Cumulative dietary risk assessment overarching different regulatory silos using a margin of exposure approach: A case study with three chemical silos.

Risk assessment of chemicals occurring in our diet is commonly performed for single chemicals without considering exposure to other chemicals. We performed a case study on risk assessment of combined dietary exposure to chemicals from different regulatory silos, i.e. pesticides (PPRs), persistent organic pollutants (POPs) and food additives (FAs). Chemicals were grouped into the cumulative assessment group (CAG) liver steatosis using a component-based approach. Based on literature, the CAG included 144 PPRs, 49 POPS and 7 FAs for which concentration data were available. For each silo, chronic combined dietary exposure was assessed for adults and children of nine European countries following the most commonly used exposure methodologies in Europe and by using a relative potency factor approach. For risk characterization, a Margin of Exposure (MOE) was calculated. To overarch the risk across silos, a normalised combined margin of exposure (nMOET) approach was proposed. This case study demonstrated that risk assessment of combined exposure to chemicals can be performed within regulatory silos. It also highlighted important differences in the conservatism of exposure scenarios, the derivation of point of departures and the subsequent acceptable MOEs between the silos. To overarch the risk despite these differences, a nMOET approach can be used.

A retain and refine approach to cumulative risk assessment.

Mixtures of substances to which humans are exposed may lead to cumulative exposure and health effects. To study their effects, it is first necessary to identify a cumulative assessment group (CAG) of substances for risk assessment or hazard testing. Excluding substances from consideration before there is sufficient evidence may underestimate the risk. Conversely, including everything and treating the inevitable uncertainties using conservative assumptions is inefficient and may overestimate the risk, with an unknown level of protection. An efficient, transparent strategy is described to retain a large group, quantifying the uncertainty of group membership and other uncertainties. Iterative refinement of the CAG then focuses on adding information for the substances with high probability of contributing significantly to the risk. Probabilities can be estimated using expert opinion or derived from data on substance properties. An example is presented with 100 pesticides, in which the retain step identified a single substance to target refinement. Using an updated hazard characterisation for this substance reduced the mean exposure estimate from 0.43 to 0.28 µg kg-bw-1 day-1 and reduced the 99.99th percentile exposure from 24.9 to 5.1 µg kg-bw-1 day-1. Other retained substances contributed little to the risk estimates, even after accounting for uncertainty.

The MCRA toolbox of models and data to support chemical mixture risk assessment.

A model and data toolbox is presented to assess risks from combined exposure to multiple chemicals using probabilistic methods. The Monte Carlo Risk Assessment (MCRA) toolbox, also known as the EuroMix toolbox, has more than 40 modules addressing all areas of risk assessment, and includes a data repository with data collected in the EuroMix project. This paper gives an introduction to the toolbox and illustrates its use with examples from the EuroMix project. The toolbox can be used for hazard identification, hazard characterisation, exposure assessment and risk characterisation. Examples for hazard identification are selection of substances relevant for a specific adverse outcome based on adverse outcome pathways and QSAR models. Examples for hazard characterisation are calculation of benchmark doses and relative potency factors with uncertainty from dose response data, and use of kinetic models to perform in vitro to in vivo extrapolation. Examples for exposure assessment are assessing cumulative exposure at external or internal level, where the latter option is needed when dietary and non-dietary routes have to be aggregated. Finally, risk characterisation is illustrated by calculation and display of the margin of exposure for single substances and for the cumulation, including uncertainties derived from exposure and hazard characterisation estimates.

Modelling aggregate exposure to pesticides from dietary and crop spray sources in UK residents.

Human exposure to pesticide mixtures can occur from the diet and other sources. Realistic exposure and risk assessments should include multiple sources and compounds and include the relative hazards of the different compounds. The EU-funded Euromix project is developing new web-based tools to facilitate these calculations. A case study is presented that exemplifies their use for a population of UK residents, including exposure from crop-spraying. A UK pesticide usage survey provided information on real pesticide combinations applied to crops of wheat, potatoes, sugar beet and dessert apples. This information was combined with outputs from two alternative simulation models of spray drift to estimate dermal, oral and inhalation exposures of residents. These non-dietary exposures were combined with dietary exposure estimates using the Monte Carlo Risk Assessment software to produce a distribution of aggregated and cumulative exposures. Compounds are weighted by relative potency to generate a measure of overall risk. Uncertainty quantification was also included in the distribution of exposures. These tools are flexible to allow diverse sources of exposure and can provide important information to decision-makers and help to prioritise testing of pesticide mixtures. Including non-dietary sources changed the prioritisation of pesticide mixtures, when compared to dietary exposure alone.

