Swedish Institute for Health Economics-Diabetes Cohort Model (IHE-DCM)

 

Information last updated: July 2022

Participated in following Mt Hood Diabetes Challenge Meetings: 2020 Virtual. 

Publicly accessible?:  IHE-DCM is propriety.  Detailed information about model structure and model validity is publicly available, however (see “Key Publications”).

Is the model continuing to be developed?: Yes.

Brief Description:

The IHE-DCM was developed to estimate the cost effectiveness of treatment interventions for type 2 diabetes mellitus (T2DM) using the cohort (representative patient) approach. 


The IHE-DCM uses Markov health states that capture important microvascular and macrovascular complications and premature mortality resulting from T2DM. The cycle length is 1 year, and the time horizon is user-definable (up to 40 years). 
The model was constructed in Microsoft® Excel 2013 with the aid of the built-in Visual Basic for Applications (VBA) and requires no plugins or external programs to use. To ensure the flexibility necessary to model many different applications, the model contains many user-definable parameters, including baseline characteristics of the cohort, choice of risk equations, treatment algorithms, unit costs and quality-adjusted life year (QALY) weights. The baseline characteristics of the cohort are demographics (e.g. age and gender), biomarkers (e.g. glycated hemoglobin [A1C] and blood pressure) and pre-existing complications (e.g. microalbuminuria and stroke). 


At the start of the simulation, a cohort of hypothetical patients is defined from user-defined baseline characteristics and cloned for study arm. Each cohort is assigned a unique treatment algorithm. The treatment algorithms allow for modification of doses and addition of new medications when the initial treatment regimen does not achieve adequate A1C control. Medication to control blood pressure, blood lipids and overweight may also be applied. Treatment effects are modeled as absolute changes applied at simulation start or, for treatment intensification, during the year when it occurs in combination with annual drifts for each treatment line. The evolution of biomarkers is simulated annually until the predefined time horizon is reached. Adverse events, including up to 3 levels of severity of hypoglycemia, are applied using an annual event rate. Development and progression of complications and mortality are simulated next to the evolution of biomarkers. Risk equations govern the progression of the cohort between different health states. 
The macrovascular and microvascular health states were selected to capture the most important complications for T2DM. To make the cohort approach feasible, the sets of micro- and macrovascular health states were divided into 2 separate Markov sub-models. The 120 microvascular health states express the possible combinations of eye disease, kidney disease and lower extremity amputation states. The 100 macrovascular health states combine stages of ischemic heart disease (IHD), myocardial infarction (MI), stroke and heart failure. The user can choose to form a set of 4 macrovascular risk prediction equations, including the United Kingdom Prospective Diabetes Study (UKPDS) 68, UKPDS 82, Swedish National Diabetes Registry (NDR) and Australian Freemantle Diabetes Study (FDS), which are applied individually to each macrovascular health state. The user can choose between 2 sets of mortality equations, either the UKPDS 68 or UKPDS 82.


Unit costs and QALY weights, matching current treatment, distribution of health states and adverse events are applied to the cohort in each cycle. Model outcomes include mean survival, expected life-years, QALYs and direct costs. The outcomes are combined to compute incremental cost-effectiveness ratios (ICERs) and cost-effectiveness acceptability curves (CEACs), among other outcomes.

Funding source for model development: 

 Internal

Key Publications:

Lundqvist A, Steen Carlsson K, et al. Validation of the IHE Cohort Model of type 2 diabetes and the impact of choice of macrovascular risk equations. PLoS One. 2014;9(10): e110235.

Willis M, Fridhammar A, Gundgaard J, Nilsson A, Johansen P. Comparing the Cohort and Micro-Simulation Modeling Approaches in Cost-Effectiveness Modeling of Type 2 Diabetes Mellitus: A Case Study of the IHE Diabetes Cohort Model and the Economics and Health Outcomes Model of T2DM. PharmacoEconomics. 2020;38(9):953-69.