An empirical method to cluster objective nebulizer adherence data among adults with cystic fibrosis
Received 3 January 2017
Accepted for publication 7 March 2017
Published 24 March 2017 Volume 2017:11 Pages 631—642
Checked for plagiarism Yes
Review by Single-blind
Peer reviewers approved by Dr Akshita Wason
Peer reviewer comments 2
Editor who approved publication: Dr Johnny Chen
Zhe H Hoo,1,2 Michael J Campbell,1 Rachael Curley,1,2 Martin J Wildman1,2
1School of Health and Related Research (ScHARR), University of Sheffield, 2Sheffield Adult Cystic Fibrosis Centre, Northern General Hospital, Sheffield, UK
Background: The purpose of using preventative inhaled treatments in cystic fibrosis is to improve health outcomes. Therefore, understanding the relationship between adherence to treatment and health outcome is crucial. Temporal variability, as well as absolute magnitude of adherence affects health outcomes, and there is likely to be a threshold effect in the relationship between adherence and outcomes. We therefore propose a pragmatic algorithm-based clustering method of objective nebulizer adherence data to better understand this relationship, and potentially, to guide clinical decisions.
Methods to cluster adherence data: This clustering method consists of three related steps. The first step is to split adherence data for the previous 12 months into four 3-monthly sections. The second step is to calculate mean adherence for each section and to score the section based on mean adherence. The third step is to aggregate the individual scores to determine the final cluster (“cluster 1” = very low adherence; “cluster 2” = low adherence; “cluster 3” = moderate adherence; “cluster 4” = high adherence), and taking into account adherence trend as represented by sequential individual scores. The individual scores should be displayed along with the final cluster for clinicians to fully understand the adherence data.
Three illustrative cases: We present three cases to illustrate the use of the proposed clustering method.
Conclusion: This pragmatic clustering method can deal with adherence data of variable duration (ie, can be used even if 12 months’ worth of data are unavailable) and can cluster adherence data in real time. Empirical support for some of the clustering parameters is not yet available, but the suggested classifications provide a structure to investigate parameters in future prospective datasets in which there are accurate measurements of nebulizer adherence and health outcomes.
Keywords: cystic fibrosis, medication adherence, nebulizers and vaporizers, epidemiologic methods, cluster analysis
This work is published by Dove Medical Press Limited, and licensed under a Creative Commons Attribution License. The full terms of the License are available at http://creativecommons.org/licenses/by/4.0/. The license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.Download Article [PDF] View Full Text [HTML][Machine readable]