No Longer Undertreated? Depression Diagnosis and Antidepressant Therapy in Elderly Long‐Stay Nursing Home Residents, 1999 to 2007
Bibliographic record
Abstract
OBJECTIVES: To examine the evolution of depression identification and use of antidepressants in elderly long-stay nursing home residents from 1999 through 2007 and the associated sociodemographic and facility characteristics. DESIGN: Annual cross-sectional analysis of merged resident assessment data from the Minimum Data Set (MDS) and facility characteristics from the Online Survey Certification and Reporting data. SETTING: Nursing homes in eight states (5,445 facilities). PARTICIPANTS: Long-stay nursing home residents aged 65 and older (2,564,687 assessments). MEASUREMENTS: Physician-documented depression diagnoses recorded in the MDS were used to identify residents with depression; antidepressant use was measured using MDS information about residents' receipt of an antidepressant in the 7 days before assessment. RESULTS: Diagnosis of depression and antidepressant therapy in residents diagnosed increased at a rapid rate. By 2007, 51.8% of residents were diagnosed with depression, 82.8% of whom received an antidepressant. Adjusted odds of treatment were higher for younger residents, whites, and those with moderate impairment of cognitive function. CONCLUSION: This study demonstrates striking increases in depression diagnosis and treatment with antidepressant medications, but disparities persist without clear evidence about underlying mechanisms. More research is needed to assess effectiveness of antidepressant prescribing.
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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 | 0.000 | 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 | 0.001 |
| 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".