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BACKGROUND: Physical activity and sleep are behavioral risk factors for cancer that may be influenced by environmental exposures, including built and natural environments. However, many studies in this area are limited by residence-based exposure assessment and/or self-reported, time-aggregated measures of behavior. METHODS: The Nurses Health Study 3 (NHS3) Mobile Health Substudy is a pilot study of 500 participants in the prospective NHS3 cohort who use a smartphone application and a Fitbit for seven-day periods, four times over a year, to measure minute-level location, physical activity, heart rate, and sleep. RESULTS: We have collected data on 435 participants, comprising over 6 million participant-minutes of heart rate, step, sleep, and location. Over 90% of participants had five days of ≥600 minutes of Fitbit wear-time in their first sampling week, and this percentage dropped to 70% for weeks 2 to 4. Over 819 sampling weeks, we observed an average of 7,581 minutes of heart rate and step data [interquartile range (IQR): 6,651-9,645] per participant-week, and >2 million minutes of sleep in over 5,700 sleep bouts. We have recorded location data for 5,237 unique participant-days, averaging 104 location observations per participant-day (IQR: 103-107). CONCLUSIONS: This study describes a protocol to incorporate mobile health technology into a nationwide prospective cohort to measure high-resolution objective data on environment and behavior. IMPACT: This project could provide translational insights into interventions for urban planning to optimize opportunities for physical activity and healthy sleep patterns to reduce cancer risk.See all articles in this CEBP Focus section, Modernizing Population Science.

In anticipation of the expanding appreciation for air quality models in health outcomes studies, we develop and evaluate a reduced-complexity model for pollution transport that intentionally sacrifices some of the sophistication of full-scale chemical transport models in order to support applicability to a wider range of health studies. Specifically, we introduce the HYSPLIT average dispersion model, HyADS, which combines the HYSPLIT trajectory dispersion model with modern advances in parallel computing to estimate ZIP code level exposure to emissions from individual coal-powered electricity generating units in the United States. Importantly, the method is not designed to reproduce ambient concentrations of any particular air pollutant; rather, the primary goal is to characterize each ZIP code's exposure to these coal power plants specifically. We show adequate performance towards this goal against observed annual average air pollutant concentrations (nationwide Pearson correlations of 0.88 and 0.73 with SO 4 2 - and PM2.5, respectively) and coal-combustion impacts simulated with a full-scale chemical transport model and adjusted to observations using a hybrid direct sensitivities approach (correlation of 0.90). We proceed to provide multiple examples of HyADS's single-source applicability, including to show that 22% of the population-weighted coal exposure comes from 30 coal-powered electricity generating units.

Environ Health Perspect 131(3):037005 (2023), https://doi.org/10.1289/EHP11605 Table 1 incorrectly labeled low-operation year population-weighted exposure as 'High-operation years' in the 'Avoided: reduced operation after scrubber' naming convention. In addition, Figure 2 [...]

BACKGROUND: Emissions from coal power plants have decreased over recent decades due to regulations and economics affecting costs of providing electricity generated by coal vis-a-vis its alternatives. These changes have improved regional air quality, but questions remain about whether benefits have accrued equitably across population groups. OBJECTIVES: We aimed to quantify nationwide long-term changes in exposure to particulate matter (PM) with an aerodynamic diameter [less than or equal to]2.5 [micro]m (P[M.sub.2.5]) associated with coal power plant S[O.sub.2] emissions. We linked exposure reductions with three specific actions taken at individual power plants: scrubber installations, reduced operations, and retirements. We assessed how emissions changes in different locations have influenced exposure inequities, extending previous source-specific environmental justice analyses by accounting for location-specific differences in racial/ethnic population distributions. METHODS: We developed a data set of annual P[M.sub.2.5] source impacts ('coal P[M.sub.2.5]') associated with S[O.sub.2] emissions at each of 1,237 U.S. coal-fired power plants across 1999-2020. We linked population-weighted exposure with information about each coal unit's operational and emissions-control status. We calculate changes in both relative and absolute exposure differences across demographic groups. Results: Nationwide population-weighted coal P[M.sub.2.5] declined from 1.96 [micro]g/[m.sup.3] in 1999 to 0.06 [micro]g/[m.sup.3] in 2020. Between 2007 and 2010, most of the exposure reduction is attributable to S[O.sub.2] scrubber installations, and after 2010 most of the decrease is attributable to retirements. Black populations in the South and North Central United States and Native American populations in the western United States were inequitably exposed early in the study period. Although inequities decreased with falling emissions, facilities in states across the North Central United States continue to inequitably expose Black populations, and Native populations are inequitably exposed to emissions from facilities in the West. DISCUSSION: We show that air quality controls, operational adjustments, and retirements since 1999 led to reduced exposure to coal power plant related P[M.sub.2.5]. Reduced exposure improved equity overall, but some populations continue to be inequitably exposed to P[M.sub.2.5] associated with facilities in the North Central and western United States. https://doi.org/10.1289/EHP11605, Introduction Electricity generation from U.S. coal power plants was historically a major contributor to poor air quality--notably in the form of particulate matter (PM) and ozone from sulfur and nitrogen [...]

