Prevalence and determinants of adherence to statin therapy: a systematic review and meta-analysis
Bibliographic record
Abstract
AIM: To estimate the prevalence of good adherence to statin therapy and identify demographic and clinical factors associated with adherence among adults prescribed lipid-lowering therapy (LLT) for atherosclerotic cardiovascular disease (ASCVD) prevention. METHODS: We conducted a systematic search of PubMed and Scopus through May 2025 to identify randomized controlled trials, cohort, nested case-control, and cross-sectional studies evaluating adherence to statin monotherapy. Data were extracted on study design, participant demographics, comorbidities, adherence assessment method and duration, and statin type. A random-effects meta-analysis was performed. Study quality was assessed using the Newcastle-Ottawa Scale, and risk of bias in randomized trials was evaluated with the Cochrane RoB 2 tool. Subgroup and sensitivity analyses examined adherence variations by follow-up duration (<1, 1, >1 year), alternative adherence thresholds, and study quality. "Primary" non-adherence (failure to initiate prescribed therapy) was not reported in any of the included studies. RESULTS: Seventy-six studies encompassing 5,898,141 participants (median follow-up 24 months) were included. The pooled prevalence of good adherence (≥80% medication use) was 62.4% (95% CI: 58.3-66.5%), lower in primary (57.5%) than secondary (64.4%) prevention settings. Factors associated with lower adherence included female sex (RR=0.92), Black race (RR=0.66), smoking (RR=0.94), depression (RR=0.89), and heart failure (RR=0.96). Higher adherence was observed among older adults (RR=1.34), individuals with myocardial infarction (RR=1.28) or hypertension (RR=1.12), those with ≥2 comorbidities (RR=1.25), and patients with polypharmacy (RR=1.32). Subgroup and sensitivity analyses yielded consistent results. CONCLUSIONS: Adherence to statin therapy remains suboptimal and is significantly influenced by demographic and clinical factors. Targeted strategies are needed to improve adherence, particularly in high-risk groups.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 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) | 0.000 | 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".