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Understanding Adherence and Prescription Patterns Using Large-Scale Claims Data

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Abstract

Background

Advanced computing capabilities and novel visual analytics tools now allow us to move beyond the traditional cross-sectional summaries to analyze longitudinal prescription patterns and the impact of study design decisions. For example, design decisions regarding gaps and overlaps in prescription fill data are necessary for measuring adherence using prescription claims data. However, little is known regarding the impact of these decisions on measures of medication possession (e.g., medication possession ratio). The goal of the study was to demonstrate the use of visualization tools for pattern discovery, hypothesis generation, and study design.

Method

We utilized EventFlow, a novel discrete event sequence visualization software, to investigate patterns of prescription fills, including gaps and overlaps, utilizing large-scale healthcare claims data. The study analyzes data of individuals who had at least two prescriptions for one of five hypertension medication classes: ACE inhibitors, angiotensin II receptor blockers, beta blockers, calcium channel blockers, and diuretics. We focused on those members initiating therapy with diuretics (19.2 %) who may have concurrently or subsequently take drugs in other classes as well. We identified longitudinal patterns in prescription fills for antihypertensive medications, investigated the implications of decisions regarding gap length and overlaps, and examined the impact on the average cost and adherence of the initial treatment episode.

Results

A total of 790,609 individuals are included in the study sample, 19.2 % (N = 151,566) of whom started on diuretics first during the study period. The average age was 52.4 years and 53.1 % of the population was female. When the allowable gap was zero, 34 % of the population had continuous coverage and the average length of continuous coverage was 2 months. In contrast, when the allowable gap was 30 days, 69 % of the population showed a single continuous prescription period with an average length of 5 months. The average prescription cost of the period of continuous coverage ranged from US$3.44 (when the maximum gap was 0 day) to US$9.08 (when the maximum gap was 30 days). Results were less impactful when considering overlaps.

Conclusions

This proof-of-concept study illustrates the use of visual analytics tools in characterizing longitudinal medication possession. We find that prescription patterns and associated prescription costs are more influenced by allowable gap lengths than by definitions and treatment of overlap. Research using medication gaps and overlaps to define medication possession in prescription claims data should pay particular attention to the definition and use of gap lengths.

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Notes

  1. The start-date of the first treatment episode is different for each patient, as a result the year of the cost differs from one patient to the next.

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Author contributions

The interpretation and reporting of the results are the sole responsibility of the authors. Dr. Bjarnadottir will act as the guarantor of the work presented in this paper. Dr. Bjarnadottir contributed to the study design, data collection, conduct and interpretation of the analysis, and drafted and revised the manuscript with input from all co-authors. Sana Malik created all figures in the paper, and contributed to the study design, the application of EventFlow to the data, interpretation of the analysis, and commented on/edited all drafts of the manuscript. Dr. Onukwugha contributed to the study design, interpretation of the analysis, drafted and revised the manuscript. Dr. Plaisant contributed to the study design, the application of EventFlow and the interpretation of the analysis, reviewed and commented on/edited all drafts of the manuscript. Tanisha Gooden contributed to the interpretation of the analysis, drafted and/or reviewed and commented on/edited all drafts of the manuscript.

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Authors and Affiliations

  1. Robert H. Smith School of Business, 4324 Van Munching Hall, College Park, MD, 20742, USA

    Margrét V. Bjarnadóttir

  2. Human-Computer Interaction Lab, University of Maryland, College Park, MD, USA

    Sana Malik & Catherine Plaisant

  3. Department of Pharmaceutical Health Services Research, Baltimore, MD, USA

    Eberechukwu Onukwugha

  4. Pharmaceutical Research Computing, Pharmaceutical Health Services Research, University of Maryland, Baltimore, MD, USA

    Tanisha Gooden

Authors
  1. Margrét V. Bjarnadóttir
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  2. Sana Malik
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  5. Catherine Plaisant
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Corresponding author

Correspondence to Margrét V. Bjarnadóttir.

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Funding

This study was partially funded by The University of Maryland/Mpowering the State through the Center for Health-Related Informatics and Bioimaging.

Conflicts of interest

Dr. Onukwugha reports consulting income from AstraZeneca and Janssen Analytics. Dr. Bjarnadottir has no conflicts of interest to declare. Sana Malik has no conflicts of interest to declare. Tanisha Gooden has no conflicts of interest to declare. Dr. Plaisant reports being a stake-holder in EventFlow through indirect benefit from commercial use of the software. The EventFlow software has been disclosed as an invention with the University of Maryland Office of Technology Commercialization (OTC) so that the OTC can negotiate commercial licenses with companies interested in licensing the software. The IP is owned by the campus and the income is distributed among different entities on campus (OTC, Colleges and departments of the inventors) with a small percentage of the income from those licenses returned to the inventors and used to support further research. The OTC website specifies the availability of the software and the policy of distributing a portion of the income to the inventors.

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Bjarnadóttir, M.V., Malik, S., Onukwugha, E. et al. Understanding Adherence and Prescription Patterns Using Large-Scale Claims Data. PharmacoEconomics 34, 169–179 (2016). https://doi.org/10.1007/s40273-015-0333-4

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