Reduced risk of physician‐diagnosed asthma among children dwelling in a farming environment
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: Living in a farm environment has been reported to be associated with lower prevalence of asthma, based on the results of cross-sectional studies. The objective of this longitudinal study was to determine whether living in a farm environment is associated with lower incidence of asthma among children. METHODS: A total of 13 524 asthma-free children aged 0-11 years were drawn from the Cycle 1 (1994/1995) of the Canadian National Longitudinal Survey of Children and Youth (NLSCY). Subjects were categorized as dwelling in rural farming, rural non-farming and non-rural environments. Incidence of physician-diagnosed asthma was determined at Cycle 2 (1996/1997). Bootstrap logistic regression method was used to adjust for design effect in the NLSCY. RESULTS: The 2-year cumulative incidence of asthma was 2.3%, 5.3% and 5.7% among children living in farming, rural non-farming and non-rural environments, respectively. From the multivariate analysis with adjustment for important confounders, children from a farming environment had a reduced risk of asthma compared with children from rural non-farming environment with odds ratios (OR) of 0.22 (95% CI: 0.07-0.74) and 0.39 (95% CI: 0.24-0.65) for children with and without parental history of asthma, respectively. Children living in a non-rural environment with parental history of asthma had an increased risk of asthma incidence when compared with children living in rural non-farming environment (OR = 2.51, 95% CI: 1.56-4.05). CONCLUSION: This longitudinal study expands on observational study results which suggest a reduced risk of developing asthma associated with living in a farming environment.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".