Modeling long-term average exposure in occupational exposure response analysis

Objectives Estimates of long-term average exposure to occupational hazards are often imprecise because intraindividual variability in exposure can be large and exposure is usually based on one or few measurements. One potential result is bias of exposure-response relationships. The possibility was studied of a more valid measure of exposure being obtained by modeling exposure and consequently increasing the number of days with exposure estimates, using simple measurable exposure surrogates. Methods In a group of 198 Dutch pig farmers, exposure to endotoxins was measured on one workday in summer and one day in winter. Farmers recorded activity patterns during one week in both seasons, and farm characteristics were evaluated. Relationships between farm characteristics and activities and log-transformed measured exposure levels were quantified in a multiple regression analysis. Exposure was estimated for 14 d with known activity patterns. Results The ratio of intraindividual and interindividual variance in log-transformed measured exposure was 4.7. Given this ratio, the true regression coefficient of lung function on exposure would potentially be attenuated by 70%. The variance ratio for predicted exposures was only 1.2, and the potential attenuation by variation in exposure estimates was decreased to 8%. There was no relationship between lung function and measured exposure. Modeled long-term average exposure was inversely related to base-line lung function; it reached statistical significance for asymptomatic farmers. Conclusions The results suggest that the presented strategy offers a possibility to minimize measurement effort in occupational epidemiologic studies, without apparent loss of statistical power.