Improving interventional causal predictions in regulatory risk assessment.

In 2022, the US EPA published an important risk assessment concluding that "Compared to the current annual standard, meeting a revised annual standard with a lower level is estimated to reduce PM2.5-associated health risks in the 30 annually-controlled study areas by about 7-9% for a level of 11.0 µg/m3… and 30-37% for a level of 8.0 µg/m3." These are interventional causal predictions: they predict percentage reductions in mortality risks caused by different counterfactual reductions in fine particulate (PM2.5) levels. Valid causal predictions are possible if: (1) Study designs are used that can support valid causal inferences about the effects of interventions (e.g., quasi-experiments with appropriate control groups); (2) Appropriate causal models and methods are used to analyze the data; (3) Model assumptions are satisfied (at least approximately); and (4) Non-causal sources of exposure-response associations such as confounding, measurement error, and model misspecification are appropriately modeled and adjusted for. This paper examines two long-term mortality studies selected by the EPA to predict reductions in PM2.5-associated risk. Both papers use Cox proportional hazards (PH) models. For these models, none of these four conditions is satisfied, making it difficult to interpret or validate their causal predictions. Scientists, reviewers, regulators, and members of the public can benefit from more trustworthy and credible risk assessments and causal predictions by insisting that risk assessments supporting interventional causal conclusions be based on study designs, methods, and models that are appropriate for predicting effects caused by interventions.

Re-assessing human mortality risks attributed to PM2.5-mediated effects of agricultural ammonia.

How can and should epidemiologists and risk assessors assemble and present evidence for causation of mortality or morbidities by identified agents such as fine particulate matter or other air pollutants? As a motivating example, some scientists have warned recently that ammonia from the production of meat significantly increases human mortality rates in exposed populations by increasing the ambient concentration of fine particulate matter (PM2.5) in air. We reexamine the support for such conclusions, including quantitative calculations that attribute deaths to PM2.5 air pollution by applying associational results such as relative risks, odds ratios, or slope coefficients from regression models to predict the effects on mortality or morbidity of reducing PM2.5 exposures. Taking an outside perspective from the field of causal artificial intelligence (CAI), we conclude that these attribution calculations are methodologically unsound. They produce unreliable conclusions because they ignore an essential distinction between differences in outcomes observed at different levels of exposure and changes in outcomes caused by changing exposure. We find that multiple studies that have examined associations between changes over time in particulate exposure and mortality risk instead of differences in exposures and corresponding mortality risks have found no clear evidence that observed changes in exposure help to predict or explain subsequent changes in mortality risks. We conclude that there is no sound theoretical or empirical reason to believe that reducing ammonia emissions from farms has reduced or would reduce human mortality risks. More generally, applying CAI principles and methods can potentially improve current widespread practices of unsound causal inferences and policy-relevant causal claims that are made without the benefit of formal causal analysis in air pollution health effects research and in other areas of applied epidemiology and public health risk assessment.

Mechanisms and shapes of causal exposure-response functions for asbestos in mesotheliomas and lung cancers.

This paper summarizes recent insights into causal biological mechanisms underlying the carcinogenicity of asbestos. It addresses their implications for the shapes of exposure-response curves and considers recent epidemiologic trends in malignant mesotheliomas (MMs) and lung fiber burden studies. Since the commercial amphiboles crocidolite and amosite pose the highest risk of MMs and contain high levels of iron, endogenous and exogenous pathways of iron injury and repair are discussed. Some practical implications of recent developments are that: (1) Asbestos-cancer exposure-response relationships should be expected to have non-zero background rates; (2) Evidence from inflammation biology and other sources suggests that there are exposure concentration thresholds below which exposures do not increase inflammasome-mediated inflammation or resulting inflammation-mediated cancer risks above background risk rates; and (3) The size of the suggested exposure concentration threshold depends on both the detailed time patterns of exposure on a time scale of hours to days and also on the composition of asbestos fibers in terms of their physiochemical properties. These conclusions are supported by complementary strands of evidence including biomathematical modeling, cell biology and biochemistry of asbestos-cell interactions in vitro and in vivo, lung fiber burden analyses and epidemiology showing trends in human exposures and MM rates.

