Currently Viewing:
The American Journal of Managed Care March 2013
Rates of Guideline Adherence Among US Community Oncologists Treating NSCLC
Zhaohui Wang, MD, PhD; Inga Aksamit, RN, MBA; Lisa Tuscher, BA; and Kim Bergstrom, PharmD
Currently Reading
Effectiveness and Cost-Effectiveness of Diabetes Prevention Among Adherent Participants
William H. Herman, MD, MPH; Sharon L. Edelstein, ScM; Robert E. Ratner, MD; Maria G. Montez, RN, MSHP; Ronald T. Ackermann, MD, MPH; Trevor J. Orchard, MD; Mary A. Foulkes, PhD; Ping Zhang, PhD; Christopher D. Saudek, MD†; and Morton B. Brown, PhD; The Diabetes Prevention Program Research Group
Drug Adherence After Price Changes in a Previously Compliant Population
James J. Hill III, MD, MPH; Deron Galusha, MS; Martin D. Slade, MPH; and Mark R. Cullen, MD
Measuring Quality in the Early Years of Health Insurance Exchanges
Ledia M. Tabor, MPH; Phyllis Torda, MA; Sarah S. Thomas, MS; and Jennifer L. Zutz, MHSA
Multilevel Predictors of Colorectal Cancer Screening Use in California
Salma Shariff-Marco, PhD, MPH; Nancy Breen, PhD; David G. Stinchcomb, MS, MA; and Carrie N. Klabunde, PhD
Engaging Providers in Underserved Areas to Adopt Electronic Health Records
Cleo A. Samuel, BS; Jennifer King, PhD; Fadesola Adetosoye, MS; Leila Samy, MPH; and Michael F. Furukawa, PhD
Factors Associated With Primary Hip Arthroplasty After Hip Fracture
Ishveen Chopra, MS; Khalid M. Kamal, PhD; Jayashri Sankaranarayanan, MPharm, PhD; and Gibbs Kanyongo, PhD
Measuring Concurrent Oral Hypoglycemic and Antidepressant Adherence and Clinical Outcomes
Hillary R. Bogner, MD, MSCE; Heather F. de Vries, MSPH; Alison J. O'Donnell, BA; and Knashawn H. Morales, ScD
Computed Tomography Scan Use Variation: Patient, Hospital, and Geographic Factors
Eric A. Vance, PhD; Xiaojin Xie, MS; Andrew Henry, BS; Christian Wernz, PhD; and Anthony D. Slonim, MD, DrPH
Low Clinical Utility of Folate Determinations in Primary Care Setting
Shlomo Vinker, MD; Eli Krantman, MD; Michal Shani, MD; and Sasson Nakar, MD
Trends in Inpatient Hospital Prices, 2008 to 2010
Jeff Lemieux, MA; and Teresa Mulligan, MHSA

Effectiveness and Cost-Effectiveness of Diabetes Prevention Among Adherent Participants

William H. Herman, MD, MPH; Sharon L. Edelstein, ScM; Robert E. Ratner, MD; Maria G. Montez, RN, MSHP; Ronald T. Ackermann, MD, MPH; Trevor J. Orchard, MD; Mary A. Foulkes, PhD; Ping Zhang, PhD; Christopher D. Saudek, MD†; and Morton B. Brown, PhD; The Diabetes Prevention Program Research Group
Over 10 years, among adherent participants, lifestyle intervention and metformin were effective and cost-effective for diabetes prevention compared with placebo.
In these analyses, from a health system perspective and without discounting, lifestyle, DPP group lifestyle, and metformin were all cost saving relative to placebo. In our previous undiscounted intent-to-treat analysis, lifestyle cost approximately $6700 per QALY gained compared with placebo but both DPP group lifestyle and metformin were cost saving.2 In both this analysis and our previous intent-to-treat analysis, lifestyle was more expensive than metformin but produced greater health benefits.2 The undiscounted cost per QALY was $14,213 and $10,555, respectively.2 In these analyses, from a health system perspective and with both costs and QALYs discounted at 3%, neither lifestyle, DPP group lifestyle, nor metformin was cost saving. These differences likely reflect the impact of discounting on early treatment costs. In these analyses, we assumed that all participants randomized to lifestyle and metformin remained adherent during the first year. Because early treatment costs were greater, discounting resulted in the early, relatively expensive preventive interventions being less cost-effective.

