Association of living in a farming environment with asthma incidence in Canadian children
Bibliographic record
Abstract
<b>Background:</b> Living in a farming environment during early life has been shown to have a protective effect against asthma and this has given rise to the 9hygiene hypothesis9; however, only a limited number of longitudinal studies have been conducted to examine this relationship, to date. <b>Objective:</b> To examine the effect of living in a farm environment on asthma incidence in children in a population-based 14-year follow-up study. <b>Methods:</b> In total, 10,941 children free of asthma and wheeze at the baseline in the National Longitudinal Survey of Children and Youth were considered. Children9s living environment was classified, based on street-level frontage, into three categories: non-rural, rural non-farming and farming. <b>Results:</b> The 14-year cumulative incidence of asthma was 16.50%, 13.12%, and 10.18% for children residing in non-rural, rural non-farming, and farming environments, respectively. After adjusting for potential confounders, children residing in farming environments had a significantly reduced risk of asthma [hazard ratio (HR): 0.56; 95% confidence interval (CI): (0.41, 0.76)] in comparison to those living in non-rural environments. Children residing in rural non-farming environments also had a reduced risk of asthma in comparison to those living in non-rural environments. In an analysis, restricting to children aged 0 to 4 years and considering children living in rural non-farming environments as the comparison group, children living in farming environments had a reduced risk of asthma [HR = 0.59; 95% CI: (0.35, 0.99)]. <b>Conclusion:</b> Our study provides further evidence that living in a farming environment during childhood is protective of asthma incidence in adolescence and adulthood.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".