Methodologic issues in the use of workers' compensation databases for the study of work injuries with days away from work. I. Sensitivity of case ascertainment

Background Case ascertainment costs vary substantially between primary and secondary data sources. This review summarizes information on the sensitivity of state administrative databases in workers' compensation systems for the ascertainment of days-away-from-work (DAFW) work injuries for use in modeling studies. Methods Review of the literature supplemented by data from governmental or organizational reports or produced for this report. Results Employers currently appear to provide workers' compensation insurance coverage for 98.9% of wage and salary workers. Wage and salary jobs account for approximately 90% of jobs in the United States. In industries such as manufacturing, the fraction of covered jobs is probably closer to 98%. In Minnesota, the number of DAFW cases ascertained by the Bureau of Labor Statistics' annual survey of occupational injuries and illnesses is approximately 92–97% concordant with the number of wage compensation claims for injuries producing DAFW over the period 1992–2000, once adjustments are made to permit direct comparisons of the numbers. The workers' compensation databases provide information for more than 95% of the total DAFW resulting from work injuries. Covariate estimates are unaffected by this less than 5% loss because effects appear dependent on time from injury. Conclusions Statewide workers' compensation administrative databases can have substantial utility for epidemiologic study of work injuries with DAFW because of their size, using high sensitivity for case ascertainment as the evaluative criterion. Am. J. Ind. Med. 45:260–274, 2004. © 2004 Wiley-Liss, Inc.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/34825/1/10333_ftp.pd

Predicting movements of onsite workers and mobile equipment for enhancing construction site safety

Tens of thousands of time-loss injuries and deaths are annually reported from the construction sector, and a high percentage of them are due to the workers being struck by mobile equipment on sites. In order to address this site safety issue, it is necessary to provide proactive warning systems. One critical part in such systems is to locate the current positions of onsite workers and mobile equipment and also predict their future positions to prevent immediate collisions. This paper proposes novel Kalman filters for predicting the movements of the workers and mobile equipment on the construction sites. The filters take the positions of the equipment and workers estimated from multiple video cameras as input, and output the corresponding predictions on their future positions. Moreover, the filters could adjust their predictions based on the worker or equipment's previous movements. The effectiveness of the filters has been tested with real site videos and the results show the high prediction accuracy of the filters

Self-reported physical work exposures and incident carpal tunnel syndrome

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/109320/1/ajim22359.pd