<p>Zero-inflated models are commonly used for longitudinal count data with excess zeros. When the data exhibit overdispersion, conventional zero-inflated Poisson mixed models may fail to provide an adequate fit. Moreover, missing outcomes induced by subject dropout represent a common challenge in longitudinal studies, necessitating the incorporation of informative dropout into modeling frameworks to mitigate bias. To address these issues, we propose a novel Bayesian model for analyzing longitudinal count data characterized by excess zeros, overdispersion, and informative dropout. Unlike frequentist methods, our approach treats unobserved dropout outcomes as latent variables through data augmentation, thereby transforming high-dimensional integration problems into posterior sampling tasks. By integrating Pólya-Gamma data augmentation, we develop an efficient Gibbs sampling algorithm. The simulation results demonstrate that ignoring missing data can lead to severe bias, even reversing time trend estimates, whereas the proposed method maintains accurate parameter estimation. In a health research application, our model identified a positive temporal trend in hospitalization duration that was not captured by models ignoring informative dropout. It suggests a possible population-wide deterioration in health habits or systemic and behavioral shifts, pointing to the need for targeted policy interventions.</p>