Joint Effect of Depression and Chronic Conditions on Disability: Results From a Population-Based Study
Bibliographic record
Abstract
OBJECTIVES: To estimate and compare the prevalence of functional disability in individuals with both chronic medical conditions and comorbid major depression and individuals with either chronic medical conditions or major depression alone and to determine the joint effect of depression and chronic conditions on functional disability. Evidence exists that major depression interacts with physical illness to amplify the functional disability associated with many medical conditions. METHODS: We used data from the Canadian Community and Health Survey Cycle 2.1 (n = 46,262), a nationally representative survey conducted in 2003 by Statistics Canada. Depression, chronic conditions, and functional disability were assessed by personal/telephone interview. RESULTS: Prevalence of functional disability was higher in subjects with chronic conditions and comorbid major depression (46.3%) than in individuals with either chronic conditions (20.9%) or major depression (27.8%) alone. With no chronic conditions and no major depression as reference and after adjusting for relevant covariates, the odds ratio of functional disability was 2.49 (95% confidence interval (CI), 1.91-3.26) for major depression, 2.12 (95% CI, 1.93-2.32) for chronic conditions, and 6.34 (95% CI, 5.35-7.51) for chronic conditions and comorbid major depression. CONCLUSIONS: The results suggest that there is a joint effect of depression and chronic conditions on functional disability. Research and social policies should focus on the treatment of depression in chronic 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 | 0.004 | 0.008 |
| 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) | 0.001 | 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".