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      Agreement and Accuracy of Medication Persistence Identified by Patient Self-report vs Pharmacy Fill : A Secondary Analysis of the Cluster Randomized ARTEMIS Trial

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          Abstract

          Pharmacy fill data are increasingly accessible to evaluate medication-taking behavior, yet how often do patient-reported and pharmacy fill–based medication persistence agree? Of 8373 patients included in this post hoc hypothesis-driven secondary analysis of the cluster randomized ARTEMIS trial, 50.0% were concordantly persistent by both self-report and pharmacy fills, 36.5% were discordantly persistent, and 13.5% were concordantly nonpersistent. Observed rates of death, myocardial infarction, or stroke were highest for patients who were concordantly nonpersistent. Patient-reported and pharmacy fill–based persistence measures are frequently discordant, but patients who are nonpersistent by both methods have the worst clinical outcomes and should be prioritized for interventions to improve medication-taking behavior. Pharmacy fill data are increasingly accessible to clinicians and researchers to evaluate longitudinal medication persistence beyond patient self-report. To assess the agreement and accuracy of patient-reported and pharmacy fill–based medication persistence. This post hoc analysis of the cluster randomized clinical trial ARTEMIS (Affordability and Real-world Antiplatelet Treatment Effectiveness After Myocardial Infarction Study) enrolled patients at 287 US hospitals (131 randomized to intervention and 156 to usual care) from June 5, 2015, to September 30, 2016, with 1-year follow-up and blinded adjudication of major adverse cardiovascular events. In total, 8373 patients with myocardial infarction and measurement of P2Y12 inhibitor persistence by both patient self-report and pharmacy data were included. Serum P2Y12 inhibitor drug levels were measured for 944 randomly selected patients. Data were analyzed from May 2018 to November 2019. Patients treated at intervention-arm hospitals received study vouchers to offset copayments at each P2Y12 inhibitor fill for 1 year after myocardial infarction. Nonpersistence was defined as a gap of 30 days or more in P2Y12 inhibitor use (patient report) or supply (pharmacy fill) and as serum P2Y12 inhibitor levels below the lower limit of quantification (drug level). Among patients in the intervention arm, a “criterion standard” definition of nonpersistence was a gap of 30 days or more in P2Y12 inhibitor use by both voucher use and pharmacy fill. Major adverse cardiovascular events were defined as adjudicated death, recurrent myocardial infarction, or stroke. Of 8373 patients included in this analysis, the median age was 62 years (interquartile range, 54-70 years), 5664 were men (67.7%), and 990 (11.8%) self-reported as nonwhite race/ethnicity. One-year estimates of medication nonpersistence rates were higher using pharmacy fills (4042 patients [48.3%]) compared with patient self-report (1277 patients [15.3%]). Overall, 4185 patients (50.0%) were persistent by both pharmacy fill data and patient report, 1131 patients (13.5%) were nonpersistent by both, and 3057 patients (36.5%) were discordant. By application of the criterion standard definition, the 1-year nonpersistence rate was 1184 of 3703 patients (32.0%); 892 of 3318 patients (26.9%) in the intervention arm who self-reported persistence were found to be nonpersistent, and 303 of 1487 patients (20.4%) classified as nonpersistent by pharmacy fill data were actually persistent. Agreement between serum P2Y12 inhibitor drug levels and either patient-reported (κ = 0.11-0.23) or fill-based (κ = 0.00-0.19) persistence was poor. Patients who were nonpersistent by both pharmacy fill data and self-report had the highest 1-year major adverse cardiac event rate (18.3%; 95% CI, 16.0%-20.6%) compared with that for discordant patients (9.7%; 8.7%-10.8%) or concordantly persistent patients (8.2%; 95% CI, 7.4%-9.0%). Patient report overestimated medication persistence rates, and pharmacy fill data underestimated medication persistence rates. Patients who are nonpersistent by both methods have the worst clinical outcomes and should be prioritized for interventions that improve medication-taking behavior. ClinicalTrials.gov Identifier: NCT02406677 This ad hoc secondary analysis of the cluster randomized ARTEMIS trial assesses the agreement and accuracy of medication persistence rates measured in a 1-year follow-up of usual care by patient report, pharmacy fill, voucher use for medication copayment (intervention arm), and serum P2Y12 inhibitor levels among patients with major adverse cardiovascular events.

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          Most cited references15

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          Impact of medication adherence on hospitalization risk and healthcare cost.

