Subsyndromal depression in the United States: prevalence, course, and risk for incident psychiatric outcomes
Bibliographic record
Abstract
BACKGROUND: Subsyndromal depression (SD) may increase risk for incident major depressive and other disorders, as well as suicidality. However, little is known about the prevalence, course, and correlates of SD in the US general adult population. Method Structured diagnostic interviews were conducted to assess DSM-IV Axis I and II disorders in a nationally representative sample of 34 653 US adults who were interviewed at two time-points 3 years apart. RESULTS: A total of 11.6% of US adults met study criteria for lifetime SD at Wave 1. The majority (9.3%) had <5 total symptoms required for a diagnosis of major depression; the remainder (2.3%) reported ⩾5 symptoms required for a diagnosis of major depression, but denied clinically significant distress or functional impairment. SD at Wave 1 was associated with increased likelihood of developing incident major depression [odds ratios (ORs) 1.72-2.05], as well as dysthymia, social phobia, and generalized anxiety disorder (GAD) at Wave 2 (ORs 1.41-2.92). Among respondents with SD at Wave 1, Cluster A and B personality disorders, and worse mental health status were associated with increased likelihood of developing incident major depression at Wave 2. CONCLUSIONS: SD is prevalent in the US population, and associated with elevated rates of Axis I and II psychopathology, increased psychosocial disability, and risk for incident major depression, dysthymia, social phobia, and GAD. These results underscore the importance of a dimensional conceptualization of depressive symptoms, as SD may serve as an early prognostic indicator of incident major depression and related disorders, and could help identify individuals who may benefit from preventive interventions.
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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 | 0.000 | 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".