Economic Modelling of the Prevention of Type 2 Diabetes

Slide 1:Economic Modelling of the Prevention of Type 2 Diabetesin Australia

Presentation to the Technical Workshops of the1st General Conference of the International Microsimulation AssociationVienna, 22 August 2007

Slide 2:Background

In 2005 NATSEM was commissioned to construct a model that would facilitate: The economic analyses evaluating whether or not type 2 diabetes prevention programs are likely to be cost-effective in the Australian context

Slide 3:Research Questions

The model was to be used to address two specific research questions: What are the relative benefits of investing in primary prevention compared to secondary prevention; and What is the optimal balance of investment across strategies aimed at the whole population versus those targeting high risk groups?

Slide 4:Overview of the Diabetes Model

A cell based population projections model Generates a time-series of cross-sectional prevalence based ‘snap shots’ of the adult Australian population Models up to 15 three-year cycles: Current base year is 2005 ? project to 2050 The cell-based structure of the model is underpinned by unit record data from the 1999-00 AusDiab survey The population base is also updated in each cycle to reflect population ageing

Slide 5:Overview of the Diabetes Model (cont)

3,456 population cells representing the unique combinations of eight diabetes risk factors used in the model: Age Sex Income Lifestyle factors In each cycle of the simulation, people are shifted between risk factor states reflecting changes in the prevalence of modifiable risk factors Two logistic regression models are used to predict the proportion of a cell population that is likely to have pre­diabetes or diabetes

Slide 6:Diabetes Model Risk Factors

Waist circumference (binary) Blood pressure (binary) Abnormal cholesterol (binary) Adequacy of exercise (three levels) Smoking status (three levels) Income (four levels)

Slide 7:Diabetes Model Flow Diagram

1. AusDiab Survey

Slide 8:Moving People Between Risk Factor States

The movement of people within the Diabetes Model is largely based on the idea of binary paired states For every particular combination of sex, age and risk factor profile there is a paired combination which is identical in all respects except that one of the risk factors has the opposite status

Slide 9:Binary Paired States

Slide 10:Example – Base Case

What might happen to diabetes prevalence if current trends in population ageing, diabetes risk factors, existing screening and detection practices, and diabetes care persist. Quantify current trends in diabetes risk factors using data from sources such as AIHW’s risk factor data online cube. Used as comparator

Slide 11:Predicted Change in Diabetes Prevalence

Slide 12:Projected Growth in Adults with Type 2 Diabetes – Nums/Prevalence by Risk Factor

Number of Adults Aged 25 Years and Over ('000)

Slide 13:Impact of Primary Prevention - Prevalence of Diabetes in Adults Aged ? 25 years

Slide 14:Three Year Disability Adjusted Life Years

Slide 15:Direct Health Care Costs – Diagnosed With Type 2 Diabetes (Not Discounted)

Slide 16:User Interface

Slide 17:Navigation Panel

Slide 18:Updated Version of the Diabetes Model

Diabetes Model v06.1 released last year Version 06.2 recently released. Major enhancements: How secondary interventions are modeled Interaction between the model and the user Improved computational efficiency Additional output on a given simulation Various technical changes to make the model more robust Currently working on v07.1

Slide 19:Future Developments

Alternative risk factor classifications (v07.1) Joint estimation of diabetic states (v07.1) Some dependence between age groups (v07.1) Modelling complications (v07.1) Second wave of the AusDiab survey (v07.1?) Improved computational efficiency (v07.1) Alternative software (v0x.x)? Measures of uncertainty Modelling different medical services