title: A model-based meta-analysis of the influence of factors that impact adherence to medications.
abstract: OBJECTIVE
  Several studies have investigated factors that may influence adherence for a given disease. The influence of disease on adherence has received limited attention. Less work has been conducted to investigate the influence of other factors in conjunction with disease on adherence. The aim of this study was to determine the independent influence of disease and other factors on adherence.
  
  
  METHODS
  A literature search was conducted to retrieve adherence studies using medication event monitoring system devices. Studies were categorized into different therapeutic areas. Only the two most commonly studied therapeutic areas were selected. Pseudopatient-level data were extracted from each study. The extracted data were analysed using a model-based meta-analysis technique. Univariate and multivariate models were developed. Model selection was based on a likelihood ratio test and visual plots.
  
  
  RESULTS
  The most commonly studied therapeutic areas were HIV and hypertension. The most commonly recorded adherence criterion was percentage of prescribed doses taken per day. Based on this adherence criterion, ultimately, 24 HIV papers and 12 hypertension papers were included for data extraction. The statistically significant factors were disease, age and dosing regimen. The independent influences of each factor on adherence were as follows: an increase in adherence of approximately 8% per 10-year increase of age, a 15-19% reduction from once to thrice daily dosing and that patients with HIV were 5% more adherent than those with hypertension.
  
  
  CONCLUSIONS
  Although the influence of disease on adherence was significant, it was of limited clinical significance in the diseases studied here. Adherence appears to improve with age and decline with more frequent dosing. Additionally, the influence of dosing regimen wanes with increasing age. These results should be treated as exploratory and require prospective assessment.
authors.affiliation: School of Pharmacy, University of Otago, Dunedin, New Zealand.
authors.forename: P
authors.initials: P
authors.lastname: Assawasuwannakit
authors.forename: R
authors.initials: R
authors.lastname: Braund
authors.forename: S B
authors.initials: SB
authors.lastname: Duffull
mesh_terms: D004334:Drug Administration Schedule; D006801:Humans; D015233:Models, Statistical; D016903:Drug Monitoring
keywords: adherence; age; disease; medication event monitoring system; model-based meta-analysis; nonlinear mixed effects modelling