Interventions to improve adherence to statins: a summary of current evidence
Bibliographic record
Abstract
Statin non-adherence and discontinuation is common and associated with a higher risk of cardiovascular events than adherence and persistence. We aimed to summarise the effectiveness of interventions employed to improve statin adherence and persistence. Data sources included systematic reviews (SR), meta-analyses (MA), randomised controlled trials (RCT) and alternative design studies (e.g. cohort, quasi-experimental, prospective and retrospective) from EMBASE, Medline and PubMed databases, without date restrictions. Studies that evaluated an intervention targeting adherence to self-administered statin medication for primary or secondary prevention were eligible. Adherence, as primary measure, and lipid levels as secondary measure were analysed. Findings were reported by category of intervention and study design. Potential implementation of successful interventions within the UK NHS was assessed, as well as the resources required. Nineteen SR and MAs, forty-three RCTs, and twenty studies of alternative design were included. Interventions were categorized into eight categories. Modification of statin regimen, financial considerations, and blended interventions improved statin adherence compared to usual care. Patient-targeting behavioural interventions were least likely to be successful. Pharmacists were most commonly involved in intervention delivery. All sixty-one interventions were found to be practical, albeit some were dependent on availability of resources required. HCP training was needed for almost 80% of the interventions. A variety of successful interventions to improve adherence to statins have been reported and are easily applicable within the NHS. Investment in staff training is important for successful implementation of these interventions in routine practice.
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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 | 0.012 | 0.036 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.008 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".