The results of this 10-year within-trial analysis demonstrate that lifestyle and metformin interventions are even more effective for diabetes prevention in DPP/DPPOS participants who are adherent to their randomized treatments than among the larger group of both adherent and nonadherent participants. In addition, the interventions are extremely cost-effective or even cost saving. These results are consistent with earlier analyses that assessed the cost-effectiveness of lifestyle and metformin interventions based upon the results of the Finnish Diabetes Prevention Study,15 the DPP,16,17 the DPP/DPPOS,18 and the Indian Diabetes Prevention Study.19 One study which did not find lifestyle intervention to be costeffective20 differed from the published lifetime cost-utility analyses16-18 in that it assumed that the lifestyle intervention continued over the participants’ lifetimes even after they developed diabetes. It also assumed that when participants developed diabetes, their A1C remained <7.0% for the remainder of their lives. These assumptions led to potential overestimation of intervention costs and underestimation of the costs and quality-of-life impact of the complications and comorbidities of diabetes. Taken together, these assumptions likely account at least in part for the difference in results.

This analysis has a number of limitations. First, in defining participants as adherent to the lifestyle intervention, we used the outcome (weight loss) to define adherence. This was necessary because all lifestyle participants were strongly encouraged to attend lifestyle sessions and attendance was not a good marker of adoption of the behavioral intervention. Second, the simulated group lifestyle intervention was not empirically tested within the DPP. The decision to implement the lifestyle intervention individually within DPP was pragmatic. The study group was anxious to enroll participants and begin the interventions as quickly as possible. The literature suggests, however, that group-implemented lifestyle interventions are at least as effective as individually implemented interventions, largely due to the benefits of peer support. Third, we included all participants randomized to placebo in these analyses. During DPPOS, placebo participants were offered and participated in the group lifestyle intervention and 3% were prescribed metformin outside the study. If these interventions were effective in the placebo group, they would have reduced non–intervention- related resource utilization and costs. The relative impact of these potential biases is impossible to determine, but if the intervention costs were less than the savings resulting from a decreased incidence of diabetes, the bias would be conservative, making lifestyle and metformin appear less cost-effective relative to placebo.

In summary, this assessment of outcomes among DPP/DPPOS participants who were adherent to their randomized treatment assignments indicates that lifestyle and metformin are likely to be even more effective in real-world clinical practice than they were during the randomized controlled clinical trial and its subsequent observational follow-up study. Perhaps not surprisingly, the costs of the interventions, especially the cost of the metformin intervention, were higher among adherent participants, but the benefits, assessed in terms of non–intervention-related direct medical costs, were also greater. Interestingly, the benefits in terms of QALYs gained were similar among adherent and intent-to-treat participants, perhaps reflecting the impact of non–diabetes-related comorbidities on quality of life. The impact of discounting on the cost-effectiveness equation highlights the fact that in chronic diseases, prevention is an important investment but often not cost saving in the short term.21 To the extent that intervention costs are accrued early in the natural history of disease and complications are accrued later, discounting tends to portray a less favorable cost-effectiveness picture. Nevertheless, these analyses confirm that lifestyle, group lifestyle, and metformin represent a good value for money.

