Regions With Higher Medicare Part D Spending Show Better Drug Adherence, But Not Lower Medicare Costs For Two Diseases

doi: 10.1377/hlthaff.2011.0727 HEALTH AFFAIRS 32, NO. 1 (2013): 120 126 2013 Project HOPE The People-to-People Health Foundation, Inc. By Bruce Stuart, J. Samantha Shoemaker, Mingliang Dai, and Amy J. Davidoff Regions With Higher Medicare Part D Spending Show Better Drug Adherence, But Not Lower Medicare Costs For Two Diseases Bruce Stuart is the Parke- Davis Chair in Geriatric Pharmacotherapy, Department of Pharmaceutical Health Services Research, School of Pharmacy, at the University of Maryland, Baltimore. J. Samantha Shoemaker is director of policy and research for Pharmaceutical Research and Manufacturers of America, in Washington, D.C. Mingliang Dai (mingdail@ umbc.edu) is a doctoral student in gerontology at the University of Maryland, Baltimore County, in Baltimore. Amy J. Davidoff is a senior economist in the Center for Financing, Access, and Cost Trends, Agency for Healthcare Research and Quality, in Rockville, Maryland. ABSTRACT A quarter-century of research on geographic variation in Medicare costs has failed to find any positive association between high spending and better health outcomes. We conducted this study using a 5 percent random sample of Medicare beneficiaries with diabetes or heart failure in 2006 and 2007 to see whether there was any correlation between geographic variation in Part D spending and good medicationtaking behavior and, if so, whether that correlation resulted in reduced Medicare Parts A and B spending on diabetes and heart failure treatments. We found that beneficiaries residing in areas characterized by higher adjusted drug spending had significantly more therapy days days with recommended medications on hand than did beneficiaries in lower-spending areas. However, we did not find that this factor translated into short-term savings in Medicare treatment costs for these two diseases. This result might not be surprising, since returns from medication adherence can take years to manifest. At the same time, discovering which regional factors are responsible for differences in drug spending and medication practices should be a high priority. If the observed differences are related to poor physician communication or lack of good care coordination, then appropriately designed policy tools including accountable care organizations, medical homes, and provider quality reporting initiatives might help address them. A quarter-century of research on geographic variation in Medicare costs has failed to find any positive association between high spending and better health outcomes, even after differences in regional costs and beneficiaries demographic characteristics and health status are controlled for. 1 3 Recent research has found substantial geographic variation in Part D drug spending that also cannot be explained by differences in medical costs or beneficiaries characteristics. 4,5 Work by Yuting Zhang and colleagues 6,7 concluded that high adjusted Part D spending does not contribute to better outcomes, as measured either by reduced use of traditional Medicare services or by improved quality indicators related to high risk and harmful medication use. However, it has yet to be determined whether variation in Part D spending is associated with differences in evidence-based practice in treating specific chronic diseases. Prescription drugs are the mainstay in treating conditions such as diabetes and heart failure. Thus, one might expect to see stronger associations between drug spending and good medication practices for beneficiaries with these conditions than for the Medicare population as a whole. In particular, one might expect to find a positive relationship between drug spending and good 120 Health Affairs JANUARY 2013 32:1

medication adherence, for the simple reason that higher compliance rates with guidelinerecommended therapies will raise drug spending. However, because adherence behavior may vary across therapeutic categories, drug use for a given medication may or may not correlate strongly with overall drug spending. One might also expect to see an inverse relationship between better guideline-directed medication utilization patterns and spending on Parts A and B Medicare services used to treat these conditions, since secondary prevention in the treatment of diabetes and heart failure reduces the risk of costly complications. These relationships have been documented at the patient level, 8 11 but they have yet to be explored at the regional level. If systematic regional differences do exist, then policy makers can use this information to better target interventions designed to improve the quality and efficiency of Medicare service delivery. Study Methods And Data Data All data for the study were based on a 5 percent random sample of the Medicare population from the Chronic Condition Data Warehouse for 2006 and 2007. 