Efficiency of Zero-Inflated Generalized Poisson Regression Model on Hospital Length of Stay Using Real Data and Simulation Study

Background: An important feature of Poisson distribution is the equality of mean and variance. However, additional zeroes in the data may cause over-dispersion in most cases, in which zero-inflated models are recommended. In this study, we aimed to evaluate the efficacy of zero-inflated models to predict hospital length of stay (LOS) using real data and simulated study.Methods: This study was conducted on patients admitted at Shariati hospital, Tehran, Iran. Zero inflated Poisson (ZIP), zero inflated negative binomials (ZINB) and zero inflated generalized Poisson (ZIGP) models were fitted on patient’s length of stay. The fitted models were compared using the Akaike information criterion (AIC). The simulated data was generated using a model with the lowest AIC. Different models were then compared using the AIC. Data analysis was performed in R statistical software. Results: The results of both real data and simulation study showed lower AIC for ZIGP model compared to ZIP and ZINB model. Conclusion: Given the high dispersion and Zero Inflation in hospital LOS, the zero-inflated generalized Poisson regression model is the most suitable model to predict determinants of LOS.