Comparative Human Health Impact Assessment of Engineered Nanomaterials in the Framework of Life Cycle Assessment.

For safe innovation, knowledge on potential human health impacts is essential. Ideally, these impacts are considered within a larger life-cycle-based context to support sustainable development of new applications and products. A methodological framework that accounts for human health impacts caused by inhalation of engineered nanomaterials (ENMs) in an indoor air environment has been previously developed. The objectives of this study are as follows: (i) evaluate the feasibility of applying the CF framework for NP exposure in the workplace based on currently available data; and (ii) supplement any resulting knowledge gaps with methods and data from the life cycle approach and human risk assessment (LICARA) project to develop a modified case-specific version of the framework that will enable near-term inclusion of NP human health impacts in life cycle assessment (LCA) using a case study involving nanoscale titanium dioxide (nanoTiO2 ). The intent is to enhance typical LCA with elements of regulatory risk assessment, including its more detailed measure of uncertainty. The proof-of-principle demonstration of the framework highlighted the lack of available data for both the workplace emissions and human health effects of ENMs that is needed to calculate generalizable characterization factors using common human health impact assessment practices in LCA. The alternative approach of using intake fractions derived from workplace air concentration measurements and effect factors based on best-available toxicity data supported the current case-by-case approach for assessing the human health life cycle impacts of ENMs. Ultimately, the proposed framework and calculations demonstrate the potential utility of integrating elements of risk assessment with LCA for ENMs once the data are available.

Evaluation of Decision Rules in a Tiered Assessment of Inhalation Exposure to Nanomaterials.

Tiered or stepwise approaches to assess occupational exposure to nano-objects, and their agglomerates and aggregates have been proposed, which require decision rules (DRs) to move to a next tier, or terminate the assessment. In a desk study the performance of a number of DRs based on the evaluation of results from direct reading instruments was investigated by both statistical simulations and the application of the DRs to real workplace data sets. A statistical model that accounts for autocorrelation patterns in time-series, i.e. autoregressive integrated moving average (ARIMA), was used as 'gold' standard. The simulations showed that none of the proposed DRs covered the entire range of simulated scenarios with respect to the ARIMA model parameters, however, a combined DR showed a slightly better agreement. Application of the DRs to real workplace datasets (n = 117) revealed sensitivity up to 0.72, whereas the lowest observed specificity was 0.95. The selection of the most appropriate DR is very much dependent on the consequences of the decision, i.e. ruling in or ruling out of scenarios for further evaluation. Since a basic assessment may also comprise of other type of measurements and information, an evaluation logic was proposed which embeds the DRs, but furthermore supports decision making in view of a tiered-approach exposure assessment.

Reliability of the Advanced REACH Tool (ART).

OBJECTIVES: The aim of this study was to assess the reliability of the Advanced REACH Tool (ART) by (i) studying interassessor agreement of the resulting exposure estimates generated by the ART mechanistic model, (ii) studying interassessor agreement per model parameters of the ART mechanistic model, (iii) investigating assessor characteristics resulting in reliable estimates, and (iv) estimating the effect of training on assessor agreement. METHODS: Prior to the 1-day workshop, participants had to assess four scenarios with the ART. During two 1-day workshops, 54 participants received 3-h training in applying the mechanistic model and the technical aspects of the web tool. Afterward, the participants assessed another four scenarios. The assessments of the participants were compared with gold standard estimates compiled by the workshop instructors. Intraclass correlation coefficients (ICCs) were calculated and per model parameter and the percentage agreement and Cohen kappa statistics were estimated. RESULTS: The ICCs showed good agreement before and almost perfect agreement after training. However, substantial variability was observed between individual assessors' estimates for an individual scenario. After training, only 42% of the assessments lay within a factor of three of the gold standard estimate. The reliability appeared to be influenced by several factors: (i) information provided by text and video hampered the assessors gaining additional information required to make the assessments, (ii) for some parameters, the guidance documentation implemented in the tool may have been insufficient, and (iii) in some cases, the assessors were not able to implement the information explicitly provided. CONCLUSIONS: The ART is an expert tool and extensive training is recommended prior to use. Improvements of the guidance documentation, consensus procedures, and improving the training methods could improve the reliability of ART. Nevertheless, considerable variability can be expected between assessors using ART to estimate exposure levels for a given scenario.

