Resolving an Open Science-policy question: Should the LNT still be an omnibus regulatory assumption?

Regulatory analyses, modeling the carcinogenic effect of ionizing radiations (IR) (e.g., alpha and beta particles, x-, and gamma rays, neutrons) and chemicals continue to use the linear no-threshold (LNT) model from zero to some low dose. The LNT is an omnibus causal default in regulatory occupational and health risk analysis. Its use raises four issues that make this default an open question. The first is that the LNT applied to study a single agent excludes co-exposure to other known risk factors: physical, dietary, socio-economic, and other. Causation is inappropriately specified because cancer incidence is imputed to the single agent's doses, although most cancers are multifactorial diseases. The second, linear interpolation from high to zero dose and response, is incorrect because biological and epidemiological evidence identify different mechanisms and modes of action at those doses. Third, additivity of exposure effect to background effect is questionable and certainly variable. Fourth, the default overestimates the probabilities and consequences at low doses, supplanting rational decision-making in which alternative models may be more or less likely to be correct. Recent converging scientific evidence against the LNT hypothesis answers the open question. The LNT use in regulation conflates science with administrative ease and risk aversion by policymakers. It should be replaced by models that are based on biologically motivated mechanistic understandings within an evolutionary biology framework that integrates adaptive strategies/processes in their formulation.

Lost in the woods: Finding our way back to the scientific method in systematic review.

Systematic review has become the preferred approach to addressing causality and informing regulatory and other decision-making processes, including chemical risk assessments. While advocates of systematic reviews acknowledge that they hold great potential for increasing objectivity and transparency in assessments of chemicals and human health risks, standardizing and harmonizing systematic review methods have been challenging. This review provides a brief summary of the development of systematic review methods and some of the frameworks currently in use in the US and Europe. We also provide an in-depth evaluation and comparison of two "competing" US EPA systematic review frameworks, informed by the constructively critical recommendations from the US National Academies of Science, Engineering and Medicine. We conclude with suggestions for moving forward to harmonize systematic review methods, as we believe that further criticism of individual available frameworks likely will be unproductive. Specifically, we issue a call to action for an international collaboration to work toward a blueprint that embraces the most scientifically critical elements common to most systematic review frameworks, while necessarily accommodating adaptations for specific purposes. Despite the array of available systematic review methods, it is clear that there is a shared goal and desire to promote objective assessment and synthesis of scientific evidence informing globally important issues regarding disease causality and human health risk evaluation.

Ionizing radiations epidemiology does not support the LNT model.

Most cancers are multifactorial diseases. Yet, epidemiological modeling of the effect of ionizing radiation (IR) exposures based on the linear no-threshold model at low doses (LNT) has generally not included co-exposure to chemicals, dietary, socio-economic and other risk factors also known to cause the cancers imputed to IR. When so, increased cancer incidences are incorrectly predicted by being solely associated with IR exposures. Moreover, to justify application of the LNT to low doses, high dose-response data, e.g., from the bombing of Hiroshima and Nagasaki, are linearly interpolated to background incidence (which usually has large uncertainty). In order for this interpolation to be correct, it would imply that the biological mechanisms leading to cancer and those that prevent cancer at high doses are exactly the same as at low doses. We show that linear interpolations are incorrect because both the biological and epidemiological evidence for thresholds, or other non-linearities, are more than substantial. We discuss why the LNT model suffers from misspecification errors, multiple testing, and other biases. Moreover, its use by regulatory agencies conflates vague assertions of scientific causation, by conjecturing the LNT, for administrative ease of use.

Quantifying the risk-reduction potential of new Modified Risk Tobacco Products.

