Effect modification of the association between fine particulate air pollution during a wildfire event and respiratory health by area-level measures of socio-economic status, race/ethnicity, and smoking prevalence

Fine particulate air pollution (PM2.5) is decreasing in most areas of the United States, except for areas most affected by wildfires, where increasing trends in PM2.5 can be attributed to wildfire smoke. The frequency and duration of large wildfires and the length of the wildfire season have all increased in recent decades, partially due to climate change, and wildfire risk is projected to increase further in many regions including the western United States. Increasingly, empirical evidence suggests differential health effects from air pollution by class and race; however, few studies have investigated such differential health impacts from air pollution during a wildfire event. We investigated differential risk of respiratory health impacts during the 2008 northern California wildfires by a comprehensive list of socio-economic status (SES), race/ethnicity, and smoking prevalence variables. Regardless of SES level across nine measures of SES, we found significant associations between PM2.5 and asthma hospitalizations and emergency department (ED) visits during these wildfires. Differential respiratory health risk was found by SES for ED visits for chronic obstructive pulmonary disease where the highest risks were in ZIP codes with the lowest SES levels. Findings for differential effects by race/ethnicity were less consistent across health outcomes. We found that ZIP codes with higher prevalence of smokers had greater risk of ED visits for asthma and pneumonia. Our study suggests that public health efforts to decrease exposures to high levels of air pollution during wildfires should focus on lower SES communities.

Geography, rurality, and community distress: deaths due to suicide, alcohol-use, and drug-use among Colorado Veterans

BACKGROUND

In the USA, deaths due to suicide, alcohol, or drug-related causes (e.g., alcohol-related liver disease, overdose) have doubled since 2002. Veterans appear disproportionately impacted by growing trends. Limited research has been conducted regarding the relationship between community-level factors (e.g., rurality, community distress resulting from economic conditions) and the presence of spatial clustering of suicide, alcohol-related, or drug-related deaths. We explored community-level relationships in Colorado Veterans and compared suicide, alcohol-, and drug-related death rates between the Colorado adult population and Veterans.

METHODS

2009-2020 suicide, alcohol-related, and/or drug-related deaths were identified using qualifying multiple cause-of-death International Classification of Disease (ICD)-10 codes in CDC WONDER for the general adult population and Colorado death data for Veteran populations. Age and race adjusted rates were calculated to compare risk overall and by mortality type (i.e., suicide, alcohol-related, drug-related). In Veteran decedents, age-adjusted rates were stratified by rurality and community distress, measured by the Distressed Communities Index. Standardized mortality ratios were calculated to measure spatial autocorrelation and identify clusters using global and local Moran's I, respectively.

RESULTS

6.4% of Colorado Veteran deaths (n = 6948) were identified as being related to suicide, alcohol, or drugs. Compared to rates in the general population of Colorado adults, Veterans had 1.8 times higher rates of such deaths overall (2.1 times higher for suicide, 1.8 times higher for alcohol-related, 1.3 times higher for drug-related). Among Veterans, community distress was associated with an increased risk of alcohol-related [age-adjusted rate per 100,000 (95% CI) = 129.6 (89.9-193.1)] and drug-related deaths [95.0 (48.6-172.0)]. This same significant association was not identified among those that died by suicide. Rurality was not associated with risk for any of the deaths of interest. There was significant spatial clustering for alcohol-related deaths in southeast Colorado.

CONCLUSIONS

Colorado Veterans have higher rates of deaths due to suicide, alcohol-related, and drug-related causes compared to members of the general adult population. Upstream prevention efforts, such as community-based interventions targeting alcohol-use and community economic distress, are warranted. More research is also needed to understand how community distress and other social determinants of health impact the community burden of suicide, alcohol-related, and drug-related mortality.

Evaluation of Model-Based PM2.5 Estimates for Exposure Assessment during Wildfire Smoke Episodes in the Western U.S

Investigating the health impacts of wildfire smoke requires data on people's exposure to fine particulate matter (PM2.5) across space and time. In recent years, it has become common to use machine learning models to fill gaps in monitoring data. However, it remains unclear how well these models are able to capture spikes in PM2.5 during and across wildfire events. Here, we evaluate the accuracy of two sets of high-coverage and high-resolution machine learning-derived PM2.5 data sets created by Di et al. and Reid et al. In general, the Reid estimates are more accurate than the Di estimates when compared to independent validation data from mobile smoke monitors deployed by the US Forest Service. However, both models tend to severely under-predict PM2.5 on high-pollution days. Our findings complement other recent studies calling for increased air pollution monitoring in the western US and support the inclusion of wildfire-specific monitoring observations and predictor variables in model-based estimates of PM2.5. Lastly, we call for more rigorous error quantification of machine-learning derived exposure data sets, with special attention to extreme events.