Selecting mixtures on the basis of dietary exposure and hazard data: application to pesticide exposure in the European population in relation to steatosis.

Populations are exposed to mixtures of pesticides through their diet on a daily basis. The question of which substances should be assessed together remains a major challenge due to the complexity of the mixtures. In addition, the associated risk is difficult to characterise. The EuroMix project (European Test and Risk Assessment Strategies for Mixtures) has developed a strategy for mixture risk assessment. In particular, it has proposed a methodology that combines exposures and hazard information to identify relevant mixtures of chemicals belonging to any cumulative assessment group (CAG) to which the European population is exposed via food. For the purposes of this study, food consumption and pesticide residue data in food and drinking water were obtained from national surveys in nine European countries. Mixtures of pesticides were identified by a sparse non-negative matrix underestimation (SNMU) applied to the specific liver steatosis effect in children from 11 to 15 years of age, and in adults from 18 to 64 years of age in nine European countries. Exposures and mixtures of 144 pesticides were evaluated through four different scenarios: (1) chronic exposure with a merged concentration dataset in the adult population, (2) chronic exposure with country-specific concentration datasets in the adult population, (3) acute exposure with a merged concentration dataset in the adult population, and (4) chronic exposure with a merged concentration dataset in the paediatric population. The relative potency factors of each substance were calculated to express their potency relative to flusilazole, which was chosen as the reference compound. The selection of mixtures and the evaluation of exposures for each country were carried out using the Monte Carlo Risk Assessment (MCRA) software. Concerning chronic exposure, one mixture explained the largest proportion of the total variance for each country, while in acute exposure, several mixtures were often involved. The results showed that there were 15 main pesticides in the mixtures, with a high contribution of imazalil and dithiocarbamate. Since the concentrations provided by the different countries were merged in the scenario using merged concentration data, differences between countries result from differences in food consumption behaviours. These results support the approach that using merged concentration data to estimate exposures in Europe seems to be realistic, as foods are traded across European borders. The originality of the proposed approach was to start from a CAG and to integrate information from combined exposures to identify a refined list of mixtures with fewer components. As this approach was sensitive to the input data and required significant resources, efforts should continue regarding data collection and harmonisation among the different aspects within the pesticides regulatory framework, and to develop methods to group substances and mixtures to characterise the risk.

Improvements in Modelling Bystander and Resident Exposure to Pesticide Spray Drift: Investigations into New Approaches for Characterizing the 'Collection Efficiency' of the Human Body.

The BREAM (Bystander and Resident Exposure Assessment Model) (Kennedy et al. in BREAM: A probabilistic bystander and resident exposure assessment model of spray drift from an agricultural boom sprayer. Comput Electron Agric 2012;88:63-71) for bystander and resident exposure to spray drift from boom sprayers has recently been incorporated into the European Food Safety Authority (EFSA) guidance for determining non-dietary exposures of humans to plant protection products. The component of BREAM, which relates airborne spray concentrations to bystander and resident dermal exposure, has been reviewed to identify whether it is possible to improve this and its description of variability captured in the model. Two approaches have been explored: a more rigorous statistical analysis of the empirical data and a semi-mechanistic model based on established studies combined with new data obtained in a wind tunnel. A statistical comparison between field data and model outputs was used to determine which approach gave the better prediction of exposures. The semi-mechanistic approach gave the better prediction of experimental data and resulted in a reduction in the proposed regulatory values for the 75th and 95th percentiles of the exposure distribution.

Assessing and reporting uncertainties in dietary exposure analysis - Part II: Application of the uncertainty template to a practical example of exposure assessment.

A previous publication described methods for assessing and reporting uncertainty in dietary exposure assessments. This follow-up publication uses a case study to develop proposals for representing and communicating uncertainty to risk managers. The food ingredient aspartame is used as the case study in a simple deterministic model (the EFSA FAIM template) and with more sophisticated probabilistic exposure assessment software (FACET). Parameter and model uncertainties are identified for each modelling approach and tabulated. The relative importance of each source of uncertainty is then evaluated using a semi-quantitative scale and the results expressed using two different forms of graphical summary. The value of this approach in expressing uncertainties in a manner that is relevant to the exposure assessment and useful to risk managers is then discussed. It was observed that the majority of uncertainties are often associated with data sources rather than the model itself. However, differences in modelling methods can have the greatest impact on uncertainties overall, particularly when the underlying data are the same. It was concluded that improved methods for communicating uncertainties for risk management is the research area where the greatest amount of effort is suggested to be placed in future.

