"When people see me, they know me; they trust what I say": characterizing the role of trusted sources for smoke risk communication in the Okanogan River Airshed Emphasis Area.

INTRODUCTION: As wildfire smoke events increase in intensity and frequency in the Pacific Northwest, there is a growing need for effective communication on the health risks of smoke exposure. Delivery through a trusted source or intermediary has been shown to improve reception of risk communication messages. This is especially salient in rural and tribal communities who may be hesitant to trust information from state and federal agency sources. This study aims to identify and characterize trusted sources for smoke risk information in the Okanogan River Airshed Emphasis Area (ORAEA), a rural region of North Central Washington state that is heavily impacted by smoke from wildfires and prescribed fire. METHODS: The research team conducted a qualitative study using data collected through key informant interviews and focus groups to assess the role of various sources and intermediaries in disseminating smoke risk information. We used a consensual coding approach in NVivo Qualitative Analysis Software to sort data into preliminary categories, which were grouped into themes using a thematic analysis approach. We used member checking and iterative feedback processes with local project partners throughout the project to ensure credibility of results. RESULTS: Through the analysis, we identified three themes characterizing trusted sources for smoke risk communication in the ORAEA. These themes were: (1) local and tribal sources of information are perceived as more trustworthy than state and federal government sources, (2) trustworthiness is determined by an evaluation of multiple factors, in particular, perceived credibility, quality of information, and relationship with the source, and (3) conservative political ideology and perceived parallels with COVID-19 communication influence perception of trust. Within each theme, we identified several sub-themes, which contributed additional nuance to our analysis. CONCLUSION: This study provides insights into which sources of information are trusted by rural and tribal community members in the ORAEA and why. Results from our study emphasize the importance of relationships and collaboration with local and tribal partners in smoke risk communication. In this paper, we discuss implications for state and federal agency practitioners and present recommendations for how to work with local and tribal partners on smoke risk communication.

Estimating Fuel Moisture in Grasslands Using UAV-Mounted Infrared and Visible Light Sensors.

Predicting wildfire behavior is a complex task that has historically relied on empirical models. Physics-based fire models could improve predictions and have broad applicability, but these models require more detailed inputs, including spatially explicit estimates of fuel characteristics. One of the most critical of these characteristics is fuel moisture. Obtaining moisture measurements with traditional destructive sampling techniques can be prohibitively time-consuming and extremely limited in spatial resolution. This study seeks to assess how effectively moisture in grasses can be estimated using reflectance in six wavelengths in the visible and infrared ranges. One hundred twenty 1 m-square field samples were collected in a western Washington grassland as well as overhead imagery in six wavelengths for the same area. Predictive models of vegetation moisture using existing vegetation indices and components from principal component analysis of the wavelengths were generated and compared. The best model, a linear model based on principal components and biomass, showed modest predictive power (r² = 0.45). This model performed better for the plots with both dominant grass species pooled than it did for each species individually. The presence of this correlation, especially given the limited moisture range of this study, suggests that further research using samples across the entire fire season could potentially produce effective models for estimating moisture in this type of ecosystem using unmanned aerial vehicles, even when more than one major species of grass is present. This approach would be a fast and flexible approach compared to traditional moisture measurements.

Combining Multi-Source Remotely Sensed Data and a Process-Based Model for Forest Aboveground Biomass Updating.

Monitoring and understanding the spatio-temporal variations of forest aboveground biomass (AGB) is a key basis to quantitatively assess the carbon sequestration capacity of a forest ecosystem. To map and update forest AGB in the Greater Khingan Mountains (GKM) of China, this work proposes a physical-based approach. Based on the baseline forest AGB from Landsat Enhanced Thematic Mapper Plus (ETM+) images in 2008, we dynamically updated the annual forest AGB from 2009 to 2012 by adding the annual AGB increment (ABI) obtained from the simulated daily and annual net primary productivity (NPP) using the Boreal Ecosystem Productivity Simulator (BEPS) model. The 2012 result was validated by both field- and aerial laser scanning (ALS)-based AGBs. The predicted forest AGB for 2012 estimated from the process-based model can explain 31% (n = 35, p < 0.05, RMSE = 2.20 kg/m²) and 85% (n = 100, p < 0.01, RMSE = 1.71 kg/m²) of variation in field- and ALS-based forest AGBs, respectively. However, due to the saturation of optical remote sensing-based spectral signals and contribution of understory vegetation, the BEPS-based AGB tended to underestimate/overestimate the AGB for dense/sparse forests. Generally, our results showed that the remotely sensed forest AGB estimates could serve as the initial carbon pool to parameterize the process-based model for NPP simulation, and the combination of the baseline forest AGB and BEPS model could effectively update the spatiotemporal distribution of forest AGB.

