In it’s May 2022 edition, the Journal of Chronic Illness published, “Using latent class analysis to inform the design of an EHR-based national chronic disease surveillance model,” a peer-reviewed article describing how MENDS provides an approach to strategically select and engage partner sites to inform other public health surveillance modernization efforts that leverage timely EHR data in supporting increased representativeness of data for surveillance.
MENDS conducted a latent class analysis (LCA) and grouped 50 states and the District of Columbia by similarities in socioeconomics, demographics, chronic disease and behavioral risk factor prevalence, health outcomes, and health insurance coverage. The column was co-authored by subject matter experts from the National Association of Chronic Disease Directors and the National Center for Chronic Disease Prevention and Health Promotion at the Centers for Disease Control and Prevention.
LCA can be considered as a strategy for engaging partner sites in other public health surveillance modernization efforts at the national scale.