Quantitative risk assessment of novel Modified Risk Tobacco Products (MRTP) must rest on indirect measurements that are indicative of disease development prior to epidemiological data becoming available. For this purpose, a Population Health Impact Model (PHIM) has been developed to estimate the reduction in the number of deaths from smoking-related diseases following the introduction of an MRTP. One key parameter of the model, the F-factor, describes the effective dose upon switching from cigarette smoking to using an MRTP. Biomarker data, collected in clinical studies, can be analyzed to estimate the effects of switching to an MRTP as compared to quitting smoking. Based on transparent assumptions, a link function is formulated that translates these effects into the F-factor. The concepts of 'lack of sufficiency' and 'necessity' are introduced, allowing for a parametrization of a family of link functions. These can be uniformly sampled, thus providing different 'scenarios' on how biomarker-based evidence can be translated into the F-factor to inform the PHIM.

Assessing Catastrophes-Dragon-Kings, Black, and Gray Swans-for Science-Policy.

The threat of catastrophic incidents-from nonroutine events to extreme ones, such as Dragon-Kings (DK), Black Swans (BS), and Gray Swans-induces precautionary initiatives that, before the fact, may encounter public resistance or after the fact recriminations. This study develops three aspects of these events: (1) generating mechanisms, (2) the statistical distributions of near and far-term consequences, and (3) the aggregation of expert opinions about assumptions, mechanisms, and consequences that informs science-policy. This study shows how causal analysis should account for the: (1) nonlinear catastrophic behaviors that generate predictions, (2) common and power-law distributions of the consequences, (3) self-organizing criticality and self-similarity, and (4) feedbacks and couplings between mechanisms that produce snaps, crackles, and pops as precursor, warning signals. The distribution of the consequences associated with catastrophic incidents has longer and fatter right tails than those expected from failure analysis based on known nonroutine events. DK are extreme events that deviate from these fat tail distributions, have a much higher frequency than expected, and can be predicted unlike BS. This shows how to combine divergent expert individual beliefs over assumptions, causation, and results, and a paradox that affects agreements obtained by majority rule.

Endocrine Disruptors: Improving Regulatory Science Policy.

Law and science combine in the estimation of risks from endocrine disruptors (EDs) and actions for their regulation. For both, dose-response models are the causal link between exposure and probability (or percentage change) of adverse response. The evidence that leads to either regulations or judicial decrees is affected by uncertainty and limited knowledge, raising difficult policy issues that we enumerate and discuss. In the United States, some courts have dealt with EDs, but causation based on animal studies has been a stumbling block for plaintiffs seeking compensation, principally because those courts opt for epidemiological evidence. The European Union (EU) has several regulatory tools and ongoing research on the risks associated with bisphenol A, under the Registration, Evaluation, Authorisation and Restriction of Chemicals (REACH) Regulation and other regulations or directives. The integration of a vast (in kind and in scope) number of research papers into a statement of causation for either policy or to satisfy legal requirements, in both the United States and the EU, relies on experts. We outline the discursive dilemma and issues that may affect consensus-based results and a Bayesian causal approach that accounts for the evolution of information, yielding both value of information and flexibility associated with public choices.

Warmer is healthier: effects on mortality rates of changes in average fine particulate matter (PM2.5) concentrations and temperatures in 100 U.S. cities.

Recent studies have indicated that reducing particulate pollution would substantially reduce average daily mortality rates, prolonging lives, especially among the elderly (age ≥ 75). These benefits are projected by statistical models of significant positive associations between levels of fine particulate matter (PM2.5) levels and daily mortality rates. We examine the empirical correspondence between changes in average PM2.5 levels and temperatures from 1999 to 2000, and corresponding changes in average daily mortality rates, in each of 100 U.S. cities in the National Mortality and Morbidity Air Pollution Study (NMMAPS) data base, which has extensive PM2.5, temperature, and mortality data for those 2 years. Increases in average daily temperatures appear to significantly reduce average daily mortality rates, as expected from previous research. Unexpectedly, reductions in PM2.5 do not appear to cause any reductions in mortality rates. PM2.5 and mortality rates are both elevated on cold winter days, creating a significant positive statistical relation between their levels, but we find no evidence that reductions in PM2.5 concentrations cause reductions in mortality rates. For all concerned, it is crucial to use causal relations, rather than statistical associations, to project the changes in human health risks due to interventions such as reductions in particulate air pollution.

