Evidence map›Paper›PMID 34387355›Full record

ArticleHealth services research2022

Comparison of measures of medication adherence from pharmacy dispensing and insurer claims data.

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

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Article in Health services research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

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0cells of the map it votes in
10citing papers in PubMed
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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.

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3 · Its place in the literature

Who cites it

10 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

10 authors.

Constance P FontanetCenter for Healthcare Delivery Sciences (C4HDS), Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA.ORCID 0000-0002-8253-1598
Niteesh K ChoudhryCenter for Healthcare Delivery Sciences (C4HDS), Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA.
Thomas IsaacDepartment of Internal Medicine, Atrius Health, Newton, Massachusetts, USA.
Thomas D SequistDivision of General Internal Medicine and Department of Health Care Policy, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA.
Chandrasekar GopalakrishnanDivision of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA.
Joshua J GagneDivision of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA.
Cynthia A JackeviciusPharmacy Practice and Administration Department, Western University of Health Sciences, Pomona, California, USA.
Michael A FischerDivision of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA.
Daniel H SolomonDivision of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA.
Julie C LauffenburgerCenter for Healthcare Delivery Sciences (C4HDS), Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA.ORCID 0000-0002-4940-4140

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

objectiveMedication nonadherence is linked to worsened clinical outcomes and increased costs. Existing system-level adherence interventions rely on insurer claims for patient identification and outcome measurement, yet suffer from incomplete capture and lags in data acquisition. Data from pharmacies regarding prescription filling, captured in retail dispensing, may be more efficient. DATA SOURCES: Pharmacy fill and insurer claims data. STUDY

designWe compared adherence measured using pharmacy fill data to adherence using insurer claims data, expressed as proportion of days covered (PDC) over 12 months. Agreement was evaluated using correlation/validation metrics. We also explored the relationship between adherence in both sources and disease control using prediction modeling. DATA EXTRACTION

methodsLarge pragmatic trial of cardiometabolic disease in an integrated delivery network. PRINCIPAL

findingsAmong 1113 patients, adherence was higher in pharmacy fill (mean = 50.0%) versus claims data (mean = 47.4%), although they had moderately high correlation (R = 0.57, 95% CI: 0.53-0.61) with most patients (86.9%) being similarly classified as adherent or nonadherent. Sensitivity and specificity of pharmacy fill versus claims data were high (0.89, 95% CI: 0.86-0.91 and 0.80, 95% CI: 0.75-0.85). Pharmacy fill-based PDC predicted better disease control slightly more than claims-based PDC, although the difference was nonsignificant.

conclusionsPharmacy fill data may be an alternative to insurer claims for adherence measurement.

Indexed as

PharmaciesPharmacyHumansInsurance CarriersMedication AdherenceRetrospective Studiesadherenceclaimsdiabeteshyperlipidemiahypertensionpharmacy

Identifiers

PMID34387355
PMCPMC9108079

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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.