Many safety studies as based on the analysis carried out on injury surveillance data. The
injury surveillance data gathered for the analysis include information on number of employees
at risk of injury in each of several strata where the strata are defined in terms of a series of
important predictor variables. Further insight into the relationship between fatal injury rates
and predictor variables may be obtained by tha Poisson regression approach. Poisson
regression is widely used in analyzing count data. In this study, Poisson regression is used to
model the relationshiop between fatal injury rates and predictor variables which are year
(1995-2002), gender, recording system and industry type. Data for the analysis were obtained
from PERKESO and Jabatan Perangkaan Malaysia. It is found that the assumption that the
data follow Poisson distribution has ben violated. After correction for the problem of
overdispersion, the predictor variables that are round to be significant in the model are
gender, system of recording, industry type, and two interaction effects (interaction between
recording system and industry type, and between year and industry type