An Investigation of Medicare Advantage Dual Eligible Member- Level Performance on CMS Five-Star Quality Measures

Transcription

1 An Investigation of Medicare Advantage Dual Eligible Member-Level Performance on CMS Five-Star Quality Measures An Investigation of Medicare Advantage Dual Eligible Member- Level Performance on CMS Five-Star Quality Measures

2 About Inovalon Inovalon is a leading technology company that combines advanced cloud-based data analytics and data-driven intervention platforms to achieve meaningful insight and impact in clinical and quality outcomes, utilization, and financial performance across the healthcare landscape. Inovalon s unique achievement of value is delivered through the effective progression of Turning Data into Insight, and Insight into Action. Large proprietary datasets, advanced integration technologies, sophisticated predictive analytics, data-driven intervention platforms, and deep subject matter expertise deliver a seamless, end-to-end capability that brings the benefits of big data and large-scale analytics to the point of care. Driven by data, Inovalon uniquely identifies gaps in care, quality, data integrity, and financial performance while bringing to bear the unique capabilities to resolve them. Providing technology that supports hundreds of healthcare organizations in 98.2% of U.S. counties and Puerto Rico, Inovalon s cloud-based analytical and data-driven intervention platforms are informed by data pertaining to more than 754,000 physicians, 248,000 clinical facilities, and more than 120 million Americans providing a powerful solution suite that drives high-value impact, improving quality and economics for health plans, ACOs, hospitals, physicians, consumers and pharma/life-sciences researchers. For more information, visit

3 TABLE OF CONTENTS Executive Summary...5 Background...11 Objective...16 Methods...16 Study Population and Data Sources...17 Plan-Level Analyses...18 Multivariate Analyses...27 Appendix A Variable Descriptions...40 Appendix B Measure Definitions for Selected CMS Five-Star Quality and Display Measures...46 Appendix C Plan Benefit Package Level Analyses: Technical Notes...52 Appendix D Plan Benefit Package Level Analyses: Detailed Results...54 Appendix E Analysis: Technical Notes...63 Appendix F Multivariate Decomposition Analyses: Detailed Results...66 Dual Eligible Study Advisory Panel...85 Inovalon Project Team...86 References

4 ACKNOWLEDGEMENTS This study was independently conducted by Inovalon s Division of Statistical Research, with additional funding provided by Cigna HealthSpring, WellCare, Healthfirst, Gateway Health, Blue Cross Blue Shield Minnesota and Blue Plus, and Health Care Service Corporation (HCSC). We would like to extend our appreciation and thanks to the staff of the Centers for Medicare and Medicaid Services for their technical comments on the methodologies used in this report, and to the Special Needs Plan (SNP) Alliance, Medicaid Health Plans of America (MHPA) and Pharmacy Quality Alliance (PQA) for their feedback on the analyses and findings. If you have any questions, or would like to request more information on the study, please contact Inovalon via [email protected] 4

5 EXECUTIVE SUMMARY The Centers for Medicare and Medicaid Services (CMS) evaluates Medicare Advantage (MA) health plan performance using a Five-Star Rating System based on a wide range of quality measures. MA Star Ratings are published annually, and thus influence health plan member recruitment and retention. Since 2012, the Star Ratings affect MA health plan reimbursement through quality-bonus payments made to plans that achieve higher ratings. A 2013 Inovalon study investigated the association of dual eligible populations Medicare beneficiaries who are eligible for both Medicare and Medicaid and the performance of MA health plans and found that dual eligible members had significantly lower scores compared to non-dual eligible members on nine of the 10 Star Measures evaluated. The objective of this follow-up investigation was to identify the factors associated with the lower Star Ratings among dual eligible members. To our knowledge, this is the first study to use a large-scale claims database comprised of more than 2.2 million MA beneficiaries, supplemented with new detailed sources of sociodemographic and community resource data at the member-level in conjunction with monthly data on dual eligible status. This unique database enables a statistically valid evaluation of the influence of clinical, sociodemographic and community resource risk factors on Star Measure outcomes at the individual beneficiary level. This is also the first study to leverage a database large enough to evaluate Star Measure outcomes of dual eligible members compared to non-dual eligible members enrolled in the same plan benefit package. MA health plans have contracts with CMS to offer Medicare covered benefits in defined geographic areas, and within those contracts, plans can offer different plan benefit packages. Research to date has compared MA health plan quality at the contract level, which can confound results because contracts often aggregate beneficiaries across different plan benefit packages and provider networks. Analyses at the plan benefit package level allow analyses to answer the question, To what degree are MA beneficiary outcomes related to the dual eligible status of individual plan members versus the quality of care provided by the plan? 5 Key Findings 1. Dual eligible beneficiaries have significantly worse outcomes than non-dual eligible members who are enrolled in the same plan benefit package for five of the eight current Star Measures analyzed. 2. Worse outcomes of dual eligible members are not statistically related to the proportion of dual eligible members enrolled in the plan. Dual eligible members have worse outcomes no matter how large or how small the number of dual eligible beneficiaries in the plan. 3. Worse outcomes of dual eligible members are not statistically associated with enrollment in lower-performing plans, but appear to be related to a higher prevalence of risk factors among dual eligible members.

6 4. Clinical and sociodemographic risk factors affect outcomes in all MA members, but the impact is magnified for MA health plans serving more dual eligible members due to (1) the higher prevalence of these risk factors in the dual eligible population; and (2) the differential impact of these factors on dual eligible members. 5. Clinical, sociodemographic and community resource factors are significantly associated with worse outcomes among dual eligible members accounting for at least 70% of the observed disparities in outcomes between dual eligible and non-dual eligible members for seven Star Measures analyzed using a multivariate technique. 6. Differences in sociodemographic characteristics were consistently a main contributor to the differences in outcomes between dual eligible and non-dual eligible members, accounting for at least 30% of the observed disparities in outcomes of dual eligible members compared to non-dual eligible members. 7. If MA Star Measures were adjusted to control for characteristics statistically associated with higher risk for the outcomes evaluated, the observed performance gaps between dual eligible and non-dual eligible members would be reduced by 70% or more based on this research. Summary of Study Findings 6 The first series of analyses were designed to evaluate the impact of a member s dual eligible status on Star Measure scores within individual plan benefit packages (plans). These analyses assess whether the observed worse outcomes among dual eligible plan members are related to enrollment in poor-performing plans or whether they are related to higher risk profiles of dual eligible beneficiaries. For this study, dual eligible members were defined broadly as those who were dual eligible for at least one month during the study period (2013), including those in Dual Eligible Special Needs plans (D-SNPs). The effect of dual eligible status on individual member outcomes was negative for five of eight current Star Measures evaluated, indicating that dual eligible members have worse outcomes than non-dual eligible members within the same plan. In general, there was no relationship between individual plan member outcomes and the proportion of dual eligible members served by the plan.

7 An important illustration of the negative impact of dual status on outcomes is Plan All-Cause Readmissions, a quality measure that is given triple weight in calculating MA Plan Five-Star Ratings. Hospital readmissions have received significant attention in recent years due to the high cost and the effect on individuals quality of life. This is the only measure among those studied that is currently statistically adjusted for clinical and demographic risk factors associated with higher rates of readmission. The analysis found that dual eligible members are more likely to be readmitted to hospitals than nondual eligible members in the same plan, even after accounting for the measure s existing adjustment factors. The percent of dual eligible members enrolled in the plan was not associated with higher readmissions of individual plan members. This finding suggests that the current adjustments to this measure for age, gender and chronic clinical conditions do not fully correct MA Plan Five-Star Rating scores for the impact of dual eligible status. Simply put, a dual eligible member is at higher risk for hospital readmission compared to a non-dual eligible member with the same clinical characteristics, and this added risk is not accounted for in the current Star Ratings. In summary, the first series of analyses found that the lower performance of dual eligible members does not appear to be associated with the quality of care provided by the plan. If the disparities were due to the quality of care provided by the plan, the analysis would have shown similar results between dual eligible and non-dual eligible members within the same plan benefit package (i.e., the dual status effect would not have been significant). If the disparities were associated with plans serving a larger proportion of dual eligible members, the analysis would have shown a significant effect related to this plan characteristic (i.e., the contextual effect would have been significant). Neither of these results was found in this study. In other words, this research indicates that lower performance of dual eligible members does not appear to be due to enrollment in lower-performing plans. 7 The second series of analyses focused on identifying the specific clinical, sociodemographic and community resource factors contributing to the disparities in seven current Star Measures evaluated using a multivariate approach ( decomposition analysis ). Differences in clinical, sociodemographic and community resource characteristics between dual eligible and non-dual eligible members accounted for 70% or more of the performance gaps observed in the seven Star Measures analyzed. Sociodemographic characteristics were consistently a main contributor to the disparity between dual eligible and nondual eligible MA plan members, explaining 30% or more of the observed differences in outcomes.

8 Using the Plan All-Cause Readmissions measure to illustrate these findings, the decomposition of factors contributing to the disparity in readmission rates between dual eligible and non-dual eligible beneficiaries found that 81.8% of the disparity in readmission rates was attributed to differences in the prevalence of clinical, sociodemographic and community resource factors associated with higher risk of readmission. If dual eligible and non-dual eligible members had similar characteristics or if the measure was statistically adjusted to account for these factors 82% of the observed disparity could be mitigated. The 18% of the disparity in rates not explained by these factors can be attributed to the differential impact of the risk factors on dual eligible members. Of the 81.8% of the disparity explained by the risk factors identified, over half 54.3% was attributed to 17 chronic conditions associated with higher rates of readmission; 27.5% of the disparity was attributed to 3 sociodemographic factors. 8 Living in a neighborhood with a high poverty rate (greater than 23% of households) contributed more than any other risk factor to the likelihood of readmission. This sociodemographic factor explained 18.1% of the disparity in readmission rates. Being poor increases the risk of readmission among all MA members, but the dual eligible population is impacted disproportionately because 41.2% lived in a high poverty neighborhood compared to only 15.8% of non-dual eligible members. Renal disease contributed the most of any chronic condition to the performance gap 14.9% due to the higher prevalence of the disease among dual eligible members (50.7% vs. 43.9%). Dementia is also more prevalent in dual eligible members (42.8% vs. 35.9%) and explained 14.5% of the gap. Living in a county designated as having a physician shortage (less than 50 physicians per 10,000 people) explained 11.9% of the disparity in readmission rates. This result substantiates recent studies reporting that a lack of social- and community-based supports leads to higher rates of readmission. Three Medication Adherence measures included in the Five-Star Rating System were evaluated. The decomposition analyses found that if these measures were statistically adjusted for factors associated with non-adherence, MA plans would score better on medication adherence among their dual eligible members compared to non-dual eligible members with similar risk profiles. Early results of this study released in October 2014 showed that dual eligible members have more outpatient visits on average compared to non-dual eligible members, and that more office visits are associated with better adherence. Since these measures have no statistical adjustments for any of the risk factors associated with lower adherence, and these factors are more prevalent in dual eligible members, reported Star Ratings do not reflect this quality-of-care difference. Because these three

9 measures are triple-weighted in the Star Rating System, the failure to adjust for these risk factors has three times the impact on Star Ratings of MA plans serving a large proportion of dual eligible members. In summary, the second series of analyses found that 70% or more of the observed disparities in outcomes between dual eligible and non-dual eligible members were attributable to differences in the prevalence of clinical, sociodemographic and community resource factors associated with higher risk of a worse outcome and not under the control of the health plan. If these Star Measures were statistically adjusted for the risk factors associated with worse performance, the observed disparities in scores could be reduced by 70% or more. Conclusions This study demonstrates that dual eligible members have significantly worse outcomes on a majority of MA Star Rating measures evaluated. The worse outcomes are statistically associated with dual eligible status, and are not associated with the proportion of dual eligible members in the plan. Longstanding social epidemiological research suggests that patient sociodemographic and community resource factors influence health utilization and outcomes just as clinical risk factors influence health utilization and outcomes. This study affirms that these factors significantly affect outcomes in all MA members, but have a larger impact on the dual eligible population and on MA plans serving larger proportions of dual eligible members due to (1) the higher prevalence of these risk factors in dual eligible members; and (2) the differential impact of these factors on dual eligible members. 9 The findings of this investigation suggest that under the current Star Rating System, MA plans serving disadvantaged members such as those who are dual eligible may be providing a higher quality of care than they appear to provide based on the current Star Rating system. The issue of accurate evaluation of the quality of care provided by health plans and providers extends more broadly to a wide range of existing quality measures, which prompted the National Quality Forum (NQF) to amend their longstanding policy against risk adjustment for sociodemographic factors in Beginning in January 2015, the NQF implemented a robust trial period to allow measure developers to test the feasibility of adding sociodemographic factors to the risk adjustment of measures meeting specified criteria. 1 The results of this study support the need for adjustment to measures in the MA Five- Star Rating System that account for clinical, sociodemographic and community resource factors in order to ensure a fair evaluation of the actual performance of MA plans. These adjustments would provide a more accurate comparison of quality across all MA plans by accounting for factors statistically associated with higher risk of worse outcomes. While

10 the appropriateness of risk adjusting for clinical risk factors is well accepted in quality measurement, the majority of clinical Star Measures still have no such adjustments. The appropriateness of adjusting quality measures for sociodemographic factors is still under debate, but the results of this study demonstrate that these factors are important contributors to disparities in outcomes and affect all MA members with those characteristics. The appropriateness of adjusting for community resource availability is less controversial, but no Star Measures include adjustments for such factors. The contribution of clinical, sociodemographic and community resource factors varies by measure a characteristic may impact one outcome but not another and a characteristic can serve to reduce the disparity for one measure but increase the disparity for another. Thus, the argument that adjusting for these factors would allow a lower standard of care for members with these characteristics is not supported by the results of this investigation. With risk adjustment, plans doing a relatively worse job at achieving good outcomes among members with high-risk characteristics will still have lower performance scores relative to plans doing a relatively better job at achieving good outcomes among members with similar risk profiles. The adjustments provide a more level playing field to allow fair comparisons of quality of care across health plans serving different populations. 10 Future measurement development efforts should account for factors that affect health plan performance and that are outside the control of the health plan in order to have valid and useful measures that support health plan evaluation and ongoing qualityimprovement efforts.

11 BACKGROUND A 2013 Inovalon study titled The Impact of Dual Eligible Populations on CMS Five-Star Quality Measures and Member Outcomes in Medicare Advantage Health Plans 2 presented new quantitative evidence based on member-level analysis of 1.6 million Medicare Advantage (MA) plan beneficiaries that dual eligible members performed significantly worse on nine of 10 Star Measures examined. Consumers are increasingly using Star Ratings to select a health plan, and thus the ratings impact the plan s ability to recruit and retain members. Since 2012, the ratings also affect health plan reimbursement through quality-bonus payments made to MA plans based on Star Ratings. There is evidence of large shifts of members from low Star to higher Star MA plans, primarily attributed to the better benefits high Star plans are able to provide due to higher reimbursement. 3 Research over the last 25 years has demonstrated the role of social determinants of health, such as income, education, occupation and social supports as significant contributors to health outcomes. 4 The 2002 Institute of Medicine report titled The Future of the Public s Health in the 21st Century observed that research has increasingly demonstrated the important contributions to health of factors beyond the physical environment, medical care, and health behaviors, e.g., socioeconomic position, race and ethnicity, social networks and social support, and work conditions, as well as economic inequality and social capital. 5 A large meta-analysis seeking to assign weights to determinants of health found that, on average, access and quality of clinical care contribute about 20% to health outcomes, while social and economic factors such as education, income and family/social supports contribute 40%. Health behaviors such as alcohol and drug abuse contribute 30% to health outcomes In 2014, the National Quality Forum (NQF) released recommendations that pointed to the need for risk adjustment of some quality measures to account for the impact of sociodemographic risk factors defined as inclusive of socioeconomic status and other social risk factors on health outcomes in order to make correct and fair inferences about quality and improve outcomes in vulnerable populations with these characteristics. 7 The NQF expert panel recommended a measure-by-measure determination of the appropriateness of sociodemographic adjustment based on two criteria: (1) there should be a conceptual relationship between the factor and the outcome or process reflected in the measure; and (2) there should be empirical evidence that the sociodemographic factor affects the measure. 8 The NQF report noted that a lack of available data on sociodemographic factors has limited the ability to scientifically test the validity and feasibility of these factors as potential risk adjustors to the quality measures. This study used new data sources to enable testing the impact of various sociodemographic and community resource, clinical, and demographic factors on outcomes at the member-level.

12 On Sept. 9, 2014, CMS issued a request for information (RFI), Data on Differences in Medicare Advantage (MA) and Part D Star Rating Quality Measurements for Dual- Eligible versus Non-Dual-Eligible Enrollees. 9 The RFI specifically requested [a]nalysis of the difference in measurement scores between dual and non-dual enrollees in the same contract or plan for all contracts under a parent organization for the Star Ratings measures. Analyses would be more helpful if all enrollees from all contracts under a parent organization are included in the analysis. The RFI further requested [i]n-depth analyses using a multivariate modeling approach to explore the relationship between dual status and measure scores. The analysis that CMS requested was addressed in this report Phase one of this follow-up study was to update and expand upon the 2013 Inovalon report. The Member-Level Analyses results were published in October 2014 in a preliminary study report in order to provide MA health plans additional information and data needed to respond to the CMS RFI. 11 The Member-Level Analyses evaluated how clinical, sociodemographic and community resource factors differ for dual eligible and non-dual eligible members and how quality measure scores differ between dual eligible and non-dual eligible MA members by those factors for a subset of 18 quality measures; eight current Star Measures and 10 Star Display measures (see Table 1). The Star Display measures are reported but not factored into the Five-Star Rating System. These 18 measures were selected because they can be readily calculated using available claims data. Other Star Rating Measures are derived from beneficiary surveys, medical record reviews, and other sources not readily available for analysis. Detailed definitions of the measures are included in Appendix B.

