Targeting of the diabetes prevention program leads to substantial benefits when capacity is constrained.

Publisher: Springer Verlag Country of Publication: Germany NLM ID: 9200299 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1432-5233 (Electronic) Linking ISSN: 09405429 NLM ISO Abbreviation: Acta Diabetol Subsets: MEDLINE

Publication: Berlin : Springer Verlag
Original Publication: Berlin : Springer International, c1991-

Patient Selection* ; Primary Prevention*/economics ; Primary Prevention*/methods ; Primary Prevention*/organization & administration ; Primary Prevention*/statistics & numerical data; Diabetes Mellitus, Type 2/*prevention & control ; Prediabetic State/*therapy; Adult ; Cohort Studies ; Cost-Benefit Analysis ; Diabetes Mellitus, Type 2/economics ; Diabetes Mellitus, Type 2/epidemiology ; Female ; Health Services Accessibility/economics ; Health Services Accessibility/organization & administration ; Health Services Accessibility/statistics & numerical data ; Humans ; Hypoglycemic Agents/economics ; Hypoglycemic Agents/therapeutic use ; Life Expectancy ; Life Style ; Male ; Metformin/economics ; Metformin/therapeutic use ; Middle Aged ; Prediabetic State/economics ; Prediabetic State/epidemiology ; Quality of Life ; Risk Factors ; Standard of Care/economics ; Standard of Care/organization & administration ; Standard of Care/standards ; United States/epidemiology

Objective: Approximately 84 million people in the USA have pre-diabetes, but only a fraction of them receive proven effective therapies to prevent type 2 diabetes. We estimated the value of prioritizing individuals at highest risk of progression to diabetes for treatment, compared to non-targeted treatment of individuals meeting inclusion criteria for the Diabetes Prevention Program (DPP).
Methods: Using microsimulation to project outcomes in the DPP trial population, we compared two interventions to usual care: (1) lifestyle modification and (2) metformin administration. For each intervention, we compared targeted and non-targeted strategies, assuming either limited or unlimited program capacity. We modeled the individualized risk of developing diabetes and projected diabetic outcomes to yield lifetime costs and quality-adjusted life expectancy, from which we estimated net monetary benefits (NMB) for both lifestyle and metformin versus usual care.
Results: Compared to usual care, lifestyle modification conferred positive benefits and reduced lifetime costs for all eligible individuals. Metformin's NMB was negative for the lowest population risk quintile. By avoiding use when costs outweighed benefits, targeted administration of metformin conferred a benefit of $500 per person. If only 20% of the population could receive treatment, when prioritizing individuals based on diabetes risk, rather than treating a 20% random sample, the difference in NMB ranged from $14,000 to $20,000 per person.
Conclusions: Targeting active diabetes prevention to patients at highest risk could improve health outcomes and reduce costs compared to providing the same intervention to a similar number of patients with pre-diabetes without targeted selection.

