Cost effectiveness of newborn screening for cystic fibrosis: A simulation study
Bibliographic record
Abstract
BACKGROUND: Early detection of cystic fibrosis (CF) by newborn screening (NBS) reduces the rate of avoidable complications. NBS protocols vary by jurisdiction and the cost effectiveness of these different protocols is debated. OBJECTIVE: To compare the cost effectiveness of various CF NBS options. METHODS: A Markov model was built to simulate the cost effectiveness of various CF-NBS options for a hypothetical CF-NBS program over a 5-year time horizon assuming its integration into an existing universal NBS program. NBS simulated options were based on a combination of tests between the two commonly used immunoreactive trypsinogen (IRT) cutoffs (96th percentile and 99.5th percentile) as first tier tests, and, as a second tier test, either a second IRT, pancreatic-associated protein (PAP) or CFTR mutation panels. CFTR mutation panels were also considered as an eventual third tier test. Data input parameters used were retrieved from a thorough literature search. Outcomes considered were the direct costs borne by the Quebec public health care system and the number of cases of CF detected through each strategy, including the absence of screening option. RESULTS: IRT-PAP with an IRT cutoff at the 96th percentile is the most favorable option with a ratio of CAD$28,432 per CF case detected. The next most favorable alternative is the IRT1-IRT2 option with an IRT1 cutoff at the 96th percentile. The no-screening option is dominated by all NBS screening protocols considered. Results were robust in sensitivity analyses. CONCLUSION: This study suggests that NBS for cystic fibrosis is a cost-effective strategy compared to the absence of NBS. The IRT-PAP newborn screening algorithm with an IRT cutoff at the 96th percentile is the most cost effective NBS approach for Quebec.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".