Plutonium Disposition: Using and Explaining Complex Risk-Related Methods.

Applying risk assessment and management tools to plutonium disposition is a long-standing challenge for the U.S. government. The science is complicated, which has helped push risk assessment and management tools in new creative directions. Yet, communicating effectively about increasingly complicated risk-science issues like plutonium disposition requires careful planning and speakers who can address why specific tools are selected, the past record of applying these tools, why assumptions sometimes are applied instead of reliable data, and how uncertainty is characterized. Speakers addressing risk issues must also overcome obstacles in communication arising from expert-audience differences in knowledge and legal restrictions on disclosing information. This perspective seeks to highlight and illustrate five key risk questions, about probabilistic risk assessment (PRA) and performance assessment (PA) in the context of managing plutonium defense nuclear waste: objectives, experience, gaps, transparency, and difficulty of applying and communicating using each tool. While the general public needs to be involved, some issues require a level of expertise that is typically beyond local communities and therefore an expert panel should support community access.

Information Structures for Causally Explainable Decisions.

For an AI agent to make trustworthy decision recommendations under uncertainty on behalf of human principals, it should be able to explain why its recommended decisions make preferred outcomes more likely and what risks they entail. Such rationales use causal models to link potential courses of action to resulting outcome probabilities. They reflect an understanding of possible actions, preferred outcomes, the effects of action on outcome probabilities, and acceptable risks and trade-offs-the standard ingredients of normative theories of decision-making under uncertainty, such as expected utility theory. Competent AI advisory systems should also notice changes that might affect a user's plans and goals. In response, they should apply both learned patterns for quick response (analogous to fast, intuitive "System 1" decision-making in human psychology) and also slower causal inference and simulation, decision optimization, and planning algorithms (analogous to deliberative "System 2" decision-making in human psychology) to decide how best to respond to changing conditions. Concepts of conditional independence, conditional probability tables (CPTs) or models, causality, heuristic search for optimal plans, uncertainty reduction, and value of information (VoI) provide a rich, principled framework for recognizing and responding to relevant changes and features of decision problems via both learned and calculated responses. This paper reviews how these and related concepts can be used to identify probabilistic causal dependencies among variables, detect changes that matter for achieving goals, represent them efficiently to support responses on multiple time scales, and evaluate and update causal models and plans in light of new data. The resulting causally explainable decisions make efficient use of available information to achieve goals in uncertain environments.

Using Bayesian networks to clarify interpretation of exposure-response regression coefficients: blood lead-mortality association as an example.

We examine how Bayesian network (BN) learning and analysis methods can help to meet several methodological challenges that arise in interpreting significant regression coefficients in exposure-response regression modeling. As a motivating example, we consider the challenge of interpreting positive regression coefficients for blood lead level (BLL) as a predictor of mortality risk for nonsmoking men. We first note that practices such as dichotomizing or categorizing continuous confounders (e.g. income), omitting potentially important socioeconomic confounders (e.g. education), and assuming specific parametric regression model forms leave unclear to what extent a positive regression coefficient reflects these modeling choices, rather than a direct dependence of mortality risk on exposure. Therefore, significant exposure-response coefficients in parametric regression models do not necessarily reveal the extent to which reducing exposure-related variables (e.g. BLL) alone, while leaving fixed other correlates of exposure and mortality risks (e.g. education, income, etc.) would reduce adverse outcome risks (e.g. mortality risks). We then consider how BN structure-learning and inference algorithms and nonparametric estimation methods (partial dependence plots) can be used to clarify dependencies between variables, variable selection, confounding, and quantification of joint effects of multiple factors on risk, including possible high-order interactions and nonlinearities. We conclude that these details must be carefully modeled to determine whether a data set provides evidence that exposure itself directly affects risks; and that BN and nonparametric effect estimation and uncertainty quantification methods can complement regression modeling and help to improve the scientific basis for risk management decisions and policy-making by addressing these issues.

