Performance Assessment of Some Count Data Models to Immunization Coverage Data

Abstract

This research evaluates the performance of various count data models, including Poisson Regression (PR), Zero-Inflated Poisson Regression (ZIP), Zero-Truncated Poisson Regression (ZTP), Truncated Negative Binomial Poisson Regression (TNBP), and Negative Binomial Poisson Regression (NBP), using immunization coverage data from the National Primary Health Care Development Agency (NPHCDA). The study focuses on children under 12 months, assessing model fit using Likelihood Ratio (LR), Akaike Information Criterion (AIC), and Bayesian Information Criterion (BIC) criteria. Analysis conducted with STATA indicates that the Truncated Negative Binomial Poisson Regression (TNBP) outperformed other models in fit and efficiency. Both the ZTeeP and TNBP models demonstrated the best fit, with lower AIC (1959.107) and BIC (2037.649) values and higher Pseudo R-squared values (0.0677 for ZTP and 0.0590 for TNBP), compared to standard models. Age was identified as a significant predictor, negatively associated with immunization status, implying that older infants in the under-12-month category are less likely to receive all vaccinations. The ZTP model showed significant positive effects for antigens such as HepB0, OPV0, BCG, and Measles, with age having a significant negative association. The findings highlight the importance of selecting appropriate statistical models for accurate public health data analysis, enhancing decision-making in immunization programs.