Universal or Targeted Screening for Fetal Alcohol Exposure: A Cost-Effectiveness Analysis
Bibliographic record
Abstract
OBJECTIVE: In this article, we compared the costs of testing meconium for alcohol exposure in newborns with the lifetime benefits of early detection and intervention. METHOD: A decision analytic model was developed to assess the cost-effectiveness of testing meconium for two scenarios: (1) all infants in the Canadian province of Ontario and (2) infants who have an older sibling diagnosed with fetal alcohol spectrum disorder (FASD). The model incorporated the costs of early screening, early intervention, and the lifetime societal benefits of early intervention. RESULTS: The cost of the meconium test is Can. $150. The lifetime societal cost of the disease is Can. $1.3 million per incident case. The benefit of early intervention is an improvement in literacy, which improves the quality of life parameter by 0.17 and increases adult lifetime earnings by $26,400 per year. The ratio of the incremental cost to the incremental benefits results in an incremental cost-effectiveness ratio for mandating a universal screen of all newborns in Ontario of $65,874 per quality-adjusted life years. When considering targeted screening, there is a cost savings for society and improvements in quality of life. CONCLUSIONS: Depending on society's willingness-to-pay threshold for improving infants' lives in a setting of considerable equity concerns, universal screening and targeted screening of infants who have an older sibling diagnosed with FASD both represent policies that are good value for the money.
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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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".