Policy-makers seeking to limit the impact of coal electricity-generating units (EGUs, also known as power plants) on air quality and climate justify regulations by quantifying the health burden attributable to exposure from these sources. We defined “coal PM2.5” as fine particulate matter associated with coal EGU sulfur dioxide emissions and estimated annual exposure to coal PM2.5 from 480 EGUs in the US. We estimated the number of deaths attributable to coal PM2.5 from 1999 to 2020 using individual-level Medicare death records representing 650 million person-years. Exposure to coal PM2.5 was associated with 2.1 times greater mortality risk than exposure to PM2.5 from all sources. A total of 460,000 deaths were attributable to coal PM2.5, representing 25% of all PM2.5-related Medicare deaths before 2009 and 7% after 2012. Here, we quantify and visualize the contribution of individual EGUs to mortality [ABSTRACT FROM AUTHOR]

BACKGROUND: Emissions from coal power plants have decreased over recent decades due to regulations and economics affecting costs of providing electricity generated by coal vis-à-vis its alternatives. These changes have improved regional air quality, but questions remain about whether benefits have accrued equitably across population groups. OBJECTIVES: We aimed to quantify nationwide long-term changes in exposure to particulate matter (PM) with an aerodynamic diameter ≤2.5 μm (PM2.5) associated with coal power plant SO2 emissions. We linked exposure reductions with three specific actions taken at individual power plants: scrubber installations, reduced operations, and retirements. We assessed how emissions changes in different locations have influenced exposure inequities, extending previous source-specific environmental justice analyses by accounting for location-specific differences in racial/ethnic population distributions. METHODS: We developed a data set of annual PM2.5 source impacts (“coal PM2.5”) associated with SO2 emissions at each of 1,237 U.S. coal-fired power plants across 1999–2020. We linked population-weighted exposure with information about each coal unit’s operational and emissions-control status. We calculate changes in both relative and absolute exposure differences across demographic groups. RESULTS: Nationwide population-weighted coal PM2.5 declined from 1.96 μg/m³ in 1999 to 0:06 μg/m³ in 2020. Between 2007 and 2010, most of the exposure reduction is attributable to SO2 scrubber installations, and after 2010 most of the decrease is attributable to retirements. Black populations in the South and North Central United States and Native American populations in the western United States were inequitably exposed early in the study period. Although inequities decreased with falling emissions, facilities in states across the North Central United States continue to inequitably expose Black populations, and Native populations are inequitably exposed to emissions from facilities in the West. DISCUSSION: We show that air quality controls, operational adjustments, and retirements since 1999 led to reduced exposure to coal power plant related PM2.5. Reduced exposure improved equity overall, but some populations continue to be inequitably exposed to PM2.5 associated with facilities in the North Central and western United States. [ABSTRACT FROM AUTHOR]

Since the beginning of the COVID-19 pandemic, many dashboards have emerged as useful tools to monitor its evolution, inform the public, and assist governments in decision-making. Here, we present a globally applicable method, integrated in a daily updated dashboard that provides an estimate of the trend in the evolution of the number of cases and deaths from reported data of more than 200 countries and territories, as well as 7-d forecasts. One of the significant difficulties in managing a quickly propagating epidemic is that the details of the dynamic needed to forecast its evolution are obscured by the delays in the identification of cases and deaths and by irregular reporting. Our forecasting methodology substantially relies on estimating the underlying trend in the observed time series using robust seasonal trend decomposition techniques. This allows us to obtain forecasts with simple yet effective extrapolation methods in linear or log scale. We present the results of an assessment of our forecastingmethodology and discuss its application to the production of global and regional risk maps. [ABSTRACT FROM AUTHOR]

