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Comparative Effectiveness Research and Formulary Placement: The Case of Diabetes
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Comparative Effectiveness Research and Formulary Placement: The Case of Diabetes

Michael E. Chernew, PhD; Rick McKellar, BS; Wade Aubry, MD; Roy Beck, MD, PhD; Joshua Benner, PharmD, ScD; Jan E. Berger, MD, MJ; A. Mark Fendrick, MD; Felicia Forma, BSc; Dana Goldman, PhD; Anne Peters, MD; Rebecca Killion, MA; Darius Lakdawalla, PhD; Douglas K. Owens, MD; and Joe Stahl, MA
Formularies of the future should use evidence-produced CER to better target, not limit, diabetes care.
In incorporating CER into formularies a few basic principles should be considered. First, CER should take a broad perspective. Notably, many CER studies will not compare one drug against another, but instead will compare one broad treatment strategy (eg, drug treatment) against another broad strategy (eg, surgery). In fact the Institute of Medicine CER priority list generally focused on these broad strategies as opposed to drug versus drug studies.13 Formulary placement should not be based only on drug versus drug information. For example, the Clinical Outcomes Utilizing Revascularization and Aggressive Drug Evaluation (COURAGE) trial demonstrated that outcomes from stents were no better than those with medical management.14 Yet, use of drugs did not change with these findings, suggesting the need for improvement in the transition from research to implementation. Second, CER should incorporate all outcomes, not just those directly related to the service. This includes the possible reduction in use of other services associated with greater medication adherence or disease management. While CER may not include economic outcomes (existing rules prohibit PCORI-funded CER from examining cost), assessment of value, and thus formulary placement, does require some attention to cost. Previous work has demonstrated that the spending offset associated with better drug adherence may be significant. For instance, a Medicare policy change in California in 2002 that raised office visit and drug copayments by $5 to $20 was associated with a 6% greater likelihood of being hospitalized.5 This greater expenditure on inpatient hospitalization offset around 20% of the savings associated with increased patient cost sharing. Similar research on the impact of Medicare Part D shows that increased medication coverage was associated with fewer hospitalizations,15 and that for groups with the greatest change in benefit generosity, the increased expenditure on drugs was totally offset by a reduction in medical expenditure.16 While these studies provide useful analysis for several types of medical expenditure, offsets should be even broader to include non-medical components such as productivity in the workplace. Better chronic care management may reduce disability and absenteeism. Currently, data limitations often prevent such analysis. Advocating greater investment in building such data is crucial to the next stages of CER and cost containment.

A third principle to guide CER-related formulary placement is recognition that the goal of the healthcare system is not to save money, but to improve health. Similarly, CER-guided formulary placement should aim to encourage high-value uses, which will often increase drug and even total expenditures. That said, the system must be fiscally sustainable, which is a far cry from today’s reality. CER can help achieve financial sustainability by allowing targeting of populations that will benefit from specific products and by identifying low-value services. Strategies that finance favorable formulary placement of high value by spreading the cost across other services can improve the efficiency of the system.

Formulary design traditionally has focused on saving money through cost-sharing tiers that differentiate medications with therapeutically similar effects. Over time, the number of tiers and associated cost-sharing amounts have increased, and there is reason for concern that these growing costs may increasingly impact patient adherence and adversely affect health outcomes. Historically, the decisions have been made largely on the basis of the cost of medications across a range of therapeutically similar alternatives, without much consideration of the clinical benefit achieved in one disease area versus another, one disease sub-class versus another, or even in one patient type versus another. Without a strong investment in CER that enables greater sophistication in formulary structure, patients are more likely to face “across the board” increases in cost sharing. That trend makes it increasingly likely that the unintended consequences of discouraging appropriate management of chronic disease will grow. In the case of diabetes—partially due to the fact that, in many cases, the management of hyperglycemia, hypertension, and hypercholesterolemia require more than 1 medication—suboptimal management (ie, lack of adherence due to cost-related issues) may lead to expensive complications. CER can provide the knowledge base necessary to add clinical nuance to formularies—possibly by distinguishing across disease states (eg, insulin vs acne), within disease drug classes (eg, insulin vs DPP-4s), and within drug uses (eg, Januvia first vs fourth line)—in addition to distinguishing across a class of similar products (eg, generic vs brand vs non-preferred brand), which has been the main focus of formulary management to date. Cost-containment efforts that rely on an improved evidence base are probably preferable to current efforts to drive all practice toward those of the lowest cost. Thus, formularies of the future can use findings from CER is to better target, not limit, care.

