Using exposure prediction rules for exposure assessment: an example on whole-body vibration in taxi drivers

It is often difficult and expensive to make direct measurements of an individual's occupational or environmental exposures in large epidemiologic studies.

In this study, we used information collected in validation studies to develop a prediction rule for assessing exposure in a study with no direct measurement. We established a prediction rule through mixed-effect modeling of direct measurement data and information on observable exposure predictors and their interactions. Specifically, we used 383 measures of whole-body vibration from 247 professional taxi drivers and attempted to quantify vibration exposures for individuals in a large study on low back pain.

Using the "jackknife method," we found that our prediction rule had an acceptably low relative prediction error of 11% (95% confidence interval-10-12%). Implementing the prediction rule would result in measurement errors independent of low back pain and of all identified and observable predictors of whole-body vibration. We applied the predicted levels to compute each person's daily exposure, and found a strong association between the predicted daily whole-body vibration exposure and prevalence of low back pain. This supported the construct validity of the exposure prediction rule.

The predictive and construct validity of our prediction rule suggests that this general statistical approach can be useful in other occupational settings to improve the quality of exposure assessment.