The Prevalence and Incidence of Treated Major Depressive Disorder among National Health Insurance Enrollees in Taiwan, 1996 to 2003
Bibliographic record
Abstract
OBJECTIVE: We used the National Health Insurance (NHI) database to examine the prevalence and incidence of treated major depressive disorder (MDD) and their associated factors. METHOD: The National Health Research Institute provided a database of 200 432 randomly selected subjects for study. We obtained a population-based random sample aged 15 years or older (n = 136 045) as a fixed cohort dated 1996 to 2003. We identified study subjects with a principal diagnosis of MDD who had at least one service claim during these years for either ambulatory or inpatient care. RESULTS: From 1996 to 2003, the cumulative treated prevalence increased from 1.67 per 1000 to 17.24 per 1000. From 1997 to 2003, the annual treated incidence increased from 1.89 per 1000 to 2.58 per 1000. A higher incidence of treated MDD was detected in the groups aged 25 to 44 years (hazard ratio [HR] 1.28; 95% confidence interval [CI], 1.13 to 1.45), 45 to 64 years (HR 1.90; 95% CI, 1.66 to 2.16), and 65 years or older (HR 1.87; 95% CI, 1.59 to 2.20); in female subjects (HR 1.97; 95% CI, 1.80 to 2.15); in those with with an insurance amount of US $1281 or more (HR 1.15; 95% CI, 1.01 to 1.31); in those with a fixed premium (HR 1.44; 95% CI, 1.27 to 1.62); and among those who lived in urban areas (HR 1.22; 95% CI, 1.10 to 1.35). CONCLUSIONS: For treated MDD, the prevalence and incidence in Taiwan were lower than in community studies in Western countries. Individuals with MDD are underdiagnosed and undertreated in Taiwan.
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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".