Regulatory-Science: Biphasic Cancer Models or the LNT-Not Just a Matter of Biology!

There is no doubt that prudence and risk aversion must guide public decisions when the associated adverse outcomes are either serious or irreversible. With any carcinogen, the levels of risk and needed protection before and after an event occurs, are determined by dose-response models. Regulatory law should not crowd out the actual beneficial effects from low dose exposures-when demonstrable-that are inevitably lost when it adopts the linear non-threshold (LNT) as its causal model. Because regulating exposures requires planning and developing protective measures for future acute and chronic exposures, public management decisions should be based on minimizing costs and harmful exposures. We address the direct and indirect effects of causation when the danger consists of exposure to very low levels of carcinogens and toxicants. The societal consequences of a policy can be deleterious when that policy is based on a risk assumed by the LNT, in cases where low exposures are actually beneficial. Our work develops the science and the law of causal risk modeling: both are interwoven. We suggest how their relevant characteristics differ, but do not attempt to keep them separated; as we demonstrate, this union, however unsatisfactory, cannot be severed.

Water quality and non-point sources of risk: the Jiulong River Watershed, P. R. of China.

Retrospective water quality assessment plays an essential role in identifying trends and causal associations between exposures and risks, thus it can be a guide for water resources management. We have developed empirical relationships between several time-varying social and economic factors of economic development, water quality variables such as nitrate-nitrogen, COD(Mn), BOD(5), and DO, in the Jiulong River Watershed and its main tributary, the West River. Our analyses used alternative statistical methods to reduce the dimensionality of the analysis first and then strengthen the study's causal associations. The statistical methods included: factor analysis (FA), trend analysis, Monte Carlo/bootstrap simulations, robust regressions and a coupled equations model, integrated into a framework that allows an investigation and resolution of the issues that may affect the estimated results. After resolving these, we found that the concentrations of nitrogen compounds increased over time in the West River region, and that fertilizer used in agricultural fruit crops was the main risk with regard to nitrogen pollution. The relationships we developed can identify hazards and explain the impact of sources of different types of pollution, such as urbanization, and agriculture.

Systems Cancer Biology and the Controlling Mechanisms for the J-Shaped Cancer Dose Response: Towards Relaxing the LNT Hypothesis.

The hormesis phenomena or J-shaped dose response have been accepted as a common phenomenon regardless of the involved biological model, endpoint measured and chemical class/physical stressor. This paper first introduced a mathematical dose response model based on systems biology approach. It links molecular-level cell cycle checkpoint control information to clonal growth cancer model to predict the possible shapes of the dose response curves of Ionizing Radiation (IR) induced tumor transformation frequency. J-shaped dose response curves have been captured with consideration of cell cycle checkpoint control mechanisms. The simulation results indicate the shape of the dose response curve relates to the behavior of the saddle-node points of the model in the bifurcation diagram. A simplified version of the model in previous work of the authors was used mathematically to analyze behaviors relating to the saddle-node points for the J-shaped dose response curve. It indicates that low-linear energy transfer (LET) is more likely to have a J-shaped dose response curve. This result emphasizes the significance of systems biology approach, which encourages collaboration of multidiscipline of biologists, toxicologists and mathematicians, to illustrate complex cancer-related events, and confirm the biphasic dose-response at low doses.

Temperature, Not Fine Particulate Matter (PM2.5), is Causally Associated with Short-Term Acute Daily Mortality Rates: Results from One Hundred United States Cities.

Exposures to fine particulate matter (PM2.5) in air (C) have been suspected of contributing causally to increased acute (e.g., same-day or next-day) human mortality rates (R). We tested this causal hypothesis in 100 United States cities using the publicly available NMMAPS database. Although a significant, approximately linear, statistical C-R association exists in simple statistical models, closer analysis suggests that it is not causal. Surprisingly, conditioning on other variables that have been extensively considered in previous analyses (usually using splines or other smoothers to approximate their effects), such as month of the year and mean daily temperature, suggests that they create strong, nonlinear confounding that explains the statistical association between PM2.5 and mortality rates in this data set. As this finding disagrees with conventional wisdom, we apply several different techniques to examine it. Conditional independence tests for potential causation, non-parametric classification tree analysis, Bayesian Model Averaging (BMA), and Granger-Sims causality testing, show no evidence that PM2.5 concentrations have any causal impact on increasing mortality rates. This apparent absence of a causal C-R relation, despite their statistical association, has potentially important implications for managing and communicating the uncertain health risks associated with, but not necessarily caused by, PM2.5 exposures.

