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HCIL-2015-17

Bjarnadottir, M., Malik, S., Onukwugha, E., Gooden, T., Plaisant, C. (October 2015)
Understanding Adherence and Prescription Patterns Using Large Scale Claims Data
To appear in PharmacoEconomics
HCIL-2015-17

Purpose: 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 is to demonstrate the use of visualization tools for pattern discovery, hypothesis generation and study design.

Method: We utilize 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 (ACE-I), Angiotensin II receptor blockers (ARB), Beta blockers (Beta), Calcium channel blockers (CCB) and Diuretics (Diur).

We focus on those members initiating therapy with Diuretics (19.2%) who may concurrently or subsequently take drugs in other classes as well. We identify longitudinal patterns in prescription fills for antihypertensive medications, investigate the implications of decisions regarding gap length and overlaps, and examine 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 is 52.4 years and 53.1% of the population is female. When the allowable gap is zero, 34% of the population has continuous coverage and the average length of continuous coverage is 2 months. In contrast, when the allowable gap is 30 days, 69% of the population shows a single continuous prescription period with an average length of 5 months. The average prescription cost of the period of continuous coverage ranges from $3.44 (when the maximum gap is 0 days) to $9.08 (when the maximum gap is 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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