Meta-Analysis of the Prevalence of Major Depressive Disorder Among Older Adults With Dementia
Bibliographic record
Abstract
OBJECTIVE: Little is known about the overall prevalence of major depressive disorder (MDD) in persons with dementia (ie, "depression in dementia": DpD). The aim of this systematic review and meta-analysis was to determine the prevalence and factors associated with DpD among older adults (age range 58.7-87.8 years). The protocol was registered in the PROSPERO registry (2015:CRD42015020681). DATA SOURCES: We searched the following electronic databases: MEDLINE (1946-February 2017), Embase (1980-2017 week 5), and PsycINFO (1967-February 2017) using medical subject headings and free-text search terms for studies in the English language. STUDY SELECTION: We screened 9,421 studies, and 55 met the inclusion criteria (ie, used validated criteria for both MDD and dementia). DATA EXTRACTION: Two independent reviewers extracted data from included studies. Meta-analysis was used to determine the pooled estimates and 95% confidence intervals for the prevalence of DpD. Prevalence across dementia subtypes, study setting, diagnostic criteria, and dementia severity was compared in subgroup analyses. RESULTS: The prevalence of MDD in all-cause dementia was 15.9% (95% CI, 12.6%-20.1%). The prevalence of MDD was higher among individuals with vascular dementia (24.7%) compared to Alzheimer's disease (14.8%). Studies using the provisional diagnostic criteria for DpD reported a higher prevalence (35.6%) compared to studies using either the DSM-III-R (13.2%) or DSM-IV (17.3%) criteria. CONCLUSIONS: Depression is common among individuals with dementia, and the type of dementia and diagnostic criteria affect prevalence estimates of DpD. Further studies are required to understand factors that lead to the development of DpD and strategies to prevent and treat DpD.
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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 | 0.021 | 0.042 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.049 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".