Cost-efficient measurement of physical load

Correct exposure information is essential to any study aiming at documenting, surveying, or intervening on exposures, as well as to studies of associations between exposure and outcome.

While extensive research has been devoted to developing instruments for measuring physical load in occupational settings, far less attention has been paid to the statistical properties of these instruments when used as intended, i.e. their ability to produce unbiased and precise exposure estimates in a specific setting.

For a certain exposure assessment approach, this ability is influenced by, in particular, the size of the collected data set, the allocation among subjects and in time of the measurement efforts, and uncertainties associated with the measurement instrument per se. A variety of methodologies are available for examining the statistical properties of sampling strategies, including exposure modeling, and using the results for designing and interpreting studies, but they have not been used to any notable extent in occupational contexts.

In addition to facing the need to design statistically effective exposure assessment strategies, researchers and practitioners also play against restricted resources, and so wish to optimize the cost-efficiency of exposure assessment, i.e. to identify methods providing exposure information with a “good” quality at a “low” cost. Cost and cost-efficiency of data collection and processing – an issue in the borderland between statistics and economics – has been very little explored in science, in spite of its obvious practical relevance.

The superior goal of programme C is thus to develop principles confirmed by empirical evidence for designing exposure data collection strategies that can effectively and with an optimal cost-efficiency answer to the statistical needs of occupational studies.   

Principal investigator

Marina Heiden

 

Research in this program


Published by: Zara Lindahl Page responsible: Annika Strömberg Updated: 2017-11-30
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