  In many healthcare and social science applications, information about units
is dispersed across multiple data files. Linking records across files is
necessary to estimate the associations of interest. Common record linkage
algorithms only rely on similarities between linking variables that appear in
all the files. Moreover, analysis of linked files often ignores errors that may
arise from incorrect or missed links. Bayesian record linking methods allow for
natural propagation of linkage error, by jointly sampling the linkage structure
and the model parameters. We extend an existing Bayesian record linkage method
to integrate associations between variables exclusive to each file being
linked. We show analytically, and using simulations, that this method can
improve the linking process, and can yield accurate inferences. We apply the
method to link Meals on Wheels recipients to Medicare Enrollment records.
