Association of living in a farming environment with asthma incidence in Canadian children
Bibliographic record
Abstract
OBJECTIVE: The objective of this population-based 14-year follow-up study was to examine the effect of living in a farm environment on asthma incidence in children. METHODS: A total of 10,941 children of ages 0 to 11 years who were free of asthma and wheeze at the baseline (1994-1995) in the National Longitudinal Survey of Children and Youth were considered in the study. Children's living environment was classified, based on interviewer's observation, into three categories: non-rural, rural non-farming, and farming. An incidence of asthma was obtained from health-professional diagnosed asthma reported either by the person most knowledgeable for children under 15 years or by the children themselves if they were of ages 16 years and over. RESULTS: The 14-year cumulative incidence of asthma among children living in farming environments was 10.18%, which was significantly lower than that observed for children living in rural non-farming (13.12%) and non-rural environments (16.50%). After adjusting for age group, number of older siblings, allergy, parental history of asthma, dwelling in need of repairs and SES index, a dose-response relationship was observed with children living in rural non-farming and farming environments having significantly reduced risk of asthma [hazard ratio (HR): 0.77; 95% confidence interval (CI): (0.60, 1.00); p = 0.047 and HR: 0.56; 95% CI: (0.41,0.77); p < 0.001] in comparison to those living in non-rural environments. CONCLUSION: This cohort study provides further evidence that living in a farming environment during childhood is protective of asthma incidence in adolescence and adulthood and this finding provides further support for the hygiene hypothesis.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".