ExpoQual: Evaluating measured and modeled human exposure data

Development of a Range of Plausible Noncancer Toxicity Values for 2,3,7,8-Tetrachlorodibenzo-p-Dioxin Based on Effects on Sperm Count: Application of Systematic Review Methods and Quantitative Integration of Dose Response Using Meta-Regression

Regulatory agencies have derived noncancer toxicity values for 2,3,7,8-tetrachlorodibenzo-p-dioxin based on reduced sperm counts relying on single studies from a large body of evidence. Techniques such as meta-regression allow for greater use of the available data while simultaneously providing important information regarding the uncertainty associated with the underlying evidence base when conducting risk assessments. The objective herein was to apply systematic review methods and meta-regression to characterize the dose-response relationship of gestational exposure and epididymal sperm count. Twenty-three publications (20 animal studies consisting of 29 separate rat experimental data sets, and 3 epidemiology studies) met inclusion criteria. Risk of bias evaluation was performed to critically appraise study validity. Low to very low confidence precluded use of available epidemiological data as candidate studies for dose-response due to inconsistencies across the evidence base, high risk of bias, and general lack of biological coherence, including lack of clinical relevance and dose-response concordance. Experimental animal studies, which were found to have higher confidence following the structured assessment of confidence (eg, controlled exposure, biological consistency), were used as the basis of a meta-regression. Multiple models were fit; points of departure were identified and converted to human equivalent doses. The resulting reference dose estimates ranged from approximately 4 to 70 pg/kg/day, depending on model, benchmark response level, and study validity integration approach. This range of reference doses can be used either qualitatively or quantitatively to enhance understanding of human health risk estimates for dioxin-like compounds.

Facilitation of risk assessment with evidence-based methods - A framework for use of systematic mapping and systematic reviews in determining hazard, developing toxicity values, and characterizing uncertainty

Systematic review tools and approaches developed for clinical medicine are often difficult to apply "off the shelf" in order to meet the needs of chemical risk assessments. To address such, we propose an approach that can be used by practitioners for using evidence-based methods to facilitate the risk assessment process. The framework builds on and combines efforts conducted to date by a number of agencies and researchers; the novelty is in combining these efforts with a practical understanding of risk assessment, and translating such into a 'step-by-step' guide. The approach relies on three key components: problem formulation, systematic evidence mapping, and systematic review, applied using a stepwise approach. Unique to this framework is the consideration of exposure in selecting, prioritizing, and evaluating data (e.g., dose-relevance, routes of exposure, etc.). Using the proposed step-by-step process, critical appraisal of individual studies (e.g., formal and structured assessment of both relevance and reliability) and integration efforts are considered in context of specified risk assessment objectives (e.g., mode of action, dose-response) as well as chemical-specific considerations. The resulting framework provides a logical approach of how evidence-based methods can be used to facilitate risk assessment, and elevates the use of systematic methods beyond hazard identification to directly facilitating transparent and objective selection of candidate studies and/or datasets used to quantitatively characterize risk, and to better use the underlying process to inform the approaches used to develop toxicity values.

GRADE Guidelines 30: the GRADE approach to assessing the certainty of modeled evidence-An overview in the context of health decision-making

OBJECTIVES

The objective of the study is to present the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) conceptual approach to the assessment of certainty of evidence from modeling studies (i.e., certainty associated with model outputs).

STUDY DESIGN AND SETTING

Expert consultations and an international multidisciplinary workshop informed development of a conceptual approach to assessing the certainty of evidence from models within the context of systematic reviews, health technology assessments, and health care decisions. The discussions also clarified selected concepts and terminology used in the GRADE approach and by the modeling community. Feedback from experts in a broad range of modeling and health care disciplines addressed the content validity of the approach.

RESULTS

Workshop participants agreed that the domains determining the certainty of evidence previously identified in the GRADE approach (risk of bias, indirectness, inconsistency, imprecision, reporting bias, magnitude of an effect, dose-response relation, and the direction of residual confounding) also apply when assessing the certainty of evidence from models. The assessment depends on the nature of model inputs and the model itself and on whether one is evaluating evidence from a single model or multiple models. We propose a framework for selecting the best available evidence from models: 1) developing de novo, a model specific to the situation of interest, 2) identifying an existing model, the outputs of which provide the highest certainty evidence for the situation of interest, either "off-the-shelf" or after adaptation, and 3) using outputs from multiple models. We also present a summary of preferred terminology to facilitate communication among modeling and health care disciplines.

CONCLUSION

This conceptual GRADE approach provides a framework for using evidence from models in health decision-making and the assessment of certainty of evidence from a model or models. The GRADE Working Group and the modeling community are currently developing the detailed methods and related guidance for assessing specific domains determining the certainty of evidence from models across health care-related disciplines (e.g., therapeutic decision-making, toxicology, environmental health, and health economics).

Translation of Exposure and Epidemiology for Risk Assessment: A Shifting Paradigm

Risk assessment is a well-established process used for various types of public health decision-making, such as setting chemical site clean-up levels, developing limits on exposures to chemicals in soil, water, air and food, and determining occupational exposure limits[...].

Wrangling environmental exposure data: guidance for getting the best information from your laboratory measurements

BACKGROUND

Environmental health and exposure researchers can improve the quality and interpretation of their chemical measurement data, avoid spurious results, and improve analytical protocols for new chemicals by closely examining lab and field quality control (QC) data. Reporting QC data along with chemical measurements in biological and environmental samples allows readers to evaluate data quality and appropriate uses of the data (e.g., for comparison to other exposure studies, association with health outcomes, use in regulatory decision-making). However many studies do not adequately describe or interpret QC assessments in publications, leaving readers uncertain about the level of confidence in the reported data. One potential barrier to both QC implementation and reporting is that guidance on how to integrate and interpret QC assessments is often fragmented and difficult to find, with no centralized repository or summary. In addition, existing documents are typically written for regulatory scientists rather than environmental health researchers, who may have little or no experience in analytical chemistry.

OBJECTIVES

We discuss approaches for implementing quality assurance/quality control (QA/QC) in environmental exposure measurement projects and describe our process for interpreting QC results and drawing conclusions about data validity.

DISCUSSION

Our methods build upon existing guidance and years of practical experience collecting exposure data and analyzing it in collaboration with contract and university laboratories, as well as the Centers for Disease Control and Prevention. With real examples from our data, we demonstrate problems that would not have come to light had we not engaged with our QC data and incorporated field QC samples in our study design. Our approach focuses on descriptive analyses and data visualizations that have been compatible with diverse exposure studies with sample sizes ranging from tens to hundreds of samples. Future work could incorporate additional statistically grounded methods for larger datasets with more QC samples.

CONCLUSIONS

This guidance, along with example table shells, graphics, and some sample R code, provides a useful set of tools for getting the best information from valuable environmental exposure datasets and enabling valid comparison and synthesis of exposure data across studies.