1. Effects of Medicare Part D on Disparity Implications of Medicare Medication Therapy Management Eligibility Criteria Junling Wang, Ph.D. Associate Professor Health Outcomes and Policy Research University of Tennessee College of Pharmacy

2. Co-Authors Yanru Qiao, M.S. Ya-Chen Tina Shih, Ph.D. JoEllen J. Jamison, B.S. Christina A. Spivey, Ph.D. Liyuan Li, Ph.D. Jim Y. Wan, Ph.D. Shelley I. White-Means, Ph.D. Samuel Dagogo-Jack, M.D., F.R.C.P. William C. Cushman, M.D. Marie A. Chisholm-Burns, Pharm.D. M.P.H, M.B.A., F.C.C.P, F.A.S.H.P. 2

3. Medicare Prescription Drug & Modernization Act (MMA) established Medicare prescription drug (Part D) benefit in 2006. Centers for Medicare & Medicaid Services (CMS) required prescription drug plans to provide medication therapy management (MTM) services for Medicare beneficiaries as part of Part D benefits. 3 Background

4. The purpose: to “ensure that covered Part D drugs prescribed to targeted beneficiaries are appropriately used to optimize therapeutic outcomes through improved medication use.” MTM services are particularly beneficial for patients with chronic diseases, in whose management pharmacotherapy plays a major role, such as hypertension and diabetes. Prevalence of some of these diseases is higher in minority populations. 4 Background

5. Three criteria for eligibility: ◦ Have multiple chronic conditions ◦ Use multiple covered drugs ◦ Be likely to exceed a drug cost threshold of $4,000 (2006-2009) or $3,000 with inflation adjustment (2010 and after) 5 Background

6. Racial and ethnic minorities would be less likely to be eligible for MTM services than were Whites. There were greater racial and ethnic disparities in health status among MTM- ineligible individuals than among MTM-eligible before the implementation of Medicare Part D in 2006. MTM eligibility criteria may aggravate existing racial and ethnic disparities in health status. 6 What we found…

7. To determine whether the implementation of Medicare Part D in 2006 correlates to changes in racial and ethnic disparities among MTM-ineligible and MTM-eligible beneficiaries 7 Study Objective

8. Methods-Data Source Medicare Current Beneficiaries Survey (MCBS; 2001-2002, 2004-2005, 2007-2008) ◦ Sponsored by CMS and started in 1991. ◦ A continuous, multi-purpose survey of health status, healthcare utilizations, health insurance coverage, and socio-demographic characteristics ◦ A comprehensive data source for a nationally representative sample of Medicare population. 8

9. Methods Electronic Orange Book Query Data Files ◦ Maintained by Food and Drug Administration to determine characteristics of medications ◦ Information include drug name, approval date, etc. 9

10. Methods-Conceptual Framework Anderson’s Behavioral Model of Health Services Utilization ◦ Predisposing factors: race, ethnicity, age, gender, and marital status; Enabling factors: education, insurance, poverty level, metropolitan statistical area, and geographic regions; Need factors: self-perceived health status and a risk adjustment summary score Iezzoni’s Risk Adjustment Model ◦ To analyze health status ◦ Categorizing risk dimensions into socio- demographic variables and health status measures 10

11. Methods Examined lower limit, median, and upper limit values for MTM eligibility criteria 2010 eligibility criteria ◦ For the criterion based on the number of covered prescriptions: 2,5,8; for the criterion based on the number of chronic conditions: 2, 3; and for the criterion based on Part D drug costs: $3,000 ◦ Six possible threshold combinations  Median values for main analysis  All other 5 combinations in sensitivity analyses 11

12. Health status: self-perceived good health status, number of chronic conditions, number of activities of daily living (ADLs), and number of instrumental activities of daily living (IADLs). Health services: number and cost of ER visits, physician visits, hospitalizations, and total health care costs. Medication utilization patterns: Generic Dispensing Ratio (GDR). 12 Methods-Study Outcomes