The Research Group gratefully acknowledges the commitment and dedication of the participants of the DPP and DPPOS. During the DPPOS, the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) of the National Institutes of Health provided funding to the clinical centers and the Coordinating Center for the design and conduct of the study, and collection, management, analysis, and interpretation of the data. The Southwestern American Indian Centers were supported directly by the NIDDK, including its Intramural Research Program, and the Indian Health Service. The General Clinical Research Center Program, National Center for Research Resources, supported data collection at many of the clinical centers. Funding was also provided by the National Institute of Child Health and Human Development, the National Institute on Aging, the National Eye Institute, the National Heart Lung and Blood Institute, the Office of Research on Women’s Health, the National Center for Minority Health and Human Disease, the Centers for Disease Control and Prevention, and the American Diabetes Association. Bristol-Myers Squibb and Parke-Davis provided additional funding and material support during the DPP, Lipha (Merck-Sante) provided medication, and LifeScan Inc donated materials during the DPP and DPPOS. Economic analyses were supported in part by the Michigan Diabetes Research and Training Center (P60 DK020572) and the Michigan Center for Diabetes Translational Research (P30 DK092926). The opinions expressed are those of the investigators and do not necessarily reflect the views of the funding agencies. A complete list of Centers, investigators, and staff can be found in the eAppendix.

Author Affiliations: From Department of Endocrinology (WHH), University of Michigan, Ann Arbor, MI; Biostatistics Center (SLE), George Washington University, Rockville, MD; Medstar Research Institute (RER), Washington, DC; Department of Medicine (MGM), University of Texas Health Science Center, San Antonio, TX; Department of Medicine (RTA), Indiana University School of Medicine, Indianapolis, IN; University of Pittsburgh (TJO), Pittsburgh, PA; Biostatistics Center (MAF), George Washington University, Rockville, MD; Centers for Disease Control and Prevention (PZ), Atlanta, GA; Department of Internal Medicine (MBB), University of Michigan, Ann Arbor, MI; Biostatistics Center (Research Group DPP), George Washington University, Rockville, MD; John Hopkins University School of Medicine (CDS), Baltimore, MD.

Funding Source: NIH 5U01-DK048375-12.

Author Disclosures: Dr Ratner reports that he has received consulting fees from Novo Nordisk and sanofi. Dr Orchard reports that he has received consulting fees from Abbott Laboratories and lecture fees from Gilead. The other authors (WHH, SLE, MGM, RTA, MAF, PZ, MBB) report 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 (WHH, SLE, RER, MGM, RTA, TJO, MAF, PZ, CDS); acquisition of data (WHH, RER, MGM, TJO); analysis and interpretation of data (WHH, SLE, RER, MAF, PZ, MBB); drafting of the manuscript (WHH, SLE, RTA, MAF); critical revision of the manuscript for important intellectual content (WHH, SLE, RTA, RER, TJO, MAF, PZ); statistical analysis (SLE, MAF, MBB); provision of study materials or patients (WHH, RER, MGM, TJO, CDS); obtaining funding (WHH, TJO, CDS); administrative, technical, or logistic support (WHH, TJO); and supervision (WHH, MGM, MAF).

Address correspondence to: The Diabetes Prevention Program Coordinating Center, George Washington University Biostatistics Center, 6110 Executive Blvd, Ste 750, Rockville, MD 20852. E-mail:
1. Diabetes Prevention Program Research Group. 10-year follow-up of diabetes incidence and weight loss in the Diabetes Prevention Program Outcomes Study. Lancet. 2009;374(9702):1677-1686.

2. The Diabetes Prevention Program Research Group. The 10-year cost-effectiveness of lifestyle intervention or metformin for diabetes prevention: an intent-to-treat analysis of the DPP/DPPOS. Diabetes Care. 2012;35(4):723-730.

3. The Diabetes Prevention Program Research Group. Reduction in the incidence of type 2 diabetes with lifestyle modification or metformin. N Engl J Med. 2002;346(6):393-403.

4. Hoerger TJ, Hicks KA, Sorensen SW, et al. Cost-effectiveness of screening for pre-diabetes among overweight and obese U.S. adults. Diabetes Care. 2007;30(11):2874-2879.

5. Tabaei BP, Engelgau MM, Herman WH. A multivariate logistic regression equation to screen for dysglycemia: development and validation [published correction appears in Diabetic Med. 2006;23:221]. Diabetic Med. 22:599-605.