Modeling Dose-response at Low Dose: A Systems Biology Approach for Ionization Radiation.

For ionization radiation (IR) induced cancer, a linear non-threshold (LNT) model at very low doses is the default used by a number of national and international organizations and in regulatory law. This default denies any positive benefit from any level of exposure. However, experimental observations and theoretical biology have found that both linear and J-shaped IR dose-response curves can exist at those very low doses. We develop low dose J-shaped dose-response, based on systems biology, and thus justify its use regarding exposure to IR. This approach incorporates detailed, molecular and cellular descriptions of biological/toxicological mechanisms to develop a dose-response model through a set of nonlinear, differential equations describing the signaling pathways and biochemical mechanisms of cell cycle checkpoint, apoptosis, and tumor incidence due to IR. This approach yields a J-shaped dose response curve while showing where LNT behaviors are likely to occur. The results confirm the hypothesis of the J-shaped dose response curve: the main reason is that, at low-doses of IR, cells stimulate protective systems through a longer cell arrest time per unit of IR dose. We suggest that the policy implications of this approach are an increasingly correct way to deal with precautionary measures in public health.

Causal regulations vs. political will: why human zoonotic infections increase despite precautionary bans on animal antibiotics.

Using precautionary principles when facing incomplete facts and causal conjectures raises the possibility of a Faustian bargain. This paper applies systems dynamics based on previously unavailable data to show how well intended precautionary policies for promoting food safety may backfire unless they are informed by quantitative cause-and-effect models of how animal antibiotics affect animal and human health. We focus on European Union and United States formulations of regulatory precaution and then analyze zoonotic infections in terms of the consequences of relying on political will to justify precautionary bans. We do not attempt a political analysis of these issues; rather, we conduct a regulatory analysis of precautionary legal requirements and use Quantitative Risk Assessment (QRA) to assess a set of policy outcomes. Thirty-seven years ago, the Joint Committee on the Use of Antibiotics in Animal Husbandry and Veterinary Medicine (the Swann Report) warned that uncontrolled use of similar antibiotics in humans and food animals could promote the emergence of resistant strains of foodborne bacteria that could endanger human health. Since then, many countries have either banned or restricted antibiotics as feed additives for promoting animal growth. Others, including the United States, have relied on prudent use guidelines and programs that reduce total microbial loads, rather than focusing exclusively on antibiotic-resistant bacteria. In retrospect, the regulatory strategy of banning or restricting animal antibiotic uses has had limited success: it has been followed in many cases by deteriorating animal health and increases in human illnesses and resistance rates. Conversely, a combination of continued prudent use of antibiotics to prevent and control animal infections, together with HACCP and other improvements, has been followed by large improvements in the microbial safety of chickens and other food animals in the United States, leaving both animals and people better off now than they were decades ago. A quantitative risk assessment model of microbiological risks (Campylobacter because of data availability) suggests that these outcomes may be more than coincidental: prudent use of animal antibiotics may actually improve human health, while bans on animal antibiotics, intended to be precautionary, inadvertently may harm human health.

Precautionary principles: a jurisdiction-free framework for decision-making under risk.

