Health and equity implications of individual adaptation to air pollution in a changing climate.

Future climate change can cause more days with poor air quality. This could trigger more alerts telling people to stay inside to protect themselves, with potential consequences for health and health equity. Here, we study the change in US air quality alerts over this century due to fine particulate matter (PM2.5), who they may affect, and how they may respond. We find air quality alerts increase by over 1 mo per year in the eastern United States by 2100 and quadruple on average. They predominantly affect areas with high Black populations and leakier homes, exacerbating existing inequalities and impacting those less able to adapt. Reducing emissions can offer significant annual health benefits ($5,400 per person) by mitigating the effect of climate change on air pollution and its associated risks of early death. Relying on people to adapt, instead, would require them to stay inside, with doors and windows closed, for an extra 142 d per year, at an average cost of $11,000 per person. It appears likelier, however, that people will achieve minimal protection without policy to increase adaptation rates. Boosting adaptation can offer net benefits, even alongside deep emission cuts. New adaptation policies could, for example: reduce adaptation costs; reduce infiltration and improve indoor air quality; increase awareness of alerts and adaptation; and provide measures for those working or living outdoors. Reducing emissions, conversely, lowers everyone's need to adapt, and protects those who cannot adapt. Equitably protecting human health from air pollution under climate change requires both mitigation and adaptation.

Prescribed fires, smoke exposure, and hospital utilization among heart failure patients.

BACKGROUND: Prescribed fires often have ecological benefits, but their environmental health risks have been infrequently studied. We investigated associations between residing near a prescribed fire, wildfire smoke exposure, and heart failure (HF) patients' hospital utilization. METHODS: We used electronic health records from January 2014 to December 2016 in a North Carolina hospital-based cohort to determine HF diagnoses, primary residence, and hospital utilization. Using a cross-sectional study design, we associated the prescribed fire occurrences within 1, 2, and 5 km of the patients' primary residence with the number of hospital visits and 7- and 30-day readmissions. To compare prescribed fire associations with those observed for wildfire smoke, we also associated zip code-level smoke density data designed to capture wildfire smoke emissions with hospital utilization amongst HF patients. Quasi-Poisson regression models were used for the number of hospital visits, while zero-inflated Poisson regression models were used for readmissions. All models were adjusted for age, sex, race, and neighborhood socioeconomic status and included an offset for follow-up time. The results are the percent change and the 95% confidence interval (CI). RESULTS: Associations between prescribed fire occurrences and hospital visits were generally null, with the few associations observed being with prescribed fires within 5 and 2 km of the primary residence in the negative direction but not the more restrictive 1 km radius. However, exposure to medium or heavy smoke (primarily from wildfires) at the zip code level was associated with both 7-day (8.5% increase; 95% CI = 1.5%, 16.0%) and 30-day readmissions (5.4%; 95% CI = 2.3%, 8.5%), and to a lesser degree, hospital visits (1.5%; 95% CI: 0.0%, 3.0%) matching previous studies. CONCLUSIONS: Area-level smoke exposure driven by wildfires is positively associated with hospital utilization but not proximity to prescribed fires.

The Health and Climate Benefits of Economic Dispatch in China's Power System.

China's power system is highly regulated and uses an "equal-share" dispatch approach. However, market mechanisms are being introduced to reduce generation costs and improve system reliability. Here, we quantify the climate and human health impacts brought about by this transition, modeling China's power system operations under economic dispatch. We find that significant reductions in mortality related to air pollution (11%) and CO2 emissions (3%) from the power sector can be attained by economic dispatch, relative to the equal-share approach, through more efficient coal-powered generation. Additional health and climate benefits can be achieved by incorporating emission externalities in electricity generation costs. However, the benefits of the transition to economic dispatch will be unevenly distributed across China and may lead to increased health damage in some regions. Our results show the potential of dispatch decision-making in electricity generation to mitigate the negative impacts of power plant emissions with existing facilities in China.

Diverse Pathways for Power Sector Decarbonization in Texas Yield Health Cobenefits but Fail to Alleviate Air Pollution Exposure Inequities.