13 MEASURE ACRONYM STAR MEASURE MEASURE NAME RISK ADJUSTMENT HIGHER SCORE ART Yes Rheumatoid Arthritis Management None Better BPD Yes Diabetes Treatment None Better HRM Yes High Risk Medication None Worse MA-C Yes Medication Adherence for Cholesterol (Statins) None Better MA-D Yes Medication Adherence for Diabetes Medications None Better MA-H OMW Yes Yes Medication Adherence for Hypertension (RAS Antagonists) Osteoporosis Management in Women Who Had a Fracture PCR Yes Plan All-Cause Readmissions None None Age, gender, chronic conditions Better Better Worse AAP No Access to Primary Care Doctor Visits None Better AMM No Antidepressant Medication Management None Better BCS No* Breast Cancer Screening None Better DDI No Drug-Drug Interactions None Worse IET-E No Engagement of Alcohol or other Drug Treatment None Better IET-I No Initiation of Alcohol or other Drug Treatment None Better 13 PBH No Continuous Beta-Blocker Treatment None Better PCE-B PCE-S SPR No No No Pharmacotherapy Management of COPD Exacerbation-Bronchodilator Pharmacotherapy Management of COPD Exacerbation-Systemic Corticosteroid Testing to Confirm Chronic Obstructive Pulmonary Disease None None None Better Better Better *will be included in 2016 Star Rating System pursuant to the CMS 2016 call letter issued Nov. 21, 2014 Table 1: Quality Measures Evaluated The Member-Level Analyses provided new and detailed evidence that dual eligible MA beneficiaries have different clinical and sociodemographic profiles compared to non-dual eligible beneficiaries, and that many of these characteristics are associated with worse performance on a majority of measures evaluated. Dual eligible members are younger, more likely to be female, and more ethnically/racially diverse. They are more likely to have chronic conditions that impact health outcomes, such as alcohol/drug/substance abuse, anxiety, dementia, and bipolar/major depression. Dual eligible members comprise 75% of MA beneficiaries with HIV and schizophrenia, more than 80% of members with inadequate/lack of housing, 86% of members with intellectual disability, and more than 50% of MA members using a wheelchair.

14 Dual eligible members are more likely to live in areas designated as shortage areas for primary care physicians and mental health professionals. They have more emergency room visits, more hospitalizations and readmissions within 30 days of hospitalization, and are more likely to take seven or more different medications. They are more likely to live in an urban neighborhood with a median income less than $20,000, and 75% of the population lives in a high-poverty neighborhood. In addition, few are married or own their own home, and they are more likely to live in a neighborhood where more than 40% are singleperson households. Measure rates were calculated separately for all dual eligible and non-dual eligible members in the study population for the 18 measures analyzed. Results comparing the differences in rates are shown in Figure 1. The bars in the graph represent the percentage difference in rates between dual eligible and non-dual eligible members. A bar below 0% indicates dual eligible members perform worse than non-dual eligible members, while a bar above 0% indicates dual eligible members perform better. 14 Results showed that dual eligible members had significantly worse outcomes on six of the eight current Star Measures (75%) and on 10 of the 18 measures overall (56%) (see Figure 1). Dual eligible members performed significantly better on one current Star Measure Diabetes Treatment (BPD) but that measure was retired by the measure developer in late Dual eligible members also performed better on four Star Display measures related to drug treatment, including two measures related to initiation and engagement of alcohol/drug/substance abuse treatment. Dual eligible members performed similar to non-dual eligible members on three of 18 measures, including access to primary care visits (AAP) and two other measures.

15 FIGURE 1: 2013 STAR RATING COMPARISON PERCENT DIFFERENCE IN AVERAGE STAR RATINGS DUAL ELIGIBLE VERSUS NON-DUAL ELIGIBLE MA PLAN MEMBERS 70% 60% 59% 50% 40% PERCENT 30% 20% 10% 7% 13% 0% -10% -20% 0%* -14% 3% 2%* 2% -6% -3% -4% -2% -4% -2%* -4% -16% -16% -13% -30% NOTE 1: *not statistically significantly different (AAP, OMW, PCE-S) NOTE 2: The signs of the inverse measures where a higher rate indicates worse performance Drug-Drug Interactions (DDI), High Risk Medications (HRM) and Plan All-Cause Readmissions (PCR) were reversed so that a bar below 0% always indicates worse performance by dual eligible members. NOTE 3: The percent difference in rates for the engagement measure (IET-E) stands out as relatively large because outcome rates for that measure are so low compared to other measures in the chart (5.4% for dual eligible members and 3.4% for non-dual eligible members, resulting in a 59% difference in rates). 15 The large member-level database allowed for calculation of rates for the 18 measures further stratified by a large number of member clinical, sociodemographic and community resource characteristics. The stratified rates identify specific groups of members experiencing the worst outcomes for the measures evaluated. For example, dual eligible members are far more likely to have disability as the original reason for entitlement (46.3% versus 16.9%), underscoring a major difference in this population. The stratified measure rates were worse for dual eligible members who qualified for Medicare based on disability compared to members who qualified for Medicare based on the traditional criteria of age for a majority of measures evaluated. These findings address the often-voiced fears that adjustment of the quality measures for sociodemographic factors would mask disparities. Reporting the stratified measure results as recommended by the NQF will make the disparities more transparent. These results can be tied to meaningful quality-improvement programs and changes in service delivery that can improve overall plan performance and impact outcomes in the most vulnerable members. As noted by the NQF, disparities currently exist by reporting unadjusted scores that do not accurately reflect the quality of care being provided by MA plans, particularly those serving large, disadvantaged populations, such as dual eligible beneficiaries.

16 OBJECTIVE The objective of phase one of this study was to evaluate how clinical, sociodemographic and community resource factors differ for dual eligible and non-dual eligible members and how stratified quality-measure scores differ by those factors. These results were published in October 2014 and summarized above. The objective of phase two of this study was twofold: (1) conduct a series of complex statistical analyses to better understand the impact of a member s dual eligible status on Star Measure scores for MA members enrolled in the same plan benefit package; and (2) identify specific clinical, sociodemographic and community resource characteristics underlying observed disparities in Star Measure scores between dual eligible and non-dual eligible members and quantify the relative contribution of these factors to the performance gaps. METHODS Findings from two separate analyses designed to support the two main objectives are included in this report: The first series of analyses were designed to evaluate the impact of dual eligible member status on Star Measure scores for members within the same plan. This is the first study to leverage a sufficiently large database to evaluate performance of MA members within the same plan benefit package in order to weigh the effect of plan characteristics versus the effect of dual eligible status on member outcomes. This approach allows researchers to answer the question, To what degree are MA beneficiary outcomes related to the dual eligible status of individual plan members versus the quality of care provided by the plan? 2. The second series of analyses focused on identifying and quantifying the specific clinical, sociodemographic and community resource factors contributing to the disparities in seven Star Measures evaluated using a multivariate decomposition approach. Detailed methodologies for each analysis are included below, with additional technical notes included in the Appendices. The study protocol was reviewed by the Chesapeake Institutional Review Board (IRB) and determined not to require IRB oversight as per Department of Health and Human Services regulations 45 CFR 46.

17 STUDY POPULATION AND DATA SOURCES A base population of 2,207,940 MA members in 81 separate MA contracts with 364 individual plan benefit packages in 2013 was utilized in this study. The study utilized member-level data including age, gender, race/ethnicity, and comprehensive information on diseases/diagnoses, chronic conditions, and medical and pharmacy utilization. These data were supplemented with dual eligible status, low-income subsidy status, and institutional status. All dual eligible members were included in this study, including those enrolled in Dual Eligible Special Needs Plans (D-SNPs). These member-level data were linked with sociodemographic characteristics (such as income, education, household size) and data on availability of community resources (such as shortage of physicians or mental health professionals). Previous research has demonstrated that sociodemographic and community resource characteristics of the neighborhood where the individual member resides can serve as close proxies for these characteristics at the member-level (e.g., income, education). 12 The main data source for this study was member-level MA data extracted from Inovalon s MORE² Registry (Medical Outcomes Research for Effectiveness and Economics Registry referred to as MORE 2 ). MORE² is a large, nationally representative and statistically deidentified administrative claims database. The database includes longitudinal patient-level data for more than 120 million individual eligible members from a broad range of sources across all payer types (Commercial, Medicare and Medicaid), geographic regions (capturing virtually all U.S. counties), healthcare settings (inpatient and outpatient services), and provider specialties. 17 CMS monthly membership reports (MMR) were utilized to identify members Medicaid dual eligible status, original reason for entitlement, amount of low-income drug subsidy received, and institutional status. Dual eligible beneficiaries are members who are eligible for Medicare and Medicaid. Dual eligible members with incomes below 150% of the federal poverty level qualify for the Part D Low-Income Subsidy (LIS). The key source of data on sociodemographic characteristics in this study was from Acxiom s Market Indices ACS data, which is an aggregation of the American Community Survey (ACS) and Acxiom s InfoBase Geo files. The files include data aggregated from multiple, comprehensive individual and household databases (e.g., public records such as government information, self-reported data, buying activity, financial behavior). 13

18 These sources result in roughly 30 million discrete data points based on Zip+4 areas, which include an average of eight households. The files include a broad range of data elements on sociodemographic factors at the member s near neighborhood level, including financial information, education levels, mean household size, and other social, economic, and demographic characteristics. Previous studies examining sociodemographic characteristics have generally utilized data available at the Census 5-digit ZIP code level that cover only about 40,000 discrete data points, or U.S. Census Bureau ACS area block group data that cover about 250,000 areas. These sources provide information averaged across multiple disparate neighborhoods, resulting in a relatively imprecise assignment of characteristics to individual members compared to the 30 million discrete neighborhoods utilized in this study. The area health resource file (AHRF) was used to provide information on community resource availability at the county level. 14 This file contains information such as primary care and mental health professional shortage areas, number of physicians per 10,000 people, and hospital admissions per 10,000 people. A detailed description of the key variables used in the study is included in Appendix A. PLAN-LEVEL ANALYSES 18 Objective The purpose of these analyses was to use the large study sample of MA members to examine variation in Star Measure scores between dual eligible members and non-dual eligible members after controlling for variations in performance of beneficiaries enrolled in the same plan benefit package within an individual MA health plan contract. MA health plans have contracts with CMS to offer Medicare covered benefits in defined geographic areas, and within those contracts, plans can offer different plan benefit packages. However, CMS reports MA Five-Star Ratings at the contract level only. While some previous studies have examined the association of the percentage of dual eligible members in a contract and contract Five-Star Ratings using available published data, this is the first large-scale study to evaluate Star Measure performance at the individual MA member-level, and the first to compare outcomes of dual eligible and non-dual eligible members enrolled in the same plan benefit package. The specific objective of these analyses was to investigate systematic differences in Star Measure scores in dual eligible versus non-dual eligible members (referred to as the within effect) after controlling for the effect of the individual plan benefit package and for the percent of dual eligible members in the plan benefit package (referred to as the contextual effect).

19 Methods The observations in these analyses are individual members of MA plan benefit packages. Both members and health plans are characterized in terms of dual eligible status; that is, members are categorized as dual eligible or not, and plans benefit packages are categorized by the percent of dual eligible membership. The analyses employ a set of statistical approaches that seek to estimate the relative impact of both individual member and group characteristics on the 18 quality measures evaluated. The premise of these approaches is that members of a group are more similar to members of the same group such as employees within the same company or students within the same school and these within-group inter-correlations must be taken into account. 15 These statistical techniques are used when individuals are nested within different groups (in this case, members enrolled in a specific MA plan benefit package). 16 Three different specifications were tested using the generalized linear mixed-model approach. i All three models included a random effects variable to account for the nonindependence of data from members of the same plan benefit package. This controls for the different characteristics of the separate benefit packages. 1. Model 1 examined the effect of the member s dual eligible status on outcomes. The question addressed by this model was, After controlling for the effect of the plan benefit package the members were enrolled in, do dual eligible and non-dual eligible members differ on the outcome measure? 2. Model 2 examined the effect of the member s dual eligible status on outcomes after adding the effect of the proportion of dual eligible members in the plan benefit package. The question addressed by this model is, After controlling for the effect of the plan benefit package and controlling for the effect of the percent of dual eligible members in the plan benefit package (the contextual effect ), do outcomes of dual eligible and non-dual eligible members differ? 3. Model 3 is a specific form of Model 2, which is used when the group variable is derived from the individual variable (in this case, the group variable percent of dual eligible members in the plan benefit package is derived from the individual members dual eligible status). It is specified so that the effect of the two explanatory variables can be estimated independent of one another Detailed technical specifications for the three models are presented in Appendix C. i These models are known by different names depending on discipline (e.g., multilevel models, random effects models, generalized linear mixed models, hierarchical linear models).

20 Sample Selection The number of plan benefit packages and MA members grouped by the percent of dual eligible membership is shown in Table 2. Since most plans have either relatively few dual eligible members or a large portion of dual eligible members (i.e., they tend to be clustered at the ends of the distribution), using the sample of plan benefit packages with at least 30% of both dual eligible and non-dual eligible members eliminated over 90% of the data and was dropped from the analyses. Analyses for the 18 quality measures were completed using three different samples that included (1) the subset of plan benefit packages with at least 20% of each group; (2) the subset of plan benefit packages with at least 10% of each group; and (3) all MA members in all plan benefit packages in the study population. PLAN BENEFIT PACKAGES (PBPs) MA MEMBERS PBPs % DUAL ELIGIBLE N % N % < , , , , < < , <0.1 All ,207, Green: At least 30% of each Gray: At least 20% of each Blue: At least 10% of each NOTE: Adding in the highlighted plan benefit packages tends to add non-dual eligible members disproportionately. Table 2: Number of Plan Benefit Packages and Medicare Advantage Members by Percent Dual Eligible Membership

21 Outcome Measures (Dependent Variables) The dependent variables in the models were the individual outcome for each member for each measure we are seeking to explain outcomes at the member-level. The number of members included in the analysis for any given measure changes based on the measure definition that determines whether or not an individual member qualifies for inclusion in measure calculation. Detailed definitions for the 18 measures including denominator criteria, numerator criteria, and any exclusions or risk adjustments are included in Appendix B. Explanatory Variables (Independent Variables) The dual eligible status of each individual MA member was used as an explanatory variable in the models in order to estimate the effect of dual status on the outcomes evaluated. Each member was categorized as dual eligible or non-dual eligible during each month of the measurement year of 2013 using the CMS MMR files. A member was categorized as dual eligible for this study based on exploratory analyses if they qualified for Medicaid for at least one month during the year. The study population included all dual eligible members, regardless of whether they were enrolled in a D-SNP or not. The percentage of dual eligible members in the plan benefit package was also used as an explanatory variable. For each plan benefit package, the number of dual eligible members was divided by the total membership to calculate the plan percent dual eligible. This variable estimates the effect of the proportion of dual eligible members served by the plan benefit package on Star Ratings (i.e., is the percent of dual eligible members in a plan significantly related to the outcomes of individual members of the plan?). The effect of the percent of dual eligible members in the plan benefit package is referred to as the contextual effect. The plan percent dual eligible variable is a plan characteristic and thus does not change from one measure to another. 21

22 Results As described above, these analyses were conducted using three different study populations and three different generalized linear mixed models. The separate analyses yielded remarkably consistent results, regardless of sampling method and regardless of modeling approach, which greatly strengthens the generalizability of the findings. Summary results are presented in Table 3 and Table 4 below. Due to the large volume and similarity of findings across samples, results are shown only for the most inclusive sample of all MA members in all plan benefit packages in the study population (i.e., closest to the real-world Star Rating System). Similarly, because results for Models 2 and 3 were statistically equivalent in this study, we present only results for Model 1 and Model 3 in this section. Complete results for all models and samples are included in Appendix D. The effect of dual eligible status was significant and negative for five Star Measures and three Star Display Measures after controlling for plan benefit package (Model 1), and after controlling for both plan benefit package and the percent of dual eligible members in the plan (Model 3) (Table 3). For these measures, dual eligible members perform worse than non-dual eligible members within the same plan benefit package ( plan ), regardless of the percent of dual eligible members served by the plan. 22 The measures with a significant negative dual status effect include five of the eight current Star Measures and three of the Star Display Measures, including the Breast Cancer Screening measure that is expected to be returned to the Five-Star Rating System in For one measure Rheumatoid Arthritis Management dual eligible status was no longer significant when plan percent dual eligible was added to the model, but the effect of plan percent dual eligible was also insignificant (Model 3). The effect of plan percent dual eligible (the contextual effect ) was insignificant in most cases, indicating no relationship between the proportion of dual eligible members served by the plan and individual member outcomes on the measure. The effect of plan percent dual eligible was significant in only two of the measures with a significant dual status effect, and in those cases the contextual effect was inconsistent. In one case, there was a positive relationship between the percent of dual eligible members in the plan and the outcomes of members of the plan. In the other case, the relationship was negative, indicating that as the percent of dual eligible members in the plan increased, member outcomes were more likely to be worse. These results indicate that, in general, the worse outcomes observed in dual eligible members were not statistically related to the percentage of dual eligible members in the plan, i.e., dual eligible members had worse outcomes than non-dual eligible members in plans with a small number of dual beneficiaries, as well as in plans with a large number of dual eligible beneficiaries.

