The Medicare Part D program was implemented in 2006 according to the Medicare Prescription Drug, Improvement, and Modernization Act (MMA).1 Medication therapy management (MTM) services were established by the Centers for Medicare & Medicaid Services (CMS) as part of the Part D benefit. MTM services may be furnished by a pharmacist or by other healthcare providers to “ensure that covered Part D drugs prescribed to targeted beneficiaries…are appropriately used to optimize therapeutic outcomes.”1 The core components of MTM are the formulation of a medication treatment plan and integration of the plan with all health services provided to patients.2
In consideration of limited resources, the MMA restricted MTM services to Medicare beneficiaries meeting all 3 criteria, including (1) having multiple chronic conditions, (2) using multiple Part D drugs, and (3) being likely to exceed a drug cost threshold of $4000.1,3 For the year 2010 and onward, CMS required the eligibility thresholds to be lowered to no more than 3 chronic conditions, 8 drugs, and $3000 in annual drug costs.3
Of note, 2 of the 3 eligibility criteria depend significantly on the utilization of medications by the beneficiary, whereas multiple studies on medication use patterns have shown that racial and ethnic minorities use fewer medications and incur lower drug costs compared with nonminorities.4-9 Therefore, as Wang and colleagues have found, minorities may be less likely to meet the Medicare MTM eligibility criteria.10
Furthermore, in a recent study, Wang and colleagues found that non-Hispanic blacks and Hispanics were less likely than non-Hispanic whites to report self-perceived good health status, and that there were greater racial and ethnic disparities among the MTM-ineligible than MTM-eligible beneficiary population before the implementation of Part D based on the 2006 and 2010 MTM eligibility criteria.11 This suggests that MTM eligibility criteria perpetuate the existing racial and ethnic disparities in health status.
The purpose of this present study was to determine the effects of Part D implementation on the health implications of Medicare MTM eligibility across racial and ethnic groups. If this study found that Part D implementation was not associated with reductions in greater racial and ethnic disparities in the MTM-ineligible than the MTM-eligible individuals, the urgency for modifying MTM eligibility criteria would be even greater than established by the previous studies by Wang and colleagues.10,11
We conducted a retrospective observational analysis using data from the Medicare Current Beneficiary Survey (MCBS; study periods 2001-2002, 2004-2005, and 2007-2008).12 The MCBS, which is sponsored by CMS, includes a nationwide sample of Medicare beneficiaries linked to patients’ Medicare claims.12 This continuous, multipurpose survey provides information on Medicare beneficiaries’ health status, healthcare utilizations, health insurance coverage, and socioeconomic and demographic characteristics.12 The Electronic Orange Book Query data files (Orange Book) from the US Food and Drug Administration (FDA) were used to determine characteristics specific to prescription medications.13 These files provide comprehensive information for both brand-name and generic medications.
To examine racial and ethnic disparities, 3 major groups were included—non-Hispanic whites, non-Hispanic blacks, and Hispanics. Racial disparities were examined by comparing whites and blacks, and ethnic disparities were examined by comparing whites and Hispanics. To reduce the heterogeneity of the study population, the sample included only home-dwelling Medicare beneficiaries who were eligible for Medicare based on their age. Beneficiaries in a health maintenance organization with a closed network were excluded from the analysis, because not all claims for these individuals were included in the databases.
Andersen’s Behavioral Model for Health Services Utilization and Iezzoni’s Risk Adjustment Model were applied in this study.14,15 According to Andersen’s model, independent variables that govern prescription and health services utilization and costs were categorized into 3 groups: predisposing factors (race, ethnicity, age, sex, and marital status), enabling factors (socioeconomic status, education, health insurance, and region of residence), and need factors (self-perceived health status and a risk adjustment summary score).14 Iezzoni’s Risk Adjustment Model was used to analyze health status by categorizing risk dimensions into sociodemographic variables and health status measures.15
The 3 aspects of disparities that were analyzed include health status, health services utilization and costs, and medication utilization patterns. To identify disparities in health status, the following measures were used: self-perceived good health status (classified as good [excellent, very good, or good] or poor [fair or poor]), number of chronic conditions, number of activities of daily living (ADLs), and number of instrumental ADLs (IADLs). To identify chronic conditions, a raw count among a list of 25 chronic conditions was obtained using the Clinical Classifications Software (Rockville, MD).16 This list was devised by Daniel and Malone and includes all major conditions that were specifically targeted by Medicare Part D.17
Disparities in health services utilization and costs were measured by number and cost of emergency department visits, physician visits, hospitalizations, and total healthcare costs. Medication utilization patterns were based on the generic-dispensing ratio.11,18
The generic-dispensing ratio was defined as the proportion of generic prescriptions among total prescriptions.11,18 The MCBS was linked to the FDA’s Orange Book to determine if the medications prescribed were generic or brand-name agents. A pharmacist manually determined if any unmatched medications were generic agents.
