Diagnosis and Treatment of Depression in Older Community‐Dwelling Adults: 1992–2005
Bibliographic record
Abstract
OBJECTIVE: To examine evolving patterns of depression diagnosis and treatment in older U.S. adults in the era of newer-generation antidepressants. DESIGN: Trend analysis using data from the Medicare Current Beneficiary Survey, a nationally representative survey of Medicare enrollees, from 1992 to 2005. SETTING: Community, usual care. PARTICIPANTS: Older Medicare fee-for-service beneficiaries. MEASUREMENTS: Depression diagnoses and psychotherapy use identified from Medicare claims; antidepressant use identified from detailed medication inventories conducted by interviewers. RESULTS: The proportion of older adults who received a depression diagnosis doubled, from 3.2% to 6.3%, with rates increasing substantially across all demographic subgroups. Of those diagnosed, the proportion receiving antidepressants increased from 53.7% to 67.1%, whereas the proportion receiving psychotherapy declined from 26.1% to 14.8%. Adjusting for other characteristics, odds of antidepressant treatment in older adults diagnosed with depression were 86% greater for women, 53% greater for men, 89% greater for whites, 13% greater for African Americans, 84% greater for metropolitan-area residents, and 55% greater for nonmetropolitan-area residents. Odds of antidepressant treatment were 54% greater for those diagnosed with major depressive disorder (MDD) and 83% greater for those with other depression diagnoses, whereas the odds of receiving psychotherapy was 29% lower in those with MDD diagnoses and 74% lower in those with other depression diagnoses. CONCLUSION: Overall diagnosis and treatment rates increased over time. Antidepressants are assuming a more-prominent and psychotherapy a less-prominent role. These shifts are most pronounced in groups with less-severe depression, in whom evidence of efficacy of treatment with antidepressants alone is less clear.
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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 | 0.002 |
| 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 | 0.001 |
| 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".