Evidence map›Paper›PMID 32768690›Full record

Trial reportAmerican heart journal2020

Comparison of a new 3-item self-reported measure of adherence to medication with pharmacy claims data in patients with cardiometabolic disease.

Julie C Lauffenburger, Constance P Fontanet, Thomas Isaac, Chandrasekar Gopalakrishnan, Thomas D Sequist, Joshua J Gagne, Cynthia A Jackevicius, Michael A Fischer, Daniel H Solomon, Niteesh K Choudhry

Open access · greenAbstract readPragmatic Clinical Trial
In one paragraph

Trial report in American heart journal, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed, 1 pooled it
2.5field-weighted citation impact, top 12% 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

12 citing papers in PubMed, 1 synthesis or guideline pooled it, 27 citations in OpenAlex.

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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

10 authors at 3 institutions in 2 countries.

Julie C LauffenburgerCenter for Healthcare Delivery Sciences (C4HDS), Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA; Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA. Electronic address: jlauffenburger@bwh.harvard.edu.
Constance P FontanetCenter for Healthcare Delivery Sciences (C4HDS), Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA; Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA.
Thomas IsaacAtrius Health, Newton, MA.
Chandrasekar GopalakrishnanDivision of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA.
Thomas D SequistDivision of General Internal Medicine and Department of Health Care Policy, Brigham and Women's Hospital and Harvard Medical School, Boston, MA.
Joshua J GagneDivision of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA.
Cynthia A JackeviciusWestern University of Health Sciences, Pomona, CA, USA; VA Greater Los Angeles Healthcare System, Los Angeles, CA; Institute for Health Policy, Management and Evaluation, University of Toronto; and ICES, University Health Network, Toronto, Canada.
Michael A FischerDivision of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA.
Daniel H SolomonDivision of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA; Division of Rheumatology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
Niteesh K ChoudhryCenter for Healthcare Delivery Sciences (C4HDS), Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA; Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA.
Harvard University · USBrigham and Women's Hospital · USWestern University of Health Sciences · US

Funding

Interactive Tele-Pharmacy to Improve Chronic Medication AdherenceR01HL117918 · NHLBI · BRIGHAM AND WOMEN'S HOSPITAL · PI CHOUDHRY, NITEESH K, SEQUIST, THOMAS D · 2014 to 2017
$2.8M
Development of a novel medication adherability tool to improve cardiovascular disease managementK01HL141538 · NHLBI · BRIGHAM AND WOMEN'S HOSPITAL · PI LAUFFENBURGER, JULIE CHRISTINE · 2018 to 2022
$718k
NHLBI NIH HHS K01 HL141538NHLBI NIH HHS R01 HL117918
6 · The paper itself

Abstract

backgroundLess than half of patients with cardiometabolic disease consistently take prescribed medications. While health insurers and some delivery organizations use claims to measure adherence, most clinicians do not have access during routine interactions. Self-reported scales exist, but their practical utility is often limited by length or cost. By contrast, the accuracy of a new 3-item self-reported measure has been demonstrated in individuals with HIV. We evaluated its concordance with claims-based adherence measures in cardiometabolic disease.

methodsWe used data from a recently-completed pragmatic trial of patients with cardiometabolic conditions. After 12 months of follow-up, intervention subjects were mailed a survey with the 3-item measure that queries about medication use in the prior 30 days. Responses were linearly transformed and averaged. Adherence was also measured in claims in month 12 and months 1-12 of the trial using proportion of days covered (PDC) metrics. We compared validation metrics for non-adherence for self-report (average <0.80) compared with claims (PDC <0.80).

resultsOf 459 patients returning the survey (response rate: 43.5%), 50.1% were non-adherent in claims in month 12 while 20.9% were non-adherent based on the survey. Specificity of the 3-item metric for non-adherence was high (month 12: 0.83). Sensitivity was relatively poor (month 12: 0.25). Month 12 positive and negative predictive values were 0.59 and 0.52, respectively.

conclusionsA 3-item self-reported measure has high specificity but poor sensitivity for non-adherence versus claims in cardiometabolic disease. Despite this, the tool could help target those needing adherence support, particularly in the absence of claims data.

Indexed as

Drug MonitoringPatient Reported Outcome MeasuresFemaleHumansInsurance Claim ReviewMaleMetabolic SyndromeMiddle AgedOutcome Assessment, Health CarePharmaceutical Services, OnlineRemote ConsultationSelf ReportSensitivity and SpecificitySurveys and QuestionnairesUnited States

Identifiers

PMID32768690
PMCPMC7508975
OpenAlexW3037620423

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

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.