Should air pollution health effects assumptions be tested? Fine particulate matter and COVID-19 mortality as an example.

In the first half of 2020, much excitement in news media and some peer reviewed scientific articles was generated by the discovery that fine particulate matter (PM2.5) concentrations and COVID-19 mortality rates are statistically significantly positively associated in some regression models. This article points out that they are non-significantly negatively associated in other regression models, once omitted confounders (such as latitude and longitude) are included. More importantly, positive regression coefficients can and do arise when (generalized) linear regression models are applied to data with strong nonlinearities, including data on PM2.5, population density, and COVID-19 mortality rates, due to model specification errors. In general, statistical modeling accompanied by judgments about causal interpretations of statistical associations and regression coefficients - the current weight-of-evidence (WoE) approach favored in much current regulatory risk analysis for air pollutants - is not a valid basis for determining whether or to what extent risk of harm to human health would be reduced by reducing exposure. The traditional scientific method based on testing predictive generalizations against data remains a more reliable paradigm for risk analysis 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.

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. We also found that the prediction model correlating cancer pathway bioactivity scores with in vivo carcinogenic effects in rodents was not robust. A change of classification of a single chemical in the test set was capable of changing the overall study conclusion about the statistical significance of the correlation. Furthermore, in the subset of pesticide compounds used in model validation, the accuracy of prediction was no better than chance for about three quarters of the chemicals (those with fewer than 7 positive outcomes in HTS assays representing the 11 histopathological endpoints used in model development), suggesting that the prediction model was not adequate to predict cancer hazard for most of these chemicals. Although the utility of the model for humans is also unclear because a number of the rodent responses modeled (e.g., mouse liver tumors, rat thyroid tumors, rat testicular tumors, etc.) are not considered biologically relevant to human responses, the data examined imply the need for further research with HTS assays and improved models, which might help to predict classifications of in vivo carcinogenic responses in rodents for the pesticide considered, and thus reduce the need for testing in laboratory animals.

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. Past studies have demonstrated associations and subsequently either interpreted associations causally or relied on subjective judgments to infer causation. This article applies more quantitative methods to assess causality. METHODS: This article examines data from these "natural experiments" of changing pollutant levels for 483 counties in the 15 most populated US states using quantitative methods for causal hypothesis testing, such as conditional independence and Granger causality tests. We assessed whether changes in historical pollution levels helped to predict and explain changes in CVD and AC mortality rates. RESULTS: A causal relation between pollutant concentrations and AC or CVD mortality rates cannot be inferred from these historical data, although a statistical association between them is well supported. There were no significant positive associations between changes in PM2.5 or O3 levels and corresponding changes in disease mortality rates between 2000 and 2010, nor for shorter time intervals of 1 to 3 years. CONCLUSIONS: These findings suggest that predicted substantial human longevity benefits resulting from reducing PM2.5 and O3 may not occur or may be smaller than previously estimated. Our results highlight the potential for heterogeneity in air pollution health effects across regions, and the high potential value of accountability research comparing model-based predictions of health benefits from reducing air pollutants to historical records of what actually occurred.

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. This article integrates available data from several sources to construct a conservative (plausible upper bound) probability estimate for the actual human health harm (MRSA infections and fatalities) arising from ST398-MRSA from pigs. The model provides plausible upper bounds of approximately one excess human infection per year among all U.S. pig farm workers, and one human infection per 31 years among the remaining total population of the United States. These results assume the possibility of transmission events not yet observed, so additional data collection may reduce these estimates further.

Developing scientific confidence in HTS-derived prediction models: lessons learned from an endocrine case study.

High throughput (HTS) and high content (HCS) screening methods show great promise in changing how hazard and risk assessments are undertaken, but scientific confidence in such methods and associated prediction models needs to be established prior to regulatory use. Using a case study of HTS-derived models for predicting in vivo androgen (A), estrogen (E), thyroid (T) and steroidogenesis (S) endpoints in endocrine screening assays, we compare classification (fitting) models to cross validation (prediction) models. The more robust cross validation models (based on a set of endocrine ToxCast™ assays and guideline in vivo endocrine screening studies) have balanced accuracies from 79% to 85% for A and E, but only 23% to 50% for T and S. Thus, for E and A, HTS results appear promising for initial use in setting priorities for endocrine screening. However, continued research is needed to expand the domain of applicability and to develop more robust HTS/HCS-based prediction models prior to their use in other regulatory applications. Based on the lessons learned, we propose a framework for documenting scientific confidence in HTS assays and the prediction models derived therefrom. The documentation, transparency and the scientific rigor involved in addressing the elements in the proposed Scientific Confidence Framework could aid in discussions and decisions about the prediction accuracy needed for different applications.

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.

