A bivariate zero-inflated Poisson regression model to analyze occupational injuries.
The aim of many occupational safety interventions is to reduce the incidence of injury. However, when measuring intervention effectiveness within a period, population-based accident count data typically contain a large proportion of zero observations (no injury). This situation is compounded where injuries are categorized in a binary manner according to an outcome of interest. The distribution thus comprises a point mass at zero mixed with a non-degenerate parametric component, such as the bivariate Poisson. In this paper, a bivariate zero-inflated Poisson (BZIP) regression model is proposed to evaluate a participatory ergonomics team intervention conducted within the cleaning services department of a public teaching hospital. The findings highlight that the BZIP distribution provided a satisfactory fit to the data, and that the intervention was associated with a significant reduction in overall injury incidence and the mean number of musculoskeletal (MLTI) injuries, while the decline in injuries of a non-musculoskeletal (NMLTI) nature was marginal. In general, the method can be applied to assess the effectiveness of intervention trials on other populations at high risk of occupational injury.
School of Public Health, Curtin University of Technology, P.O. Box U 1987, 6845, WA, Perth, Australia.
Kui
K
Wang
Andy H
AH
Lee
Kelvin K W
KK
Yau
Philip J W
PJ
Carrivick
D000062:Accidents, Occupational / Q000706:statistics & numerical data*; D006804:Ergonomics / Q000706:statistics & numerical data; D006801:Humans; D015233:Models, Statistical; D016012:Poisson Distribution; D012044:Regression Analysis; D014947:Wounds and Injuries / Q000453:epidemiology*