12 The Chronic Condition Data Warehouse is a Centers for Medicare and Medicaid Services database designed to make Medicare, Medicaid, and Part D prescription drug data more readily available to support research to improve the quality of care and reduce costs. To test the hypothesis that chronic medication use varies regionally, we examined the behavior of Medicare beneficiaries diagnosed with diabetes and heart failure. We selected the diabetes cohort from Chronic Condition Data Warehouse indicators that identify first date of a diabetes diagnosis (going back to 1999) based on the presence of International Classification of Diseases, Ninth Revision (ICD-9), codes 250.00 93, 357.2, 362.01, 362.02, or 366.41 on at least one inpatient or two outpatient or carrier claims. To ensure that all subjects had prevalent diabetes, we selected only those with a first diagnosis prior to January 1, 2006. In like fashion, the heart failure cohort included beneficiaries with Chronic Condition Data Warehouse indicators for heart failure based on ICD-9 codes 398.91, 402.xx, and 428.xx on Medicare claims prior to January 1, 2006. Both samples were restricted to fee-forservice beneficiaries who survived through December 31, 2007, and who maintained continuous coverage for Parts A, B, and D. Drug use was captured from the Chronic Condition Data Warehouse Part D Prescription Drug Event files for each study subject. Analysis The analysis involved five steps. First, using ZIP code of residence, we assigned each beneficiary to a geographic region either a Metropolitan Statistical Area or a rest-of-state area replicating procedures used in the 2011 report to Congress on regional variation in Medicare service use prepared by the Medicare Payment Advisory Commission. 13 Metropolitan Statistical Areas with fewer than thirty beneficiaries were excluded to eliminate potentially unstable estimates. The total number of beneficiaries assigned was 133,904 for diabetes in 388 regions and 103,045 for heart failure in 367 regions. Second, we computed monthly expenditures for Part D drugs for 2006 and 2007 from the Prescription Drug Event files for each beneficiary and used those measures to compute regional means. Expenditures were based on total payments to pharmacies including plan payments and patient cost sharing. As in the MedPAC report, 13 we adjusted individual-level spending values for age, sex, Social Security Disability Insurance status, Medicaid dual eligibility, and the Part D risk adjuster for prescription drug hierarchical coexisting conditions. There was little regional variation in Part D drug prices, 6 so we did not control for that factor. Third, we computed mean utilization measures for guideline-recommended medications for beneficiaries in each region. For the diabetes sample, we followed guidelines developed by the American Diabetes Association 14 and the American Geriatrics Society. 15 The drugs selected for review were oral antidiabetic agents (metformin, sulfonylureas, thiazolidinediones, alpha-glucosidase inhibitors, meglitinides, and sitagliptan), angiotensin-converting enzyme inhibitors or angiotensin receptor blockers, and antihyperlipidemic agents (statins, bile acid sequestrants, fibrates, ezetimibe, and niacin). For heart failure, we used guidelines developed by the American College of Cardiology/ American Heart Association Task Force on Practice Guidelines. 16 These guidelines list many medications appropriate for treating heart failure. However, we limited our selection to angiotensin-converting enzyme inhibitors, angiotensin receptor blockers, and beta-blockers, which are nearly universally recommended treatments. Because the individual drugs within each class may be considered substitutes for each other, we defined take-up as one or more fills for any drug within a class. Our measure of adherence was total therapy days with all drugs in each class over the two-year study period. Because our sample was restricted to survivors, therapy days can be divided by 730 days to find the percentage of time with drug availability in each class. Both JANUARY 2013 32:1 Health Affairs 121

