Strategies for improving adherence to antiepileptic drug treatment in people with epilepsy
Bibliographic record
Abstract
BACKGROUND: Poor adherence to antiepileptic medication is associated with increased mortality, morbidity and healthcare costs. In this review, we focus on interventions designed and tested in randomised controlled trials (RCTs) and quasi-RCTs to assist people with adherence to antiepileptic medication. This is an update of a Cochrane review first published in 2011, and last updated in 2017. OBJECTIVES: To determine the effectiveness of interventions aimed at improving adherence to antiepileptic medication in adults and children with epilepsy. SEARCH METHODS: For the latest update, we searched the following databases on 18 February 2020: Cochrane Register of Studies (CRS Web), MEDLINE, CINAHL Plus and PsycINFO. CRS Web includes RCTs or quasi-RCTs from PubMed, Embase, ClinicalTrials.gov, the World Health Organization International Clinical Trials Registry Platform (ICTRP), CENTRAL, and the Specialized Registers of Cochrane Review Groups including Epilepsy. We also searched the reference lists of relevant articles. SELECTION CRITERIA: RCTs and quasi-RCTs of adherence-enhancing interventions aimed at people with a clinical diagnosis of epilepsy (as defined in individual studies), of any age and treated with antiepileptic drugs in a primary care, outpatient or other community setting. DATA COLLECTION AND ANALYSIS: All review authors independently assessed lists of potentially relevant citations and abstracts. At least two review authors independently extracted data and performed a quality assessment of each study according to the Cochrane tool for assessing risk of bias. We graded the level of evidence for each outcome according to GRADE. The studies differed widely according to the type of intervention and measures of adherence; therefore combining data was not appropriate. MAIN RESULTS: We included 20 studies reporting data on 2832 participants. Thirteen studies targeted adults with epilepsy, one study included participants of all ages, one study included participants older than two years, one recruited pediatric patients aged between 1 month to 15 years, one study targeted caregivers of children with epilepsy, one targeted adolescents and caregivers, and two studies targeted families of children with epilepsy. We identified three ongoing studies. Follow-up time was generally short in most studies, ranging from 1 to 12 months. The studies examined three main types of interventions: educational interventions, behavioural interventions and mixed interventions. All but three studies compared treatment with usual care or 'no intervention'. Due to heterogeneity between studies in terms of interventions, methods used to measure adherence and the way the studies were reported, we did not pool the results and these findings were inappropriate to be included in a meta-analysis. Education and counselling of participants with epilepsy had mixed success (moderate-certainty evidence). Behavioural interventions such as the use of intensive reminders provided more favourable effects on adherence (moderate-certainty evidence). The effect on adherence to antiepileptic drugs described by studies of mixed interventions showed improved adherence in the intervention groups compared to the control groups (high-certainty evidence). Eleven studies described seizure frequency or seizure severity or both, with four of them, reporting improved adherence and decreased seizure frequency in the intervention groups (moderate-certainty evidence). Findings related to self-efficacy and quality of life were mixed, with no clear pattern across types of intervention. AUTHORS' CONCLUSIONS: Behavioural interventions such as intensive reminders and the use of mixed interventions demonstrate some positive results, however, we need more reliable evidence on their efficacy, derived from carefully-designed RCTs before we can draw a firm conclusion. None of the newly included studies have provided additional information that would lead to significant changes in our conclusions.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".