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 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 | 0.020 | 0.045 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.023 | 0.044 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".