How much atopy is attributable to common childhood environmental exposures? A population-based birth cohort study followed to adulthood
Bibliographic record
Abstract
Background: The rising prevalence of atopic diseases implies a strong influence of environmental determinants. Epidemiological studies have identified several early life exposures that appear to influence the risk of developing atopic sensitization, but the combined influence of these exposures is unknown. We sought to estimate the proportion of atopy that could be attributed to common childhood exposures associated with atopic sensitization in adolescence and young adulthood. Methods: Atopic sensitization was measured by skin-prick tests for common aeroallergens in a population-based New Zealand birth cohort at ages 13 and 32 years. The independent effects of previously identified risk and protective factors for atopic sensitization were assessed using multiple logistic regression. Population attributable fractions were calculated for atopic sensitization in childhood and adulthood. Results: Tobacco smoke exposure, dog and cat ownership, nail-biting and thumb-sucking, attending pre-school day care, and household crowding were associated with a lower risk of atopic sensitization whereas breastfeeding was associated with a higher risk. Population attributable fractions for combined effects of these environmental factors suggest that they may account for 58% of atopic sensitization at age 13 and 49% at age 32 years. Conclusions: A substantial proportion of atopic sensitization appears to be attributable to common childhood environmental and lifestyle factors, and the influence of these exposures persists into adulthood. The absolute risks attributable to these exposures will be different in other cohorts and we cannot assume that these associations are necessarily causal. Nevertheless, the findings suggest that identifiable childhood environmental factors contribute substantially to atopic sensitization.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".