The OSIRIS Weight of Evidence approach: ITS mutagenicity and ITS carcinogenicity.

Risk assessment of chemicals usually implies data evaluation of in vivo tests in rodents to conclude on their hazards. The FP7 European project OSIRIS has developed integrated testing strategies (ITS) for relevant toxicological endpoints to avoid unnecessary animal testing and thus to reduce time and costs. This paper describes the implementation of ITS mutagenicity and carcinogenicity in the public OSIRIS webtool. The data requirements of REACH formed the basis for these ITS. The main goal was to implement procedures to reach a conclusion on the adequacy and validity of available data. For the mutagenicity ITS a quantitative Weight of Evidence approach based on Bayesian statistics was developed and implemented. The approach allows an overall quality assessment of all available data for the five types of mutagenicity data requirements: in vitro bacterial mutagenicity, in vitro and in vivo chromosome aberration, in vitro and in vivo mammalian mutagenicity. For the carcinogenicity ITS a tool was developed to evaluate the quality of studies not conforming (entirely) to guidelines. In a tiered approach three quality aspects are assessed: documentation (reliability), study design (adequacy) and scope of examination (validity). The quality assessment is based on expert and data driven quantitative Weight of Evidence.

A mixture model for the joint analysis of latent developmental trajectories and survival.

A general joint modeling framework is proposed that includes a parametric stratified survival component for continuous time survival data, and a mixture multilevel item response component to model latent developmental trajectories given mixed discrete response data. The joint model is illustrated in a real data setting, where the utility of longitudinally measured cognitive function as a predictor for survival is investigated in a group of elderly persons. The object is partly to determine whether cognitive impairment is accompanied by a higher mortality rate. Time-dependent cognitive function is measured using the generalized partial credit model given occasion-specific mini-mental state examination response data. A parametric survival model is applied for the survival information, and cognitive function as a continuous latent variable is included as a time-dependent explanatory variable along with other explanatory information. A mixture model is defined, which incorporates the latent developmental trajectory and the survival component. The mixture model captures the heterogeneity in the developmental trajectories that could not be fully explained by the multilevel item response model and other explanatory variables. A Bayesian modeling approach is pursued, where a Markov chain Monte Carlo algorithm is developed for simultaneous estimation of the joint model parameters. Practical issues as model building and assessment are addressed using the DIC and various posterior predictive tests.

Evaluating cognitive theory: a joint modeling approach using responses and response times.

In current psychological research, the analysis of data from computer-based assessments or experiments is often confined to accuracy scores. Response times, although being an important source of additional information, are either neglected or analyzed separately. In this article, a new model is developed that allows the simultaneous analysis of accuracy scores and response times of cognitive tests with a rule-based design. The model is capable of simultaneously estimating ability and speed on the person side as well as difficulty and time intensity on the task side, thus dissociating information that is often confounded in current analysis procedures. Further, by integrating design matrices on the task side, it becomes possible to assess the effects of design parameters (e.g., cognitive processes) on both task difficulty and time intensity, offering deeper insights into the task structure. A Bayesian approach, using Markov Chain Monte Carlo methods, has been developed to estimate the model. An application of the model in the context of educational assessment is illustrated using a large-scale investigation of figural reasoning ability.

Prioritisation of allergenic foods with respect to public health relevance: Report from an ILSI Europe Food Allergy Task Force Expert Group.