23 This means that we generally cannot attribute worse outcomes of dual eligible members on these eight measures to the quality of care provided by the plan. If the disparity was related to plan performance, we would observe similar results between dual eligible members and non-dual eligible members within the same plan (i.e., the dual status effect would not be significant). If the disparity was related to the higher population of dual eligible members in the plan, we would see a significant contextual effect, which was not observed in the majority of measures evaluated. The most compelling illustration of these findings is the Plan All-Cause Readmissions (PCR) measure because it is the only measure among the 18 measures evaluated that has been statistically adjusted for clinical and demographic characteristics associated with higher risk for re-hospitalization. The measure is defined as the percentage of MA members 65 years or older discharged from an acute care hospital and readmitted for any diagnosis within 30 days of discharge. The measure was analyzed two different ways to provide maximum insight into the performance gap between dual eligible and non-dual eligible plan members. In the unadjusted model, the outcome (dependent) variable was an indicator of whether the member was readmitted or not. Dual eligible status was negative and significant in both Model 1 and Model 3, indicating that dual eligible members have higher rates of readmission compared to non-dual eligible members within the same plan. There was no contextual effect related to the percent of dual eligible members in the plan; in fact, the dual eligible status effect was even stronger after controlling for the percent of dual eligible members in the plan. This indicates that within the same plan dual eligible members are more likely to be readmitted than non-dual eligible members, regardless of the percent of dual eligible members in the plan. 23 In the adjusted model, the outcome variable was the same, but an explanatory variable representing the individual member s likelihood of readmission (based on the adjusted readmissions measure) was included as an additional covariate in the model. Dual eligible status was still negative and significant in both Model 1 and Model 3, though the effect was slightly smaller as expected after adjusting for some risk factors (i.e., the coefficient values are slightly lower). This indicates that dual eligible members have higher rates of readmission compared to non-dual eligible members after controlling for the risk factors included in the adjusted Star Measure (i.e., age, gender and clinical risk factors) as well as controlling for the members plan benefit package and the percent of dual eligible members in the plan. There was again no relationship of individual plan member outcomes to the percent of dual eligible members enrolled in the plan.

24 These results suggest that the adjustments applied to the reported readmission rates do not fully capture all of the risk factors associated with higher likelihood of readmission among dual eligible members. The multivariate analysis for the Plan All- Cause Readmissions measure presented in the next section of this report explores additional factors beyond clinical and demographic characteristics contributing to the disparity in readmission rates between dual eligible and non-dual eligible MA plan members. STAR OR DISPLAY MEASURE MEASURE # PBPs DUAL VS. NON DUAL RATING DIFFERENCE MODEL 1: DUAL STATUS (WITHIN EFFECT) DUAL STATUS (WITHIN EFFECT) MODEL 3: PLAN % DUAL (CONTEXTUAL EFFECT) STAR Plan All-Cause Readmissions (PCR) PCR Unadjusted 62-13%* (<.0001)* (<.0001)* 0.10 (0.1240) PCR Adjusted (0.0293)* (0.0067)* 0.11 (0.1176) STAR Rheumatoid Arthritis Mgmt. (ART) 171-6%* (0.0439)* (0.3294) (0.0537) 24 STAR Medication Adherence for Hypertension (MA-H) 203-4%* (<.0001)* (<.0001)* (0.0013)* STAR Osteoporosis Mgmt. Women w/fracture (OMW) 103 0% (0.0006)* (<.0001)* (0.0046)* STAR High Risk Medications (HRM) %* (<.0001)* (<.0001)* 0.04 (0.6938) DISPLAY Breast Cancer Screening (BCS) 272-3%* (<.0001)* (<.0001)* 0.12 (0.1728) DISPLAY Drug-Drug Interactions (DDI) %* (<.0001)* (<.0001)* 0.10 (0.1203) DISPLAY Use of Spirometry Testing in Assessment & Dx COPD (SPR) 251-4%* (<.0001)* (<.0001)* (0.9676) * Statistically significant at 95% confidence level (p-value in parenthesis). Table 3. Star Measures with Significant Negative Dual Status Effect

25 The dual status effect was positive and significant for three Star Display Measures in both Model 1 and Model 3 (see Table 4). The positive relationship indicates that these outcomes were better for dual eligible members than for non-dual eligible members within the same plan benefit package, regardless of the percent of dual eligible members in the plan. The aggregate results shown in Figure 1 showed no significant difference in overall rates between dual eligible members and non-dual eligible members on the access-to-care measure, Access to Preventive/Ambulatory Health Services (AAP). The results of this analysis show that within the same plan benefit package, dual eligible members performed better on the access to care measure, indicating that dual eligible members have similar or better access to care as non-dual eligible members within the same plan. Dual eligible members also had better outcomes both overall and within the same plan benefit package on two other treatment measures (see Figure 1 and Table 4). The contextual effect was insignificant for all three measures. STAR OR DISPLAY MEASURE MEASURE # PBPs DUAL VS. NON DUAL RATING DIFFERENCE MODEL 1: DUAL STATUS (WITHIN EFFECT) DUAL STATUS (WITHIN EFFECT) MODEL 3: PLAN % DUAL (CONTEXTUAL EFFECT) DISPLAY Adults Access to Preventive/ Ambulatory Health Services (AAP) 293 0% 0.07 (<.0001)* 0.07 (<.0001)* 0.09 (0.5228) 25 DISPLAY Persistence of Beta- Blocker Treatment After Heart Attack (PBH) %* 0.25 (0.0383)* 0.30 (0.0422)* (0.5918) DISPLAY Pharmacotherapy Mgmt. COPD- Bronchodilators (PCE-B) %* 0.48 (<.0001)* 0.44 (<.0001)* 0.17 (0.3393) * Statistically significant at 95% confidence level (p-value in parenthesis). Table 4. Star Measures with Significant Positive Dual Status Effect

26 Conclusions Across three sampling methods and three statistical models, five of eight Star Measures and three Star Display Measures show a consistent statistically significant negative effect of member dual status on outcomes. Dual eligible members have worse outcomes on these measures than non-dual eligible members within the same plan benefit package, regardless of the percent of dual eligible members in the plan. These results indicate that worse performance of dual eligible members on these measures cannot be attributed to the quality of care provided by the plan. If the disparity was related to individual plan benefit package performance, we would have observed similar results between dual eligible members and non-dual eligible members within the same plan (i.e., the dual status effect would not be significant). If the disparity in member outcomes was related to the higher population of dual eligible members in the plan, we would have observed a significant contextual effect, which was not found in the majority of measures evaluated. Thus, in these analyses, lower performance of dual eligible members was not statistically related to enrollment in lower-performing plans. 26 These findings are especially important for Plan All-Cause Readmission rates, where the existing risk adjustments for age, gender and chronic conditions do not fully correct for the effect of dual status a dual eligible member is at higher risk for readmission compared to a non-dual eligible member with the same demographic characteristics and same chronic conditions. This suggests that the adjustments included in the readmission rates reported for MA plans do not fully capture all of the risk factors associated with dual eligible members having a greater likelihood of readmission. Moreover, the likelihood of readmission was not statistically associated with the percent of dual eligible members in the plan indicating that having a higher proportion of dual eligible members in a plan benefit package does not impact outcomes in dual eligible members differentially.

27 MULTIVARIATE ANALYSES Objective The objective of this large-scale member-level multivariate analysis was to identify patient clinical, sociodemographic, and community resource factors underlying the observed disparity in quality-of-care outcomes between dual eligible and non-dual eligible members of MA plans. Using new sources of data on sociodemographic and community resource availability, these analyses uniquely explore the degree to which identified risk factors contribute to the performance gaps identified in the Inovalon 2013 and 2014 reports. Methods The differences in quality measure rates between dual eligible members and non-dual eligible members were analyzed using the Blinder-Oaxaca decomposition technique. 17,18 This technique has been used in previous research-investigating disparities in healthcare. 19,20,21,22,23 The decomposition approach breaks down the difference in measure rates into two components that include (1) the explained proportion of the gap attributed to differences in the prevalence of characteristics associated with a higher likelihood of a worse outcome (e.g., to what degree does the higher prevalence of disabled individuals in the dual eligible population contribute to the difference in outcomes between the two groups?); and (2) the unexplained proportion of the gap attributed to the differential impact of the characteristic on dual eligible members compared to non-dual eligible members. A detailed technical description of the methodological approach is included in Appendix E. 27 Outcome Measures (Dependent Variables) The multivariate analyses focused on six of the eight current Star Measures included in earlier analyses. Two Star Measures were not analyzed due to technical issues. ii 2 A multivariate model was also developed for one additional Star Display Measure Breast Cancer Screening because this measure is expected to be returned to the Five-Star Rating System in Thus a total of seven measures were evaluated using a comprehensive multivariate decomposition approach (see Table 5; detailed definitions for the measures are included in Appendix B). ii We did not attempt to model Diabetes Treatment (BPD) as this measure was retired in 2014 by the measure developer, the Pharmacy Quality Alliance (PQA), due to changes in evidence-based practice guidelines. We did not model Osteoporosis Management in Women Who Had a Fracture (OMW) due to the instability of this measure and sensitivity to small changes in the denominator population.

28 Member-level outcome scores for each of these measures were calculated and used as the dependent outcome variables in the models. Explanatory Variables (Independent Variables) All member characteristics in the study database deemed potentially associated with the measure outcome (including clinical, sociodemographic, geographic, and community resource factors) were examined. First, bivariate analyses were conducted to test which potential explanatory variables were independently associated with the outcome. Second, variables found to be statistically significant (p-value 0.05) were then included and tested in the multivariate regression model. Finally, the variables in the final model were reviewed for clinical relevance or a conceptual relationship to the outcome (consistent with National Quality Forum criteria). MEASURE ACRONYM STAR MEASURE MEASURE NAME RISK ADJUSTMENT HIGHER SCORE ART Yes Rheumatoid Arthritis Management None Better HRM Yes High Risk Medication None Worse MA-C Yes Medication Adherence for Cholesterol (Statins) None Better 28 MA-D Yes Medication Adherence for Diabetes Medications None Better MA-H Yes Medication Adherence for Hypertension (RAS Antagonists) None Better PCR Yes Plan All-Cause Readmissions Age, gender, chronic conditions Worse BCS No* Breast Cancer Screening None Better *Will be included in 2016 Star Rating System per CMS 2016 Call Letter issued Nov. 21, Table 5: Quality Measures Evaluated Using Multivariate Approach Results Differences in clinical, sociodemographic and community resource characteristics between dual eligible and non-dual eligible members accounted for 70% or more of the performance gap observed in the seven Star Measures analyzed. Sociodemographic characteristics were consistently a main contributor to the performance gaps, explaining at least 30% or more of the observed disparities in outcomes. These results indicate that if these Star Measures were statistically adjusted for the risk factors found to be significantly associated with worse outcomes, the observed disparities in Star Measure scores could be reduced by 70% or more.

29 The proportion of the disparity explained was less than 100% for four measures Plan All-Cause Readmissions (PCR), rheumatoid arthritis management (ART), high risk medications (HRM) and breast cancer screening (BCS). An explained proportion less than 100% indicates that while 70% or more of the disparity observed for these measures was attributed directly to the difference in the prevalence of risk factors associated with the outcome, there was a remaining unexplained portion of the gap attributable to the differential impact of the risk factor on dual eligible members. The explained proportion of the disparity was greater than 100% for the three Medication Adherence Measures analyzed Cholesterol (MA-C), Diabetes (MA-D), and Hypertension (MA-H). An explained proportion greater than 100% indicates that, after controlling for risk factors associated with higher likelihood of non-adherence, MA plans do a better job achieving compliance with medication adherence in dual eligible members than in nondual eligible members with similar risk profiles. This is due in part to the fact that dual eligible members have more outpatient visits on average compared to non-dual eligible members, and more office visits are associated with better medication adherence as shown in the Member-Level Analysis published by Inovalon in These results indicate that if the three Medication Adherence measures were statistically adjusted to control for the differences in characteristics associated with non-adherence, the adjusted rates for MA plans with a high proportion of members with those characteristics would be higher (better) and adjusted rates for MA plans with relatively fewer members with these risk factors would be lower (worse) compared to currently reported rates. Since these Medication Adherence measures have no statistical adjustments for factors associated with lower adherence scores, and these factors are more prevalent in dual eligible members, reported Star Ratings do not reflect this quality-of-care difference. Because these measures are triple-weighted in the Star Rating System, the failure to adjust for these differences has three times the impact on Star Ratings of MA plans serving a large proportion of dual eligible members. 29 Results of the decomposition analyses are summarized below; complete results are presented in Appendix F. Plan All-Cause Readmissions (PCR) is defined as the percentage of MA members 65 years or older discharged from an acute care hospital and readmitted for any diagnosis within 30 days of discharge. A total of 32,563 index discharges in 2013 (28.9% dual; 71.1% non-dual) were included in the analysis. The unadjusted readmission rate was 13.4% higher in dual eligible members compared to non-dual eligible members (16.9% vs. 14.9%). iii iii All-Cause Readmissions is an inverse measure, meaning that higher rates indicate worse performance.

30 The decomposition analysis found that differences in the prevalence of risk factors for readmission accounted for 81.8% of the disparity in readmission rates between dual eligible and non-dual eligible members (see Figure 2). If dual eligible and non-dual eligible members had similar characteristics or if the measure was statistically adjusted to account for these factors 82% of the observed disparity could be mitigated. The 18% of the disparity in rates not explained by these factors can be attributed to the differential impact of the risk factors on dual eligible members. These risk factors affect risk of readmission for all MA members, but the dual eligible population and MA plans serving a relatively larger proportion of members with these characteristics will be impacted more overall. Contribution of Factors Associated with Risk of Readmission: Differences in the prevalence of 17 chronic conditions explained 54.3% of the disparity (see Figure 3). These conditions were significantly associated with readmission after controlling for other patient characteristics. Differences in the prevalence of three sociodemographic factors explained 27.5% of the disparity (see Figure 4). Two sociodemographic factors explained 30.0% of the gap living in a county with a physician shortage and living in a high-poverty neighborhood but differences in gender composition reduced the gap by 2.5% since fewer dual eligible members are male, and males have a higher risk of readmission. 30 Importantly, one sociodemographic factor living in a poor neighborhood contributed more than any other risk factor to the disparity in readmission rates. This characteristic explained 18.1% of the observed performance gap between dual eligible members and non-dual eligible members (see Figure 2). This is attributed to the difference in prevalence 41.2% of dual members live in a poor neighborhood compared to only 15.8% of non-dual eligible members (see Figure 5). The chronic condition contributing most to the disparity in readmission rates was renal disease. Renal disease is more prevalent in dual eligible members (50.7% vs. 43.9%), and the difference in prevalence explained 14.9% of the performance gap. Dementia was the next most important chronic condition associated with readmission likelihood. Dementia is more prevalent in dual members (42.8% vs. 35.9%) and explained 14.5% of the gap. A second sociodemographic factor living in a county designated as having a physician shortage (i.e., less than 50 physicians per 10,000 people) explained 11.9% of the gap. A higher percentage of dual eligible members live in these areas compared to non-dual eligible members (84.1% vs. 67.5%). This community resource factor impacts likelihood of MA members being re-hospitalized and is out of the control of the health plan.

31 Several factors tend to reduce the readmission rates gap. Having a diagnosis of cancer is associated with higher risk of hospital readmission, but cancer is more prevalent in nondual eligible members (28.6% vs. 22.5%) (see Figure 3). The decomposition results show that statistically adjusting readmission rates for cancer would increase the performance gap by 15.8%. The lower prevalence of males among dual eligible members also reduces the gap (36.5% vs. 44.6%). The detailed analytic results for the covariates associated with higher rates of readmission are presented in Table 13 in Appendix F. The means represent the prevalence of the factor within each group; for example, 36.5% of dual eligible members are male compared to 44.6% of non-dual eligible members. The difference in prevalence of these covariates between dual eligible members and non-dual eligible members result in the explained component of the disparity in the decomposition analysis. The coefficient estimates quantify the differential impact of the covariates on dual eligible members compared to non-dual eligible members. This comprises the unexplained component of the disparity in the decomposition analysis. The interpretation of the coefficient within a cohort group (e.g., among dual eligible members) shows the increased risk for a member with the characteristic to have the outcome compared to a member in the same group who does not have the characteristic, after controlling for other risk factors. For example, within dual eligible members, the probability of readmission was points higher for a dual member with dementia than for a similar dual member who did not have dementia (see Table 13). 31 Comparing the coefficients between the two cohort groups, the difference in coefficient values measures the differential impact of the covariate on dual eligible members compared to non-dual eligible members. For example, the section above indicated that the coefficient estimate for dementia for dual eligible members was 5.2% (0.052). The coefficient estimate for dementia for non-dual eligible members was 4.0% (0.040), indicating that a non-dual eligible member with dementia is also more likely to be readmitted, but is 1.2% percentage points less likely than a dual eligible member with dementia to be admitted (0.040 minus = differential impact of dementia on dual eligible members compared to non-dual eligible members), holding all other characteristics constant. The summary of the explained and unexplained contributions of the various risk factors to the performance gap for readmissions is shown in Table 14 in Appendix F. Dissecting the impact of the largest contributor to the performance gap is an interesting example. The fact that more dual eligible members live in a poor neighborhood (41.2% vs. 15.8% as shown in Table 13) explains 18.1% of the disparity in readmission rates. However, the differential impact of this sociodemographic factor is actually larger for non-dual eligible members i.e., the coefficient is 1.6% for non-dual eligible

32 members compared to 1.2% for dual eligible members as shown in Table 13. This means that holding all other factors constant, being poor contributes slightly more to the likelihood of readmission for a non-dual eligible member than for a dual eligible member. This differential impact on of living in a poor neighborhood acts to reduce the performance gap by -6.82% (see Table 14). The overall result is that while the larger prevalence of dual eligible members living in a poor neighborhood explains 18.1% of the gap, the total contribution of this factor to the disparity in rates is only 11.27% due to the larger impact of poverty on non-dual eligible members. While this finding needs further investigation, one theory is that poor non-dual eligible members who do not qualify for Medicaid in their State have fewer supports/resources than similarly poor dual eligible members, and are thus at greater risk for hospital readmission. In summary, differences in the prevalence of covariates associated with higher risk of hospital readmission explain 81.8% of the observed difference in readmission rates between dual eligible members and non-dual eligible members. The remaining 18.2% of the performance gap is attributed to differential impact of the risk factors on dual eligible members compared to non-dual eligible members. 32 FIGURE 2. CONTRIBUTION OF DIFFERENCES IN CHARACTERISTICS BETWEEN DUAL ELIGIBLE AND NON-DUAL ELIGIBLE MA PLAN MEMBERS TO THE PLAN ALL-CAUSE READMISSIONS RATE GAP (%) Living in a poor neighborhood (poverty rate>23%) Renal Disease Dementia Living in a county with physicians per 10,000 COPD Congestive Heart Failure Schizophrenia Liver Disease Amputation Paraplegia/Hemiplegia Renal Dialysis Status Cerebrovascular Disease Anxiety Brain Damage Acute Mycocardial Infraction Ostomy Peptic Ulcers/Gastrointestinal Disease Gender Oxygen/Ventilator Dependence 11.9% 8.1% 7.4% 6.3% 6.2% 5.2% 4.1% 3.3% 2.6% 1.7% 0.4% 0.0% -0.1% -0.2% -2.5% -4.2% 14.9% 14.5% 18.1% Cancer -15.8% -20% -10% 0% 10% 20% TOTAL CONTRIBUTION TO PERFORMANCE GAP = 81.8%

