The cost of lost productivity due to fetal alcohol spectrum disorder-related premature mortality.
Bibliographic record
Abstract
BACKGROUND: Individuals with Fetal Alcohol Spectrum Disorder (FASD) have increased mortality as compared to the general population. OBJECTIVES: To estimate the productivity losses due to premature mortality of individuals with FASD in Canada in 2011. METHODS: A demographic approach with a counterfactual scenario in which nobody in Canada is born with FASD was used. Population estimates were calculated using data on the labour force, unemployment rate, and average weekly wage obtained from Statistics Canada. The number of FASD-related deaths, coded in the International Classification of Diseases, version 10, was estimated based on data from Statistics Canada and pooled prevalence estimates of the major disease conditions associated with FASD were obtained from a meta-analysis. The estimates of FASD-related mortality rates served as a basis for the length of working life span estimation. Once the number of working years lost to premature deaths was derived, productivity losses were computed. RESULTS: It was estimated that in total 327 individuals with FASD aged 20 to 69 (almost twice as many men as women) died in Canada in 2011. As a result, there were 2,877 years of potential employment lost, which translated to a loss ranging from $88 million to $126 million. This amount represents the increase in national income, had there been no premature mortality from FASD and the workers with FASD had been typical members of the labour force (without compromised productivity due to FASD). CONCLUSIONS: The estimates of productivity losses further reinforce the value of FASD prevention as a primary strategy.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".