Regulators and risk managers in general need to decide whether an allergenic food or ingredient is of such public health importance that it needs to be actively managed. There is therefore a need to scale the relative allergenicity of foods and ingredients according to the hazards they pose. Objective criteria increase transparency and trust in this decision-making process and its conclusions. This paper proposes a framework that allows categorisation and prioritisation of allergenic foods according to their public health importance. The challenge is to find a basis on which the allergenicity of foods can best be described and a method to combine the relevant measures of allergenicity into a scoring system that prioritises allergenic foods on the basis of their public health relevance. The framework is designed in accordance with the generic risk analysis principles used in food safety and can be used by regulators to decide whether or not a specific allergenic food or ingredient is of sufficient public health importance that it warrants regulation (i.e. mandatory labelling) when used in the production of food products.

Bayesian inference for an illness-death model for stroke with cognition as a latent time-dependent risk factor.

Longitudinal data can be used to estimate the transition intensities between healthy and unhealthy states prior to death. An illness-death model for history of stroke is presented, where time-dependent transition intensities are regressed on a latent variable representing cognitive function. The change of this function over time is described by a linear growth model with random effects. Occasion-specific cognitive function is measured by an item response model for longitudinal scores on the Mini-Mental State Examination, a questionnaire used to screen for cognitive impairment. The illness-death model will be used to identify and to explore the relationship between occasion-specific cognitive function and stroke. Combining a multi-state model with the latent growth model defines a joint model which extends current statistical inference regarding disease progression and cognitive function. Markov chain Monte Carlo methods are used for Bayesian inference. Data stem from the Medical Research Council Cognitive Function and Ageing Study in the UK (1991-2005).

A microsimulation model for the development and progression of chronic obstructive pulmonary disease.

Chronic obstructive pulmonary disease (COPD) is a chronic lung disease that is thought to affect over one million people in Great Britain. The main factor contributing to the development of COPD is tobacco smoke. This paper presents a microsimulation model for the development of COPD, incorporating population dynamics and trends in smoking. The model simulates a population longitudinally throughout their lifetimes, providing projections of future COPD prevalence and evaluation of the effects of changes in risk factor prevalence such as smoking. Sensitivity analysis provides information on the most influential model parameters. The model-predicted prevalence of COPD in 2040 was 17% in males over the age of 35 years (13% amongst non-smokers and 22% amongst smokers), and a modest decline over the next 25 years due to recent trends in smoking rates. The simulation model provides us with valuable information on current and future trends in COPD in Great Britain. It was developed primarily to enable easy extension to evaluate the effects of occupational and environmental exposures on lung function and the prevalence of COPD and to allow evaluation of interventions, such as introducing health surveillance or policy changes. As longitudinal studies for investigating COPD are difficult due to the lengthy follow-up time required and the potentially large number of drop-outs, we anticipate that the model will provide a valuable tool for health impact assessment. An extended model for occupational exposures is under development and will be presented in a subsequent paper.

Food allergy population thresholds: an evaluation of the number of oral food challenges and dosing schemes on the accuracy of threshold dose distribution modeling.

For most allergenic foods, limited availability of threshold dose information within the population restricts the advice on action levels of unintended allergenic foods which should trigger advisory labeling on packaged foods. The objective of this paper is to provide guidance for selecting an optimal sample size for threshold dosing studies for major allergenic foods and to identify factors influencing the accuracy of estimation. A simulation study was performed to evaluate the effects of sample size and dosing schemes on the accuracy of the threshold distribution curve. The relationships between sample size, dosing scheme and the employed statistical distribution on the one hand and accuracy of estimation on the other hand were obtained. It showed that the largest relative gains in accuracy are obtained when sample size increases from N=20 to N=60. Moreover, it showed that the EuroPrevall dosing scheme is a useful start, but that it may need revision for a specific allergen as more data become available, because a proper allocation of the dosing steps is important. The results may guide risk assessors in minimum sample sizes for new studies and in the allocation of proper dosing schemes for allergens in provocation studies.