Study objective: To identify predictors of medication-related problems (MRPs) 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 MRPs detected during the initial medication therapy review. Secondary dependent variables were the detection of one or more MRPs 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 MRPs: 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 MRPs 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 MRPs (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 MRPs, 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 MRPs, the model was significant (p<0.001, R(2) = 0.054), and predictors included bone disease and dyslipidemia (p<0.05). For safety-related MRPs, 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 MRPs were identified.

Conclusion: This analysis supports the relative importance of number of medications as a predictor of MRPs 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.