Guidelines & other publications from Mt Hood network

American Diabetes Association consensus panel guidelines on diabetes simulation modeling, 2004


With the assistance of groups in the Mt Hood Diabetes modeling network the American Diabetes Association developed guidelines for the development of computer simulation models of the progression of diabetes and its complications. These guidelines provide definitions of concepts such as transparency, validation and uncertainty, as well as key challenges associated with modeling diabetes.

A recent study How Do Diabetes Models Measure Up? A Review of Diabetes Economic Models and ADA Guidelines has tried to assess a range of simulation models using these guidelines.

Mt Hood network guidelines on transparency in diabetes simulation modeling, 2018


The Eighth Mount Hood Challenge (held in St. Gallen, Switzerland, in September 2016) evaluated the transparency of model input documentation from two published health economics studies and developed guidelines for improving transparency in the reporting of input data underlying model-based economic analyses in diabetes.


Transparency of diabetes model inputs is important to promote the reproducibility and credibility of simulation results. In the Eighth Mount Hood Challenge, the Diabetes Modeling Input Checklist was developed with the goal of improving the transparency of input data reporting and reproducibility of diabetes simulation model results.

Mt Hood network publication promoting transparency in diabetes simulation modeling, 2019


Transparency in health economic decision modelling is important for engendering confidence in the models and in the reliability of model-based cost-effectiveness analyses. Model registration is a potential method for promoting transparency, while also reducing the duplication of effort. An important network initiative is the ongoing construction of a diabetes model registry.We recommend that modelling groups provide technical and non-technical documentation sufficient to enable model reproduction, but not necessarily provide the model code. We also request that modelling groups upload documentation on the methods and outcomes of validation efforts, and run reference case simulations so that model outcomes can be compared.


Diabetes Simulation Model Registry

The database is intended to provide a description and sources of further information on any health economic diabetes models that have been developed, or are in the process of development. The starting point for the database is the material provided by various research groups participating in previous Mt Hood Challenge Meetings. 

The database contains general information for researchers and users of diabetes simulation models. It provides descriptions of models, and in time may be expanded to become a registry of health economic diabetes simulation models.

Those wishing to register (or update information) on a model on the database can do so by downloading the form below. The purpose of this form is collect information on any type of diabetes computer simulation model that is used to inform health economic evaluations or resource allocation decisions.

It is the responsibility of the person or group submitting this form to ensure that all information supplied is readable, understandable and accurate. Models under development can be pre-registered.

To enable model simulations to be compared across time the Mt Hood Diabetes Challenge network has developed a reference simulation that can be run each time there are any changes to the model to determine the impact on simulated outcomes of representative patient. These reference simulations will be reported on the Mt Hood Diabetes Simulation Model registry. Each time the is model changed the developers should re-run these simulations and update the information contained in the registry.

We would also encourage groups or researchers that have not participated in previous Mt Hood Challenge meetings to register diabetes computer simulation models, so that a comprehensive database can be developed. This will be a step towards creating a general register of health economic decision models. 

Health State Costs 

While many cost of illness studies estimate the total cost of diabetes, most of costs are related to complications of diabetes such as stroke (see figure 1).

Simulation models require estimates of the health care costs associated with specific complications. There are now a range of studies that report estimates of the acute and long-term costs of many complications of diabetes. Many studies report equations that are specifically designed for use with diabetes simulation models. 

As health care costs vary across different countries and change over time there is a need to choose appropriate costs for your application. 

Fig 1. Contributions of specific complications to total hospital use during the ADVANCE study, by region.

Source: Clarke PM, et al. (2010) Event Rates, Hospital Utilization, and Costs Associated with Major Complications of Diabetes: A Multicountry Comparative Analysis. PLoS Med 7(2): e1000236. doi:10.1371/journal.pmed.1000236

Tools for building simulation models 

Simulation models use equations to predict the occurrence of events such as complications that can impact of the lives of people with diabetes.

Most simulation models normally include both macro-vascular (e.g. myocardial infarction, other ischaemic heart disease, congestive heart failure, stroke) and micro-vascular (e.g. blindness) complications. Health economic models often use lifetime profiles of complications to estimate outcomes in terms of life expectancy and Quality Adjusted Life Years (QALYs), as these are the most commonly used metrics in economic evaluation.  Reduced rates of complications may also reduce health care costs, producing savings which may offset some of the costs of improving treatment. To capture these benefits, the simulation model is also used to estimate the lifetime health care costs that are related to diabetes-related complications.

An example of the equations that form the basis of a UKPDS Outcomes Model are summarised in figure 2.

These equations are used to estimate the probability of occurrence of different complications given risk factors such as patient’s age, sex, duration of diabetes, systolic blood pressure, HbA1c, lipid levels and smoking status. The relative risk for each of these factors by type of complication is also reported. An intervention that reduces a risk factor such as systolic blood pressure (SBP) can have a beneficial effect on multiple complications (e.g. 10mm Hg reduction in SBP reduce the risk of stroke by 32%; and MI by 11% and renal failure by 60%) as well as increases life expectancy. Further, the dependencies between complications (i.e. having some type of complication puts one at higher risk of other complications occurring) are also accounted for to fully capture the burden of the disease on some individuals. 

Fig. 2. Summary of model equations showing event-related dependencies and hazard/odds ratio for each risk factor.

Source: Diabetologia (2004) 47:1747–1759 DOI 10.1007/s00125-004-1527-z

Quality of life

Health economic models often use lifetime profiles of complications to estimate outcomes in terms of life expectancy and Quality Adjusted Life Years (QALYs), as these are the most commonly used metrics in economic evaluation. The QALY, which adjusts life-expectancy by the degree of morbidity, is usually measured on a “utility” scale in which 0 represents a health state equivalent to death and 1 represents full health.

In recent years a large number of studies have estimated utility values that can be used in diabetes either to capture the impact of complications, or the side effects of treatments.

Several meta-analyses and systematic reviews have been undertaken to synthesize values from the literature. 

Applications of diabetes models 

The use of diabetes model is continually expanding. A major application of simulation models is to help inform economic evaluations of diabetes interventions and new technologies. We provide a list of published cost-effectiveness studies that use simulation models to estimate long-term outcomes such as improvements in life expectancy and Quality Adjusted Life Years (QALYs). 

It is based on a recent systematic review of computer simulations of estimating the benefits of interventions to control blood glucose in people with Type 2 diabetes.