Latent factors of adverse childhood experiences and adult-onset asthma
Bibliographic record
Abstract
Asthma is a chronic respiratory disease with complex etiology. Adverse childhood experiences (ACEs) have been linked to asthma in adulthood. Underlying potential mechanisms for the ACE-asthma relationship include stress-induced inflammatory pathways and immune dysregulation. We conducted a cross-sectional secondary data analysis of the 2013 Alberta ACE Survey to explore the relationship between latent ACE factors and self-reported adult asthma. We evaluated the underlying correlation structure among eight different ACEs using exploratory factor analysis. We conducted a logistic regression model to evaluate whether ACE factors retained from the factor analysis predicted self-reported asthma in adulthood. Results were reported as odds ratios (ORs) with 95% confidence intervals (CIs). We analyzed ACE survey results from 1207 participants. Factor analysis yielded four ACE latent factors: factor 1/relational violence, factor 2/negative home environment, factor 3/illness at home, and factor 4/sexual abuse. Results of the logistic regression showed that experiencing sexual abuse (OR: 3.23; 95% CI: 1.89, 5.23), relational violence (OR: 1.99; 95% CI: 1.17, 3.38), and being exposed to a negative home environment (OR: 1.86; 95% CI: 1.03, 3.35) were predictive of a diagnosis of asthma in adulthood, whereas living in a household with someone experiencing illness did not show an effect (OR: 1.38; 95% CI: 0.75, 2.56). Factor analysis provides an effectual approach to understand the long-term impact of ACEs on respiratory health. Our findings have important implications to understand the developmental origins of asthma in adulthood and inform interventions aimed at reducing the lasting negative impact of childhood adversities on future respiratory health.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".