Determining MTM Eligibility
To determine MTM eligibility, the 2010 CMS criteria were applied.19 MTM eligibility thresholds used by Part D plans in 2010 included 2 to 8 Part D drugs (median, 5), 2 to 3 chronic conditions (median, 3), and at least $3000 in drug costs. Upper, median, and lower limits were used as representative values for the eligibility thresholds used by Part D plans based on the number of Part D drugs and chronic conditions for each beneficiary.19 The median and upper limits were the same for chronic conditions, thus 1 main analysis and 5 sensitivity analyses were conducted (3*2*1, representing the number of representative values for the thresholds based on the number of Part D drugs, number of chronic conditions, and the only drug cost threshold; 6 in total).
The main analysis examined the combination of median threshold values for the number of chronic conditions (3 conditions), the number of Part D drugs (5 drugs), and the $3000 drug cost thresholds, whereas the other 5 of the 6 combinations of those thresholds were calculated as sensitivity analyses. The drug cost was converted to the study year dollars using the Consumer Price Index for medical care.20
A generalized difference-in-differences (DD) model, a difference-in-differences-in-differences-in-differences (DDDD) model was used. Specifically, differences in patterns of racial and ethnic disparities between MTM-ineligible and MTM-eligible beneficiaries between 2004-2005 and 2007-2008, relative to the changes from 2001-2002 and 2004-2005, were compared and are described in the Figure.
(For example, when examining racial disparities, disparities between non-Hispanic whites and blacks were referred to as “difference,” whereas DD represented difference in disparities between the MTM-ineligible and MTM-eligible populations. There were 3 DDs in this study, one for each of the time periods—2007-2008, 2004-2005, and 2001-2002. Difference in differences in differences [DDD] in this study refers to changes in racial disparities from one period to the next. Specifically, there were 2 DDDs, one representing changes in DD from 2004-2005 to 2007-2008, the other for changes in DD from 2001-2002 to 2004-2005. DDDD in this study represents the difference in these 2 DDDs.)
Racial and ethnic disparities were analyzed separately in regression models (see Appendix). The functional forms of the regression models varied according to the types of dependent variables. For example, a logistic regression was used when analyzing self-perceived good health status and high-risk medication use.
A negative binomial model was used for count variables, including ADLs, IADLs, number of emergency department visits, number of physician visits, and number of hospitalizations. A Poisson regression was used for the number of chronic diseases, because a negative binomial model would not converge. A generalized linear model was analyzed using log link and gamma distribution on all cost variables. An ordinary least squares regression was used for the generic-dispensing ratio.
The highest levels of differences (ie, DDDD) calculated in this study were carried out with a creative programming method using STATA (StataCorp LP, College Station, TX) that is based on the interpretative method on the additive term (also called marginal effect). This method takes into account only the baseline effect among the reference group.21
The complex sampling structure of MCBS, including primary sampling units, strata, and cross-sectional full sample weights, was accounted for in all data analyses using SAS 9.3 (SAS Institute Inc, Cary, NC) and STATA 12.0. This study was deemed exempt from further Institutional Review Board review at the lead author’s institution.
The 2001-2002 study sample included a total of 15,787 (weighted to 54,259,004) Medicare beneficiaries. The sample included 13,299 whites (weighted to 45,997,416 or 84.77f%), 1408 non-Hispanic blacks (weighted to 4,489,293 or 8.27%), and 1080 Hispanics (weighted to 3,772,315 or 6.95%).
Significant differences were noted between whites and minorities on several demographic characteristics (Table 1). In comparison to whites, minorities were less likely to be married, and were more likely to have lower levels of education, belong to poorer income categories, have Medicaid, and have reported poorer health status (P <.05). The 2004-2005 and 2007-2008 samples had similar characteristics.
Based on unadjusted and adjusted multivariate regression models in the DDDD part of the analyses, the main analysis (representing the combination of 5 Part D drugs, 3 chronic conditions, and $3000 in drug costs) did not find any significant DDDD (Table 2, Panel 1).
However, significant findings were seen in other levels of differences and in some variables’ sensitivity analyses. For example, the marginal effects were higher among whites than among blacks in the model (Table 3, Panel 1). In the DD part of the analysis, the difference was calculated for the differences between whites and blacks among the MTM-ineligible and the MTM-eligible beneficiaries.
