NOIRS 1997 Abstracts of the National Occupational Injury Research Symposium 1997. Washington, DC: National Institute for Occupational Safety and Health, 1997 Oct; :13
Surveillance plays a very significant role in the NIOSH epidemiological model as applied to occupational health and safety. Surveillance studies are used in identifying occupational problems, evaluating the effectiveness of intervention procedures, establishing research priorities, and allocating resources for its health and safety program. The NIOSH surveillance system has been highly successful in serving NIOSH's mission of prevention of occupational injuries as applied to relatively large number, relatively high frequency events such as falls, machinery mishaps, and environmental exposures. However in the case of relatively low number and low frequency industrial disasters such as explosions and fires, and commercial aircraft accidents, the applicability of the surveillance model is far less clear. I believe that some of the problem lies with worker exposure - its interpretation and evaluation, and some of the problem lies with assumptions (both explicit and implicit) as to weighting factors (i.e., economic, societal and hazard impacts) to be or not to be considered in the data analysis. I believe there is a definite need to broaden the data base that exists in current surveillance S&H models; particularly in the area of worker exposure. These points will be elaborated on in terms of the 'numerator' and the 'denominator' of surveillance information, in which BLS and NIOSH surveillance studies revolve around a count of the injured (the numerator) normalized to some count of the workers exposed (the denominator). If one interprets the 'numerator' of surveillance rate data as referring to those individuals, institutions and events which are directly affected by accidents that occurred, then weighting factors that relate to the numerator (referred to as N-factors) can be defined based on degrees of hazard or injury and the economic and societal impacts that are directly associated with the occurrence of the accident. The 'denominator' of surveillance data will likewise be affected by weighting factors, but the D-factors will refer to those individuals, institutions and events which are in harms way, i.e., those who will be affected by future accidents. As will be described, the D-factors being different from the N-factors, will lead to a ranking scheme which is different from that currently employed by NIOSH, but one that could lead to an all-inclusive model for epidemiologic analysis of accidents. Another aspect of the model employed by NIOSH for prevention of occupational injury is what might be called the 'Haddon Strategy' for reducing injuries. With this paradigm, remediation efforts focus on those means available for reducing injuries associated with an accident, rather than on determining the exact cause(s) or 'anatomy' of an accident. For example, water purification will control disease even in the absence of specific knowledge as to the pathogens that would cause disease. Unfortunately, low frequency disasters, such as mine explosions and fires, often involve the liberation of energies so great that prevention of occupational injury dictates that the event must be prevented from occurring. This requires a fundamental understanding of the exact causes that can lead to the event. In these cases, it is only through a detailed anatomy (research) of an accident that solutions for prevention will be found.