Assessing human health risks from pesticide use in conventional and innovative cropping systems with the BROWSE model.

BACKGROUND: Reducing the risks and impacts of pesticide use on human health and on the environment is one of the objectives of the European Commission Directive 2009/128/EC in the quest for a sustainable use of pesticides. This Directive, developed through European national plans such as Ecophyto plan in France, promotes the introduction of innovative cropping systems relying, for example, on integrated pest management. Risk assessment for human health of the overall pesticide use in these innovative systems is required before the introduction of those systems to avoid that an innovation becomes a new problem. OBJECTIVES: The objectives of this work were to assess and to compare (1) the human exposure to pesticides used in conventional and innovative cropping systems designed to reduce pesticide needs, and (2) the corresponding risks for human health. METHODS: Humans (operator and residents) exposure to pesticides and risks for human health were assessed for each pesticide with the BROWSE model. Then, a method was proposed to represent the overall risk due to all pesticides used in one system. This study considers 3 conventional and 9 associated innovative cropping systems, and 116 plant protection products containing 89 different active substances (i.e. pesticides). RESULTS: The modelling results obtained with BROWSE showed that innovative cropping systems such as low input or no herbicide systems would reduce the risk for human health in comparison to the corresponding conventional cropping systems. On the contrary, BROWSE showed that conservation tillage system would lead to unacceptable risks in the conditions of our study, because of a high number of pesticide applications, and especially of some herbicides. For residents, the dermal absorption was the main exposure route while ingestion was found to be negligible. For operators, inhalation was also a predominant route of exposure. In general, human exposure to pesticides and human health risks were found to be correlated to the treatment frequency index TFI (number of registered doses of pesticides used per hectare for one copping season), confirming the relationship between the reduction of pesticide use and the reduction of risks. CONCLUSIONS: Assessment with the BROWSE model helped to identify cropping systems with decreased risks from pesticides for human health and to propose some improvements to the cropping systems by identifying the pesticides that led to unacceptable risks.

Assessing and reporting uncertainties in dietary exposure analysis: Mapping of uncertainties in a tiered approach.

Uncertainty analysis is an important component of dietary exposure assessments in order to understand correctly the strength and limits of its results. Often, standard screening procedures are applied in a first step which results in conservative estimates. If through those screening procedures a potential exceedance of health-based guidance values is indicated, within the tiered approach more refined models are applied. However, the sources and types of uncertainties in deterministic and probabilistic models can vary or differ. A key objective of this work has been the mapping of different sources and types of uncertainties to better understand how to best use uncertainty analysis to generate more realistic comprehension of dietary exposure. In dietary exposure assessments, uncertainties can be introduced by knowledge gaps about the exposure scenario, parameter and the model itself. With this mapping, general and model-independent uncertainties have been identified and described, as well as those which can be introduced and influenced by the specific model during the tiered approach. This analysis identifies that there are general uncertainties common to point estimates (screening or deterministic methods) and probabilistic exposure assessment methods. To provide further clarity, general sources of uncertainty affecting many dietary exposure assessments should be separated from model-specific uncertainties.

New approaches to uncertainty analysis for use in aggregate and cumulative risk assessment of pesticides.

Risk assessments for human exposures to plant protection products (PPPs) have traditionally focussed on single routes of exposure and single compounds. Extensions to estimate aggregate (multi-source) and cumulative (multi-compound) exposure from PPPs present many new challenges and additional uncertainties that should be addressed as part of risk analysis and decision-making. A general approach is outlined for identifying and classifying the relevant uncertainties and variabilities. The implementation of uncertainty analysis within the MCRA software, developed as part of the EU-funded ACROPOLIS project to address some of these uncertainties, is demonstrated. An example is presented for dietary and non-dietary exposures to the triazole class of compounds. This demonstrates the chaining of models, linking variability and uncertainty generated from an external model for bystander exposure with variability and uncertainty in MCRA dietary exposure assessments. A new method is also presented for combining pesticide usage survey information with limited residue monitoring data, to address non-detect uncertainty. The results show that incorporating usage information reduces uncertainty in parameters of the residue distribution but that in this case quantifying uncertainty is not a priority, at least for UK grown crops. A general discussion of alternative approaches to treat uncertainty, either quantitatively or qualitatively, is included.

A European model and case studies for aggregate exposure assessment of pesticides.

