An equity and environmental justice assessment of anti-science actions during the Trump administration.

In the United States, science shapes federal health and safety protections, but political officials can and do politicize federal science and science-based safeguards. Many presidential administrations have politicized science, but under the administration of President Trump, these attacks on science-such as buried research, censored scientists, halted data collection-increased in number to unprecedented levels. Underserved communities bore the brunt of the harms. Such attacks disproportionately harm Black, Indigenous, low-income communities, and communities of color, all of whom have long been burdened by pollution exposure and other stressors. We analyze the effects on underserved communities of the Trump administration's anti-science environmental and public health policy actions and offer policy recommendations for current and future administrations. Our goal is to strengthen scientific integrity, prioritize health disparity research, and meaningfully engage affected communities in federal rulemaking.

The disinformation playbook: how industry manipulates the science-policy process-and how to restore scientific integrity.

For decades, corporate undermining of scientific consensus has eroded the scientific process worldwide. Guardrails for protecting science-informed processes, from peer review to regulatory decision making, have suffered sustained attacks, damaging public trust in the scientific enterprise and its aim to serve the public good. Government efforts to address corporate attacks have been inadequate. Researchers have cataloged corporate malfeasance that harms people's health across diverse industries. Well-known cases, like the tobacco industry's efforts to downplay the dangers of smoking, are representative of transnational industries, rather than unique. This contribution schematizes industry tactics to distort, delay, or distract the public from instituting measures that improve health-tactics that comprise the "disinformation playbook." Using a United States policy lens, we outline steps the scientific community should take to shield science from corporate interference, through individual actions (by scientists, peer reviewers, and editors) and collective initiatives (by research institutions, grant organizations, professional associations, and regulatory agencies).

Perceived losses of scientific integrity under the Trump administration: A survey of federal scientists.

President Trump and his administration have been regarded by news outlets and scholars as one of the most hostile administrations towards scientists and their work. However, no study to-date has empirically measured how federal scientists perceive the Trump administration with respect to their scientific work. In 2018, we distributed a survey to over 63,000 federal scientists from 16 federal agencies to assess their perception of scientific integrity. Here we discuss the results of this survey for a subset of these agencies: Department of Interior (DOI) agencies (the US Fish and Wildlife Service (FWS), the US Geological Survey, and the National Park Service); the Centers for Disease Control and Prevention (CDC); the US Environmental Protection Agency (EPA); the Food and Drug Administration (FDA); and the National Oceanic and Atmospheric Administration (NOAA). We focus our analysis to 10 key questions fitting within three core categories that relate to perceptions of integrity in science. Additionally, we analyzed responses across agencies and compare responses in the 2018 survey to prior year surveys of federal scientists with similar survey questions. Our results indicate that federal scientists perceive losses of scientific integrity under the Trump Administration. Perceived loss of integrity in science was greater at the DOI and EPA where federal scientists ranked incompetent and untrustworthy leadership as top barriers to science-based decision-making, but this was not the case at the CDC, FDA, and NOAA where scientists positively associated leadership with scientific integrity. We also find that reports of political interference in scientific work and adverse work environments were higher at EPA and FWS in 2018 than in prior years. We did not find similar results at the CDC and FDA. These results suggest that leadership, positive work environments, and clear and comprehensive scientific integrity policies and infrastructure within agencies play important roles in how federal scientists perceive their agency's scientific integrity.

Hazardous air pollutant emissions implications under 2018 guidance on U.S. Clean Air Act requirements for major sources.