Exhibit 1 Regions With Highest And Lowest Adjusted Part D Drug Spending For Beneficiaries With Diabetes, 2006 07 Highest-spending regions Region Mean adjusted monthly Lowest-spending regions Region Elmira, NY 510 Flagstaff, AZ 224 Boulder, CO 506 Farmington, NM 224 Sheboygan, WI 493 Albuquerque, NM 241 Williamsport, PA 492 Gulfport-Biloxi, MS 253 La Crosse, WI 486 Brownsville- Harlingen, TX 259 Kingston, NY 477 Laredo, TX 259 Johnstown, PA 477 NM, rest of state 263 Appleton, WI 474 McAllen-Edinburg- Mission, TX 265 Grand Forks, ND 472 Lubbock, TX 266 Oshkosh-Neenah, WI 471 AZ, rest of state 269 Mean adjusted monthly SOURCE Authors analysis of 5 percent random sample of the Medicare population from the Medicare Chronic Condition Data Warehouse. NOTE Drug spending was adjusted for age, sex, Social Security Disability Insurance status, Medicaid dual eligibility, and the Part D risk adjuster for prescription drug hierarchical coexisting conditions. drug utilization measures were adjusted using the same regression-based procedures employed in adjusting drug spending. Fourth, to determine whether there was any evidence of cost offset from drug spending, we calculated mean monthly regional Medicare spending for services relating to the treatment of diabetes and heart failure for the beneficiaries in our two disease cohorts. We included all Parts A and B services with diagnostic codes (any position) for the two diseases using the same adjustment procedures as for drug spending and drug use. Fifth, we grouped the adjusted regional drug spending results for the two disease cohorts into ascending deciles and computed the mean monthly spending per decile. Mean regional values for drug use and Medicare spending were also computed for each drug spending decile. We used correlation analysis to test for systematic associations between the regional values for drug spending, drug use, and Medicare spending. Study Results Exhibit 1 shows the ten regions with the highest adjusted monthly Part D drug spending for the diabetes sample, and the ten regions with the lowest spending. Mean spending in the ten highest-spending regions was almost double that in the lowest-spending areas. There were also striking regional patterns. All of the ten lowest-spending areas were located in southern states, and all of the ten highest-spending areas were in northern or central states. Indeed, forty-three of the fifty lowest-cost regions were south of the thirty-seventh parallel (a line extending roughly from the Virginia North Carolina border to central California), and forty-three of the fifty highest-cost regions were above that line (data not shown). Similar patterns were found for the heart failure sample. Exhibits 2 and 3 summarize our findings regarding the relationship between Part D drug spending, use of guideline-recommended medications, and Medicare Parts A and B spending for diabetes and heart failure. Regional assignments to spending deciles were virtually identical for the diabetes and heart failure samples. Beneficiaries with diabetes residing in regions with the lowest risk-adjusted drug spending levels (decile 1) averaged $285 dollars per month on Part D services, compared to $294 for beneficiaries with heart failure. In regions with the highest drug spending, beneficiaries with diabetes averaged $453 per month on Part D services just $3 above the average for those with heart failure. We found no consistent association between probability of take-up of guideline-recommended medications and drug spending by region. There were small significant negative correlations of 0.10 (for diabetes) and 0.13 (for heart failure) between regional drug spending and take-up of angiotensin-converting enzyme inhibitors or angiotensin receptor blockers, but no association for the other drug classes. The percentage of beneficiaries with diabetes who filled prescriptions for guideline-recommended medications differed across the deciles by no more than 2 percent. In the case of beneficiaries with heart failure, the difference did not exceed 4 percent. However, there were significant positive correlations between regional drug spending and mean therapy days for each drug class, ranging from 0.26 (for patients with heart failure using angiotensin-converting enzyme inhibitors or angiotensin receptor blockers) to 0.48 (for patients with diabetes using antihyperlipidemics). Patients with diabetes in the regions with the highest drug spending received fifty-four more therapy days with oral antidiabetic drugs over two years, compared to those in the regions with the lowest spending. The difference was fortytwo days for patients using angiotensin-converting enzyme inhibitors or angiotensin receptor blockers, and sixty-eight days for antihyperlipidemic users. For patients with heart failure, the difference was forty-two days with betablockers and thirty days with angiotensin- 122 Health Affairs JANUARY 2013 32:1