Vaccination and mortality from COVID-19: a comparative study between hemodialysis, peritoneal dialysis, and non-dialysis adult population in Panama.

PURPOSE: Dialysis patients have a different response than the non-dialysis population to infection with COVID-19. This study evaluates the prevalence of infection and lethality in patients receiving hemodialysis or peritoneal dialysis in Panama, compared to non-dialysis adult population, and reports of adverse events of vaccination. METHODS: This is a prospective, multi-center cohort study of spatients aged 18 years or older and receiving in-center hemodialysis or ambulatory peritoneal dialysis in 13 centers in Panama from March 2021 to 2022. For comparison with general population, the study used an extended period of two years. RESULTS: A total of 1531 patients receiving dialysis treatment accepted to participate. PD patients represented an 18% of study patients. Lethality was higher in peritoneal dialysis patients with COVID-19 infection than in hemodialysis in the study group (p 0.02). Total deaths in dialysis patients for 2020 were 156 patients, before vaccination; 79 in 2021; and 25 for the first trimester of 2022. Lethality for the period of 2020-2022 was 9.3% for dialysis patients and 0.2% for non-dialysis population. There was no difference in symptoms in first dose, but with second dose, hemodialysis patients reported fewer symptoms than peritoneal dialysis patients (p < 0.0001). CONCLUSION: Ninety one percent of people in the country received BNT162b2 Pfizer BionTech vaccine. Lethality decreased from 30 to 5% once vaccination was available. There were no severe adverse effects and symptoms reported were less frequent than in general population, probably due to low reactogenicity in dialysis patients, or better tolerance to pain.

Wildfire, Smoke Exposure, Human Health, and Environmental Justice Need to be Integrated into Forest Restoration and Management.

PURPOSE OF REVIEW: Increasing wildfire size and severity across the western United States has created an environmental and social crisis that must be approached from a transdisciplinary perspective. Climate change and more than a century of fire exclusion and wildfire suppression have led to contemporary wildfires with more severe environmental impacts and human smoke exposure. Wildfires increase smoke exposure for broad swaths of the US population, though outdoor workers and socially disadvantaged groups with limited adaptive capacity can be disproportionally exposed. Exposure to wildfire smoke is associated with a range of health impacts in children and adults, including exacerbation of existing respiratory diseases such as asthma and chronic obstructive pulmonary disease, worse birth outcomes, and cardiovascular events. Seasonally dry forests in Washington, Oregon, and California can benefit from ecological restoration as a way to adapt forests to climate change and reduce smoke impacts on affected communities. RECENT FINDINGS: Each wildfire season, large smoke events, and their adverse impacts on human health receive considerable attention from both the public and policymakers. The severity of recent wildfire seasons has state and federal governments outlining budgets and prioritizing policies to combat the worsening crisis. This surging attention provides an opportunity to outline the actions needed now to advance research and practice on conservation, economic, environmental justice, and public health interests, as well as the trade-offs that must be considered. Scientists, planners, foresters and fire managers, fire safety, air quality, and public health practitioners must collaboratively work together. This article is the result of a series of transdisciplinary conversations to find common ground and subsequently provide a holistic view of how forest and fire management intersect with human health through the impacts of smoke and articulate the need for an integrated approach to both planning and practice.

A multi-analysis approach for estimating regional health impacts from the 2017 Northern California wildfires.

Smoke impacts from large wildfires are mounting, and the projection is for more such events in the future as the one experienced October 2017 in Northern California, and subsequently in 2018 and 2020. Further, the evidence is growing about the health impacts from these events which are also difficult to simulate. Therefore, we simulated air quality conditions using a suite of remotely-sensed data, surface observational data, chemical transport modeling with WRF-CMAQ, one data fusion, and three machine learning methods to arrive at datasets useful to air quality and health impact analyses. To demonstrate these analyses, we estimated the health impacts from smoke impacts during wildfires in October 8-20, 2017, in Northern California, when over 7 million people were exposed to Unhealthy to Very Unhealthy air quality conditions. We investigated using the 5-min available GOES-16 fire detection data to simulate timing of fire activity to allocate emissions hourly for the WRF-CMAQ system. Interestingly, this approach did not necessarily improve overall results, however it was key to simulating the initial 12-hr explosive fire activity and smoke impacts. To improve these results, we applied one data fusion and three machine learning algorithms. We also had a unique opportunity to evaluate results with temporary monitors deployed specifically for wildfires, and performance was markedly different. For example, at the permanent monitoring locations, the WRF-CMAQ simulations had a Pearson correlation of 0.65, and the data fusion approach improved this (Pearson correlation = 0.95), while at the temporary monitor locations across all cases, the best Pearson correlation was 0.5. Overall, WRF-CMAQ simulations were biased high and the geostatistical methods were biased low. Finally, we applied the optimized PM2.5 exposure estimate in an exposure-response function. Estimated mortality attributable to PM2.5 exposure during the smoke episode was 83 (95% CI: 0, 196) with 47% attributable to wildland fire smoke.Implications: Large wildfires in the United States and in particular California are becoming increasingly common. Associated with these large wildfires are air quality and health impact to millions of people from the smoke. We simulated air quality conditions using a suite of remotely-sensed data, surface observational data, chemical transport modeling, one data fusion, and three machine learning methods to arrive at datasets useful to air quality and health impact analyses from the October 2017 Northern California wildfires. Temporary monitors deployed for the wildfires provided an important model evaluation dataset. Total estimated regional mortality attributable to PM2.5 exposure during the smoke episode was 83 (95% confidence interval: 0, 196) with 47% of these deaths attributable to the wildland fire smoke. This illustrates the profound effect that even a 12-day exposure to wildland fire smoke can have on human health.

