HEDS is part of the School of Health and Related Research (ScHARR) at the University of Sheffield. We undertake research, teaching, training and consultancy on all aspects of health related decision science, with a particular emphasis on health economics, HTA and evidence synthesis.

Wednesday, 19 August 2015

Diabetes Prevention Model

With calls from Diabetes UK to invest more in diabetes prevention, it is timely to flag the latest addition to HEDS Discussion Paper Series “SPHR Diabetes Prevention Model: Detailed Description of Model Background, Methods, Assumptions and Parameters”.  HEDS contributions are from Penny Breeze, Chloe Thomas, Hazel Squires, Alan Brennan, Louise Preston and Jim Chilcott.  The abstract is below:

“Type-2 diabetes is a complex disease with multiple risk factors and health consequences whose prevention is a major public health priority. We have developed a microsimulation model written in the R programming language that can evaluate the effectiveness and cost-effectiveness of a comprehensive range of different diabetes prevention interventions, either in the general population or in subgroups at high risk of diabetes.

Within the model individual patients with different risk factors for diabetes follow metabolic trajectories (for body mass index, cholesterol, systolic blood pressure and glycaemia), develop diabetes, complications of diabetes and related disorders including cardiovascular disease and cancer, and eventually die. Lifetime costs and quality-adjusted life-years are collected for each patient. The model allows assessment of the wider social impact on employment and the equity impact of different interventions. Interventions may be population-based, community-based or individually targeted, and administered singly or layered together. The model is fully enabled for probabilistic sensitivity analysis (PSA) to provide an estimate of decision uncertainty.

This discussion paper provides a detailed description of the model background, methods and assumptions, together with details of all parameters used in the model, their sources and distributions for PSA.”