Socio-demographic and health vulnerability in prescribed-burn exposed versus unexposed counties near the National Forest System

Prescribed fire is an increasingly important tool in restoring ecological conditions and reducing uncontrolled wildfire. Prescribed burn techniques could reduce public health impacts associated with wildfire smoke exposure. However, there have been few assessments of the health impacts of prescribed burning, and potential vulnerabilities among populations exposed to smoke from prescribed fires. Our study area focused on counties in and near U.S. National Forests - a set of lands distributed across the U.S. In county-level analyses, we compared the sociodemographic and health characteristics of areas that were exposed with those that were not exposed to prescribe burns during the years 2010-2019 on a national level and within three regions. In addition, using spatial error regression models, we looked for associations between prescribed fire exposure and health behaviors and outcomes while controlling for spatial autocorrelation. On a national level, we found disproportionate prescribed fire exposure in rural counties with higher percentage mobile home and vacant housing units, and higher percentage African-American and white populations. Regionally, we found evidence of disproportionate exposure to prescribed burns among counties with lower percentage white population, higher percentage Hispanic population and mobile homes in the southern region, and to high poverty counties with high vacancy in the western region. These findings could indicate that vulnerable populations face potential health risks from prescribed burning smoke exposure, but also that they are not missing out on the benefits of prescribed burning, which could involve considerably lower smoke exposure compared to uncontrolled wildfire. In addition, in regression analyses, we found no evidence of disproportionate health burden in exposed compared to unexposed counties. Awareness of these patterns could influence both large-scale or institutional polices about prescribed burning practice, and could be used to build decision-making factors into modeling tools and smoke management plans, as well as community-engagement around wildfire risk reduction.

Assessment of Rural-Urban and Geospatial Differences in Perceived Handgun Access and Reported Suicidality Among Youth in Colorado

IMPORTANCE

Suicide is the second leading cause of death in adolescents, with firearms the most common method, especially in rural communities. Identifying where to target lethal means safety interventions could better leverage limited resources.

OBJECTIVES

To understand the associations of rurality, school-level prevalence of easy handgun access, and suicidality measures in Colorado youth, to explore spatial distribution of school-level measures, and to identify communities with high prevalence of both easy handgun access and suicidality.

DESIGN, SETTING, AND PARTICIPANTS

This cross-sectional study used data from the 2019 Healthy Kids Colorado Survey, an anonymous cross-sectional school-based survey conducted at 256 participating Colorado high schools. Participants included students from schools recruited for statewide population-based estimates and additional schools opting in. Data were analyzed from November 9, 2020, to March 13, 2021.

EXPOSURES

Urban-centric locale according to a 7-level continuum. Geocoded location of schools was used for spatial analysis.

MAIN OUTCOMES AND MEASURES

The main outcomes were weighted prevalence for easy handgun access and 4 measures of mental health and suicidality in the previous year (ie, feeling sad for 2 weeks and considering suicide, planning suicide attempt, or attempting suicide in the past year).

RESULTS

A total of 59 556 students (49.7% [95% CI, 49.3%-50.1%] male and 50.3% [95% CI, 49.9%-50.7%] female; 53.9% [95% CI, 53.5%-54.3%] in 9th and 10th grade; 36.4% [95% CI, 36.0%-36.8%] Hispanic and 50.8% [95% CI, 50.4%-51.2%] non-Hispanic White) from 256 schools participated. Most schools were rural or in small towns (56.8% [95% CI, 50.7%-62.9%]), while more students participated from urban and suburban schools (57.8% [95% CI, 57.6%-58.0%]). Prevalence of perceived easy access to handguns increased with increasing rurality, with 36.2% (95% CI, 35.2%-37.1%) of students in rural (remote) schools reporting easy access, compared with 18.2% (95% CI, 17.3%-19.1%) for city (large) schools. The spatial distribution of easy handgun access and suicidality measures had minimal overlap, but there was correlation at school-level between easy handgun access and considering suicide (ρ = 0.203 [95% CI, 0.0748-0.331]), planning suicide (ρ = 0.300 [95% CI, 0.173-0.427]), and attempting suicide (ρ = 0.218 [95% CI, 0.0869-0.350) in the previous year. The highest quartile for prevalence of both perceived easy access to handguns and planning suicide in the previous year included 21 schools (81.0% [95% CI, 64.0%-97.9%] rural [remote] or rural [distant]).

CONCLUSIONS AND RELEVANCE

These findings suggest that rural-remote communities in Colorado may benefit most from interventions focused on limiting youth access to handguns when youth are in crisis, with some communities at especially high risk. Spatially referenced data may improve targeting interventions to where they are needed most.

Green infrastructure can limit but not solve air pollution injustice

Outdoor air pollution contributes to millions of deaths worldwide yet air pollution has differential exposures across racial/ethnic groups and socioeconomic status. While green infrastructure has the potential to decrease air pollution and provide other benefits to human health, vegetation alone cannot resolve health disparities related to air pollution injustice. We discuss how unequal access to green infrastructure can limit air quality improvements for marginalized communities and provide strategies to move forward.