Herman WH, Edelstein SL, Ratner RE et al (2013) Effectiveness and cost-effectiveness of diabetes prevention among adherent participants. Am J Manag Care 19:194–202. (PMID: 235447613985133)
Herman WH, Hoerger TJ, Brandle M et al (2005) The cost-effectiveness of lifestyle modification or metformin in preventing type 2 diabetes in adults with impaired glucose tolerance. Ann Intern Med 142:323–332. (PMID: 15738451270139210.7326/0003-4819-142-5-200503010-00007)
Rehm CD, Marquez ME, Spurrell-Huss E, Hollingsworth N, Parsons AS (2017) Lessons from launching the diabetes prevention program in a large integrated health care delivery system: a case study. Popul Health Manag 20:262–270. (PMID: 28075695556404210.1089/pop.2016.0109)
Hafez D, De Michele M, Sachdev N (2016) Frontline account: resident-led implementation of the national diabetes prevention program within primary care clinics of a large, academic medical center. J Gen Intern Med 31:573–575. (PMID: 26921155483537510.1007/s11606-016-3613-6)
Vojta D, Koehler TB, Longjohn M, Lever JA, Caputo NF (2013) A coordinated national model for diabetes prevention: linking health systems to an evidence-based community program. Am J Prev Med 44:S301–S306. (PMID: 2349829110.1016/j.amepre.2012.12.018)
Caffrey M (2018) Blog post suggests medicare diabetes prevention program capacity crunch, but CMS is short on details. American Journal of Managed Care. https://www.ajmc.com/view/blog-post-suggests-medicare-diabetes-prevention-program-capacity-crunch-but-cms-is-short-ondetia.
Sussman JB, Kent DM, Nelson JP, Hayward RA (2015) Improving diabetes prevention with benefit based tailored treatment: risk based reanalysis of diabetes prevention program. BMJ 350:h454. (PMID: 25697494435327910.1136/bmj.h454)
Herman WH, Pan Q, Edelstein SL et al (2017) Impact of lifestyle and metformin interventions on the risk of progression to diabetes and regression to normal glucose regulation in overweight or obese people with impaired glucose regulation. Diabetes Care 40:1668–1677. (PMID: 29021207571133610.2337/dc17-1116)
Kent DM, Nelson J, Dahabreh IJ, Rothwell PM, Altman DG, Hayward RA (2016) Risk and treatment effect heterogeneity: re-analysis of individual participant data from 32 large clinical trials. Int J Epidemiol 45(6):2075–2088. (PMID: 273752875841614)
Wilson PW, Meigs JB, Sullivan L, Fox CS, Nathan DM, D’Agostino RB Sr (2007) Prediction of incident diabetes mellitus in middle-aged adults: the framingham offspring study. Arch Intern Med 167:1068–1074. (PMID: 1753321010.1001/archinte.167.10.1068)
Sullivan SD, Garrison LP Jr, Rinde H, Kolberg J, Moler EJ (2011) Cost-effectiveness of risk stratification for preventing type 2 diabetes using a multi-marker diabetes risk score. J Med Econ 14:609–616. (PMID: 2174029110.3111/13696998.2011.602160)
Breeze PR, Thomas C, Squires H et al (2017) The impact of type 2 diabetes prevention programmes based on risk-identification and lifestyle intervention intensity strategies: a cost-effectiveness analysis. Diabet Med 34:632–640. (PMID: 2807554410.1111/dme.13314)
Muhlenbruch K, Zhuo X, Bardenheier B et al (2020) Selecting the optimal risk threshold of diabetes risk scores to identify high-risk individuals for diabetes prevention: a cost-effectiveness analysis. Acta Diabetol 57:447–454. (PMID: 3174564710.1007/s00592-019-01451-1)
Chen L, Magliano DJ, Balkau B et al (2011) Maximizing efficiency and cost-effectiveness of type 2 diabetes screening: the AusDiab study. Diabet Med 28:414–423. (PMID: 2139206210.1111/j.1464-5491.2010.03188.x)
Watson P, Preston L, Squires H, Chilcott J, Brennan A (2014) Modelling the economics of type 2 diabetes mellitus prevention: a literature review of methods. Appl Health Econ Health Policy 12:239–253. (PMID: 2459552210.1007/s40258-014-0091-z)
Roberts S, Barry E, Craig D, Airoldi M, Bevan G, Greenhalgh T (2017) Preventing type 2 diabetes: systematic review of studies of cost-effectiveness of lifestyle programmes and metformin, with and without screening, for pre-diabetes. BMJ Open 7:e017184. (PMID: 29146638569535210.1136/bmjopen-2017-017184)
Li R, Qu S, Zhang P et al (2015) Economic evaluation of combined diet and physical activity promotion programs to prevent type 2 diabetes among persons at increased risk: a systematic review for the community preventive services task force. Ann Intern Med 163:452–460. (PMID: 26167962491389010.7326/M15-0469)
Stinnett AA, Mullahy J (1998) Net health benefits: a new framework for the analysis of uncertainty in cost-effectiveness analysis. Med Decis Making 18:S68-80. (PMID: 956646810.1177/0272989X98018002S09)
Neumann P, Ganiats T, Russell L, Sanders G, Siegel J (2016) Cost-effectiveness in health and medicine. Oxford University Press, Oxford. (PMID: 10.1093/acprof:oso/9780190492939.001.0001)
Sanders GD, Neumann PJ, Basu A et al (2016) Recommendations for conduct, methodological practices, and reporting of cost-effectiveness analyses: second panel on cost-effectiveness in health and medicine. JAMA 316:1093–1103. (PMID: 2762346310.1001/jama.2016.12195)
Knowler WC, Barrett-Connor E, Fowler SE et al (2002) Reduction in the incidence of type 2 diabetes with lifestyle intervention or metformin. N Engl J Med 346:393–403. (PMID: 1183252710.1056/NEJMoa012512)