Exhibit 2 Mean Adjusted Values For Monthly Part D Drug Spending, Drug Utilization, And Medicare Spending For Beneficiaries With Diabetes, By Decile Of Spending, 2006 07 Monthly drug Oral antidiabetic drugs ACE inhibitors/arbs Antihyperlipidemic agents Monthly Take-up Mean days Take-up Mean days Take-up Mean days Decile (%) therapy (%) therapy (%) therapy All deciles 368 62 513 70 503 66 482 540 Medicare spending on diabetes ($) 1 285 61 483 70 471 64 441 533 2 320 a 62 505 a 70 494 a 64 468 a 548 3 334 a 61 507 a 72 a 500 a 67 a 469 a 556 4 350 a 62 512 a 70 503 a 65 476 a 550 5 363 a 62 504 a 70 502 a 67 a 479 a 539 6 374 a 63 a 517 a 70 501 a 67 a 491 a 544 7 385 a 62 524 a 69 515 a 65 495 a 530 8 396 a 62 527 a 69 509 a 65 487 a 545 9 417 a 63 a 519 a 70 516 a 68 a 499 a 543 10 453 a 63 537 a 68 513 a 65 509 a 510 Decile 10 decile 1 168 a 2 54 a 2 42 a 1 68 a 23 Correlation with drug spending b 0.08 0.42 a 0.10 a 0.38 a 0.09 0.48 a 0.07 SOURCE Authors analysis of 5 percent random sample of the Medicare population from the Medicare Chronic Condition Data Warehouse. NOTES Spending and drug utilization measures were adjusted for age, sex, Social Security Disability Insurance status, Medicaid dual eligibility, and the Part D risk adjuster for prescription drug hierarchical coexisting conditions. Assignment to 388 Metropolitan Statistical Areas and rest-of-state regions based on the rank of adjusted mean monthly drug spending for beneficiaries residing in each region. ACE is angiotensin-converting enzyme. ARB is angiotensin receptor blocker. a Value differs from decile 1 value; p < 0:05. b Not applicable. Exhibit 3 Mean Adjusted Values For Monthly Part D Drug Spending, Drug Utilization, And Medicare Spending For Beneficiaries With Heart Failure, By Decile Of Spending, 2006 07 Monthly drug Beta-blockers ACE inhibitors/arbs Take-up Mean days Take-up Mean days Decile (%) therapy (%) therapy All deciles 368 57 492 65 493 405 1 294 55 464 67 475 432 2 323 a 58 a 480 a 66 481 422 3 338 a 57 483 a 65 484 387 a 4 350 a 56 491 a 67 490 a 440 5 361 a 58 a 486 a 65 487 a 429 6 373 a 60 a 495 a 64 a 492 a 345 a 7 385 a 58 504 a 66 502 a 420 8 396 a 58 a 511 a 66 510 a 397 9 410 a 55 496 a 63 a 499 a 407 10 450 a 58 506 a 63 a 505 a 369 a Decile 10 decile 1 156 a 3 42 a 4 a 30 a 63 a Correlation with drug spending b 0.04 0.29 a 0.13 a 0.26 a 0.10 Monthly Medicare spending on heart failure ($) SOURCE Authors analysis of 5 percent random sample of the Medicare population from the Medicare Chronic Condition Data Warehouse. NOTES Spending and drug utilization measures were adjusted for age, sex, Social Security Disability Insurance status, Medicaid dual eligibility, and the Part D risk adjuster for prescription drug hierarchical coexisting conditions. Assignment to 388 Metropolitan Statistical Areas and rest-of-state regions based on the rank of adjusted mean monthly drug spending for beneficiaries residing in each region. ACE is angiotensin-converting enzyme. ARB is angiotensin receptor blocker. a Value differs from decile 1 value; p < 0:05. b Not applicable. JANUARY 2013 32:1 Health Affairs 123

converting enzyme inhibitors or angiotensin receptor blockers. Exhibits 2 and 3 also show mean adjusted monthly spending amounts for Medicare services used to treat diabetes and heart failure in the spending deciles. There were negative correlations between Part D and Medicare spending across all regions for both diseases, but neither correlation was significant. Beneficiaries in the decile with the highest drug spending did have lower Medicare spending compared to beneficiaries in the lowestspending decile, and for patients with heart failure, the difference was $63 and was significant. However, the extent of the cost offset was small. Discussion And Implications We found mixed results in our assessment of whether the use of medications recommended in diabetes and heart failure guidelines varied systematically between regions with high and low spending on Part D. On the one hand, there was no consistent evidence that take-up of