Urinary Cotinine Levels Among Latino Tobacco Farmworkers in North Carolina Compared to Latinos Not Employed in Agriculture
Bibliographic record
Abstract
INTRODUCTION: This analysis describes urinary cotinine levels of North Carolina Latino farmworkers, compares cotinine levels of farmworkers to those of Latinos non-farmworkers, determines factors associated with farmworker cotinine levels, and determines if differences in farmworker and non-farmworker cotinine levels are associated with smoking. METHODS: Data are from 63 farmworkers and 44 non-farmworkers who participated in a larger study of occupational exposures. Questionnaire data and urine samples collected in 2012 and 2013 are analyzed. RESULTS: Farmworkers had urinary cotinine levels that were far greater than the non-farmworker group. Geometric mean (GM) urinary cotinine levels for farmworkers were 1808.22ng/ml in 2012, and 396.03ng/ml in 2013; corresponding GM levels for non-farmworkers were 4.68ng/ml and 9.03ng/ml. Farmworker GM cotinine levels were associated with harvesting tobacco (1242.77ng/ml vs. 471.26ng/ml; P = .0048), and working in wet shoes (1356.41ng/ml vs. 596.93ng/ml; P = .0148). Smoking did not account for cotinine level differences; the GM cotinine level for farmworkers who did not smoke was 541.31ng/ml; it was 199.40ng/ml for non-farmworkers who did smoke. CONCLUSION: North Carolina farmworkers experience large nicotine doses. The long-term health effects of these doses are not known. Although procedures to reduce occupational nicotine exposure are known, no changes in work practices or in policies to protect workers have been implemented. Research on the health effects of occupational nicotine exposure must become a priority. Current knowledge of occupational transdermal nicotine exposure must be used to improve occupational safety practice and policy for tobacco workers. IMPLICATIONS: This study documents the heavy burden of nicotine exposure and dose experienced by tobacco workers in North Carolina. Hundreds of thousands of farmworkers and farmers in the United States and Canada, as well as agricultural workers around the world, share this burden of nicotine exposure and dose. These results support the need to change work practices and regulations to protect workers. They also document the need to delineate the health effects of long-term exposure to high transdermal nicotine doses.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".