{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,24]],"date-time":"2026-06-24T05:46:40Z","timestamp":1782280000490,"version":"3.54.5"},"reference-count":38,"publisher":"Wiley","issue":"3","license":[{"start":{"date-parts":[[2019,1,13]],"date-time":"2019-01-13T00:00:00Z","timestamp":1547337600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Pain Practice"],"published-print":{"date-parts":[[2019,3]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Objective<\/jats:title><jats:p>To develop and validate a risk assessment tool called the Prescription Medication Non\u2010Adherence Prediction Tool (Rx\u2010<jats:styled-content style=\"fixed-case\">NAPT<\/jats:styled-content>) to predict medication nonadherence in patients with fibromyalgia.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>This was a retrospective cohort study using claims data from South Carolina Medicaid. Patients with fibromyalgia who were \u226518 years old and who had filled at least 1 prescription medication for pregabalin, duloxetine, or milnacipran from January 1, 2005, through June 30, 2011 were included. Medication possession ratios (<jats:styled-content style=\"fixed-case\">MPR<\/jats:styled-content>s) were calculated to classify patients as adherent (<jats:styled-content style=\"fixed-case\">MPR<\/jats:styled-content> \u2265 80%) or nonadherent (<jats:styled-content style=\"fixed-case\">MPR<\/jats:styled-content> &lt; 80%). Multivariable logistic models using 100 bootstrap replications (with replacement) were used to identify factors associated with medication nonadherence, including age, gender, race, days\u2019 supply, medication type, and fibromyalgia\u2010related comorbidity score. Weighted \u03b2 coefficients of the predictors were used to create the Rx\u2010<jats:styled-content style=\"fixed-case\">NAPT<\/jats:styled-content>. Youden's J statistic was used to classify nonadherent patients into different levels of risk.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The study sample comprised 6,626 patients with fibromyalgia, where 4,804 (72.50%) were non\u2010adherent and 1,822 (27.50%) were adherent to their prescribed medication(s). Logistic regression models showed that 7 predictors (gender, age, race, fibromyalgia\u2010related comorbidity score, medication type, health maintenance organization coverage, emergency room visit) were statistically significant in \u226550% of the bootstrapped samples. The final model demonstrated reasonable discrimination (area under the curve [<jats:styled-content style=\"fixed-case\">AUC<\/jats:styled-content>] = 0.6224) and calibration (Hosmer\u2010Lemeshow goodness\u2010of\u2010fit; <jats:italic>P<\/jats:italic> &gt; 0.05) statistics and was validated internally (<jats:styled-content style=\"fixed-case\">AUC<\/jats:styled-content> = 0.6372).<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>Poor adherence with medication remains an important barrier to providing optimal care. Our risk prediction model provides an easy tool to help clinicians better identify patients with fibromyalgia who may not take their medications as prescribed.<\/jats:p><\/jats:sec>","DOI":"10.1111\/papr.12743","type":"journal-article","created":{"date-parts":[[2018,10,28]],"date-time":"2018-10-28T05:04:36Z","timestamp":1540703076000},"page":"295-302","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Risk for Medication Nonadherence Among Medicaid Enrollees With Fibromyalgia: Development of a Validated Risk Prediction Tool"],"prefix":"10.1111","volume":"19","author":[{"given":"Raj","family":"Desai","sequence":"first","affiliation":[{"name":"Health Services Research Management and Policy University of Florida Gainesville Florida U.S.A."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ara","family":"Jo","sequence":"additional","affiliation":[{"name":"Health Services Research Management and Policy University of Florida Gainesville Florida U.S.A."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nicole M.","family":"Marlow","sequence":"additional","affiliation":[{"name":"Health Services Research Management and Policy University of Florida Gainesville Florida U.S.A."}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2019,1,13]]},"reference":[{"key":"e_1_2_8_2_1","doi-asserted-by":"publisher","DOI":"10.1586\/eci.10.64"},{"key":"e_1_2_8_3_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1742-1241.2007.01480.x"},{"key":"e_1_2_8_4_1","first-page":"1318","article-title":"Economic cost and epidemiological characteristics of patients with fibromyalgia claims","volume":"30","author":"Robinson RL","year":"2003","journal-title":"J Rheumatol"},{"key":"e_1_2_8_5_1","doi-asserted-by":"publisher","DOI":"10.3899\/jrheum.170098"},{"key":"e_1_2_8_6_1","doi-asserted-by":"publisher","DOI":"10.1185\/03007995.2011.576663"},{"key":"e_1_2_8_7_1","doi-asserted-by":"publisher","DOI":"10.1093\/rheumatology\/keh141"},{"key":"e_1_2_8_8_1","doi-asserted-by":"publisher","DOI":"10.1002\/wps.20060"},{"key":"e_1_2_8_9_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.rmed.2013.08.044"},{"key":"e_1_2_8_10_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.rmed.2013.04.005"},{"key":"e_1_2_8_11_1","first-page":"35","article-title":"Adherence and health care costs","volume":"7","author":"Iuga AO","year":"2014","journal-title":"Risk Manag Healthcare Policy"},{"key":"e_1_2_8_12_1","unstructured":"ParadiseJ.10 Things to Know About Medicaid: Setting the Facts Straight. 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