evidence-based therapies was related to regional drug spending. Overall, take-up rates varied between 55 percent and 72 percent, which are much lower than recommended in guidelines for these two diseases. We are not the first to report poor levels of exposure to critical medications within the Medicare population. However, our findings underscore the fact that access to drugs under Part D is no guarantee that evidence-based drug therapy will be initiated. On the other hand, we found strong associations between spending and therapy days among users of these drugs. Beneficiaries residing in areas characterized by higher adjusted drug spending had significantly more days with recommended medications than did beneficiaries in lower-spending areas. The differences were relatively modest in magnitude at the drug class level between 7 percent and 13 percent more therapy days in regions in the highest-spending decile compared to those in the lowest decile, or roughly one to two months supply of medications over a two-year period (data not shown). But because most patients with diabetes and heart failure take multiple medications, the impact of region on aggregate days of therapy can be substantial. We did not find any strong evidence for Medicare savings from higher Part D spending. The direction of the association was negative for both diabetes and heart failure, but the magnitudes were small and not significant. Care must be taken in deriving causal inferences about individual behavior, given that our data are aggregated at the level of metropolitan and rest-of-state areas. Nonetheless, there are several plausible conjectures behind these findings. First, the combination of both a strong positive association between drug spending and therapy days and the lack of any consistent association between spending and drug takeup rates would seem to point to differences in patient behavior rather than physician practice patterns, given that physicians have much greater control over who gets prescribed medications than over how well they are taken. Nonetheless, there is solid evidence that physician communication is an important influence on patient compliance with treatment, 17 so lack of consistent physician messages regarding the importance of medication adherence may also be a factor. Second, the consistent positive relationship between therapy days and drug spending strongly suggests that patient adherence is a major contributor to regional differences in adjusted drug spending. However, adherence to medication regimens has yet to translate into cost savings for Medicare. Finally, the consistency in findings across two disease states and multiple medications suggests the presence of important underlying regional factors affecting patients behavior in treating chronic disease. Our purpose was not to identify these factors, but rather to demonstrate the importance of geographic region as a focal point for understanding differences in the pharmacological management of chronic disease in the Medicare population. Discovering which regional factors are responsible for differences in medication practices should be a high priority for future research. In particular, it will be important to determine if poor physician communication and care coordination are responsible for the observed differences, as these shortcomings can be alleviated though appropriately designed policy tools including accountable care organizations, medical homes, and provider quality reporting initiatives. Conclusion We conducted this study to see whether there was any correlation between geographic variation in Part D spending and good medicationtaking behavior, as measured by take-up and therapy days with evidence-based drugs. Consistent with prior work in this area, our study measured drug use via drug claims. Thus, we cannot be certain that all filled prescriptions were taken as directed. In contrast to prior work, our study restricted the analysis to disease states 124 Health Affairs JANUARY 2013 32:1

