An overview of human biomonitoring of environmental chemicals in the Canadian Health Measures Survey: 2007–2019
Bibliographic record
Abstract
Human biomonitoring (HBM) is used to indicate and quantify exposure by measuring environmental chemicals, their metabolites or reaction products in biological specimens. The biomonitoring component of the Canadian Health Measures Survey (CHMS) is the most comprehensive initiative providing general population HBM data in Canada. The CHMS is an ongoing cross-sectional direct measures survey implemented in 2-year cycles. It provides nationally-representative data on health, nutritional status, environmental exposures, and related risks and protective characteristics. The survey follows a robust planning, design and sampling protocol as well as a comprehensive quality assurance and quality control regime implemented for all aspect of the survey to ensure the validity of the HBM results. HBM blood and urine data are available for CHMS cycles 1 (2007-2009), 2 (2009-2011) and 3 (2012-2013). Field collection has been completed for cycle 4 (2014-2015), with cycle 5 (2016-2017) in progress and cycle 6 planning (2018-2019) being finalized. Biomonitoring results for 279 chemicals are expected over the six cycles of the CHMS (220 in individual blood, urine or hair samples, and 59 in pooled serum samples). The chemicals include metals and trace elements, polychlorinated biphenyls (PCBs), organochlorines, flame retardants, perfluoroalkyl substances, volatile organic compounds (VOCs) and metabolites, environmental phenols, triclocarban, acrylamide, pesticides (e.g., triazines, carbamates, organophosphates, phenoxy, pyrethroids) and/or their metabolites, chlorophenols, polycyclic aromatic hydrocarbon (PAH) metabolites, phthalates and alternate plasticizer metabolites, and tobacco biomarkers. Approximately one half of the chemicals measured in individual blood and urine samples over the first three cycles were detected in more than 60% of samples. CHMS biomonitoring data have been used to establish baseline HBM concentrations in Canadians; inform public health, regulatory risk assessment and management decisions; and fulfil national and international reporting requirements. Concurrent efforts are underway in Canada to develop statistically- and risk-based concepts and tools to interpret biomonitoring data.
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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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.013 | 0.033 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".