Dynamic simulation models

Dynamic simulation models are designed to track changes in an outcome over a specified time period. Groups of individuals, or simulated individuals themselves, are followed, with transitions through different BMI groups, ages, or migration routes determined by transition probabilities or rates98_{. They are useful in that} future demographic changes, changes in incidence of a condition and parameter uncertainty can be packaged in a fashion whereby these various dynamics operate simultaneously and internally within the model, and different future scenarios can be tested with relative ease. Dynamic simulation models work well in the prediction of future overweight and obesity due to its ability to simultaneously incorporate non-linear trends, the delay between changes in past incidence and its effect on total prevalence, in addition to feedback loops98_{. A feedback loop, in the} context of forecasting future overweight and obesity, could involve a reduction in future incidence of overweight and obesity in response to increased awareness of excess weight as the prevalence increases109_{. The packaging of epidemiological,} societal (for instance urbanisation) and policy related effects in such a framework

has made simulation modelling an appealing method in the forecasting of overweight and obesity.

2.2.4.1. Microsimulation models

One family of dynamic simulation models are microsimulation models. Microsimulation approaches in overweight and obesity forecasting generally begin with a simulated population with a particular BMI distribution at a baseline time point. This population is designed to represent a real population98_{. The} model’s forecasts are based on the random simulation of the population’s BMI trajectory as they age up to a pre-determined forecast year110_{. Estimated transition} parameters from one BMI value to another, based on a starting BMI, age and cohort, are then applied to the simulated population to inform an individual’s BMI trajectory over the life course. Mortality rates for individuals based on one’s sex, age, cohort and BMI are usually introduced as a competing transition for any simulated individual, and probabilities of giving birth are usually added to make the simulated population as realistic as possible110_{. Commonly, models operate in} discrete-time and adopt a simplifying Markov assumption (that an individual’s risk of transitioning to a different BMI, remaining at the same BMI, or dying, are based on current characteristics rather than any previous transitions)98_.

Microsimulation has been adopted in a number of overweight and obesity forecasting studies in HICs110–114_{. The Foresight team, affiliated with the} Government Office for Science in the UK, estimated that the proportion of men aged 21-60 years classified as overweight will decline from around 44% in 1993 to 35% in 2050, whereas the proportion of women who are overweight will increase from 30% to 33% over the same period110_{. On the other hand, the proportion of} adult men who are obese is expected to reach 60% in 2050, from 13% in 1993; among women the prevalence will increase from 16% to 54%, although a larger proportion of obese women are expected to have a BMI greater than 40kg/m2110_.

Similarly, another microsimulation study in Australia found that the prevalence of overweight through 2025 is likely to remain relatively stable among adults. On the other hand, the prevalence of obesity is predicted to increase from 19% to 35% among adults between 1995 and 2025, whereas the prevalence of severe obesity was projected to almost triple, from 5% in 1995 to 13% in 2025113_.

Simulation models offer an easily interpretable framework within which one can test the effects of interventions designed to tackle overweight or obesity. Webber et al (2014) used microsimulation resembling the Foresight team’s model to forecast the future burden of obesity and obesity related diseases in 53 countries, primarily in Europe. In 2030 their model projected that 4.0%, 4.6% and 2.1% of people across the 53 countries would have diabetes, CHD and stroke and cancer, respectively. Furthermore, the authors established that a reduction in the population BMI of 1% would cause around 365 incident cases per 100000 of CHD and stoke to be avoided, whereas a 5% reduction in population BMI would result in 1317 incident cases per 10000 of CHD and stroke being averted114_.

2.2.4.2. Macrosimulation models

In contrast to microsimulation models, macrosimulation models group individuals by particular characteristics, for instance, age, BMI group or urban/rural residence. Rather than track individuals, macrosimulation models track proportions of the population in each group; sometimes referred to as health states. Similar to microsimulations, transitions between health states or a death state, are determined based on a set of transition rates and mortality rates. Macrosimulation models generally use the proportions of the population in each health state, an estimate of the total population in the baseline period, the number of individuals entering the model at each discrete time step (in addition to their distribution across the states) and an understanding of how the transition (including mortality) rates are likely to evolve over the forecast period as input parameters. Uncertainty is often incorporated into the models by running multiple

simulations, each time selecting a random set of parameter values from a specified range and distribution, also known as Monte Carlo simulations98_.

Basu’s (2010) age-classified macrosimulation model, forecasting BMI distributions between 2004 and 2014 in the United States among children/adolescents and people aged 17 years or more, found that obesity levels are expected to remain relatively constant among US adults, whereas the prevalence of overweight is expected to increase over the period. One-year transitions between BMI groups were estimated using longitudinal data from the Medical Expenditure Panel Survey115_.

Simple macrosimulation models are usually sufficiently flexible to expand and answer more nuanced research questions. A study aiming to examine the reasons for a plateauing obesity prevalence in the United States for instance, expanded the Basu (2010) model to compartmentalise the population below the BMI classification for overweight into groups with different risks of transitioning to overweight (susceptible, exposed, and recovered). This expansion aimed to account for some diversity in the probability of becoming overweight among those who are not overweight (for instance, the exposed category represented individuals born into or residing in an obesogenic environment). Their results provided a particularly detailed understanding of the dynamics of future obesity in the United States, particularly the fact that the future level at which obesity will plateau is primarily driven by a combination of the probability of being born into an obesogenic environment and the birth rate. Additionally, the study found that obesity prevalence is likely to plateau around 2030, irrespective of any interventions. Such findings can play a crucial role in accurately monitoring the impact of future interventions116_.

Similar models have also been used to explore predicted future socioeconomic inequalities in obesity. In Australia, one study found that if all educational groups had the same probabilities of becoming obese, the projected difference in prevalence in 2025 between the lowest and highest educational group would decrease from 14% to 6%40_{. Another study incorporated an indicator of caloric}

imbalance to influence transition rates between BMI categories, which were determined by changes affecting the food environment or activity environments or changes to either the effectiveness or the use of services to regulate one’s weight. Their findings suggested that efforts to maintain caloric balance among school children would do little to halt the increase in adult obesity117_.