Author Affiliations: From Department of Health Care Policy, Harvard Medical School (MEC, RM), Boston, MA; Stanford University School of Medicine (DKO), Stanford, CA; Philip R. Lee Institute for Health Policy Studies (WA), University of California San Francisco, San Francisco, CA; Jaeb Center for Health Research (RB), Tampa, FL; RxAnte Inc (JB), McLean, VA; Health Intelligence Partners (JEB), Chicago, IL; Evidence Based Medicine, sanofi (FF), Bridgewater, NJ; University of Michigan (AMF), Ann Arbor, MI; University of Southern California (DG), Los Angeles, CA; McKenna Long and Aldridge (RK), Washington DC; University of Southern California (DL), Los Angeles, CA; Joe Stahl Consulting LLC (JS), Minneapolis, MN; USC Keck School of Medicine (AP), USC Westside Center for Diabetes Los Angeles, CA.

Funding Source: Funding was provided by sanofi.

Author Disclosures: Dr Chernew reports receiving consultancies from Peer Health Exchange. Dr Chernew, Dr Aubry, Dr Beck, Dr Benner, Dr Berger, Dr Peters, Ms Killion, Mr McKellar, Dr Owens, and Mr Stahl report receipt of payment from sanofi for involvement in the preparation of this manuscript. Dr Owens’ contribution to this publication was as a paid consultant to sanofi-US and was not part of his Stanford University duties or responsibilities. Dr Fendrick reports being a consultant for Abbott; ActiveHealth Management/Aetna; AstraZeneca; BlueCross BlueShield Association; Center for Medicare & Medicaid Services; Genentech; GlaxoSmithKline; Health Alliance Plan; Aon Hewitt; Highmark BlueCross BlueShield; Integrated Benefits Institue; MedImpact HeathCare Systems, Inc; Merck; National Business Coalition on Health; National Pharmacutical Council; Pfizer; POZEN, Inc; Regence BlueCross BlueShield of Oregon; sanofi-aventis Pharmaceuticals; Thomson Reuters; TriZetto; and zanzors. He also reports that he is a speaker for Merck and Pfizer; and receives research support from Abbott; AstraZeneca; Eli Lilly; Genentech; GlaxoSmithKline; Merck; Novartis; Pfizer; and sanofiaventis Pharmaceuticals. Drs Goldman and Lakdawalla are partners at Precision Health Economics, which received financial compensation from sanofi to support this work. Dr Goldman reports consulting income from sanofi, Novartis, Eli Lilly, Bristol Myers-Squibb, and Pfizer. Dr Peters reports board membership with ADA, and has received consultancies from Amylin, Lilly, Abbott, Takeda, sanofi, Roche, and Janssen. She also has grants pending with sanofi and has received lecture fees from Amylin, Lilly, and Novo Nordisk. Ms Forma reports no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

Authorship Information: Concept and design (MEC, RM, WA, RB, JB, JEB, FF, DG, AP, RK, DL, JS); acquisition of data (DG, AP); analysis and interpretation of data (WA, AMF, DG, AP); drafting of the manuscript (MEC, DKO, JB, AMF, DG, RK, RM, JS, AP); critical revision of the manuscript for important intellectual content (MEC, DKO, WA, RB, JB, JEB, AMF, FF, DG, RK, RM, DL, JS, AP); obtaining funding (FF); and administrative, technical, or logistic support (JEB, RM).

Address correspondence to: Michael E. Chernew, PhD, Department of Health Care Policy, Harvard Medical School, 180 Longwood Ave, Ste 207, Boston, MA 02115. E-mail:
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