Search

Introduction: In occupational epidemiology, differences in the temporal coverage of the exposure history by available exposure measurement data may affect the uncertainty of exposure estimates. In the reporting of results of studies, greater attention should be paid to the extent to which exposure assessments require extrapolation outside the timeframe for which exposure measurements are available. We propose a simple graphical method that can be used to visualise the temporal coverage of exposure history with exposure measurements and the extent of temporal extrapolation needed., Methods: We construct a graph that displays the accumulated work history years for which exposure had to be assessed in each calendar year. Years for which exposure measurements were available are shaded. The proportion of work history years covered by exposure measurements and the proportion of work history years accrued before the first measurements are summarised. When available, the actual number of measurements available in each calendar year is shown., Results: We demonstrate the application of the graphical tool in three nested case-control studies that reported on leukaemia in relation to low-level benzene exposures in the petroleum industry. Considerable differences in temporal coverage between the studies were illustrated, which may have resulted in differences in the reliability of the retrospective exposure estimates derived for these studies., Conclusion: We introduce a graphical tool for visualising the temporal coverage by available exposure measurement data in epidemiological studies and encourage others to use similar graphs to derive and share better qualitative insights into the uncertainty in exposure assessment.

Objectives: For regulatory risk assessment under REACH a tiered approach is proposed in which the first tier models should provide a conservative exposure estimate that can discriminate between scenarios which are of concern and those which are not. The Stoffenmanager is mentioned as a first tier approach in the REACH guidance. In an attempt to investigate the validity of the Stoffenmanager algorithms, a cross-validation study was performed., Methods: Exposure estimates using the Stoffenmanager algorithms were compared with exposure measurement results (n=254). Correlations between observed and predicted exposures, bias and precision were calculated. Stratified analyses were performed for the scenarios "handling of powders and granules" (n=82), "handling solids resulting in comminuting" (n=60), "handling of low-volatile liquids" (n=40) and "handling of volatile liquids" (n=72)., Results: The relative bias of the four algorithms ranged between -9% and -77% with a precision of approximately 1.7. The 90th percentile estimate of one out of four algorithms was not conservative enough. Based on these statistics and analyses of residual plots the underlying algorithm was adapted. Subsequently, the calibration and the cross-validation dataset were merged into one dataset (n=952) used for calibrating the adapted Stoffenmanager algorithms. This new calibration resulted in new exposure algorithms for the four scenarios., Conclusions: The Stoffenmanager is capable of discriminating among exposure levels mainly between scenarios in different companies. The 90th percentile estimates of the Stoffenmanager are verified to be sufficiently conservative. Therefore, the Stoffenmanager could be a useful tier 1 exposure assessment tool for REACH.

Objective: The main goal was to investigate the potential of a probabilistic approach for exposure assessment and use this information to evaluate the impact of a complex of policy actions/interventions on dermal exposure to antineoplastic agents among oncology nurses. The central theme of this study was to make optimal use of existing data, supplemented only with limited additional information from a questionnaire survey., Methods: A task based exposure model was used to estimate dermal exposure of the hands among oncology nurses in non-academic hospitals in the Netherlands. Monte Carlo simulation was used to integrate information from available (exposure) studies and generate exposure distributions for the total population of oncology nurses in both pre- and post-intervention situation. Graphs and descriptive statistics of the simulated exposure distributions were used to evaluate trends in population exposure., Results: The inventory showed that important intervention occurred in the preparation and administering of antineoplastic agents and in the handling of urine. Hardly any changes were identified in de nursing tasks. The use of gloves seemed to have decreased for a number of tasks. The results of the analysis show that the interventions did not affect the median exposure. However frequencies of occurrence of individuals with very high and very low total dermal exposures decreased substantially in the post-intervention situation. Analysis of the effect of pregnancy showed that pregnancy is very unlikely to influence exposure or any of the key input variables., Conclusions: The present study shows that the probabilistic approach adds valuable information to deterministic exposure assessment, especially when extrapolating data on a subpopulation to populations of individuals at large. The results show that the identified changes in the past decade in Dutch non-academic hospitals resulted in changes in the exposure distribution of antineoplastic agents among oncology nurses.

Background: The authors recently developed a Dermal Exposure Assessment Method (DREAM), an observational semiquantitative method to assess dermal exposures by systematically evaluating exposure determinants using pre-assigned default values., Aim: To explore the accuracy of the DREAM method by comparing its estimates with quantitative dermal exposure measurements in several occupational settings., Methods: Occupational hygienists observed workers performing a certain task, whose exposure to chemical agents on skin or clothing was measured quantitatively simultaneously, and filled in the DREAM questionnaire. DREAM estimates were compared with measurement data by estimating Spearman correlation coefficients for each task and for individual observations. In addition, mixed linear regression models were used to study the effect of DREAM estimates on the variability in measured exposures between tasks, between workers, and from day to day., Results: For skin exposures, spearman correlation coefficients for individual observations ranged from 0.19 to 0.82. DREAM estimates for exposure levels on hands and forearms showed a fixed effect between and within surveys, explaining mainly between-task variance. In general, exposure levels on clothing layer were only predicted in a meaningful way by detailed DREAM estimates, which comprised detailed information on the concentration of the agent in the formulation to which exposure occurred., Conclusions: The authors expect that the DREAM method can be successfully applied for semiquantitative dermal exposure assessment in epidemiological and occupational hygiene surveys of groups of workers with considerable contrast in dermal exposure levels (variability between groups >1.0). For surveys with less contrasting exposure levels, quantitative dermal exposure measurements are preferable.