A systematic overview of systematic reviews evaluating medication adherence interventions
Bibliographic record
Abstract
PURPOSE: To systematically summarize evidence from multiple systematic reviews (SRs) examining interventions addressing medication nonadherence and to discern differences in effectiveness by intervention, patient, and study characteristics. SUMMARY: MEDLINE, the Cochrane Database of Systematic Reviews, and the Database of Abstracts of Reviews of Effects were searched for papers published from January 2004 to February 2017. English-language SRs examining benefits of medication adherence interventions were eligible. Inclusion was limited to adult patients prescribed medication for 1 of the following disease conditions: diabetes and prediabetes, heart conditions, hypertension and prehypertension, stroke, and cognitive impairment. Non-disease-specific SRs that considered medication adherence interventions for older adults, adults with chronic illness, and adults with known medication adherence problems were also included. Two researchers independently screened titles, abstracts, and full-text articles. They then extracted key variables from eligible SRs, reconciling discrepancies via discussion. A MeaSurement Tool to Assess systematic Reviews (AMSTAR) was used to assess SRs; those with scores below 8 were excluded. Conclusions regarding intervention effectiveness were extracted. Grades of Recommendation, Assessment, Development and Evaluation (GRADE) methodology was applied to assess evidence quality. RESULTS: Of 390 SRs, 25 met the inclusion criteria and assessed adherence as a primary outcome. Intervention types most consistently found to be effective were dose simplification, patient education, electronic reminders to patients, and reduced patient cost sharing or incentives. Of 50 conclusions drawn by the SRs, the underlying evidence was low or very low quality for 45 SRs. CONCLUSION: Despite an abundance of primary studies and despite only examining high-quality SRs, the vast majority of primary studies supporting SR authors' conclusions were of low or very low quality. Nonetheless, health system leaders seeking to improve medication adherence should prioritize interventions that have been studied and found to be effective at improving patient adherence, including dose simplification, education, reminders, and financial incentives.
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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 | 0.043 | 0.157 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.017 | 0.015 |
| Bibliometrics | 0.038 | 0.023 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".