For Publishers
DiscoveryMetadataPeer reviewHostingPublishing
For Researchers
JoinPublishReviewCollect
Register
Blog
About
Search
Advanced search
My ScienceOpen
Search
For Publishers
For Researchers
Blog
About
18
views
24
references
 Top references
cited by
36
    
0 reviews
 Review
0
comments
 Comment
0
recommends
+1 Recommend
0 collections
    0
    shares
      116
      similar
       All similar
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Computation of adherence to medication and visualization of medication histories in R with AdhereR: Towards transparent and reproducible use of electronic healthcare data

      research-article
      Author(s): 1 , 2 , 3 , 4 , *
      Editor(s):
      Publication date (Electronic):
      Journal: PLoS ONE
      Publisher: Public Library of Science

      Read this article at

      ScienceOpenPublisherPMC
      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Adherence to medications is an important indicator of the quality of medication management and impacts on health outcomes and cost-effectiveness of healthcare delivery. Electronic healthcare data (EHD) are increasingly used to estimate adherence in research and clinical practice, yet standardization and transparency of data processing are still a concern. Comprehensive and flexible open-source algorithms can facilitate the development of high-quality, consistent, and reproducible evidence in this field. Some EHD-based clinical decision support systems (CDSS) include visualization of medication histories, but this is rarely integrated in adherence analyses and not easily accessible for data exploration or implementation in new clinical settings. We introduce AdhereR, a package for the widely used open-source statistical environment R, designed to support researchers in computing EHD-based adherence estimates and in visualizing individual medication histories and adherence patterns. AdhereR implements a set of functions that are consistent with current adherence guidelines, definitions and operationalizations. We illustrate the use of AdhereR with an example dataset of 2-year records of 100 patients and describe the various analysis choices possible and how they can be adapted to different health conditions and types of medications. The package is freely available for use and its implementation facilitates the integration of medication history visualizations in open-source CDSS platforms.

          Related collections

          Most cited references24

          • Record: found
          • Abstract: found
          • Article: not found

          The assessment of refill compliance using pharmacy records: methods, validity, and applications.

          Ricardo A. Prochazka, F. Steiner (1996)
          The refill records of computerized pharmacy systems are used increasingly as a source of compliance information. We reviewed the English-language literature to develop a typology of methods for assessing refill compliance (RC), to describe the epidemiology of compliance in obtaining medications, to identify studies that attempted to validate RC measures, to describe clinical features that predicted RC, and to describe the uses of RC measures in epidemiologic and health services research. In most of the 41 studies reviewed, patients obtained less medication than prescribed; gaps in treatment were common. Of the studies that assessed the validity of RC measures, most found significant associations between RC and other compliance measures, as well as measures of drug presence (e.g., serum drug levels) or physiologic drug effects. Refill compliance was generally not correlated with demographic characteristics of study populations, was higher among drugs with fewer daily doses, and was inconsistently associated with the total number of drugs prescribed. We conclude that, though some methodologic problems require further study, RC measures can be a useful source of compliance information in population-based studies when direct measurement of medication consumption is not feasible.
          Article 0 comments Cited 140 times – based on 0 reviews     Review now
            Bookmark
            • Record: found
            • Abstract: found
            • Article: found
            Is Open Access

            Comparing Adherence and Persistence Across 6 Chronic Medication Classes

            Jason Yeaw, Joshua S. Benner, John G. Walt … (2009)
            BACKGROUND: The National Quality Forum recently endorsed the proportion of days covered (PDC)—a measure of medication adherence—as an indicator of quality in drug therapy management. OBJECTIVES: To inform initial efforts to improve the quality of drug therapy management, we compared PDC and persistence among new users of 6 commonly used chronic medication categories. METHODS: A retrospective analysis of pharmacy claims in a database of more than 64 million members enrolled in 100 health plans assessed persistence and adherence to drug therapy in 6 chronic conditions. Patients were included in the analysis if they initiated a prescription drug of interest in any of 6 drug classes—prostaglandin analogs, statins, bisphosphonates,oral antidiabetics, angiotensin II receptor blockers (ARBs), and overactive bladder (OAB) medications—between January 1 and December 31, 2005.The first claim for a drug of interest during this period was considered a patient’s index date. Patients were required to have a minimum of 12months of continuous enrollment both preceding and following their index date. New users of a treatment were identified by excluding patients who filled a prescription for any drug in the same class during the previous 12months and were followed for a minimum of 12 months. Nonpersistence was defined as discontinuation of the therapy class following an allowed gap between refills—30-, 60-, and 90-day refill gaps were used. Adherence was defined as a continuous measure of the proportion of days covered (PDC) during the 12-month post-index period. Logistic regression analyses predicted (a) nonpersistence during the 12-month post-index period and (b) adherence (PDC) of at least 80%, with drug class as the predictor variable of interest, controlling for demographic variables, insurance and plan type, history of hospitalization, Charlson comorbidity score,copayment for index medication, and number of medications at index. RESULTS: A total of 167,907 patients were identified across 6 cohorts.Using the 60-day gap, 6-month persistence rates were prostagl and in analogs 47%, statins 56%, bisphosphonates 56%, oral antidiabetics 66%,ARBs 63%, and OAB medications 28%. After the first 90 days of therapy,relative persistence was stable across cohorts, and rates declined consistently from 6 months post-index to study end. Logistic regression models showed that oral antidiabetic users had a 59%, 36%, 37%, and 79% decreased risk of nonpersistence in a 12-month follow-up period compared with patients taking prostaglandin analogs, statins, bisphosphonates, orOAB medications, respectively. Risk of nonpersistence decreased with increasing age. Mean (SD) 12-month adherence rates were: prostagl and in analogs 37% (26%), statins 61% (33%), bisphosphonates 60% (34%), oral antidiabetics 72% (32%), ARBs 66% (32%), and OAB medications 35%(32%). Logistic regression indicated that oral antidiabetic use was a significantpredictor of adherence (PDC) of at least 80% compared with other therapy classes. Adjusted odds ratios for oral antidiabetics were 17.60(95% confidence interval [CI]=15.38-20.14) versus prostaglandin analogs,2.06 (95% CI=1.99-2.12) versus statins, 1.92 (95% CI=1.83-2.02) versus bisphosphonates, 1.29 (95% CI=1.24-1.34) versus ARBs, and 5.77 (95%CI=5.38-6.19) versus OAB medications.
            Article 0 comments Cited 132 times – based on 0 reviews     Review now
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Assessing medication adherence: options to consider.