Summary: Coal burning power plants are a frequent target of regulatory programmes because of their emission of chemicals that are known precursors to the formation of ambient particulate air pollution. Health impact assessments of emissions from coal power plants typically rely on assumed causal relationships between emissions, ambient pollution and health, many of which have never been empirically verified. We offer a novel statistical evaluation of some of these presumed causal relationships, integrating the formality of causal inference methods with repurposed tools from atmospheric science to accommodate the central challenge of long‐range pollution transport of emissions from power plants to exposed populations. The statistical approach follows recent work on Bayesian methods for deploying principal stratification and causal mediation analysis in tandem to characterize the extent to which decreased sulphur dioxide emissions from 410 power plants across the USA impact mortality and hospitalization outcomes across Medicare beneficiaries residing across 12370 locations in a manner that is mediated through reductions of ambient fine particulate pollution. The result is the first epidemiological investigation integrating causal inference methodology with direct measurements of coal emissions, pollution transport, ambient pollution and human health in a single analysis, indicating the potential for data science at the intersection of statistics, epidemiology and atmospheric science. [ABSTRACT FROM AUTHOR]

This study examines events at the conclusion of the 2006 stock option backdating scandal: the departures of C-level executives from firms implicated in backdating. The authors ask whether removing executives brings closure to the scandal, or if executive turnover creates greater uncertainty. Using a sample of 236 executive departures, the authors find that although overall market reaction to executive departures is negative, those departures involving a firm's CEO or CFO ameliorate the market reaction. The authors also find that market reaction worsens when the CEO "resigns," versus being terminated, and when the firm involved has been previously identified as socially responsible. Results suggest that firms, by shaping who and how executives depart, may amplify or dampen the damage of existing scandalizing events. [ABSTRACT FROM AUTHOR]

This study seeks to examine the mechanisms by which a corporation's use of philanthropy affects its reputation for corporate social performance (CSP), which the authors conceive of as consisting of two dimensions: CSP awareness and CSP perception. Using signal detection theory (SDT), the authors model signal amplitude (the amount contributed), dispersion (number of areas supported), and consistency (presence of a corporate foundation) on CSP awareness and perception. Overall, this study finds that characteristics of firms' portfolio of philanthropic activities are a greater predictor of CSP awareness than of CSP perception. Awareness increases with signal amplitude, dispersion, and consistency. CSP perception is driven by awareness and corporate reputation. The authors' contention that corporate philanthropy is a complex variable is upheld, as we find that CSP signal characteristics influence CSP awareness and perception independently and asymmetrically. The authors conclude by proposing avenues for future research. [ABSTRACT FROM AUTHOR]

A large body of literature looks at how firms develop and maintain their reputation. Little is known, however, about factors leading to a damaged corporate reputation. In this article, the authors compare two sets of predictors of reputational damage following a reported breach of norms: the characteristics of the breach and the characteristics of the actor reporting the breach. Theoretically, the authors argue that the latter is likely to prevail over the former. The authors test this proposition in the highly normative context of corporate social responsibility (CSR). Building on a global data set of over 8,600 CSR-related norm breaches, directed against 451 firms on the Fortune ranking reputation over the 2006-2009 period, the authors find empirical support for the idea that reputation damage is not really driven by the severity and novelty of the allegation, but by the type of source reporting the issue as well as the credibility of this source. Hence, these results lend some support to a socially constructed view of reputational damage, in which being portrayed by prominent actors as deviating from the norm is more important than the actual deviation from the goal of the norm itself. [ABSTRACT FROM AUTHOR]

This article introduces the special issue on "Corporate Reputation: Being Good and Looking Good." Three of the five included articles help to reinforce a conclusion that "being good" and "looking good" are not dichotomous, mutually exclusive conditions. Rather, the two dimensions are linked in some kind of causal relationship for which continuing conceptual and empirical research is desirable. A fourth article concerns the reputational effects of the stock-option backdating scandal. The fifth article offers a critique of conventional approaches to defining and measuring reputation. [ABSTRACT FROM AUTHOR]

The most popular measure of corporate reputation is the ranking of the most admired companies. But what exactly do we admire in people and firms of good reputation? This article is about the ethical dimension of corporate reputation. It integrates the trait approach in personality psychology and philosophical ethics to the study of reputation and related concepts as a way to account for the discontinuities between reputation at the individual and corporate levels under conditions of uncertainty. Through an examination of the distinction between perceptions and grounding of reputation, the article also sheds light on the explanatory power of reputation concepts and critically appraises the possibility of character and organizational virtue. [ABSTRACT FROM AUTHOR]

