Heterogeneity in sources of exposure variability among groups of workers exposed to inorganic mercury

Many exposure assessment strategies rely on the occupational group as the unit of analysis in which workers are classified on the basis of job title, location, or on other characteristics related to the workplace or the job. Although statistical methods that combine exposure data collected on workers from different occupational groups are more efficient, the underlying assumption that the degree of variation over time and among workers is the same for all groups has yet to be fully investigated. Given the utility of different modeling approaches when assessing exposures, we investigated assumptions of homogeneity of variance within and between workers using both random- and mixed-effects models. In our study of four groups of workers exposed to inorganic mercury (Hg) at a chloralkali plant, there was no evidence of significant heterogeneity in the levels of variation over time or between workers for air Hg levels. For the biological monitoring data, however, our findings indicate that groups did not share common levels of variability and that it was not appropriate to pool the data and obtain single estimates of the within- and between-worker variance components. Classification of job group as a random or fixed effect had no effect on the results and yielded the same conclusions when the models were compared. To illustrate effects related to the proper specification of a model, the likelihood of exceeding certain levels (which is a function of the parameters of the underlying distribution of the natural log-transformed exposures) was evaluated using the results obtained from the different models. Although the probability that workers' mean exposures exceeded occupational exposure limits for air, urine and blood Hg was generally low (<10%) for all groups except maintenance workers, the estimated values sometimes varied depending upon the particular model that was applied. Given the growing use of random- and mixed-effects models that combine data across occupational groups, additional studies are warranted to evaluate whether it is reasonable to assume common variances and covariances among measurements collected on workers from different groups.