              Audrey Lehmann, Parisa Aslani, Rana Ahmed … (2014)
              Adherence to chronic therapy is a key determinant of patient health outcomes in chronic disease. However, only about 50 % of patients adhere to chronic therapy. One of the challenges in promoting adherence is having an accurate understanding of adherence rates and the factors that contribute to non-adherence. There are many measures available to assess patient medication adherence.
              Article 0 comments Cited 98 times – based on 0 reviews     Review now
                Bookmark
                All references

                Author and article information

                Contributors
                Christophe Leroyer: Role: Editor
                Journal
                Journal ID (nlm-ta): PLoS One
                Journal ID (iso-abbrev): PLoS ONE
                Journal ID (publisher-id): plos
                Journal ID (pmc): plosone
                Title: PLoS ONE
                Publisher: Public Library of Science (San Francisco, CA USA )
                ISSN (Electronic): 1932-6203
                Publication date (Electronic): 26 April 2017
                Publication date Collection: 2017
                Volume: 12
                Issue: 4
                Electronic Location Identifier: e0174426
                Affiliations
                [1 ]Amsterdam School of Communication Research ASCoR, University of Amsterdam, Amsterdam, the Netherlands
                [2 ]Health Services and Performance Research (HESPER EA 7425), University Claude Bernard Lyon 1, Lyon, France
                [3 ]Language and Genetics Department, Max Planck Institute for Psycholinguistics, Nijmegen, The Netherlands
                [4 ]Donders Institute for Brain, Cognition and Behavior, Radboud University, Nijmegen, The Netherlands
                Universite de Bretagne Occidentale, FRANCE
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                • Conceptualization: ALD.

                • Data curation: DD.

                • Formal analysis: ALD DD.

                • Investigation: ALD DD.

                • Methodology: ALD DD.

                • Software: DD ALD.

                • Validation: ALD DD.

                • Visualization: DD.

                • Writing – original draft: ALD.

                • Writing – review & editing: ALD DD.

                Author information
                http://orcid.org/0000-0002-0704-6365
                Article
                Publisher ID: PONE-D-16-48957
                DOI: 10.1371/journal.pone.0174426
                PMC ID: 5405929
                PubMed ID: 28445530
                SO-VID: 9b3a361b-725a-4020-80e2-874aecb69981
                Copyright © © 2017 Dima, Dediu
                License:

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                Date received : 11 December 2016
                Date accepted : 8 March 2017
                Page count
                Figures: 2, Tables: 5, Pages: 14
                Funding
                Funded by: funder-id http://dx.doi.org/10.13039/501100000780, European Commission;
                Award ID: 282593
                ALD has received funding from the European Community’s 7th Framework (FP7/2007-2013) under grant agreement n°282593 (ASTRO-LAB project) during the development of this tool.
                Categories
                Subject: Research Article
                Subject: Medicine and Health Sciences
                Subject: Pharmaceutics
                Subject: Drug Therapy
                Subject: Computer and Information Sciences
                Subject: Information Technology
                Subject: Data Processing
                Subject: Medicine and Health Sciences
                Subject: Computer and Information Sciences
                Subject: Computer Architecture
                Subject: Computer Hardware
                Subject: Computer and Information Sciences
                Subject: Data Visualization
                Subject: Biology and Life Sciences
                Subject: Taxonomy
                Subject: Computer and Information Sciences
                Subject: Data Management
                Subject: Taxonomy
                Subject: Computer and Information Sciences
                Subject: Computer Software
                Subject: Open Source Software
                Subject: Science Policy
                Subject: Open Science
                Subject: Open Source Software
                Subject: Research and Analysis Methods
                Subject: Research Assessment
                Subject: Reproducibility
                Custom metadata
                Data Availability The AdhereR package is available on CRAN (The Comprehensive R Archive Network; https://cran.r-project.org/), and source code, data, and documentation are available on GitHub ( https://github.com/ddediu/AdhereR). Additionally, S1 File contains a supplementary R script that, when used in conjunction with the package, produces all analyses and figures included in the paper.

                ScienceOpen disciplines: Uncategorized
                Data availability:
                ScienceOpen disciplines: Uncategorized

                Comments

                Comment on this article

                scite_

                Similar content116

                See all similar

                Cited by36

                See all cited by

                Most referenced authors367

                See all reference authors