33 33 Living in a county with physicians per 10,000 population less than 50 FIGURE 4: PREVALENCE OF SOCIODEMOGRAPHIC CHARACTERISTICS ASSOCIATED WITH PLAN ALL-CAUSE READMISSIONS Living in a poor neighborhood (poverty rate greater than 23%) Dual Non-Dual Male PERCENT SAMPLE WITH INDICATOR

34 Rheumatoid Arthritis Management (ART) is defined as the percentage of MA members diagnosed with rheumatoid arthritis during the measurement year who were dispensed at least one ambulatory prescription for a disease modifying anti-rheumatic drug. The measure criteria resulted in a sample of 12,836 members (33.5% dual eligible and 66.5% non-dual eligible). The treatment rate was 5.8% lower in dual eligible members (73.5% vs. 78.0%). The decomposition analysis found that 82.7% of the gap was explained by differences in the prevalence of characteristics that reduce the likelihood of receiving treatment (Table 2, Appendix F): Sociodemographic factors explained the majority of the performance gap for this measure 56.7% including age, gender, geographic region, neighborhood median household income, and neighborhood percent home ownership. Five clinical conditions explained 26.0% of the disparity in treatment, including alcohol/drug abuse, anxiety, dementia, liver disorder and paraplegia/hemiplegia. Thus, over 80% of the observed gap in ART rates would be mitigated if dual and non-dual eligible members had similar characteristics related to likelihood of arthritis treatment. Alternatively, if the measure were risk adjusted to control for those differences, the gap in measure scores would be reduced by more than 80%. 34 The top factors contributing to disparity in rheumatoid arthritis treatment rates were: 1. Median household income <$15,000 explained 47.5% of the gap due to the difference in the percentage of dual eligible members with this characteristic (20.7% vs. 5.8% of non-dual eligible members); 2. Percent of households that own their home explained 32.2% of the gap due to the percentage of dual eligible members who live in a neighborhood with low home ownership (49.2% vs. 18.0%); 3. Census division explained 10.8% of the gap for example, dual eligible members were more concentrated in Middle Atlantic (34.0% vs. 21.7%) and less concentrated in East North Central (20.6% vs. 6.4%) than non-dual eligible members, and the likelihood of receiving ART drugs varies significantly across geographic regions of the U.S.; 4. Alcohol, drug, or substance abuse explained 8.9% of the gap because these members were less likely to receive ART drugs than a similar member without this condition and alcohol/drug/substance abuse was more prevalent in dual eligible members (8.0% vs. 3.9%).

35 The top factors reducing the disparity in treatment rates were: Differences in age distribution reduced the gap by 18.4% since dual eligible members are younger (age <55 years: 15.3% vs. 4.5%, years: 20.2% vs. 13.9%, >65 years: 64.5% vs. 81.6%), and older members are less likely to receive ART drugs; Differences in gender distribution reduced the gap by 15.4% since fewer dual eligible members are male (15.9% vs. 26.5%), and males are less likely to receive ART. Detailed analytic results for this measure are in Tables 1 and 2, Appendix F. Breast Cancer Screening (BCS) is defined as the percentage of female MA members aged who had a mammogram within the past 27 months as of the end of the measurement year. This analysis used data for 247,774 eligible women (23.6% dual eligible and 76.4% non-dual eligible). The breast cancer screening rate was 3.2% lower in dual eligible members (74.8% vs. 77.3%). The decomposition analysis found that 90.5% of the performance gap was explained by differences in the prevalence of characteristics associated with likelihood of breast cancer screening. The remaining 9.5% of the disparity was due to the differential impact of these characteristics on the likelihood of getting a mammogram in dual eligible members compared to non-dual eligible members. Top factors contributing positively to the disparity in Breast Cancer Screening rates (i.e., increasing the gap) were: Census Division was the most important factor contributing 61.4% of the disparity in rates. The likelihood of receiving breast cancer screening varies significantly across U.S. regions and the distribution of dual eligible and non-dual eligible members varies across regions as well. For example, dual eligible members are more concentrated in the Middle Atlantic (41.5% vs. 21.6%) where MA members are less likely to have a mammogram and less concentrated in the East North Central region (5.5% vs. 30.1%) where members are more likely to have a mammogram (Table 3, Appendix F). This result indicates that comparing results for this measure for MA plans in different regions of the U.S. may not provide an accurate comparison of plan performance on this measure. Disability as Original Reason for Medicare entitlement was the next most important contributor to the performance gap (54.4%). Members with disability or ESRD as their original reason for entitlement are less likely to have a mammogram and a larger proportion of dual eligible members are in these groups (49.3% vs. 20.6%). 35

36 Living in a neighborhood with low home ownership rates (54% or fewer of households) explained 49.2% of the gap. These members are less likely to have breast cancer screening, and far more dual eligible members live in these areas (52.6% vs. 14.0%). Dementia explained 11.6% of the disparity in rates. Dementia is more prevalent in dual eligible members (19.9% vs. 12.9%) and patients with dementia are less likely to have breast cancer screening. Institutionalized status contributed 10.4% to the performance gap as more dual eligible members are institutionalized (0.5% vs. 0.02%) and these patients are less likely to have screening than non-institutionalized members. Some factors contribute negatively to the disparity in Breast Cancer Screening rates (i.e., reduce the gap). Blacks and Hispanics are more likely to get breast cancer screenings and members in these race/ethnic groups are more prevalent among dual eligible members than non-dual eligible members. The total effect of this characteristic reduced the disparity in rates by 18.95%. Differences in the age distribution of dual eligible members compared to non-dual eligible members also tended to reduce the gap. Members aged years are less likely to have a mammogram than younger members, and dual eligible members are younger on average. Table 4 presents the contribution of each variable on the gap for both components. 36 High Risk Medications (HRM) is defined as the percentage of MA members aged 65 years or older who received two or more prescription fills for a drug with a high risk of serious side effects in the elderly. This analysis used data for 837,238 members (27.6% dual eligible members and 72.4% non-dual eligible members). The rate of high-risk medication use was 16.0% higher in dual eligible members (11.6% vs. 10.0%; note that this is an inverse measure, so a higher rate indicates worse performance). Differences in the prevalence of characteristics associated with higher use of High Risk Medications explained 71.1% of the performance gap. If dual eligible members and non-dual eligible members had similar prevalence of these factors, or if this measure was statistically adjusted for these risk factors, the disparity in rates would be reduced from 16.0% to 4.6%. The differential impact of the effects of the risk factors on dual eligible members accounted for the remaining 28.9% of the disparity in rates. MA members with disability or ESRD as their original reason for Medicare entitlement were more prevalent among dual eligible members (19.7% vs. 10.5%) and this factor explained 25.8% of the performance gap. These members are more likely to receive High Risk Medications than members with age as reason for Medicare entitlement.

37 High Risk Medication rates are impacted by a wide range of clinical characteristics, such as alcohol/drug/substance abuse, anxiety, bipolar/major depression, cancer, CHF, COPD, dementia, diabetes, HIV, PVD, rheumatoid arthritis, schizophrenia. Use of High Risk Medications is also influenced by sociodemographic and community resource factors, including age, gender, race/ethnicity, living in an area designated as having a shortage of primary care physicians or mental health professionals, living in a metropolitan area, and regional differences in the use of high risk drugs. Details of the covariates and contributions to the performance gap are seen in Tables 5 and 6. Medication Adherence Results of the decomposition models for the three Medication Adherence models were different from the measures discussed above in that the covariates explained more than 100% of the performance gap observed for these measures. This indicates that after controlling for factors associated with non-adherence, MA plans tend to achieve better medication adherence in their dual eligible members compared to non-dual eligible members with similar characteristics (i.e., adjusted scores would be higher better in the dual eligible population compared to the non-dual eligible population). Since these measures are not risk adjusted for these factors, the currently reported rates do not accurately reflect this quality of care difference. Detailed results for all three models are included in Tables 7-12, Appendix F. Since the models are similar, one measure Medication Adherence for Hypertension is discussed below. 37 Medication Adherence for Hypertension (MA-H) is defined as the percentage of MA members 18 years or older who had a proportion of days covered (PDC) of 80% or higher for Renin Angiotensin System (RAS) antagonist medications (i.e., Angiotensin Converting Enzyme ACE inhibitors, Angiotensin Receptor Blocker ARBs and direct renin inhibitors) during the measurement period. The PDC was adjusted to account for inpatient stays, hospice enrollment, and/or Skilled Nursing Facility (SNF) stays. The study population included 512,327 members (35.3% dual eligible) that met the measure criteria during The overall adherence rate was 74.3%, but the rate was significantly lower in dual eligible members (72.2% vs. 75.2%). The differences in prevalence of the covariates accounted for 135.8% of the disparity in adherence to antihypertensive medications between dual and non-dual eligible members. As discussed above, this means that if the prevalence of the covariates were the same in dual and non-dual eligible members, or if the measure was statistically adjusted to control for these risk factors, the performance rate would actually be higher in dual eligible members.

38 The five top factors positively contributing to the Medication Adherence for Hypertension performance measure gap were: 1. Race/ethnicity differences explained 50.4% of the gap due to the higher percentage of Blacks among dual eligible members (35.2% vs. 18.1%) Blacks are less likely to be adherent to hypertension medications; 2. Age differences explained 24.8% of the gap the percent of members aged 64 years or younger is significantly higher among dual eligible members (23.8% vs. 9.5%) and younger members are less likely to be adherent than older members; 3. Living in a neighborhood with a high proportion of population never married explained 19.4% of the gap 47.0% of dual eligible members vs. 20.3% of nondual eligible members are in this group of members less likely to have social supports, and these members are at higher risk for non-adherence; 4. Living in a neighborhood with a large proportion of population below poverty level (poverty rate > 23%) explained 14.7% of the gap 50.4% of dual eligible members live in a high-poverty area where people are at greater risk for non-adherence, compared to 21.9% of non-dual eligible members; 5. Having an original reason for Medicare entitlement of disability and/or ESRD explained 11.8% of the gap 39.2% of dual eligible members were in this group compared to 19.8% of non-dual eligible members and these members have higher risk for non-adherence to medications. 38 Conclusions The decomposition analyses revealed that differences in member characteristics between dual eligible and non-dual eligible members account for the majority 70% or more of the performance gaps observed in the Star Measures evaluated. This result indicates that if dual eligible members and non-dual eligible members had the same characteristics, the observed performance gaps would be reduced by 70% or more for the measures examined. Alternatively, if the measures were statistically adjusted to control for the differences in risk factors, the disparity in Star Ratings could be similarly reduced. Differences in sociodemographic characteristics between dual eligible and non-dual eligible members were consistently a main contributor to the performance gap, explaining at least 30% or more of the gap in the seven measures evaluated, though the contribution of the risk factors varied across measures. Differences in the prevalence of clinical conditions contributed most to the performance gaps for two of the measures evaluated high risk medications (HRM) and plan all-cause readmission (PCR) measures respectively accounting for 98.7% and 54.3% of the disparity.

39 Differences in sociodemographic characteristics contributed most to the disparities for the other measures evaluated. However, it is important to note that differences in sociodemographic factors contribute differently to different measures; i.e., differences in age, gender and race/ethnicity distribution between dual eligible and non-dual eligible members tend to reduce the gap for some measures, but increase the gap for others. For example, differences in racial/ethnic distribution contributed negatively to the disparities in Breast Cancer Screening (BCS) and High Risk Medications (HRM) measures, but contributed positively to the disparities in the three Medication Adherence measures. This means that if the racial/ethnic distribution was the same in dual and non-dual eligible members, the gaps in Breast Cancer Screening and High Risk Medications would be larger, but the gaps in the three Medication Adherence measures would be smaller. These findings provide data-driven evidence that patient sociodemographic factors influence outcomes through a variety of pathways as concluded by the NQF in their 2014 report and dispel the concerns that adjusting for sociodemographic factors is setting a lower standard of care for beneficiaries with these characteristics. This study shows that both clinical and sociodemographic factors impact different outcomes in different ways; adjusting for these differences in risk assures a more accurate evaluation of the quality of care provided by all MA plans, not just those serving a larger proportion of disadvantaged members. The results support the calls to further explore risk adjustment of quality performance measures to account for the greater risk of experiencing poor outcomes among MA members with certain clinical, sociodemographic and environmental characteristics. 39 The results demonstrate that the identified risk factors affect all MA members with the characteristics, but the impact tends to be larger on the dual eligible population and on MA plans serving a large proportion of dual eligible members because these characteristics are more prevalent among dual eligible members. Adjusting the quality measures for differences in member characteristics serves to account for the fact that members with certain characteristics have a higher risk for the adverse outcomes. The measure scores will more accurately reflect the relative risk of the population of an MA plan, but will still clearly show which plans are performing worse compared to plans serving similar members.

40 APPENDIX A VARIABLE DESCRIPTIONS General Considerations Geographic Variables Many of the sociodemographic member characteristics used are based on statistics taken from regional characteristics. These are the Acxiom and AHRF variables and RUCA. These data elements were merged with member data by first validating the member s address using a commercial address-checking program. The data for members with valid addresses were then matched with Acxiom, AHRF and RUCA variables, respectively, using 9-digit zip code, county, and census tract. The zip code/county/census tract mapping table was provided by Acxiom. Members with invalid addresses or addresses without a match in the Acxiom crossreference table were included in the report but treated as having Unknown values for all the geographic variables. Because these Unknown values are identical for all geographic variables, the Unknown row was excluded from the member characteristics and measure statistics tables. These values are shown in the table below. 40 MATCHING RESULT DUAL NON-DUAL TOTAL N % N % N % UNKNOWN ADDRESS 53, , , Utilization Variables The Utilization measures (Emergency Department Visits per Year, Outpatient Visits per Year) are member characteristics that are also HEDIS measures. As such, they were subjected to the same quality checks as the Star Measures. Data from contracts that failed those criteria were treated as Unknown and excluded from the tables. These values were 60,616 (10.6%) for Dual eligible members and 121,970 (8.1%) for Non-Dual eligible members.

41 Variables Dual Eligible Member s Medicare/Medicaid (i.e., dual eligible) status computed using data available in the MMR data (specifically the Medicaid, Medicaid Add-on, and Medicaid Status indicator fields), where the member was identified as having Medicaid coverage for at least one month during the measurement year. Demographics Age Group: Denotes the member s age calculated as of the end of the measurement year. Gender: Denotes the member s gender. Race/Ethnicity: Denotes the member s race/ethnicity. Geography Rural-Urban Commuting Area (Census Tract): The Rural-Urban Commuting Area (RUCA) classification defined by the Washington State Department of Health. Address Type: Postal delivery classification of the address (i.e., street, high rise, non-residential delivery, and firm). 41 Acxiom Acxiom, Inc. provides aggregated socioeconomic and sociodemographic data from ACS and the Acxiom s proprietary InfoBase GEO database. It encompasses over 144 million households and 214 million individual eligible members. Acxiom InfoBase information is available at the ZIP+4 level (approximately 30 million discrete records), and ACS data is available at the census block level. Acxiom Demographic: Mean Household Size Percent of Single-Person Households Percent of Households with Completed High School or Less Percent of Households with Completed College Percent of Households with Completed Graduate School Percent of Households with Attended Vocational/Technical Acxiom Property: Percent of Households that Own Their Home

42 Acxiom Financial-Market: Median Household Income Median Household Net Worth American Community Survey (ACS): Percent of Population Married Percent of Population Never Married Percent of Population Separated or Divorced Percent of Population Widowed Percent of Population Below Poverty Level Percent of Population Unemployed Percent of Population with Disability Area Health Resources Files (AHRF) AHRF data were obtained from the U.S. Department of Health and Human Services on ahrf.hrsa.gov. AHRF is a collection of data resources products compiled annually from over 50 sources. 42 Primary Care Shortage Area: Denotes whether the member resides in a county that was designated by the Health Resources and Services Administration (HRSA) as having a shortage in primary care practitioners as reported in HRSA uses the following criteria to determine shortage areas: 1. The area is a rational area for the delivery of primary medical services. 2. One of the following conditions prevails within the area: a. The area has a population to full-time-equivalent primary care physician ratio of at least 3,500:1; or b. The area has a population to full-time-equivalent primary care physician ratio of less than 3,500:1 but greater than 3,000:1 and has unusually high needs for primary care services or insufficient capacity of existing primary care providers. 3. Primary medical care professionals in contiguous areas are over-utilized, excessively distant or inaccessible to the population of the area under consideration. Primary Care Practitioners include non-federal doctors of medicine (M.D.) and doctors of osteopathy (D.O.) providing direct patient care who practice principally in one of the four primary care specialties-general or family practice, general internal medicine, pediatrics, and obstetrics and gynecology. Those physicians engaged solely in administration, research and teaching will be excluded. Mental Health Shortage Area: Denotes whether the member resides in a county that was designated by HRSA as having a shortage in mental health practitioners as reported in AHRF. HRSA uses the following criteria to determine shortage areas:

43 1. The area is a rational area for the delivery of mental health services. 2. One of the following conditions prevails within the area: a. The area has: a) population-to-core-mental-health-professional ratio greater than or equal to 6,000:1 and a population-to-psychiatrist ratio greater than or equal to 20,000:1, or b) a population-to-coreprofessional ratio greater than or equal to 9,000:1, or c) a populationto-psychiatrist ratio greater than or equal to 30,000:1; or b. The area has unusually high needs for mental services, and has the following: i. Population-to-core-mental-health-professional ratio greater than or equal to 4,500:1 and a population-to-psychiatrist ratio greater than or equal to 15,000:1; ii. Population-to-core-professional ratio greater than or equal to 6,000:1; or iii. Population-to-psychiatrist ratio greater than or equal to 20,000:1. 3. Mental health professionals in contiguous areas are over utilized, excessively distant or inaccessible to residents of the area under construction. Mental health professionals include those psychiatrists, clinical psychologists, clinical social workers, psychiatric nurse specialists, and marriage and family therapists who meet the definitions set forth in the ruling. Total Number Physicians per 10,000 People: The total number active MDs, including federal and non-federal, in the member s county per 10,000 people in 2011 from American Medical Association Physician Master files as reported in AHRF. 43 Hospital Admissions per 10,000 People: The total number of hospital admissions in the member s county per 10,000 people in 2010 from American Hospital Association Annual Survey Database as reported in AHRF. Monthly Member Report (MMR) Original Reason for Entitlement: Denotes the member s original reason for entitlement to Medicare benefits. Low-Income Drug Subsidy: The amount of monthly financial assistance the member received towards paying for their prescription drug costs (e.g., the monthly Part D premium, annual deductible, and copayments). Institutional Status: Indicates whether the member has been institutionalized for at least 90 days as of the end of the measurement year.

