Allergies and major depression: a longitudinal community study
Bibliographic record
Abstract
BACKGROUND: Cross-sectional studies have reported associations between allergies and major depression but in the absence of longitudinal data, the implications of this association remain unclear. Our goal was to examine this association from a longitudinal perspective. METHODS: The data source was the Canadian National Population Health Survey (NPHS). This study included a short form version of the Composite International Diagnostic Interview (CIDI-SF) to assess major depression and also included self report items for professionally diagnosed allergies of two types: non-food allergies and food allergies. A longitudinal cohort was followed between 1994 and 2002. Proportional hazards models for grouped time data were used to estimate unadjusted and adjusted hazard ratios. RESULTS: A slightly increased incidence of non-food allergies in respondents with major depression was observed: adjusted hazard ratio 1.2 (95% 1.0 - 1.5, p = 0.046). Some evidence for an increased incidence of major depression in association with non-food allergies was found in unadjusted analyses, but the association did not persist after multivariate adjustment. Food allergies were not associated with major depression incidence, nor was major depression associated with an increased incidence of food allergies. CONCLUSION: Findings from the present study support the idea that major depression is associated with an increased risk of developing non-food allergies. An effect in the opposite direction could not be confirmed. The observed effect may be due to shared genetic factors, epigenetic factors, or immunological changes that occur during depression.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".