Patterns of association of chronic medical conditions and major depression
Bibliographic record
Abstract
AIMS: Age and sex-related patterns of association between medical conditions and major depressive episodes (MDE) are important for understanding disease burden, anticipating clinical needs and for formulating etiological hypotheses. General population estimates are especially valuable because they are not distorted by help-seeking behaviours. However, even large population surveys often deliver inadequate precision to adequately describe such patterns. In this study, data from a set of national surveys were pooled to increase precision, supporting more precise characterisation of these associations. METHODS: The data were from a series of Canadian national surveys. These surveys used comparable sampling strategies and assessment methods for MDE. Chronic medical conditions were assessed using items asking about professionally diagnosed medical conditions. Individual-level meta-analysis methods were used to generate unadjusted, stratified and adjusted prevalence odds ratios for 11 chronic medical conditions. Random effects models were used in the meta-analysis. A procedure incorporating rescaled replicate bootstrap weights was used to produce 95% confidence intervals. RESULTS: Overall, conditions characterised by pain and inflammation tended to show stronger associations with MDE. The meta-analysis uncovered two previously undescribed patterns of association. Effect modification by age was observed in varying degrees for most conditions. This effect was most prominent for high blood pressure and cancer. Stronger associations were found in younger age categories. Migraine was an exception: the strength of association increased with age, especially in men. Second, especially for conditions predominantly affecting older age groups (arthritis, diabetes, back pain, cataracts, effects of stroke and heart disease) confounding by age was evident. For each condition, age adjustment resulted in strengthening of the associations. In addition to migraine, two conditions displayed distinctive patterns of association. Age adjusted odds ratios for thyroid disease reflected a weak association that was only significant in women. In epilepsy, a similar strength of association was found irrespective of age or sex. CONCLUSIONS: The prevalence of MDE is elevated in association with most chronic conditions, but especially those characterised by inflammation and pain. Effect modification by age may reflect greater challenges or difficulties encountered by young people attempting to cope with these conditions. This pattern, however, does not apply to migraine or epilepsy. Neurobiological changes associated with these conditions may offset coping-related effects, such that the association does not weaken with age. Prominent confounding by age for several conditions suggests that age adjustments are necessary in order to avoid underestimating the strength of these associations.
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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 | 0.002 | 0.005 |
| 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) | 0.000 | 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".