Abstract
The application of syndromic surveillance systems has expanded beyond early event detection to include long-term disease trend monitoring. To address this wider set of priorities, we propose using a general linear mixed model (GLMM) for examining syndrome trends spatially and over time. With the GLMM, we found that New York City asthma rates varied by ZIP code and fluctuated seasonally, but that annual citywide rates did not change from 2007 to 2012. The GLMM estimated rates at multiple spatial and temporal levels, adjusted for clustering with random effects, and integrated covariate demographic data to reduce bias.