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. Spurious spatial regression and other threats to valid causal inference may undermine practical efforts to causally link health effects to geographic sources, even when there are clear statistical associations between them. This paper demonstrates the methodological problems by examining statistical associations and regression coefficients between spatially distributed exposure and response variables in a realistic data set for California. We find that distance from "nonsense" sources (such as arbitrary points or lines) are highly statistically significant predictors of cause-specific risks, such as traffic fatalities and incidence of Kaposi's sarcoma. However, the signs of such associations typically depend on the distance scale chosen. This is consistent with theoretical analyses showing that random spatial trends (which tend to fluctuate in sign), rather than true causal relations, can create statistically significant regression coefficients: spatial location itself becomes a confounder for spatially distributed exposure and response variables. Hence, extreme caution and careful application of spatial statistical methods are warranted before interpreting proximity-based exposure-response relations as evidence of a possible or probable causal relation.

A cautionary tale: the characteristics of two-dimensional distributions and their effects on epidemiological studies employing an ecological design.

In recent years, many spatial epidemiological studies that use proximity of subjects to putative sources as a surrogate for exposure have been published and are increasingly cited as evidence of environmental problems requiring public health interventions. In these studies, the simple finding of a significant, positive association between proximity and disease incidence has been interpreted as evidence of causality. However, numerous authors have pointed out limitations to such interpretations. This, the first of two companion studies, examines the effects of analyzing (real and simulated) spatial data using logistic regression. Simulation is also employed to explore the statistical power of such analyses to detect true effects, quantify the probabilities of Type I and Type II errors, and to evaluate a proposed mechanism that explains the observed effects. Results indicate that, even when the odds ratios of cases and controls are regressed against random or nonsense sources, significant, positive associations are observed at frequencies substantially greater than chance. These frequencies increase when targets are highly non-uniformly distributed such that, for example, false-positive associations are more likely than not when odds ratios are regressed against the actual distribution of ultramafic rocks in California. The coefficients of true, causal associations are substantially attenuated under realistic conditions so that, absent corroborating analyses, there is no non-arbitrary means of distinguishing causal from spurious or real but non-causal associations. Factors affecting where people choose to live act as powerful confounders, creating spurious or real but non-causal associations between exposure and response variables (as well as between other pairs of variables). Consequently, future epidemiological studies that use proximity as a surrogate for exposure should be required to include adequate negative control analyses and/or other kinds of corroborating analyses before they are accepted for publication.

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.

Assessing potential human health hazards and benefits from subtherapeutic antibiotics in the United States: tetracyclines as a case study.

Many scientists, activists, regulators, and politicians have expressed urgent concern that using antibiotics in food animals selects for resistant strains of bacteria that harm human health and bring nearer a "postantibiotic era" of multidrug resistant "super-bugs." Proposed political solutions, such as the Preservation of Antibiotics for Medical Treatment Act (PAMTA), would ban entire classes of subtherapeutic antibiotics (STAs) now used for disease prevention and growth promotion in food animals. The proposed bans are not driven by formal quantitative risk assessment (QRA), but by a perceived need for immediate action to prevent potential catastrophe. Similar fears led to STA phase-outs in Europe a decade ago. However, QRA and empirical data indicate that continued use of STAs in the United States has not harmed human health, and bans in Europe have not helped human health. The fears motivating PAMTA contrast with QRA estimates of vanishingly small risks. As a case study, examining specific tetracycline uses and resistance patterns suggests that there is no significant human health hazard from continued use of tetracycline in food animals. Simple hypothetical calculations suggest an unobservably small risk (between 0 and 1.75E-11 excess lifetime risk of a tetracycline-resistant infection), based on the long history of tetracycline use in the United States without resistance-related treatment failures. QRAs for other STA uses in food animals also find that human health risks are vanishingly small. Whether such QRA calculations will guide risk management policy for animal antibiotics in the United States remains to be seen.

Human health risk assessment of penicillin/aminopenicillin resistance in enterococci due to penicillin use in food animals.

Penicillin and ampicillin drugs are approved for use in food animals in the United States to treat, control, and prevent diseases, and penicillin is approved for use to improve growth rates in pigs and poultry. This article considers the possibility that such uses might increase the incidence of ampicillin-resistant Enterococcus faecium (AREF) of animal origin in human infections, leading to increased hospitalization and mortality due to reduced response to ampicillin or penicillin. We assess the risks from continued use of penicillin-based drugs in food animals in the United States, using several assumptions to overcome current scientific uncertainties and data gaps. Multiplying the total at-risk population of intensive care unit (ICU) patients by a series of estimated factors suggests that not more than 0.04 excess mortalities per year (under conservative assumptions) to 0.14 excess mortalities per year (under very conservative assumptions) might be prevented in the whole U.S. population if current use of penicillin drugs in food animals were discontinued and if this successfully reduced the prevalence of AREF infections among ICU patients. These calculations suggest that current penicillin usage in food animals in the United States presents very low (possibly zero) human health risks.