Decarbonizing power systems is a critical component of climate change mitigation, which can have public health cobenefits by reducing air pollution. Many studies have examined strategies to decarbonize power grids and quantified their health cobenefits. However, few of them focus on near-term cobenefits at community levels, while comparing various decarbonization pathways. Here, we use a coupled power system and air quality modeling framework to quantify the costs and benefits of decarbonizing the Texas power grid through a carbon tax; replacing coal with natural gas, solar, or wind; and internalizing human health impacts into operations. Our results show that all decarbonization pathways can result in major reductions in CO2 emissions and public health impacts from power sector emissions, leading to large net benefits when considering the costs to implement these strategies. Operational changes with existing infrastructure can serve as a transitional strategy during the process of replacing coal with renewable energy, which offers the largest benefits. However, we also find that Black and lower-income populations receive disproportionately higher air pollution damages and that none of the examined decarbonization strategies mitigate this disparity. These findings suggest that additional interventions are necessary to mitigate environmental inequity while decarbonizing power grids.

Uncertainty in Health Impact Assessments of Smoke From a Wildfire Event.

Wildfires cause elevated air pollution that can be detrimental to human health. However, health impact assessments associated with emissions from wildfire events are subject to uncertainty arising from different sources. Here, we quantify and compare major uncertainties in mortality and morbidity outcomes of exposure to fine particulate matter (PM2.5) pollution estimated for a series of wildfires in the Southeastern U.S. We present an approach to compare uncertainty in estimated health impacts specifically due to two driving factors, wildfire-related smoke PM2.5 fields and variability in concentration-response parameters from epidemiologic studies of ambient and smoke PM2.5. This analysis, focused on the 2016 Southeastern wildfires, suggests that emissions from these fires had public health consequences in North Carolina. Using several methods based on publicly available monitor data and atmospheric models to represent wildfire-attributable PM2.5, we estimate impacts on several health outcomes and quantify associated uncertainty. Multiple concentration-response parameters derived from studies of ambient and wildfire-specific PM2.5 are used to assess health-related uncertainty. Results show large variability and uncertainty in wildfire impact estimates, with comparable uncertainties due to the smoke pollution fields and health response parameters for some outcomes, but substantially larger health-related uncertainty for several outcomes. Consideration of these uncertainties can support efforts to improve estimates of wildfire impacts and inform fire-related decision-making.

Potential impacts of prescribed fire smoke on public health and socially vulnerable populations in a Southeastern U.S. state.

Prescribed fire is an essential tool for wildfire risk mitigation and ecosystem restoration in the Southeastern United States. It is also one of the region's largest sources of atmospheric emissions. The public health impacts of prescribed fire smoke, however, remain uncertain. Here, we use digital burn permit records, reduced-complexity air quality modeling, and epidemiological associations between fine particulate matter concentrations and multiple health endpoints to assess the impacts of prescribed burning on public health across Georgia. Additionally, we examine the social vulnerability of populations near high prescribed burning activity using a demographic- and socioeconomic-based index. The analysis identifies spatial clusters of burning activity in the state and finds that areas with intense prescribed fire have levels of social vulnerability that are over 25% higher than the state average. The results also suggest that the impacts of burning in Georgia can potentially include hundreds of annual morbidity and mortality cases associated with smoke pollution. These health impacts are concentrated in areas with higher fractions of low socioeconomic status, elderly, and disabled residents, particularly vulnerable to air pollution. Estimated smoke-related health incidence rates are over 3 times larger than the state average in spatial clusters of intense burning activity, and over 40% larger in spatial clusters of high social vulnerability. Spatial clusters of low social vulnerability experience substantially lower negative health effects from prescribed burning relative to the rest of the state. The health burden of smoke from prescribed burns in the state is comparable to that estimated for other major emission sectors, such as vehicles and industrial combustion. Within spatial clusters of socially-vulnerable populations, the impacts of prescribed fire considerably outweigh those of other emission sectors. These findings call for greater attention to the air quality impacts of prescribed burning in the Southeastern U.S. and the communities most exposed to fire-related smoke.

Application and evaluation of a low-cost PM sensor and data fusion with CMAQ simulations to quantify the impacts of prescribed burning on air quality in Southwestern Georgia, USA.