Social Susceptibility to Multiple Air Pollutants in Cardiovascular Disease

INTRODUCTION

Cardiovascular disease (CVD) is the leading cause of death in the United States, and substantial research has linked ambient air pollution to elevated rates of CVD etiology and events. Much of this research identified increased effects of air pollution in lower socioeconomic position (SEP) communities, where pollution exposures are also often higher. The complex spatial confounding between air pollution and SEP makes it very challenging, however, to disentangle the impacts of these very different exposure types and to accurately assess their interactions. The specific causal components (i.e., specific social stressors) underlying this SEP-related susceptibility remain unknown, because there are myriad pathways through which poverty and/or lower-SEP conditions may influence pollution susceptibility - including diet, smoking, coexposures in the home and occupational environments, health behaviors, and healthcare access. Growing evidence suggests that a substantial portion of SEP-related susceptibility may be due to chronic psychosocial stress - given the known wide-ranging impacts of chronic stress on immune, endocrine, and metabolic function - and to a higher prevalence of unpredictable chronic stressors in many lower-SEP communities, including violence, job insecurity, and housing instability. As such, elucidating susceptibility to pollution in the etiology of CVD, and in the risk of CVD events, has been identified as a research priority. This interplay among social and environmental conditions may be particularly relevant for CVD, because pollution and chronic stress both impact inflammation, metabolic function, oxidative stress, hypertension, atherosclerosis, and other processes relevant to CVD etiology. Because pollution exposures are often spatially patterned by SEP, disentangling their effects - and quantifying any interplay - is especially challenging. Doing so, however, would help to improve our ability to identify and characterize susceptible populations and to improve our understanding of how community stressors may alter responses to multiple air pollutants. More clearly characterizing susceptible populations will improve our ability to design and target interventions more effectively (and cost-effectively) and may reveal greater benefits of pollution reduction in susceptible communities, strengthening cost-benefit and accountability analyses, ultimately reducing the disproportionate burden of CVD and reducing health disparities.

METHODS

In the current study, we aimed to quantify combined effects of multiple pollutants and stressor exposures on CVD events, using a number of unique datasets we have compiled and verified, including the following. 1. Poverty metrics, violent crime rates, a composite socioeconomic deprivation index (SDI), an index of racial and economic segregation, noise disturbance metrics, and three composite spatial factors produced from a factor analysis of 27 community stressors. All indicators have citywide coverage and were verified against individual reports of stress and stressor exposure, in citywide focus groups and surveys. 2. Spatial surfaces for multiple pollutants from the New York City (NYC) Community Air Survey (NYCCAS), which monitored multiple pollutants year-round at 150 sites and used land use regression (LUR) modeling to estimate fine-scale (100-m) intra-urban spatial variance in fine particles (PM2.5), nitrogen dioxide (NO2), sulfur dioxide (SO2), and ozone (O3). 3. Daily data and time-trends derived from all U.S. Environmental Protection Agency (EPA) Air Quality System (AQS) monitors in NYC for 2005-2011, which we combined with NYCCAS surfaces to create residence- and day-specific spatiotemporal exposure estimates. 4. Complete data on in- and out-patient unscheduled CVD events presented in NYC hospitals for 2005-2011 (n = 1,113,185) from the New York State (NYS) Department of Health's Statewide Planning and Research Cooperative System (SPARCS). In the study, we quantified relationships between multiple pollutant exposures and both community CVD event rates and individual risk of CVD events in NYC and tested whether pollution-CVD associations varied by community SEP and social stressor exposures. We hypothesized (1) that greater chronic community-level SEP, stressor, and pollution exposures would be associated with higher community CVD rates; (2) that spatiotemporal variations in multiple pollutants would be associated with excess risk of CVD events; and (3) that pollution-CVD associations would be stronger in communities of lower SEP or higher stressor exposures.

