Use of employer administrative databases to identify systematic causes of injury in aluminum manufacturing.
BACKGROUND
Employer administrative files are an underutilized source of data in epidemiologic studies of occupational injuries.


METHODS
Personnel files, occupational health surveillance data, industrial hygiene data, and a real-time incident and injury management system from a large multi-site aluminum manufacturer were linked deterministically. An ecological-level measure of physical job demand was also linked. This method successfully created a database containing over 100 variables for 9,101 hourly employees from eight geographically dispersed U.S. plants.


RESULTS
Between 2002 and 2004, there were 3,563 traumatic injuries to 2,495 employees. The most common injuries were sprain/strains (32%), contusions (24%), and lacerations (14%). A multivariable logistic regression model revealed that physical job demand was the strongest predictor of injury risk, in a dose dependent fashion. Other strong predictors of injury included female gender, young age, short company tenure and short time on current job.


CONCLUSIONS
Employer administrative files are a useful source of data, as they permit the exploration of risk factors and potential confounders that are not included in many population-based surveys. The ability to link employer administrative files with injury surveillance data is a valuable analysis strategy for comprehensively studying workplace injuries, identifying salient risk factors, and targeting workforce populations disproportionately affected.
Pollack
Keshia M
KM
Department of Health Policy and Management, Center for Injury Research and Policy, The Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland 21218, USA. kpollack@jhsph.edu
Agnew
Jacqueline
J
Slade
Martin D
MD
Cantley
Linda
L
Taiwo
Oyebode
O
Vegso
Sally
S
Sircar
Kanta
K
Cullen
Mark R
MR
D000062:Accidents, Occupational / Q000706:statistics & numerical data*; D000293:Adolescent; D000328:Adult; D000367:Age Factors; D000535:Aluminum; D016208:Databases, Factual; D005260:Female; D006801:Humans; D008297:Male; D008669:Metallurgy; D008875:Middle Aged; D016272:Occupational Health / Q000706:statistics & numerical data*; D010559:Personnel Management / Q000706:statistics & numerical data*; D011159:Population Surveillance; D012737:Sex Factors; D013647:Task Performance and Analysis; D014481:United States
D000535:Aluminum