Three-Year Incidence and Predictors of First-Onset of DSM-IV Mood, Anxiety, and Substance Use Disorders in Older Adults
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to determine the incidence rates of Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV) mood disorders, anxiety disorders, and substance use disorders in older adults and to identify sociodemographic, psychopathological, health-related, and stress-related predictors of onset of these disorders. METHOD: A nationally representative sample of 8,012 community-dwelling adults aged 60 and above was interviewed twice over a period of 3 years, in 2000-2001 and 2004-2005. First incidence of mood, anxiety, and substance use disorders was assessed over a period of 3 years using the Alcohol Use Disorder and Associated Disabilities Interview Schedule-DSM-IV Version. RESULTS: The 3-year incidence rates of DSM-IV mood, anxiety, and substance use disorders were highest for nicotine dependence (3.38%) and major depressive disorder ([MDD] 3.28%) and lowest for drug use disorder (0.29%) and bipolar II disorder (0.34%). Incidence rates were significantly greater among older women for MDD (99% CI, 1.22-3.13) and generalized anxiety disorder (GAD; 99% CI, 1.20-4.26) and greater among older men for nicotine dependence and alcohol abuse and dependence. Posttraumatic stress disorder predicted incidence of MDD, bipolar I disorder, panic disorder, specific phobia, and GAD, while Cluster B personality disorders predicted incident MDD, bipolar I and II disorders, panic disorder, social phobia, GAD, nicotine dependence, and alcohol dependence. Poor self-rated health increased the risk for the onset of MDD, whereas obesity decreased the incidence of nicotine dependence. CONCLUSIONS: Information about disorders that are highly incident in late life and risk factors for the onset of psychiatric disorders among older adults are important for effective early intervention and prevention initiatives.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".