Academic journal article Scandinavian Journal of Work, Environment & Health

Cost-Efficient Assessment of Biomechanical Exposure in Occupational Groups, Exemplified by Posture Observation and Inclinometry

Academic journal article Scandinavian Journal of Work, Environment & Health

Cost-Efficient Assessment of Biomechanical Exposure in Occupational Groups, Exemplified by Posture Observation and Inclinometry

Article excerpt

(ProQuest: ... denotes formulae omitted.)

A suitable exposure assessment is central to occupa- tional health research, not the least as guided by the need to obtain data assuring that statistical power is suf- ficient to render the study informative. Power in studies comparing exposures between groups or conditions within groups is directly dependent on the precision of the mean exposure estimate, as exemplified by studies of biomechanical exposure (physical workload) (1, 2). This precision, in turn, depends on the sample size, and the variability in exposure caused by differences in behav- ior between subjects and within subjects across time, as well as by uncertainty associated with the exposure measurement method per se (3, 4). Exposure variability, often expressed in terms of exposure variance compo- nents, has been an issue in occupational epidemiology for more than two decades (5-7), with applications reaching far beyond the design of data collection strate- gies (8). In the field of biomechanical exposures, to which the present paper is specifically devoted, exposure variability was introduced in the mid-1990s (9-11), and a number of papers have discussed exposure assessment strategies, including necessary sample sizes to ensure a certain precision of a mean exposure estimate and/or a sufficient power in comparison studies (1, 3, 12-17). Although these articles have furthered inquiry into efficient exposure assessment from a statistical point of view, they have been criticized for rarely acknowledging the actual costs of exposure assessment. Thus, a 2010 systematic review of literature focusing on cost-efficient collection of exposure data (18) found only nine studies dealing with the trade-off between statistical perfor- mance and monetary resources invested in obtaining that performance, even if some studies have appeared after 2010 (19-21). Only some of the publications identified in the 2010 review dealt specifically with occupational or environmental exposures (7, 22-26); only two of these included empirical data to illustrate cost and effi- ciency (24, 26), and none were devoted to assessment of biomechanical exposure. In the context of study design, the trade-off between cost and statistical performance appears in the form of either one of two questions: (i) for a given research budget, which measurement method and sampling strategy delivers the highest statistical performance with respect to producing unbiased and precise data? and (ii) facing a required statistical performance, for instance in order to obtain sufficient power, which method and sampling strategy is the most cost efficient?

Biomechanical exposure assessment methods are often grouped into three broad categories or "classes" of measurement: direct measurement, observation on-site or from video, and worker self-report. Objective direct mea- surement methods are generally preferred for accuracy (27-31). Observation has long been believed to represent a middle ground in that they are more objective than self-reports (19, 27, 29), yet less consistent than direct measurements. The suspected inferior precision associ- ated with using observational methods would mainly be a result of variability introduced by within- and between- observer differences in opinions when viewing the same posture (21, 32-34). At the same time, observations have been anecdotally claimed to be cheaper in use than direct measurements (27). This would mean that, for a certain budget, more data could be collected by observation, which might make up for the larger exposure variability, and eventually lead to a more precise estimate of group mean exposure than that obtained by direct measurements at the same total cost. In order to appreciate this trade-off and decide which measurement method to prefer from a cost-efficiency point of view, costs and efficiency must be quantified for each measurement class under realistic data collection scenarios and then compared (18). …

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