A1621
Title: Copula-based joint modeling of emergency department visits with time-varying dependence
Authors: Cindy Feng - Dalhousie University (Canada) [presenting]
Guanjie Lyu - University of Windsor (Canada)
Abstract: Jointly modeling multiple correlated count time series is essential in health services research, where outcomes like emergency visits for mental health and substance use often evolve together. Ignoring these dependencies can obscure meaningful trends and limit the effectiveness of policy evaluation. A copula-based framework is proposed that combines negative binomial regression with penalized splines for flexible marginal trends and a smoothly time-varying copula to capture evolving dependence. Simulations show improved accuracy and uncertainty quantification over models assuming constant or no dependence. Applied to monthly emergency visits in Nova Scotia (2017-2023), the model reveals dynamic associations: strong positive correlation pre-COVID 19, a temporary reversal during lockdowns, and partial recovery post-reopening. This approach offers a powerful tool for analyzing complex, time-varying relationships in health care utilization data to inform public health planning.
