Can Mathematics Help Answer the Big Questions About Global Obesity?

The three big questions about the global obesity epidemic are: what are the current and projected burdens of obesity; what has driven the simultaneous, global rise in obesity and what explains the variation in the trajectories of countries, and; what are effective and cost-effective interventions to reduce prevalence?  The questions can in part be answered using descriptive, explanatory and evaluative mathematical models, respectively. 

The physiological, behavioural, and environmental influences on obesity are enormously complex.  However, simplicity can be achieved if populations rather than individuals are modelled (determinants causing individual variability drop out), when questions are narrowed (unchanging determinants can be dropped for models examining the rise over time), or when solutions are modelled (influences not included in the intervention can be dropped). 

Progress with modelling obesity has been variable. Methods for estimating the current burden of obesity have been around for some time with more recent approaches assessing the added burden per rise in mean population body mass index (BMI) above 21 kg/m² (the value which minimises the combined prevalence of underweight and overweight).  Projecting future burdens from changing trends is much more difficult, although with sufficient prevalence time series estimates, useful projections can be made.  Models explaining the rise in obesity consistently point to a dominant driver effect of increasing energy intake.  Explaining variations between countries needs to take account of different environments: physical (eg urban transport systems), economic (eg wealth), policy (eg regulations of markets), and socio-cultural (eg body size perceptions).  Only some of these can be quantified and models have not been developed to explain the enormous variations in BMI between countries.  The evaluative modelling approaches to assessing effectiveness or cost-effectiveness of specified interventions has provided evidence for several highly cost-effective (but politically resisted) interventions and identified some other cost-ineffective (but politically favoured) interventions.

Mathematical models can greatly assist in the understanding the global obesity epidemic and can help guide priority-setting for policy action.