Toward an Economic Analysis of the Environmental Burden of Disease Among Canadian Children
Bibliographic record
Abstract
There have been calls for increased investments in research in Canada to determine the extent of exposure and the associated health effects of environmental risks to child health. When allocating scare public health resources, decision makers often rely on cost-benefit analysis to determine whether specific expenditures will yield significant economic benefits by reducing adverse health outcomes. This article describes the elements required for an economic analysis of the environmental burden of disease among Canadian children. Such analysis would require reviewing the strength of the association between environmental exposures and specific adverse pregnancy outcomes and childhood diseases. Second, it would determine the prevalence of childhood diseases and conditions in order to estimate the total economic and social costs associated with the overall burden of childhood diseases in Canada. The next step is to determine how much of the overall burden of disease among Canadian children can be attributed to environmental exposures. Recent environmental burden of disease analyses in other jurisdictions have led to advancement in methodologies that could support this work in Canada. Finally, the economic and social costs attributable to the environmental burden of childhood diseases in Canada could be estimated. Estimates of the economic costs of environmentally related diseases can then be used to determine the appropriate level of investments in scientific research. This article argues for initial investments in establishing a biomonitoring program of children's and pregnant women's exposure to environmental chemicals, as well as a Canadian longitudinal cohort study on the environmental influences on child health and development.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 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".