44 Conditions The medical conditions and disability status were identified based on ICD-9-CM codes (International Classification of Diseases, 9th revision, Clinical Modification Codes) the member had during the measurement year. The ICD-9-CM codes were selected on the basis of clinical expertise, Centers for Medicare and Medicaid Services (CMS) Hierarchical Condition Categories (HCC), Charlson Comorbidity Index (CCI) 24, and/or Healthcare Effectiveness Data and Information Set (HEDIS) definitions for calculating performance measures. 25 Here is the list of conditions listed: 44 Alcohol/Drug/Substance Abuse; Acute Myocardial Infarction; Amputation; Anxiety; Bipolar/Major Depression; Blindness; Brain Damage; Cancer; Cerebrovascular Disease; Congestive Heart Failure; COPD (Chronic Obstructive Pulmonary Disorder); Deaf/Deaf & Mute/Deaf & Blind; Dementia; Diabetes; HIV (Human Immunodeficiency Virus); Huntington s Disease; Inadequate/Lack of Housing; Intellectual Disability; Liver Disease; Multiple Sclerosis; Ostomy; Oxygen/Ventilator Dependence; Paraplegia/Hemiplegia; Peptic Ulcers/Gastrointestinal Disease; Peripheral Vascular Disease; Rheumatoid Arthritis; Renal Dialysis Status; Renal Disease; Schizophrenia; Traumatic Brain Injury, History of; Wheelchair.

45 Utilization Ambulatory Care (AMB) is among a set of HEDIS healthcare quality measures designed by NCQA that summarizes healthcare utilization services in an ambulatory care setting into the following categories: Emergency Department Visits per Year and Outpatient Visits per Year. Drugs Number of Fills: The total number of unique fills for any prescription drugs a member had during the measurement year. Number of Drugs: The total number of unique prescription drugs a member had filled at least once during the measurement year. Contract Plan: Percent Dual: Percent of dual eligible members enrolled in the plan. Plan Type: Denotes the Medicare Advantage plan type (e.g., PPO, HMO, POS, MSA, and PFFS). Special Needs Plan: Indicates whether the plan is a Medicare Advantage Special Needs Plan. 45 Employer Group Waiver Plan: Indicates whether the plan is a Medicare Advantage Employer Group Waiver Plan (also known as EGWP, Egg Whip, and 800-series plan). Contract: Number of States Served: The number of states (including D.C. and U.S. territories) that the contract has at least one member who received services. Contract: Number of Counties Served: The number of counties that the contract has at least member who received services. Contract: MA Market Share in Service Area: The contract s Medicare Advantage market share in the service area they cover.

46 APPENDIX B MEASURE DEFINITIONS FOR SELECTED CMS FIVE-STAR QUALITY AND DISPLAY MEASURES The Five-Star Rating system includes several types of the following measures: (1) Healthcare Effectiveness and Data Information Set (HEDIS) quality measures developed by the National Committee for Quality Assurance (NCQA); (2) Prescription Drug Event (PDE) quality measures developed by the Pharmacy Quality Alliance (PQA); (3) satisfaction measures from the Consumer Assessment of Healthcare Providers and Systems (CAHPS) survey developed by the Agency for Healthcare Research and Quality (AHRQ); (4) measures based on the Medicare Health Outcomes Survey (HOS); and (5) administrative data gathered by CMS. 46 The analyses completed for this study include in-depth evaluation of 18 quality measures. Of these, eight are included in the Five-Star Rating System and 10 are Star Display Measures. These 18 measures were selected because they can be calculated with readily available administrative claims data. The HEDIS Hybrid measures rely on claims data supplemented by data obtained from medical record reviews, and the process, CAHPS and HOS survey measures, rely on data collected through surveys of members. Together, the eight current Star Measures comprise 23.39% of the overall Star Rating for plans providing both Part C and Part D services (MA-PD Contracts), and 47.62% of the overall rating for Prescription Drug Plan (PDP)-only plans. In order to isolate and understand the impact of dual eligibility and other sociodemographic factors on these outcomes, it is important to first understand how these measures are defined, including any exclusions or case-mix (e.g., severity or risk ) adjustments that are applied that may account for some differences in populations across health plans. The types of exclusions and adjustments applied to these 18 measures are reflective of those applied to other Star and Display measures. Only one of the Star Measures evaluated Plan All-Cause Readmissions has risk adjustments applied. All-Cause Readmissions is adjusted for age, gender, presence of surgeries, discharge condition, and comorbidities to control for how sick patients were when they were discharged from the hospital. Due to the characteristics of the dual eligible population, these adjustments can potentially control for the impact of some of the sociodemographic factors found in dual eligible members. Several Star Measures have exclusions, mostly related to prior history of the event being measured (e.g., prior fracture) or conditions making individual eligible members not eligible for the measure (e.g., HIV).

47 HEDIS Admin Star Measures Osteoporosis Management in Women Who Had a Fracture (OMW) The percent of women 67 years and older who suffered a fracture (denominator) and who had either a bone mineral density (BMD) test or prescription for a drug to treat or prevent osteoporosis in the six months after the fracture (numerator). Exclusions: (1) Members are excluded if they had a previous fracture (documented in an outpatient visit, observation stay, emergency department visit, non-acute inpatient encounter or acute inpatient encounter) during the 60 days (i.e., two months) prior to the index fracture. (2) Members are excluded if they had a Bone Mineral Density (BMD) test or a claim/encounter for osteoporosis therapy or received a dispensed prescription to treat osteoporosis during the 365 days (i.e., 12 months) prior to the index fracture. (3) The measure does not include fractures of skull, face, toes or fingers. Plan All-Cause Readmissions (PCR) The percent of members 65 years of age and older discharged from an acute-care hospital (denominator) and readmitted for any diagnosis within 30 days for members (numerator). Exclusions: Non-acute inpatient rehabilitation services, including non-acute inpatient stays at rehabilitation facilities; same day hospitalizations; discharges for members with another discharge in prior 30 days; discharges for death; and stays with a principal diagnosis of pregnancy or conditions originating in the perinatal period. 47 Risk adjustment: This is the only measure among those evaluated that is adjusted for case-mix severity to account for how sick patients were when they went to the hospital the first time. Categories include: age, gender, presence of surgeries, discharge condition, and comorbidities. Disease-Modifying Anti-Rheumatic Drug Therapy for Rheumatoid Arthritis (ART) The percent of members who were diagnosed with rheumatoid arthritis (denominator) and who were dispensed at least one ambulatory prescription for a disease-modifying antirheumatic drug (DMARD) in the measurement year (numerator). Optional exclusions: (1) diagnosis of HIV; or (2) pregnancy anytime during the member s history through December 31 of the measurement year. Prescription Drug Event (PDE) Star Measures Diabetes Treatment (BPD) The percent of Medicare Part D beneficiaries, 18 years or older, dispensed a medication for diabetes and a medication for hypertension who were receiving at least one fill for a Renin Angiotensin System (RAS) antagonist (Angiotensin Converting Enzyme ACE inhibitor, Angiotensin Receptor Blocker ARB or Direct

48 Renin Inhibitor) which are recommended for people with diabetes (numerator). The population includes members dispensed at least one prescription for an oral hypoglycemic agent or insulin and at least one prescription for an antihypertensive agent (denominator). There are no exclusions or adjustments for this measure. High Risk Medication (HRM) The percent of Medicare members 65 years of age or older who received two or more fills of at least one drug with a high risk of side effects in the elderly (numerator). The denominator is number of member-years of enrolled beneficiaries. There are no exclusions or adjustments for this measure. Medication Adherence for Diabetes Medications (MA-D) The percent of Medicare Part D beneficiaries 18 years or older who had proportion of days covered (PDC) of 80% or higher across the classes of diabetes medications (biguanides, sulfonylureas, thiazolidinediones, DPP-IV Inhibitors, meglitinides and incretin mimetic agents) during the measurement period (numerator). The population includes members with at least two fills of medication(s) across the six classes (denominator). The PDC is adjusted to account for inpatient stays, hospice enrollment and/or Skilled Nursing Facility (SNF) stays. There are no exclusions or adjustments for this measure. 48 Medication Adherence for Cholesterol (Statins) (MA-C) The percent of beneficiaries 18 years or older who had proportion of days covered (PDC) of 80% or over for statin cholesterol medication(s) during the measurement period (numerator). The population includes members with at least two fills of any statin medication (denominator). The PDC is adjusted to account for inpatient stays, hospice enrollment and/or Skilled Nursing Facility (SNF) stays. There are no exclusions or adjustments for this measure. Medication Adherence for Hypertension (RAS antagonists) (MA-H) The percent of beneficiaries 18 years or older who had proportion of days covered (PDC) of 80% or higher for Renin Angiotensin System (RAS) antagonist medications (Angiotensin Converting Enzyme ACE inhibitors, Angiotensin Receptor Blocker ARBs and direct renin inhibitors) during the measurement period (numerator). The population includes members with at least two fills of any RAS antagonist (denominator). The PDC is adjusted to account for inpatient stays, hospice enrollment and/or Skilled Nursing Facility (SNF) stays. There are no exclusions or adjustments for this measure.

49 HEDIS Admin Display Measures Access to Primary Care Doctor Visits (AAP) The percentage of members 20 years and older (denominator) who had an ambulatory or preventive care visit during the measurement year (numerator). There are no exclusions or adjustments for this measure. Antidepressant Medication Management (6 months) (AMM) The percentage of members 18 years of age and older with a diagnosis of major depression (denominator) who were newly treated with antidepressant medication, and remained on an antidepressant medication treatment (numerator). Exclusions: Members who did not have a diagnosis of major depression in an inpatient, outpatient, ED, intensive outpatient or partial hospitalization setting during the 60 days prior to the Index Prescription Start Date (inclusive) through 60 days after the Index Prescription Start Date (inclusive). Breast Cancer Screening (BCS) The percentage of women years of age (denominator) who had a mammogram to screen for breast cancer (numerator). Optional Exclusions: Bilateral mastectomy any time during the member s history through December 31 of the measurement year. Any of the following meet criteria for bilateral mastectomy. 49 Engagement of Alcohol and Other Drug Dependence Treatment (IET-E) The percentage of members who initiated treatment and who had two or more additional services with a diagnosis of Alcohol and Other Drug Dependence (AOD) within 30 days of the initiation visit. There are no exclusions or adjustments for this measure. Initiation of Alcohol and Other Drug Dependence Treatment (IET-I) The percentage of members who initiate treatment through an inpatient Alcohol and Other Drug Dependence (AOD) admission, outpatient visit, intensive outpatient encounter or partial hospitalization within 14 days of the diagnosis. There are no exclusions or adjustments for this measure.

50 Continuous Beta-Blocker Treatment (PBH) The percentage of members 18 years of age and older during the measurement year who were hospitalized and discharged alive from July 1 of the year prior to the measurement year to June 30 of the measurement year with a diagnosis of AMI (denominator) and who received persistent beta-blocker treatment for six months after discharge (numerator). Optional Exclusions: Members identified as having an intolerance or allergy to betablocker therapy. Any of the following anytime during the member s history through the end of the continuous enrollment period meet criteria: asthma, COPD, obstructive chronic bronchitis, chronic respiratory conditions due to fumes and vapors, hypotension, heart block >1 degree or sinus bradycardia, a medication dispensing event indicative of a history of asthma, and intolerance or allergy to beta-blocker therapy. Pharmacotherapy Management of COPD Exacerbation-Bronchodilator (PCE-B) The percentage of COPD exacerbations for members 40 years of age and older who had an acute inpatient discharge or ED visit on or between January 1 November 30 of the measurement year and who were dispensed a bronchodilator within 30 days of the event. 50 Exclusions: ED visits that result in an inpatient admission, episode dates when the member was transferred directly to an acute or non-acute care facility for any diagnosis, episode dates when the member was readmitted to an acute or non-acute care facility for any diagnosis within 14 days after the episode date, episode dates when the member had an ED visit for any diagnosis within 14 days after the episode date. Pharmacotherapy Management of COPD Exacerbation-Systemic Corticosteroid (PCE-S) The percentage of COPD exacerbations for members 40 years of age and older who had an acute inpatient discharge or ED visit on or between January 1 November 30 of the measurement year and who were dispensed a systemic corticosteroid within 14 days of the event. Exclusions: ED visits that result in an inpatient admission, episode dates when the member was transferred directly to an acute or non-acute care facility for any diagnosis, episode dates when the member was readmitted to an acute or non-acute care facility for any diagnosis within 14 days after the episode date, episode dates when the member had an ED visit for any diagnosis within 14 days after the episode date. Testing to Confirm Chronic Obstructive Pulmonary Disease (SPR) The percentage of members 40 years of age and older with a new diagnosis of COPD or newly active COPD during the measurement year (denominator), who received appropriate spirometry testing to confirm the diagnosis (numerator). Exclusions: Members who had an outpatient visit, an observation visit, an ED visit or an acute inpatient encounter during the 730 days (2 years) prior to the IESD, with a diagnosis of COPD, emphysema or chronic bronchitis.

51 PDE Display Measures Drug-Drug Interactions (DDI) The percent of Medicare Part D beneficiaries who received a prescription for a target medication during the measurement period and who were dispensed a prescription for a contraindicated medication with or subsequent to the initial prescription. Number of member-years of beneficiaries enrolled during the measurement period who were dispensed a target medication with at least one day overlap with a contraindicated medication (numerator). Number of member-years of beneficiaries enrolled during the measurement period who were dispensed a target medication (denominator). There are no exclusions or adjustments for this measure. 51

52 APPENDIX C PLAN BENEFIT PACKAGE LEVEL ANALYSES: TECHNICAL NOTES Generalized Linear Mixed Model Descriptions In the description of the models, the following notation is used: 52 : Response (dependent) variable member j from plan benefit package i, typically 1 if the member is included in that measure s numerator and 0 otherwise : Dual status of member j from plan benefit package i. It is equal to 1 if the member is dual eligible and 0 otherwise : Plan % dual membership for plan benefit package i: category identifier (model 2) or continuous proportion of dual eligible members (model 3) : Overall intercept : Within-plan, or individual, effect of dual status ( within ) : Between-plan effect of % dual membership ( between ) : Plan benefit package-specific random effect : Individual error term Model 1, which tests only for the main effect of members dual status on outcome, controlling for the plan benefit package random effect, is defined as: Model 2, which includes member dual status and adds plan benefit package % dual, tests for the main effects of members dual status controlling for plan benefit package % dual and the plan benefit package random effect. Using plan benefit package data that are grouped into categories based on % dual, the model is defined as: In Model 2, plan benefit package % dual is specified as dummy variables, defined as Dik = 1 for plan benefit package i with % dual enrollment from kth group and Dik = 1 otherwise, to capture a possible non-linear relationship between plan benefit package % dual and the outcome. Five groups were used: < 10%, 10 < 20%, 20 < 30%, 30 < 90% and %. This model can also be estimated treating % dual as a continuous variable. However, including % dual defined as a continuous variable results in precisely the same coefficient estimates as shown in the contextual effect coefficient estimates in Model 3, thus those results were not shown here.

53 Model 3, which transforms the member s dual status to a deviation from the group plan percent dual (group mean centering) in order to independently estimate the within (individual) and between effects, is defined as: The contextual effect between coefficients: can be tested by testing the equality of the within and Failing to reject the null hypothesis implies that there is no contextual effect of plan benefit package. 53

54 APPENDIX D PLAN BENEFIT PACKAGE LEVEL ANALYSES: DETAILED RESULTS 54 RANDOM EFFECT (PBPs) MEMBER DUAL STATUS (WITHIN EFFECT) MODEL 1. TABLE 1: MEASURE = MEMBER DUAL STATUS WITH RANDOM EFFECT OF PLAN BENEFIT PACKAGES F VALUE P-VALUE CHISQ P-VALUE UPPER 95% CI LOWER 95% CI ODDS RATIO ESTIMATE STANDARD ERROR NUMBER OF MEMBERS NUMBER OF PBPs SAMPLE MEASURE MEASURE NAME 293 1,533, < <.0001 Adults' Access to Preventive/ Ambulatory Health Services All PBPs AAP , <.0001 Antidepressant Medication Management-Effective Continuation Phase Treatment AMM ART Rheumatoid Arthritis Management , <.0001 BCS Breast Cancer Screening , < <.0001 BPD Diabetes Treatment , <.0001 DDI Drug-Drug Interactions , < <.0001 HRM High Risk Medication 216 1,132, < < , <.0001 Initiation and Engagement of Alcohol and Other Drug Dependence Treatment- Engagement IET-E , <.0001 Initiation and Engagement of Alcohol and Other Drug Dependence Treatment-Initiation IET-I , <.0001 Medication Adherence for Cholesterol (Statins) MA-C , <.0001 Medication Adherence for Diabetes Medications MA-D , < <.0001 Medication Adherence for Hypertension (RAS antagonists) MA-H 102 9, <.0001 Osteoporosis Management in Women Who Had a Fracture OMW 162 4, <.0001 Persistence of Beta-Blocker Treatment After a Heart Attack PBH , < <.0001 Pharmacotherapy Management of COPD Exacerbation- Bronchodilators PCE-B , <.0001 Pharmacotherapy Management of COPD Exacerbation-Systemic Corticosteroids PCE-S , < <.0001 Use of Spirometry Testing in the Assessment and Diagnosis of COPD SPR 62 32, < Plan All-Cause Readmissions, Unadjusted PCR 62 32, Plan All-Cause Readmissions, Adjusted Notes: Odds ratio less than 1 means that dual eligible members perform worse. Blue color indicates the effect is negative and statistically significant meaning that dual eligibles perform worse than non-dual eligibles; Green color indicates the effect is positive and statistically significant meaning that dual eligibles perform better than non-dual eligibles. For reverse measures (HRM, PCR, DDI), the coefficient estimates measure the effect of the variable on NOT having an outcome.

