Depression and antecedent medication adherence in a cohort of new metformin users
Bibliographic record
Abstract
AIMS: The association between depression and poor medication adherence is based on cross-sectional studies and cohort studies that measure adherence rates after depression status is determined. However, depressive symptoms occur well before diagnosis. This study examined adherence patterns in the year before a depressive episode. METHODS: This retrospective cohort study followed new metformin users identified in Alberta Health's administrative data between 2008 and 2018. Depressive episodes starting ≥1 year after metformin initiation were identified using a validated case definition. Controls were randomly assigned a pseudo depression date. Adherence to oral antihyperglycemic medications was estimated using proportion of days covered (PDC) and group-based trajectory models to explore the association between depression and poor adherence (PDC<0.8). RESULTS: A depressive episode occurred in 17,418 (10.6%) of 165,056 new metformin users. Individuals with depression were more likely to have poor adherence compared to controls (adjusted odds ratio 1.21; 95% CI 1.17, 1.26). Five trajectories were identified: nearly perfect adherence (PDC >0.95 [34.8% of cohort]), discontinued (PDC=0 [18.3% of cohort], poor initial adherence (PDC 0.75) that declined either rapidly (9.2% of cohort) or gradually (30.1% of cohort), and poor initial adherence (PDC 0.26) that increased gradually (7.6% of cohort). Individuals with depression were more likely to be in one of the four trajectories of poor adherence compared to controls (adjusted odds ratio 1.24; 95% CI 1.19-1.29). CONCLUSIONS: Poor medication adherence occurs in the year before a depressive episode; therefore, poor medication use patterns could be used as an early warning sign for 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 | 0.000 | 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".