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Risk for Medication Nonadherence Among Medicaid Enrollees With Fibromyalgia: Development of a Validated Risk Prediction Tool
Author(s) -
Desai Raj,
Jo Ara,
Marlow Nicole M.
Publication year - 2019
Publication title -
pain practice
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.899
H-Index - 58
eISSN - 1533-2500
pISSN - 1530-7085
DOI - 10.1111/papr.12743
Subject(s) - medicine , fibromyalgia , logistic regression , medicaid , comorbidity , duloxetine , medical prescription , physical therapy , pregabalin , emergency medicine , psychiatry , health care , alternative medicine , pathology , economics , pharmacology , economic growth
Objective To develop and validate a risk assessment tool called the Prescription Medication Non‐Adherence Prediction Tool (Rx‐ NAPT ) to predict medication nonadherence in patients with fibromyalgia. Methods This was a retrospective cohort study using claims data from South Carolina Medicaid. Patients with fibromyalgia who were ≥18 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 ( MPR s) were calculated to classify patients as adherent ( MPR ≥ 80%) or nonadherent ( MPR < 80%). Multivariable logistic models using 100 bootstrap replications (with replacement) were used to identify factors associated with medication nonadherence, including age, gender, race, days’ supply, medication type, and fibromyalgia‐related comorbidity score. Weighted β coefficients of the predictors were used to create the Rx‐ NAPT . Youden's J statistic was used to classify nonadherent patients into different levels of risk. Results The study sample comprised 6,626 patients with fibromyalgia, where 4,804 (72.50%) were non‐adherent and 1,822 (27.50%) were adherent to their prescribed medication(s). Logistic regression models showed that 7 predictors (gender, age, race, fibromyalgia‐related comorbidity score, medication type, health maintenance organization coverage, emergency room visit) were statistically significant in ≥50% of the bootstrapped samples. The final model demonstrated reasonable discrimination (area under the curve [ AUC ] = 0.6224) and calibration (Hosmer‐Lemeshow goodness‐of‐fit; P > 0.05) statistics and was validated internally ( AUC = 0.6372). Conclusion 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.