Prescribed burning (PB) is a prominent source of PM2.5 in the southeastern US and exposure to PB smoke is a health risk. As demand for burning increases and stricter controls are implemented for other anthropogenic sources, PB emissions tend to be responsible for an increasing fraction of PM2.5 concentrations. Here, to quantify the effect of PB on air quality, low-cost sensors are used to measure PM2.5 concentrations in Southwestern Georgia. The feasibility of using low-cost sensors as a supplemental measurement tool is evaluated by comparing them with reference instruments. A chemical transport model, CMAQ, is also used to simulate the contribution of PB to PM2.5 concentrations. Simulated PM2.5 concentrations are compared to observations from both low-cost sensors and reference monitors. Finally, a data fusion method is applied to generate hourly spatiotemporal exposure fields by fusing PM2.5 concentrations from the CMAQ model and all observations. The results show that the severe impact of PB on local air quality and public health may be missed due to the dearth of regulatory monitoring sites. In Southwestern Georgia PM2.5 concentrations are highly non-homogeneous and the spatial variation is not captured even with a 4-km horizontal resolution in model simulations. Low-cost PM sensors can improve the detection of PB impacts and provide useful spatial and temporal information for integration with air quality models. R2 of regression with observations increases from an average of 0.09 to 0.40 when data fusion is applied to model simulation withholding the observations at the evaluation site. Adding observations from low-cost sensors reduces the underestimation of nighttime PM2.5 concentrations and reproduces the peaks that are missed by the simulations. In the future, observations from a dense network of low-cost sensors could be fused with the model simulated PM2.5 fields to provide better estimates of hourly exposures to smoke from PB.Implications: Prescribed burning emissions are a major cause of elevated PM2.5 concentrations, posing a risk to human health. However, their impact cannot be quantified properly due to a dearth of regulatory monitoring sites in certain regions of the United States such as Southwestern Georgia. Low-cost PM sensors can be used as a supplemental measurement tool and provide useful spatial and temporal information for integration with air quality model simulations. In the future, data from a dense network of low-cost sensors could be fused with model simulated PM2.5 fields to provide improved estimates of hourly exposures to smoke from prescribed burning.

Air quality modeling to inform pollution mitigation strategies in a Latin American megacity.

Poor air quality disproportionally impacts cities in low- and middle-income countries. In Bogotá, Colombia, a metropolitan area with over 10 million inhabitants, fine particulate matter (PM2.5) levels regularly exceed air quality guidelines, leading to detrimental effects on health. Although there is public interest to improve the city's air quality, the main sources of PM2.5 pollution have not been clearly identified and the use of modeling for policy development in Bogotá has been limited. Here, we apply a modeling framework based on the Community Multiscale Air Quality Modeling System (CMAQ) to conduct seasonal simulations of air pollution in Bogotá and reveal the emissions sectors with the largest contributions to PM2.5. Based on these results, we project and compare the air quality benefits of potential pollution mitigation strategies focused on these sources. The analysis finds that resuspended dust from unpaved roads is the largest local source of PM2.5 and can contribute over 30% of seasonally-averaged concentration across the city. Vehicles, industrial activity, and unpaved road dust combined are responsible for over 60% of PM2.5 pollution in Bogotá. A scenario analysis shows that paving roads can lead to PM2.5 decreases of nearly 10 µg/m3 by 2030 in some areas of the city, but air quality will deteriorate significantly over others in the absence of additional emissions control measures. Mitigation strategies designed to target the sectors with the largest contributions to PM2.5, including road cleaning systems, controls for industrial point sources, cleaner transportation fuels, and updated vehicle fleets, can largely avert projected increases in concentrations, although the impacts of different approaches vary throughout the city. This study is the first to use a comprehensive model to determine sector contributions to air pollution and inform potential emissions control policies in Bogotá, demonstrating an approach to guide pollution management in developing cities facing comparable challenges.

Investigating Sex Differences in Emotion Recognition, Learning, and Regulation Among Youths With Conduct Disorder.

