Long-Term Medical Conditions and Major Depression: Strength of Association for Specific Conditions in the General Population
Bibliographic record
Abstract
BACKGROUND: The prevalence of major depression (MD) in persons with nonpsychiatric medical conditions is an indicator of clinical need in those groups, an indicator of the feasibility of screening and case-finding efforts, and a source of etiologic hypotheses. This analysis explores the prevalence of MD in the general population in relation to various long-term medical conditions. METHODS: We used a dataset from a large-scale Canadian national health survey, the Canadian Community Health Survey (CCHS). The sample consisted of 115 071 subjects aged 18 years and over, randomly sampled from the Canadian population. The survey interview recorded self-reported diagnoses of various long-term medical conditions and employed a brief predictive interview for MD, the Composite International Diagnostic Interview Short Form for Major Depression. Logistic regression was used to adjust estimates of association for age and sex. RESULTS: The conditions most strongly associated with MD were chronic fatigue syndrome (adjusted odds ratio [AOR] 7.2) and fibromyalgia (AOR 3.4). The conditions least strongly associated were hypertension (AOR 1.2), diabetes, heart disease, and thyroid disease (AOR 1.4 in each case). We found associations with various gastrointestinal, neurologic, and respiratory conditions. CONCLUSIONS: A diverse set of long-term medical conditions are associated with MD, although previous studies might have lacked power to detect some of these associations. The strength of association in prevalence data, however, varies across specific conditions.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".