Machine Learning-Based Integration of High-Resolution Wildfire Smoke Simulations and Observations for Regional Health Impact Assessment.

Large wildfires are an increasing threat to the western U.S. In the 2017 fire season, extensive wildfires occurred across the Pacific Northwest (PNW). To evaluate public health impacts of wildfire smoke, we integrated numerical simulations and observations for regional fire events during August-September of 2017. A one-way coupled Weather Research and Forecasting and Community Multiscale Air Quality modeling system was used to simulate fire smoke transport and dispersion. To reduce modeling bias in fine particulate matter (PM2.5) and to optimize smoke exposure estimates, we integrated modeling results with the high-resolution Multi-Angle Implementation of Atmospheric Correction satellite aerosol optical depth and the U.S. Environmental Protection Agency AirNow ground-level monitoring PM2.5 concentrations. Three machine learning-based data fusion algorithms were applied: An ordinary multi-linear regression method, a generalized boosting method, and a random forest (RF) method. 10-Fold cross-validation found improved surface PM2.5 estimation after data integration and bias correction, especially with the RF method. Lastly, to assess transient health effects of fire smoke, we applied the optimized high-resolution PM2.5 exposure estimate in a short-term exposure-response function. Total estimated regional mortality attributable to PM2.5 exposure during the smoke episode was 183 (95% confidence interval: 0, 432), with 85% of the PM2.5 pollution and 95% of the consequent multiple-cause mortality contributed by fire emissions. This application demonstrates both the profound health impacts of fire smoke over the PNW and the need for a high-performance fire smoke forecasting and reanalysis system to reduce public health risks of smoke hazards in fire-prone regions.

Anfibios y reptiles del departamento de Ica con comentarios sobre su distribución

En este trabajo se presenta el listado actualizado de anfibios y reptiles de Ica. Las especies son comentadas, acompañadas de mapas de distribución y claves de identificación para reptiles y anfibios. Se recopiló información de registros en colecciones científicas, artículos científicos, informes de agencias gubernamentales de vida silvestre y bases de datos taxonómicas especializadas. Adicionalmente, entre los años 2019 y 2020 se realizaron evaluaciones de campo en zonas con vacíos de información. Se encontraron un total de 22 especies (anfibios 3 y reptiles 19). Los nuevos registros de reptiles para Ica incluyen la lagartija Liolaemus evaristoi y la serpiente Incaspis tachymenoides en la provincia de Chincha a 4200 y 3200 m de altitud respectivamente, la lagartija Stenocercus ornatissimus y la serpiente ciega Epictia tesselata en la provincia de Pisco a 3500 y 2800 m respectivamente. Los nuevos registros de anfibios incluyen a Pleurodema marmoratum en la provincia de Chincha (3900 m) y Telmatobius rimac en las provincias de Chincha y Palpa a 3900 y 2350 m respectivamente. La mayor riqueza y endemismos locales se encontraron en el desierto costero, mientras que, las ampliaciones de distribución y endemismos regionales se registraron en la vertiente occidental de los Andes. También, se observa que las familias Leptotyphlopidae y Viperidae, y los géneros Dicrodon, Stenocercus y Oxyrophus terminan su distribución en la vertiente occidental y la zona costera de los departamentos de Ica y Arequipa sin representantes en el extremo sur del Perú y Chile.

This paper presents the updated list of amphibians and reptiles of Ica. Species are commented, accompanied by distribution maps and identification keys for reptiles and amphibians. Information was collected from scientific collections, scientific articles, governmental wildlife agency reports, and specialized taxonomic database. Additionally, between 2019 and 2020, field surveys were carried out in areas with information gaps. A total of 22 species were found (amphibians 3 and reptiles 19). The new records of reptiles include the Liolaemus evaristoi lizard and the Incaspis tachymenoides snake from Chincha province at 4200 and 3200 m altitude respectively, the Stenocercus ornatissimus lizard and the blind snake Epictia tesselata from Pisco province at 3500 and 2800 m respectively. New amphibian records include Pleurodema marmoratum in Chincha province (3900 m) and Telmatobius rimac in Chincha and Palpa provinces at 3900 and 2350 m respectively. The greatest richness and local endemism were found in the coastal desert, while the expansion of distribution and regional endemism were recorded on the western slope of the Andes. Also, it is observed that the families Leptotyphlopidae and Viperidae, and the genera Dicrodon, Stenocercus and Oxyrophus are finishing their distribution in the western slope and the coastal zone of the departments of Ica and Arequipa without representatives in the extreme south of Peru and Chile.

