Susan Arnold

Using publicly available information to create exposure and risk-based ranking of chemicals used in the workplace and consumer products.

Mandates that require the estimation of exposure and human health risk posed by large numbers of chemicals present regulatory managers with a significant challenge. Although these issues have been around for some time, the estimation of human exposure to chemicals from use of products in the workplace and by the consumer has been generally hindered by the lack of good tools. Logically and in the interest of cost-effective resource allocation and regulation one would typically and naturally first attempt to rank-order or prioritize the chemicals according to the human exposure potential that each might pose. We have developed an approach and systematic modeling construct that accomplishes this critical task by providing a quantitative estimate of human exposure for as many as several hundred chemicals initially; however, it could ultimately do this for any number of regulated chemicals starting only with the identity (Chemical Abstract Service number) for each chemical under consideration. These exposure estimates can then be readily linked to toxicological benchmarks for each item to estimate and rank the human health risk for the chemicals under consideration in a “worst things first” listing. This modeling construct, entitled Complex Exposure Tool (ComET) was developed by The LifeLine Group as a proof of concept under the sponsorship of Health Canada. ComET considers multiple routes of exposure, multiple subpopulations and different possible durations of exposure. A β-version of ComET was issued and demonstrated in which users can change the assumptions in the model and see the impacts of these changes and the quality of information as they relate to the predicted exposure potential. We have advanced the operational elements of ComET into a tool entitled the Chemical Exposure Priority Setting Tool (CEPST) designed to provide quantitative estimation of the exposure potential of large groups of chemicals with little data and possibly multiple exposure scenarios. A basic feature of this tool is the utilization of an internally consistent approach and assumptions that are completely transparent. It uses publicly available information as critical input and is specifically designed to be continually reviewed, refined, expanded and updated using scientific peer review and stakeholder input.

Modeling framework for human exposure assessment

We are at the dawn of a new era of quantitative consumer exposure and risk assessment of chemicals driven by regulatory mandates. This remarkable development also signals the beginning of a dramatic resurgence in the need for and development of human exposure models. This paper presents some of the philosophical background underlying exposure modeling in the context of human health risk assessment. The basic types of and structure of inhalation exposure models are discussed, as well as the research needed to move us forward into this exciting new period of development.

Evaluating well-mixed room and near-field–far-field model performance under highly controlled conditions

ABSTRACT Exposure judgments made without personal exposure data and based instead on subjective inputs tend to underestimate exposure, with exposure judgment accuracy not significantly more accurate than random chance. Therefore, objective inputs that contribute to more accurate decision making are needed. Models have been shown anecdotally to be useful in accurately predicting exposure but their use in occupational hygiene has been limited. This may be attributable to a general lack of guidance on model selection and use and scant model input data. The lack of systematic evaluation of the models is also an important factor. This research addresses the need to systematically evaluate two widely applicable models, the Well-Mixed Room (WMR) and Near-Field–Far-Field (NF-FF) models. The evaluation, conducted under highly controlled conditions in an exposure chamber, allowed for model inputs to be accurately measured and controlled, generating over 800 pairs of high quality measured and modeled exposure estimates. By varying conditions in the chamber one at a time, model performance across a range of conditions was evaluated using two sets of criteria: the ASTM Standard 5157 and the AIHA Exposure Assessment categorical criteria. Model performance for the WMR model was excellent, with ASTM performance criteria met for 88–97% of the pairs across the three chemicals used in the study, and 96% categorical agreement observed. Model performance for the NF-FF model, impacted somewhat by the size of the chamber was nevertheless good to excellent. NF modeled estimates met modified ASTM criteria for 67–84% of the pairs while 69–91% of FF modeled estimates met these criteria. Categorical agreement was observed for 72% and 96% of NF and FF pairs, respectively. These results support the use of the WMR and NF–FF models in guiding decision making towards improving exposure judgment accuracy.

