Cost-Effectiveness Evaluation of a Remote Monitoring Programme Including Lifestyle Education Software in Type 2 Diabetes: Results of the Educ@dom Study

Michael Mounié, Nadège Costa, Pierre Gourdy, Christelle Latorre, Solène Schirr-Bonnans, Jean-Marc Lagarrigue, Henri Roussel, Jacques Martini, Jean-Christophe Buisson, Marie-Christine Chauchard, Jacqueline Delaunay, Soumia Taoui, Marie-France Poncet, Valeria Cosma, Sandrine Lablanche, Magali Coustols-Valat, Lucie Chaillous, Charles Thivolet, Caroline Sanz, Alfred Penfornis, Benoît Lepage, Hélène Colineaux, Hélène Hanaire, Laurent Molinier, Marie-Christine Turnin, Educ@dom Study Group, Michael Mounié, Nadège Costa, Pierre Gourdy, Christelle Latorre, Solène Schirr-Bonnans, Jean-Marc Lagarrigue, Henri Roussel, Jacques Martini, Jean-Christophe Buisson, Marie-Christine Chauchard, Jacqueline Delaunay, Soumia Taoui, Marie-France Poncet, Valeria Cosma, Sandrine Lablanche, Magali Coustols-Valat, Lucie Chaillous, Charles Thivolet, Caroline Sanz, Alfred Penfornis, Benoît Lepage, Hélène Colineaux, Hélène Hanaire, Laurent Molinier, Marie-Christine Turnin, Educ@dom Study Group

Abstract

Introduction: Telemedicine programs using health technological innovation to remotely monitor the lifestyles of patients with type 2 diabetes (T2D) can improve glycaemic control and thus reduce the incidence of complications as well as management costs. In this context, an assessment was made of the 1-year and 2-year cost-effectiveness of the EDUC@DOM telemonitoring and tele-education program.

Methods: The EDUC@DOM study was a multicentre randomized controlled trial conducted between 2013 and 2017 that compared a telemonitoring group (TMG) to a control group (CG) merged with health insurance databases to extract economic data on resource consumption. Economic analysis was performed from the payer perspective, and direct costs and indirect costs were considered. The clinical outcome used was the intergroup change in glycated haemoglobin (HbA1c) levels from baseline. Missing economic data were imputed using multiple imputation, and fitted values from a generalized linear mixed model were used to calculate the incremental cost-effectiveness ratio (ICER). Bootstrapped 95% confidence ellipses were drawn in the cost-effectiveness plan.

Results: The main analysis included data from 256 patients: 126 in the TMG and 130 in the CG. Incremental costs over 1 and 2 years were equal to €2129 and €5101, respectively, in favour of the TMG. Once imputed and adjusted for confounding factors, the TMG trends to a 21% cost decrease over 1 and 2 years of follow-up (0.79 [0.58; 1.08], p = 0.1452 and 0.79 [0.61; 1.03], p = 0.0879, respectively). The EDUC@DOM program led to a €1334 cost saving and a 0.17 decrease in HbA1c over 1 year and a €3144 cost saving and a 0.14 decrease in HbA1c over 2 years. According to the confidence ellipse, EDUC@DOM was a cost-effective strategy.

Conclusion: This study provides additional economic information on telemonitoring and tele-education programs to enhance their acceptance and promote their use. In the light of this work, the EDUC@DOM program is a cost-saving strategy in T2D management.

Trial registration: This trial was registered in the Clinical Trials Database on 27 September 2013 under no. NCT01955031 and bears ID-RCB no. 2013-A00391-44.

Keywords: Cost-effectiveness; EDUC@DOM; Economic assessment; Lifestyle management; Tele-education; Telemonitoring; Type 2 diabetes.

© 2022. The Author(s).

Figures

Fig. 1
Fig. 1
a Incremental cost-effectiveness ratio and its 95% confidence ellipse over a 1-year period. b Incremental cost-effectiveness ratio and its 95% confidence ellipse over a 2-year period