Grounded in stakeholder theory and a resource-based view of the firm, this longitudinal research demonstrates the evolution of corporate social responsibility (CSR) and firm reputation over time. Drawing on a 5-year sample of 285 major U.S. firms obtained from the KLD database and Fortune's Most Admired Companies, we find that the proposed dynamic relationship predicts evolving stakeholder expectations to incite organizations to improve their social performance to earn reputational benefits. Contrary to the often labeled stickiness of reputation, we find a "Red Queen" effect that supports reputation as a dynamic construct where the change in CSR does predict a change in corporate reputation. Similarly, we find that the change in reputation over time varies by industry, being most pronounced for manufacturing. From a practical perspective, this relationship across time may incite managers to create sustainable competitive advantage by continuously investing in doing good to reap the benefits of looking good and looking even better with time. [ABSTRACT FROM AUTHOR]

Propensity score matching is a common tool for adjusting for observed confounding in observational studies, but is known to have limitations in the presence of unmeasured confounding. In many settings, researchers are confronted with spatially-indexed data where the relative locations of the observational units may serve as a useful proxy for unmeasured confounding that varies according to a spatial pattern. We develop a new method, termed distance adjusted propensity score matching (DAPSm) that incorporates information on units' spatial proximity into a propensity score matching procedure. We show that DAPSm can adjust for both observed and some forms of unobserved confounding and evaluate its performance relative to several other reasonable alternatives for incorporating spatial information into propensity score adjustment. The method is motivated by and applied to a comparative effectiveness investigation of power plant emission reduction technologies designed to reduce population exposure to ambient ozone pollution. Ultimately, DAPSm provides a framework for augmenting a "standard" propensity score analysis with information on spatial proximity and provides a transparent and principled way to assess the relative trade-offs of prioritizing observed confounding adjustment versus spatial proximity adjustment. [ABSTRACT FROM AUTHOR]

Importance: The US Environmental Protection Agency is required to reexamine its National Ambient Air Quality Standards (NAAQS) every 5 years, but evidence of mortality risk is lacking at air pollution levels below the current daily NAAQS in unmonitored areas and for sensitive subgroups.Objective: To estimate the association between short-term exposures to ambient fine particulate matter (PM2.5) and ozone, and at levels below the current daily NAAQS, and mortality in the continental United States.Design, Setting, and Participants: Case-crossover design and conditional logistic regression to estimate the association between short-term exposures to PM2.5 and ozone (mean of daily exposure on the same day of death and 1 day prior) and mortality in 2-pollutant models. The study included the entire Medicare population from January 1, 2000, to December 31, 2012, residing in 39 182 zip codes.Exposures: Daily PM2.5 and ozone levels in a 1-km × 1-km grid were estimated using published and validated air pollution prediction models based on land use, chemical transport modeling, and satellite remote sensing data. From these gridded exposures, daily exposures were calculated for every zip code in the United States. Warm-season ozone was defined as ozone levels for the months April to September of each year.Main Outcomes and Measures: All-cause mortality in the entire Medicare population from 2000 to 2012.Results: During the study period, there were 22 433 862 million case days and 76 143 209 control days. Of all case and control days, 93.6% had PM2.5 levels below 25 μg/m3, during which 95.2% of deaths occurred (21 353 817 of 22 433 862), and 91.1% of days had ozone levels below 60 parts per billion, during which 93.4% of deaths occurred (20 955 387 of 22 433 862). The baseline daily mortality rates were 137.33 and 129.44 (per 1 million persons at risk per day) for the entire year and for the warm season, respectively. Each short-term increase of 10 μg/m3 in PM2.5 (adjusted by ozone) and 10 parts per billion (10-9) in warm-season ozone (adjusted by PM2.5) were statistically significantly associated with a relative increase of 1.05% (95% CI, 0.95%-1.15%) and 0.51% (95% CI, 0.41%-0.61%) in daily mortality rate, respectively. Absolute risk differences in daily mortality rate were 1.42 (95% CI, 1.29-1.56) and 0.66 (95% CI, 0.53-0.78) per 1 million persons at risk per day. There was no evidence of a threshold in the exposure-response relationship.Conclusions and Relevance: In the US Medicare population from 2000 to 2012, short-term exposures to PM2.5 and warm-season ozone were significantly associated with increased risk of mortality. This risk occurred at levels below current national air quality standards, suggesting that these standards may need to be reevaluated. [ABSTRACT FROM AUTHOR]