Background: With increasing urbanisation rates, assessments must be made on the impact of the built environment on the health of populations. As the bulk of healthcare expenditure in developed countries is borne by the elderly through chronic disease management and treatment costs, intervening using the built environment can have lasting population-wide effects.
Methods: Using two cohort studies for training and validation, we quantified each individual’s local context based on their residential address and derived geographical exposures adapted from the International Physical Activity and the Environment Network guidelines. Bayesian inference was used to develop a regression model that examines the impacts of the geographical exposures and predicts mean body mass index and prevalence of type 2 diabetes mellitus, acute myocardial infarction and stroke by communities.
Results: The distance to the nearest retail outlet was found to be negatively associated with body mass index. Our prediction model shows good accuracy (AUC > 0.75) for predicting type 2 diabetes mellitus, acute myocardial infarction and stroke. National-level maps were generated that predict the health of communities by mean body mass index and overall chronic disease risk.
Conclusions: The predictive model has the ability to predict on a macro scale the overall health of a community. Understanding the geospatial distribution of chronic disease risk allows for evidence-based policymaking with urban–specific interventions that improve overall population health.