55 RANDOM EFFECT (PBPs) MEMBER DUAL STATUS (WITHIN EFFECT) MODEL 1. TABLE 2: MEASURE = MEMBER DUAL STATUS WITH RANDOM EFFECT OF PLAN BENEFIT PACKAGES F VALUE P-VALUE CHISQ P-VALUE UPPER 95% CI LOWER 95% CI ODDS RATIO ESTIMATE STANDARD ERROR NUMBER OF MEMBERS NUMBER OF PBPs SAMPLE MEASURE MEASURE NAME , <.0001 Adults' Access to Preventive/ Ambulatory Health Services AAP PBPs with 10-90% Dual Enrollment 84 9, <.0001 Antidepressant Medication Management-Effective Continuation Phase Treatment AMM ART Rheumatoid Arthritis Management 87 6, <.0001 BCS Breast Cancer Screening , < <.0001 BPD Diabetes Treatment 42 51, <.0001 DDI Drug-Drug Interactions , < <.0001 HRM High Risk Medication , < < , <.0001 Initiation and Engagement of Alcohol and Other Drug Dependence Treatment- Engagement IET-E 46 8, <.0001 Initiation and Engagement of Alcohol and Other Drug Dependence Treatment-Initiation IET-I , <.0001 Medication Adherence for Cholesterol (Statins) MA-C , <.0001 Medication Adherence for Diabetes Medications MA-D , < <.0001 Medication Adherence for Hypertension (RAS antagonists) MA-H 42 4, <.0001 Osteoporosis Management in Women who had a Fracture OMW 78 2, Persistence of Beta-Blocker Treatment After a Heart Attack PBH 84 4, < <.0001 Pharmacotherapy Management of COPD Exacerbation- Bronchodilators PCE-B 84 4, <.0001 Pharmacotherapy Management of COPD Exacerbation-Systemic Corticosteroids PCE-S 98 14, <.0001 Use of Spirometry Testing in the Assessment and Diagnosis of COPD SPR 27 11, n/a Plan All-Cause Readmissions, Unadjusted PCR 27 11, n/a Plan All-Cause Readmissions, Adjusted Notes: Odds ratio less than 1 means that dual eligible members perform worse. Blue color indicates the effect is negative and statistically significant meaning that dual eligibles perform worse than non-dual eligibles; Green color indicates the effect is positive and statistically significant meaning that dual eligibles perform better than non-dual eligibles. For reverse measures (HRM, PCR, DDI), the coefficient estimates measure the effect of the variable on NOT having an outcome. 55

56 56 RANDOM EFFECT (PBPs) MEMBER DUAL STATUS (WITHIN EFFECT) MODEL 1. TABLE 3: MEASURE = MEMBER DUAL STATUS WITH RANDOM EFFECT OF PLAN BENEFIT PACKAGES F VALUE P-VALUE CHISQ P-VALUE UPPER 95% CI LOWER 95% CI ODDS RATIO ESTIMATE STANDARD ERROR NUMBER OF MEMBERS NUMBER OF PBPs SAMPLE MEASURE MEASURE NAME , < <.0001 Adults' Access to Preventive/ Ambulatory Health Services AAP PBPs with 20-80% Dual Enrollment 45 5, <.0001 Antidepressant Medication Management-Effective Continuation Phase Treatment AMM ART Rheumatoid Arthritis Management 43 3, <.0001 BCS Breast Cancer Screening 44 50, < <.0001 BPD Diabetes Treatment 23 35, <.0001 DDI Drug-Drug Interactions 47 74, < <.0001 HRM High Risk Medication , < < , <.0001 Initiation and Engagement of Alcohol and Other Drug Dependence Treatment- Engagement IET-E 27 5, <.0001 Initiation and Engagement of Alcohol and Other Drug Dependence Treatment-Initiation IET-I , <.0001 Medication Adherence for Cholesterol (Statins) MA-C 46 52, <.0001 Medication Adherence for Diabetes Medications MA-D , < <.0001 Medication Adherence for Hypertension (RAS antagonists) MA-H 15 1, <.0001 Osteoporosis Management in Women who had a Fracture OMW 42 1, Persistence of Beta-Blocker Treatment After a Heart Attack PBH 44 2, < <.0001 Pharmacotherapy Management of COPD Exacerbation- Bronchodilators PCE-B 44 2, Pharmacotherapy Management of COPD Exacerbation-Systemic Corticosteroids PCE-S 42 6, <.0001 Use of Spirometry Testing in the Assessment and Diagnosis of COPD SPR 16 6, n/a Plan All-Cause Readmissions, Unadjusted PCR 16 6, n/a Plan All-Cause Readmissions, Adjusted Notes: Odds ratio less than 1 means that dual eligible members perform worse. Blue color indicates the effect is negative and statistically significant meaning that dual eligibles perform worse than non-dual eligibles; Green color indicates the effect is positive and statistically significant meaning that dual eligibles perform better than non-dual eligibles. For reverse measures (HRM, PCR, DDI), the coefficient estimates measure the effect of the variable on NOT having an outcome.

57 RANDOM EFFECT (PBPs) PLAN % DUAL (CONTEXTUAL/ BETWEEN EFFECT) MEMBER DUAL STATUS (WITHIN EFFECT) MODEL 2. TABLE 1: MEASURE = MEMBER DUAL STATUS + PLAN % DUAL WITH RANDOM EFFECT OF PLAN BENEFIT PACKAGES Dual percentage groups: <10%, 10-<20%, 20-<30%, 30-<90%, % F VALUE P-VALUE F VALUE P-VALUE CHISQ P-VALUE UPPER 95% CI LOWER 95% CI ODDS RATIO ESTIMATE STANDARD ERROR NUMBER OF MEMBERS NUMBER OF PBPs SAMPLE MEASURE MEASURE NAME 293 1,533, < <.0001 Adults' Access to Preventive/ Ambulatory Health Services All PBPs AAP , < <.0001 Antidepressant Medication Management-Effective Continuation Phase Treatment AMM ART Rheumatoid Arthritis Management , <.0001 BCS Breast Cancer Screening , < <.0001 BPD Diabetes Treatment , <.0001 DDI Drug-Drug Interactions , < <.0001 HRM High Risk Medication 216 1,132, < < , <.0001 Initiation and Engagement of Alcohol and Other Drug Dependence Treatment- Engagement IET-E , <.0001 Initiation and Engagement of Alcohol and Other Drug Dependence Treatment-Initiation IET-I , <.0001 Medication Adherence for Cholesterol (Statins) MA-C , <.0001 Medication Adherence for Diabetes Medications MA-D , < <.0001 Medication Adherence for Hypertension (RAS antagonists) MA-H 102 9, <.0001 Osteoporosis Management in Women who had a Fracture OMW 162 4, <.0001 Persistence of Beta-Blocker Treatment After a Heart Attack PBH , < <.0001 Pharmacotherapy Management of COPD Exacerbation- Bronchodilators PCE-B , <.0001 Pharmacotherapy Management of COPD Exacerbation-Systemic Corticosteroids PCE-S , < <.0001 Use of Spirometry Testing in the Assessment and Diagnosis of COPD SPR 62 32, < Plan All-Cause Readmissions, Unadjusted PCR 62 32, Plan All-Cause Readmissions, Adjusted Notes: Odds ratio less than 1 means that dual eligible members perform worse. Blue color indicates the effect is negative and statistically significant meaning that dual eligibles perform worse than non-dual eligibles; Green color indicates the effect is positive and statistically significant meaning that dual eligibles perform better than non-dual eligibles. For reverse measures (HRM, PCR, DDI), the coefficient estimates measure the effect of the variable on NOT having an outcome. 57

58 58 RANDOM EFFECT (PBPs) PLAN % DUAL (CONTEXTUAL/ BETWEEN EFFECT) MEMBER DUAL STATUS (WITHIN EFFECT) MODEL 2. TABLE 2: MEASURE = MEMBER DUAL STATUS + PLAN % DUAL WITH RANDOM EFFECT OF PLAN BENEFIT PACKAGES Dual percentage groups: <10%, 10-<20%, 20-<30%, 30-<90%, % F VALUE P-VALUE F VALUE P-VALUE CHISQ P-VALUE UPPER 95% CI LOWER 95% CI ODDS RATIO ESTIMATE STANDARD ERROR NUMBER OF MEMBERS NUMBER OF PBPs SAMPLE MEASURE MEASURE NAME , <.0001 Adults' Access to Preventive/ Ambulatory Health Services AAP PBPs with 10-90% Dual Enrollment 84 9, <.0001 Antidepressant Medication Management-Effective Continuation Phase Treatment AMM ART Rheumatoid Arthritis Management 87 6, <.0001 BCS Breast Cancer Screening , < <.0001 BPD Diabetes Treatment 42 51, <.0001 DDI Drug-Drug Interactions , < <.0001 HRM High Risk Medication , < < < , <.0001 Initiation and Engagement of Alcohol and Other Drug Dependence Treatment- Engagement IET-E 46 8, <.0001 Initiation and Engagement of Alcohol and Other Drug Dependence Treatment-Initiation IET-I , <.0001 Medication Adherence for Cholesterol (Statins) MA-C , <.0001 Medication Adherence for Diabetes Medications MA-D , < <.0001 Medication Adherence for Hypertension (RAS antagonists) MA-H 42 4, <.0001 Osteoporosis Management in Women who had a Fracture OMW 78 2, Persistence of Beta-Blocker Treatment After a Heart Attack PBH 84 4, < <.0001 Pharmacotherapy Management of COPD Exacerbation- Bronchodilators PCE-B 84 4, Pharmacotherapy Management of COPD Exacerbation-Systemic Corticosteroids PCE-S 98 14, <.0001 Use of Spirometry Testing in the Assessment and Diagnosis of COPD SPR 27 11, n/a Plan All-Cause Readmissions, Unadjusted PCR 27 11, n/a Plan All-Cause Readmissions, Adjusted Notes: Odds ratio less than 1 means that dual eligible members perform worse. Blue color indicates the effect is negative and statistically significant meaning that dual eligibles perform worse than non-dual eligibles; Green color indicates the effect is positive and statistically significant meaning that dual eligibles perform better than non-dual eligibles. For reverse measures (HRM, PCR, DDI), the coefficient estimates measure the effect of the variable on NOT having an outcome.

59 RANDOM EFFECT (PBPs) PLAN % DUAL (CONTEXTUAL/ BETWEEN EFFECT) MEMBER DUAL STATUS (WITHIN EFFECT) MODEL 2. TABLE 3: MEASURE = MEMBER DUAL STATUS + PLAN % DUAL WITH RANDOM EFFECT OF PLAN BENEFIT PACKAGES Dual percentage groups: <10%, 10-<20%, 20-<30%, 30-<90%, % F VALUE P-VALUE F VALUE P-VALUE CHISQ P-VALUE UPPER 95% CI LOWER 95% CI ODDS RATIO ESTIMATE STANDARD ERROR NUMBER OF MEMBERS NUMBER OF PBPs SAMPLE MEASURE MEASURE NAME , < <.0001 Adults' Access to Preventive/ Ambulatory Health Services AAP PBPs with 20-80% Dual Enrollment 45 5, <.0001 Antidepressant Medication Management-Effective Continuation Phase Treatment AMM ART Rheumatoid Arthritis Management 43 3, <.0001 BCS Breast Cancer Screening 44 50, < <.0001 BPD Diabetes Treatment 23 35, <.0001 DDI Drug-Drug Interactions 47 74, < <.0001 HRM High Risk Medication , < < < , <.0001 Initiation and Engagement of Alcohol and Other Drug Dependence Treatment- Engagement IET-E 27 5, <.0001 Initiation and Engagement of Alcohol and Other Drug Dependence Treatment-Initiation IET-I , <.0001 Medication Adherence for Cholesterol (Statins) MA-C 46 52, <.0001 Medication Adherence for Diabetes Medications MA-D , < <.0001 Medication Adherence for Hypertension (RAS antagonists) MA-H 15 1, <.0001 Osteoporosis Management in Women who had a Fracture OMW 42 1, Persistence of Beta-Blocker Treatment After a Heart Attack PBH 44 2, < Pharmacotherapy Management of COPD Exacerbation- Bronchodilators PCE-B 44 2, Pharmacotherapy Management of COPD Exacerbation-Systemic Corticosteroids PCE-S 42 6, <.0001 Use of Spirometry Testing in the Assessment and Diagnosis of COPD SPR 16 6, n/a Plan All-Cause Readmissions, Unadjusted PCR 16 6, n/a Plan All-Cause Readmissions, Adjusted Notes: Odds ratio less than 1 means that dual eligible members perform worse. Blue color indicates the effect is negative and statistically significant meaning that dual eligibles perform worse than non-dual eligibles; Green color indicates the effect is positive and statistically significant meaning that dual eligibles perform better than non-dual eligibles. For reverse measures (HRM, PCR, DDI), the coefficient estimates measure the effect of the variable on NOT having an outcome. 59

60 60 CONTEXTUAL EFFECT (BETWEEN - WITHIN) PLAN % DUAL (BETWEEN EFFECT) MEMBER DUAL STATUS (WITHIN EFFECT) MODEL 3. TABLE 1: IDENTIFIES CONTEXTUAL EFFECT OF PLAN % DUAL ON MEMBER OUTCOMES WITH RANDOM EFFECT OF PLAN BENEFIT PACKAGES T VALUE P-VALUE ESTIMATE P-VALUE F VALUE P-VALUE ESTIMATE STANDARD ERROR NUMBER OF MEMBERS NUMBER OF PBPs SAMPLE MEASURE MEASURE NAME 293 1,533, < Adults' Access to Preventive/ Ambulatory Health Services All PBPs AAP , < <.0001 Antidepressant Medication Management-Effective Continuation Phase Treatment AMM ART Rheumatoid Arthritis Management , BCS Breast Cancer Screening , < BPD Diabetes Treatment , DDI Drug-Drug Interactions , < HRM High Risk Medication 216 1,132, < , Initiation and Engagement of Alcohol and Other Drug Dependence Treatment- Engagement IET-E , Initiation and Engagement of Alcohol and Other Drug Dependence Treatment-Initiation IET-I , Medication Adherence for Cholesterol (Statins) MA-C , Medication Adherence for Diabetes Medications MA-D , < < Medication Adherence for Hypertension (RAS antagonists) MA-H 102 9, < Osteoporosis Management in Women who had a Fracture OMW 162 4, Persistence of Beta-Blocker Treatment After a Heart Attack PBH , < Pharmacotherapy Management of COPD Exacerbation- Bronchodilators PCE-B , Pharmacotherapy Management of COPD Exacerbation-Systemic Corticosteroids PCE-S , < Use of Spirometry Testing in the Assessment and Diagnosis of COPD SPR 62 32, < Plan All-Cause Readmissions, Unadjusted PCR 62 32, Plan All-Cause Readmissions, Adjusted Notes: Odds ratio less than 1 means that dual eligible members perform worse. Blue color indicates the effect is negative and statistically significant meaning that dual eligibles perform worse than non-dual eligibles; Green color indicates the effect is positive and statistically significant meaning that dual eligibles perform better than non-dual eligibles. For reverse measures (HRM, PCR, DDI), the coefficient estimates measure the effect of the variable on NOT having an outcome.