On January 25, 2018, the United States Environmental Protection Agency withdrew a 1995 policy that mandates the use of maximum achievable control technology (MACT) to regulate emissions from major sources of hazardous air pollutants (HAPs), a category of toxic chemicals that may be carcinogenic, mutagenic, or cause other adverse health effects. To better understand the implications and scope of the change in regulatory guidance for HAP emissions of major sources that may reclassify as area sources, the increase in emissions that could legally occur under the new policy is assessed here. Based on facility-level data from a 2014 HAP national emissions inventory, it is estimated that 70% of major sources of HAPs qualify for reclassification as area sources, which could result in a maximum of 35,030 tons per year (tpy) of additional HAP emissions if all sources successfully reclassified. This amount would nearly triple the total volume of HAPs that qualifying major sources emitted in 2014. On average, qualifying sources could emit individually an additional 18.4 tpy. In the 21 states and territories that follow only federal guidelines for controlling HAPs, it is more likely that the estimates presented here could materialize compared to states that have additional guidelines for area sources of HAPs. The quantitative analysis of the potential emission changes resulting from regulatory change is instructive for industry, state and federal decisionmakers, and interested members of the public looking to understand and anticipate how relevant stakeholders will be affected by this policy change.Implications: Withdrawal of a U.S. Environmental Protection Agency policy that mandates the use of maximum achievable control technology (MACT) to regulate emissions from major sources of hazardous air pollutants (HAPs) could result in higher emissions of toxic chemicals that may be carcinogenic, mutagenic, or cause other adverse health effects. Analysis of potential emission changes resulting from regulatory change is instructive for industry, state, and federal decisionmakers, and interested members of the public looking to understand and anticipate how relevant stakeholders will be affected by this policy change.

Defending the scientific integrity of conservation-policy processes.

Government agencies faced with politically controversial decisions often discount or ignore scientific information, whether from agency staff or nongovernmental scientists. Recent developments in scientific integrity (the ability to perform, use, communicate, and publish science free from censorship or political interference) in Canada, Australia, and the United States demonstrate a similar trajectory. A perceived increase in scientific-integrity abuses provokes concerted pressure by the scientific community, leading to efforts to improve scientific-integrity protections under a new administration. However, protections are often inconsistently applied and are at risk of reversal under administrations publicly hostile to evidence-based policy. We compared recent challenges to scientific integrity to determine what aspects of scientific input into conservation policy are most at risk of political distortion and what can be done to strengthen safeguards against such abuses. To ensure the integrity of outbound communications from government scientists to the public, we suggest governments strengthen scientific integrity policies, include scientists' right to speak freely in collective-bargaining agreements, guarantee public access to scientific information, and strengthen agency culture supporting scientific integrity. To ensure the transparency and integrity with which information from nongovernmental scientists (e.g., submitted comments or formal policy reviews) informs the policy process, we suggest governments broaden the scope of independent reviews, ensure greater diversity of expert input and transparency regarding conflicts of interest, require a substantive response to input from agencies, and engage proactively with scientific societies. For their part, scientists and scientific societies have a responsibility to engage with the public to affirm that science is a crucial resource for developing evidence-based policy and regulations in the public interest.

Effects of ambient air pollution measurement error on health effect estimates in time-series studies: a simulation-based analysis.

In this study, we investigated bias caused by spatial variability and spatial heterogeneity in outdoor air-pollutant concentrations, instrument imprecision, and choice of daily pollutant metric on risk ratio (RR) estimates obtained from a Poisson time-series analysis. Daily concentrations for 12 pollutants were simulated for Atlanta, Georgia, at 5 km resolution during a 6-year period. Viewing these as being representative of the true concentrations, a population-level pollutant health effect (RR) was specified, and daily counts of health events were simulated. Error representative of instrument imprecision was added to the simulated concentrations at the locations of fixed site monitors in Atlanta, and these mismeasured values were combined to create three different city-wide daily metrics (central monitor, unweighted average, and population-weighted average). Given our assumptions, the median bias in the RR per unit increase in concentration was found to be lowest for the population-weighted average metric. Although the Berkson component of error caused bias away from the null in the log-linear models, the net bias due to measurement error tended to be towards the null. The relative differences in bias among the metrics were lessened, although not eliminated, by scaling results to interquartile range increases in concentration.

Characterization of Ambient Air Pollution Measurement Error in a Time-Series Health Study using a Geostatistical Simulation Approach.

