A hierarchical model for analyzing multisite individual-level disease surveillance data from multiple systems.

ABSTRACT: Passive surveillance systems are widely used to monitor diseases occurrence over wide spatial areas due to their cost-effectiveness and integration into broadly distributed healthcare systems. However, such systems are generally associated with imperfect ascertainment of disease cases and with heterogeneous capture probabilities arising from factors such as differential access to care. Augmenting passive surveillance systems with other surveillance efforts provides a way to estimate the true number of incident cases. We develop a hierarchical modeling framework for analyzing data from multiple surveillance systems that allows for individual-level covariate-dependent heterogeneous capture probabilities, and borrows information across surveillance sites to improve estimation of the true numb

SUBMITTER: Zhang Y 

PROVIDER: S-EPMC10027420 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature