Background: In a study of wood dust exposure and lung function, we tested the effect on the exposure-response relationship of six different exposure metrics using the mean measured exposure of each subject versus the mean exposure based on various methods of grouping subjects, including job-based groups and groups based on an empirical model of the determinants of exposure.
Methods: Multiple linear regression was used to examine the association between wood dust concentration and forced expiratory volume in 1s (FEV(1)), adjusting for age, sex, height, race, pediatric asthma, and smoking.
Results: Stronger point estimates of the exposure-response relationships were observed when exposures were based on increasing levels of aggregation, allowing the relationships to be found statistically significant in four of the six metrics. The strongest point estimates were found when exposures were based on the determinants of exposure model.
Conclusions: Determinants of exposure modeling offers the potential for improvement in risk estimation equivalent to or beyond that from job-based exposure grouping.