Exposures to plant protection products (PPPs) are assessed using risk analysis methods to protect public health. Traditionally, single sources, such as food or individual occupational sources, have been addressed. In reality, individuals can be exposed simultaneously to multiple sources. Improved regulation therefore requires the development of new tools for estimating the population distribution of exposures aggregated within an individual. A new aggregate model is described, which allows individual users to include as much, or as little, information as is available or relevant for their particular scenario. Depending on the inputs provided by the user, the outputs can range from simple deterministic values through to probabilistic analyses including characterisations of variability and uncertainty. Exposures can be calculated for multiple compounds, routes and sources of exposure. The aggregate model links to the cumulative dietary exposure model developed in parallel and is implemented in the web-based software tool MCRA. Case studies are presented to illustrate the potential of this model, with inputs drawn from existing European data sources and models. These cover exposures to UK arable spray operators, Italian vineyard spray operators, Netherlands users of a consumer spray and UK bystanders/residents. The model could also be adapted to handle non-PPP compounds.

The MCRA model for probabilistic single-compound and cumulative risk assessment of pesticides.

Pesticide risk assessment is hampered by worst-case assumptions leading to overly pessimistic assessments. On the other hand, cumulative health effects of similar pesticides are often not taken into account. This paper describes models and a web-based software system developed in the European research project ACROPOLIS. The models are appropriate for both acute and chronic exposure assessments of single compounds and of multiple compounds in cumulative assessment groups. The software system MCRA (Monte Carlo Risk Assessment) is available for stakeholders in pesticide risk assessment at mcra.rivm.nl. We describe the MCRA implementation of the methods as advised in the 2012 EFSA Guidance on probabilistic modelling, as well as more refined methods developed in the ACROPOLIS project. The emphasis is on cumulative assessments. Two approaches, sample-based and compound-based, are contrasted. It is shown that additional data on agricultural use of pesticides may give more realistic risk assessments. Examples are given of model and software validation of acute and chronic assessments, using both simulated data and comparisons against the previous release of MCRA and against the standard software DEEM-FCID used by the Environmental Protection Agency in the USA. It is shown that the EFSA Guidance pessimistic model may not always give an appropriate modelling of exposure.

Testing a cumulative and aggregate exposure model using biomonitoring studies and dietary records for Italian vineyard spray operators.

The need for improved tools to estimate the cumulative and aggregate exposure to compounds such as plant protection products (PPPs) is recognised in the EU Regulation 1107/2009. A new model has been developed to estimate the exposure within a population to single compounds or compounds within a Cumulative Action Group, considering dietary and non-dietary sources and multiple exposure routes. To test the model a field study was carried out in Italy with operators applying tebuconazole fungicides, with measurements of dermal exposure collected. Whole urine samples were collected and analysed to provide values for the absorbed dose of tebuconazole, with duplicate diet samples collected and analysed as a measure of dietary exposures. The model provided predicted values of exposure for combined dietary and non-dietary routes of exposures which were compared to the measured absorbed dose values based on urinary analysis. The model outputs provided mean daily exposure values of 1.77 (± 1.96) µg a.s./kg BW which are comparable to measured mean values from the biomonitoring field study of 1.73 (± 1.31) µg a.s./kg BW. To supplement the limited measurement data available, comparisons against other models were also made and found to be comparable.

A framework to determine the effectiveness of dietary exposure mitigation to chemical contaminants.

In order to ensure the food safety, risk managers may implement measures to reduce human exposure to contaminants via food consumption. The evaluation of the effect of a measure is often an overlooked step in risk analysis process. The aim of this study was to develop a systematic approach for determining the effectiveness of mitigation measures to reduce dietary exposure to chemical contaminants. Based on expert opinion, a general framework for evaluation of the effectiveness of measures to reduce human exposure to food contaminants was developed. The general outline was refined by application to three different cases: 1) methyl mercury in fish and fish products, 2) deoxynivalenol in cereal grains, and 3) furan in heated products. It was found that many uncertainties and natural variations exist, which make it difficult to assess the impact of the mitigation measure. Whenever possible, quantitative methods should be used to describe the current variation and uncertainty. Additional data should be collected to cover natural variability and reduce uncertainty. For the time being, it is always better for the risk manager to have access to all available information, including an assessment of uncertainty; however, the proposed methodology provides a conceptual framework for addressing these systematically.

Benefit-risk assessment of plant sterols in margarine: a QALIBRA case study.