This study found significant differences for every individual time period in the unadjusted model for the self-perceived good health status, including 2001-2002 (difference in odds, 2.49; P <.001; 95% confidence interval [CI], 1.96-3.02); 2004-2005 (difference in odds, 2.51; P <.001; 95% CI, 1.77-3.25); and 2007-2008 (difference in odds, 2.24; P <.001; 95% CI, 1.17-3.3). These results suggest that racial disparities may be greater among MTM-ineligible beneficiaries than among MTM-eligible beneficiaries. Similar patterns were found in the adjusted model (Table 2, Panel 1).
Nonetheless, neither the difference-in-differencesin-differences (DDD) nor the DDDD part of the analyses revealed significant findings, which indicates that Part D was not associated with significant changes in patterns of disparities (DDDD in the unadjusted model = –0.30; P = .72; 95% CI, –1.97 to 1.37; DDDD in the adjusted model = –0.56; P = .63; 95% CI, –2.82 to 1.71). The sensitivity analyses for self-perceived good health status had similar findings.
For a few variables, however, when examining racial disparities, some sensitivity analyses did produce significant findings for the DDDD part of the analysis, suggesting that Part D may be associated with significant changes in disparities in some situations. Specifically, in the analysis of ADLs, only sensitivity analysis 5 (thresholds of ≥8 drugs, ≥2 chronic conditions, and >$3000 in drug costs) produced a significant DDDD estimate of 1.13 in the adjusted model (P = .03; 95% CI, 0.09-2.17).
The interpretation of this estimate requires an examination of all levels of differences involved. The marginal effects were typically lower among whites than blacks (Table 3, Panel 1). The same patterns predominantly held for 2004-2005 and 2007-2008 (Table 3, Panel 1). The DD between the MTM-ineligible group and the MTM-eligible group was –0.02 (P = .94) for 2001-2002; –0.31 for 2004-2005 (P = .09); and 0.54 (P = .09) for 2007-2008, suggesting there may not be significant difference in the abovementioned disparity patterns between the MTM-ineligible and MTM-eligible groups for any time periods examined.
Furthermore, the DDD was found to be –0.29 (P = .36) for 2004-2005 versus 2001-2002 and 0.84 (P = .01) for 2007-2008 versus 2004-2005, indicating that there may be a decrease in the greater racial disparity among the MTM-ineligible than in the MTM-eligible populations when comparing 2007-2008 and 2004-2005, but there may not be significant changes when comparing 2001-2002 and 2004-2005.
These findings, combined with the significant DDDD value of 1.13, suggest that Part D implementation may be associated with a decrease in any racial disparities in ADLs among the MTM-ineligible group versus the MTM-eligible group using the combinations of eligibility thresholds for sensitivity analysis 5. For the variable IADLs, the same patterns as ADLs were found in sensitivity analysis 4 (for thresholds of ≥8 drugs, ≥3 chronic conditions, and >$3000 in drug costs) and sensitivity analysis 5. This suggests that Part D implementation may be also associated with a greater decrease in any racial disparities in IADLs among the MTM-ineligible group than among the MTM-eligible group.
With regard to racial disparities, no other variables were found to be associated with significant results in the DDDD part of the analysis.
Concerning the comparison between whites and Hispanics, the main analysis did not find significant DDDD for any variable (Table 2, Panel 2). However, the analysis of costs of physician visits did show significant findings for some sensitivity analyses. For costs of physician visits, the DDDD was found to be significant in the adjusted models for sensitivity analysis 1 (thresholds of ≥2 drugs, ≥2 chronic conditions, and >$3000 in drug costs) and analysis 3 (thresholds of ≥5 drugs, ≥2 chronic conditions, and >$3000 in drug costs). The DDDD estimate was –4613.71 (P = .04; 95% CI, –8907.29 to –320.13) for sensitivity analysis 1.
The marginal effects were quantitatively higher among whites than among Hispanics under most situations, although the CIs did not overlap for the MTM-ineligible groups in 2004-2005 and in 2007-2008 (Table 3, Panel 2). The DD estimates were –1064.86 (P = .17) for 2001-2002; 1909.27 (P = .06) for 2004-2005; and 269.69 (P = .63) for 2007-2008, suggesting that disparities between whites and Hispanics were similar between MTM-ineligible and MTM-eligible populations for all time periods.
The DDD values were 2974.13 (P = .02) when comparing 2004-2005 and 2001-2002, and –1639.58 when comparing 2007-2008 and 2004-2005 (P = .15). When combined with the DDDD value of –4613.71 (P = .04), these findings suggest that Part D implementation was associated with a reduction in greater ethnic disparities in the costs of physician visits for the MTM-ineligible than for the MTM-eligible groups.
