Modeling of fatal injury rates among Malaysian workers using poisson regression approach

Abstract

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)