National Center for Health Statistics U.S., and U.S. National vital statistics system (2016) Age- and sex-specific United States life tables for years 1981–2013: Department of Health and Human Services, Centers for Disease Control and Prevention.
Blewett LA, Rivera Drew JA, Griffin R, Williams KCW (2019) IPUMS health surveys: medical expenditure panel survey, version 1.1. [dataset]. IPUMS, Minneapolis, MN. https://meps.ipums.org/meps/.
Ye W, Brandle M, Brown MB, Herman WH (2015) The Michigan model for coronary heart disease in type 2 diabetes: development and validation. Diabetes Technol Ther 17:701–711. (PMID: 26222704469643310.1089/dia.2014.0304)
Zhou H, Isaman DJ, Messinger S et al (2005) A computer simulation model of diabetes progression, quality of life, and cost. Diabetes Care 28:2856–2863. (PMID: 1630654510.2337/diacare.28.12.2856)
University of Michigan, Michigan Center of Diabetes Translational Research (MCDTR) Disease Modeling Group. The Michigan model for diabetes user manual. http://diabetesresearch.med.umich.edu/peripherals/DiseaseModel/MDRTC%20Diabetes%20Model/UserManual_MichiganModel_for_Diabetes_ver2.pdf2015.
Knowler WC, Fowler SE, Diabetes Prevention Program Research G et al (2009) 10-year follow-up of diabetes incidence and weight loss in the diabetes prevention program outcomes study. Lancet 374:1677–1686. (PMID: 10.1016/S0140-6736(09)61457-4)
Haw JS, Galaviz KI, Straus AN et al (2017) Long-term sustainability of diabetes prevention approaches: a systematic review and meta-analysis of randomized clinical trials. JAMA Intern Med 177:1808–1817. (PMID: 29114778582072810.1001/jamainternmed.2017.6040)
van Klaveren D, Wong JB, Kent DM, Steyerberg EW (2017) Biases in individualized cost-effectiveness analysis: influence of choices in modeling short-term, trial-based, mortality risk reduction and post-trial life expectancy. Med Decis Making 37:770–778. (PMID: 28854143563864410.1177/0272989X17696994)
Pandya A, Sy S, Cho S, Weinstein MC, Gaziano TA (2015) Cost-effectiveness of 10-year risk thresholds for initiation of statin therapy for primary prevention of cardiovascular disease. JAMA 314:142–150. (PMID: 26172894479763410.1001/jama.2015.6822)
Vijan S, Sussman JB, Yudkin JS, Hayward RA (2014) Effect of patients’ risks and preferences on health gains with plasma glucose level lowering in type 2 diabetes mellitus. JAMA Intern Med 174:1227–1234. (PMID: 24979148429986510.1001/jamainternmed.2014.2894)
Kent DM, Steyerberg E, van Klaveren D (2018) Personalized evidence based medicine: predictive approaches to heterogeneous treatment effects. BMJ 363:k4245. (PMID: 30530757688983010.1136/bmj.k4245)
Kent DM, Paulus JK, van Klaveren D et al (2020) The predictive approaches to treatment effect heterogeneity (PATH) statement. Ann Intern Med 172:35–45. (PMID: 3171113410.7326/M18-3667)
Kumar V, Cohen JT, van Klaveren D et al (2018) Risk-targeted lung cancer screening: a cost-effectiveness analysis. Ann Intern Med 168:161–169. (PMID: 29297005653391810.7326/M17-1401)
Lavelle TA, Kent DM, Lundquist CM, Thorat T, Cohen JT, Wong JB, Olchanski N, Neumann PJ (2018) Patient variability seldom assessed in cost-effectiveness studies. Med Decis Making 38(4):487–494.
Palmer AJ, Tucker DM (2012) Cost and clinical implications of diabetes prevention in an Australian setting: a long-term modeling analysis. Prim Care Diabetes 6:109–121. (PMID: 2215388810.1016/j.pcd.2011.10.006)
Hoerger TJ, Hicks KA, Sorensen SW et al (2007) Cost-effectiveness of screening for pre-diabetes among overweight and obese U.S. adults. Diabetes Care 30:2874–2879. (PMID: 1769861410.2337/dc07-0885)
Gillies CL, Lambert PC, Abrams KR et al (2008) Different strategies for screening and prevention of type 2 diabetes in adults: cost effectiveness analysis. BMJ 336:1180–1185. (PMID: 18426840239470910.1136/bmj.39545.585289.25)
Mortaz S, Wessman C, Duncan R, Gray R, Badawi A (2012) Impact of screening and early detection of impaired fasting glucose tolerance and type 2 diabetes in Canada: a Markov model simulation. Clinicoecon Outcomes Res 4:91–97. (PMID: 225534253340109)
Brandle M, Zhou H, Smith BR et al (2003) The direct medical cost of type 2 diabetes. Diabetes Care 26:2300–2304. (PMID: 1288285210.2337/diacare.26.8.2300)
Coffey JT, Brandle M, Zhou H et al (2002) Valuing health-related quality of life in diabetes. Diabetes Care 25:2238–2243. (PMID: 1245396710.2337/diacare.25.12.2238)
Herman WH, Ye W, Griffin SJ et al (2015) Early detection and treatment of type 2 diabetes reduce cardiovascular morbidity and mortality: a simulation of the results of the anglo-danish-dutch study of intensive treatment in people with screen-detected diabetes in primary care (ADDITION-Europe). Diabetes Care 38:1449–1455. (PMID: 25986661451213810.2337/dc14-2459)
Kent DM, Alsheikh-Ali A, Hayward RA (2008) Competing risk and heterogeneity of treatment effect in clinical trials. Trials 9:30. (PMID: 18498644242318210.1186/1745-6215-9-30)

P30 DK092926 United States DK NIDDK NIH HHS; TL1 TR001062 United States TR NCATS NIH HHS; U01 NS086294 United States NS NINDS NIH HHS; U01 NS086294 United States NH NIH HHS

Keywords: Diabetes prevention; Economic analysis; Heterogeneity of treatment effect; Lifestyle modification; Risk based; Type 2 diabetes; Value

0 (Hypoglycemic Agents)
9100L32L2N (Metformin)

Date Created: 20210131 Date Completed: 20210614 Latest Revision: 20221109

20231215

PMC8276501

10.1007/s00592-021-01672-3

33517494