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Res Social Adm Pharm. 2018 May 19. pii: S1551-7411(18)30407-8. doi: 10.1016/j.sapharm.2018.05.010. [Epub ahead of print]

An innovative and comprehensive technique to evaluate different measures of medication adherence: The network meta-analysis.

Author information

1
Pharmaceutical Sciences Postgraduate Programme, Federal University of Paraná, Curitiba, Brazil. Electronic address: stumpf.tonin@ufpr.br.
2
Graduate School of Health, University of Technology Sydney, Australia. Electronic address: Elyssa.wiecek@student.uts.edu.au.
3
Graduate School of Health, University of Technology Sydney, Australia. Electronic address: Andrea.j.torresrobles@student.uts.edu.au.
4
Department of Pharmacy, Federal University of Paraná, Curitiba, Brazil. Electronic address: pontarolo@ufpr.br.
5
Graduate School of Health, University of Technology Sydney, Australia. Electronic address: shalom.benrimoj@uts.edu.au.
6
Research Institute for Medicines (iMed.ULisboa), Department of Social Pharmacy, Faculty of Pharmacy, Universidade de Lisboa, Lisbon, Portugal. Electronic address: f-llimos@ff.ul.pt.
7
Graduate School of Health, University of Technology Sydney, Australia. Electronic address: victoria.garciacardenas@uts.edu.au.

Abstract

BACKGROUND:

Poor medication adherence is associated with adverse health outcomes and higher costs of care. However, inconsistencies in the assessment of adherence are found in the literature.

OBJECTIVE:

To evaluate the effect of different measures of adherence in the comparative effectiveness of complex interventions to enhance patients' adherence to prescribed medications.

METHODS:

A systematic review with network meta-analysis was performed. Electronic searches for relevant pairwise meta-analysis including trials of interventions that aimed to improve medication adherence were performed in PubMed. Data extraction was conducted with eligible trials evaluating short-period adherence follow-up (until 3 months) using any measure of adherence: self-report, pill count, or MEMS (medication event monitoring system). To standardize the results obtained with these different measures, an overall composite measure and an objective composite measure were also calculated. Network meta-analyses for each measure of adherence were built. Rank order and surface under the cumulative ranking curve analyses (SUCRA) were performed.

RESULTS:

Ninety-one trials were included in the network meta-analyses. The five network meta-analyses demonstrated robustness and reliability. Results obtained for all measures of adherence were similar across them and to both composite measures. For both composite measures, interventions comprising economic + technical components were the best option (90% of probability in SUCRA analysis) with statistical superiority against almost all other interventions and against standard care (odds ratio with 95% credibility interval ranging from 0.09 to 0.25 [0.02, 0.98]).

CONCLUSION:

The use of network meta-analysis was reliable to compare different measures of adherence of complex interventions in short-periods follow-up. Analyses with longer follow-up periods are needed to confirm these results. Different measures of adherence produced similar results. The use of composite measures revealed reliable alternatives to establish a broader and more detailed picture of adherence.

KEYWORDS:

Intervention; Measurement methods; Medication adherence; Network meta-analysis; Systematic review

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