Abstract
Diabetes mellitus is a major healthcare concern from both a treatment and a funding perspective. Although decision makers frequently rely on models to evaluate the long-term costs and consequences associated with diabetes interventions, no recent article has reviewed the methods used in long-term cost-effectiveness models of diabetes treatment.
The following databases were searched up to April 2008 to identify published economic models evaluating treatments for diabetes mellitus: OVID MEDLINE, EMBASE and the Thomson’s Biosis Previews, NHS EED via Wiley’s Cochrane Library, and Wiley’s HEED database. Identified articles were reviewed and grouped according to unique models. When a model was applied in different settings (e.g. country) or compared different treatment alternatives, only the original publication describing the model was included. In some cases, subsequent articles were included if they provided methodological advances from the original model. The following data were captured for each study: (i) study characteristics; (ii) model structure; (iii) long-term complications, data sources, methods reporting and model validity; (iv) utilities, data sources and methods reporting; (v) costs, data sources and methods reporting; (vi) model data requirements; and (vii) economic results including methods to deal with uncertainty.
A total of 17 studies were identified, 12 of which allowed for the conduct of a cost-effectiveness analysis and a cost-utility analysis. Although most models were Markov-based microsimulations, models differed with respect to the number of diabetes-related complications included. The majority of the studies used a lifetime time horizon and a payer perspective. The DCCT for type 1 diabetes and the UKPDS for type 2 diabetes were the trial data sources most commonly cited for the efficacy data, although several non-randomized data sources were used. While the methods used to derive the efficacy data were commonly reported, less information was given regarding the derivation of the utilities or the costs. New interventions were generally deemed cost effective based on ten studies presenting results. Authors relied mostly on univariate sensitivity analyses to test the robustness of their models.
Although diabetes is a complex disease, several models have been developed to predict the long-term costs and consequences associated with diabetes treatment. Combined with previous findings, this review supports the development of a reference case that could be used for any new diabetes models. Models could be enhanced if they had the capacity to deal with both first- and second-order uncertainty. Future research should continue to compare economic models for diabetes treatment in terms of clinical outcomes, costs and QALYs when applicable.
Similar content being viewed by others
References
International Diabetes Federation. Diabetes atlas. 3rd ed. Brussels: International Diabetes Federation, 2009 [online]. Available from URL: http://www.diabetesatlas.org/content/diabetes [Accessed 2009 Dec 10]
Goeree R, Lim ME, Hopkins R, et al. Prevalence, total and excess costs of diabetes and related complications in Ontario, Canada. Can J Diabetes 2009; 33 (1): 35–45
Goeree R, Lim ME, Hopkins R, et al. Excess risk of mortality and complications associated with newly diagnosed cases of diabetes in Ontario, Canada. Can J Diabetes 2009; 33 (2): 93–104
Jonsson B. Revealing the cost of type II diabetes in Europe. Diabetologia 2002; 45 (7): S5–12
American Diabetes Association. Economic costs of diabetes in the US in 2007. Diabetes Care 2008; 31 (3): 596–615
The Diabetes Control and Complications Trial Research Group. The effect of intensive treatment of diabetes on the development and progression of long-term complications in insulin-dependent diabetes mellitus. N Engl J Med 1993; 329 (14): 977–86