In recent years, geostatistical modeling has been used to inform air pollution health studies. In this study, distributions of daily ambient concentrations were modeled over space and time for 12 air pollutants. Simulated pollutant fields were produced for a 6-year time period over the 20-county metropolitan Atlanta area using the Stanford Geostatistical Modeling Software (SGeMS). These simulations incorporate the temporal and spatial autocorrelation structure of ambient pollutants, as well as season and day-of-week temporal and spatial trends; these fields were considered to be the true ambient pollutant fields for the purposes of the simulations that followed. Simulated monitor data at the locations of actual monitors were then generated that contain error representative of instrument imprecision. From the simulated monitor data, four exposure metrics were calculated: central monitor and unweighted, population-weighted, and area-weighted averages. For each metric, the amount and type of error relative to the simulated pollutant fields are characterized and the impact of error on an epidemiologic time-series analysis is predicted. The amount of error, as indicated by a lack of spatial autocorrelation, is greater for primary pollutants than for secondary pollutants and is only moderately reduced by averaging across monitors; more error will result in less statistical power in the epidemiologic analysis. The type of error, as indicated by the correlations of error with the monitor data and with the true ambient concentration, varies with exposure metric, with error in the central monitor metric more of the classical type (i.e., independent of the monitor data) and error in the spatial average metrics more of the Berkson type (i.e., independent of the true ambient concentration). Error type will affect the bias in the health risk estimate, with bias toward the null and away from the null predicted depending on the exposure metric; population-weighting yielded the least bias.

Impact of exposure measurement error in air pollution epidemiology: effect of error type in time-series studies.

BACKGROUND: Two distinctly different types of measurement error are Berkson and classical. Impacts of measurement error in epidemiologic studies of ambient air pollution are expected to depend on error type. We characterize measurement error due to instrument imprecision and spatial variability as multiplicative (i.e. additive on the log scale) and model it over a range of error types to assess impacts on risk ratio estimates both on a per measurement unit basis and on a per interquartile range (IQR) basis in a time-series study in Atlanta. METHODS: Daily measures of twelve ambient air pollutants were analyzed: NO2, NOx, O3, SO2, CO, PM10 mass, PM2.5 mass, and PM2.5 components sulfate, nitrate, ammonium, elemental carbon and organic carbon. Semivariogram analysis was applied to assess spatial variability. Error due to this spatial variability was added to a reference pollutant time-series on the log scale using Monte Carlo simulations. Each of these time-series was exponentiated and introduced to a Poisson generalized linear model of cardiovascular disease emergency department visits. RESULTS: Measurement error resulted in reduced statistical significance for the risk ratio estimates for all amounts (corresponding to different pollutants) and types of error. When modelled as classical-type error, risk ratios were attenuated, particularly for primary air pollutants, with average attenuation in risk ratios on a per unit of measurement basis ranging from 18% to 92% and on an IQR basis ranging from 18% to 86%. When modelled as Berkson-type error, risk ratios per unit of measurement were biased away from the null hypothesis by 2% to 31%, whereas risk ratios per IQR were attenuated (i.e. biased toward the null) by 5% to 34%. For CO modelled error amount, a range of error types were simulated and effects on risk ratio bias and significance were observed. CONCLUSIONS: For multiplicative error, both the amount and type of measurement error impact health effect estimates in air pollution epidemiology. By modelling instrument imprecision and spatial variability as different error types, we estimate direction and magnitude of the effects of error over a range of error types.

Ambient air pollutant measurement error: characterization and impacts in a time-series epidemiologic study in Atlanta.

In time-series studies of ambient air pollution and health in large urban areas, measurement errors associated with instrument precision and spatial variability vary widely across pollutants. In this paper, we characterize these errors for selected air pollutants and estimate their impacts on epidemiologic results from an ongoing study of air pollution and emergency department visits in Atlanta. Error was modeled for daily measures of 12 air pollutants using collocated monitor data to characterize instrument precision and data from multiple study area monitors to estimate population-weighted spatial variance. Time-series simulations of instrument and spatial error were generated for each pollutant, added to a reference pollutant time-series, and used in a Poisson generalized linear model of air pollution and cardiovascular emergency department visits. Reductions in risk ratio due to instrument precision error were less than 6%. Error due to spatial variability resulted in average risk ratio reductions of less than 16% for secondary pollutants (O(3), PM(2.5) sulfate, nitrate and ammonium) and between 43% and 68% for primary pollutants (NO(x), NO(2), SO(2), CO, PM(2.5) elemental carbon); pollutants of mixed origin (PM(10), PM(2.5), PM(2.5) organic carbon) had intermediate impacts. Quantifying impacts of measurement error on health effect estimates improves interpretation across ambient pollutants.