Hereditary hemochromatosis screening: effect of mutation penetrance and prevalence on cost‐effectiveness of testing algorithms
Bibliographic record
Abstract
Screening for hereditary hemochromatosis, although largely discussed, is not yet implemented in clinical practice. We evaluated the cost-effectiveness of 165 hemochromatosis population-screening algorithms involving two or three of several screening tests by developing a computer program that simulates all possible screening scenarios. Input data comprised government estimates of health services data and costs and a virtual population with user-defined demographic characteristics (including variable HFE mutation frequencies and penetrance values). We show that when C282Y homozygote prevalence is set at 3:1000, population screening appears cost-effective when penetrance of the biochemical phenotype is >0.70. When only hepatocellular carcinoma and cirrhosis are considered as the cost-driving complications, population-based screening is not significantly more cost-efficient than no screening, but life expectancy of individuals identified with hereditary hemochromatosis and treated is still improved by 7 years. Among the 165 screening algorithms tested in 91 different virtual populations of one million individuals, biochemical tests usually perform better as the initial test than genetic testing. Indeed, the genetic testing is most cost-effective as the final confirmatory test. Finally, for most combinations of prevalence and penetrance of HFE, one screening algorithm--unbound iron-binding capacity + transferrin saturation--appeared robust enough to be always within the top 5 most cost-effective strategies.
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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 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".