Nick Warren

Conceptual Model for Assessment of Inhalation Exposure: Defining Modifying Factors

The present paper proposes a source-receptor model to schematically describe inhalation exposure to help understand the complex processes leading to inhalation of hazardous substances. The model considers a stepwise transfer of a contaminant from the source to the receptor. The conceptual model is constructed using three components, i.e. (i) the source, (ii) various transmission compartments and (iii) the receptor, and describes the contaminants emission and its pattern of transport. Based on this conceptual model, a list of nine mutually independent principal modifying factors (MFs) is proposed: activity emission potential, substance emission potential, localized control, separation, segregation, dilution, worker behavior, surface contamination and respiratory protection. These MFs describe the exposure process at a high level of abstraction so that the model can be generically applicable. A list of exposure determinants underlying each of these principal MFs is proposed to describe the exposure process at a more detailed level. The presented conceptual model is developed in conjunction with an activity taxonomy as described in a separate paper. The proposed conceptual model and MFs should be seen as building blocks for development of higher tier exposure models.

Advanced Reach Tool (ART): Development of the Mechanistic Model

This paper describes the development of the mechanistic model within a collaborative project, referred to as the Advanced REACH Tool (ART) project, to develop a tool to model inhalation exposure for workers sharing similar operational conditions across different industries and locations in Europe. The ART mechanistic model is based on a conceptual framework that adopts a source receptor approach, which describes the transport of a contaminant from the source to the receptor and defines seven independent principal modifying factors: substance emission potential, activity emission potential, localized controls, segregation, personal enclosure, surface contamination, and dispersion. ART currently differentiates between three different exposure types: vapours, mists, and dust (fumes, fibres, and gases are presently excluded). Various sources were used to assign numerical values to the multipliers to each modifying factor. The evidence used to underpin this assessment procedure was based on chemical and physical laws. In addition, empirical data obtained from literature were used. Where this was not possible, expert elicitation was applied for the assessment procedure. Multipliers for all modifying factors were peer reviewed by leading experts from industry, research institutes, and public authorities across the globe. In addition, several workshops with experts were organized to discuss the proposed exposure multipliers. The mechanistic model is a central part of the ART tool and with advancing knowledge on exposure, determinants will require updates and refinements on a continuous basis, such as the effect of worker behaviour on personal exposure, best practice values that describe the maximum achievable effectiveness of control measures, the intrinsic emission potential of various solid objects (e.g. metal, glass, plastics, etc.), and extending the applicability domain to certain types of exposures (e.g. gas, fume, and fibre exposure).

Tools for regulatory assessment of occupational exposure: development and challenges

REACH (Registration, Evaluation and Authorization of CHemicals) requires improved exposure models that can be incorporated into screening tools and refined assessment tools. These are referred to as tier 1 and 2 models, respectively. There are a number of candidate in tier 1 models that could be used with REACH. Tier 2 models, producing robust and realistic exposure assessments, are currently not available. A research programme is proposed in this paper that will result in a new, advanced exposure assessment tool for REACH. In addition, issues related to variability and uncertainty are discussed briefly, and some examples of tier 1 screening tools are presented. The proposed framework for the tier 2 tool is based on a Bayesian approach, and makes full use of mechanistically modelled estimates and any relevant measurements of exposure. The new approach will preclude the necessity to conduct of case-by-case exposure measurements for each chemical and scenario, since the system will allow for the use of analogous exposure data from relatively comparable scenarios. The development of the new approach requires substantial effort in the area of mechanistic modelling, database development and Bayesian statistical techniques. In this paper, the data gaps and areas for future research are identified to help realise and further improve this type of approach within REACH. A structured data collection and storage system is a central element of the research programme and the availability of this type of tool may also facilitate the sharing of exposure data down and up the supply chain. In addition, new data that are stored according to the proposed structure could enable the validation of any exposure model and thus this programme enhances the exposure assessment field as a whole.

Advanced REACH Tool (ART): Overview of Version 1.0 and Research Needs

This paper provides an outline of the Advanced REACH Tool (ART) version 1.0 and a discussion of how it could be further developed. ART is a higher tier exposure assessment tool that combines mechanistically modelled inhalation exposure predictions with available exposure data using a Bayesian approach. ART assesses exposure for scenarios across different plants and sites. Estimates are provided for different percentiles of the exposure distribution and confidence intervals around the estimate. It also produces exposure estimates in the absence of data, but uncertainty of the estimates will decrease when results of exposure measurements are included. The tool has been calibrated using a broad range of exposure data and provides estimates for exposure to vapours, mists, and dusts. ART has a robust and stable conceptual basis but will be refined in the future and should therefore be considered an evolving system. High-priority areas for future research are identified in this paper and include the integration of partially analogous measurement series, inclusion of company and site-specific assessments, user decision strategies linked to ART predictions, evaluation of validity and reliability of ART, exploring the possibilities for incorporating the dermal route and integration of ART predictions with tools for modelling internal dose. ART is initially developed in the scope of REACH but is equally useful for exposure assessment in other areas.