This paper presents the benefit-risk assessment of adding plant sterols to margarine as an illustration of the QALIBRA method and software. With the QALIBRA tool health effects, risks as well as benefits are expressed in a common metric (DALY) which allows quantitative balancing of benefits and risks of food intake. The QALIBRA software can handle uncertainties in a probabilistic simulation. This simple case study illustrates the data need and assumptions that go into a quantitative benefit-risk assessment. The assessment shows that the benefits of plant sterols added to margarine outweigh the risks, if any.

Fish consumption during child bearing age: a quantitative risk-benefit analysis on neurodevelopment.

The fish ingredient N3-docosahexaenoic acid 22:6 n-3 (DHA) stimulates brain development. On the other hand methylmercury (MeHg) in fish disturbs the developing central nervous system. In this Context the IQ score in children is considered as an aggregate measure of in utero brain development. To determine the effect of DHA exposure on prenatal neurodevelopment the maternal DHA intake during pregnancy was compared with its epidemiologically observed effect on the IQ score of children. For MeHg the maternal intake was converted into its accumulation in the maternal body. The maternal body burden then was compared with its epidemiologically observed relationship with the IQ score. Taking the MeHg and DHA content of 33 fish species the net effect of these compounds on the IQ score was quantified. For most fish species the adverse effect of MeHg on the IQ score exceeded the beneficial effect of DHA. In the case of long-living predators a negative effect up to 10 points on the IQ score was found. The results of this study indicate that food interventions aiming at the beneficial effects of fish consumption should focus on fish species with a high DHA content, while avoiding fish species with a high MeHg content.

Qalibra: a general model for food risk-benefit assessment that quantifies variability and uncertainty.

The EU project BRAFO proposed a framework for risk-benefit assessment of foods, or changes in diet, that present both potential risks and potential benefits to consumers (Hoekstra et al., 2012a). In higher tiers of the BRAFO framework, risks and benefits are integrated quantitatively to estimate net health impact measured in DALYs or QALYs (disability- or quality-adjusted life years). This paper describes a general model that was developed by a second EU project, Qalibra, to assist users in conducting these assessments. Its flexible design makes it applicable to a wide range of dietary questions involving different nutrients, contaminants and health effects. Account can be taken of variation between consumers in their diets and also other characteristics relevant to the estimation of risk and benefit, such as body weight, gender and age. Uncertainty in any input parameter may be quantified probabilistically, using probability distributions, or deterministically by repeating the assessment with alternative assumptions. Uncertainties that are not quantified should be evaluated qualitatively. Outputs produced by the model are illustrated using results from a simple assessment of fish consumption. More detailed case studies on oily fish and phytosterols are presented in companion papers. The model can be accessed as web-based software at www.qalibra.eu.

A hierarchical Bayesian model for extreme pesticide residues.

The number of residue measurements in an individual field trial, carried out to provide data for a pesticide registration for a particular crop, is generally too small to estimate upper tails of the residue distribution for that crop with any certainty. We present a new method, using extreme value theory, which pools information from various field trials, with different crop and pesticide combinations, to provide a common model for the upper tails of residue distributions generally. The method can be used to improve the estimation of high quantiles of a particular residue distribution. It provides a flexible alternative to the direct fitting of a distribution to each individual dataset, and does not require strong distributional assumptions. By using a hierarchical Bayesian model, our method also accounts for parameter uncertainty. The method is applied to a range of supervised trials containing residues on individual items (e.g. on individual apples), and the results illustrate the variation in tail properties amongst all commodities and pesticides. The outputs could be used to select conservative high percentile residue levels as part of a deterministic risk assessment, taking account of the variability between crops and pesticides and also the uncertainty due to relatively small datasets.

Bayesian modelling of long-term dietary intakes from multiple sources.

Human exposure to a specific pesticide or other chemical can occur from a combination of food and drink products. Probabilistic risk assessments are used to quantify the distribution of mean total daily exposures in the population, from the available data on residues and consumptions. We present a new statistical method for estimating this distribution, based on dietary survey data for multiple food types and residue monitoring data. The model allows for between-food correlations in both frequency and amounts of consumption. Three case studies are presented based on consumption data for UK children, considering the distribution of daily intakes of pyrimethanil, captan and chlorpyrifos aggregated over 4, 6 and 10 food types, respectively. We compared three alternative approaches, each using a Bayesian approach to quantify uncertainty: (i) a multivariate model that explicitly includes correlation parameters; (ii) separate independent parametric models for individual food types and (iii) a single parametric model applied to intakes aggregated directly from the data. The results demonstrate the importance of accounting for correlations between foods, using model (i) or (iii), for example, but also show that model (iii) can produce very different results when the aggregated intakes distribution is bimodal. The influence of residue uncertainty is also demonstrated.