Prevalence and correlates of major depressive disorder, bipolar disorder and schizophrenia among nursing home residents without dementia: systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: The elderly population and numbers of nursing homes residents are growing at a rapid pace globally. Uncertainty exists regarding the actual rates of major depressive disorder (MDD), bipolar disorder and schizophrenia as previous evidence documenting high rates relies on suboptimal methodology. AIMS: To carry out a systematic review and meta-analysis on the prevalence and correlates of MDD, bipolar disorder and schizophrenia spectrum disorder among nursing homes residents without dementia. METHOD: Major electronic databases were systematically searched from 1980 to July 2017 for original studies reporting on the prevalence and correlates of MDD among nursing homes residents without dementia. The prevalence of MDD in this population was meta-analysed through random-effects modelling and potential sources of heterogeneity were examined through subgroup/meta-regression analyses. RESULTS: Across 32 observational studies encompassing 13 394 nursing homes residents, 2110 people were diagnosed with MDD, resulting in a pooled prevalence rate of 18.9% (95% CI 14.8-23.8). Heterogeneity was high (I2 = 97%, P≤0.001); no evidence of publication bias was observed. Sensitivity analysis indicated the highest rates of MDD among North American residents (25.4%, 95% CI 18-34.5, P≤0.001). Prevalence of either bipolar disorder or schizophrenia spectrum disorder could not be reliably pooled because of the paucity of data. CONCLUSIONS: MDD is highly prevalent among nursing homes residents without dementia. Efforts towards prevention, early recognition and management of MDD in this population are warranted.
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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 | 0.013 | 0.034 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.035 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".