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Predictors of Medication‐Related Problems among Medicaid Patients Participating in a Pharmacist‐Provided Telephonic Medication Therapy Management Program
Author(s) -
Snyder Margie E.,
Frail Caitlin K.,
Jaynes Heather,
Pater Karen S.,
Zillich Alan J.
Publication year - 2014
Publication title -
pharmacotherapy: the journal of human pharmacology and drug therapy
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.227
H-Index - 109
eISSN - 1875-9114
pISSN - 0277-0008
DOI - 10.1002/phar.1462
Subject(s) - medicine , medication therapy management , medicaid , pharmacy , dyslipidemia , pharmacist , medical prescription , retrospective cohort study , emergency medicine , bivariate analysis , family medicine , emergency department , observational study , health care , obesity , psychiatry , statistics , mathematics , economics , pharmacology , economic growth
Study Objective To identify predictors of medication‐related problems ( MRP s) among Medicaid patients participating in a telephonic medication therapy management ( MTM ) program. Design Retrospective analysis of data from patients enrolled in a previously published study. Data Sources Two Medicaid administrative claims file databases (for health care utilization and prescription dispensing information) and one pharmacy organization file for MTM program information. Patients Seven hundred twelve adult Medicaid patients who participated in a statewide pharmacist‐provided telephone‐based MTM program and who received an initial medication therapy review. Measurements and Main Results The primary dependent variable was the number of MRP s detected during the initial medication therapy review. Secondary dependent variables were the detection of one or more MRP s related to indication, effectiveness, safety, and adherence. Predictor variables were selected a priori that, from the literature and our own practice experiences, were hypothesized as being potentially associated with MRP s: demographics, comorbidities, medication use, and health care utilization. Bivariate analyses were performed, and multivariable models were constructed. All predictor variables with significant associations (defined a priori as p<0.1) with the median number of MRP s detected were then entered into a three‐block multiple linear regression model. The overall model was significant (p<0.001, R 2 = 0.064). Significant predictors of any MRP s (p<0.05) were total number of medications, obesity, dyslipidemia, and one or more emergency department visits in the past 3 months. For indication‐related MRP s, the model was significant (p<0.001, R 2 = 0.049), and predictors included female sex, obesity, dyslipidemia, and total number of medications (p<0.05). For effectiveness‐related MRP s, the model was significant (p<0.001, R 2 = 0.054), and predictors included bone disease and dyslipidemia (p<0.05). For safety‐related MRP s, the model was significant (p<0.001, R 2 = 0.046), and dyslipidemia was a predictor (p<0.05). No significant predictors of adherence‐related MRP s were identified. Conclusion This analysis supports the relative importance of number of medications as a predictor of MRP s in the Medicaid population and identifies other predictors. However, given the models' low R 2 values, these findings indicate that other unknown factors are clearly important and that criteria commonly used for determining MTM eligibility may be inadequate in identifying appropriate patients for MTM in a Medicaid population.