Exposure Load: Using biomonitoring data to quantify multi-chemical exposure burden in a population
Bibliographic record
Abstract
People are often concurrently exposed to numerous chemicals. Here we sought to leverage existing large biomonitoring datasets to improve our understanding of multi-chemical exposures in a population. Using nationally-representative data from the 2012-2015 Canadian Health Measures Survey (CHMS), we developed Exposure Load, a metric that counts the number of chemicals measured in people above a defined concentration threshold. We calculated Exposure Loads based on five concentration thresholds: the analytical limit of detection (LOD) and the 50th, 75th, 90th and 95th percentiles. Our analysis considered 44 analyte biomarkers representing 26 chemicals from the 2012-2015 CHMS; complete biomarker data were available for 1858 participants aged 12-79 years following multiple imputation of results that were missing due to sample loss. Chemicals may have one or more biomarkers, and for the purposes of Exposure Load calculation, participants were considered to be exposed to a chemical if at least one biomarker was above the threshold. Distributions of Exposure Loads are reported for the total population, as well as by age group, sex and smoking status. Canadians had an Exposure Load between 9 and 21 (out of 26) when considering LOD as the threshold, with the majority between 13 and 18. At higher thresholds, such as the 95th percentile, the majority of Canadians had an Exposure Load between 0 and 3, although some people had an Exposure Load of up to 15, indicating high exposures to multiple chemicals. Adolescents aged 12-19 years had significantly lower Exposure Loads than adults aged 40-79 years at all thresholds and adults aged 20-39 years at the 50th and 75th percentiles. Smokers had significantly higher Exposure Loads than nonsmokers at all thresholds except the LOD, which was expected given that tobacco smoke is a known source of certain chemicals included in our analysis. No differences in Exposure Loads were observed between males and females at any threshold. These findings broadly suggest that Canadians are concurrently exposed to many chemicals at lower concentrations and to fewer chemicals at high concentrations. They should assist in identifying vulnerable subpopulations disproportionately exposed to numerous chemicals at high concentrations. Future work will use Exposure Loads to identify prevalent chemical combinations and their link with adverse health outcomes in the Canadian population. The Exposure Load concept can be applied to other large datasets, through collaborative efforts in human biomonitoring networks, in order to further improve our understanding of multiple chemical exposures in different populations.
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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 | 0.011 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| 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".