Stratton IM, Adler AI, Neil HA, et al. Association of glycaemia with macrovascular and microvascular complications of type 2 diabetes (UKPDS 35): prospective observational study. BMJ 2000; 321 (7258): 405–12
UK Prospective Diabetes Study (UKPDS) Group. Intensive blood-glucose control with sulphonylureas or insulin compared with conventional treatment and risk of complications in patients with type 2 diabetes (UKPDS 33). Lancet 1998; 352 (9131): 837–53
Nixon J, Stoykova B, Glanville J, et al. The UK NHS economic evaluation database: economic issues in evaluations of health technology. Int J Technol Assess Health Care 2000; 16 (3): 731–42
Briggs A, Sculpher M, Claxton K. Decision modelling for health economic evaluation. Oxford: Oxford University Press, 2006
American Diabetes Association. Guidelines for computer modeling of diabetes and its complications. Diabetes Care 2004; 27 (9): 2262–5
Weinstein MC, O’Brien B, Hornberger J, et al. Principles of good practice for decision analytic modeling in health-care evaluation: report of the ISPOR Task Force on Good Research Practices — Modeling Studies. Value Health 2003; 6 (1): 9–17
Coyle D, Lee KM, O’Brien BJ. The role of models within economic analysis: focus on type 2 diabetes mellitus. Pharmacoeconomics 2002; 20 Suppl. 1: 11–9
Mount Hood 4 Modeling Group. Computer modeling of diabetes and its complications: a report on the Fourth Mount Hood Challenge Meeting. Diabetes Care 2007; 30 (6): 1638–46
Vijgen SM, Hoogendoorn M, Baan CA, et al. Cost effectiveness of preventive interventions in type 2 diabetes mellitus: a systematic literature review. Pharmacoeconomics 2006; 24 (5): 425–41
Waugh N, Scotland G, McNamee P, et al. Screening for type 2 diabetes: literature review and economic modelling. Health Technol Assess 2007; 11 (17): iii-xi, 1
Klonoff DC, Schwartz DM. An economic analysis of interventions for diabetes. Diabetes Care 2000; 23 (3): 390–404
Oliver A, Pritchard C. Economic evaluations relating to diabetes: a descriptive review and their compliance with guidance. Value Health 2000; 3 Suppl. 1: 7–14
Clouse JC, Zitter M, Herman ME. Health economic considerations in the management of type 2 diabetes. Manag Care Interface 2002; 15 (1): 66–71
Veenstra DL, Ramsey SD, Sullivan SD. A guideline for the use of pharmacoeconomic models of diabetes treatment in the US managed-care environment. Pharmacoeconomics 2002; 20 Suppl. 1: 21–30
Raikou M, McGuire A. The economics of screening and treatment in type 2 diabetes mellitus. Pharmacoeconomics 2003; 21 (8): 543–64
Zhang P, Engelgau MM, Norris SL, et al. Application of economic analysis to diabetes and diabetes care. Ann Intern Med 2004; 140 (11): 972–7
The Diabetes Control and Complications Trial Research Group. Lifetime benefits and costs of intensive therapy as practiced in the diabetes control and complications trial: The Diabetes Control and Complications Trial Research Group. JAMA 1996; 276 (17): 1409–15
Palmer AJ, Weiss C, Sendi PP, et al. The cost-effectiveness of different management strategies for type I diabetes: a Swiss perspective. Diabetologia 2000; 43 (1): 13–26
Eastman RC, Javitt JC, Herman WH, et al. Model of complications of NIDDM: II. Analysis of the health benefits and cost-effectiveness of treating NIDDM with the goal of normoglycemia. Diabetes Care 1997; 20 (5): 735–44
Caro JJ, Klittich WS, Raggio G, et al. Economic assessment of troglitazone as an adjunct to sulfonylurea therapy in the treatment of type 2 diabetes. Clin Ther 2000; 22 (1): 116–27
Caro JJ, Salas M, Ward AJ, et al. Combination therapy for type 2 diabetes: what are the potential health and cost implications in Canada? Can J Diabetes 2003; 27 (1): 33–41
Ward AJ, Salas M, Caro JJ, et al. Health and economic impact of combining metformin with nateglinide to achieve glycemic control: comparison of the lifetime costs of complications in the UK. Cost Eff Resour Alloc 2004; 2 (1): 2