RESULTS

To first understand the separate and combined associations with CVD for both stressors and pollutants measured at the same spatial and temporal scale of resolution, we used ecological cross-sectional models to examine spatial relationships between multiple chronic pollutant and stressor exposures and age-adjusted community CVD rates. Using census-tract-level annual averages (n = 2,167), we compared associations with CVD rates for multiple pollutant concentrations and social stressors. We found that associations with community CVD rates were consistently stronger for social stressors than for pollutants, in terms of both magnitude and significance. We note, however, that this result may be driven by the relatively greater variation (on a proportional basis) for stressors than for pollutants in NYC. We also tested effect modification of pollutant-CVD associations by each social stressor and found evidence of stronger associations for NO2, PM2.5, and wintertime SO2 with CVD rates, particularly across quintiles of increasing community violence or assault rates (P trend < 0.0001). To examine individual-level associations between spatiotemporal exposures to multiple pollutants and the risk of CVD events, across multiple lag days, we examined the combined effects of multiple pollutant exposures, using spatiotemporal (day- and residence-specific) pollution exposure estimates and hospital data on individual CVD events in case-crossover models, which inherently adjust for nontime-varying individual confounders (e.g., sex and race) and comorbidities. We found consistent significant relationships only for same-day pollutant exposures and the risk of CVD events, suggesting very acute impacts of pollution on CVD risk. Associations with CVD were positive for NO2, PM2.5, and SO2, as hypothesized, and we found inverse associations for O3 (a secondary pollutant chemically decreased ["scavenged"] by fresh emissions that, in NYC, displays spatial and temporal patterns opposite those of NO2). Finally, to test effect modification by chronic community social stressors on the relationships between spatiotemporal pollution measures and the risk of CVD events, we used individual-level case-crossover models, adding interaction terms with categorical versions of each social stressor. We found that associations between NO2 and the risk of CVD events were significantly elevated only in communities with the highest exposures to social stressors (i.e., in the highest quintiles of poverty, socioeconomic deprivation, violence, or assault). The largest positive associations for PM2.5 and winter SO2 were generally found in the highest-stressor communities but were not significant in any quintile. We again found inverse associations for O3, which were likewise stronger for individuals living in communities with greater stressor exposures.

CONCLUSIONS

In ecological models, we found stronger relationships with community CVD rates for social stressors than for pollutant exposures. In case-crossover analyses, higher exposures to NO2, PM2.5, and SO2 were associated with greater excess risk of CVD events but only on the case day (there were no consistent significant lagged-day effects). In effect-modification analyses at both the community and individual level, we found evidence of stronger pollution-CVD associations in communities with higher stressor exposures. Given substantial spatial confounding across multiple social stressors, further research is needed to disentangle these effects in order to identify the predominant social stressors driving this observed differential susceptibility.

Daily PM2.5 concentration estimates by county, ZIP code, and census tract in 11 western states 2008-2018

We created daily concentration estimates for fine particulate matter (PM2.5) at the centroids of each county, ZIP code, and census tract across the western US, from 2008-2018. These estimates are predictions from ensemble machine learning models trained on 24-hour PM2.5 measurements from monitoring station data across 11 states in the western US. Predictor variables were derived from satellite, land cover, chemical transport model (just for the 2008-2016 model), and meteorological data. Ten-fold spatial and random CV R2 were 0.66 and 0.73, respectively, for the 2008-2016 model and 0.58 and 0.72, respectively for the 2008-2018 model. Comparing areal predictions to nearby monitored observations demonstrated overall R2 of 0.70 for the 2008-2016 model and 0.58 for the 2008-2018 model, but we observed higher R2 (>0.80) in many urban areas. These data can be used to understand spatiotemporal patterns of, exposures to, and health impacts of PM2.5 in the western US, where PM2.5 levels have been heavily impacted by wildfire smoke over this time period.

Extreme Weather and Climate Change: Population Health and Health System Implications

Extreme weather and climate events, such as heat waves, cyclones, and floods, are an expression of climate variability. These events and events influenced by climate change, such as wildfires, continue to cause significant human morbidity and mortality and adversely affect mental health and well-being. Although adverse health impacts from extreme events declined over the past few decades, climate change and more people moving into harm's way could alter this trend. Long-term changes to Earth's energy balance are increasing the frequency and intensity of many extreme events and the probability of compound events, with trends projected to accelerate under certain greenhouse gas emissions scenarios. While most of these events cannot be completely avoided, many of the health risks could be prevented through building climate-resilient health systems with improved risk reduction, preparation, response, and recovery. Conducting vulnerability and adaptation assessments and developing health system adaptation plans can identify priority actions to effectively reduce risks, such as disaster risk management and more resilient infrastructure. The risks are urgent, so action is needed now.

