Simultaneous assessment of occupational exposures from multiple worker groups

The methods developed by Rappaport et al. [Ann. Occup. Hyg. 39 (1995) 469] and Lyles et al. [J. Agri. Bio. Environ. Stat. 2 (1997a) 64; Ann. Occup. Hyg. 41 (1997b) 63]) for assessing workplace exposures on a group-by-group basis are extended to allow for the simultaneous assessment of data from multiple worker groups within the same industry. These extended methods allow models to be fit simultaneously to data on all groups in a study, even when some of the groups might not contribute adequate information to be modeled separately. We assume that the exposures are log-normally distributed, and that they can be adequately modeled by a mixed effects regression model with parameters for exposure levels and for between- and within-worker variance components. Simultaneously analyzing data from multiple groups is only advantageous when at least one of these variance components can be assumed to be homogeneous across the groups. Here, we advocate testing an assumption of homogeneous within-worker variance components, sigma(2)(w,h), using a likelihood ratio test to choose between a full model (distinct sigma(2)(w,h) for each group) and a reduced model (common sigma(2)(w) across groups). We then develop a procedure, which is conditional on the results of the likelihood ratio test, for testing whether or not each group of workers is overexposed to the contaminant of interest. This modeling and testing procedure was applied to 39 different data sets, each containing data for multiple groups, from a wide variety of industries. For these data, the testing procedure generally resulted in the same conclusion regarding overexposure under both models, even in those data sets where the within-worker variance components appeared to be quite heterogeneous. We also conducted a small simulation study to estimate the significance level of the proposed testing procedure, and found that the significance levels tended to be adequately close to the specified nominal level when a likelihood ratio test with significance level of at least 0.01 was used as a preliminary test. Additionally, we make specific recommendations for designing studies and suggest a method for determining whether engineering and administrative controls or individual-level interventions would be of most benefit to an overexposed group of workers.