Prevalence and Incidence Studies of Mood Disorders: A Systematic Review of the Literature
Bibliographic record
Abstract
OBJECTIVE: To present the results of a systematic review of literature published between January 1, 1980, and December 31, 2000, that reports findings on the prevalence and incidence of mood disorders in both general population and primary care settings. METHOD: We conducted a literature search of epidemiologic studies of mood disorders, using Medline and HealthSTAR databases and canvassing English-language publications. Eligible publications were restricted to studies that examined subjects aged at least 15 years and over. We used a set of predetermined inclusion and exclusion criteria to identify relevant studies. We extracted and analyzed prevalence and incidence data for heterogeneity. RESULTS: Of general population studies, a total of 18 prevalence and 5 incidence studies met eligibility criteria. We found heterogeneity across 1-year and lifetime prevalence of major depressive disorder (MDD), dysthymic disorder and bipolar I disorder. The corresponding pooled rates for 1-year prevalence were 4.1 per 100, 2.0 per 100, and 0.72 per 100, respectively. For lifetime prevalence, the corresponding pooled rates were 6.7 per 100, 3.6 per 100, and 0. per 100, respectively. Significant variation was observed among 1-year incidence rates of MDD, with a correspond ing pooled rate of 2.9 per 100. CONCLUSIONS: The prevalence of mood disorders reported in high-quality studies is generally lower than rates commonly reported in the general psychiatric literature. When controlled for common methodological confounds, variation in prevalence rates persists across studies and deserves continued study. Methodological variation among studies that have examined the prevalence of depression in primary health care services is so large that comparative analyses cannot be achieved.
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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 | 0.030 | 0.129 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.012 | 0.011 |
| Bibliometrics | 0.030 | 0.025 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".