Wildland firefighter exposure to smoke and COVID-19: A new risk on the fire line

Throughout the United States, wildland firefighters respond to wildfires, performing arduous work in remote locations. Wildfire incidents can be an ideal environment for the transmission of infectious diseases, particularly for wildland firefighters who congregate in work and living settings. In this review, we examine how exposure to wildfire smoke can contribute to an increased likelihood of SARS-CoV-2 infection and severity of coronavirus disease (COVID-19). Human exposure to particulate matter (PM), a component of wildfire smoke, has been associated with oxidative stress and inflammatory responses; increasing the likelihood for adverse respiratory symptomology and pathology. In multiple epidemiological studies, wildfire smoke exposure has been associated with acute lower respiratory infections, such as bronchitis and pneumonia. Co-occurrence of SARS-CoV-2 infection and wildfire smoke inhalation may present an increased risk for COVID-19 illness in wildland firefighters due to PM based transport of SARS CoV-2 virus and up-regulation of angiotensin-converting enzyme II (ACE-2) (i.e. ACE-2 functions as a trans-membrane receptor, allowing the SARS-CoV-2 virus to gain entry into the epithelial cell). Wildfire smoke exposure may also increase risk for more severe COVID-19 illness such as cytokine release syndrome, hypotension, and acute respiratory distress syndrome (ARDS). Current infection control measures, including social distancing, wearing cloth masks, frequent cleaning and disinfecting of surfaces, frequent hand washing, and daily screening for COVID-19 symptoms are very important measures to reduce infections and severe health outcomes. Exposure to wildfire smoke may introduce additive or even multiplicative risk for SARS-CoV-2 infection and severity of disease in wildland firefighters. Thus, additional mitigative measures may be needed to prevent the co-occurrence of wildfire smoke exposure and SARS-CoV-2 infection.

Improving accuracy of air pollution exposure measurements: Statistical correction of a municipal low-cost airborne particulate matter sensor network

Low-cost air quality sensors can help increase spatial and temporal resolution of air pollution exposure measurements. These sensors, however, most often produce data of lower accuracy than higher-end instruments. In this study, we investigated linear and random forest models to correct PM_2.5 measurements from the Denver Department of Public Health and Environment (DDPHE)'s network of low-cost sensors against measurements from co-located U.S. Environmental Protection Agency Federal Equivalence Method (FEM) monitors. Our training set included data from five DDPHE sensors from August 2018 through May 2019. Our testing set included data from two newly deployed DDPHE sensors from September 2019 through mid-December 2019. In addition to PM_2.5, temperature, and relative humidity from the low-cost sensors, we explored using additional temporal and spatial variables to capture unexplained variability in sensor measurements. We evaluated results using spatial and temporal cross-validation techniques. For the long-term dataset, a random forest model with all time-varying covariates and length of arterial roads within 500 m was the most accurate (testing RMSE = 2.9 μg/m³ and R² = 0.75; leave-one-location-out (LOLO)-validation metrics on the training set: RMSE = 2.2 μg/m³ and R² = 0.93). For on-the-fly correction, we found that a multiple linear regression model using the past eight weeks of low-cost sensor PM_2.5, temperature, and humidity data plus a near-highway indicator predicted each new week of data best (testing RMSE = 3.1 μg/m³ and R² = 0.78; LOLO-validation metrics on the training set: RMSE = 2.3 μg/m³ and R² = 0.90). The statistical methods detailed here will be used to correct low-cost sensor measurements to better understand PM_2.5 pollution within the city of Denver. This work can also guide similar implementations in other municipalities by highlighting the improved accuracy from inclusion of variables other than temperature and relative humidity to improve accuracy of low-cost sensor PM_2.5 data.

Hurricane Harvey Hospital Flood Impacts: Accuracy of Federal Emergency Management Agency Flood Hazard Areas in Harris County, Texas

Objectives. To compare the flood impacts experienced by Harris County, Texas, hospitals with Federal Emergency Management Agency (FEMA) flood hazard areas and Hurricane Harvey's inundation boundary.Methods. One year following Hurricane Harvey, we created a novel data set of Hurricane Harvey's flood impacts in Harris County hospitals. We then mapped the hospital flood impact data in ArcGIS alongside FEMA flood hazard areas and Hurricane Harvey's inundation boundary to classify each hospital's location in high flood-risk areas and in areas purportedly affected by Hurricane Harvey.Results. Of the 66 hospitals for which flood impact information was ascertained, 16 (24%) hospitals experienced flood impacts during Hurricane Harvey. Of these 16 hospitals, 5 (31%) were located outside a FEMA flood hazard area and 8 (50%) were located outside Hurricane Harvey's inundation boundary.Conclusions. FEMA flood hazard areas did not accurately predict all areas of Harris County, Texas, that flooded during Hurricane Harvey or which hospitals experienced flood impacts.

Putting Co-Exposures on Equal Footing: An Ecological Analysis of Same-Scale Measures of Air Pollution and Social Factors on Cardiovascular Disease in New York City

Epidemiologic evidence consistently links urban air pollution exposures to health, even after adjustment for potential spatial confounding by socioeconomic position (SEP), given concerns that air pollution sources may be clustered in and around lower-SEP communities. SEP, however, is often measured with less spatial and temporal resolution than are air pollution exposures (i.e., census-tract socio-demographics vs. fine-scale spatio-temporal air pollution models). Although many questions remain regarding the most appropriate, meaningful scales for the measurement and evaluation of each type of exposure, we aimed to compare associations for multiple air pollutants and social factors against cardiovascular disease (CVD) event rates, with each exposure measured at equal spatial and temporal resolution. We found that, in multivariable census-tract-level models including both types of exposures, most pollutant-CVD associations were non-significant, while most social factors retained significance. Similarly, the magnitude of association was higher for an IQR-range difference in the social factors than in pollutant concentrations. We found that when offered equal spatial and temporal resolution, CVD was more strongly associated with social factors than with air pollutant exposures in census-tract-level analyses in New York City.