          The objective of this study was to evaluate the impact of medication adherence on healthcare utilization and cost for 4 chronic conditions that are major drivers of drug spending: diabetes, hypertension, hypercholesterolemia, and congestive heart failure. The authors conducted a retrospective cohort observation of patients who were continuously enrolled in medical and prescription benefit plans from June 1997 through May 1999. Patients were identified for disease-specific analysis based on claims for outpatient, emergency room, or inpatient services during the first 12 months of the study. Using an integrated analysis of administrative claims data, medical and drug utilization were measured during the 12-month period after patient identification. Medication adherence was defined by days' supply of maintenance medications for each condition. The study consisted of a population-based sample of 137,277 patients under age 65. Disease-related and all-cause medical costs, drug costs, and hospitalization risk were measured. Using regression analysis, these measures were modeled at varying levels of medication adherence. For diabetes and hypercholesterolemia, a high level of medication adherence was associated with lower disease-related medical costs. For these conditions, higher medication costs were more than offset by medical cost reductions, producing a net reduction in overall healthcare costs. For diabetes, hypercholesterolemia, and hypertension, cost offsets were observed for all-cause medical costs at high levels of medication adherence. For all 4 conditions, hospitalization rates were significantly lower for patients with high medication adherence. For some chronic conditions, increased drug utilization can provide a net economic return when it is driven by improved adherence with guidelines-based therapy.
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            Relationship between adherence to evidence-based pharmacotherapy and long-term mortality after acute myocardial infarction.

            The extent to which drug adherence may affect survival remains unclear, in part because mortality differences may be attributable to "healthy adherer" behavioral attributes more so than to pharmacological benefits. To explore the relationship between drug adherence and mortality in survivors of acute myocardial infarction (AMI). Population-based, observational, longitudinal study of 31 455 elderly AMI survivors between 1999 and 2003 in Ontario. All patients filled a prescription for statins, beta-blockers, or calcium channel blockers, with the latter drug considered a control given the absence of clinical trial-proven survival benefits. Patient adherence was subdivided a priori into 3 categories--high (proportion of days covered, > or =80%), intermediate (proportion of days covered, 40%-79%), and low (proportion of days covered, <40%)--and compared with long-term mortality (median of 2.4 years of follow-up) using multivariable survival models (and propensity analyses) adjusted for sociodemographic factors, illness severity, comorbidities, and concomitant use of evidence-based therapies. Among statin users, compared with their high-adherence counterparts, the risk of mortality was greatest for low adherers (deaths in 261/1071 (24%) vs 2310/14,345 (16%); adjusted hazard ratio, 1.25; 95% confidence interval, 1.09-1.42; P = .001) and was intermediary for intermediate adherers (deaths in 472/2407 (20%); adjusted hazard ratio, 1.12; 95% confidence interval, 1.01-1.25; P = .03). A similar but less pronounced dose-response-type adherence-mortality association was observed for beta-blockers. Mortality was not associated with adherence to calcium channel blockers. Moreover, sensitivity analyses demonstrated no relationships between drug adherence and cancer-related admissions, outcomes for which biological plausibility do not exist. The long-term survival advantages associated with improved drug adherence after AMI appear to be class-specific, suggesting that adherence outcome benefits are mediated by drug effects and do not merely reflect an epiphenomenon of "healthy adherer" behavioral attributes.
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              Methods for measuring and monitoring medication regimen adherence in clinical trials and clinical practice.

              K C Farmer (1999)
              Researchers and clinicians have used numerous methods in their attempts to adequately assess patient compliance (adherence) with medication regimens and to identify noncompliant patients. Large variations have been reported in the extent of noncompliance in individual patients and large populations. In addition, nonadherence has often been poorly defined. Direct measures of adherence include drug assays of blood or urine, use of drug markers with the target medication, and direct observation of the patient receiving the medication. Indirect measures of adherence imply that the medication has been used by the patient; these measures include various forms of self-reporting by the patient, medication measurement (pill count), use of electronic monitoring devices, and review of prescription records and claims. Compliance measures should be assessed on the basis of their validity (sensitivity and specificity or statistical correlation) and the reference standard used. Many early studies used pill counts as a reference standard, but electronic monitoring devices such as the Medication Event Monitoring System have replaced pill counts as the reference standard. The choice of a method for measuring adherence to a medication regimen should be based on the usefulness and reliability of the method in light of the researcher's or clinician's goals. Specific methods may be more applicable to certain situations, depending on the type of adherence being assessed, the precision required, and the intended application of the results.
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                Author and article information

                Journal
                JAMA Cardiology
                JAMA Cardiol
                American Medical Association (AMA)
                2380-6583
                May 01 2020
                May 01 2020
                : 5
                : 5
                : 532
                Affiliations
                [1 ]Leonard Davis Institute of Health Economics, Penn Cardiovascular Outcomes, Quality and Evaluative Research Center, Cardiovascular Medicine Division, University of Pennsylvania, Philadelphia
                [2 ]Department of Medicine, Duke University, Durham, North Carolina
                [3 ]Duke Clinical Research Institute, Duke University, Durham, North Carolina
                [4 ]Cardiovascular Division, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts
                [5 ]Center for Healthcare Delivery Sciences, Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts
                [6 ]The Carl and Edyth Lindner Center for Research and Education, The Christ Hospital, Cincinnati, Ohio
                [7 ]University of Missouri–Kansas City School of Medicine, Kansas City
                [8 ]AstraZeneca, Wilmington, Delaware
                [9 ]Division of Cardiology, Ronald Reagan UCLA Medical Center, Los Angeles, California
                [10 ]Associate Editor for Health Care Quality and Guidelines, JAMA Cardiology
                Article
                10.1001/jamacardio.2020.0125
                7057174
                32129795
                a073c70c-8a34-4b95-aaea-5e8ab1e4a06e
                © 2020
                History

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