How were ranks created for the data?
Ranks are based on the age-adjusted prevalence rates of diagnosed diabetes and obesity. Models are fit using a Bayesian simulation method known as Markov Chain Monte Carlo (MCMC).3, 5 As part of the model fitting process we generate and save two thousand draws from the distribution of each county’s age-adjusted prevalence rate. For each of these draws we sort the counties by prevalence and save the counties’ ranks. This gives us two thousand draws from the distribution of each county’s rank. We then use the median for the rank estimate and the 5th and 95th percentiles for a 90% confidence interval.
Ranks for county-level data of diagnosed diabetes and selected risk factors were based on age-adjusted prevalence rates. Models were fit using a Bayesian simulation method known as Markov Chain Monte Carlo.2-4 As part of the model fitting process we generated and saved two thousand draws from the distribution of each county’s age-adjusted prevalence rate. For each of these draws we sorted the counties by prevalence and saved the counties’ ranks. This gave us two thousand draws from the distribution of each county’s rank. We then used the median for the rank estimate and the 5th and 95th percentiles for a 90% confidence interval.