Machine learning models accurately predict ozone exposure during wildfire events

Epidemiologists use prediction models to downscale (i.e., interpolate) air pollution exposure where monitoring data is insufficient. This study compares machine learning prediction models for ground-level ozone during wildfires, evaluating the predictive accuracy of ten algorithms on the daily 8-hour maximum average ozone during a 2008 wildfire event in northern California. Models were evaluated using a leave-one-location-out cross-validation (LOLO CV) procedure to account for the spatial and temporal dependence of the data and produce more realistic estimates of prediction error. LOLO CV avoids both the well-known overly optimistic bias of k-fold cross-validation on dependent data and the conservative bias of evaluating prediction error over a coarser spatial resolution via leave-k-locations-out CV. Gradient boosting was the most accurate of the ten machine learning algorithms with the lowest LOLO CV estimated root mean square error (0.228) and the highest LOLO CV Rˆ² (0.677). Random forest was the second best performing algorithm with an LOLO CV Rˆ² of 0.661. The LOLO CV estimates of predictive accuracy were less optimistic than 10-fold CV estimates for all ten models. The difference in estimated accuracy between the 10-fold CV and LOLO CV was greater for more flexible models like gradient boosting and random forest. The order of estimated model accuracy depended on the choice of evaluation metric, indicating that 10-fold CV and LOLO CV may select different models or sets of covariates as optimal, which calls into question the reliability of 10-fold CV for model (or variable) selection. These prediction models are designed for interpolating ozone exposure, and are not suited to inferring the effect of wildfires on ozone or extrapolating to predict ozone in other spatial or temporal domains. This is demonstrated by the inability of the best performing models to accurately predict ozone during 2007 southern California wildfires.

Prevalence and characterisation of wounds in sheep attributed to attacks by kea (Nestor notabilis) on high country farms in New Zealand

Aims: To characterise and classify wounds in sheep suspected to have been caused by attacks by kea (Nestor notabilis) (kea strike), and to report the prevalence of these wounds on five high country farms in the South Island of New Zealand.Methods: Data were collected from farms between 28 August 2012 and 20 September 2013. Sheep were examined opportunistically immediately after shearing for signs of wounds caused by kea. The age and sex of sheep were also recorded. Wounds were measured and characterised as recent, healing, or healed, and the estimated true prevalence was calculated for each farm.Results: Injuries consistent with kea strike wounds were identified in 70/13,978 (0.5%) sheep examined. The estimated true prevalence varied between farms, from 0 (95% CI = 0-0.16) to 1.25 (95% CI = 0.97-1.61)%. Of the 76 wounds identified, 61 (80%) were located in the lumbar region, and 74 (97%) consisted of full-thickness ulceration of the skin, one showed evidence of injury to muscle and one to bone. The median length of the 63 wounds measured was 6 (min 1, max 23.5) cm, and 10/63 (13%) were categorised as recently healed, 47/63 (62%) as healing, and 17/63 (22%) as recent wounds.Conclusions: The results of this study show that kea strike on sheep was occurring at a low prevalence on the high country farms surveyed. The wounds identified were survivable, but the welfare impact of kea strike on sheep should be considered in balance with the conservation status of kea. There was clear variation in the prevalence of wounds attributed to kea strike between the farms but we were not able to identify the risk factors contributing to these differences. Future studies of kea strike should examine variables such as altitude, local kea density and distribution, and differences in kea strike management and husbandry practices, and should include high country farms without a history of kea strike.

Associations between respiratory health and ozone and fine particulate matter during a wildfire event

Wildfires have been increasing in frequency in the western United States (US) with the 2017 and 2018 fire seasons experiencing some of the worst wildfires in terms of suppression costs and air pollution that the western US has seen. Although growing evidence suggests respiratory exacerbations from elevated fine particulate matter (PM_2.5) during wildfires, significantly less is known about the impacts on human health of ozone (O₃) that may also be increased due to wildfires. Using machine learning, we created daily surface concentration maps for PM_2.5 and O₃ during an intense wildfire in California in 2008. We then linked these daily exposures to counts of respiratory hospitalizations and emergency department visits at the ZIP code level. We calculated relative risks of respiratory health outcomes using Poisson generalized estimating equations models for each exposure in separate and mutually-adjusted models, additionally adjusted for pertinent covariates. During the active fire periods, PM_2.5 was significantly associated with exacerbations of asthma and chronic obstructive pulmonary disease (COPD) and these effects remained after controlling for O₃. Effect estimates of O₃ during the fire period were non-significant for respiratory hospitalizations but were significant for ED visits for asthma (RR = 1.05 and 95% CI = (1.022, 1.078) for a 10 ppb increase in O₃). In mutually-adjusted models, the significant findings for PM_2.5 remained whereas the associations with O₃ were confounded. Adjusted for O₃, the RR for asthma ED visits associated with a 10 μg/m³ increase in PM_2.5 was 1.112 and 95% CI = (1.087, 1.138). The significant findings for PM_2.5 but not for O₃ in mutually-adjusted models is likely due to the fact that PM_2.5 levels during these fires exceeded the 24-hour National Ambient Air Quality Standard (NAAQS) of 35 μg/m³ for 4976 ZIP-code days and reached levels up to 6.073 times the NAAQS, whereas our estimated O₃ levels during the fire period only occasionally exceeded the NAAQS of 70 ppb with low exceedance levels. Future studies should continue to investigate the combined role of O₃ and PM_2.5 during wildfires to get a more comprehensive assessment of the cumulative burden on health from wildfire smoke.