What is the best strategy to reduce the burden of occupational asthma and allergy in bakers

Rationale Insight into the effectiveness of intervention strategies will help realise a decrease in the occupational disease burden from (allergic) respiratory diseases in the bakery population. Objectives To use a simulation model to assess the impact of different intervention strategies on the disease burden of the bakery population over time. Methods A recently developed dynamic population based model was used to prospectively evaluate the impact on disease burden resulting from different intervention strategies. We distinguished interventions based on exposure reductions for flour dust and fungal α-amylase, health surveillance combined with reduction in exposure, and pre-employment screening. Main Results The impact of most interventions on disease burden was limited, generally less than 50% for lower respiratory symptoms and disabling occupational asthma. Only the rigorous health surveillance strategy, identifying workers who are sensitised or report upper respiratory symptoms and decreasing their individual exposures by 90% shortly after diagnosis, resulted in a decrease of almost 60% in disease burden after 20u2005years. Conclusions This study demonstrates that different intervention strategies have substantially different impacts on the burden of disease. The time window during which changes occur differs considerably between strategies. This information can assist policy makers in their choice of intervention and gives guidance for achievable reductions in disease burden.

Inter- and intra-individual variation in urinary biomarker concentrations over a 6-day sampling period. Part 1: Metals

The aim of the current HBM-study is to further the understanding of the impact of inter- and intra-individual variability in HBM surveys as it may have implications for the design and interpretation of the study outcomes. As spot samples only provide a snapshot in time of the concentrations of chemicals in an individual, it remains unclear to what extent intra-individual variability plays a role in the overall variability of population-wide HBM surveys. The current paper describes the results of an intensive biomonitoring study, in which all individual urine samples of 8 individuals were collected over a 6-day sampling period (a total of 352 unique samples). By analyzing different metals (As, Cd, Mn, Ni) in each individual sample, inter- and intra-individual variability for these four metals could be determined, and the relationships between exposure, internal dose, and sampling protocol assessed. Although the range of biomarker values for different metals was well within the normal range reported in large-scale population surveys, large intra-individual differences over a 6-day period could also be observed. Typically, measured biomarker values span at least an order of magnitude within an individual, and more if specific exposure episodes could be identified. Fish consumption for example caused a twenty- to thirty-fold increase in urinary As-levels over a period of 2-6h. Intra-class correlation coefficients (ICC) were typically low for uncorrected biomarker values (between 0.104 and 0.460 for the 4 metals), but improved when corrected for creatinine or specific gravity (SG). The results show that even though urine is a preferred matrix for HBM studies, there are certain methodological issues that need to be taken into account in the interpretation of urinary biomarker data, related to the intrinsic variability of the urination process itself, the relationship between exposure events and biomarker quantification, and the timing of sampling. When setting up HBM-projects, this expected relationship between individual exposure episode and urinary biomarker concentration needs to be taken into account.

Classification of Occupational Activities for Assessment of Inhalation Exposure

There is a large variety of activities in workplaces that can lead to emission of substances. Coding systems based on determinants of emission have so far not been developed. In this paper, a system of Activity Classes and Activity Subclasses is proposed for categorizing activities involving chemical use. Activity Classes share their so-called emission generation mechanisms and physical state of the product handled and the underlying determinants of emission. A number of (industrial) stakeholders actively participated in testing and fine-tuning the system. With the help of these stakeholders, it was found to be relatively easy to allocate a large number of activities to the Activity Classes and Activity Subclasses. The system facilitates a more structured classification of activities in exposure databases, a structured analysis of the analogy of exposure activities, and a transparent quantification of the activity emission potential in (new) exposure assessment models. The first use of the system is in the Advanced REACH Tool.

