Application of evidence-based methods to construct mechanism-driven chemical assessment frameworks.

The workshop titled "Application of evidence-based methods to construct mechanism-driven chemical assessment frameworks" was co-organized by the Evidence-based Toxicology Collaboration and the European Food Safety Authority (EFSA) and hosted by EFSA at its headquarters in Parma, Italy on October 2 and 3, 2019. The goal was to explore integration of systematic review with mechanistic evidence evaluation. Participants were invited to work on concrete products to advance the exploration of how evidence-based approaches can support the development and application of adverse outcome pathways (AOP) in chemical risk assessment. The workshop discussions were centered around three related themes: 1) assessing certainty in AOPs, 2) literature-based AOP development, and 3) integrating certainty in AOPs and non-animal evidence into decision frameworks. Several challenges, mostly related to methodology, were identified and largely determined the workshop recommendations. The workshop recommendations included the comparison and potential alignment of processes used to develop AOP and systematic review methodology, including the translation of vocabulary of evidence-based methods to AOP and vice versa, the development and improvement of evidence mapping and text mining methods and tools, as well as a call for a fundamental change in chemical risk and uncertainty assessment methodology if to be conducted based on AOPs and new approach methodologies (NAM). The usefulness of evidence-based approaches for mechanism-based chemical risk assessments was stressed, particularly the potential contribution of the rigor and transparency inherent to such approaches in building stakeholders' trust for implementation of NAM evidence and AOPs into chemical risk assessment.

Cumulative dietary risk characterisation of pesticides that have acute effects on the nervous system.

A retrospective acute cumulative risk assessment of dietary exposure to pesticide residues, supported by an uncertainty analysis based on expert knowledge elicitation, was conducted for two effects on the nervous system: brain and/or erythrocyte acetylcholinesterase inhibition, and functional alterations of the motor division. The pesticides considered in this assessment were identified and characterised in the scientific report on the establishment of cumulative assessment groups of pesticides for their effects on the nervous system. Cumulative exposure assessments were conducted through probabilistic modelling by EFSA and the Dutch National Institute for Public Health and the Environment (RIVM) using two different software tools and reported separately. These exposure assessments used monitoring data collected by Member States under their official pesticide monitoring programmes in 2014, 2015 and 2016 and individual consumption data from 10 populations of consumers from different countries and different age groups. This report completes the characterisation of cumulative risk, taking account of the available data and the uncertainties involved. For each of the 10 populations, it is concluded with varying degrees of certainty that cumulative exposure to pesticides that have the acute effects on the nervous system mentioned above does not exceed the threshold for regulatory consideration established by risk managers.

Cumulative dietary risk characterisation of pesticides that have chronic effects on the thyroid.

A retrospective chronic cumulative risk assessment of dietary exposure to pesticide residues, supported by an uncertainty analysis based on expert knowledge elicitation, was conducted for two effects on the thyroid, hypothyroidism and parafollicular cell (C-cell) hypertrophy, hyperplasia and neoplasia. The pesticides considered in this assessment were identified and characterised in the scientific report on the establishment of cumulative assessment groups of pesticides for their effects on the thyroid. Cumulative exposure assessments were conducted through probabilistic modelling by EFSA and the Dutch National Institute for Public Health and the Environment (RIVM) using two different software tools and reported separately. These exposure assessments used monitoring data collected by Member States under their official pesticide monitoring programmes in 2014, 2015 and 2016 and individual consumption data from 10 populations of consumers from different countries and different age groups. This report completes the characterisation of cumulative risk, taking account of the available data and the uncertainties involved. For each of the 10 populations, it is concluded with varying degrees of certainty that cumulative exposure to pesticides that have the chronic effects on the thyroid mentioned above does not exceed the threshold for regulatory consideration established by risk managers.

Towards a landscape scale management of pesticides: ERA using changes in modelled occupancy and abundance to assess long-term population impacts of pesticides.

Pesticides are regulated in Europe and this process includes an environmental risk assessment (ERA) for non-target arthropods (NTA). Traditionally a non-spatial or field trial assessment is used. In this study we exemplify the introduction of a spatial context to the ERA as well as suggest a way in which the results of complex models, necessary for proper inclusion of spatial aspects in the ERA, can be presented and evaluated easily using abundance and occupancy ratios (AOR). We used an agent-based simulation system and an existing model for a widespread carabid beetle (Bembidion lampros), to evaluate the impact of a fictitious highly-toxic pesticide on population density and the distribution of beetles in time and space. Landscape structure and field margin management were evaluated by comparing scenario-based ERAs for the beetle. Source-sink dynamics led to an off-crop impact even when no pesticide was present off-crop. In addition, the impacts increased with multi-year application of the pesticide whereas current ERA considers only maximally one year. These results further indicated a complex interaction between landscape structure and pesticide effect in time, both in-crop and off-crop, indicating the need for NTA ERA to be conducted at landscape- and multi-season temporal-scales. Use of AOR indices to compare ERA outputs facilitated easy comparison of scenarios, allowing simultaneous evaluation of impacts and planning of mitigation measures. The landscape and population ERA approach also demonstrates that there is a potential to change from regulation of a pesticide in isolation, towards the consideration of pesticide management at landscape scales and provision of biodiversity benefits via inclusion and testing of mitigation measures in authorisation procedures.

Father absence and age at first birth in a Western sample.

