Risk Analysis Implications of Dose-Response Thresholds for NLRP3 Inflammasome-Mediated Diseases: Respirable Crystalline Silica and Lung Cancer as an Example.

Chronic inflammation mediates an extraordinarily wide range of diseases. Recent progress in understanding intracellular inflammasome assembly, priming, activation, cytokine signaling, and interactions with mitochondrial reactive oxygen species, lysosome disruption, cell death, and prion-like polymerization and spread of inflammasomes among cells, has potentially profound implications for dose-response modeling. This article discusses mechanisms of exposure concentration and duration thresholds for NOD-like receptor protein 3 (NLRP3)-mediated inflammatory responses and develops a simple biomathematical model of the onset of exposure-related tissue-level chronic inflammation and resulting disease risks, focusing on respirable crystalline silica (RCS) and lung cancer risk as an example.

Socioeconomic and particulate air pollution correlates of heart disease risk.

How does risk of heart disease depend on age, sex, smoking, income, education, marital status, and outdoor concentrations of fine particulate matter (PM2.5)? We join data available from the Centers for Disease Control and Prevention (CDC) Behavioral Risk Factor Surveillance (BRFSS) System for years 2008-2012 to US Environmental Protection Agency (EPA) data on county-specific concentrations of fine particulate matter (PM2.5) to quantify associations among these variables and to explore possible causal interpretations.

Biological mechanisms of non-linear dose-response for respirable mineral fibers.

Sufficiently high and prolonged inhalation exposures to some respirable elongated mineral particles (REMPs), notably including amphibole asbestos fibers, can increase risk of inflammation-mediated diseases including malignant mesothelioma, pleural diseases, fibrosis, and lung cancer. Chronic inflammation involves ongoing activation of the NLRP3 inflammasome, which enables immune cells to produce potent proinflammatory cytokines IL-1β and IL-18. Reactive oxygen species (ROS) (in particular, mitochondrial ROS) contribute to NRLP3 activation via a well-elucidated mechanism involving oxidation of reduced thioredoxin and association of thioredoxin-interacting protein with NLRP3.

Applying Nonparametric Methods to Analyses of Short-Term Fine Particulate Matter Exposure and Hospital Admissions for Cardiovascular Diseases among Older Adults.

Short-term exposure to fine particulate matter (PM) has been associated with increased risks of cardiovascular diseases (CVDs), but whether such associations are supportive of a causal relationship is unclear, and few studies have employed formal causal analysis methods to address this. We employed nonparametric methods to examine the associations between daily concentrations of PM and hospital admissions (HAs) for CVD among adults aged 75 years and older in Texas, USA. We first quantified the associations in partial dependence plots generated using the random forest approach.

Do causal concentration-response functions exist? A critical review of associational and causal relations between fine particulate matter and mortality.

Concentration-response (C-R) functions relating concentrations of pollutants in ambient air to mortality risks or other adverse health effects provide the basis for many public health risk assessments, benefits estimates for clean air regulations, and recommendations for revisions to existing air quality standards. The assumption that C-R functions relating levels of exposure and levels of response estimated from historical data usefully predict how future changes in concentrations would change risks has seldom been carefully tested. This paper critically reviews literature on C-R functions for fine particulate matter (PM2.

Socioeconomic and air pollution correlates of adult asthma, heart attack, and stroke risks in the United States, 2010-2013.

Asthma in the United States has become an important public health issue, with many physicians, regulators, and scientists elsewhere expressing concern that criterion air pollutants have contributed to a rising tide of asthma cases and symptoms. This paper studies recent associations (from 2008 to 2012) between self-reported asthma experiences and potential predictors, including age, sex, income, education, smoking, and county-level average annual ambient concentrations of ozone (O3) and fine particulate matter (PM2.5) levels recorded by the U.

How accurately and consistently do laboratories measure workplace concentrations of respirable crystalline silica?

Permissible exposure limits (PELs) for respirable crystalline silica (RCS) have recently been reduced from 0.10 to 0.05 mg/m.

Rethinking the Meaning of Concentration-Response Functions and the Estimated Burden of Adverse Health Effects Attributed to Exposure Concentrations.

Four articles by Anenberg et al., Fann et al., Shin et al.

How well can in vitro data predict in vivo effects of chemicals? Rodent carcinogenicity as a case study.

A recent research article by the National Center for Computational Toxicology (NCCT) (Kleinstreuer et al., 2013), indicated that high throughput screening (HTS) data from assays linked to hallmarks and presumed pathways of carcinogenesis could be used to predict classification of pesticides as either (a) possible, probable or likely rodent carcinogens; or (b) not likely carcinogens or evidence of non-carcinogenicity. Using independently developed software to validate the computational results, we replicated the majority of the results reported.

Overcoming Learning Aversion in Evaluating and Managing Uncertain Risks.

Decision biases can distort cost-benefit evaluations of uncertain risks, leading to risk management policy decisions with predictably high retrospective regret. We argue that well-documented decision biases encourage learning aversion, or predictably suboptimal learning and premature decision making in the face of high uncertainty about the costs, risks, and benefits of proposed changes. Biases such as narrow framing, overconfidence, confirmation bias, optimism bias, ambiguity aversion, and hyperbolic discounting of the immediate costs and delayed benefits of learning, contribute to deficient individual and group learning, avoidance of information seeking, underestimation of the value of further information, and hence needlessly inaccurate risk-cost-benefit estimates and suboptimal risk management decisions.

