Association of Tobacco and Lead Exposures With Attention-Deficit/Hyperactivity Disorder
Bibliographic record
Abstract
OBJECTIVE: The study objective was to determine the independent and joint associations of prenatal tobacco and childhood lead exposures with attention-deficit/hyperactivity disorder (ADHD), as defined by current diagnostic criteria, in a national sample of US children. METHODS: Data are from the 2001-2004 National Health and Nutrition Examination Survey, a cross-sectional, nationally representative sample of the US population. Participants were 8 to 15 years of age (N = 2588). Prenatal tobacco exposure was measured by report of maternal cigarette use during pregnancy. Lead exposure was assessed by using current blood lead levels. The Diagnostic Interview Schedule for Children was used to ascertain the presence of ADHD in the past year, on the basis of Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, criteria. RESULTS: A total of 8.7% (95% confidence interval [CI]: 7.3%-10.1%) of children met criteria for ADHD. Prenatal tobacco exposure (adjusted odds ratio [aOR]: 2.4 [95% CI: 1.5-3.7]) and higher current blood lead concentrations (aOR for third versus first tertile: 2.3 [95% CI: 1.5-3.8]) were independently associated with ADHD. Compared with children with neither exposure, children with both exposures (prenatal tobacco exposure and third-tertile lead levels) had an even greater risk of ADHD (aOR: 8.1 [95% CI: 3.5-18.7]) than would be expected if the independent risks were multiplied (tobacco-lead exposure interaction term, P < .001). CONCLUSIONS: Prenatal tobacco and childhood lead exposures are associated with ADHD in US children, especially among those with both exposures. Reduction of these common toxicant exposures may be an important avenue for ADHD prevention.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".