  Electronic health records (EHRs) have become a platform for data-driven
surveillance on a granular level in recent years. In this paper, we make use of
EHRs for early prevention of childhood obesity. The proposed method
simultaneously provides smooth disease mapping and outlier information for
obesity prevalence, which are useful for raising public awareness and
facilitating targeted intervention. More precisely, we consider a penalized
multilevel generalized linear model. We decompose regional contribution into
smooth and sparse signals, which are automatically identified by a combination
of fusion and sparse penalties imposed on the likelihood function. In addition,
we weigh the proposed likelihood to account for the missingness and potential
non-representativeness arising from the EHR data. We develop a novel
alternating minimization algorithm, which is computationally efficient, easy to
implement, and guarantees convergence. Simulation studies demonstrate superior
performance of the proposed method. Finally, we apply our method to the
University of Wisconsin Population Health Information Exchange database.