61 CONTEXTUAL EFFECT (BETWEEN - WITHIN) PLAN % DUAL (BETWEEN EFFECT) MEMBER DUAL STATUS (WITHIN EFFECT) MODEL 3. TABLE 2: IDENTIFIES CONTEXTUAL EFFECT OF PLAN % DUAL ON MEMBER OUTCOMES WITH RANDOM EFFECT OF PLAN BENEFIT PACKAGES T VALUE P-VALUE ESTIMATE P-VALUE F VALUE P-VALUE ESTIMATE STANDARD ERROR NUMBER OF MEMBERS NUMBER OF PBPs SAMPLE MEASURE MEASURE NAME , Adults' Access to Preventive/ Ambulatory Health Services AAP PBPs with 10-90% Dual Enrollment 84 9, Antidepressant Medication Management-Effective Continuation Phase Treatment AMM ART Rheumatoid Arthritis Management 87 6, BCS Breast Cancer Screening , < BPD Diabetes Treatment 42 51, DDI Drug-Drug Interactions , < HRM High Risk Medication , < , Initiation and Engagement of Alcohol and Other Drug Dependence Treatment- Engagement IET-E 46 8, Initiation and Engagement of Alcohol and Other Drug Dependence Treatment-Initiation IET-I , Medication Adherence for Cholesterol (Statins) MA-C , Medication Adherence for Diabetes Medications MA-D , < Medication Adherence for Hypertension (RAS antagonists) MA-H 42 4, Osteoporosis Management in Women who had a Fracture OMW 78 2, Persistence of Beta-Blocker Treatment After a Heart Attack PBH 84 4, < Pharmacotherapy Management of COPD Exacerbation- Bronchodilators PCE-B 84 4, Pharmacotherapy Management of COPD Exacerbation-Systemic Corticosteroids PCE-S 98 14, Use of Spirometry Testing in the Assessment and Diagnosis of COPD SPR 27 11, Plan All-Cause Readmissions, Unadjusted PCR 27 11, Plan All-Cause Readmissions, Adjusted Notes: Odds ratio less than 1 means that dual eligible members perform worse. Blue color indicates the effect is negative and statistically significant meaning that dual eligibles perform worse than non-dual eligibles; Green color indicates the effect is positive and statistically significant meaning that dual eligibles perform better than non-dual eligibles. For reverse measures (HRM, PCR, DDI), the coefficient estimates measure the effect of the variable on NOT having an outcome. 61

62 62 CONTEXTUAL EFFECT (BETWEEN - WITHIN) PLAN % DUAL (BETWEEN EFFECT) MEMBER DUAL STATUS (WITHIN EFFECT) MODEL 3. TABLE 3: IDENTIFIES CONTEXTUAL EFFECT OF PLAN % DUAL ON MEMBER OUTCOMES WITH RANDOM EFFECT OF PLAN BENEFIT PACKAGES T VALUE P-VALUE ESTIMATE P-VALUE F VALUE P-VALUE ESTIMATE STANDARD ERROR NUMBER OF MEMBERS NUMBER OF PBPs SAMPLE MEASURE MEASURE NAME , < Adults' Access to Preventive/ Ambulatory Health Services AAP PBPs with 20-80% Dual Enrollment 45 5, Antidepressant Medication Management-Effective Continuation Phase Treatment AMM ART Rheumatoid Arthritis Management 43 3, BCS Breast Cancer Screening 44 50, < BPD Diabetes Treatment 23 35, DDI Drug-Drug Interactions 47 74, < HRM High Risk Medication , < , Initiation and Engagement of Alcohol and Other Drug Dependence Treatment- Engagement IET-E 27 5, Initiation and Engagement of Alcohol and Other Drug Dependence Treatment-Initiation IET-I , Medication Adherence for Cholesterol (Statins) MA-C 46 52, Medication Adherence for Diabetes Medications MA-D , < Medication Adherence for Hypertension (RAS antagonists) MA-H 15 1, Osteoporosis Management in Women who had a Fracture OMW 42 1, Persistence of Beta-Blocker Treatment After a Heart Attack PBH 44 2, < Pharmacotherapy Management of COPD Exacerbation- Bronchodilators PCE-B 44 2, Pharmacotherapy Management of COPD Exacerbation-Systemic Corticosteroids PCE-S 42 6, Use of Spirometry Testing in the Assessment and Diagnosis of COPD SPR 16 6, Plan All-Cause Readmissions, Unadjusted PCR 16 6, Plan All-Cause Readmissions, Adjusted Notes: Odds ratio less than 1 means that dual eligible members perform worse. Blue color indicates the effect is negative and statistically significant meaning that dual eligibles perform worse than non-dual eligibles; Green color indicates the effect is positive and statistically significant meaning that dual eligibles perform better than non-dual eligibles. For reverse measures (HRM, PCR, DDI), the coefficient estimates measure the effect of the variable on NOT having an outcome.

63 APPENDIX E MULTIVARIATE DECOMPOSITION ANALYSIS: TECHNICAL NOTES The study utilized Linear Probability Model (LPM) with weighting formula proposed by Neumark 26 and a normalization technique proposed by Yun 27 for categorical variables. The outcome variables of the measures included in this study are binary variables with value equals to 1 if a member is in the numerator of the measure and 0 otherwise. Compared to a non-linear model (i.e., logit and probit), linear probability model allows for easier interpretation and it has been used in another study. 28 The estimates of contributions based on the two models do not differ substantially as shown by Fairlie. 29 Member characteristics included as explanatory variables in the model are clinical, demographic, geographic, and sociodemographic characteristics. Suppose variable y is a measure outcome, which is explained by a vector of explanatory variables x. We can write two regression equations for dual and non-dual groups as follows: Dual: Non-dual: Where: and are vectors of coefficients, respectively, for dual and non-dual groups. The measure rate for dual is average denoted as, and the measure rate for non-dual is average denoted as. The measure gap between dual and non-dual is equal to: Explained Unexplained 63 Where: and are measure rates, respectively, for dual and non-dual; and are vectors of average explanatory variables for dual and non-dual, respectively;,, and are vectors of coefficient estimates for dual, non-dual, and combined dual and nondual, respectively.

64 All member characteristics in the study database deemed potentially associated with the measure outcome (including clinical, demographic, geographic, and sociodemographic factors) were examined. Bivariate analyses were conducted to test which potential explanatory variables were statistically associated with the outcome. Variables found to be statistically significant (p-value 0.05) were then included and tested in the multivariate regression model. Variance inflation factors (VIF) were calculated to detect multicollinearity between variables; those with VIF greater than five were considered to be highly correlated with one or more variable and excluded from the model (e.g., poverty rate and use of food stamps were highly correlated as both signify economic status, thus only poverty rate was retained). The variables in the final model were reviewed for clinical relevance or a conceptual relationship to the outcome. In the models, the differences in means (i.e., prevalence of the characteristic) between dual eligible and non-dual eligible members determine the explained component of the observed gap, and the differences in the coefficient estimates determine the unexplained component of the gap. 64 When the explained proportion of the performance gap is less than 100%, there is an unexplained component remaining. The unexplained proportion quantifies the differential impact of the characteristics on outcomes in dual eligible members compared to non-dual eligible members (e.g., does age have a larger effect on the outcome in dual members than in non-dual eligible members in the same age group?). The unexplained gap could be attributable to several things: 1. A systematic difference in the way a risk factor impacts dual eligible members. The factor may have a differential impact on dual eligible members; (e.g., having dementia may impact a dual member more than a non-dual member with other similar characteristics). The factor could also be more severe in dual eligible members and thus impact dual eligible members more. For example, a chronic condition like dementia is specified as binary (present or not), which does not capture severity; if the measurement were refined, it could explain more of the performance gap. 2. Factors not included in the model because we don t have the data (e.g., information on health behaviors such as illegal substance abuse). 3. Differences in services, such as plan characteristics not captured in the model. Further investigation of the unexplained gap is needed to further understand the underlying cause.

65 In cases with an unexplained proportion (explained component is <100%) due to differential impact of the covariates, even if covariates were made to be identical between dual eligible members and non-dual eligible members, this portion of the performance gap in the measure would persist. In some cases the explained proportion is >100%. This means that if the prevalence of the covariates were the same dual and non-dual eligible members, the performance rate on that measure would actually be higher in dual members. 65

66 APPENDIX F MULTIVARIATE DECOMPOSITION ANALYSES: DETAILED RESULTS Table 1. Means and Coefficient Estimates of Explanatory Variables in ART Model DUAL NON-DUAL ALL VARIABLE GROUP MEAN COEF. P-VALUE MEAN COEF. P-VALUE MEAN COEF. P-VALUE VIF 66 Intercept < < <.0001 Age Group Age Group Age Group Age Group Age Group < Age Group < < < Age Group Ref Ref Ref Gender Male < < Gender Female Ref Ref Ref Median Household $15,000 - Income $19, Median Household $20,000 - Income $29, Median Household $30,000 - Income $39, Median Household $40,000 - Income $49, Median Household $50,000 - Income $74, < < Median Household $75,000 - Income $99, < Median Household Income $100, < Median Household $0 - Income $15, Ref Ref Ref Percent of Households that Own Their Home Percent of Households that Own Their Home Percent of Households that Own Their Home < < Percent of Households that Ref Ref Ref Own Their Home Census Division East North Central Census Division East South Central Census Division Mountain

67 Table 1. Means and Coefficient Estimates of Explanatory Variables in ART Model (Continued) DUAL NON-DUAL ALL VARIABLE GROUP MEAN COEF. P-VALUE MEAN COEF. P-VALUE MEAN COEF. P-VALUE VIF Census Division New England < < Census Division Pacific Census Division South Atlantic Census Division West North Central < Census Division West South Central Census Division Middle Atlantic Ref Ref Ref Clinical Condition Alcohol/ Drug/ Substance Abuse < < Clinical Condition Anxiety < < Clinical Condition Dementia Liver Clinical Condition Disease Paraplegia/ Clinical Condition Hemiplegia 67 Table 2. Contribution from Differences in Characteristics ( Explained ) and Differences Coefficients ( Unexplained ) to ART Gap PERCENT CONTRIBUTION VARIABLE EXPLAINED UNEXPLAINED TOTAL Intercept Age Gender Median Household Income Percent of Households that Own Their Home Census Division Alcohol/Drug/Substance Abuse Anxiety Dementia Liver Disease Paraplegia/Hemiplegia Total

68 Table 3. Means and Coefficient Estimates of Explanatory Variables in BCS Model DUAL NON-DUAL ALL VARIABLE GROUP MEAN COEF. P-VALUE MEAN COEF. P-VALUE MEAN COEF. P-VALUE VIF 68 Intercept < < <.0001 Age Group Age Group < < < Age Group < < < Age Group Ref Ref Ref Race/Ethnicity Asian Race/Ethnicity Black < < < Race/Ethnicity Hispanic or Latino < < < Race/Ethnicity Other < Race/Ethnicity White Ref Ref Ref Percent of Households that < < Own Their Home Percent of Households that < < Own Their Home Percent of Households that < < Own Their Home Percent of Households that Ref Ref Ref Own Their Home Percent Households with Completed High < < School or Less Percent Households with Completed High Ref Ref Ref School or Less Primary Care Shortage Area Shortage area < < Primary Care Non/partial Shortage Area shortage area Ref Ref Ref Census Division East North Central < < Census Division East South Central < < Census Division Mountain < < Census Division New England < Census Division Pacific < < Census Division South Atlantic < Census Division West North Central < < <

69 Table 3. Means and Coefficient Estimates of Explanatory Variables in BCS Model (Continued) Census Division West South Central < < < DUAL NON-DUAL ALL VARIABLE GROUP MEAN COEF. P-VALUE MEAN COEF. P-VALUE MEAN COEF. P-VALUE VIF Census Division Middle Atlantic Ref Ref Ref Census Division Metropolitan (Urban) (50, < < or more) Census Division Other than metropolitan with 50, Ref Ref Ref or more Census Division Alcohol/Drug/ Substance < < < Abuse Census Division Acute Myocardial < < Infarction Clinical Condition Amputation < < < Clinical Bipolar/Major Condition Depression < < < Clinical Condition Cancer < < < Clinical Congestive Condition Heart Failure < < Deaf/Deaf & Clinical Mute/Deaf & Condition Blind < < < Clinical Condition Dementia < < < Clinical Condition Liver Disease < < < Clinical Condition Clinical Condition Clinical Condition Clinical Condition Clinical Condition Clinical Condition Clinical Condition Institutional Status Original Reason for Entitlement Original Reason for Entitlement Ostomy < < < Oxygen/ Ventilator Dependence Paraplegia/ Hemiplegia Peripheral Vascular Disease Rheumatoid Arthritis Renal Dialysis Status < < < < < < < < < < < < < < < Schizophrenia < Institutionalized status Disability or ESRD < < < < < < Age Ref Ref Ref 69

70 Table 4. Contribution from Differences in Characteristics ( Explained ) and Differences Coefficients ( Unexplained ) to BCS Gap PERCENT CONTRIBUTION 70 VARIABLE EXPLAINED UNEXPLAINED TOTAL Intercept Age Race/Ethnicity Percent of Households that Own Their Home Percent Households with Completed High School or Less Living in Primary Care Shortage Area Census Division Metropolitan Area Alcohol/Drug/Substance Abuse Acute Myocardial Infarction Amputation Bipolar/Major Depression Cancer Congestive Heart Failure Deaf/Deaf & Mute/Deaf & Blind Dementia Liver Disease Ostomy Oxygen/Ventilator Dependence Paraplegia/Hemiplegia Peripheral Vascular Disease Rheumatoid Arthritis Renal Dialysis Status Schizophrenia Institutional Status Original Reason for Entitlement Total Table 5. Means and Coefficient Estimates of Explanatory Variables in HRM Model DUAL NON-DUAL ALL VARIABLE GROUP MEAN COEF. P-VALUE MEAN COEF. P-VALUE MEAN COEF. P-VALUE VIF Intercept < < <.0001 Age Group < < Age Group < < < Age Group < < < Age Group < < < Age Group Ref Ref Ref Gender Male < < < Gender Female Ref Ref Ref Race/Ethnicity Asian < < < Race/Ethnicity Black < < < Race/Ethnicity Hispanic or Latino < < <

71 Table 5. Means and Coefficient Estimates of Explanatory Variables in HRM Model (Continued) DUAL NON-DUAL ALL VARIABLE GROUP MEAN COEF. P-VALUE MEAN COEF. P-VALUE MEAN COEF. P-VALUE VIF Race/Ethnicity Other < < < Race/Ethnicity White Ref Ref Ref Primary Care Shortage Area Primary Care Shortage Area Mental Health Shortage Area Mental Health Shortage Area Census Division Shortage area < < Non/partial shortage area Ref Ref Ref Shortage area < < < Non/partial shortage area East North Central Ref Ref Ref < < Census Division East South Central < < < Census Division Middle Atlantic < < < Census Division Mountain < < Census Division New England < < < Census Division Pacific Census Division Census Division West North Central West South Central < < < Census Division South Atlantic Ref Ref Ref Rural-Urban Commuting Area (Census Tract) Rural-Urban Commuting Area (Census Tract) Rural-Urban Commuting Area (Census Tract) Rural-Urban Commuting Area (Census Tract) Clinical Condition Clinical Condition Micropolitan (Large Rural Town) (10,000-49,999) Small Rural Town (2,500 9,999) Isolated Rural (under 2,500) Metropolitan (Urban) (50,000 or more) Alcohol/Drug/ Substance Abuse < < < < < < < < < Ref Ref Ref < < < Anxiety < < <

72 Table 5. Means and Coefficient Estimates of Explanatory Variables in HRM Model (Continued) DUAL NON-DUAL ALL VARIABLE GROUP MEAN COEF. P-VALUE MEAN COEF. P-VALUE MEAN COEF. P-VALUE VIF 72 Clinical Condition Clinical Condition Clinical Condition Clinical Condition Clinical Condition Clinical Condition Clinical Condition Clinical Condition Clinical Condition Clinical Condition Clinical Condition Clinical Condition Clinical Condition Clinical Condition Clinical Condition Original Reason for Entitlement Original Reason for Entitlement Bipolar/Major Depression < < < Cancer < < < Cerebrovascular Disease Congestive Heart Failure < < < < < COPD < < < Dementia < Diabetes < < < HIV < < Huntington's Disease < < < Liver Disease < < Paraplegia/ Hemiplegia Peptic Ulcers/ Gastrointestinal Disease Peripheral Vascular Disease Rheumatoid Arthritis < < < < < < < < Schizophrenia < < < Disability or ESRD < < < Age Ref Ref Ref Table 6. Contribution from Differences in Characteristics ( Explained ) and Differences Coefficients ( Unexplained ) to HRM Gap PERCENT CONTRIBUTION VARIABLE EXPLAINED UNEXPLAINED TOTAL Intercept Age Gender Race/Ethnicity Living in Primary Care Shortage Area Living in Mental Health Care Shortage Area Census Division Metropolitan Area Alcohol/Drug/Substance Abuse Anxiety

73 Table 6. Contribution from Differences in Characteristics ( Explained ) and Differences Coefficients ( Unexplained ) to HRM Gap (Continued) PERCENT CONTRIBUTION VARIABLE EXPLAINED UNEXPLAINED TOTAL Bipolar/Major Depression Cancer Cerebrovascular Disease Congestive Heart Failure COPD Dementia Diabetes HIV Huntington's Disease Liver Disease Paraplegia/Hemiplegia Peptic Ulcers/Gastrointestinal Disease Peripheral Vascular Disease Rheumatoid Arthritis Schizophrenia Original Reason for Entitlement Total Table 7. Means and Coefficient Estimates of Explanatory Variables in MA-C Model DUAL NON-DUAL ALL 73 VARIABLE GROUP MEAN COEF. P-VALUE MEAN COEF. P-VALUE MEAN COEF. P-VALUE VIF Intercept < < <.0001 Age Group < < < Age Group < < < Age Group < < < Age Group < < < Age Group < < < Age Group Ref Ref Ref Gender Male < < < Gender Female Ref Ref Ref Percent of Population Never < < < Married Percent of Population Never < < < Married Percent of Population Never < < Married Percent of Population Never Married Ref Ref Ref Race/Ethnicity Asian < Race/Ethnicity Black < < <

74 Table 7. Means and Coefficient Estimates of Explanatory Variables in MA-C Model (Continued) DUAL NON-DUAL ALL VARIABLE GROUP MEAN COEF. P-VALUE MEAN COEF. P-VALUE MEAN COEF. P-VALUE VIF Race/Ethnicity Hispanic or Latino < < < Race/Ethnicity Other < < Race/Ethnicity White Ref Ref Ref Percent Households with Completed High School or Less Percent Households with Completed High School or Less Percent of Households that Own Their Home Percent of Households that Own Their Home Percent of Households that Own Their Home Percent of Households that Own Their Home Percent of Population Below Poverty Level Primary Care Shortage Area Primary Care Shortage Area Census Division Census Division Ref Ref Ref < < < Ref Ref Ref Living in a poor neighborhood with poverty rate greater than 23% < < Shortage area < < Non/Partial shortage area East North Central East South Central Ref Ref Ref < < Census Division Mountain < < < Census Division New England Census Division Pacific Census Division Census Division Census Division Census Division South Atlantic West North Central West South Central Middle Atlantic < < < < < < < < Ref Ref Ref

75 Table 7. Means and Coefficient Estimates of Explanatory Variables in MA-C Model (Continued) DUAL NON-DUAL ALL VARIABLE GROUP MEAN COEF. P-VALUE MEAN COEF. P-VALUE MEAN COEF. P-VALUE VIF Rural-Urban Commuting Area (Census Tract) Rural-Urban Commuting Area (Census Tract) Clinical Condition Clinical Condition Metropolitan (Urban) (50,000 or more) Other than metropolitan with 50,000 or more Alcohol/Drug/ Substance Abuse Bipolar/Major Depression < < Ref Ref Ref < < < < Clinical Condition COPD < < Clinical Condition Dementia < < < Clinical Condition Diabetes < < Clinical Condition HIV < < < Clinical Condition Liver Disease < < < Clinical Condition Clinical Condition Peptic Ulcers/ Gastrointestinal Disease Peripheral Vascular Disease < < < < < Clinical Condition Renal Disease < < < Clinical Condition Schizophrenia < < Institutional Status Original Reason for Entitlement Original Reason for Entitlement Institutionalized Status Disability or ESRD < < Age Ref Ref Ref Table 8. Contribution from Differences in Characteristics ( Explained ) and Differences Coefficients ( Unexplained ) to MA-C Gap PERCENT CONTRIBUTION VARIABLE EXPLAINED UNEXPLAINED TOTAL Intercept Age Gender Percent of Population Never Married Race/Ethnicity Percent Households with Completed High School or Less Percent of Households that Own Their Home

