Exposure to Pesticides and Breast Cancer in an Agricultural Region in Brazil
Bibliographic record
Abstract
Rural workers are disproportionally exposed to pesticides and might be at an increased risk of developing chronic diseases. Here, we investigated the impact of pesticide exposure on breast cancer (BC) risk and disease profile in rural female workers. This is a case-control study that prospectively included 758 individuals. The study was conducted in the Southwest region of Paraná state in Brazil, a region characterized by family-based agriculture and intensive use of pesticides. We found that this region has a 41% higher BC diagnosis rate and 14% higher BC mortality rate than the mean rates in Brazil, as well as a pesticide trade volume about 6 times higher than the national average. We showed substantial exposure in this population and found that even women who did not work in the fields but performed equipment decontamination and clothes washing of male partners who worked in the fields had urine samples positive for glyphosate, atrazine, and/or 2,4-D. The crude association showed a significantly higher risk of BC among women exposed to pesticides (OR: 1.58, 95% CI 1.18-2.13). Adjusted analyses showed a lower and nonstatistically significant association (OR: 1.30, 95% CI 41 0.87-1.95). Stratification on disease profile showed a significantly higher risk of lymph node metastasis (adjusted OR: 2.19, 95% CI 1.31-3.72) in women exposed to pesticides. Our findings suggest that female populations exposed to pesticides are at a higher risk of developing BC with a more aggressive profile and draw attention to the need to monitor rural populations potentially exposed to pesticides in the field or at home.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".