Fetal alcohol spectrum disorder: a systematic review of the cost of and savings from prevention in the United States and Canada
Bibliographic record
Abstract
BACKGROUND AND AIMS: Fetal alcohol spectrum disorder (FASD) is a preventable condition that imposes a significant financial burden on societies. Funding of FASD prevention is a small portion of the total expenditures associated with FASD. This paper aimed to review the literature on the costs of and savings from prevention of FASD and present a model for the United States and Canada of projected savings based on expansion of existing evidence-based prevention models. METHODS: A systematic review of published literature on the cost of FASD prevention was conducted and experts in the field were interviewed. Studies that reported the cost of primary prevention of FASD were eligible for further consideration. RESULTS: Applying evidenced-based prevention programs to women at highest risk to have a future child with FASD greatly reduces the cost of prevention. In the United States, one case of FASD can be prevented for as little as USD $20 200 - 47 615. Cost of prevention is considerably less expensive than cost of care for a case of FASD. CONCLUSION: Expansion of risk-based prevention strategies for fetal alcohol spectrum disorder in the United States and Canada would be an economically efficient and worthwhile investment for society.
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 | 0.006 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.012 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".