EPA underestimates, oversimplifies, miscommunicates, and mismanages cancer risks by ignoring human susceptibility

Beyond the cancer slope factor: Broad application of Bayesian and probabilistic approaches for cancer dose-response assessment

Traditional cancer slope factors derived from linear low-dose extrapolation give little consideration to uncertainties in dose-response model choice, interspecies extrapolation, and human variability. As noted previously by the National Academies, probabilistic methods can address these limitations, but have only been demonstrated in a few case studies. Here, we applied probabilistic approaches for Bayesian Model Averaging (BMA), interspecies extrapolation, and human variability distributions to 255 animal cancer bioassay datasets previously used by governmental agencies. We then derived predictions for both population cancer incidence and individual cancer risk. For model uncertainty, we found that lower confidence limits from BMA and from U.S. Environmental Protection Agency (EPA)'s Benchmark Dose Software (BMDS) correlated highly, with 86% differing by <10-fold. Incorporating other uncertainties and human variability, the lower confidence limits of the probabilistic risk-specific dose (RSD) at 10-6 population incidence were typically 3- to 30-fold lower than traditional slope factors. However, in a small (<7%) number of cases of highly non-linear experimental dose-response, the probabilistic RSDs were >10-fold less stringent. Probabilistic RSDs were also protective of individual risks of 10-4 in >99% of the population. We conclude that implementing Bayesian and probabilistic methods provides a more scientifically rigorous basis for cancer dose-response assessment and thereby improves overall cancer risk characterization.

Current practice and recommendations for advancing how human variability and susceptibility are considered in chemical risk assessment

A key element of risk assessment is accounting for the full range of variability in response to environmental exposures. Default dose-response methods typically assume a 10-fold difference in response to chemical exposures between average (healthy) and susceptible humans, despite evidence of wider variability. Experts and authoritative bodies support using advanced techniques to better account for human variability due to factors such as in utero or early life exposure and exposure to multiple environmental, social, and economic stressors.This review describes: 1) sources of human variability and susceptibility in dose-response assessment, 2) existing US frameworks for addressing response variability in risk assessment; 3) key scientific inadequacies necessitating updated methods; 4) improved approaches and opportunities for better use of science; and 5) specific and quantitative recommendations to address evidence and policy needs.Current default adjustment factors do not sufficiently capture human variability in dose-response and thus are inadequate to protect the entire population. Susceptible groups are not appropriately protected under current regulatory guidelines. Emerging tools and data sources that better account for human variability and susceptibility include probabilistic methods, genetically diverse in vivo and in vitro models, and the use of human data to capture underlying risk and/or assess combined effects from chemical and non-chemical stressors.We recommend using updated methods and data to improve consideration of human variability and susceptibility in risk assessment, including the use of increased default human variability factors and separate adjustment factors for capturing age/life stage of development and exposure to multiple chemical and non-chemical stressors. Updated methods would result in greater transparency and protection for susceptible groups, including children, infants, people who are pregnant or nursing, people with disabilities, and those burdened by additional environmental exposures and/or social factors such as poverty and racism.

The Pebble Remains in the Master's Hand: Two Careers Spent Learning (Still) from John Evans

In this article, we discuss four vexing problems in risk-based decision making that John Evans has addressed over the last nearly 40 years and has perennially challenged the two of us and others to think about. We tackle the role in decision making of potential thresholds in dose-response functions, how the lack of health reference values for many chemicals may distort risk management, the challenge of model uncertainty for risk characterization, and the yet-untapped potential for value-of-information analysis to enhance public health decision making. Our theme is that work remains to be done on each of these, but that some of that work would merely involve listening to ideas that John has already offered.

A screening-level approach to quantifying risk from glacial release of organochlorine pollutants in the Alaskan Arctic

Widespread distribution of atmospherically mobilized organochlorine pollutants (OCPs) has been documented throughout the Arctic. A fraction of these OCPs have become entrained in glacial ice, and during melting, they can be released into downstream reservoirs. Though this remobilization is known, an assessment of risk from glacial meltwater to collocated human communities in the Arctic, including Alaska, had not been accomplished. Here, we use a screening-level risk assessment model for glacial watersheds, based on US Environmental Protection Agency (EPA) methodology, which we apply to the glaciated Jarvis Creek watershed of interior Alaska. Model results indicate that even with low levels of OCPs in glacial meltwater, high fish consumption by subsistence communities in the area increases the risk of cancer and hazard impacts above acceptable limits. Though this model is specific to one watershed, our results imply that further investigation of an increasing OCP signal in glacial meltwater and fish throughout the North American Arctic is warranted.

Quantitative screening level assessment of human risk from PCBs released in glacial meltwater: Silvretta Glacier, Swiss Alps

Persistent organic pollutants (POPs) are entrained within glaciers globally, reemerging in many alpine ecosystems. Despite available data on POP flux from glaciers, a study of human health risk caused by POPs released in glacial meltwater has never been attempted. Glaciers in the European Alps house the largest known quantity of POPs in the Northern Hemisphere, presenting an opportunity for identification of potential risk in an endmember scenario case study. With methodology developed by the US Environmental Protection Agency (EPA), we provide a regional screening level human risk analysis of one class of POPs, polychlorinated-biphenyls (PCB) that have been measured in melt waters from the Silvretta Glacier in the Swiss Alps. Our model suggests the potential for both cancer and non-cancer impacts in residents with lifetime exposure to current levels of PCB in glacial meltwater and average consumption of local fish. For residents with an abbreviated 30-year exposure timeframe, the risk for cancer and non-cancer impacts is low. Populations that consume higher quantities of local fish are predicted to be at a greater risk, with risk to lifetime consumers higher by an order of magnitude. Based on the results of our screening study, we suggest that local government move to the next step within the risk assessment framework: local monitoring and management. Within the Alps, other glacial watersheds of a similar size and latitude may see comparable risk and our model framework can be adapted for further implementation therein.

Unveiling variability and uncertainty for better science and decisions on cancer risks from environmental chemicals

The National Research Council 2009 "Silver Book" panel report included a recommendation that the U.S. Environmental Protection Agency (EPA) should increase all of its chemical carcinogen (CC) potency estimates by ∼7-fold to adjust for a purported median-vs.-mean bias that I recently argued does not exist (Bogen KT. "Does EPA underestimate cancer risks by ignoring susceptibility differences?," Risk Analysis, 2014; 34(10):1780-1784). In this issue of the journal, my argument is critiqued for having flaws concerning: (1) intent, bias, and conservatism of EPA estimates of CC potency; (2) bias in potency estimates derived from epidemiology; and (3) human-animal CC-potency correlation. However, my argument remains valid, for the following reasons. (1) EPA's default approach to estimating CC risks has correctly focused on bounding average (not median) individual risk under a genotoxic mode-of-action (MOA) assumption, although pragmatically the approach leaves both inter-individual variability in CC-susceptibility, and widely varying CC-specific magnitudes of fundamental MOA uncertainty, unquantified. (2) CC risk estimates based on large epidemiology studies are not systematically biased downward due to limited sampling from broad, lognormal susceptibility distributions. (3) A good, quantitative correlation is exhibited between upper-bounds on CC-specific potency estimated from human vs. animal studies (n = 24, r = 0.88, p = 2 × 10(-8)). It is concluded that protective upper-bound estimates of individual CC risk that account for heterogeneity in susceptibility, as well as risk comparisons informed by best predictions of average-individual and population risk that address CC-specific MOA uncertainty, should each be used as separate, complimentary tools to improve regulatory decisions concerning low-level, environmental CC exposures.