inform formulary decision-making.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".