The cost-effectiveness of neonatal screening for cystic fibrosis: an analysis of alternative scenarios using a decision model.
Bibliographic record
Abstract
BACKGROUND: The use of neonatal screening for cystic fibrosis is widely debated in the United Kingdom and elsewhere, but the evidence available to inform policy is limited. This paper explores the cost-effectiveness of adding screening for cystic fibrosis to an existing routine neonatal screening programme for congenital hypothyroidism and phenylketonuria, under alternative scenarios and assumptions. METHODS: The study is based on a decision model comparing screening to no screening in terms of a number of outcome measures, including diagnosis of cystic fibrosis, life-time treatment costs, life years and QALYs gained. The setting is a hypothetical UK health region without an existing neonatal screening programme for cystic fibrosis. RESULTS: Under initial assumptions, neonatal screening (using an immunoreactive trypsin/DNA two stage screening protocol) costs 5,387 pound sterling per infant diagnosed, or 1.83 pound sterling per infant screened (1998 costs). Neonatal screening for cystic fibrosis produces an incremental cost-effectiveness of 6,864 pound sterling per QALY gained, in our base case scenario (an assumed benefit of a 6 month delay in the emergence of symptoms). A difference of 11 months or more in the emergence of symptoms (and mean survival) means neonatal screening is both less costly and produces better outcomes than no screening. CONCLUSION: Neonatal screening is expensive as a method of diagnosis. Neonatal screening may be a cost-effective intervention if the hypothesised delays in the onset of symptoms are confirmed. Implementing both antenatal and neonatal screening would undermine potential economic benefits, since a reduction in the birth incidence of cystic fibrosis would reduce the cost-effectiveness of neonatal screening.
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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 | 0.011 | 0.025 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".