The Experience of Sexual Stigma and the Increased Risk of Attempted Suicide in Young Brazilian People from Low Socioeconomic Group.

This study was intended to analyze the intersection of experience of sexual stigma low-socioeconomic status, and suicide attempt amongst young Brazilians (11-24 years old). In each of the data collection periods (2004-2006: n = 7185; 2010-2012: n = 2734), participants completed a questionnaire-based instrument. Network analysis provided support for a Minority Stress Model, oriented around whether participants had experienced sexual stigma. Although suicide attempts decreased by 20% for participants who had not experienced sexual stigma, there was a 60% increase for those who had experienced sexual stigma. Of particular note were the increases in rates of reported community and familial physical assault, molestation, and rape for those who had experienced sexual stigma. An analysis of centrality statistics demonstrated that both experiences of this Minority Stress Model were fundamentally different, and that those disparities increased over the time frame observed in this study. At the center of this model, shortest paths statistics exhibited a direct conditioned connection between experiencing sexual stigma and suicide attempts. We discuss the social and historical contexts that contributed to these dynamics, and emphasize the need for policy change.

Infección por COVID-19 en pacientes en Hemodiálisis en Panamá 2020

Antecedentes y objetivo: Los pacientes con Enfermedad Renal Crónica (ERC) en Hemodiálisis son pacientes que tienen condiciones que los hacen pacientes de riesgo para la infección por COVID-19. Los pacientes en hemodiálisis han sido un grupo muy afectado, debido a que no pueden suspender sus tratamientos para mantener el aislamiento domiciliario, lo que aumenta su exposición y riesgo a infección por COVID-19. Para evaluar el comportamiento de la infección por SARS-CoV-2 en los pacientes en hemodiálisis crónica en Panamá, realizamos un estudio prospectivo de los pacientes infectados por COVID-19 en las Unidades de Hemodiálisis de la CSS de todo el país, para determinar las características de los pacientes afectados, los síntomas que presentaron, su evolución clínica y el desenlace de los pacientes infectados. Materiales y Médodos: Realizamos un estudio longitudinal descriptivo prospectivo multicéntrico de los casos positivos que se diagnosticaron desde el 15 de julio hasta el 31 de diciembre de 2020 en las 14 Unidades de Hemodiálisis de la CSS del país.  Resultados y conclusiones: Fueron incluidos un total de 333 pacientes en hemodiálisis con diagnóstico positivo para  infección  por SARS-CoV-2, de un total de 2194 pacientes que realizan hemodiálisis en las Unidades de la Caja de Seguro Social.  El 59.5% de los afectados fueron de sexo masculino.  La edad promedio fue de 56.75 años (DS 15.1 años).  La Tasa de Mortalidad encontrada en nuestro estudio fue de 26%. La incidencia acumulada de COVID-19 en pacientes en Hemodiálisis fue de 16% para el período de estudio del 2020. (provisto por Infomedic International)

Background and objective: Patients with Chronic Kidney Disease (CKD) on Hemodialysis are patients who have conditions that make them patients at risk for COVID-19 infection. Hemodialysis patients have been a highly affected group because they cannot stop their treatments to maintain home isolation, which increases their exposure and risk of COVID-19 infection. To evaluate the behavior of SARS-CoV-2 infection in patients on chronic hemodialysis in Panama, we conducted a prospective study of patients infected by COVID-19 in hemodialysis units throughout the country, to determine the characteristics of the affected patients, the symptoms they presented, their clinical evolution and the outcome of the infected patients. Materials and Methods: We conducted a multicenter prospective descriptive longitudinal study of the positive cases that were diagnosed from July 15, 2020 to December 31, 2020 in the 14 Hemodialysis Units of the Social Security Fund of the country. Results and conclusions: A total of 333 hemodialysis patients with a positive diagnosis for SARS-CoV-2 infection were included, out of a total of 2194 patients undergoing hemodialysis in the Units of the Social Security Fund. Fifty nine percet of those affected were male.  The mean age was 56.75 years (DS 15.1 years).  The Mortality Rate found in our study was 26%. The cumulative incidence of COVID-19 in hemodialysis patients was 16% for the 2020 study period. (provided by Infomedic International)