Implications of nonlinearity, confounding, and interactions for estimating exposure concentration-response functions in quantitative risk analysis.

Recent advances in understanding of biological mechanisms and adverse outcome pathways for many exposure-related diseases show that certain common mechanisms involve thresholds and nonlinearities in biological exposure concentration-response (C-R) functions. These range from ultrasensitive molecular switches in signaling pathways, to assembly and activation of inflammasomes, to rupture of lysosomes and pyroptosis of cells. Realistic dose-response modeling and risk analysis must confront the reality of nonlinear C-R functions. This paper reviews several challenges for traditional statistical regression modeling of C-R functions with thresholds and nonlinearities, together with methods for overcoming them. Statistically significantly positive exposure-response regression coefficients can arise from many non-causal sources such as model specification errors, incompletely controlled confounding, exposure estimation errors, attribution of interactions to factors, associations among explanatory variables, or coincident historical trends. If so, the unadjusted regression coefficients do not necessarily predict how or whether reducing exposure would reduce risk. We discuss statistical options for controlling for such threats, and advocate causal Bayesian networks and dynamic simulation models as potentially valuable complements to nonparametric regression modeling for assessing causally interpretable nonlinear C-R functions and understanding how time patterns of exposures affect risk. We conclude that these approaches are promising for extending the great advances made in statistical C-R modeling methods in recent decades to clarify how to design regulations that are more causally effective in protecting human health.

Nonlinear dose-time-response functions and health-protective exposure limits for inflammation-mediated diseases.

Why have occupational safety regulations in the United States not been more successful in protecting worker health from mesothelioma risks, while apparently succeeding relatively well in reducing silicosis risks? This paper briefly discusses biological bases for thresholds and nonlinearities in exposure-response functions for respirable crystalline silica (RCS) and asbestos, based on modeling a chronic inflammation mode of action (mediated by activation of the NLRP3 inflammasome, for both RCS and asbestos). It applies previously published physiologically based pharmacokinetic (PBPK) models to perform computational experiments illuminating how different time courses of exposure with the same time-weighted average (TWA) concentration affect internal doses in target tissues (lung for RCS and mesothelium for asbestos). Key conclusions are that (i) For RCS, but not asbestos, limiting average (TWA) exposure concentrations also tightly constrains internal doses and ability to trigger chronic inflammation and resulting increases in disease risks (ii) For asbestos, excursions (i.e., spikes in concentrations); and especially the times between them are crucial drivers of internal doses and time until chronic inflammation; and hence (iii) These dynamic aspects of exposure, which are not addressed by current occupational safety regulations, should be constrained to better protect worker health. Adjusting permissible average exposure concentration limits (PELs) and daily excursion limits (ELs) is predicted to have little impact on reducing mesothelioma risks, but increasing the number of days between successive excursions is predicted to be relatively effective in reducing worker risks, even if it has little or no impact on TWA average concentrations.

Chronic inflammation, Adverse Outcome Pathways, and risk assessment: A diagrammatic exposition.

Inflammasomes are a family of pro-inflammatory signaling complexes that orchestrate inflammatory responses in many tissues. The NLRP3 inflammasome has been implicated in several diseases associated with chronic inflammation. In this paper, we present an Adverse Outcome Pathway (AOP) for NLRP3-induced chronic inflammatory diseases that demonstrates how NLRP3 can cause a transition from acute to chronic inflammation, and ultimately the onset of disease. We present a simple graphical description of the main features of internal dose time courses that are important when pharmacodynamics are governed by an activation threshold. Similar considerations hold for other AOPs that are rate-limited by processes with activation thresholds. The risk analysis implications of AOPs with threshold or threshold-like pharmacodynamic responses include the need to consider how cumulative dose per unit time is distributed over time and the possibility that safe, or virtually safe, exposure concentrations can be defined for such processes.

Answerable and Unanswerable Questions in Risk Analysis with Open-World Novelty.

