Lifestyle factors as predictors of nonadherence to statin therapy among patients with and without cardiovascular comorbidities
Bibliographic record
Abstract
BACKGROUND: Easily detectable predictors of nonadherence to long-term drug treatment are lacking. We investigated the association between lifestyle factors and nonadherence to statin therapy among patients with and without cardiovascular comorbidities. METHODS: We included 9285 participants from the Finnish Public Sector Study who began statin therapy after completing the survey. We linked their survey data with data in national health registers. We used prescription dispensing data to determine participants' nonadherence to statin therapy during the first year of treatment (defined as < 80% of days covered by filled prescriptions). We used logistic regression to estimate the association of several lifestyle factors with nonadherence, after adjusting for sex, age and year of statin initiation. RESULTS: Of the participants without cardiovascular comorbidities (n = 6458), 3171 (49.1%) were nonadherent with their statin therapy. Obesity (adjusted odds ratio [OR] 0.86, 95% confidence interval [CI] 0.74-0.99), overweight (adjusted OR 0.88, 95% CI 0.79-0.98) and former smoking (adjusted OR 0.82, 95% CI 0.74-0.92) predicted a reduced risk of nonadherence in this group after adjustment for sex, age and year of statin initiation. Of the participants with cardiovascular comorbidities (n = 2827), 1155 (40.9%) were nonadherent. In this group, high alcohol consumption (adjusted OR 1.55, 95% CI 1.12-2.15), extreme drinking occasions (adjusted OR 1.48, 95% CI 1.11-1.97) and a cluster of 3-4 lifestyle risks (adjusted OR 1.61, 95% CI 1.15-2.27) predicted increased odds of nonadherence after adjustment for sex, age and year of statin initiation. INTERPRETATION: People with cardiovascular comorbidities who had risky drinking behaviours or a cluster of lifestyle risks were at increased risk of nonadherence. Among individuals without cardiovascular comorbidities, information on lifestyle factors was unhelpful in identifying those at increased risk of nonadherence; that overweight, obesity and former smoking were predictors of better adherence in this group provides insight into mechanisms of adherence to preventive medication that deserve further study.
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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".