OBJECTIVE: Conduct disorder (CD) is a serious neurodevelopmental disorder marked by notably higher prevalence rates for boys than girls. Converging evidence suggests that CD is associated with impairments in emotion recognition, learning, and regulation. However, it is not known whether there are sex differences in the relationship between CD and emotion dysfunction. Prior studies on emotion functioning in CD have so far been underpowered for investigating sex differences. Therefore, our primary aim was to characterize emotion processing skills in a large sample of girls and boys with CD compared to typically developing controls (TDCs) using a comprehensive neuropsychological test battery. METHOD: We included 542 youths with CD (317 girls) and 710 TDCs (479 girls), 9 to 18 years of age, from a European multisite study (FemNAT-CD). Participants completed three experimental tasks assessing emotion recognition, learning, and regulation, respectively. Data were analyzed to test for effects of group and sex, and group-by-sex interactions, while controlling for potentially confounding factors. RESULTS: Relative to TDCs, youths with CD showed impaired emotion recognition (that was related to more physical and proactive aggression, and higher CU traits), emotional learning (specifically from punishment), and emotion regulation. Boys and girls with CD, however, displayed similar impairments in emotion processing. CONCLUSION: This study provides compelling evidence for a relationship between CD and deficient neurocognitive functioning across three emotional domains that have previously been linked to CD etiology. However, there was no support for sex-specific profiles of emotion dysfunction, suggesting that current neurocognitive models of CD apply equally to both sexes.

Real-world evidence of the effectiveness of ombitasvir-paritaprevir/r ± dasabuvir ± ribavirin in patients monoinfected with chronic hepatitis C or coinfected with human immunodeficiency virus-1 in Spain.

AIM: We describe the effectiveness and safety of the interferon-free regimen ombitasvir/paritaprevir/ritonavir plus dasabuvir with or without ribavirin (OBV/PTV/r ± DSV ± RBV) in a nationwide representative sample of the hepatitis C virus (HCV) monoinfected and human immunodeficiency virus-1/hepatitis C virus (HIV/HCV) coinfected population in Spain. MATERIAL AND METHODS: Data were collected from patients infected with HCV genotypes 1 or 4, with or without HIV-1 coinfection, treated with OBV/PTV/r ± DSV ± RBV at 61 Spanish sites within the initial implementation year of the first government-driven "National HCV plan." Effectiveness was assessed by sustained virologic response at post-treatment week 12 (SVR12) and compared between monoinfected and coinfected patients using a non-inferiority margin of 5% and a 90% confidence interval (CI). Sociodemographic and clinical characteristics or patients and adverse events (AEs) were also recorded. RESULTS: Overall, 2,408 patients were included in the intention-to-treat analysis: 386 (16%) were patients with HIV/HCV. Patient selection reflected the real distribution of patients treated in each participating region in Spain. From the total population, 96.6% (95% CI, 95.8-97.3%) achieved SVR12. Noninferiority of SVR12 in coinfected patients was met, with a difference between monoinfected and coinfected patients of -2.2% (90% CI, -4.5% - 0.2%). Only genotype 4 was associated with non-response to OBV/PTV/r ± DSV ± RBV treatment (p<0.001) in the multivariate analysis. Overall, 286 patients (11.9%) presented AEs potentially related to OBV/PTV/r ± DSV, whereas 347 (29.0%) presented AEs potentially related to ribavirin and 61 (5.1%) interrupted ribavirin. CONCLUSIONS: Our results confirm that OBV/PTV/r ± DSV ± RBV is effective and generally well tolerated in a representative sample of the HCV monoinfected and HCV/HIV coinfected population in Spain within the experience of a national strategic plan to tackle HCV.

African American Exposure to Prescribed Fire Smoke in Georgia, USA.

