Estimating Exposure to Three Commonly Used, Potentially Carcinogenic Pesticides (Chlorolathonil, 2,4-D, and Glyphosate) Among Agricultural Workers in Canada
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».