Fundamental principles of precaution are legal maxims that ask for preventive actions, perhaps as contingent interim measures while relevant information about causality and harm remains unavailable, to minimize the societal impact of potentially severe or irreversible outcomes. Such principles do not explain how to make choices or how to identify what is protective when incomplete and inconsistent scientific evidence of causation characterizes the potential hazards. Rather, they entrust lower jurisdictions, such as agencies or authorities, to make current decisions while recognizing that future information can contradict the scientific basis that supported the initial decision. After reviewing and synthesizing national and international legal aspects of precautionary principles, this paper addresses the key question: How can society manage potentially severe, irreversible or serious environmental outcomes when variability, uncertainty, and limited causal knowledge characterize their decision-making? A decision-analytic solution is outlined that focuses on risky decisions and accounts for prior states of information and scientific beliefs that can be updated as subsequent information becomes available. As a practical and established approach to causal reasoning and decision-making under risk, inherent to precautionary decision-making, these (Bayesian) methods help decision-makers and stakeholders because they formally account for probabilistic outcomes, new information, and are consistent and replicable. Rational choice of an action from among various alternatives--defined as a choice that makes preferred consequences more likely--requires accounting for costs, benefits and the change in risks associated with each candidate action. Decisions under any form of the precautionary principle reviewed must account for the contingent nature of scientific information, creating a link to the decision-analytic principle of expected value of information (VOI), to show the relevance of new information, relative to the initial (and smaller) set of data on which the decision was based. We exemplify this seemingly simple situation using risk management of BSE. As an integral aspect of causal analysis under risk, the methods developed in this paper permit the addition of non-linear, hormetic dose-response models to the current set of regulatory defaults such as the linear, non-threshold models. This increase in the number of defaults is an important improvement because most of the variants of the precautionary principle require cost-benefit balancing. Specifically, increasing the set of causal defaults accounts for beneficial effects at very low doses. We also show and conclude that quantitative risk assessment dominates qualitative risk assessment, supporting the extension of the set of default causal models.

Precaution, uncertainty and causation in environmental decisions.

What measures of uncertainty and what causal analysis can improve the management of potentially severe, irreversible or dreaded environmental outcomes? Environmental choices show that policies intended to be precautionary (such as adding MTBE to petrol) can cause unanticipated harm (by mobilizing benzene, a known leukemogen, in the ground water). Many environmental law principles set the boundaries of what should be done but do not provide an operational construct to answer this question. Those principles, ranging from the precautionary principle to protecting human health from a significant risk of material health impairment, do not explain how to make environmental management choices when incomplete, inconsistent and complex scientific evidence characterizes potentially adverse environmental outcomes. Rather, they pass the task to lower jurisdictions such as agencies or authorities. To achieve the goals of the principle, those who draft it must deal with scientific casual conjectures, partial knowledge and variable data. In this paper we specifically deal with the qualitative and quantitative aspects of the European Union's (EU) explanation of consistency and on the examination of scientific developments relevant to variability and uncertain data and causation. Managing hazards under the precautionary principle requires inductive, empirical methods of assessment. However, acting on a scientific conjecture can also be socially unfair, costly, and detrimental when applied to complex environmental choices. We describe a constructive framework rationally to meet the command of the precautionary principle using alternative measures of uncertainty and recent statistical methods of causal analysis. These measures and methods can bridge the gap between conjectured future irreversible or severe harm and scant scientific evidence, thus leading to more confident and resilient social choices. We review two sets of measures and computational systems to deal with uncertainty and link them to causation through inductive empirical methods such as Bayesian Networks. We conclude that primary legislation concerned with large uncertainties and potential severe or dreaded environmental outcomes can produce accurate and efficient choices. To do so, primary legislation should specifically indicate what measures can represent uncertainty and how to deal with uncertain causation thus providing guidance to an agency's rulemaking or to an authority's writing secondary legislation. A corollary conclusion with legal, scientific and probabilistic implications concerns how to update past information when the state of information increases because a failure to update can result in regretting past choices. Elected legislators have the democratic mandate to formulate precautionary principles and are accountable. To preserve that mandate, imbedding formal methods to represent uncertainty in the statutory language of the precautionary principle enhances subsequent judicial review of legislative actions. The framework that we propose also reduces the Balkanized views and interpretations of probabilities, possibilities, likelihood and uncertainty that exists in environmental decision-making.