Overcoming confirmation bias in causal attribution: a case study of antibiotic resistance risks.

When they do not use formal quantitative risk assessment methods, many scientists (like other people) make mistakes and exhibit biases in reasoning about causation, if-then relations, and evidence. Decision-related conclusions or causal explanations are reached prematurely based on narrative plausibility rather than adequate factual evidence. Then, confirming evidence is sought and emphasized, but disconfirming evidence is ignored or discounted. This tendency has serious implications for health-related public policy discussions and decisions. We provide examples occurring in antimicrobial health risk assessments, including a case study of a recently reported positive relation between virginiamycin (VM) use in poultry and risk of resistance to VM-like (streptogramin) antibiotics in humans. This finding has been used to argue that poultry consumption causes increased resistance risks, that serious health impacts may result, and therefore use of VM in poultry should be restricted. However, the original study compared healthy vegetarians to hospitalized poultry consumers. Our examination of the same data using conditional independence tests for potential causality reveals that poultry consumption acted as a surrogate for hospitalization in this study. After accounting for current hospitalization status, no evidence remains supporting a causal relationship between poultry consumption and increased streptogramin resistance. This example emphasizes both the importance and the practical possibility of analyzing and presenting quantitative risk information using data analysis techniques (such as Bayesian model averaging (BMA) and conditional independence tests) that are as free as possible from potential selection, confirmation, and modeling biases.

Some limitations of aggregate exposure metrics.

Aggregate exposure metrics based on sums or weighted averages of component exposures are widely used in risk assessments of complex mixtures, such as asbestos-associated dusts and fibers. Allowed exposure levels based on total particle or fiber counts and estimated ambient concentrations of such mixtures may be used to make costly risk-management decisions intended to protect human health and to remediate hazardous environments. We show that, in general, aggregate exposure information alone may be inherently unable to guide rational risk-management decisions when the components of the mixture differ significantly in potency and when the percentage compositions of the mixture exposures differ significantly across locations. Under these conditions, which are not uncommon in practice, aggregate exposure metrics may be "worse than useless," in that risk-management decisions based on them are less effective than decisions that ignore the aggregate exposure information and select risk-management actions at random. The potential practical significance of these results is illustrated by a case study of 27 exposure scenarios in El Dorado Hills, California, where applying an aggregate unit risk factor (from EPA's IRIS database) to aggregate exposure metrics produces average risk estimates about 25 times greater - and of uncertain predictive validity - compared to risk estimates based on specific components of the mixture that have been hypothesized to pose risks of human lung cancer and mesothelioma.

Quantifying potential human health impacts of animal antibiotic use: enrofloxacin and macrolides in chickens.

Use of similar or identical antibiotics in both human and veterinary medicine has come under increasing scrutiny by regulators concerned that bacteria resistant to animal antibiotics will infect people and resist treatment with similar human antibiotics, leading to excess illnesses and deaths. Scientists, regulators, and interest groups in the United States and Europe have urged bans on nontherapeutic and some therapeutic uses of animal antibiotics to protect human health. Many regulators and public health experts have also expressed dissatisfaction with the perceived limitations of quantitative risk assessment and have proposed alternative qualitative and judgmental approaches ranging from "attributable fraction" estimates to risk management recommendations based on the precautionary principle or on expert judgments about the importance of classes of compounds in human medicine. This article presents a more traditional quantitative risk assessment of the likely human health impacts of continuing versus withdrawing use of fluoroquinolones and macrolides in production of broiler chickens in the United States. An analytic framework is developed and applied to available data. It indicates that withdrawing animal antibiotics can cause far more human illness-days than it would prevent: the estimated human BENEFIT:RISK health ratio for human health impacts of continued animal antibiotic use exceeds 1,000:1 in many cases. This conclusion is driven by a hypothesized causal sequence in which withdrawing animal antibiotic use increases illnesses rates in animals, microbial loads in servings from the affected animals, and hence human health risks. This potentially important aspect of human health risk assessment for animal antibiotics has not previously been quantified.

Optimal tracking and testing of U.S. and Canadian herds for BSE: a value-of-information (VOI) approach.

The U.S. Department of Agriculture (USDA) tests a subset of cattle slaughtered in the United States for bovine spongiform encephalitis (BSE). Knowing the origin of cattle (U.S. vs. Canadian) at testing could enable new testing or surveillance policies based on the origin of cattle testing positive. For example, if a Canadian cow tests positive for BSE, while no U.S. origin cattle do, the United States could subject Canadian cattle to more stringent testing. This article illustrates the application of a value-of-information (VOI) framework to quantify and compare potential economic costs to the United States of implementing tracking cattle origins to the costs of not doing so. The potential economic value of information from a tracking program is estimated to exceed its costs by more than five-fold if such information can reduce future losses in export and domestic markets and reduce future testing costs required to reassure or win back customers. Sensitivity analyses indicate that this conclusion is somewhat robust to many technical, scientific, and market uncertainties, including the current prevalence of BSE in the United States and/or Canada and the likely reactions of consumers to possible future discoveries of BSE in the United States and/or Canada. Indeed, the potential value of tracking information is great enough to justify locating and tracking Canadian cattle already in the United States when this can be done for a reasonable cost. If aggressive tracking and testing can win back lost exports, then the VOI of a tracking program may increase to over half a billion dollars per year.