Using checklists and algorithms to improve qualitative exposure judgment accuracy

ABSTRACT Most exposure assessments are conducted without the aid of robust personal exposure data and are based instead on qualitative inputs such as education and experience, training, documentation on the process chemicals, tasks and equipment, and other information. Qualitative assessments determine whether there is any follow-up, and influence the type that occurs, such as quantitative sampling, worker training, and implementing exposure and risk management measures. Accurate qualitative exposure judgments ensure appropriate follow-up that in turn ensures appropriate exposure management. Studies suggest that qualitative judgment accuracy is low. A qualitative exposure assessment Checklist tool was developed to guide the application of a set of heuristics to aid decision making. Practicing hygienists (n = 39) and novice industrial hygienists (n = 8) were recruited for a study evaluating the influence of the Checklist on exposure judgment accuracy. Participants generated 85 pre-training judgments and 195 Checklist-guided judgments. Pre-training judgment accuracy was low (33%) and not statistically significantly different from random chance. A tendency for IHs to underestimate the true exposure was observed. Exposure judgment accuracy improved significantly (p <0.001) to 63% when aided by the Checklist. Qualitative judgments guided by the Checklist tool were categorically accurate or over-estimated the true exposure by one category 70% of the time. The overall magnitude of exposure judgment precision also improved following training. Fleiss’ κ, evaluating inter-rater agreement between novice assessors was fair to moderate (κ = 0.39). Cohens weighted and unweighted κ were good to excellent for novice (0.77 and 0.80) and practicing IHs (0.73 and 0.89), respectively. Checklist judgment accuracy was similar to quantitative exposure judgment accuracy observed in studies of similar design using personal exposure measurements, suggesting that the tool could be useful in developing informed priors and further demonstrating its usefulness in producing accurate qualitative exposure judgments.

Turbulent Eddy Diffusion Models in Exposure Assessment - Determination of the Eddy Diffusion Coefficient

ABSTRACT The use of the turbulent eddy diffusion model and its variants in exposure assessment is limited due to the lack of knowledge regarding the isotropic eddy diffusion coefficient, DT. But some studies have suggested a possible relationship between DT and the air changes per hour (ACH) through a room. The main goal of this study was to accurately estimate DT for a range of ACH values by minimizing the difference between the concentrations measured and predicted by eddy diffusion model. We constructed an experimental chamber with a spatial concentration gradient away from the contaminant source, and conducted 27 3-hr long experiments using toluene and acetone under different air flow conditions (0.43–2.89 ACHs). An eddy diffusion model accounting for chamber boundary, general ventilation, and advection was developed. A mathematical expression for the slope based on the geometrical parameters of the ventilation system was also derived. There is a strong linear relationship between DT and ACH, providing a surrogate parameter for estimating DT in real-life settings. For the first time, a mathematical expression for the relationship between DT and ACH has been derived that also corrects for non-ideal conditions, and the calculated value of the slope between these two parameters is very close to the experimentally determined value. The values of DT obtained from the experiments are generally consistent with values reported in the literature. They are also independent of averaging time of measurements, allowing for comparison of values obtained from different measurement settings. These findings make the use of turbulent eddy diffusion models for exposure assessment in workplace/indoor environments more practical.

Evaluation of the well mixed room and near-field far-field models in occupational settings

ABSTRACT Drawing appropriate conclusions about a scenario for which the exposure is truly unacceptable drives appropriate exposure and risk management, and protects the health and safety of those individuals. To ensure the vast majority of these decisions are accurate, these decisions must be based upon proven approaches and tools. When these decisions are based solely on professional judgment guided by subjective inputs, however, they are more than likely wrong, and biased, underestimating the true exposure. Models have been shown anecdotally to be useful in accurately predicting exposure but their use in occupational hygiene has been limited. Possible reasons are a general lack of guidance on model selection and use and scant model input data. The lack of systematic evaluation of the models is also an important factor. This research is the second phase of work building upon the robust evaluation of the Well Mixed Room (WMR) and Near Field Far Field (NF-FF) models under controlled conditions in an exposure chamber,[5] in which good concordance between measured and modeled airborne concentrations of three solvents under a range of conditions was observed. In real world environments, the opportunity to control environmental conditions is limited and measuring the model inputs directly can be challenging; in many cases, model inputs must be estimated indirectly without measurement. These circumstances contribute to increased model input uncertainty and consequent uncertainty in the output. Field studies of model performance directly inform us about how well models predict exposures given these practical limitations, and are, therefore, an important component of model evaluation. The evaluation included ten diverse contaminant-exposure scenarios at five workplaces involving six different contaminants. A database of parameter values and measured and modeled exposures was developed and will be useful for modeling similar scenarios in the future.

