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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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).

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.

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.