Key Data Gaps in the Available Exposure Information

Identifying Key Data Gaps in the Available Exposure Information 

Once all available and relevant information has been assembled, it can be critically analyzed for missing data elements or significant data gaps. The exposure assessor must determine where existing exposure measurements can be used directly and where other techniques must be applied to extrapolate or estimate the exposures of interest. Key types of data gaps that may need to be addressed include insufficient existing exposure data, inadequate information to characterize exposure scenarios for individual job tasks or titles, limited inputs for exposure models, or a lack of certainty regarding appropriate exposure classification. 

Data gaps can often be addressed by using a combination of exposure reconstruction techniques simultaneously this approach is common, and there are many examples of it in the peer-reviewed literature. A number of these studies are discussed and compared below. The use of a combination of methods may also be the most effective approach for exposure reconstruction for a particular scenario, given a unique dataset with certain robust elements and otho- relatively weak elements. Uncertainty analyses, such as a Monte Carlo statistical assessment, can also be used to address data gaps generated by uncertainty in existing data or information, as well as to increase the likelihood that true exposures are captured (Cohen Hubal et al. 

Once key data gaps have been identified, an appropriate method or combination of methods can be selected to ensure that they are adequately addressed. 

Selecting the Appropriate Methodology to Reconstruct Exposure Values 

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