Prevalence and incidence of depressive disorder: the Baltimore ECA follow‐up, 1981–2004
Bibliographic record
Abstract
OBJECTIVE: To describe trends in prevalence and incidence of depressive disorder in a cohort from Eastern Baltimore. METHOD: Twenty-three-year-old longitudinal cohort, the Baltimore Epidemiologic Catchment Area Follow-up. Participants were selected probabilistically from the household population in 1981, and interviewed in 1981, 1993, and 2004. Diagnoses were made via the Diagnostic Interview Schedule according to successive editions of the American Psychiatric Association Diagnostic and Statistical Manual. RESULTS: Older age, lower education, non-White race, and cognitive impairment are independent predictors of attrition due to death and loss of contact, but depressive disorder is not related to attrition. Prevalence rates rise for females between 1981, 1993, and 2004. Incidence rates in the period 1993-2004 are lower than the period 1981-1993, suggesting the rise in prevalence is due to increasing chronicity. CONCLUSION: There has been a rise in the prevalence of depression in the prior quarter century among middle-aged females.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".