Prevalence of psychiatric disorders in community-dwelling older men and women with cognitive impairment no dementia: results from the ESA study
Bibliographic record
Abstract
OBJECTIVES: To assess the prevalence rate of mood disorders, anxiety disorders, benzodiazepine dependence, and insomnia in older men and women with probable cognitive impairment no dementia (CIND) and to examine the independent associations between each disorder and CIND. METHOD: Participants were a random sample of community-dwelling individuals aged 65-96 (N = 2414). Semi-structured in-home interviews based on DSM-IV-TR (DSM, Diagnostic and Statistical Manual of Mental Disorders) criteria evaluated the prevalence rates of mood disorders, anxiety disorders, benzodiazepine dependence, and insomnia. Participants were classified as probable CIND based on their Mini-Mental State Examination score using sex, age, and education-stratified cut-offs (lower than the 15th percentile). RESULTS: In men, 22.7% of individuals with probable CIND and 12.1% of those with normal cognition had at least one psychiatric disorder (crude odds ratio (OR): 2.13, 95% confidence interval (CI): 1.23-3.69). More specifically, mood disorders (3.43, 1.74-6.75), benzodiazepine dependence (5.10, 1.23-21.11), and comorbid anxiety and mood disorders (8.67, 2.00-37.68) were significantly associated with probable CIND, but not anxiety disorders alone and insomnia. The prevalence rate of psychiatric disorders was similar in women with probable CIND (23.1%) and in women without CIND (23.9%; 0.95, 0.64-1.42). No specific psychiatric disorder was significantly associated with probable CIND in women. All associations remained unchanged after adjustments for potential confounders. CONCLUSIONS: The association between psychiatric disorders and probable CIND appears to be sex-specific. In clinical practice, mood disorders, and benzodiazepine dependence should receive particular attention since these disorders are associated with a condition increasing the risk of dementia.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".