OP43 Incorporating Mathematical Modeling To Improve Accuracy Of Budget Impact Analysis: Using Screening For Hepatitis C As An Example
Bibliographic record
Abstract
Introduction The majority of those infected with chronic hepatitis C (CHC) are asymptomatic. Population screening has proven to be both effective and cost effective. When considering whether to implement screening or not, the uncertainty of the budget impact plays an important role. This study aims to develop methods that improve the accuracy of budget impact analysis for a one-time CHC screening program. Methods We developed a back-calculation mathematical model that employs a Markov chain Monte Carlo algorithm to estimate the prevalence and proportion of undiagnosed CHC. Subsequently, we utilized a state-transition model to assess the budget impact of two strategies: (i) no screening; and (ii) screen-and-treat with direct-acting antiviral (DAA) for individuals born between 1945 and 1965 (“baby-boomer” birth cohort). Model data were gathered from published literature. Our analysis adopted a Canadian provincial payer perspective, employed a 10-year time horizon, and followed best-practice recommendations by not applying discounting. Results For individuals born between 1945 and 1965, the estimated prevalence of CHC was 1.74 percent (95% confidence interval [CI]: 1.52, 2.30) with an undiagnosed proportion of 15.72 percent (95% CI: 11.27, 18.54). The initial budget impact analysis indicated an additional cost of CAD61.5 million (USD45.0 million) over 10 years for screening related individuals for CHC in Ontario. With these updated prevalence and undiagnosed proportion estimates, our projection suggests a 29.6 percent reduction in the budget impact, now estimated at CAD43.3 million (USD31.7 million). Conclusions By comparing the budget impact of the CHC screening strategy with other recommended health services and technologies in Ontario, we have concluded that CHC screening may be considered affordable. To enhance the accuracy of budget impact analysis for population-level screening decision-making, it is crucial to develop precise methodologies for estimating the underlying prevalence and undiagnosed proportions.
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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 | 0.007 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".