Palmer AJ, Sendi PP, Spinas GA. Applying some UK Prospective Diabetes Study results to Switzerland: the costeffectiveness of intensive glycaemic control with metformin versus conventional control in overweight patients with type-2 diabetes. Schweiz Med Wochenschr 2000; 130 (27-28): 1034–40
Brown JB, Russell A, Chan W, et al. The global diabetes model: user friendly version 3.0. Diabetes Res Clin Pract 2000; 50 Suppl. 3: S15–46
Bagust A, Hopkinson PK, Maier W, et al. An economic model of the long-term health care burden of type II diabetes. Diabetologia 2001; 44 (12): 2140–55
CDC Diabetes Cost-effectiveness Group. Cost-effectiveness of intensive glycemic control, intensified hypertension control, and serum cholesterol level reduction for type 2 diabetes. JAMA 2002; 287 (19): 2542–51
Clarke PM, Gray AM, Briggs A, et al. A model to estimate the lifetime health outcomes of patients with type 2 diabetes: the United Kingdom Prospective Diabetes Study (UKPDS) outcomes model (UKPDS no. 68). Diabetologia 2004; 47 (10): 1747–59
Bagust A, Evans M, Beale S, et al. A model of long-term metabolic progression of type 2 diabetes mellitus for evaluating treatment strategies. Pharmacoeconomics 2006; 24 Suppl. 1: 5–19
Zhou H, Isaman DJ, Messinger S, et al. A computer simulation model of diabetes progression, quality of life, and cost. Diabetes Care 2005; 28 (12): 2856–63
Tilden DP, Mariz S, O’Bryan-Tear G, et al. A lifetime modelled economic evaluation comparing pioglitazone and rosiglitazone for the treatment of type 2 diabetes mellitus in the UK. Pharmacoeconomics 2007; 25 (1): 39–54
Palmer AJ, Roze S, Valentine WJ, et al. The CORE diabetes model: projecting long-term clinical outcomes, costs and costeffectiveness of interventions in diabetes mellitus (types 1 and 2) to support clinical and reimbursement decisionmaking. Curr Med Res Opin 2004; 20 Suppl. 1: S5–26
Mueller E, Maxion-Bergemann S, Gultyaev D, et al. Development and validation of the Economic Assessment of Glycemic Control and Long-Term Effects of diabetes (EAGLE) model. Diabetes Technol Ther 2006; 8 (2): 219–36
Grima DT, Thompson MF, Sauriol L. Modelling cost effectiveness of insulin glargine for the treatment of type 1 and 2 diabetes in Canada. Pharmacoeconomics 2007; 25 (3): 253–66
Glucose tolerance and mortality: comparison of WHO and American Diabetes Association diagnostic criteria. The DECODE study group. European Diabetes Epidemiology Group. Diabetes Epidemiology: Collaborative analysis Of Diagnostic criteria in Europe. Lancet 1999; 354 (9179): 617–21
UKPDS Group. Effect of intensive blood-glucose control with metformin on complications in overweight patients with type-2 diabetes (UKPDS 34). Lancet 1998; 352: 854–65
Anderson KM, Odell PM, Wilson PW, et al. Cardiovascular disease risk profiles. Am Heart J 1991 Jan; 121 (1 Pt 2): 293–8
Levy J, Atkinson AB, Bell PM, et al. Beta-cell deterioration determines the onset and rate of progression of secondary dietary failure in type 2 diabetesmellitus: the 10 year followup of the Belfast Diet Study. Diabet Med 1998; 15: 290–6
Beale S, Bagust A, Shearer AT, et al. Cost-effectiveness of rosiglitazone combination therapy for the treatment of type 2 diabetes mellitus in the UK. Pharmacoeconomics 2006; 24 Suppl. 1: 21–34
Donnan PT, Steinke DT, Newton RW, et al. Changes in treatment after the start of oral hypoglycaemic therapy in type 2 diabetes: a population-based study. Diabet Med 2002; 19 (7): 606–10
Javitt JC, Aiello LP, Bassi LJ, et al. Detecting and treating retinopathy in patients with type I diabetes mellitus: savings associated with improved implementation of current guidelines. American Academy of Ophthalmology. Ophthalmology 1991; 98 (10): 1565–73
Horton ES, Whitehouse F, Ghazzi NM, et al., for the Troglitazone Study Group. Troglitazone in combination with sulfonylurea restores glycemic control in patients with tye 2 diabetes. Diabetes Care 1998; 21: 1462–9
Klein R, Klein BEK, Moss SE, et al. The Wisconsin Epidemilogic Study of Diabetic Retinopathy, II: prevalence and risk of diabetic retinopathy when age at diagnosis is less than 30 years. Arch Ophthalmol 1984; 102: 520–6