Influence of Parameter Values and Variances and Algorithm Architecture in ConsExpo Model on Modeled Exposures

This study evaluated the influence of parameter values and variances and model architecture on modeled exposures, and identified important data gaps that influence lack-of-knowledge-related uncertainty, using Consexpo 4.1 as an illustrative case study. Understanding the influential determinants in exposure estimates enables more informed and appropriate use of this model and the resulting exposure estimates. In exploring the influence of parameter placement in an algorithm and of the values and variances chosen to characterize the parameters within ConsExpo, “sensitive” and “important” parameters were identified: product amount, weight fraction, exposure duration, exposure time, and ventilation rate were deemed “important,” or “always sensitive.” With this awareness, exposure assessors can strategically focus on acquiring the most robust estimates for these parameters. ConsExpo relies predominantly on three algorithms to assess the default scenarios: inhalation vapors evaporation equation using the Langmuir mass transfer, the dermal instant application with diffusion through the skin, and the oral ingestion by direct uptake algorithm. These algorithms, which do not necessarily render health conservative estimates, account for 87, 89 and 59% of the inhalation, dermal and oral default scenario assessments,respectively, according them greater influence relative to the less frequently used algorithms. Default data provided in ConsExpo may be useful to initiate assessments, but are insufficient for determining exposure acceptability or setting policy, as parameters defined by highly uncertain values produce biased estimates that may not be health conservative. Furthermore, this lack-of-knowledge uncertainty makes the magnitude of this bias uncertain. Significant data gaps persist for product amount, exposure time, and exposure duration. These “important” parameters exert influence in requiring broad values and variances to account for their uncertainty. Prioritizing them for research will not only help fill a large and influential knowledge gap, but also lead to more accurate assessments and thus refine the studies informing policy decisions.

Naturally Occurring Canine Glioma as a Model for Novel Therapeutics

Abstract Background: Current animal models of glioma are limited to small animal models, which are less predictive of treatment of human disease. Canines often develop gliomas de novo, but the natural history of the disease is not well described. Objective: We provide data for naturally occurring canine gliomas; evaluating medical and surgical therapies. Methods: We reviewed medical records of pet dogs with a presumptive diagnosis of glioma from MRI imaging that underwent surgery as part of the Canine Brain Tumor Clinical Trials Program. Breed, age, sex, median progression-free, and overall survival times and cause of death were recorded for multivariate analysis. Results: Ninety five dogs (56 male; mean age = 8.3 years) were included, but nine were excluded as final pathology was non-neoplastic. Gross total resection was reported in 81 cases based on postoperative MRI. Seventy had high-grade tumors (grade III or IV). Eighty three dogs presented with seizures, being the most common presenting clinical sign. Median survival after surgery was 723 days (95% CI 343–1103) for grade II tumors, 301 days (197–404) for grade III and 200 days (126–274) for grade IV (p = .009 Kaplan–Meier survival analysis; Log Rank test). Age (cox regression, p = .14) or sex (Kaplan–Meier test, p = .22) did not predict survival. Conclusions: This study establishes normative data for a model exploiting dogs with naturally occurring glioma, which can be used to test novel therapies prior to translation to human trials. Further work will focus on the effects of different therapies, including chemotherapy, radiation therapy, and immunotherapy.

Testing an Intervention to Decrease Healthcare Workers’ Exposure to Antineoplastic Agents

PURPOSE/OBJECTIVES To develop and test a worksite intervention that protects healthcare workers who handle antineoplastic drugs from work-related exposures. 
. DESIGN Intervention study. 
. SETTING A university hospital in a large midwestern metropolitan area and its outpatient chemotherapy infusion clinic.
. SAMPLE 163 staff (nurses, pharmacists, and pharmacy technicians) who work with antineoplastic agents.
. METHODS A self-report survey measured workplace and individual factors to assess use of personal protective equipment (PPE). Wipe samples were tested for surface contamination. An intervention incorporating study findings and worker input was developed.
. MAIN RESEARCH VARIABLES PPE use was the dependent variable, and the independent variables included knowledge of the hazard, perceived risk, perceived barriers, interpersonal influence, self-efficacy, conflict of interest, and workplace safety climate. 
. FINDINGS PPE use was lower than recommended and improved slightly postintervention. Self-efficacy and perceived risk increased on the post-test survey. Chemical residue was found in several areas. Awareness of safe-handling precautions improved postintervention. The unit where nurses worked was an important predictor of safety climate and PPE use on the pretest but less so following the intervention. 
. CONCLUSIONS Involving staff in developing an intervention for safety ensures that changes made will be feasible. Units that implemented workflow changes had decreased contamination. 
. IMPLICATIONS FOR NURSING Worksite analysis identifies specific targets for interventions to improve antineoplastic drug handling safety.