Yanru Qiao1, Christina A Spivey2, Junling Wang3, Ya-Chen Tina Shih4, Jim Y Wan5, Julie Kuhle6, Samuel Dagogo-Jack7, William C Cushman8, Marie Chisholm-Burns9. 1. Health Outcomes and Policy Research, Department of Clinical Pharmacy & Translational Science, University of Tennessee College of Pharmacy, 881 Madison Avenue, Room 212, Memphis, TN 38163, , , yqiao1@uthsc.edu. 2. Department of Clinical Pharmacy & Translational Science, University of Tennessee College of Pharmacy, 881 Madison Avenue, Room 258, , , cspivey3@uthsc.edu. 3. Health Outcomes and Policy Research, Department of Clinical Pharmacy & Translational Science, University of Tennessee College of Pharmacy, 881 Madison Avenue, Room 221, Memphis, TN 38163, , , jwang26@uthsc.edu. 4. Department of Health Services Research, The University of Texas MD Anderson Cancer Center & Chief, Section of Cancer Economics and Policy, Department of Health Services Research, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd., Unit 1444, Houston, TX 77030, , , yashih@mdanderson.org. 5. Department of Preventive Medicine, University of Tennessee Health Science Center, 66 N. Pauline, Suite 633, Memphis, TN 38163, , , jwan@uthsc.edu. 6. Pharmacy Quality Alliance, 5911 Kingstowne Village Parkway, Suite 130, Alexandria, Virginia 22315, , , jkuhle@pqaalliance.org. 7. Division of Endocrinology, Diabetes & Metabolism & Director, Clinical Research Center, University of Tennessee Health Science Center, 920 Madison Avenue, Suite 300A, Memphis, TN 38163, , , sdj@uthsc.edu. 8. Department of Preventive Medicine and Medicine, University of Tennessee College of Medicine & Chief, Preventive Medicine Section, Veterans Affairs Medical Center, 1030 Jefferson Avenue, Room 5159, Memphis, TN 38104, , , william.cushman@va.gov. 9. University of Tennessee College of Pharmacy, 881 Madison Avenue, Room 264, Memphis, TN 38163, , , mchisho3@uthsc.edu.
Abstract
OBJECTIVES: To compare the predictive value positives (PVP) of medication therapy management eligibility criteria under the Medicare Modernization Act (MMA) and Affordable Care Act (ACA) in identifying individuals with medication utilization issues (MUI). METHODS: This is a retrospective analysis of Medicare database (2012-2013). MUI were determined based on medication utilization measures related to Medicare Part D Star Ratings. PVP or proportions of individuals with MUI were compared between individuals eligible for MTM under MMA and ACA. Need-based and demand-based logistic regression was used to adjust for patient characteristics. MTM eligibility thresholds in 2009 and 2013 and proposed 2015 MTM eligibility thresholds under MMA were examined. Main/sensitivity/disease-specific analyses were conducted to cover the range of eligibility thresholds and combinations. KEY FINDINGS: MMA has higher PVP in identifying patients with MUI than ACA. Proportions of individuals with MUI were higher based on MMA than ACA (e.g., 74.96% for 2009 MMA, 73.51% for 2013 MMA, and 62.46% for proposed 2015 MMA vs. 52.17% for ACA in main analysis; P<0.05). Adjusted findings were similar. For example, based on the demand-based model in the main analysis, the odds ratios were 2.474 (95% CI: 2.454-2.494) for 2013 MMA in comparison to ACA. These numbers indicate that the MMA MTM eligibility criteria for 2013 had 147.4% higher PVP in identifying patients with MUI than ACA. Similar patterns were found in most sensitivity and disease-specific analyses. CONCLUSIONS: MMA has higher PVP than ACA in identifying patients with MUI. This study may inform the government on future MTM policy.
OBJECTIVES: To compare the predictive value positives (PVP) of medication therapy management eligibility criteria under the Medicare Modernization Act (MMA) and Affordable Care Act (ACA) in identifying individuals with medication utilization issues (MUI). METHODS: This is a retrospective analysis of Medicare database (2012-2013). MUI were determined based on medication utilization measures related to Medicare Part D Star Ratings. PVP or proportions of individuals with MUI were compared between individuals eligible for MTM under MMA and ACA. Need-based and demand-based logistic regression was used to adjust for patient characteristics. MTM eligibility thresholds in 2009 and 2013 and proposed 2015 MTM eligibility thresholds under MMA were examined. Main/sensitivity/disease-specific analyses were conducted to cover the range of eligibility thresholds and combinations. KEY FINDINGS: MMA has higher PVP in identifying patients with MUI than ACA. Proportions of individuals with MUI were higher based on MMA than ACA (e.g., 74.96% for 2009 MMA, 73.51% for 2013 MMA, and 62.46% for proposed 2015 MMA vs. 52.17% for ACA in main analysis; P<0.05). Adjusted findings were similar. For example, based on the demand-based model in the main analysis, the odds ratios were 2.474 (95% CI: 2.454-2.494) for 2013 MMA in comparison to ACA. These numbers indicate that the MMA MTM eligibility criteria for 2013 had 147.4% higher PVP in identifying patients with MUI than ACA. Similar patterns were found in most sensitivity and disease-specific analyses. CONCLUSIONS: MMA has higher PVP than ACA in identifying patients with MUI. This study may inform the government on future MTM policy.
Entities:
Keywords:
Improvement, and Modernization Act; Medicare Prescription Drug; Patient Protection & Affordable Care Act; Predictive value positive; efficiency; eligibility criteria; medication therapy management services; medication utilization issues; performance
Authors: Ilene H Zuckerman; Patricia Langenberg; Mona Baumgarten; Denise Orwig; Patricia J Byrns; Linda Simoni-Wastila; Jay Magaziner Journal: Med Care Date: 2006-08 Impact factor: 2.983