6. Mooy JM, Grootenhuis PA, de Vries H, et al. Intra-individual variation of glucose, specific insulin and proinsulin concentrations measured by two oral glucose tolerance tests in a general Caucasian population: the Hoorn Study. Diabetologia. 1996;39(3):298-305.

7. Diabetes Prevention Program Research Group. The Diabetes Prevention Program (DPP): description of lifestyle intervention. Diabetes Care. 2002;25(12):2165-2171.

8. Venditti EM, Bray GA, Carrion-Petersen ML, et al; the Diabetes Prevention Program Research Group. First versus repeat treatment with a lifestyle intervention program: attendance and weight loss outcomes. Int J Obes (Lond). 2008;32(10):1537-1544.

9. Kingsley RG, Wilson GT. Behavior therapy for obesity: a comparative investigation of long-term efficacy. J Consult Clin Psychol. 1977;45(2):288-298.

10. Renjilian DA, Perri MG, Nexu AM, McKelvey WF, Shermer RL, Anton SD. Individual versus group therapy for obesity: effects of matching participants to their treatment preferences. J Consult Clin Psychol. 2001;69(4):717-721.

11. Hernan WH, Brandle M, Zhang P, et al; The Diabetes Prevention Program Research Group. Costs associated with the primary prevention of type 2 diabetes mellitus in the Diabetes Prevention Program. Diabetes Care. 2003;26(1):36-47.

12. Hatziandreu EI, Koplan JP, Weinstein MC, Caspersen CJ, Warner KE. A cost-effectiveness analysis of exercise as a health promotion activity. Am J Public Health. 1988;78(11):1417-1421.

13. Gold MR, Siegel JE, Russell LB, Weinstein MC, eds. Cost-effectiveness in Health and Medicine. New York, NY: Oxford University Press; 1996.

14. The Diabetes Prevention Program Research Group. Within-trial costeffectiveness of lifestyle intervention or metformin for the primary prevention of type 2 diabetes. Diabetes Care. 2003;26(9):2518-2523.

15. Avenell A, Broom J, Brown TJ, et al. Systematic review of the longterm effects and economic consequences of treatments for obesity and implications for health improvement. Health Technol Assess. 2004;8(21):155-162.

16. Palmer AJ, Roze S, Valentine WJ, Spinas GA, Shaw JE, Zimmet PZ. Intensive lifestyle changes or metformin in patients with impaired glucose tolerance: modeling the long-term health economic implications of the diabetes prevention program in Australia, France, Germany, Switzerland, and the United Kingdom. Clin Ther. 2004;26(2):304-321.

17. Herman WH, Hoerger TJ, Brandle M, et al; Diabetes Prevention Program Research Group. The cost-effectiveness of lifestyle modification or metformin in preventing type 2 diabetes in adults with impaired glucose tolerance. Ann Intern Med. 2005;142(5):323-332.

18. Palmer AJ, Tucker DM. Cost and clinical implications of diabetes prevention in an Australian setting: a long-term modeling analysis. Prim Care Diabetes. 2012;6(2):109-121.

19. Ramachandran A, Snehalatha C, Yamuna A, Mary S, Ping Z. Costeffectiveness of the interventions in the primary prevention of diabetes among Asian Indians: within-trial results of the Indian Diabetes Prevention Programme (IDPP). Diabetes Care. 2007;30(10):2548-2552.

20. Eddy DM, Schlessinger L, Kahn R. Clinical outcomes and cost-effectiveness of strategies for managing people at high risk for diabetes. Ann Intern Med. 2005;143(4):251-264.

21. Russell LB. Preventing chronic disease: an important investment, but don’t count on cost savings. Health Affairs. 2009;28(1):42-45.
Copyright AJMC 2006-2020 Clinical Care Targeted Communications Group, LLC. All Rights Reserved.
Welcome the the new and improved, the premier managed market network. Tell us about yourself so that we can serve you better.
Sign Up