Our project examines the association between percent African American and smoke pollution in the form of prescribed burn-sourced, fine particulate matter (PM2.5) in the U.S. state of Georgia for 2018. (1) Background: African Americans constitute 32.4% of Georgia's population, making it the largest racial/ethnic minority group in the state followed by Hispanic Americans at 9.8%. African Americans, Hispanic Americans, and lower wealth groups are more likely than most middle and upper income White Americans to be exposed to environmental pollutants. This is true because racial and ethnic minorities are more likely to live in urban areas where pollution is more concentrated. As a point of departure, we examine PM2.5 concentrations specific to prescribed fire smoke, which typically emanates from fires occurring in rural or peri-urban areas. Two objectives are specified: a) examine the association between percent African American and PM2.5 concentrations at the census tract level for Georgia, and b) identify emitters of PM2.5 concentrations that exceed National Ambient Air Quality Standards (NAAQS) for the 24-h average, i. e., >35 µg/m3. (2) Methods: For the first objective, we estimate a spatial Durbin error model (SDEM) where pollution concentration (PM2.5) estimates for 1683 census tracts are regressed on percent of the human population that is African American or Hispanic; lives in mobile homes; and is employed in agriculture and related occupations. Also included as controls are percent evergreen forest, percent mixed evergreen/deciduous forest, and variables denoting lagged explanatory and error variables, respectively. For the second objective, we merge parcel and prescribed burn permit data to identify landowners who conduct prescribed fires that produce smoke exceeding the NAAQS. (3) Results: Percent African American and mobile home dweller are positively related to PM2.5 concentrations; and government and non-industrial private landowners are the greatest contributors to exceedance levels (4) Conclusions: Reasons for higher PM2.5 concentrations in areas with higher African American and mobile home percent are not clear, although we suspect that neither group is a primary contributor to prescribed burn smoke but rather tend to live proximate to entities, both public and private, that are. Also, non-industrial private landowners who generated prescribed burn smoke exceeding NAAQS are wealthier than others, which suggests that African American and other environmental justice populations are less likely to contribute to exceedance levels in the state.

Effect of Health-Related Uncertainty and Natural Variability on Health Impacts and Cobenefits of Climate Policy.

Climate policy can mitigate health risks attributed to intensifying air pollution under climate change. However, few studies quantify risks of illness and death, examine their contribution to climate policy benefits, or assess their robustness in light of natural climate variability. We employ an integrated modeling framework of the economy, climate, air quality, and human health to quantify the effect of natural variability on U.S. air pollution impacts under future climate and two global policies (2 and 2.5 °C stabilization scenarios) using 150 year ensemble simulations for each scenario in 2050 and 2100. Climate change yields annual premature deaths related to fine particulate matter and ozone (95CI: 25 000-120 000), heart attacks (900-9400), and lost work days (3.6M-4.9M) in 2100. It raises air pollution health risks by 20%, while policies avert these outcomes by 40-50% in 2050 and 70-88% in 2100. Natural variability introduces "climate noise", yielding some annual estimates with negative cobenefits, and others that reach 100% of annual policy costs. This "noise" is three times the magnitude of uncertainty (95CI) in health and economic responses in 2050. Averaging five annual simulations reduces this factor to two, which is still substantially larger than health-related uncertainty. This study quantifies the potential for inaccuracy in climate impacts projected using too few annual simulations.

Effectiveness and safety of elbasvir/grazoprevir therapy in patients with chronic HCV infection: Results from the Spanish HEPA-C real-world cohort.

In randomized controlled trials of patients with chronic HCV infection, elbasvir/grazoprevir (EBR/GZR) demonstrated high cure rates and a good safety profile. This study assessed the effectiveness and safety of EBR/GZR, with and without ribavirin, in a real-world HCV patient cohort. HEPA-C is a collaborative, monitored national registry of HCV patients directed by the Spanish Association for the Study of the Liver and the Networked Biomedical Research Centre for Hepatic and Digestive Diseases. Patients entered into HEPA-C between December 2016 and May 2017, and treated with EBR/GZR with at least end-of-treatment response data, were included. Demographic, clinical and virologic data were analysed, and adverse events (AEs) recorded. A total of 804 patients were included in the study. The majority were male (57.9%), with a mean age of 60 (range, 19-92) years. Genotype (GT) distribution was GT 1, 86.8% (1a, 14.3%; 1b, 72.5%); GT 4, 13.2% and 176 patients (21.9%) were cirrhotic. Overall, among 588 patients with available data, 570 (96.9%) achieved sustained virologic response at 12 weeks post-treatment (SVR12). SVR12 rates by genotype were GT 1a, 97.7%; GT 1b, 98.6%; and GT 4, 98.1%. No significant differences in SVR12 according to fibrosis stage were observed. Eighty patients experienced an AE, resulting in treatment discontinuation in three. In this large cohort of patients with chronic HCV managed in a real-world setting in Spain, EBR/GZR achieved high rates of SVR12, comparable to those observed in randomized controlled trials, with a similarly good safety profile.

