Budget Impact Analysis of an Epigenetic Test Used for Diagnosing Fetal Alcohol Spectrum Disorder from the Perspective of a Laboratory Budget Holder in Manitoba, Canada
Bibliographic record
Abstract
BACKGROUND AND AIMS: Fetal alcohol spectrum disorder (FASD) is a condition that results from prenatal alcohol exposure. Though diagnosis is important for individuals with FASD to receive appropriate care, diagnosis can be difficult to obtain. Accurate diagnoses can be impeded because of an inability to confirm prenatal alcohol exposure. This is particularly problematic in instances when family cannot confirm prenatal alcohol exposure. DNA methylation testing represents a novel approach to identifying prenatal alcohol exposure via epigenetic biomarkers. The objective was to assess the impact on laboratory expenditures from adopting DNA methylation additively to the diagnostic workup for patients suspected of having FASD for whom prenatal alcohol exposure cannot be otherwise confirmed. METHODS: A budget impact model was developed that incorporates laboratory cost data, population data for Manitoba Canada, literature, and expert opinion. Probabilistic analysis was conducted for the primary analysis and deterministic sensitivity analyses were conducted to assess the sensitivity of the budget impact to changes in model parameters. The perspective of the present study is that of the laboratory budget holder at a centralized laboratory in Manitoba, Canada. RESULTS: Over a 5-year period, it was estimated that there would be 500 DNA methylation tests and the predicted budget impact to the laboratory budget holder was $207,574 (95% credible interval: 70,208-408,161) in Canadian dollars (CAD). Over a 10-year period, it was estimated that there would be 1017 DNA methylation tests and the predicted budget impact to the laboratory budget holder was CAD$439,470 (95% credible interval: 148,902-867,328). CONCLUSIONS: Findings provide insight into the impact that DNA methylation testing would have on laboratory budgets if used in the diagnostic workup for FASD in individuals for whom prenatal alcohol exposure cannot be confirmed otherwise.
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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 | 0.008 | 0.018 |
| 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 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".