Zero-Inflated Regression Models for Modeling the Effect of Air Pollutants on Hospital Admissions

Abstract

Count regression methods are the fundamental tool used for modeling the association between environmental pollution and hospital admissions. Data with many zeros are often encountered in count regression models. Failure to account for the extra zeros may result in biased parameter estimates and misleading inferences. Zero-inflated Poisson and zero-inflated negative binomial regression models have been proposed for situations where the data generating process results in too many zeros.