Predictors, sources, and health risk of exposure to neonicotinoids in Chinese school children: A biomonitoring-based study
Bibliographic record
Abstract
BACKGROUND: Recent studies have suggested an extensive exposure to neonicotinoids in human, but the information on predictors, sources, and health risk remains limited in children. OBJECTIVE: To explore the predictors, sources, and health risk for exposure of Chinese school children to neonicotinoids by biomonitoring method. METHODS: In 2019, 309 school children aged 7-11 years were selected from a dynamic cohort of children established in Shanghai, East China. Eight neonicotinoids and four metabolites were determined in first morning urine. After detailed information on demographic and socioeconomic indices, intake of drinking water, food consumption, and anthropometric measurements was collected, the predictors and sources of exposure to neonicotinoids were explored by binary and ordinal logistic regression models. Health risk was assessed by the hazard quotient (HQ) and hazard index (HI) based on estimated daily exposure dose. RESULTS: Six neonicotinoids (thiamethoxam, clothianidin, imidacloprid, acetamiprid, nitenpyram, and dinotefuran) and three metabolites (N-desmethyl-thiamethoxam, N-desmethyl-clothianidin, and N-desmethyl-acetamiprid) were detected in 81.3% of urine samples. Children in nuclear family or girls had a higher detection frequency of thiamethoxam. N-desmethyl-acetamiprid was more likely to be detected in children who were older, physically active, or obese. Parents' occupation was heterogeneously associated with the detection of thiamethoxam and clothianidin. Thiamethoxam and clothianidin were more likely to be detected in children consuming more fresh vegetables in all or its specific items of cabbage, nori, and kelp. N-desmethyl-acetamiprid was more likely to be detected in children who drank more tap water. The maximum HQ and HI were 0.3522 and 0.5187, respectively, and 2.8% of children had a HI between 0.1 and 1. CONCLUSION: Predictors for the exposure of children to neonicotinoids included demographic and socioeconomic factors, physical exercise, and relative body weight. Tap water and fresh vegetables were potential sources. A low risk was posed on children's health by the neonicotinoids.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".