Cost-benefit analysis in occupational health: a comparison of intervention scenarios for occupational asthma and rhinitis among bakery workers

Objectives Use of cost-benefit analysis in occupational health increases insight into the intervention strategy that maximises the cost-benefit ratio. This study presents a methodological framework identifying the most important elements of a cost-benefit analysis for occupational health settings. One of the main aims of the methodology is to evaluate cost-benefit ratios for different stakeholders (employers, employees and society). The developed methodology was applied to two intervention strategies focused on reducing respiratory diseases. Methods A cost-benefit framework was developed and used to set up a calculation spreadsheet containing the inputs and algorithms required to calculate the costs and benefits for all cost elements. Inputs from a large variety of sources were used to calculate total costs, total benefits, net costs and the benefit-to-costs ratio for both intervention scenarios. Results Implementation of a covenant intervention program resulted in a net benefit of €16u2008848u2008546 over 20u2005years for a population of 10u2008000 workers. Implementation was cost-effective for all stakeholders. For a health surveillance scenario, total benefits resulting from a decreased disease burden were estimated to be €44u2008659u2008352. The costs of the interventions could not be calculated. Conclusion This study provides important insights for developing effective intervention strategies in the field of occupational medicine. Use of a model based approach enables investigation of those parameters most likely to impact on the effectiveness and costs of interventions for work related diseases. Our case study highlights the importance of considering different perspectives (of employers, society and employees) in assessing and sharing the costs and benefits of interventions.

Variability of biomarkers in volunteer studies: The biological component

Biological monitoring has become one of the methods to measure exposure, with the advantage that it gives information about the concentration of a substance that actually enters the body and reflects the inter-individual differences in uptake and metabolic variation. However, limited information is available on inter- and intra-individual variability of biomarkers. The aim of this study was to gather information about the biological component of inter-individual variation in biomarkers using results from volunteer studies. Open literature and other (internal) sources were searched to find human volunteer studies utilizing biological monitoring. Ultimately 41 studies were included in our analysis, with a total of 6747 observations for one or more biomarkers from 223 volunteers. The data from these studies were grouped on the basis of study, substance under investigation, exposure route, biological matrix, exposure duration, dose and number of exposure events to obtain 278 homogeneous groups (strata) for statistical analysis. Variability was assessed in two ways. Firstly, estimates of biomarker half-life were calculated for each individual, thereby allowing the estimation of inter-individual variability in half-lives within the homogeneous groups. Secondly, variation in biomarker concentrations at a given time point was estimated. For estimated half-lives the GSDs ranged from 1.0 to 6.8. The variability in estimated half-lives did not differ much for the different types of substances. For concentrations at a given time point the average GSDs within strata ranged from 1.0 to 5.6. Again, variability did not differ much for different groups (e.g., type of substance). The median variability component was 0.11 (range 0-3.0). In conclusion, volunteer studies enable the estimation of both variation in half-lives and variation in biomarker levels in the well-defined homogeneous groups. Comparison of our results with other studies indicates that variation due to biological differences within and between people is quite substantial in homogeneous exposure groups. The relative contribution of this biological component to the total variation will be smaller when variance components are estimated in less homogeneous groups, such as those in occupational and environmental settings.

A dynamic population-based model for the development of work-related respiratory health effects among bakery workers

Objectives: This paper presents a dynamic population-based model for the development of sensitisation and respiratory symptoms in bakery workers. The model simulates a population of individual workers longitudinally and tracks the development of work-related sensitisation and respiratory symptoms in each worker. Methods: The model has three components: a multi-stage disease model describing the development of sensitisation and respiratory symptoms in each worker over time; an exposure model describing occupational exposure to flour dust and allergens; and a basic population model describing the length of a worker’s career in the bakery sector and the influx of new workers. Each worker’s disease state is modelled independently using a discrete time Markov Chain, updated yearly using each individual’s simulated exposure. A Bayesian analysis of data from a recent epidemiological study provided estimates of the yearly transition probabilities between disease states. Results: For non-atopic/non-sensitised workers the estimated probabilities of developing moderate (upper respiratory) symptoms and progression to severe (lower respiratory) symptoms are 0.4% (95% CI 0.3 to 0.5%) and 1.1% (95% CI 0.6 to 1.9%) per mg/m3/year of flour dust, respectively, and approximately twice these for atopic workers. The model predicts that 36% (95% CI 26 to 46%) of workers with severe symptoms are sensitised to wheat and 22% (95% CI 12 to 37%) to α-amylase. The predicted mean latency period for respiratory symptoms was 10.3 years (95% CI 8.3 to 12.3). Conclusions: While the model provides a valuable population-level representation of the mechanisms contributing to respiratory diseases in bakers, it was primarily developed for use in quantitative health impact assessment. Future research will use the model to evaluate a range of workplace interventions, including achievable reductions in exposure and health surveillance. The general methodology is applicable to other diseases such as chronic obstructive pulmonary disease, silicosis and musculoskeletal disorders and could be particularly valuable for forecasting changes in long latency diseases.