Does co-morbid depressive illness magnify the impact of chronic physical illness? A population-based perspective
Bibliographic record
Abstract
OBJECTIVE: To examine the relative and combined impact of depressive and chronic physical conditions on functional status and health-care use in the general population. METHOD: Canadian, representative, population-based cross-sectional survey (n=130,880). Major depressive disorder (MDD) in the past 12 months was assessed by structured interview, and physical disorders, activity reduction, role impairment and work absence by self-report. The relative impact of MDD and six common chronic physical illnesses (asthma, arthritis, back problems, chronic obstructive pulmonary disease, heart disease and diabetes) was estimated using multivariate regression, adjusting for sociodemographic characteristics and overall chronic physical illness burden. RESULTS: After adjusting for sociodemographic characteristics, alcohol dependence and chronic physical illness burden, the presence of co-morbid MDD was associated with significantly greater (approximately double the) likelihood of health-care utilization and increased functional disability and work absence compared to the presence of a chronic physical illness without co-morbid MDD. This impact of MDD was seen across each of the six chronic physical illnesses examined in this study, with the strongest associations seen for work absence. CONCLUSIONS: These observations confirm prior findings of a strong association at the population level between major depression and health-care use and role impairment among persons with chronic physical disorders. They also point to the significant impact of co-morbid major depression on health-care seeking, disability and work absence in persons with chronic physical illness, underscoring the need for greater efforts to design and test the impact of detection and treatment programs for such individuals.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".