Evidence map›Paper›PMID 26660349›Full record

ArticlePharmacoEconomics2016

Understanding Adherence and Prescription Patterns Using Large-Scale Claims Data.

Margrét V Bjarnadóttir, Sana Malik, Eberechukwu Onukwugha, Tanisha Gooden, Catherine Plaisant

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In one paragraph

Article in PharmacoEconomics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
2.8field-weighted citation impact, top 11% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

9 citing papers in PubMed, 22 citations in OpenAlex.

  1. High unmet treatment needs in patients with chronic kidney disease and type 2 diabetes: real-world evidence from a US claims database.Nephrology, dialysis, transplantation : official publication of the European Dialysis and Transplant Association - European Renal Association · 2023
    Article
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  5. Detecting Patterns of Prescription Drug Use During Pregnancy and Lactation with Visualization Techniques.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2019
    Article
  6. Article
  7. Visualization of temporal patterns in patient record data.Fundamental & clinical pharmacology · 2018
    Article
  8. Giardiasis Diagnosis and Treatment Practices Among Commercially Insured Persons in the United States.Clinical infectious diseases : an official publication of the Infectious Diseases Society of America · 2017
    Article
  9. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors at 3 institutions in 1 country.

Margrét V BjarnadóttirRobert H. Smith School of Business, 4324 Van Munching Hall, College Park, MD, 20742, USA. margret@rhsmith.umd.edu.
Sana MalikHuman-Computer Interaction Lab, University of Maryland, College Park, MD, USA.
Eberechukwu OnukwughaDepartment of Pharmaceutical Health Services Research, Baltimore, MD, USA.
Tanisha GoodenPharmaceutical Research Computing, Pharmaceutical Health Services Research, University of Maryland, Baltimore, MD, USA.
Catherine PlaisantHuman-Computer Interaction Lab, University of Maryland, College Park, MD, USA.
University of Maryland, College Park · USBaltimore City Health Department · USUniversity of Maryland, Baltimore · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAdvanced 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.

methodWe 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.

resultsA 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.

conclusionsThis 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.

Indexed as

Medication AdherenceAntihypertensive AgentsDatabases, FactualDecision MakingDrug CostsFemaleHumansMaleMiddle AgedPrescription DrugsResearch DesignSoftwareAntihypertensive AgentsPrescription Drugs

Identifiers

PMID26660349
OpenAlexW2197549681

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.