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Singapore is a microcosm of Asia as a whole, and its rapidly ageing, increasingly sedentary population heralds the chronic health problems other Asian countries are starting to face and will likely face in the decades ahead. Forecasting the changing burden of chronic diseases such as type 2 diabetes in Singapore is vital to plan the resources needed and motivate preventive efforts.

This paper describes an individual-level simulation model that uses evidence synthesis from multiple data streams—national statistics, national health surveys, and four cohort studies, and known risk factors—aging, obesity, ethnicity, and genetics—to forecast the prevalence of type 2 diabetes in Singapore. This comprises submodels for mortality, fertility, migration, body mass index trajectories, genetics, and workforce participation, parameterized using Markov chain Monte Carlo methods, and permits forecasts by …