13. Methods-Study Design 13 Groups ◦ Non-Hispanic Whites (Whites) ◦ Non-Hispanic Blacks (Blacks) ◦ Hispanics

14. 14 Figure 1. Research design. Disparity Patterns between Whites and Blacks in 2001-2002 Disparity Patterns between Whites and Blacks in 2007-2008 Disparity Patterns between Whites and Blacks in 2004-2005 Difference in Difference in Differences Difference in Difference in Differences Difference in Difference in Difference in Differences Difference in Differences DDD DDDD MTM- Ineligible MTM- Eligible Study Outcomes among Whites Study Outcomes among Blacks Racial Disparity Difference in Disparity

15. In total, 43,482 Medicare beneficiaries. ◦ Whites: 37,120 (85.37%) ◦ Blacks: 3,409 (7.84%) ◦ Hispanics: 2,953 (6.79%)Results 15University of Tennessee College of Pharmacy

16. 16 VariablesGroups Non-Hispanic Whites Non-Hispanic BlacksHispanics %% Age a 65-74 51.5253.9357.46 75-84 37.0033.4531.17 >85 11.4712.6211.37 GenderFemale 56.8060.7159.51 Male 43.2039.29 40.49 Medicaid a,b No 92.9566.0962.55 Yes 7.0533.9137.45 Marital Status a,b Not married 42.0665.1147.48 Married 57.9434.8952.52 Education a,b Lower than high school 30.9164.7866.04 High School 38.1222.3119.91 Higher than high School 30.9712.9114.05 Metropolitan statistical area b No 27.9821.8811.66 Yes 72.0278.1288.34 Table 1. Socio-demographic characteristics across racial and ethnic groups among the Medicare population in 2001-2002 a P <.05 for the difference between non-Hispanic Whites (Whites) and Hispanics. b P <.05 for the difference between Whites and non-Hispanic Blacks.

17. Table 1. Socio-demographic characteristics across racial and ethnic groups among the Medicare population in 2001-2002 (continued) 17 VariablesGroups Non-Hispanic Whites Non-Hispanic BlacksHispanics %% Poverty Status a,b 100% FPL c 11.3637.7135.13 100%-149% FPL 17.6124.9824.92 150-199% FPL 14.7313.7013.90 200%-300% FPL 29.1214.8316.70 Higher than 300% FPL 27.188.79 9.36 US census Region a,b Northeast 20.5118.2116.32 Midwest 26.4415.5110.42 South 36.7058.4740.81 West 16.347.8132.45 Self-perceived health status a,b Excellent 17.4810.8814.02 Very Good 30.5822.9121.80 Good 31.6833.3833.25 Fair 14.7925.3724.44 Poor 5.477.466.50 a P <.05 for the difference between non-Hispanic Whites (Whites) and Hispanics. b P <.05 for the difference between Whites and non-Hispanic Blacks.

18. 18 Health Outcomes 2001-2002 (DD) b 2004-2005 (DD) b 2007-2008 (DD) b 2001- 2002 vs. 2004- 2005 (DDD) c 2007-2008 vs. 2004-2005 (DDD) c Part D effect (DDDD) d Health Status Self-perceived health status Unad2.49 e 2.51 e 2.24 e -0.02-0.28-0.30 Ad2.26 e 2.37 e 1.92 e -0.10-0.45-0.56 Number of chronic diseases Unad-0.26-0.19-0.14-0.070.05-0.02 Ad-0.25-0.17-0.22-0.08-0.05-0.12 Activities of daily living, N Unad-0.14-0.050.22-0.090.270.18 Ad-0.14 0.63 e -0.0010.77 e 0.76 Instrumental activities of daily livings, N Unad-0.15-0.040.27-0.100.310.21 Ad-0.17-0.060.58 e -0.110.640.53 Table2/Panel 1: Unadjusted and Adjusted Estimates of the Part D Effect on Health Implications of MTM-Eligibility Criteria across Racial groups a. Main analysis: eligibility thresholds examined: 5 Part D drugs, 3 chronic conditions, and $3,000 in drug costs. (Unad: Unadjusted; Ad: Adjusted). b. DD= (MTM-ineligible Whites – MTM-ineligible Blacks) - (MTM-eligible Whites – MTM- eligible Blacks). c. DDD=DD for (2007-2008)-DD for (2004-2005) or DD for (2004-2005)-DD for (2001-2002). d. DDDD=DDD for ([2007-2008] vs. [2004-2005])-DDD for ([2004-2005] vs. [2001-2002]). e. p< 0.05.