Decision analysis and risk analysis have grown up around a set of organizing questions: what might go wrong, how likely is it to do so, how bad might the consequences be, what should be done to maximize expected utility and minimize expected loss or regret, and how large are the remaining risks? In probabilistic causal models capable of representing unpredictable and novel events, probabilities for what will happen, and even what is possible, cannot necessarily be determined in advance. Standard decision and risk analysis questions become inherently unanswerable ("undecidable") for realistically complex causal systems with "open-world" uncertainties about what exists, what can happen, what other agents know, and how they will act. Recent artificial intelligence (AI) techniques enable agents (e.g., robots, drone swarms, and automatic controllers) to learn, plan, and act effectively despite open-world uncertainties in a host of practical applications, from robotics and autonomous vehicles to industrial engineering, transportation and logistics automation, and industrial process control. This article offers an AI/machine learning perspective on recent ideas for making decision and risk analysis (even) more useful. It reviews undecidability results and recent principles and methods for enabling intelligent agents to learn what works and how to complete useful tasks, adjust plans as needed, and achieve multiple goals safely and reasonably efficiently when possible, despite open-world uncertainties and unpredictable events. In the near future, these principles could contribute to the formulation and effective implementation of more effective plans and policies in business, regulation, and public policy, as well as in engineering, disaster management, and military and civil defense operations. They can extend traditional decision and risk analysis to deal more successfully with open-world novelty and unpredictable events in large-scale real-world planning, policymaking, and risk management.

Quantifying Human Health Risks from Virginiamycin Use in Food Animals in China.

Virginiamycin (VM), a streptogramin antibiotic, has been used to promote healthy growth and treat illnesses in farm animals in the United States and other countries. The combination streptogramin Quinupristin-Dalfopristin (QD) was approved in the United States in 1999 for treating patients with vancomycin-resistant Enterococcus faecium (VREF) infections. Many chickens and swine test positive for QD-resistant E. faecium, raising concerns that using VM in food animals might select for streptogramin-resistant strains of E. faecium that could compromise QD effectiveness in treating human VREF infections. Such concerns have prompted bans and phase-outs of VM as growth promoters in the United States and Europe. This study quantitatively estimates potential human health risks from QD-resistant VREF infections due to VM use in food animals in China. Plausible conservative (risk-maximizing) quantitative risk estimates are derived for future uses, assuming 100% resistance to linezolid and daptomycin and 100% prescription rate of QD to high-level (VanA) VREF-infected patients. Up to one shortened life every few decades to every few thousand years might occur in China from VM use in animals, although the most likely risk is zero (e.g., if resistance is not transferred from bacteria in food animals to bacteria infecting human patients). Sensitivity and probabilistic uncertainty analyses suggest that this conclusion is robust to several data gaps and uncertainties. Potential future human health risks from VM use in animals in China appear to be small or zero, even if QD is eventually approved for use in human patients.

Dose-response modeling of NLRP3 inflammasome-mediated diseases: asbestos, lung cancer, and malignant mesothelioma as examples.

Can a single fiber of amphibole asbestos increase the risk of lung cancer or malignant mesothelioma (MM)? Traditional linear no-threshold (LNT) risk assessment assumptions imply that the answer is yes: there is no safe exposure level. This paper draws on recent scientific progress in inflammation biology, especially elucidation of the activation thresholds for NLRP3 inflammasomes and resulting chronic inflammation, to model dose-response relationships for malignant mesothelioma and lung cancer risks caused by asbestos exposures. The modeling integrates a physiologically based pharmacokinetics (PBPK) front end with inflammation-driven two-stage clonal expansion (I-TSCE) models of carcinogenesis to describe how exposure leads to chronic inflammation, which in turn promotes carcinogenesis. Together, the combined PBPK and I-TSCE modeling predict that there are practical thresholds for exposure concentration below which asbestos exposure does not cause chronic inflammation in less than a lifetime, and therefore does not increase chronic inflammation-dependent cancer risks. Quantitative examples using model parameter estimates drawn from the literature suggest that practical thresholds may be within about a factor of 2 of some past exposure levels for some workers. The I-TSCE modeling framework explains previous puzzling aspects of asbestos epidemiology, such as why age at first exposure is a better predictor of lifetime MM risk than exposure duration. It may be a valuable tool for risk analysts when LNT assumptions are not justified due to inflammation response thresholds mediating dose-response relationships.

