Association of factors with childhood asthma and allergic diseases using latent class analysis
Bibliographic record
Abstract
We hypothesize that children characterized by deprived factors have poorer health outcomes. We aim to identify clustering of determinants and estimate risk of early childhood diseases. This 1993-2019 longitudinal cohort study combines three Canadian pediatric cohorts and their families. Mothers and children are clustered using latent class analysis (LCA) by 16 indicators in three domains (maternal and newborn; socioeconomic status [SES] and neighbourhood; environmental exposures). Hazard ratios (HR) of childhood asthma, allergic rhinitis (AR), and eczema are quantified with Cox proportional hazard (PH) regression. Rate ratios (RR) of children's health services use (HSU) are estimated with Poisson regression. Here we report the inclusion of 15,724 mother-child pairs; our LCA identifies four mother-clusters. Classes 1 and 2 mothers are older (30-40 s), non-immigrants with university education, living in high SES neighbourhoods; Class 2 mothers have poorer air quality and less greenspace. Classes 3 and 4 mothers are younger (20-30 s), likely an immigrant/refugee, with high school-to-college education, living in lower SES neighborhoods with poorer air quality and less greenspace. Children's outcomes differ by Class, in comparison to Class 1. Classes 3 and 4 children have higher risks of asthma (HR 1.24, 95% CI 1.11-1.37 and HR 1.39, 95% CI 1.22-1.59, respectively), and similar higher risks of AR and eczema. Children with AR in Class 3 have 20% higher all-cause physician visits (RR = 1.20, 95% CI 1.10-1.30) and those with eczema have 18% higher all-cause emergency department visits (RR = 1.18, 95% CI 1.09-1.28) and 14% higher all-cause physician visits (RR = 1.14, 95% CI 1.09-1.19). Multifactorial-LCA mother-clusters may characterize associations of children's health outcomes and care, adjusting for interrelationships.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".