19. 19 Health Outcomes 2001- 2002 (DD) b 2004- 2005 (DD) b 2007- 2008 (DD) b 2001-2002 vs. 2004-2005 (DDD) c 2007-2008 vs. 2004-2005 (DDD) c Part D Effect (DDDD) d Health Services Utilization/Costs Emergency room visits, N Unad-0.040.14 e 0.07-0.18-0.07-0.25 Ad-0.030.090.13 e -0.120.04-0.08 Costs of ER visits, $ Unad-13.9531.6121.11-45.56-10.50-56.06 Ad-35.77-25.5393.77-10.24119.31109.06 Physician visits, NUnad-4.28-1.900.04-2.381.95-0.43 Ad-6.27 e -0.83-3.82-5.44-2.99-8.43 Costs of physician visits, $ Unad-48.08-725.43510.70677.351236.131913.47 Ad-125.77-738.56-736.69612.801.87614.67 Hospitalizations, NUnad-0.08-0.09-0.0030.0020.080.09 Ad-0.07-0.11-0.050.030.060.09 Hospitalization costs, $ Unad-901.71-918.14-792.2616.43125.89142.32 Ad-891.81-598.73-975.07-293.08-376.34-669.42 Total costs, $Unad-1462.66-2793.35164.901330.692958.254288.93 Ad-1485.44-2190.91-905.71705.471285.201990.67 Medication Utilization GDR Unad -0.010.020.03-0.030.01-0.01 Ad -0.010.020.05-0.040.02-0.01 Table2/Panel 1: Continued

20. 20 Health Outcomes 2001-2002 (DD) b 2004-2005 (DD) b 2007-2008 (DD) b 2001- 2002 vs. 2004- 2005 (DDD) c 2007-2008 vs. 2004-2005 (DDD) c Part D effect (DDDD) d Health Status Self-perceived health status Unad 2.06 e 1.95 e 3.13 e 0.101.181.29 Ad 1.85 e 1.77 e 2.83 e 0.081.061.14 Number of chronic diseases Unad 0.260.070.290.190.220.40 Ad 0.240.030.260.210.230.44 Activities of daily living, N Unad 0.26-0.04-0.020.300.020.31 Ad 0.30-0.21-0.080.51-0.130.64 Instrumental activities of daily livings, N Unad 0.34 e 0.080.050.26-0.030.23 Ad 0.28-0.060.070.340.130.47 Table2/Panel 2: Unadjusted and Adjusted Estimates of the Part D Effect on Health Implications of MTM-Eligibility Criteria across Ethnic groups a. Main analysis: eligibility thresholds examined: 5 Part D drugs, 3 chronic conditions, and $3,000 in drug costs. (Unad: Unadjusted; Ad: Adjusted). b. DD= (MTM-ineligible Whites – MTM-ineligible Hispanics) - (MTM-eligible Whites – MTM- eligible Hispanics). c. DDD=DD for (2007-2008)-DD for (2004-2005) or DD for (2004-2005)-DD for (2001-2002). d. DDDD=DDD for ([2007-2008] vs. [2004-2005])-DDD for ([2004-2005] vs. [2001-2002]). e. p< 0.05.