Bayesian Monte Carlo uncertainty analysis of human health risks from animal antimicrobial use in a dynamic model of emerging resistance.

Recent qualitative analyses warn of potential future human health risks from emergence of antibiotic resistance in food-borne pathogens due to the use of similar antimicrobial drugs in both food animals and human medicine. While historical data suggest that human health risks from some animal antimicrobials, such as virginiamycin (VM), have remained low (McDonald et al., 2001), there is a widespread concern that "resistance epidemics" or endemics could arise in the future. How reassuring is the past about the future? This article applies quantitative risk assessment methods to help find out, using human health risks from VM and the nearly identical human antimicrobial quinupristin-dalfopristin (QD) as a case study. A dynamic simulation model is used to predict the risks of emerging resistance to human antimicrobials in human populations from given input assumptions. Bayesian Monte Carlo uncertainty analysis allows past data to constrain and inform selection of input parameter values, and thus to predict the possible future resistance patterns that are consistent with historical data. The results show that health risks from VM use in food animals are highly sensitive to the human prescription rate of QD. For realistic prescription rates, quantitative risks are less than 1 x 10(-6) even for members of the most-threatened (ICU patient) population, while societal risks are <1 excess statistical death per year for the whole U.S. population. Such quantitative estimates complement more qualitative assessments that discuss the possibility of future "resistance epidemics" (or endemics) without quantifying their probabilities.

Quantifying human health risks from virginiamycin used in chickens.

The streptogramin antimicrobial combination Quinupristin-Dalfopristin (QD) has been used in the United States since late 1999 to treat patients with vancomycin-resistant Enterococcus faecium (VREF) infections. Another streptogramin, virginiamycin (VM), is used as a growth promoter and therapeutic agent in farm animals in the United States and other countries. Many chickens test positive for QD-resistant E. faecium, raising concern that VM use in chickens might compromise QD effectiveness against VREF infections by promoting development of QD-resistant strains that can be transferred to human patients. Despite the potential importance of this threat to human health, quantifying the risk via traditional farm-to-fork modeling has proved extremely difficult. Enough key data (mainly on microbial loads at each stage) are lacking so that such modeling amounts to little more than choosing a set of assumptions to determine the answer. Yet, regulators cannot keep waiting for more data. Patients prescribed QD are typically severely ill, immunocompromised people for whom other treatment options have not readily been available. Thus, there is a pressing need for sound risk assessment methods to inform risk management decisions for VM/QD using currently available data. This article takes a new approach to the QD-VM risk modeling challenge. Recognizing that the usual farm-to-fork ("forward chaining") approach commonly used in antimicrobial risk assessment for food animals is unlikely to produce reliable results soon enough to be useful, we instead draw on ideas from traditional fault tree analysis ("backward chaining") to reverse the farm-to-fork process and start with readily available human data on VREF case loads and QD resistance rates. Combining these data with recent genogroup frequency data for humans, chickens, and other sources (Willems et al., 2000, 2001) allows us to quantify potential human health risks from VM in chickens in both the United States and Australia, two countries where regulatory action for VM is being considered. We present a risk simulation model, thoroughly grounded in data, that incorporates recent nosocomial transmission and genetic typing data. The model is used to estimate human QD treatment failures over the next five years with and without continued VM use in chickens. The quantitative estimates and probability distributions were implemented in a Monte Carlo simulation model for a five-year horizon beginning in the first quarter of 2002. In Australia, a Q1-2002 ban of virginiamycin would likely reduce average attributable treatment failures by 0.35 x 10(-3) cases, expected mortalities by 5.8 x 10(-5) deaths, and life years lost by 1.3 x 10(-3) for the entire population over five years. In the United States, where the number of cases of VRE is much higher, a 1Q-2002 ban on VM is predicted to reduce average attributable treatment failures by 1.8 cases in the entire population over five years; expected mortalities by 0.29 cases; and life years lost by 6.3 over a five-year period. The model shows that the theoretical statistical human health benefits of a VM ban range from zero to less than one statistical life saved in both Australia and the United States over the next five years and are rapidly decreasing. Sensitivity analyses indicate that this conclusion is robust to key data gaps and uncertainties, e.g., about the extent of resistance transfer from chickens to people.