Evidence map›Paper›PMID 37345649›Full record

ArticlePharmacoepidemiology and drug safety2023

Accuracy of administrative claims prescription fill data to estimate glucocorticoid use and dose in patients with rheumatoid arthritis.

Rachel W Galvao, Jeffrey R Curtis, Leslie R Harrold, Qufei Wu, Fenglong Xie, Michael D George

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Article in Pharmacoepidemiology and drug safety, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

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5citing papers in PubMed
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1 · What the graph read from it

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3 · Its place in the literature

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5 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Rachel W GalvaoDepartment of Internal Medicine, Yale University, New Haven, Connecticut, USA.
Jeffrey R CurtisDivision of Clinical Immunology and Rheumatology, University of Alabama at Birmingham, Birmingham, Alabama, USA.
Leslie R HarroldCorEvitas, Waltham, Massachusetts, USA.
Qufei WuCenter for Clinical Epidemiology and Biostatistics, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
Fenglong XieDivision of Clinical Immunology and Rheumatology, University of Alabama at Birmingham, Birmingham, Alabama, USA.ORCID 0000-0002-7365-4801
Michael D GeorgeDivision of Rheumatology, University of Pennsylvania, Philadelphia, Pennsylvania, USA.

Funding

Methods and Health Informatics CoreP30AR072583 · NIAMS · UNIVERSITY OF ALABAMA AT BIRMINGHAM · PI JEFFREY R. CURTIS · 2020 to 2026
$5.7M
Risk and impact of infection resulting from treatment with chronic glucocorticoids in patients with rheumatoid arthritisK23AR073931 · NIAMS · UNIVERSITY OF PENNSYLVANIA · PI GEORGE, MICHAEL D. · 2018 to 2022
$873k
NIAMS NIH HHS K23 AR073931NIAMS NIH HHS P30 AR072583
6 · The paper itself

Abstract

purposeTo assess accuracy of administrative claims prescription fill-based estimates of glucocorticoid use and dose, and approximate bias from glucocorticoid exposure misclassification.

methodsWe identified adults with rheumatoid arthritis with linked Medicare and CorEvitas registry data. An algorithm identifying glucocorticoid use and average dose over 90 days from Medicare prescription fills was compared to physician-reported measures from a CorEvitas visit during the same period, using weighted kappa to compare doses (none, ≤5 mg, 5-10 mg, >10 mg/day). A deterministic sensitivity analysis examined the effect of exposure misclassification on estimated glucocorticoid-associated infection risk from a prior study.

resultsWe identified 621 observations among 494 patients. Prescription fills identified glucocorticoid use in 41.9% of observations versus 31.1% identified by CorEvitas physician-report. For glucocorticoid use (yes/no), prescription fills had sensitivity 88.1% (95% CI 82.7-92.3), specificity 79.0% (74.8-82.7), PPV 65.4% (59.3-71.2), NPV 93.6% (90.6-95.9), and 81.8% agreement with CorEvitas, with kappa 0.61 (moderate to substantial agreement). There was 89.5% agreement between prescription fills and physician-reported doses, with weighted kappa 0.56 (moderate agreement). Applying these results to a prior Medicare study evaluating glucocorticoid-associated infection risk [risk ratio 1.44 (95% CI 1.41-1.48)] led to an externally adjusted risk ratio of 1.74 when accounting for exposure misclassification, representing -17% bias in infection risk estimate.

conclusionsThis study supports the use of claims data to estimate glucocorticoid use and dose, but investigators should account for exposure misclassification, which may lead to underestimates of glucocorticoid risks. Our results could be applied to adjust risk estimates in other studies that use prescription fills to estimate glucocorticoid use.

Indexed as

Arthritis, RheumatoidGlucocorticoidsAdultAgedHumansMedicareOdds RatioPrescriptionsUnited StatesGlucocorticoidsadministrative claimsexposure misclassificationglucocorticoids

Identifiers

PMID37345649
PMCPMC10543479

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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.