BACKGROUND: It is often difficult and expensive to make direct measurements of an individual's occupational or environmental exposures in large epidemiologic studies. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.
BACKGROUND: It is often difficult and expensive to make direct measurements of an individual's occupational or environmental exposures in large epidemiologic studies. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.
Authors: Jennifer L Bruno Garza; Paul J Catalano; Jeffrey N Katz; Maaike A Huysmans; Jack T Dennerlein Journal: J Occup Environ Hyg Date: 2012 Impact factor: 2.155
Authors: Jennifer L Bruno Garza; Belinda H W Eijckelhof; Maaike A Huysmans; Peter W Johnson; Jaap H van Dieen; Paul J Catalano; Jeffrey N Katz; Allard J van der Beek; Jack T Dennerlein Journal: BMC Musculoskelet Disord Date: 2014-09-03 Impact factor: 2.362