Modeling fatal injury rates using Poisson regression: A case study of workers in agriculture, forestry, and fishing

doi:10.1016/S0022-4375(97)80006-0

Journal of Safety Research

Volume 28, Issue 3, Autumn 1997, Pages 177-186

https://doi.org/10.1016/S0022-4375(97)80006-0 Get rights and content

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.

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¹: John Bailer, Ph.D., earned a doctorate in Biostatistics with an area of concentration in environmental health. Since 1988, he has been a professor in the Department of Mathematics and Statistics at Miami University in Oxford, Ohio. Since 1990, he has worked in conjunction with risk assessment researchers at the National Institute for Occupational Safety and Health. Dr. Bailer has published widely in the areas of occupational and environmental risk estimation with special interests in the design and analysis of occupational and environmental research studies.

²: Leslie Stayner, Ph.D., received his doctorate in Epidemiology from the University of North Carolina in 1989, and a Masters Degree in Epidemiology from the Harvard School of Public Health in 1980. He has been working with the National Institute for Occupational Safety and Health for approximately 15 years, originally involved in the conduct of industry-wide research projects, and is currently involved in managing their Risk Assessment Program.

³: Larry Reed, M.S., earned a Master's degrees in Preventive Medicine and in Industrial and Systems Engineering. He has been employed at the National Institute for Occupational Safety and Health since 1977. In his current position, Mr. Reed manages the development of NIOSH policy statements to regulatory agencies and document development including criteria documents, alerts and hazard controls. Prior to these responsibilities, he worked in the research areas of personal protective equipment, industrial hygiene, and engineering control technology. He also teaches mathematics on a part-time basis in the College of Evening and Continuing Education at the University of Cincinnati.

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Modeling fatal injury rates using Poisson regression: A case study of workers in agriculture, forestry, and fishing

Abstract

The analysis of mortality by the subject-years method

Biometrics

Divergent trends in childhood drowning rates, 1971 through 1988

Journal of the American Medical Association

Statistical methods in cancer research, Vol. II — The design and analysis of cohort studies

The analysis of rates using poisson regression models

Biometrics

Use of Poisson regression models in estimating incidence rates and ratios

American Journal of Epidemiology