Computerized Adaptive Assessment of Organizational Climate / Evaluación adaptativa informatizada del clima organizacional

Computerized Adaptive Tests (CAT) constitutes a major advance in the field of the assessment of social sciences and health. It is interesting to incorporate this methodological development to organizational field. The objective of this research is to develop a CAT for evaluating the organizational climate. In order to do this, a bank of 160 items was constructed, which was administered to a sample of 3,163 workers (M age= 51.90 years; SD= 6.28 years). The results show that the CAT works efficiently for the three item banks used (150, 130 and 50 items). The information function shows that the latent trait in the whole range of values is measured with similar accuracy. The correlation between the scores obtained with the CAT, the three item banks and the CLIOR scale of organizational climate was higher than .90 in all cases (SE< 0.32, the 150 item CAT uses a mean of 22.5 items (SD = 13.5 items). The CAT developed allows an accuracy and useful assessment of the organizational climate on organizational settings and research

Los Tests Adaptativos Informatizados (CAT) constituyen un gran avance en el campo de la evaluación de las ciencias sociales y de la salud. Es interesante incorporar este desarrollo metodológico al campo de la psicología organizacional. El objetivo de esta investigación es desarrollar un CAT para evaluar el clima organizacional. Para ello, se construyó un banco de 160 ítems, que se administró a una muestra de 3163 trabajadores (M = 51,90 años; DT = 6,28 años). Los resultados muestran que el CAT funciona de manera eficiente para los tres bancos de ítems utilizados (150, 130 y 50 ítems). La función de información muestra que el rasgo latente se mide con una precisión similar en todo el rango de valores. La correlación entre las puntuaciones obtenidas con el CAT, los tres bancos de ítems y la escala CLIOR de clima organizacional fue superior a 0,90 en todos los casos (ET < 0,32). Con una precisión fijada en ET < 0,32, el CAT de 150 ítems utiliza una media de 22,5 ítems (DT = 13,5). El CAT desarrollado permite una evaluación útil y precisa del clima organizacional en contextos organizacionales

U.S. Air Quality and Health Benefits from Avoided Climate Change under Greenhouse Gas Mitigation.

We evaluate the impact of climate change on U.S. air quality and health in 2050 and 2100 using a global modeling framework and integrated economic, climate, and air pollution projections. Three internally consistent socioeconomic scenarios are used to value health benefits of greenhouse gas mitigation policies specifically derived from slowing climate change. Our projections suggest that climate change, exclusive of changes in air pollutant emissions, can significantly impact ozone (O3) and fine particulate matter (PM2.5) pollution across the U.S. and increase associated health effects. Climate policy can substantially reduce these impacts, and climate-related air pollution health benefits alone can offset a significant fraction of mitigation costs. We find that in contrast to cobenefits from reductions to coemitted pollutants, the climate-induced air quality benefits of policy increase with time and are largest between 2050 and 2100. Our projections also suggest that increasing climate policy stringency beyond a certain degree may lead to diminishing returns relative to its cost. However, our results indicate that the air quality impacts of climate change are substantial and should be considered by cost-benefit climate policy analyses.

Simulating smoke transport from wildland fires with a regional-scale air quality model: sensitivity to spatiotemporal allocation of fire emissions.

Air quality forecasts generated with chemical transport models can provide valuable information about the potential impacts of fires on pollutant levels. However, significant uncertainties are associated with fire-related emission estimates as well as their distribution on gridded modeling domains. In this study, we explore the sensitivity of fine particulate matter concentrations predicted by a regional-scale air quality model to the spatial and temporal allocation of fire emissions. The assessment was completed by simulating a fire-related smoke episode in which air quality throughout the Atlanta metropolitan area was affected on February 28, 2007. Sensitivity analyses were carried out to evaluate the significance of emission distribution among the model's vertical layers, along the horizontal plane, and into hourly inputs. Predicted PM2.5 concentrations were highly sensitive to emission injection altitude relative to planetary boundary layer height. Simulations were also responsive to the horizontal allocation of fire emissions and their distribution into single or multiple grid cells. Additionally, modeled concentrations were greatly sensitive to the temporal distribution of fire-related emissions. The analyses demonstrate that, in addition to adequate estimates of emitted mass, successfully modeling the impacts of fires on air quality depends on an accurate spatiotemporal allocation of emissions.