The Economic Burden of Fetal Alcohol Spectrum Disorder in Canada in 2013
Bibliographic record
Abstract
AIM: To estimate the economic burden and cost attributable to Fetal Alcohol Spectrum Disorder (FASD) in Canada in 2013. METHODS: This cost-of-illness study examined the impact of FASD on the material welfare of the Canadian society in 2013 by analyzing the direct costs of resources expended on health care, law enforcement, children and youth in care, special education, supportive housing, long-term care, prevention and research, as well as the indirect costs of productivity losses of individuals with FASD due to their increased morbidity and premature mortality. RESULTS: The costs totaled approximately $1.8 billion (from about $1.3 billion as the lower estimate up to $2.3 billion as the upper estimate). The highest contributor to the overall FASD-attributable cost was the cost of productivity losses due to morbidity and premature mortality, which accounted for 41% ($532 million-$1.2 billion) of the overall cost. The second highest contributor to the total cost was the cost of corrections, accounting for 29% ($378.3 million). The third highest contributor was the cost of health care at 10% ($128.5-$226.3 million). CONCLUSIONS: FASD is a significant public health and social problem that consumes resources, both economic and societal, in Canada. Many of the costs could be reduced with the implementation of effective social policies and intervention programs.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".