Count data has been witnessed in a wide range of disciplines in real life. Poisson, negative binomial (NB), zero inflated Poisson (ZIP) and zero inflated negative binomial (ZINB) are some of the regression models proposed to model data with count response. All the count models are potential candidates that can model count data, but there is no means to choose the one that would perform better than the others. This study aimed to assess the count models mentioned earlier at various degrees of zero inflation. Datasets were simulated with ZIP distribution with different conditions of zero inflation (0%, 2%, 5%, 10%, 15%, 20%, 30% and 40%). Poisson and NB were observed to predict regression coefficients well when the proportion of zero is below 15%. The two ZIM performed well at higher degrees of zero inflation; beyond 15% for ZIP and 20% for ZINB. Exploratory examination of the caries data revealed a zero inflation below 15%, that is, 3.23%. Analysis of early childhood caries (ECC) data among 3-6 year old children who visited Lady Northey Dental Clinic was then performed with Poisson and NB. Akaike information criterion (AIC) test was used to compare all the competing models both under simulation and with real data. Poisson yielded lower AIC values at lower zero inflation rates as compared to other three models. ZIP had the lowest AIC value at 10%, 15%, 20%, 30% and 40% levels of zero inflation. NB model had the lowest AIC value when real data was analyzed. Education level of the father-primary school completed, chewing gum several times a week, Feeding habit jam several times a day, Feeding habit juice every day, Feeding habit soda every day and Feeding habit sweets several times a week were found to be significant factors causing ECC.

Count regression models were developed to model data with integer outcome variables. These models can be employed to examine occurrence and frequency of occurrence. Four common types of count regression models are applied to caries data among children aged between three and six years attending Lady Northey Dental clinic between September, 2014 and November 2014. These models include Poisson, Negative Binomial (NB), Zero Inflated Poisson (ZIP) and Zero Inflated Negative Binomial (ZINB). The simplest count regression model, Poisson, was fitted first before considering other complex models. However, it did not perform better than its improved counterparts. The NB model proved to be the the simplest model that fits the data well according to Akaike Information Criterion (AIC), and was therefore employed to determine the important predictors of caries experience among the children. Model comparison was performed on the four models by use of AIC. Deviance values for various NB models were compared and the model with the least deviance value was considered to give a subset of best predictors of Early Childhood Caries (ECC). These predictors included age, gender, brushing frequency, feeding habit biscuits, feeding habit jam and highest education of the mother.