Screening and management of depression for adults with chronic diseases: an evidence-based analysis.
Bibliographic record
Abstract
BACKGROUND: Depression is the leading cause of disability and the fourth leading contributor to the global burden of disease. In Canada, the 1-year prevalence of major depressive disorder was approximately 6% in Canadians 18 and older. A large prospective Canadian study reported an increased risk of developing depression in people with chronic diseases compared with those without such diseases. OBJECTIVES: To systematically review the literature regarding the effectiveness of screening for depression and/or anxiety in adults with chronic diseases in the community setting. To conduct a non-systematic, post-hoc analysis to evaluate whether a screen-and-treat strategy for depression is associated with an improvement in chronic disease outcomes. DATA SOURCES: A literature search was performed on January 29, 2012, using OVID MEDLINE, OVID MEDLINE In-Process and Other Non-Indexed Citations, OVID EMBASE, OVID PsycINFO, EBSCO Cumulative Index to Nursing & Allied Health Literature (CINAHL), the Wiley Cochrane Library, and the Centre for Reviews and Dissemination database, for studies published from January 1, 2002 until January 29, 2012. REVIEW METHODS: No citations were identified for the first objective. For the second, systematic reviews and randomized controlled trials that compared depression management for adults with chronic disease with usual care/placebo were included. Where possible, the results of randomized controlled trials were pooled using a random-effects model. RESULTS: Eight primary randomized controlled trials and 1 systematic review were included in the post-hoc analysis (objective 2)-1 in people with diabetes, 2 in people with heart failure, and 5 in people with coronary artery disease. Across all studies, there was no evidence that managing depression improved chronic disease outcomes. The quality of evidence (GRADE) ranged from low to moderate. Some of the study results (specifically in coronary artery disease populations) were suggestive of benefit, but the differences were not significant. LIMITATIONS: The included studies varied in duration of treatment and follow-up, as well as in included forms of depression. In most of the trials, the authors noted a significant placebo response rate that could be attributed to spontaneous resolution of depression or mild disease. In some studies, placebo groups may have had access to care as a result of screening, since it would be unethical to withhold all care. CONCLUSIONS: There was no evidence to suggest that a screen-and-treat strategy for depression among adults with chronic diseases resulted in improved chronic disease outcomes. PLAIN LANGUAGE SUMMARY: People with chronic diseases are more likely to have depression than people without chronic diseases. This is a problem because depression may make the chronic disease worse or affect how a person manages it. Discovering depression earlier may make it easier for people to cope with their condition, leading to better health and quality of life. We reviewed studies that looked at screening and treating for depression in people with chronic diseases. In people with diabetes, treatment of depression did not affect clinical measures of diabetes management. In people with heart failure and coronary artery disease, treatment of depression did not improve heart failure management or reduce rates of heart attacks or death. At present, there is no evidence that screening and treating for depression improves the symptoms of chronic diseases or lead to use of fewer health care services.
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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 | 0.019 | 0.062 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.014 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".