References

    1. Davies MJ, D’Alessio DA, Fradkin J, et al. Management of hyperglycemia in type 2 diabetes, 2018. A consensus report by the American Diabetes Association (ADA) and the European Association for the Study of Diabetes (EASD). Diabetes Care. 2018;2018(41):2669–701.
    1. Marina T, Silvia G, Luigi G, Giorgio G, Valerio M, Gabriel M, Pietro P, Laura T, Marco T, Piervincenzo B, Franco C, Massimo P. Rethink Organization to iMprove Education and Outcomes (ROMEO)—a multicenter randomized trial of lifestyle intervention by group care to manage type 2 diabetes. Diabetes Care. 2010;33:745–747. doi: 10.2337/dc09-2024.
    1. Zhang ZY, Miao LF, Qian LL, Wang N, Qi MM, Zhang YM, Dang SP, Wu Y, Wang RX. Molecular mechanisms of glucose fluctuations on diabetic complications. Front Endocrinol (Lausanne) 2019;18(10):640. doi: 10.3389/fendo.2019.00640.
    1. 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;329:997–1086.
    1. 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;352:837–53.
    1. Deshpande AD, Harris-Hayes M, Schootman M. Epidemiology of diabetes and diabetes-related complications. Phys Ther. 2008;88(11):1254–1264. doi: 10.2522/ptj.20080020.
    1. Kulzer B, Daenschel W, Daenschel I, Schramm W, Messinger D, Weissmann J, et al. Integrated personalized diabetes management improves glycemic control in patients with insulin-treated type 2 diabetes: results of the PDM-ProValue study program. Diabetes Res Clin Pract. 2018;144:200–212. doi: 10.1016/j.diabres.2018.09.002.
    1. 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;329:997–1086.
    1. UK Prospective Diabetes Study (UKPDS) Group. Intensive blood glucose control with sulphonylureas or insulin compared with conventional treatment and risk of complications in persons with type 2 diabetes (UKPDS 33). Lancet 1998;352:837–53.
    1. Lee PA, Greenfield G, Pappas Y. The impact of telehealth remote patient monitoring on glycemic control in type 2 diabetes: a systematic review and meta-analysis of systematic reviews of randomised controlled trials. BMC Health Serv Res. 2018;18(1):495. doi: 10.1186/s12913-018-3274-8.
    1. Lee JY, Lee SWH. Telemedicine cost-effectiveness for diabetes management: a systematic review. Diabetes Technol Ther. 2018;20(7):492–500. doi: 10.1089/dia.2018.0098.
    1. Tsuji S, Ishikawa T, Morii Y, Zhang H, Suzuki T, Tanikawa T, Nakaya J, Ogasawara K. Cost-effectiveness of a continuous glucose monitoring mobile app for patients with type 2 diabetes mellitus: analysis simulation. J Med Internet Res. 2020;22(9):e16053. doi: 10.2196/16053.
    1. Zhai YK, Zhu WJ, Cai YL, Sun DX, Zhao J. Clinical- and cost-effectiveness of telemedicine in type 2 diabetes mellitus: a systematic review and meta-analysis. Medicine. 2014;93(28):e312. 10.1097/MD.0000000000000312.
    1. Andersson E, Persson S, Hallén N, et al. Costs of diabetes complications: hospital-based care and absence from work for 392,200 people with type 2 diabetes and matched control participants in Sweden. Diabetologia. 2020;63:2582–2594. doi: 10.1007/s00125-020-05277-3.
    1. Stegbauer C, Falivena C, Moreno A, et al. Costs and its drivers for diabetes mellitus type 2 patients in France and Germany: a systematic review of economic studies. BMC Health Serv Res. 2020;20:1043. doi: 10.1186/s12913-020-05897-w.
    1. Schirr-Bonnans S, Costa N, Derumeaux-Burel H, Bos J, Lepage B, Garnault V, Martini J, Hanaire H, Turnin MC, Molinier L. Cost of diabetic eye, renal and foot complications: a methodological review. Eur J Health Econ. 2017;18(3):293–312. doi: 10.1007/s10198-016-0773-6.
    1. Turnin MC, Gourdy P, Martini J, Buisson JC, Chauchard MC, Delaunay et al. Educ@dom Study Group. Impact of a remote monitoring programme including lifestyle education software in type 2 diabetes: results of the Educ@dom randomised multicentre study. Diabetes Ther. 2021. 10.1007/s13300-021-01095-x.
