Michigan Model for Diabetes

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Model website

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Brief Description:

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The Michigan Model for Diabetes (MMD) is a computerized disease model that enables the users to simulate the progression of diabetes over time, its complications (retinopathy, neuropathy and nephropathy), and its major comorbidities (cardiovascular and cerebrovascular disease), and death. Transition probabilities can be a function of individual characteristics, current disease states or treatment states. The model also estimates the medical costs of diabetes and its comorbidities, as well as the quality of life related to the current health state of the subject. MMD is implemented in a disease modeling software, Indirect Estimation and Simulation Tool, programmed in python language.

 

In contrast to other models, the transition probabilities implemented in the MMD were obtained by synthesizing the published literature. Most of the risk equations adapted in the coronary heart disease sub-model and cerebrovascular disease sub-model are from the UKPDS Outcomes Model I. Transition probabilities were derived by calibrating these equations to contemporary population-based epidemiologic studies and randomized controlled clinical trials.

 

MMD explicitly models diabetes management strategies and allows users to modify them to match the specific scenarios that they are simulating. Changes in risk factors (HbA1c, BMI, lipid profiles and systolic and diastolic blood pressures) over time in simulated individual patients are determined by both treatment states and aging/disease progression. MMD allows a user to control risk factor changes by defining treatment thresholds and compliance rates for hyperglycemia, dyslipidemia, and hypertension, and compliance to quitting smoking and taking aspirin.

 

Key Publications:

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Zhou H, Isaman DJM, Messinger S, Brown MB, Klein R, Brandle M, et al. A Computer Simulation Model of Diabetes Progression, Quality of Life, and Cost. Diabetes Care. 2005; 28:2856-63.

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Isaman DJM,  BarhakJ , Ye W: Indirect Estimation of a Discrete-State Discrete-time model using Secondary Data Analysis of Regression Data. Statistics in Medicine Volume 28, Number 16, Pages 2095 - 2115, 2009.

 

​Barhak J, Isaman DJM, Ye W, Lee D: Chronic disease modelling and simulation software. Journal of Biomedical Informatics, Volume 43, Issue 5, October 2010, Pages 791-799

 

Ye W, J. Barhak J, Isaman DJM, Use of Secondary Data to Estimate Instantaneous Model Parameters of Diabetic Heart Disease: Lemonade Method. Information Fusion Volume 13, Issue 2, April 2012, Pages 137-145

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P Zhang, MB Brown, D Bilik, RT Ackermann, R Li, WH Herman (2012).  Health Utility Scores for Persons with Type 2 Diabetes in U.S. Managed Care Health Plans: Results from Translating Research into Action for Diabetes (TRIAD).  Diabetes Care 35:2250-2256.

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R Li, D Bilik, MB Brown, P Zhang, SL Ettner, RT Ackermann, JC Crosson, WH Herman (2013).  Medical Costs Associated with Type 2 Diabetes Complications and Comorbidities.  American Journal of Managed Care 19:421-430.

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Ye W, Brandle M, Brown MB, Herman W. The Michigan Model for Coronary Heart Disease in Type 2 Diabetes:  Development and Validation (2015). Journal of Diabetes Technology and Therapeutics 17(11) DOI: 10.1089/dia.2014.0304

 

Herman W, Ye W, Brown MB, Simmons R, Davies M, Khunti K, Rutten G, Sandbaek A, Lauritzen T, Borch Johnsen K, Wareham N (2015) Estimating the public health impact of early detection of type 2 diabetes: a modeling study based on the results of the Anglo-Danish-Dutch Study of Intensive Treatment in People with Screen-Detected Diabetes in Primary Care. Diabetes Care. 38: 1449-1455

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