Wildfire smoke exposure under climate change: impact on respiratory health of affected communities

PURPOSE OF REVIEW

In this review, we describe the current status of the literature regarding respiratory health related to wildfire smoke exposure, anticipated future impacts under a changing climate, and strategies to reduce respiratory health impacts of wildfire smoke.

RECENT FINDINGS

Recent findings confirm associations between wildfire smoke exposure and respiratory health outcomes, with the clearest evidence for exacerbations of asthma. Although previous evidence showed a clear association between wildfire smoke and chronic obstructive pulmonary disease, findings from recent studies are more mixed. Current evidence in support of an association between respiratory infections and wildfire smoke exposure is also mixed. Only one study has investigated long-term respiratory health impacts of wildfire smoke, and few studies have estimated future health impacts of wildfires under likely climate change scenarios.

SUMMARY

Wildfire activity has been increasing over the past several decades and is likely to continue to do so as climate change progresses, which, combined with a growing population, means that population exposure to and respiratory health impacts of wildfire smoke is likely to grow in the future. More research is needed to understand which population subgroups are most vulnerable to wildfire smoke exposure and the long-term respiratory health impacts of these high pollution events.

Is All Urban Green Space the Same? A Comparison of the Health Benefits of Trees and Grass in New York City

Living near vegetation, often called “green space” or “greenness”, has been associated with numerous health benefits. We hypothesized that the two key components of urban vegetation, trees and grass, may differentially affect health. We estimated the association between near-residence trees, grass, and total vegetation (from the 2010 High Resolution Land Cover dataset for New York City (NYC)) with self-reported health from a survey of NYC adults (n = 1281). We found higher reporting of “very good” or “excellent” health for respondents with the highest, compared to the lowest, quartiles of tree (RR = 1.23, 95% CI = 1.06–1.44) but not grass density (relative risk (RR) = 1.00, 95% CI = 0.86–1.17) within 1000 m buffers, adjusting for pertinent confounders. Significant positive associations between trees and self-reported health remained after adjustment for grass, whereas associations with grass remained non-significant. Adjustment for air pollutants increased beneficial associations between trees and self-reported health; adjustment for parks only partially attenuated these effects. Results were null or negative using a 300 m buffer. Findings imply that higher exposure to vegetation, particularly trees outside of parks, may be associated with better health. If replicated, this may suggest that urban street tree planting may improve population health.

Differential respiratory health effects from the 2008 northern California wildfires: A spatiotemporal approach

We investigated health effects associated with fine particulate matter during a long-lived, large wildfire complex in northern California in the summer of 2008. We estimated exposure to PM2.5 for each day using an exposure prediction model created through data-adaptive machine learning methods from a large set of spatiotemporal data sets. We then used Poisson generalized estimating equations to calculate the effect of exposure to 24-hour average PM2.5 on cardiovascular and respiratory hospitalizations and ED visits. We further assessed effect modification by sex, age, and area-level socioeconomic status (SES). We observed a linear increase in risk for asthma hospitalizations (RR=1.07, 95% CI=(1.05, 1.10) per 5µg/m(3) increase) and asthma ED visits (RR=1.06, 95% CI=(1.05, 1.07) per 5µg/m(3) increase) with increasing PM2.5 during the wildfires. ED visits for chronic obstructive pulmonary disease (COPD) were associated with PM2.5 during the fires (RR=1.02 (95% CI=(1.01, 1.04) per 5µg/m(3) increase) and this effect was significantly different from that found before the fires but not after. We did not find consistent effects of wildfire smoke on other health outcomes. The effect of PM2.5 during the wildfire period was more pronounced in women compared to men and in adults, ages 20-64, compared to children and adults 65 or older. We also found some effect modification by area-level median income for respiratory ED visits during the wildfires, with the highest effects observed in the ZIP codes with the lowest median income. Using a novel spatiotemporal exposure model, we found some evidence of differential susceptibility to exposure to wildfire smoke.

