Journal of Data Science

Abstract: A multilevel model (allowing for individual risk factors and geo graphic context) is developed for jointly modelling cross-sectional differences in diabetes prevalence and trends in prevalence, and then adapted to provide geographically disaggregated diabetes prevalence forecasts. This involves a weighted binomial regression applied to US data from the Behavioral Risk Factor Surveillance System (BRFSS) survey, specifically totals of diagnosed diabetes cases, and populations at risk. Both cases and populations are dis aggregated according to survey year (2000 to 2010), individual risk factors (e.g., age, education), and contextual risk factors, namely US census division and the poverty level of the county of residence. The model includes a linear growth path in decadal time units, and forecasts are obtained by extending the growth path to future years. The trend component of the model controls for interacting influences (individual and contextual) on changing prevalence. Prevalence growth is found to be highest among younger adults, among males, and among those with high school education. There are also regional shifts, with a widening of the US “diabetes belt”.