The Effect of PM2.5 on Hospital Admissions in Sydney’s South West using the Bayesian Hierarchical Model
The relationship between the incidence of hospital admissions for respiratory and cardiovascular diseases and air pollution due to fine particles (PM2.5) is modelled using a Bayesian hierarchical model. The ambient air quality standard level of PM2.5 is being considered around the world and the effect of air pollution, in particular, PM2.5, is of interests in many public health studies. Most of these studies use the classical time series Poisson GAM method, which recently was shown to overestimate the health effects of air pollution. The Bayesian method is gaining popularity as an alternative to the classical methods.
This study is a continuation of previous work which examined the correlation between PM10 levels and the frequency of hospital admission for respiratory related diseases at Liverpool hospital in Sydney. It will be shown that the ambient levels of PM2.5 have less effects than the ambient levels of PM10 on the frequency of hospital admissions due to respiratory and cardiovascular diseases.