Does antidepressant medication use affect persistence with diabetes medicines?
Bibliographic record
Abstract
PURPOSE: This study aimed to examine the effect of antidepressant use on persistence with newly initiated oral antidiabetic medicines in older people. METHODS: A retrospective study of administrative claims data from the Australian Government Department of Veterans' Affairs, from 1 July 2000 to 30 June 2008 of new users of oral antidiabetic medicines (metformin or sulfonylurea). Antidepressant medicine use was determined in the 6 months preceding the index date of the first dispensing of an oral antidiabetic medicine. The outcome was time to discontinuation of diabetes therapy in those with antidepressant use compared with those without. Competing risks regression analyses were conducted with adjustment for covariates. RESULTS: A total of 29,710 new users of metformin or sulfonylurea were identified, with 7171 (24.2%) dispensed an antidepressant. Median duration of oral antidiabetic medicines was 1.81 years (95% CI 1.72–1.94) for those who received an antidepressant at the time of diabetes medicine initiation, by comparison to 3.23 years (95% CI 3.10–3.40) for those who did not receive an antidepressant. Competing risk analyses showed a 42% increased likelihood of discontinuation of diabetes medications in persons who received an antidepressant (subdistribution hazard ratio 1.42, 95% CI 1.37–1.47, p < 0.001). CONCLUSIONS: The results of this large population-based study demonstrate that depression may be contributing to non-compliance with medicines for diabetes and highlight the need to provide additional services to support appropriate medicine use in those initiating diabetes medicines with co-morbid depression.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 teacher head, 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".