It is important to fit count data with suitable model(s), models such as Poisson Regression, Quassi
Poisson, Negative Binomial, to mention but a few have been adopted by researchers to fit zero truncated
count data in the past. In recent times, dedicated models for fitting zero truncated count data have been
developed, and they are considered sufficient. This study proposed Bayesian multi-level Poisson and
Bayesian multi-level Geometric model, Bayesian Monte Carlo Markov Chain Generalized linear Mixed
Models (MCMCglmms) of zero truncated Poisson and MCMCglmms Poisson regression model to fit
health count data that is truncated at zero. Suitable model selection criteria were used to determine
preferred models for fitting zero truncated data. Results obtained showed that Bayesian multi-level
Poisson outperformed Bayesian multi-level Poisson Geometric model; also MCMCglmms of zero
truncated Poisson outperformed MCMCglmms Poisson