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-1ß 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. Lysosomal destabilization, efflux of cytosolic potassium ions and influx of calcium ions, signals from damaged mitochondria, both translational and post-translational controls, and prion-like polymerization have increasingly clear roles in regulating NLRP3 activation. As the molecular biology of inflammation-mediated responses to REMP exposure becomes clearer, a practical question looms: What do these mechanisms imply for the shape of the dose-response function relating exposure concentrations and durations for EMPs to risk of pathological responses? Dose-response thresholds or threshold-like nonlinearities can arise from (a) Cooperativity in assembly of supramolecular signaling complexes; (b) Positive feedback loops and bistability in regulatory networks; (c) Overwhelming of defensive barriers maintaining homeostasis; and (d) Damage thresholds, as in lysosome destabilization-induced activation of NLRP3. Each of these mechanisms holds for NLRP3 activation in response to stimuli such as REMP exposures. It is therefore timely to consider the implications of these advances in biological understanding for human health risk assessment with dose-response thresholds.

Modernizing the Bradford Hill criteria for assessing causal relationships in observational data.

Perhaps no other topic in risk analysis is more difficult, more controversial, or more important to risk management policy analysts and decision-makers than how to draw valid, correctly qualified causal conclusions from observational data. Statistical methods can readily quantify associations between observed variables using measures such as relative risk (RR) ratios, odds ratios (OR), slope coefficients for exposure or treatment variables in regression models, and quantities derived from these measures. Textbooks of epidemiology explain how to calculate population attributable fractions, attributable risks, burden-of-disease estimates, and probabilities of causation from relative risk (RR) ratios. Despite their suggestive names, these association-based measures have no necessary connection to causation if the associations on which they are based arise from bias, confounding, p-hacking, coincident historical trends, or other noncausal sources. But policy analysts and decision makers need something more: trustworthy predictions - and, later, evaluations - of the changes in outcomes caused by changes in policy variables. This concept of manipulative causation differs from the more familiar concepts of associational and attributive causation most widely used in epidemiology. Drawing on modern literature on causal discovery and inference principles and algorithms for drawing limited but useful causal conclusions from observational data, we propose seven criteria for assessing consistency of data with a manipulative causal exposure-response relationship - mutual information, directed dependence, internal and external consistency, coherent causal explanation of biological plausibility, causal mediation confirmation, and refutation of non-causal explanations - and discuss to what extent it is now possible to automate discovery of manipulative causal dependencies and quantification of causal effects from observational data. We compare our proposed principles for causal discovery and inference to the traditional Bradford Hill considerations from 1965. Understanding how old and new principles are related can clarify and enrich both.

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. Low income is identified as a direct cause of increased heart disease risk in this data set. The effect depends on age and sex: it is most pronounced for men under age 70 and for women under age 80. Income is significantly associated with all of the other variables examined and confounds the association between PM2.5 and heart disease risk. This association is significant in regression models that exclude income, but not in regression models that include it, both in the data set as a whole and in the subset of observations with PM2.5 < 15 µg/m3. Causal directed acyclic graph (DAG) models and non-parametric model ensemble partial dependence plots confirm that higher incomes reduce heart disease risk, consistent with previous observations of socioeconomic gradients in health risks. They support interpretation of this as a robust causal relation apparent in non-parametric analyses, and hence independent of any specific parametric modeling assumptions.

Effects of exposure estimation errors on estimated exposure-response relations for PM2.5.