21. 21 Health Outcomes 2001- 2002 (DD) b 2004- 2005 (DD) b 2007- 2008 (DD) b 2001-2002 vs. 2004-2005 (DDD) c 2007-2008 vs. 2004-2005 (DDD) c Part D effect (DDDD) d Health Services Utilization/Costs Emergency room visits, N Unad -0.0020.060.01-0.07-0.05-0.12 Ad -0.050.040.02-0.09-0.020.10 Costs of ER visits, $ Unad -22.48-11.79-15.07-10.69-3.28-13.97 Ad -65.23-61.86-32.59-3.3729.2725.89 Physician visits, NUnad -3.854.579.70 e -8.435.13-3.30 Ad -1.045.974.43-7.01-1.54-8.54 Costs of physician visits, $ Unad -946.221793.64-136.80-2739.87-1930.45-4670.31 Ad -834.082311.65320.71-3145.73 e -1990.94-5136.67 Hospitalizations, NUnad -0.160.030.02-0.19-0.01-0.20 Ad -0.23-0.0030.01-0.220.01-0.21 Hospitalization costs, $ Unad -498.20453.10762.66-951.31309.55-641.76 Ad -1756.59-135.06-445.20-1621.53-310.14-1931.67 Total costs, $Unad -1309.962369.433150.54-3679.38781.11-2898.27 Ad -2198.012821.892172.83-5019.90-649.06-5658.95 Medication Utilization GDR Unad -0.001-0.0020.020.0010.03 Ad 0.0010.00030.030.00030.03 Table2/Panel 2: Continued

22. Results The main analysis found that Part D implementation was not associated with reductions in greater racial and ethnic disparities among MTM-ineligible than MTM-eligible beneficiaries. 22University of Tennessee College of Pharmacy

23. Results Several sensitivity analyses showed a significant reduction in greater disparities among the MTM-ineligible group ◦ Racial disparities: ADLs (sensitivity analyses 5; DDDD=1.13 [P=0.03]& IADLs ◦ Ethnic disparities: Physician visit costs (For sensitivity analysis 1:DDDD=–4613.71; P=0.04; and sensitivity analysis 3). 23

24. Discussion After Part D implementation, the Medicare MTM eligibility criteria did not mitigate the majority of variables related to existing racial and ethnic disparities in our study outcomes based on the main analysis. Significant findings in sensitivity analyses are not comforting, because the combinations of the thresholds in the sensitivity analyses were used less frequently by Part D plans. University of Tennessee College of Pharmacy24

25. Discussion Previous literature has reported that Part D implementation led to higher medication utilization. Previous literature found that Part D had mixed effects on patient health status and the use of healthcare resources other than prescription medication and across racial and ethnic groups. University of Tennessee College of Pharmacy25

26. Limitations Unavailability to the research community of MTM claims databases suitable for this study (analyses conducted were based on policy scenarios rather than on actual beneficiary enrollment data for MTM services) Disparities in eligibility were examined rather than actual receipt of MTM vices. It is necessary to examine eligibility criteria to ensure that awareness is raised among policymakers regarding the disparity effects of these criteria. 26

27. Conclusions Our findings highlight a need for the US healthcare system to develop strategies to address these health inequalities and/or gaps between nonminority and minority Medicare beneficiaries to improve the health of the population. Future studies should explore strategies to eliminate the disparity implications related to the MTM eligibility. University of Tennessee College of Pharmacy27

28. Acknowledgements Supported by the Grant Number R01AG040146 from the National Institute On Aging. The Content is solely the responsibility of the authors and does not necessarily represent the National Institute On Aging or the National Institutes of Health. 28University of Tennessee College of Pharmacy

29. 29 Contact Information: Junling Wang, PhD Associate Professor University of Tennessee College of Pharmacy Email: jwang26@uthsc.edu Phone: 901-448-3601 Fax: 901-448-1221