OBJECTIVES: Although a large literature has shown links between "father absence" during early childhood, and earlier puberty and sexual behavior in girls in Western populations, there are only a few studies which have looked at timing of reproduction, and only one of these fully incorporated childless respondents to investigate whether father absence is associated with increased hazard of becoming a parent at one time point (early) more than another. Here we sought to clarify exactly when, if at all, father absence increased the likelihood of first birth in a Western sample. METHODS: An online sample of 954 women reported on their childhood living circumstances, their age of menarche, first coitus, first pregnancy, and first birth. RESULTS: Cox regression and Kaplan-Meier plots showed an increased risk of becoming a parent for father absent women in their 20s, but no overall greater likelihood of parenthood. CONCLUSION: These data support the suggestion that father absence is associated with an acceleration of reproductive behavior in Western samples, rather than a simple increase in likelihood of reproduction.

On the quantification of intertest variability in ecotoxicity data with application to species sensitivity distributions.

Ecotoxicological hazard assessment relies on species effect data to estimate quantities such as the predicted no-effect concentration. While there is a concerted effort to quantify uncertainty in risk assessments, the uncertainty due to intertest variability in species effect measurements is an overlooked component. The European Union Registration, Evaluation, Authorisation, and Restriction of Chemicals (REACH) guidance document suggests that multiple toxicity records for a given chemical-species combination should be aggregated by the geometric mean. Ignoring this issue or applying unjustified so-called harmonization methods weakens the defensibility of uncertainty quantification and interpretation about properties of ecological models, for example, the predicted no-effect concentration. In the present study, the authors propose a simple and broadly theoretically justifiable model to quantify intertest variability and analyze it using Bayesian methods. The value of data in ecotoxicity databases is maximized by using (interval-)censored data. An exploratory analysis is provided to support the model. The authors conclude, based on a large ecotoxicity database of acute effects to aquatic species, that the standard deviation of intertest variability is approximately a factor (or fold-difference) of 3. The consequences for decision makers of (not) adjusting for intertest variability are demonstrated.

Competing statistical methods for the fitting of normal species sensitivity distributions: recommendations for practitioners.

A species sensitivity distribution (SSD) models data on toxicity of a specific toxicant to species in a defined assemblage. SSDs are typically assumed to be parametric, despite noteworthy criticism, with a standard proposal being the log-normal distribution. Recently, and confusingly, there have emerged different statistical methods in the ecotoxicological risk assessment literature, independent of the distributional assumption, for fitting SSDs to toxicity data with the overall aim of estimating the concentration of the toxicant that is hazardous to % of the biological assemblage (usually with small). We analyze two such estimators derived from simple linear regression applied to the ordered log-transformed toxicity data values and probit transformed rank-based plotting positions. These are compared to the more intuitive and statistically defensible confidence limit-based estimator. We conclude based on a large-scale simulation study that the latter estimator should be used in typical assessments where a pointwise value of the hazardous concentration is required.

On the application of loss functions in determining assessment factors for ecological risk.

Assessment factors have been proposed as a means to extrapolate from data on the concentrations hazardous to a small sample of species to the concentration hazardous to p% of the species in a given community (HCp). Aldenberg and Jaworska [2000. Uncertainty of the hazardous concentration and fraction affected for normal species sensitivity distributions. Ecotoxicol. Environ. Saf. 46, 1-18] proposed estimators that prescribed universal assessment factors which made use of distributional assumptions associated with species sensitivity distributions. In this paper we maintain those assumptions but introduce loss functions which punish over- and under-estimation. Furthermore, the final loss function is parameterised such that conservatism can be asymmetrically and non-linearly controlled which enables one to better represent the reality of risk assessment scenarios. We describe the loss functions and derive Bayes rules for each. We demonstrate the method by producing a table of universal factors that are independent of the substance being assessed and which can be combined with the toxicity data in order to estimate the HC5. Finally, through an example we illustrate the potential strength of the newly proposed estimators which rationally accounts for the costs of under- and over-estimation to choose an estimator; as opposed to arbitrarily choosing a one-sided lower confidence limit.

Making species salinity sensitivity distributions reflective of naturally occurring communities: using rapid testing and Bayesian statistics.

Species sensitivity distributions (SSDs) may accurately predict the proportion of species in a community that are at hazard from environmental contaminants only if they contain sensitivity data from a large sample of species representative of the mix of species present in the locality or habitat of interest. With current widely accepted ecotoxicological methods, however, this rarely occurs. Two recent suggestions address this problem. First, use rapid toxicity tests, which are less rigorous than conventional tests, to approximate experimentally the sensitivity of many species quickly and in approximate proportion to naturally occurring communities. Second, use expert judgements regarding the sensitivity of higher taxonomic groups (e.g., orders) and Bayesian statistical methods to construct SSDs that reflect the richness (or perceived importance) of these groups. Here, we describe and analyze several models from a Bayesian perspective to construct SSDs from data derived using rapid toxicity testing, combining both rapid test data and expert opinion. We compare these new models with two frequentist approaches, Kaplan-Meier and a log-normal distribution, using a large data set on the salinity sensitivity of freshwater macroinvertebrates from Victoria (Australia). The frequentist log-normal analysis produced a SSD that overestimated the hazard to species relative to the Kaplan-Meier and Bayesian analyses. Of the Bayesian analyses investigated, the introduction of a weighting factor to account for the richness (or importance) of taxonomic groups influenced the calculated hazard to species. Furthermore, Bayesian methods allowed us to determine credible intervals representing SSD uncertainty. We recommend that rapid tests, expert judgements, and novel Bayesian statistical methods be used so that SSDs reflect communities of organisms found in nature.