Occupational insecticide exposure and risk of n<scp>on‐Hodgkin</scp> lymphoma: A pooled c<scp>ase‐control</scp> study from the <scp>InterLymph</scp> Consortium
Bibliographic record
Abstract
Evidence for the human health effects of pesticides is needed to inform risk assessment. We studied the relationship between occupational insecticide use and risk of non-Hodgkin lymphoma (NHL) by pooling data from nine case-control studies participating in the InterLymph Consortium, including 7909 cases and 8644 controls from North America, the European Union and Australia. Insecticide use was coded using self-report or expert assessment, for insecticide groups (eg, organophosphates, pyrethroids) and active ingredients (eg, malathion, permethrin). Associations with insecticides were estimated using logistic regression to produce odds ratios (ORs) and 95% confidence intervals (CI) for all NHL and NHL subtypes, with adjustment for study site, demographic factors and use of other pesticides. Occupational insecticide use, overall, was not associated with risk of NHL. Use of organophosphate insecticides was associated with increased risk of all NHL and the subtype follicular lymphoma, and an association was found with diazinon, in particular (ever use: OR = 2.05, 95%CI: 1.24-3.37). The carbamate insecticide, carbaryl, was associated with risk of all NHL, and the strongest associations were found with T-cell NHL for ever-use (OR = 2.44, 95%CI: 1.13-5.28) and longer duration (>8 years vs never: OR = 2.90, 95%CI: 1.02-8.25). There was no association of NHL with other broad groups of insecticides, including organochlorines and pyrethroids, and some inverse associations were estimated in relation to historical DDT use. Our findings contribute to the totality of evidence available to help inform risk decisions by public health and regulatory agencies of importance given continued, widespread use of organophosphate and carbamate insecticides.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".