Trajectories of Antidiabetic Medication Adherence in Older Adults and the Effect of Depression and Anxiety Symptoms
Bibliographic record
Abstract
OBJECTIVES: Common mental health disorders, such as anxiety and depression, significantly impact medication adherence in various chronic conditions. However, few studies have captured the evolving nature of adherence behavior, indicating a need for further investigation. The objectives were to describe adherence trajectories to antidiabetic medications among older patients and to explore the potential association between these trajectories and the presence of anxiety and depression. METHODS: We conducted a secondary analysis of the Enquête sur la santé des aînés et l'utilisation des services de santé study, involving 282 elderly participants who were prevalent users of antidiabetic medications. Medication adherence was measured using claims data over 12 months. Group-based trajectory modeling was employed to identify distinct adherence trajectories. The association between the presence of common mental health disorders, assessed using self-reported symptoms and diagnostic codes from medico-administrative data, and adherence trajectories was estimated through multinomial logistic regressions. RESULTS: Four distinct adherence trajectories were identified and defined as low adherence (6.7%), fair adherence (18.1%), high adherence (37.9%), and nearly perfect adherence (37.2%). These patterns were also stable during the 12-month follow-up period. No significant association was found between common mental health disorders and medication adherence trajectories in this cohort, even after adjusting for potential confounders. CONCLUSION: Older patients with diabetes mostly depicted high adherence. Depression or anxiety did not impact adherence trajectories. However, our study was underpowered to detect small-to-moderate effects of these common mental health disorders.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".