Prevalence of Depression in Patients With Mild Cognitive Impairment
Bibliographic record
Abstract
IMPORTANCE: Depression is common in individuals with mild cognitive impairment (MCI) and may confer a higher likelihood of progression to dementia. Prevalence estimates of depression in those with MCI are required to guide both clinical decisions and public health policy, but published results are variable and lack precision. OBJECTIVE: To provide a precise estimate of the prevalence of depression in individuals with MCI and identify reasons for heterogeneity in the reported results. DATA SOURCES: A search of literature from database inception to March 2016 was performed using Medline, Embase, and PsycINFO. Hand searching of all included articles was performed, including a Google Scholar search of citations of included articles. STUDY SELECTION: Articles were included if they (1) were published in English, (2) reported patients with MCI as a primary study group, (3) reported depression or depressive symptoms using a validated instrument, and (4) reported the prevalence of depression in patients with MCI. DATA EXTRACTION AND SYNTHESIS: All abstracts, full-text articles, and other sources were reviewed, with data extracted in duplicate. The overall prevalence of depression in patients with MCI was pooled using a random-effects model. Heterogeneity was explored using stratification and random-effects meta-regression. MAIN OUTCOMES AND MEASURES: The prevalence of depression in patients with MCI, reported as a percentage with 95% CIs. Estimates were also stratified by population source (community-based or clinic-based sample), method of depression diagnosis (clinician-administered, informant-based, or self-report), and method of MCI diagnosis (cognitive vs global measure and amnestic vs nonamnestic). RESULTS: Of 5687 unique abstracts, 255 were selected for full-text review, and 57 studies, representing 20 892 patients, met all inclusion criteria. The overall pooled prevalence of depression in patients with MCI was 32% (95% CI, 27-37), with significant heterogeneity between estimates (I2 = 90.7%). When stratified by source, the prevalence of depression in patients with MCI in community-based samples was 25% (95% CI, 19-30) and was 40% (95% CI, 32-48) in clinic-based samples, which was significantly different (P < .001). The method used to diagnose depression did not significantly influence the prevalence estimate, nor did the criteria used for MCI diagnosis or MCI subtype. CONCLUSIONS AND RELEVANCE: The prevalence of depression in patients with MCI is high. A contributor to heterogeneity in the reported literature is the source of the sample, with greater depression burden prevalent in clinic-based samples.
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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 | 0.012 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 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".