Occupational injuries to oregon workers 24 years and younger: An analysis of workers' compensation claims, 2000-2007

2010 ◽  
Vol 53 (10) ◽  
pp. 984-994 ◽  
Author(s):  
Jaime K. Walters ◽  
Kari A. Christensen ◽  
Mandy K. Green ◽  
Lauren E. Karam ◽  
Laurel D. Kincl
Keyword(s):  
Occupational Injuries ◽  
Workers Compensation ◽  
Compensation Claims

scholarly journals Collider bias in administrative workers’ compensation claims data: A challenge for cross-jurisdictional research

2021 ◽  
Author(s):  
Tyler J Lane
Keyword(s):  
Work Disability ◽  
Claims Data ◽  
Occupational Injuries ◽  
Simulated Data ◽  
Workers Compensation ◽  
Compensation Systems ◽  
Compensation Claims ◽  
Analytical Approaches ◽  
Collider Bias ◽  
State Territory

Abstract Purpose Workers’ compensation claims consist of occupational injuries severe enough to meet a compensability threshold. Theoretically, systems with higher thresholds should have fewer claims but greater average severity. For research that relies on claims data, particularly cross-jurisdictional comparisons of compensation systems, this results in collider bias that can lead to spurious associations and confound analyses. In this study, I use real and simulated claims data to demonstrate collider bias and problems with methods used to account for it. Methods Using Australian claims data, I used a linear regression to test the association between claim rate and mean disability durations across Statistical Areas. Analyses were repeated with nesting by state/territory to account for variations in compensability thresholds across compensation systems. Both analyses are repeated on left-censored data. Simulated claims data are analysed with Cox survival analyses to illustrate how left-censoring can reverse effects.Results The claim rate within a Statistical Area was inversely associated with disability duration. However, this reversed when Statistical Areas were nested by state/territory. Left-censoring resulted in an attenuation of the unnested association to non-significance, while the nested association remained significantly positive. Cox regressions on simulated data showed left-censoring can also reverse effects. Conclusions Collider bias can seriously confound work disability research, particularly cross-jurisdictional comparisons. Work disability researchers must grapple with this challenge by using appropriate study designs and analytical approaches, and considering how collider bias affects interpretation of results.

The Medical Detection of Simulated Occupational Injuries: A Historical and Social Analysis

1998 ◽  
Vol 28 (2) ◽  
pp. 227-239 ◽  
Author(s):  
Allard E. Dembe
Keyword(s):  
Occupational Injuries ◽  
Historical Record ◽  
Workers Compensation ◽  
Potential Threat ◽  
Work Injuries ◽  
Financial Gain ◽  
Economic Forces ◽  
Compensation Claims ◽  
Social Biases

Occupational malingering is often thought to involve the deliberate feigning of disease by workers seeking undeserved financial gain. Concern about this form of malingering grew in the late 19th century as a result of the emergence of the new industrial economy and the institution of workers' compensation insurance. Medical judgments about the simulation of work injuries have placed physicians in a crossfire between the interests of employers and workers in numerous medicolegal debates. Because of uncertainty about the true cause of many occupational disorders and the highly charged social environment in which medical opinions are rendered, physicians' views about malingering are often swayed by cultural, political, and economic forces. The historical record shows that a medical diagnosis of occupational malingering can reflect deep-seated cultural and social biases toward women, Jews, immigrants, and other groups representing a potential threat to the privileged social class. Current efforts to eliminate fraudulent workers' compensation claims must be sensitive to the inherent ambiguities in the medical determination of work-relatedness and the potential for judgments about simulated work injuries to conceal deep-seated social biases and class prejudices.

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