(diabetes and heart failure) in which guidelinerecommended medications are known to reduce risk of disease progression and can potentially reduce spending on traditional Medicare services. We found that the number of therapy days for every drug investigated was highest in regions with the highest Part D spending. We did not find that higher adherence translated into immediate savings in Medicare treatment costs for these diseases. That result may not be surprising, given that Part D was implemented in 2006, the year our study period began, and that the returns from medication adherence can take years to manifest. This is a prime area for future research. The results were presented at the AcademyHealth Annual Research Meeting, Seattle, Washington, June 11 14, 2011. The authors acknowledge Shinobu Suzuki for contributing to this research. This study was supported by a grant from the Commonwealth Fund. The funder had no role in the study design, data collection, analysis, or preparation of the manuscript. All authors contributed to the study design, data interpretation, and writing of the manuscript. This article was prepared while Samantha Shoemaker was a doctoral student at and Amy Davidoff was employed at the University of Maryland School of Pharmacy. The views and opinions expressed in this article are the authors own and do not necessarily reflect the views of Pharmaceutical Research and Manufacturers of America, the Agency for Healthcare Research and Quality, the Department of Health and Human Services, or the US government. The authors report no conflicts of interest regarding the contents of this article. NOTES 1 Gornick M. Trends and regional variations in hospital care under Medicare. Health Care Financ Rev. 1982;3:41 73. 2 Fisher ES, Wennberg DE, Stukel TA, Gottlieb MS, Lucas FL, Pinder EL. The implications of regional variation in Medicare spending. Part 1: the content, quality and accessibility of care. N Engl J Med. 2003;138(4): 273 87. 3 Fisher ES, Wennberg DE, Stukel TA, Gottlieb MS, Lucas FL, Pinder EL. The implications of regional variation in Medicare spending. Part 2: health outcomes and satisfaction with care. N Engl J Med. 2003; 138(4):288 99. 4 MaCurdy T, Gibbs J, Theobald N, DeLeire T, Kautz T, O Brien-Strain M. Geographic variation in drug prices and spending in the Part D program [Internet]. Burlingame (CA): Acumen; 2009 Aug [cited 2012 Nov 28]. (Centers for Medicare and Medicaid Services Contract No. HHSM-500-206-00006I, T.O. 2). Available from: http://www.cms.gov/research-statistics-data-and- Systems/Statistics-Trends-and- Reports/Reports/downloads/ MaCurdy_RxGeoPrice_Tech Report_2009.pdf 5 Medicare Payment Advisory Commission. Status report on Part D. Chapter 13 in: Report to the Congress: Medicare Payment Policy. Washington (DC): MedPAC; 2011 Mar. p. 317 40. 6 Zhang Y, Baicker K, Newhouse JP. Geographic variation in Medicare drug spending. N Engl J Med. 2010; 363(5):405 9. 7 Zhang Y, Baicker K, Newhouse JP. Geographic variation in the quality of prescribing. N Engl J Med. 2010; 363(21):1985 8. 8 Balkrishnan R, Rajagopalan R, Camacho FT, Huston SA, Murray FT, Anderson RT. Predictors of medication adherence and associated health care costs in an older adult population with type 2 diabetes mellitus: a longitudinal cohort study. Clin Ther. 2003;25(11):2958 71. 9 Rosen AB, Hamel MB, Weinstein MC, Cutler DM, Fendrick AM, Vijan S. Cost-effectiveness of full Medicare coverage of angiotensin-converting enzyme inhibitors for beneficiaries with diabetes. Ann Intern Med. 2005;143:89 99. 10 Sokol MC, McGuigan KA, Verbrugge RR, Epstein RS. Impact of medication adherence on hospital risk and healthcare cost. Med Care. 2005; 43(6):521 30. 11 Stuart B, Davidoff A, Lopert R, Shaffer T, Shoemaker JS, Lloyd J. Does medication adherence lower Medicare spending among beneficiaries with diabetes? Health Serv Res. 2011;46(4):1180 99. 12 Buccaneer. Chronic Condition Data Warehouse: user guide; version 1.8 [Internet]. Warrenton (VA): Buccaneer; 2011 Sep [cited 2012 Dec 7]. Available from: http:// www.ccwdata.org/cs/groups/ public/documents/document/ccw_ userguide.pdf 13 Medicare Payment Advisory Commission. Report to the Congress: measuring regional variation in service use. Washington (DC): MedPAC; 2011 Jan. 14 American Diabetes Association. Standards of medical care in diabetes 2008. Diabetes Care. 2008;31(Suppl 1):S12 54. 15 Brown AF, Mangione CM, Saliba D, Sarkisian CA, California Healthcare Foundation/American Geriatrics Society Panel on Improving Care for Elders with Diabetes. Guidelines for improving the care of the older person with diabetes mellitus. J Am Geriatric Soc. 2003;51:S265 80. 16 Hunt SA, Abraham WT, Chin MH, Feldman AM, Francis GS, Ganiats TF, et al. ACC/AHA 2005 Guideline Update for the Diagnosis and Management of Chronic Heart Failure in the Adult: a report of the American College of Cardiology/ American Heart Association Task Force on Practice Guidelines (Writing Committee to Update the 2001 Guidelines for the Evaluation and Management of Heart Failure): developed in collaboration with the American College of Chest Physicians and the International Society for Heart and Lung Transplantation: endorsed by the Heart Rhythm Society. Circulation. 2005;112(12):e154 235. 17 Zolnierek KB, DiMatteo MR. Physician communication and patient adherence to treatment: a metaanalysis. Med Care. 2009;47:826 34. JANUARY 2013 32:1 Health Affairs 125