Has reducing fine particulate matter and ozone caused reduced mortality rates in the United States?

Purpose: Between 2000 and 2010, air pollutant levels in counties throughout the United States changed significantly, with fine particulate matter (PM2.5) declining over 30% in some counties and ozone (O3) exhibiting large variations from year to year. This history provides an opportunity to compare county-level changes in average annual ambient pollutant levels to corresponding changes in all-cause (AC) and cardiovascular disease (CVD) mortality rates over the course of a decade.

Quantitative assessment of human MRSA risks from swine.

The public health community, news media, and members of the general public have expressed significant concern that methicillin-resistant Staphylococcus aureus (MRSA) transmitted from pigs to humans may harm human health. Studies of the prevalence and dynamics of swine-associated (ST398) MRSA have sampled MRSA at discrete points in the presumed causative chain leading from swine to human patients, including sampling bacteria from live pigs, retail meats, farm workers, and hospital patients. Nonzero prevalence is generally interpreted as indicating a potential human health hazard from MRSA infections, but quantitative assessments of resulting risks are not usually provided.

Caveats for causal interpretations of linear regression coefficients for fine particulate (PM2.5) air pollution health effects.

Recent linear regression analyses have concluded that decreasing levels of fine particulate matter (PM2.5) air pollution have increased life expectancy in the United States. These findings have left unresolved questions about the causal relation between reductions in PM2.

Improving causal inferences in risk analysis.

Recent headlines and scientific articles projecting significant human health benefits from changes in exposures too often depend on unvalidated subjective expert judgments and modeling assumptions, especially about the causal interpretation of statistical associations. Some of these assessments are demonstrably biased toward false positives and inflated effects estimates. More objective, data-driven methods of causal analysis are available to risk analysts.

Causal versus spurious spatial exposure-response associations in health risk analysis.

Many recent health risk assessments have noted that adverse health outcomes are significantly statistically associated with proximity to suspected sources of health hazard, such as manufacturing plants or point sources of air pollution. Using geographic proximity to sources as surrogates for exposure to (possibly unknown) releases, spatial ecological studies have identified potential adverse health effects based on significant regression coefficients between risk rates and distances from sources in multivariate statistical risk models. Although this procedure has been fruitful in identifying exposure-response associations, it is not always clear whether the resulting regression coefficients have valid causal interpretations.

Dose-response thresholds for progressive diseases.

Many diseases, including cancers, heart diseases, and lung diseases, can usefully be viewed as arising from disruption of feedback control systems that normally maintain homeostasis of tissues and cell populations. Excessive exposure can destabilize feedback control loops, leading to sustained elevation of variables to saturated levels and clinical consequences such as chronic unresolved inflammation, destruction of tissue (as in emphysema), proliferation of cell populations (as in lung cancer), and increases in reactive oxygen species and protease levels (as in coronary heart diseases and chronic obstructive lung disease). We propose a framework for understanding how exposure can destabilize normally homeostatic feedback control systems and create sustained imbalances and elevated levels of disease-related variables, by creating a new, locally stable, alternative equilibrium for the dynamic system, in addition to its normal (homeostatic) equilibrium.

Low-dose nonlinear effects of smoking on coronary heart disease risk.

Some recent discussions of adverse human health effects of active and passive smoking have suggested that low levels of exposure are disproportionately dangerous, so that "The effects of even brief (minutes to hours) passive smoking are often nearly as large (averaging 80% to 90%) as chronic active smoking" (Barnoya and Glantz, 2005). Recent epidemiological evidence (Teo et al., 2006) suggests a more linear relation.

Hormesis for fine particulate matter (PM 2.5).

The hypothesis of hormesis - that substances that harm health at high exposures can reduce risks below background at low exposures, e.g., if they activate defenses without overwhelming them - becomes important for practical policy making if it holds for regulated substances.

Evaluating and improving risk formulas for allocating limited budgets to expensive risk-reduction opportunities.

Simple risk formulas, such as risk = probability × impact, or risk = exposure × probability × consequence, or risk = threat × vulnerability × consequence, are built into many commercial risk management software products deployed in public and private organizations. These formulas, which we call risk indices, together with risk matrices, "heat maps," and other displays based on them, are widely used in applications such as enterprise risk management (ERM), terrorism risk analysis, and occupational safety. But, how well do they serve to guide allocation of limited risk management resources? This article evaluates and compares different risk indices under simplifying conditions favorable to their use (statistically independent, uniformly distributed values of their components; and noninteracting risk-reduction opportunities).

Clarifying types of uncertainty: when are models accurate, and uncertainties small?

Professor Aven has recently noted the importance of clarifying the meaning of terms such as "scientific uncertainty" for use in risk management and policy decisions, such as when to trigger application of the precautionary principle. This comment examines some fundamental conceptual challenges for efforts to define "accurate" models and "small" input uncertainties by showing that increasing uncertainty in model inputs may reduce uncertainty in model outputs; that even correct models with "small" input uncertainties need not yield accurate or useful predictions for quantities of interest in risk management (such as the duration of an epidemic); and that accurate predictive models need not be accurate causal models.