Accessibility statement

Future modelling of chronic diseases: foresight and beyond

Thursday 18 September 2014, 12.30PM to 1.30pm

Speaker(s): Dr Martin Brown, UK Health Forum

Abstract:

Background: Many chronic diseases are interrelated and their effects under changing exposures need to be better understood. Policy makers and planners need to understand what the current distributions of avoidable chronic disease are, among whom, and how they are likely to develop in the future. In particular, what are their effects on different populations, what will be the health consequences of these trends, and how much can these consequences be attenuated with effective interventions? Modelling the effects by evidence-based extrapolation, incorporating and attributing the epidemiology of related diseases, can give rise to straightforward estimates of incidence and death rates for the most common related conditions for the next 30+ years. Related direct or indirect healthcare costs of each disease are calculated on a cost-per-case basis. Currently this module of the model is under development as part of the EConDA project (econdaproject.eu).

Methods: The model builds on work originally developed for Foresight Tackling Obesities (UK) and subsequent work modelling obesity and related diseases in a further 70 countries. A microsimulation model was used to project future health of each of millions of individuals with a given demography (of any region) through to a given year, and various scenarios were simulated. Competing risks are examined in real simulated time. Related diseases and associated health-care costs were calculated on the basis of trends in risk factors distributed among these individuals. In the case of obesity, 13 diseases were considered. We have simulated hypothetical future scenarios, e.g. trends continue unabated, 1% and 5% reductions in body-mass index (BMI) or real interventions such as a the health impact of a sugar-sweetened beverage tax or weight-management intervention. Ultimately, health and other costs incurred or saved can be compared with the costs of intervention. The simulation model was developed in discrete modules to enable radical change and updating of assumptions and parameters.

Results: We will present some highlights from our work that have been published in the Lancet (Wang and colleagues, 2012) as well as from other work (Webber et al, BMJ Open, 2014; Retat et al, in prep) and preliminary results from our EConDA project. Small reductions in risk factors can have substantial effects on future burdens of disease and avoidable. In one study we showed that in the UK, a 1% reduction in BMI rates will save £15·5 billion, whereas in the USA the medical costs will be reduced by US$686 billion. With a 5% reduction in BMI, medical cost savings in the UK will be £61·8 billion and in the USA $1·93 trillion. The figures are substantial for other countries too, reaching $45 million for Russia, $1·8 billion for Mexico, and $4·8 billion for Brazil by 2050.

Interpretation: This predictive modelling has significant resonance with policy makers. Using sensitivity analysis we can test the outcome of interventions at a national or subnational level over a timescale that is difficult to measure by conventional evaluative methods.

Location: ARRC Auditorium A/RC/014

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Economic evaluation seminar dates

  • 10 December 2014
    Claire Hulme, Professor of Health Economics, University of Leeds