Interpreting Poisson Regression Models in Dental Caries Studies

Abstract

Oral epidemiology involves studying and investigating the distribution and determinants of dental-related diseases in a specified population group to inform decisions in the management of health problems. In oral epidemiology studies, the hypothesis is typically followed by a cogent study design and data collection. Appropriate statistical analysis is essential to demonstrate the scientific association between the independent factors and the target variable. Analysis also helps to develop and build a statistical model. Poisson regression and its extensions have gained more attention in caries epidemiology than other working models such as logistic regression. This review discusses the fundamental principles and basic knowledge of Poisson regression models. It also introduces the use of a robust variance estimator with a focus on the “robust” interpretation of the model. In addition, extensions of regression models, including the zero-inflated model, hurdle model, and negative binomial model, and their interpretation in caries studies are reviewed. Principles of model fitting, including goodness-of-fit measures, are also discussed. Clinicians and researchers should pay attention to the statistical context of the models used and interpret the models to improve the oral and general health of the communities in which they live.