Associations between fine particulate matter (PM2.5) exposure concentrations and a wide variety of undesirable outcomes, from autism and auto theft to elderly mortality, suicide, and violent crime, have been widely reported. Influential articles have argued that reducing National Ambient Air Quality Standards for PM2.5 is desirable to reduce these outcomes. Yet, other studies have found that reducing black smoke and other particulate matter by as much as 70% and dozens of micrograms per cubic meter has not detectably affected all-cause mortality rates even after decades, despite strong, statistically significant positive exposure concentration-response (C-R) associations between them. This paper examines whether this disconnect between association and causation might be explained in part by ignored estimation errors in estimated exposure concentrations. We use EPA air quality monitor data from the Los Angeles area of California to examine the shapes of estimated C-R functions for PM2.5 when the true C-R functions are assumed to be step functions with well-defined response thresholds. The estimated C-R functions mistakenly show risk as smoothly increasing with concentrations even well below the response thresholds, thus incorrectly predicting substantial risk reductions from reductions in concentrations that do not affect health risks. We conclude that ignored estimation errors obscure the shapes of true C-R functions, including possible thresholds, possibly leading to unrealistic predictions of the changes in risk caused by changing exposures. Instead of estimating improvements in public health per unit reduction (e.g., per 10 µg/m3 decrease) in average PM2.5 concentrations, it may be essential to consider how interventions change the distributions of exposure concentrations.

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 (PM2.5) and mortality risks. We find that most of them describe historical associations rather than valid causal models for predicting effects of interventions that change concentrations. The few papers that explicitly attempt to model causality rely on unverified modeling assumptions, casting doubt on their predictions about effects of interventions. A large literature on modern causal inference algorithms for observational data has been little used in C-R modeling. Applying these methods to publicly available data from Boston and the South Coast Air Quality Management District around Los Angeles shows that C-R functions estimated for one do not hold for the other. Changes in month-specific PM2.5 concentrations from one year to the next do not help to predict corresponding changes in average elderly mortality rates in either location. Thus, the assumption that estimated C-R relations predict effects of pollution-reducing interventions may not be true. Better causal modeling methods are needed to better predict how reducing air pollution would affect public health.

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.S. Environmental Protection Agency, for adults 50 years old or older for whom survey data are available from the Centers for Disease Control and Prevention (CDC) Behavioral Risk Factor Surveillance System (BRFSS). We also examine associations between these variables and self-reported heart attack and stroke experience; all three health outcomes are positively associated with each other. Young divorced women with low incomes are at greatest risk of asthma, especially if they are ever-smokers. Income is an important confounder of other relations. For example, in logistic regression modeling, PM2.5 is positively associated (p<0.06) with both stroke risk and heart attack risk when these are regressed only against PM2.5, sex, age, and ever-smoking status, but not when they are regressed against these variables and income. In this data set, PM2.5 is significantly negatively associated with asthma risk in regression models, with a 10µg/m3 decrease in PM2.5 corresponding to about a 6% increase in the probability of asthma, possibly because of confounding by smoking, which is negatively associated with PM2.5 and positively associated with asthma risk. A variety of non-parametric methods are used to quantify these associations and to explore potential causal interpretations.

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/m3. This raises an important question: do current laboratory practices and standards for assessing RCS concentrations permit reliable discrimination between workplaces that are in compliance and workplaces that are not? To find out, this paper examines recent laboratory performance in quantifying RCS amounts on filters sent to them to assess their proficiency. A key finding is that accredited laboratories do not reliably (e.g., with 95% confidence) estimate RCS quantities to within a factor of 2. Thus, laboratory findings indicating that RCS levels are above or below a PEL provide little confidence that this is true. The current accreditation standard only requires laboratories to achieve estimates within three standard deviations of the correct (reference) value at least two thirds of the time, rather than a more usual standard such as within 25% of the correct value at least 95% of the time. Laboratory practices may improve as the new PEL is implemented, but they are presently essentially powerless to discriminate among RCS levels over most of the range of values that have been tested, leaving employers and regulators without a reliable means to ascertain when workplace RCS levels are above or below the PEL.