    1. Turnin MC, Schirr-Bonnans S, Martini J, Buisson JC, Taoui S, Chauchard MC, Costa N, Lepage B, Molinier L, Hanaire H. Educ@dom: comparative study of the telemonitoring of patients with type 2 diabetes versus standard monitoring—study protocol for a randomized controlled study. Diabetol Metab Syndr. 2017;11(9):52. 10.1186/s13098-017-0252-y.
    1. Buisson JC. Nutri-Educ, a nutrition software application for balancing meals, using fuzzy arithmetic and heuristic search algorithms. Artif Intell Med. 2008;42:213–227. doi: 10.1016/j.artmed.2007.12.001.
    1. Buisson JC, Garel A. Balancing meals using fuzzy arithmetic and heuristic search algorithms. IEEE Trans Fuzzy Syst. 2003;11:68–78. doi: 10.1109/TFUZZ.2002.806323.
    1. Turnin MC, Beddok R, Clottes J, et al. Telematic expert system DIABETO. New tool for diet self-monitoring for diabetic patients. Diabetes Care. 1992;15:204–212. doi: 10.2337/diacare.15.2.204.
    1. Turnin MC, Bolzonella Pene C, Dumoulin S, et al. Multicenter evaluation of the Nutri-Expert Telematic System in diabetic patients. Diabete Metab. 1995;21:26–33.
    1. Turnin MC, Bourgeois O, Cathelineau G, et al. Multicenter randomized evaluation of a nutritional education software in obese patients. Diabetes Metab. 2001;27:139–147.
    1. Bezin J, Duong M, Lassalle R, Droz C, Pariente A, Blin P, Moore N. The national healthcare system claims databases in France, SNIIRAM and EGB: powerful tools for pharmacoepidemiology. Pharmacoepidemiol Drug Saf. 2017;26(8):954–962. doi: 10.1002/pds.4233.
    1. Moulis G, Lapeyre-Mestre M, Palmaro A, Pugnet G, Montastruc J-L, Sailler L. French health insurance databases: what interest for medical research? Rev Med Interne. 2015;36(6):411–417. doi: 10.1016/j.revmed.2014.11.009.
    1. Drummond MF, Sculpher MJ, Torrance GW, O’Brien BJ, Stoddart GL. Methods for the economic evaluation of health care programmes. Oxford: Oxford University Press; 2005. p. 379.
    1. Barber JA, Thompson SG. Analysis of cost data in randomized trials: an application of the non-parametric bootstrap. Stat Med. 2000;19(23):3219–3236. doi: 10.1002/1097-0258(20001215)19:23<3219::AID-SIM623>;2-P.
    1. Morris TP, White IR, Royston P. Tuning multiple imputation by predictive mean matching and local residual draws. BMC Med Res Methodol. 2014;14:75. doi: 10.1186/1471-2288-14-75.
    1. Black WC. The CE plane: a graphic representation of cost-effectiveness. Med Decis Mak. 1990;10(3):212–214. doi: 10.1177/0272989X9001000308.
    1. Cohen DJ, Reynolds MR. Interpreting the results of cost-effectiveness studies. J Am Coll Cardiol. 2008;52(25):2119–2126. doi: 10.1016/j.jacc.2008.09.018.
    1. Schechter CB, Cohen HW, Shmukler C, Walker EA. Intervention costs and cost-effectiveness of a successful telephonic intervention to promote diabetes control. Diabetes Care. 2012;35(11):2156–2160. doi: 10.2337/dc12-0048.
    1. Moreno L, Dale SB, Chen AY, Magee CA. Costs to Medicare of the Informatics for Diabetes Education and Telemedicine (IDEATel) home telemedicine demonstration: findings from an independent evaluation. Diabetes Care. 2009;32(7):1202–4. 10.2337/dc09-0094 (Epub 2009 Apr 14).
    1. Dafoulas GE, Mavrodi A, Bargiota A, Giannakakos H, Stafylas P, Gkiata P, et al. Cost utility analysis of long-term telemonitoring of patients with DMT2: Results of the Greek pilot of the renewing health multicenter pragmatic randomized trial. Int J Integr Care. 2014;14:8. doi: 10.5334/ijic.1767.
    1. Tsuji S, Ishikawa T, Morii Y, Zhang H, Suzuki T, Tanikawa T, Nakaya J, Ogasawara K. Cost-effectiveness of a continuous glucose monitoring mobile app for patients with type 2 diabetes mellitus: analysis simulation. J Med Internet Res. 2020;22(9):e16053. 10.2196/16053.PMID:32940613.
    1. Glick HA. Sample size and power for cost-effectiveness analysis (part 1) Pharmacoeconomics. 2011;29(3):189–198. doi: 10.2165/11585070-000000000-00000.
    1. lo-Storto C, Goncharuk AG. Efficiency vs effectiveness: a benchmarking study on European healthcare systems. Econ Soc. 2017;10(3):102–115.

Source: PubMed

3
Subscribe