Citation

Bunn TL, Slavova S, Bathke A. J. Ky. Med. Assoc. 2007; 105(7): 313-320.

Affiliation

Kentucky Injury Prevention and Research Center, 333 Waller Ave, Suite 206, Lexington, KY 40504, USA. tlbunn2@uky.edu

Copyright

(Copyright © 2007, Kentucky Medical Association)

DOI

unavailable

PMID

17715679

Abstract

PROBLEM: The identification of industry, occupation, and associated injury costs for worker falls in Kentucky have not been fully examined. The purpose of this study was to determine the associations between industry and occupation and 1) hospitalization length of stay; 2) hospitalization charges; and 3) workers' claims costs in workers suffering falls, using linked inpatient hospitalization discharge and workers' claims data sets. METHODS: Hospitalization cases were selected with ICD-9-CM external cause of injury codes for falls and payer code of workers' claims for years 2000-2004. Selection criteria for workers'claims cases were International Association of Industrial Accident Boards and Commissions Electronic Data Interchange Nature (IAIABCEDIN) injuries coded as falls and/or slips. Common data variables between the two data sets such as date of birth, gender, date of injury, and hospital admission date were used to perform probabilistic data linkage using LinkSolv software. Statistical analysis was performed with non-parametric tests. RESULTS: Construction falls were the most prevalent for male workers and incurred the highest hospitalization and workers' compensation costs, whereas most female worker falls occurred in the services industry. The largest percentage of male worker falls was from one level to another, while the largest percentage of females experienced a fall, slip, or trip (not otherwise classified). When male construction worker falls were further analyzed, laborers and helpers had longer hospital stays as well as higher total charges when the worker fell from one level to another. CONCLUSIONS: Data linkage of hospitalization and workers' claims falls data provides additional information on industry, occupation, and costs that are not available when examining either data set alone.

Language: en