A Bayesian approach to assess heart disease mortality among persons with diabetes in the presence of missing data

Some states' death certificate form includes a diabetes yes/no check box that enables policy makers to investigate the change in heart disease mortality rates by diabetes status. Because the check boxes are sometimes unmarked, a method accounting for missing data is needed when estimating heart disease mortality rates by diabetes status. Using North Dakota's data (1992–2003), we generate the posterior distribution of diabetes status to estimate diabetes status among those with heart disease and an unmarked check box using Monte Carlo methods. Combining this estimate with the number of death certificates with known diabetes status provides a numerator for heart disease mortality rates. Denominators for rates were estimated from the North Dakota Behavioral Risk Factor Surveillance System. The Bayesian approach accounted for the uncertainty due to missing diabetes status as well as the uncertainty in estimating the populations with diabetes.