Citation

Bailer AJ, Reed LD, Stayner LT. J. Saf. Res. 1997; 28(3): 177-186.

Copyright

(Copyright © 1997, U.S. National Safety Council, Publisher Elsevier Publishing)

DOI

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PMID

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Abstract

Injury surveillance data serves as the foundation of many safety studies. These studies frequently gather information on the number of injuries along with the 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. It is common for analyses of such data to examine injury rates separately for each predictor variable. The analysis of the crude or unadjusted injury rates give an overall indication of injury rate changes as a function of a particular predictor variable; however, further insights may be gained from analyses using Poisson regression models.Poisson regression models are described as a means of analyzing rates adjusting for one or more predictor variables. In these models, the log rate of injury is expressed as a linear function of predictor variables. The interpretation of model parameters is given along with a presentation of the basic formulation of such models. Testing for trend, evaluation of confounding, and effect modification are illustrated using surveillance data describing occupational fatal injury rates as a function of year (1983-1992), gender and age for White workers employed in agriculture, forestry, or fishing. Data for this analysis were obtained from two sources: the National Traumatic Occupational Fatality (NTOF) database from the National Institute for Occupational Safety and Health provided counts of the fatal injuries, while data from the U.S. Bureau of Labor Statistics (BLS) provided counts on employment. Using an unadjusted trend model, a statistically nonsignificant decline in fatal injury rates over 1983-1992 is observed. Further analysis using Poisson regression revealed an interaction between gender and calendar year with males experiencing a weak, albeit significant, decrease and females experiencing a strong and significant increase.