Ballard DJ, Humphrey LL, Melton JI, et al. Epidemiology of persistent proteinuria in type II diabetes mellitus. Diabetes 1988; 37: 405–12
Humphrey LL, Ballard DJ, Frohnert PP, et al. Chronic renal failure in non-insulin-dependent diabetes mellitus. Ann Intern Med 1989; 111: 788–96
US Renal Data System. USRDS 1994 annual data report [appendix D.17]. Bethesda (MD): The National Institutes of Health, National Institute of Diabetes and Digestive and Kidney Diseases, 1994
Clarke P, Gray A, Holman R. Estimating utility values for health states of type 2 diabetic patients using the EQ-5D (UKPDS 62). Med Decis Making 2002; 22 (4): 340–9
Nease Jr RF, Kneeland T, O’Connor GT, et al. Variation in patient utilities for outcomes of the management of chronic stable angina: implications for clinical practice guidelines. Ischemic Heart Disease Patient Outcomes Research Team. JAMA 1995; 273 (15): 1185–90
Ralston S, Mera R, Wisner C, et al. Quality of life and treatment satisfaction of type 2 diabetic patients: decision and cost-effectiveness analyses [poster 865]. 36th Annual Meeting of the European Association for the Study of Diabetes; 2000 Sep 17; Jerusalem
Tsevat J, Goldman L, Soukup JR, et al. Stability of timetradeoff utilities in survivors of myocardial infarction. Med Decis Making 1993; 13 (2): 161–5
Coffey JT, Brandle M, Zhou H, et al. Valuing health-related quality of life in diabetes. Diabetes Care 2002; 25 (12): 2238–43
Bagust A, Beale S. Modelling EuroQol health-related utility values for diabetic complications from CODE-2 data. Health Econ 2005; 14 (3): 217–30
Shin AY, Porter PJ, Wallace MC, et al. Quality of life of stroke in younger individuals: utility assessment in patients with arteriovenous malformations. Stroke 1997; 28 (12): 2395–9
Lawrence WF, Grist TM, Brazy PC, et al. Magnetic resonance angiography in progressive renal failure: a technology assessment. Am J Kidney Dis 1995; 25 (5): 701–9
Dasbach EJ, Fryback DG, Thornburt JR. Health utility preference difference. Med Decis Making 1995; 12 (4)
Eckman MH, Greenfield S, Mackey WC, et al. Foot infections in diabetic patients: decision and cost-effectiveness analyses. JAMA 1995; 273 (9): 712–20
Katzmarzyk PT, Mason C. Prevalence of class I, II and III obesity in Canada. CMAJ 2006; 174 (2): 156–7
UK Prospective Diabetes Study (UKPDS). VIII: study design, progress and performance. Diabetologia 1991 Dec; 34 (12): 877–90
Colhoun HM, Betteridge DJ, Durrington PN, et al. Primary prevention of cardiovascular disease with atorvastatin in type 2 diabetes in the Collaborative Atorvastatin Diabetes Study (CARDS): a multicentre randomised placebocontrolled trial. Lancet 2004; 364: 685–96
Heeg BM, Damen J, Buskens E, et al. Modelling approaches: the case of schizophrenia. Pharmacoeconomics 2008; 26 (8): 633–48
Claxton K, Sculpher M, McCabe C, et al. Probabilistic sensitivity analysis for NICE technology assessment: not an optional extra. Health Econ 2005; 14 (4): 339–47
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): 251–64
Acknowledgements
No sources of funding were used to assist in the preparation of this review. The authors have no conflicts of interests that are directly relevant to the content of this review. Daria O’Reilly and Jean-Eric Tarride each hold a 2007 Career Scientist Award, Ontario Ministry of Health and Long-Term Care. Special thanks to Donna Wilcockson from the Programs for Assessment of Technology in Health (PATH) Research Institute, St. Joseph’s Healthcare Hamilton, ON, Canada.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Tarride, JE., Hopkins, R., Blackhouse, G. et al. A Review of Methods Used in Long-Term Cost-Effectiveness Models of Diabetes Mellitus Treatment. Pharmacoeconomics 28, 255–277 (2010). https://doi.org/10.2165/11531590-000000000-00000
Published:
Issue Date:
DOI: https://doi.org/10.2165/11531590-000000000-00000