{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,7]],"date-time":"2026-02-07T20:03:46Z","timestamp":1770494626643,"version":"3.49.0"},"reference-count":27,"publisher":"Wiley","issue":"6","license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Brit J Clinical Pharma"],"published-print":{"date-parts":[[2023,6]]},"abstract":"<jats:sec><jats:title>Aims<\/jats:title><jats:p>Using advanced longitudinal analyses, this real\u2010world investigation examined medication adherence levels and patterns for incident atrial fibrillation (AF) patients with significant cardiovascular and noncardiovascular multimorbid conditions for each of 5 medication classes (\u03b2\u2010blockers, calcium channel blockers\/digoxin, antiarrhythmics, anticoagulants, antiplatelets). The population was derived from a large cohort covering a wide age spectrum\/diversified US geographical areas\/wide range of socioeconomic\u2013disability status.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>The patients were drawn from 3 different health plans. Adherence was defined in terms of the proportion of day covered (PDC), and its patterns were modelled in terms of group\u2010based trajectory, with each pattern profiled in terms of comorbid history, demographic variables and health plan factors using multinomial regression modelling.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The total population consisted of 1\u2009978\u2009168 patients, with the AF cohort being older (average age of 64.6\u00a0years relative to 44.7\u00a0years for the non\u2010AF cohort) and having fewer females (47.8% relative to 55.4 for the non\u2010AF cohort). The AF cohort had significant cardiovascular\/noncardiovascular multimorbidities and was much sicker than the non\u2010AF cohort. A 6\u2010group based trajectory solution appears to be the most logical outcome for each medication class according to assessed criteria. For each medication class, it consisted of one consistent adherent group (PDC\u2009\u2265\u20090.84), one fast declining group (PDC\u2009\u2264\u20090.11) and 4 intermediate nonadherence groups (slow decline [0.30\u20130.74 PDC range], occasional users [0.24\u20130.55 PDC range] and early gap\/increased adherence [0.62\u20130.75]). The most consistent adherent groups were much lower than 50% of the total population and equal to 12.5\u201327.0% of the population, with the fast declining nonadherent pattern in the 5.6\u201335.0% of the population and the intermediate nonadherence equal to ~61% of the population.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>Our findings confirm that medication adherence is of major concern among multimorbid patients, with adherence levels lower much than those reported in the literature. There are 3 patterns of intermediate nonadherence (slow decline, occasional users, early gap\/increased adherence), which were found to be eligible for interventions aimed at improving their adherence levels for each medication class. This may help improve cardiovascular medication adherence using large medication nonadherence improvement programmes.<\/jats:p><\/jats:sec>","DOI":"10.1111\/bcp.15627","type":"journal-article","created":{"date-parts":[[2022,12,8]],"date-time":"2022-12-08T20:20:10Z","timestamp":1670530810000},"page":"1736-1746","source":"Crossref","is-referenced-by-count":4,"title":["Adherence levels and patterns for multiple cardiac medications prescribed to patients with incident atrial fibrillation events"],"prefix":"10.1111","volume":"89","author":[{"given":"Gregory Y. 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Indianapolis Indiana USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bobby","family":"Jones","sequence":"additional","affiliation":[{"name":"Department of Psychiatry University of Pittsburgh School of Medicine  Pittsburgh Pennsylvania USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"George","family":"Tran","sequence":"additional","affiliation":[{"name":"IngenioRX  Indianapolis Indiana USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Patricia","family":"Marroquin","sequence":"additional","affiliation":[{"name":"Anthem Inc.  Indianapolis Indiana USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Cara","family":"Estes","sequence":"additional","affiliation":[{"name":"Anthem Inc.  Indianapolis Indiana USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tatiana","family":"Shnaiden","sequence":"additional","affiliation":[{"name":"Anthem Inc.  Indianapolis Indiana USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2023,1]]},"reference":[{"key":"e_1_2_10_2_1","doi-asserted-by":"publisher","DOI":"10.1111\/ijcp.14042"},{"key":"e_1_2_10_3_1","doi-asserted-by":"publisher","DOI":"10.1002\/joa3.12555"},{"key":"e_1_2_10_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejim.2021.04.023"},{"key":"e_1_2_10_5_1","doi-asserted-by":"publisher","DOI":"10.1038\/nrcardio.2017.153"},{"key":"e_1_2_10_6_1","doi-asserted-by":"publisher","DOI":"10.1111\/eci.13498"},{"key":"e_1_2_10_7_1","doi-asserted-by":"publisher","DOI":"10.1093\/europace\/euab092"},{"key":"e_1_2_10_8_1","doi-asserted-by":"publisher","DOI":"10.1161\/JAHA.121.021601"},{"key":"e_1_2_10_9_1","doi-asserted-by":"publisher","DOI":"10.1161\/JAHA.118.011427"},{"key":"e_1_2_10_10_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jacc.2021.09.1370"},{"key":"e_1_2_10_11_1","doi-asserted-by":"publisher","DOI":"10.1093\/europace\/euw421"},{"key":"e_1_2_10_12_1","doi-asserted-by":"publisher","DOI":"10.1161\/CIRCOUTCOMES.111.962688"},{"key":"e_1_2_10_13_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cjca.2016.06.006"},{"key":"e_1_2_10_14_1","doi-asserted-by":"publisher","DOI":"10.1159\/000360229"},{"key":"e_1_2_10_15_1","doi-asserted-by":"publisher","DOI":"10.1177\/0962280216673085"},{"key":"e_1_2_10_16_1","doi-asserted-by":"publisher","DOI":"10.1177\/0049124113503141"},{"key":"e_1_2_10_17_1","doi-asserted-by":"publisher","DOI":"10.1002\/cpt.865"},{"key":"e_1_2_10_18_1","doi-asserted-by":"publisher","DOI":"10.1111\/ijpp.12539"},{"key":"e_1_2_10_19_1","doi-asserted-by":"publisher","DOI":"10.1002\/pds.3787"},{"issue":"4","key":"e_1_2_10_20_1","first-page":"202","article-title":"Group\u2010based trajectory models to identify sociodemographic and clinical predictors of adherence patterns to statin therapy among older adults","volume":"12","author":"Vadhariya A","year":"2019","journal-title":"Am Health Drug Benefits"},{"key":"e_1_2_10_21_1","doi-asserted-by":"publisher","DOI":"10.18553\/jmcp.2020.26.9.1138"},{"key":"e_1_2_10_22_1","unstructured":"LeslieRS.Using arrays to calculate medication utilization.2007:1\u20105 Paper 043\u20102007. 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