ABOUT THE AUTHORS: BRUCE STUART, J. SAMANTHA SHOEMAKER, MINGLIANG DAI & AMY J. DAVIDOFF Bruce Stuart is the Parke-Davis Chair in Geriatric Pharmacotherapy at the University of Maryland, Baltimore. In this month s Health Affairs, Bruce Stuart and coauthors report on their study of whether geographic variations in Medicare Part D spending were linked to good medication-taking behavior and, if so, whether that correlation resulted in reduced Medicare spending in treating diabetes and heart failure. They did find that beneficiaries in areas characterized by higher adjusted drug spending had significantly more therapy days days with recommended medications on hand than did beneficiaries in lower-spending areas, but they found no link to any short-term savings in Medicare treatment costs for these two diseases. The authors say it will be useful to understand the factors explaining regional differences in drug spending and medication practices, since these could be remedied if they were related to poor physician communication or lack of good patient care coordination. Stuart is a professor and the Parke-Davis Chair in Geriatric Pharmacotherapy in the Department of Pharmaceutical Health Services Research, School of Pharmacy, and executive director of the Peter Lamy Center for Drug Therapy and Aging at the University of Maryland, Baltimore. An economist, Stuart focuses his current research on evaluating eligibility criteria for medication therapy management services offered by Part D plans, the Medicare prescription drug benefit, and the impact of Part D cost sharing on medication adherence and Medicare spending. He earned amaster s degreeandadoctorate in economics from Washington State University. J. Samantha Shoemaker is director of policy and research for Pharmaceutical Research and Manufacturers of America. Samantha Shoemaker is director of policy and research for Pharmaceutical Research and Manufacturers of America. She has conducted research on the impact of Part D on previously uninsured Medicare beneficiaries with hypertension; the impact of benefit design,costsharing,and utilization management in Part D; and supplemental medical and drug insurance and cancer-related spending. She earned a doctorate in pharmaceutical health services research from the University of Maryland School of Pharmacy. Mingliang Dai is a doctoral student in gerontology at the University of Maryland, Baltimore County. Mingliang Dai is a doctoral student in gerontology at the University of Maryland, Baltimore County. His research interests include supportive housing policies and options for older adults and postretirement economic expectations, planning, and activities among baby boomers. Dai has helped with research on predictors of functional recovery for older hip fracture patients with dementia, an evaluation of Maryland Medicaid programs, and theimpactofpartddrugusage and adherence on various health outcomes. He earned a master s degree in social gerontology from the University of Central Missouri. Amy J. Davidoff is asenioreconomist at the Agency for Healthcare Research and Quality. Amy Davidoff is a senior economist in the Center for Financing, Access, and Cost Trends at the Agency for Healthcare Research and Quality. Her research has examined how public policy affects the availability and cost of private insurance; eligibility and participation in public insurance; and the impacts of insurance and benefitdesignonaccesstocare, use of services, health care spending, and health outcomes. Since 2009 she has focused on Medicare beneficiaries with cancer. She was the principal investigator on an American Cancer Society grant to study the relationship between supplemental insurance in the Medicare population and the economic burden of cancer. She also directed or participated in two National Cancer Institute funded projects related to comparative effectiveness. Davidoff earned a master s degree in health policy and management from the University of Massachusetts and a doctorate in public health economics from the Johns Hopkins University. 126 Health Affairs JANUARY 2013 32:1