Sensitivity analysis 3 had similar results, showing a DDDD estimate of –5094.36 (P = .03). No other variables were found to be associated with significant findings in the DDDD part of the analysis.
This study sought to determine the effects of Part D implementation, based on the 2010 MTM eligibility criteria, on differences in racial and ethnic disparities in health status, health services utilization and costs, and medication utilization between the MTM-ineligible group and the MTM-eligible group. Although several sensitivity analyses for a few variables showed significant association between Part D implementation and MTM disparities, the results of the main analysis did not show a significant association between Part D implementation and MTM disparities. This suggests that after Part D implementation, the Medicare MTM eligibility criteria did not mitigate the majority of variables related to existing racial and ethnic disparities in health status, health services utilization and costs, and medication utilization.
However, it is important to note that in some situations, Part D did correlate with a significant reduction in racial disparities, specifically among the MTM-ineligible group versus the MTM-eligible groups in relation to ADLs (sensitivity analysis 5) and IADLs (sensitivity analyses 4 and 5).
Furthermore, Part D implementation also may be associated with a greater reduction in ethnic disparities, if any, among the MTM-ineligible group versus the MTM-eligible groups in the costs of physician visits (sensitivity analyses 1 and 3). However, these findings are not comforting, because the combinations of the thresholds in the sensitivity analyses were used by Part D plans less frequently than the combination of the thresholds in the main analysis.
These findings are not surprising. Previous literature has reported that Part D implementation led to higher medication utilization, but also had mixed effects on patient health status and the use of healthcare resources other than prescription medications.22-24 In addition, Part D has been found to have mixed effects on racial and ethnic disparities in prescription utilization.25,26
This present study did reveal significant differences in several variables. For example, the self-perceived health status measured by marginal effects was higher among non-Hispanic whites than among non-Hispanic blacks and Hispanics. In addition, this study found that there were greater disparities in self-perceived good health status in MTM-ineligible than in the MTM-eligible population. Both results are consistent with previous research.11
The reasons for the absence of consistent and significant effects of Part D implementation on racial and ethnic disparities may be complex. For example, various barriers hinder appropriate medication utilization among minorities, including patients’ lack of accurate knowledge about medications. Omojasola and colleagues found that blacks and Hispanics were 10 times as likely as whites to believe that generic drugs had more side effects.27
Blacks and Hispanics were also 4 times as likely to agree that generic drugs were inferior to brand-name drugs when compared with their white counterparts. However, respondents who found generic drugs comparable with brand-name drugs were 3 times more likely to use generic drug discount programs.27 The negative perceptions regarding generic drugs among minority patients likely prevent these patients from enjoying the benefits of generic prescriptions, such as decreased total out-of-pocket expenses and a reduction of cost-related barriers to medication adherence.
One additional cause for the limited effects of Part D may be that blacks and Hispanics are more likely to have lower income and education levels and poorer health status, which are associated with problems related to healthcare coverage and access.28,29 Socioeconomic status is a particularly serious challenge to reducing racial and ethnic disparities in health outcomes and can also hinder the potential benefits of MTM services.
For example, Cook and colleagues assessed patient behaviors after MTM services and found that poverty status was associated with participants taking less action after a medication review, even after adjusting for factors such as insurance.30 Therefore, even if MTM services are available to eligible minorities, socioeconomic factors have a substantial impact on whether health disparity patterns improve as a result of these services. Reducing disparity implications of MTM services, therefore, may have to take a multipronged approach.
This study is important also because eliminating racial and ethnic disparities in healthcare has become an essential step to improving the healthcare system. Since the first report about racial and ethnic disparities was issued in the 1980s,31 bridging the gap between minorities and nonminorities has become a primary goal of government agencies, such as the US Department of Health and Human Services (HHS).32
The HHS initiative Healthy People 2010/2020 set the elimination of disparities as one of its goals.33 In addition, the National Institute on Minority Health and Health Disparities pursues the mission “to lead scientific research to improve minority health and eliminate health disparities.”34 Its research aims for an “America in which all populations will have an equal opportunity to live long, healthy, and productive lives.”34
Prioritization of value-based healthcare (where costs and benefits are balanced) rather than cost-based healthcare represents a key strategy in combating racial and ethnic disparities. Given the same health status, minorities tend to use fewer prescription medications and incur lower drug costs than do whites, and thus are less likely to be eligible for MTM services.10
According to Porter, within a value-based system, good health is ultimately less costly than poor health, so the best way to contain cost may be to improve the health of the population.35 Future research should explore alternative MTM eligibility criteria that would be value-based.
