Prevalence of and factors associated with primary medication non‐adherence in chronic disease: A systematic review and meta‐analysis
Bibliographic record
Abstract
BACKGROUND: Primary medication non-adherence (PMN), defined as failure to obtain newly prescribed medications, results in adverse clinical and economic outcomes. We aimed to (a) assess the prevalence of PMN in six common chronic diseases: asthma and/ or chronic obstructive pulmonary disease, depression, diabetes mellitus, hyperlipidaemia, hypertension and osteoporosis; (b) identify and categorise factors associated with PMN; (c) explore characteristics that contributed to heterogeneity between studies. METHODS: We performed a systematic search in MEDLINE, Embase, Cochrane Library, CINAHL and PsycINFO. Studies published in English between January 2008 and August 2018 assessing PMN in subjects aged ≥18 years were included. We used the Cochrane risk of bias tool, Newcastle-Ottawa Scale and National Heart, Lung and Blood Institute Quality Assessment Tool to assess the quality of randomised controlled trials, cohort and cross-sectional studies, respectively. Findings were reported using the PRISMA checklist. PMN rates were pooled using a random effects model. We summarised factors associated with PMN descriptively. Subgroup analysis was performed to explore sources of heterogeneity. RESULTS: We screened 3083 articles and included 33 (5 randomised controlled trials, 26 cohort and 2 cross-sectional studies, n = 539 156), of which 31 (n = 519 971) were used in meta-analysis. The pooled PMN rate was 17% (95% CI: 15%-20%). Pooled PMN rates were highest in osteoporosis (25%, 95% CI: 7%-44%) and hyperlipidaemia (25%, 95% CI: 18%-32%) and lowest in diabetes mellitus (10%, 95% CI: 7%-12%). Factors commonly associated with PMN include younger age, number of concurrent medications, practitioner specialty and higher co-payment. Type of chronic disease, age, study setting and PMN definition contributed to heterogeneity between studies (all P < 0.001). CONCLUSION: Primary medication non-adherence is common among patients with chronic diseases and more needs to be done to address this issue in order to improve patient outcomes. Future PMN studies could benefit from greater standardisation to enhance comparability.
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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 | 0.022 | 0.048 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.042 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 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".