Subsyndromal depression among older adults in the USA: prevalence, comorbidity, and risk for new‐onset psychiatric disorders in late life
Bibliographic record
Abstract
BACKGROUND: Population-based data are lacking on the prevalence and comorbidity of subsyndromal depression (SSD) and its associated risk for incident psychiatric disorders in older adults. METHODS: Using nationally representative data from 10,409 US adults aged 55 years and older who participated in the National Epidemiologic Survey on Alcohol and Related Conditions, we evaluated associations between lifetime SSD at Wave 1, and lifetime and incident mood, anxiety, and substance use disorders over a 3-year period. RESULTS: Some 13.8% of older adults met criteria for SSD, and 13.7% met criteria for major depressive disorder (MDD). After adjustment for sociodemographic characteristics, older adults with SSD at Wave 1 had significantly increased odds of lifetime mood (adjusted odds ratios (AORs) = 3.65-10.55), anxiety (AORs = 1.61-2.50), and any personality (AOR = 1.62) disorders. After adjustment for sociodemographic characteristics and comorbid psychiatric disorders, older adults with SSD at Wave 1 had significantly increased odds of developing new-onset MDD (AOR = 1.44, 95% confidence interval (CI) = 1.01-2.05), as well as an anxiety disorder (AOR = 1.52, 95% CI = 1.04-2.20) at Wave 2. CONCLUSION: In addition to the 13.7% of US older adults with lifetime MDD, an additional 13.8% have lifetime SSD, which is not a formally recognized diagnosis. In addition to its high prevalence, SSD is associated with elevated rates of comorbid mood, anxiety, and personality disorders, as well as the development of a new-onset MDD and anxiety disorder. These results underscore the importance of dimensional approaches to assessing depressive symptoms in older persons, as diagnostic approaches that rely on rigorous categorical classifications may fail to identify a substantial proportion of at-risk individuals.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".