Estimating Exposure to Three Commonly Used, Potentially Carcinogenic Pesticides (Chlorolathonil, 2,4-D, and Glyphosate) Among Agricultural Workers in Canada
Bibliographic record
Abstract
OBJECTIVES: Certain pesticides have been associated with adverse health outcomes including cancer and reproductive harms. However, little is known about the prevalence of occupational pesticide exposure among agricultural workers in Canada. The purpose of this study was to estimate the prevalence and likelihood of occupational exposure to pesticides in Canada's agricultural industry, using three commonly used, potentially carcinogenic pesticides [chlorothalonil, 2,4-dichlorophenoxyacetic acid (2,4-D), and glyphosate] as an example. METHODS: Estimates were calculated using the Canadian Census of Population and the Census of Agriculture. The number of workers and the proportion of farms applying 'herbicides' or 'fungicides' by farm type was estimated using survey data from the Census of Agriculture. These values were multiplied to yield the potential number of workers at risk of exposure. Likelihood of exposure (i.e. exposed, probably exposed, and possibly exposed) was then qualitatively assigned using information on crop type, primary expected tasks, crop production practices, and residue transfer data. Additional agricultural workers who are at risk of exposure but not captured by the Census of Agriculture were identified using the 2016 Census of Population. RESULTS: An estimated range of 37 700-55 800 workers (11-13% of agricultural workers) were exposed to glyphosate in Canada while 30 800-43 600 workers (9-11%) and 9000-14 100 (2.9-3.2%) were exposed to 2,4-D and chlorothalonil, respectively. Approximately 70-75% of workers at risk of exposure were considered probably or possibly exposed to any of the pesticides. Glyphosate exposure was most common among workers in oilseed (29% of oilseed farm workers exposed) and dry pea/bean farms (28%), along with those providing support activities for farms (31%). 2,4-D exposure was most common in corn (28%), other grain (28%), and soybean farms (27%), while chlorothalonil exposure was more likely among greenhouse, nursery, and floriculture workers (42%), workers on farms (28%, for occupations not captured by the Census of Agriculture, specifically), and those providing support activities for farms (20%). Regional variations broadly reflected differences in farm types by province. CONCLUSIONS: This study estimated the prevalence of occupational exposure to three pesticides in Canada. Seasonal and temporary agricultural workers, which were captured by the Census of Agriculture, contributed to many additionally exposed workers. A large percent of the workers who were considered at risk of exposure were considered probably or possibly exposed, indicating a need for enhanced data collection and availability on pesticide use data in Canada. The study's methods can be applied to estimate workers' exposures to other pesticides within the agricultural industry.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".