This study has limitations. Because of the unavailability to the research community of MTM claims databases suitable for this study, the analyses conducted were based on policy scenarios rather than on actual beneficiary enrollment data for MTM services. Similarly, disparities in MTM eligibility were examined rather than actual receipt of services. However, it is necessary to examine eligibility criteria to ensure that awareness is raised among policymakers regarding the disparity effects of these criteria.
In addition, the main analysis did not show significant differences; however, several scenarios in the sensitivity analyses did find that the Part D implementation was associated with significant reduction in greater racial and ethnic disparities among the MTM-ineligible population compared with the MTM-eligible population in measures of health status, health services utilization and services, and medication utilization patterns.
Furthermore, the categorization of the study sample into 3 racial and ethnic groups may not accurately reflect variation in biology, culture, or preferences, although data from the MCBS have been considered authoritative for the reported information on race and ethnicity when compared with other databases.4,36,37
The results of this study indicate that the greater racial and ethnic disparities seen among the Medicare MTM-ineligible population than the MTM-eligible population in measures related to health status, health services utilization and services, and medication utilization patterns may not have been significantly reduced after the implementation of Medicare Part D. These results 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 as reflected in health status, health services utilizations and costs, and medication utilization patterns.
The authors would like to acknowledge the research assistance from Kiraat D. Munshi, MS, Caroline Clark, and Yuewen Deng.
This study was funded by grant R01AG040146 from the National Institute on Aging. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institute on Aging or the National Institutes of Health.
Author Disclosure Statement
Dr Wang has received grant support from Pfizer Inc. and from Eli Lilly and Company; Dr Dagogo-Jack has received research support from AstraZeneca, Novo Nordisk, and Boehringer-Ingelheim, and is a consultant to Merck, Novo Nordisk, and Eli Lilly and Company; Dr Cushman has received an institutional research grant from Merck and from Eli Lilly and Company; Ms Qiao, Dr Shih, Ms Jamison, Dr Spivey, Dr Li, Dr Wan, Dr White-Means, and Dr Chisholm-Burns reported no real or potential conflicts of interest.
Dr Wang is Associate Professor, Department of Clinical Pharmacy, University of Tennessee College of Pharmacy, Memphis; Ms Qiao is Research Assistant, Department of Clinical Pharmacy, University of Tennessee College of Pharmacy, Memphis; Dr Shih is Associate Professor, Section of Hospital Medicine, Department of Medicine, and Director, Program in the Economics of Cancer, University of Chicago, IL; Ms Jamison is pharmacy student, University of Tennessee Health Science Center, College of Pharmacy, Memphis; Dr Spivey is Assistant Professor, Department of Clinical Pharmacy, University of Tennessee College of Pharmacy, Memphis; Dr Li is Postdoctoral Fellow, Department of Clinical Pharmacy, University of Tennessee College of Pharmacy, Memphis; Dr Wan is Professor, Department of Preventive Medicine, University of Tennessee Health Science Center, Memphis; Dr White-Means is Professor and Director, Consortium for Health Education, Economic Empowerment and Research, Department of Clinical Pharmacy, University of Tennessee College of Pharmacy, Memphis; Dr Dagogo-Jack is Mullins Professor and Director, Division of Endocrinology, Diabetes and Metabolism, and Director, Clinical Research Center, University of Tennessee Health Science Center, Memphis; Dr Cushman is Professor, Departments of Preventive Medicine, Medicine, and Physiology, University of Tennessee College of Medicine, Memphis, and Chief, Preventive Medicine Section, Veterans Affairs Medical Center, Memphis, TN; Dr Chisholm-Burns is Dean and Professor, University of Tennessee College of Pharmacy, Memphis, Knoxville, and Nashville.
- Centers for Medicare & Medicaid Services (CMS), HHS. Medicare program; Medicare prescription drug benefit. Final rule. Fed Regist. 2005;70:4193-4585.
- American Pharmacists Association, National Association of Chain Drug Stores Foundation. Medication Therapy Management in community pharmacy practice: core elements of an MTM service (version 1.0). J Am Pharm Assoc (2003). 2005;45: 573-579.
- Centers for Medicare & Medicaid Services. 2011 Medicare Part D Medication Therapy Management (MTM) programs. June 30, 2011. www.cms.gov/Medicare/Prescription-Drug-Coverage/PrescriptionDrugCovContra/downloads/mtmfactsheet 2011063011final.pdf. Accessed July 6, 2014.
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