Comparison of Count Data Generalised Linear Models: Application to Air-Pollution Related Disease in Johor Bahru, Malaysia

Poisson regression is a common approach for modelling discrete data. However, due to characteristics of Poisson distribution, Poisson regression might not be suitable since most data are over-dispersed or under-dispersed. This study compared four generalised linear models (GLMs): negative binomial, generalised Poisson, zero-truncated Poisson and zero-truncated negative binomial. An air-pollution-related disease, upper respiratory tract infection (URTI), and its relationship with various air pollution and climate factors were investigated. The data were obtained from Johor Bahru, Malaysia, from January 1, 2012, to December 31, 2013. Multicollinearity between the covariates and the independent variables was examined, and model selection was performed to find the significant variables for each model. This study showed that the negative binomial is the best model to determine the association between the number of URTI cases and air pollution and climate factors. Particulate Matter (PM10), Sulphur Dioxide (SO2) and Ground Level Ozone (GLO) are the air pollution factors that affect this disease significantly. However, climate factors do not significantly influence the number of URTI cases. The model constructed in this study can be utilised as an early warning system to prevent and mitigate URTI cases. The involved parties, such as the local authorities and hospitals, can also employ the model when facing the risk of URTI cases that may occur due to air pollution factors.

Automated summary

This study compared four generalised linear models (GLMs) to investigate the relationship between an air-pollution-related disease, upper respiratory tract infection (URTI), and various air pollution and climate factors. The results showed that the negative binomial model is the best model to determine the association between the number of URTI cases and air pollution and climate factors.