76 Table 8. Contribution from Differences in Characteristics ( Explained ) and Differences Coefficients ( Unexplained ) to MA-C Gap (Continued) PERCENT CONTRIBUTION VARIABLE EXPLAINED UNEXPLAINED TOTAL Living in a poor neighborhood (poverty rate>23%) Living in Primary Care Shortage Area Census Division Metropolitan Area Alcohol/Drug/Substance Abuse Bipolar/Major Depression COPD Dementia Diabetes HIV Liver Disease Peptic Ulcers/Gastrointestinal Disease Peripheral Vascular Disease Renal Disease Schizophrenia Institutional Status Original Reason for Entitlement Total Table 9. Means and Coefficient Estimates of Explanatory Variables in MA-D Model DUAL NON-DUAL ALL VARIABLE GROUP MEAN COEF. P-VALUE MEAN COEF. P-VALUE MEAN COEF. P-VALUE VIF Intercept < < <.0001 Age Group < < < Age Group < < < Age Group < < < Age Group < < < Age Group < Age Group Ref Ref Ref Percent of Population Never < < < Married Percent of Population Never < < < Married Percent of Population Never < < Married Percent of Population Never Married Ref Ref Ref Race/Ethnicity Asian < Race/Ethnicity Black < < <

77 Table 9. Means and Coefficient Estimates of Explanatory Variables in MA-D Model (Continued) DUAL NON-DUAL ALL VARIABLE GROUP MEAN COEF. P-VALUE MEAN COEF. P-VALUE MEAN COEF. P-VALUE VIF Race/Ethnicity Hispanic or Latino Race/Ethnicity Other < Race/Ethnicity White Ref Ref Ref Percent of Households that Own Their Home Percent of Households that Own Their Home Percent of Households that Own Their Home Percent of Households that Own Their Home Percent Households with Completed High School or Less Percent Households with Completed High School or Less Percent of Population Below Poverty Level Primary Care Shortage Area Primary Care Shortage Area Census Division Census Division < < Ref Ref Ref Ref Ref Ref Living in a poor neighborhood with poverty rate greater than 23% Shortage area Non/Partial shortage area East North Central East South Central < < Ref Ref Ref < Census Division Mountain < < < Census Division New England Census Division Pacific Census Division Census Division Census Division South Atlantic West North Central West South Central < < < < < < <

78 Table 9. Means and Coefficient Estimates of Explanatory Variables in MA-D Model (Continued) DUAL NON-DUAL ALL VARIABLE GROUP MEAN COEF. P-VALUE MEAN COEF. P-VALUE MEAN COEF. P-VALUE VIF Census Division Rural-Urban Commuting Area (Census Tract) Rural-Urban Commuting Area (Census Tract) Clinical Condition Clinical Condition Clinical Condition Middle Atlantic Metropolitan (Urban) (50,000 or more) Ref Ref Ref < < Non-Metro Ref Ref Ref Alcohol/ Drug/ Substance Abuse Bipolar/ Major Depression Cerebrovascular Disease < < < < < Clinical Condition COPD < < < Clinical Condition Dementia < < < Clinical Condition HIV Clinical Condition Clinical Condition Clinical Condition Clinical Condition Clinical Condition Original Reason for Entitlement Original Reason for Entitlement Peptic Ulcers/ Gastrointestinal Disease Peripheral Vascular Disease Renal Disease Schizophrenia Institutional Status Disability or ESRD < < < < < < < < < < < Age Ref Ref Ref Table 10. Contribution from Differences in Characteristics ( Explained ) and Differences Coefficients ( Unexplained ) to MA-D Gap PERCENT CONTRIBUTION VARIABLE EXPLAINED UNEXPLAINED TOTAL Intercept Age Percent of Population Never Married Race/Ethnicity Percent of Households that Own Their Home

79 Table 10. Contribution from Differences in Characteristics ( Explained ) and Differences Coefficients ( Unexplained ) to MA-D Gap (Continued) PERCENT CONTRIBUTION VARIABLE EXPLAINED UNEXPLAINED TOTAL Percent Households with Completed High School or Less Living in a poor neighborhood (poverty rate>23%) Living in Primary Care Shortage Area Census Division Metropolitan Area Alcohol/Drug/Substance Abuse Bipolar/Major Depression Cerebrovascular Disease COPD Dementia HIV Peptic Ulcers/Gastrointestinal Disease Peripheral Vascular Disease Renal Disease Schizophrenia Institutional Status Original Reason for Entitlement Total Table 11. Means and Coefficient Estimates of Explanatory Variables in MA-H Model 79 DUAL NON-DUAL ALL VARIABLE GROUP MEAN COEF. P-VALUE MEAN COEF. P-VALUE MEAN COEF. P-VALUE VIF Intercept < < <.0001 Age Group < < < Age Group < < < Age Group < < < Age Group < < < Age Group < < < Age Group Ref Ref Ref Gender Male < < Gender Female Ref Ref Ref Percent of Population Never < < < Married Percent of Population Never < < < Married Percent of Population Never < < < Married Percent of Population Never Married Ref Ref Ref Race/Ethnicity Asian <

80 Table 11. Means and Coefficient Estimates of Explanatory Variables in MA-H Model (Continued) DUAL NON-DUAL ALL VARIABLE GROUP MEAN COEF. P-VALUE MEAN COEF. P-VALUE MEAN COEF. P-VALUE VIF Race/Ethnicity Black < < < Race/Ethnicity Hispanic or Latino Race/Ethnicity Other < < Race/Ethnicity White Ref Ref Ref Percent Households with Completed High School or Less Percent Households with Completed High School or Less Percent of Households that Own Their Home Percent of Households that Own Their Home Percent of Households that Own Their Home Percent of Households that Own Their Home Percent of Population Below Poverty Level Primary Care Shortage Area Primary Care Shortage Area Census Division Census Division Ref Ref Ref < < < < < Ref Ref Ref Living in a poor neighborhood with poverty rate greater than 23% Shortage area Non/partial shortage area East North Central East South Central < < < < < Ref Ref Ref < < < Census Division Mountain < < < Census Division New England Census Division Pacific < < Census Division Census Division Census Division South Atlantic West North Central West South Central < < < < < < < <

81 Table 11. Means and Coefficient Estimates of Explanatory Variables in MA-H Model (Continued) DUAL NON-DUAL ALL VARIABLE GROUP MEAN COEF. P-VALUE MEAN COEF. P-VALUE MEAN COEF. P-VALUE VIF Census Division Rural-Urban Commuting Area (Census Tract) Rural-Urban Commuting Area (Census Tract) Clinical Condition Clinical Condition Clinical Condition Clinical Condition Middle Atlantic Metropolitan (Urban) (50,000 or more) Ref Ref Ref < < < Non-Metro Ref Ref Ref Alcohol/ Drug/ Substance Abuse Bipolar/Major Depression Cerebrovascular Disease Congestive Heart Failure < < < < < < < < < < Clinical Condition COPD < < < Clinical Condition Dementia < < < Clinical Condition Diabetes < < Clinical Condition HIV < Clinical Condition Oxygen/ Ventilator < < < Dependence Clinical Condition Peptic Ulcers/Gastrointestinal < < < Disease Clinical Condition Peripheral Vascular < < Disease Institutional Status Institutional Status < < Original Reason for Disability or Entitlement ESRD < < Original Reason for Entitlement Age Ref Ref Ref Table 12. Contribution from Differences in Characteristics ( Explained ) and Differences Coefficients ( Unexplained ) to MA-H Gap PERCENT CONTRIBUTION VARIABLE EXPLAINED UNEXPLAINED TOTAL Intercept Age Gender Percent of Population Never Married Race/Ethnicity

82 Table 12. Contribution from Differences in Characteristics ( Explained ) and Differences Coefficients ( Unexplained ) to MA-H Gap (Continued) 82 PERCENT CONTRIBUTION VARIABLE EXPLAINED UNEXPLAINED TOTAL Percent Households with Completed High School or Less Percent of Households that Own Their Home Living in a poor neighborhood (poverty rate>23%) Living in Primary Care Shortage Area Census Division Metropolitan Area Alcohol/Drug/Substance Abuse Bipolar/Major Depression Cerebrovascular Disease Congestive Heart Failure COPD Dementia Diabetes HIV Oxygen/Ventilator Dependence Peptic Ulcers/Gastrointestinal Disease Peripheral Vascular Disease Institutional Status Original Reason for Entitlement Total Table 13. Means and Coefficient Estimates of Explanatory Variables in PCR Model DUAL NON-DUAL ALL VARIABLE GROUP MEAN COEF. P-VALUE MEAN COEF. P-VALUE MEAN COEF. P-VALUE VIF Intercept Gender Male Gender Female Ref Ref Ref Percent of Population Below Poverty Level Living in a poor neighborhood with poverty rate>23% Total Number Physicians per 10,000 People Clinical Condition Living in a county with less than 50 physicians per 10,000 people Acute Myocardial Infarction < < Clinical Condition Amputation < < Clinical Condition Anxiety < < Clinical Condition Brain Damage < < Clinical Condition Cancer < < <

83 Table 13. Means and Coefficient Estimates of Explanatory Variables in PCR Model (Continued) DUAL NON-DUAL ALL VARIABLE GROUP MEAN COEF. P-VALUE MEAN COEF. P-VALUE MEAN COEF. P-VALUE VIF Clinical Condition Clinical Condition Cerebrovascular Disease Congestive Heart Failure < < < < < Clinical Condition COPD < < Clinical Condition Dementia < < < Clinical Condition Liver Disease < < Clinical Condition Ostomy < < Clinical Condition Clinical Condition Clinical Condition Clinical Condition < < < < < < < Clinical Condition Renal Disease < < < Clinical Condition Oxygen/ Ventilator Dependence Paraplegia/ Hemiplegia Peptic Ulcers/Gastrointestinal Disease Renal Dialysis Status Schizophrenia Table 14. Contribution from Differences in Characteristics ( Explained ) and Differences Coefficients ( Unexplained ) to PCR Gap PERCENT CONTRIBUTION VARIABLE EXPLAINED UNEXPLAINED TOTAL Intercept Gender Living in a poor neighborhood (poverty rate>23%) Living in a county with less than 50 physicians per 10,000 people Acute Myocardial Infarction Amputation Anxiety Brain Damage Cancer Cerebrovascular Disease Congestive Heart Failure COPD Dementia

84 Table 14. Contribution from Differences in Characteristics ( Explained ) and Differences Coefficients ( Unexplained ) to PCR Gap (Continued) PERCENT CONTRIBUTION VARIABLE EXPLAINED UNEXPLAINED TOTAL Liver Disease Ostomy Oxygen/Ventilator Dependence Paraplegia/Hemiplegia Peptic Ulcers/Gastrointestinal Disease Renal Dialysis Status Renal Disease Schizophrenia Total

85 DUAL ELIGIBLE STUDY ADVISORY PANEL Cigna-HealthSpring Dr. Paige Huber Reichert, Senior Medical Director of Quality WellCare Elizabeth Goodman, Vice President of Public Policy and Government Affairs Healthfirst Joyce Chan, Assistant Vice President of Clinical Performance Gateway Health Dr. Austin Ifedirah, Vice President, Medicare & Strategic Planning BCBS Minnesota & Blue Plus Dr. Dave Pautz, Senior Medical Director, Government Programs Health Care Services Corporation (HCSC) Susan Sommers, Divisional Vice President, Medicaid Programs 85 Special Needs Plan (SNP) Alliance Richard Bringewatt, President, National Health Policy Group and President Medicaid Health Plans of America (MHPA) Jeffrey M Myers, President and CEO

86 INOVALON PROJECT TEAM Christie Teigland, Ph.D., Senior Director, Statistical Research Research Team Jon Bumbaugh, Senior Director, Research, Development & Analytics Ping Chen, M.D., MS, Health Services Researcher Jeanette Hunsberger, Statistician Barton Jones, MS, Health Data Scientist Karl Kilgore, Ph.D., Senior Health Economics & Outcomes Researcher Matthew McClellan, Health Data Scientist Sandhya Mehta, Ph.D., Health Economics Outcomes Researcher Alexis Parente, Ph.D., Health Services Researcher Zulkarnain Pulungan, Ph.D., Senior Health Economics & Outcomes Researcher 86 Clinical Team Shirley Doyle, Senior Director, Care Management Outcomes Reporting Dr. Antoine Kfuri, M.D., MPH, CMQ, Medical Director Dr. Paige Killian, Vice President, Clinical Analytics Dr. Eileen MacDonald, M.D., Medical Director

87 REFERENCES 1 Burstin, H., Nerenz, D. Quality Measures and Sociodemographic Risk Factors: To Adjust or Not to Adjust. Journal of the American Medical Association 2014; 312:24: The Impact of Dual Eligible Populations on CMS Five-Star Quality Measures and Member Outcomes in Medicare Advantage Health Plans, Inovalon Research Brief, October 30, Accessed at aspx on October 30, Partial Enrollment Data for AEP Show Small Gains, but Big Ones in High Star Plans, Medicare Advantage News, January 22, 2015; 21:1. 4 Booske, B., Athens, J., Kindig, D., Park, H., Remington, P. Different perspectives for assigning weights to determinants of health, County Health Rankings Working Paper, University of Wisconsin Population Health Institute, February The Future of the Public s Health in the 21st Century, Institute of Medicine, County Health Rankings & Roadmaps, University of Wisconsin Population Health Institute. Accessed at on October 10, NQF Draft Report: Risk Adjustment for Socioeconomic Status or Other Sociodemographic Factors, March Burstin, H., Nerenz, D. Quality Measures and Sociodemographic Risk Factors: To Adjust or Not to Adjust. Journal of the American Medical Association 2014; 312:24: Request for Information Data on Differences in Medicare Advantage (MA) and Part D Star Rating Quality Measurements for Dual-Eligible versus Non-Dual- Eligible Enrollees, Centers for Medicare and Medicaid Services (CMS), September 9, Accessed at PrescriptionDrugCovGenIn/Downloads/Request-for-Information-About-the- Impact-of-Dual-Eligibles-on-Plan-Performance.pdf on 10/15/ Ibid. 11 An Investigation of Medicare Advantage Dual Eligible Member-Level Performance on CMS Five-Star Quality Measures, Part 1: Member-Level Analysis. Accessed at on January 5, Pickett, K., Pearl, M. Multilevel analyses of neighbourhood socioeconomic context and health outcomes: a critical review. J Epidemiol Comm Healt 2001; 55:

88 13 Acxiom Corporation (2014). ZIP+4 InfoBase Geo Files: Demographic, Financial and Property, Sept 2013 release; Market Indices ACS, Feb 2014 release. Acxiom Corporation Area Health Resources Files (AHRF) US Department of Health and Human Services, Health Resources and Services Administration, Bureau of Health Professions, Rockville, MD. 15 Feaster, D., Brincks, A., Robbins, M., Szapocznik, J. Multilevel models to identify contextual effects on individual group member outcomes: a family example. Fam Process. 2011; 50: Snijders, T., Bosker, R. Multilevel Analysis: An Introduction to Basic and Advanced Multilevel Modeling. Sage Publishers: Blinder, Alan S Wage Discrimination: Reduced Form and Structural Estimates. Journal of Human Resources 8 (4): Oaxaca, Ronald L Male-Female Wage Differentials in Urban Labor Markets. International Economic Review 14 (3): Dubowitz, T., Heron, M., Basturo-Davila, R., Bird, C.E., Lurie, N., Escarce, J.J Racial/ethnic Differences in U.S. Health Behaviors: A Decomposition Analysis. Am J Health Behav 35(3): Holmes, G.H., Freburger, J.K., Ku, L.E. Decomposing Racial and Ethnic Disparities in the Use of Post-Acute Rehabilitation Care. Health Services Research. 2012; 47(3): Guarnizo-Herreno, C.C., Wehby, G. Explaining Racial/Ethnic Disparities in Children s Dental Health: A Decomposition Analysis. American Journal of Public Health 2012 (102)(5): Sen, B. Using the Oaxaca Blinder Decomposition as an Empirical Tool to Analyze Racial Disparities in Obesity. Obesity (2014) 22, Langellier, B.A., Chen, J., Vargas-Bustamante, A., Inkelas, M., Ortega, A.N. Understanding Health-Care Access and Utilization Disparities Among Latino Children in the United States. J Child Health Care , first published on November 13, Charlson, M.E., Pompei, P., Ales, K.L., MacKenzie, C.R. A new method of classifying prognostic comorbidity in longitudinal studies: Development and validation. J Chron Dis 1987;40:

89 25 The Healthcare Effectiveness Data and Information Set (HEDIS) 2015 Volume 2: Technical Specifications for Health Plans. National Committee for Quality Assurance (NCQA). 26 Neumark, D Employers Discriminatory Behavior and the Estimation of Wage Discrimination. Journal of Human Resources 23(3): Yun, Myeong-Su. (2008). Identification problem and detailed Oaxaca decomposition: A general solution and inference. Journal of Economic and Social Management 33 (1): Dubowitz, T., Heron, M., Basturo-Davila, R., Bird, C.E., Lurie, N., Escarce, J.J Racial/ethnic Differences in U.S. Health Behaviors: A Decomposition Analysis. Am J Health Behav 35(3): Fairlie, Robert W An extension of the Blinder-Oaxaca decomposition technique to logit and probit models. Journal of Economic and Social Measurement 30: All charts and graphs included in this report were created by Inovalon. 89

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