A cost-utility analysis of newborn screening for spinal muscular atrophy in Canada
Bibliographic record
Abstract
Abstract Background Spinal muscular atrophy (SMA) is a neuromuscular disorder caused by the loss of the SMN1 gene, with an estimated birth prevalence of about 1 in 10,000. Early intervention with disease-modifying therapies (DMTs) significantly improves outcomes. This study evaluates the economic implications and health benefits of newborn screening (NBS) for SMA in Canada from the societal perspective. Methods A decision analytic model was developed, which combined a decision tree for the screening algorithm and a Markov model for long-term health outcomes. The Markov model included health states based on WHO motor milestones. The population cohort of 357,903 live newborns reflects the 2022–2023 births in Canada. Screening is performed on dried blood spot testing which evaluates for biallelic deletions in SMN1. Cost inputs encompassed treatment and health state costs, while utility values reflected quality of life in each health state. Results NBS for SMA is expected to identify 37.1 (95% CI: 15.0, 70.7) newborns annually in Canada. Our analysis over a lifetime horizon and a discount rate of 1.5% shows NBS and early treatment has an incremental cost of -$146,187,000 (95% CI: -249,773,777 to − 17,890,034) and incremental benefit of 872 (95% CI: -193, 2329) quality-adjusted life years (QALYs) compared to no NBS and late treatment. This resulted in a mean ICER value of -$173,572/QALY. Conclusion The decision analytic model indicated that overall NBS is cost-saving and more effective than no NBS and late treatment in the Canadian health system.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".