Critical Review of Health Impacts of Wildfire Smoke Exposure

BACKGROUND

Wildfire activity is predicted to increase in many parts of the world due to changes in temperature and precipitation patterns from global climate change. Wildfire smoke contains numerous hazardous air pollutants and many studies have documented population health effects from this exposure.

OBJECTIVES

We aimed to assess the evidence of health effects from exposure to wildfire smoke and to identify susceptible populations.

METHODS

We reviewed the scientific literature for studies of wildfire smoke exposure on mortality and on respiratory, cardiovascular, mental, and perinatal health. Within those reviewed papers deemed to have minimal risk of bias, we assessed the coherence and consistency of findings.

DISCUSSION

Consistent evidence documents associations between wildfire smoke exposure and general respiratory health effects, specifically exacerbations of asthma and chronic obstructive pulmonary disease. Growing evidence suggests associations with increased risk of respiratory infections and all-cause mortality. Evidence for cardiovascular effects is mixed, but a few recent studies have reported associations for specific cardiovascular end points. Insufficient research exists to identify specific population subgroups that are more susceptible to wildfire smoke exposure.

CONCLUSIONS

Consistent evidence from a large number of studies indicates that wildfire smoke exposure is associated with respiratory morbidity with growing evidence supporting an association with all-cause mortality. More research is needed to clarify which causes of mortality may be associated with wildfire smoke, whether cardiovascular outcomes are associated with wildfire smoke, and if certain populations are more susceptible.

CITATION

Reid CE, Brauer M, Johnston FH, Jerrett M, Balmes JR, Elliott CT. 2016. Critical review of health impacts of wildfire smoke exposure. Environ Health Perspect 124:1334-1343; http://dx.doi.org/10.1289/ehp.1409277.

Temperature deviation index and elderly mortality in Japan

Few studies have examined how the precedence of abnormal temperatures in previous neighboring years affects the population's health. In the present study, we attempted to quantify the health effects of abnormal weather patterns by creating a metric called the temperature deviation index (TDI) and estimated the effects of TDI on mortality in Japan. We used data from 47 prefectures in Japan to compute the TDI on days between May and September from 1966 to 2010. The TDI is a summed product of an indicator of absence of high temperatures in the neighboring years, and more weights were assigned to the years closest to the current year. To estimate the TDI effects on elderly mortality, we used generalized linear modeling with a Poisson distribution after adjusting for apparent temperature, barometric pressure, day of the week, and time trend. For each prefecture, we estimated the TDI effects and pooled the estimates to yield a national average for 1991-2010 in Japan. The estimated effects of TDI in middle- or high-latitude prefectures were greater than in low-latitude prefectures. The estimated national average of TDI effects was a 0.5 % (95 % confidence intervals [CI], 0.1, 1.0) increase in elderly mortality per 1-unit (around 1 standard deviation) increase in the TDI. The significant pooled estimation of TDI effects was mainly due to the TDI effects on summer days with moderate temperature (25th-49th percentile, mean temperature 22.9 °C): a 1.9 % (95 % CI, 1.1, 2.6) increase in elderly mortality per 1-unit increase in the TDI. However, TDI effects were insignificant in other temperature ranges. These findings suggest that elderly deaths increased on moderate temperature days in the summer that differed substantially from days during that time window in the neighboring years. Therefore, not only high temperature itself but also temperature deviation compared to previous years could be considered to be a risk factor for elderly mortality in the summer.

Diurnal temperature range and short-term mortality in large US communities

Research has shown that diurnal temperature range (DTR) is significantly associated with mortality and morbidity in regions of Asia; however, few studies have been conducted in other regions such as North America. Thus, we examined DTR effects on mortality in the USA. We used mortality and environmental data from the National Morbidity Mortality Air Pollution Study (NMMAPS). The data are daily mortality, air pollution, and temperature statistics from 95 large US communities collected between 1987 and 2000. To assess community-specific DTR effects on mortality, we used Poisson generalized linear models allowing for over-dispersion. After assessing community-specific DTR effects on mortality, we estimated region- and age-specific effects of DTR using two-level normal independent sampling estimation. We found a significant increase of 0.27 % [95 % confidence intervals (CI), 0.24-0.30 %] in nonaccidental mortality across 95 communities in the USA associated with a 1 °C increase in DTR, controlling for apparent temperature, day of the week, and time trend. This overall effect was driven mainly by effects of DTR on cardiovascular and respiratory mortality in the elderly: Mortality in the above 65 age group increased by 0.39 % (95 % CI, 0.33-0.44 %) and 0.33 % (95 % CI, 0.22-0.44 %), respectively. We found some evidence of regional differences in the effects of DTR on nonaccidental mortality with the highest effects in Southern California [0.31 % (95 % CI, 0.21-0.42 %)] and smallest effects in the Northwest and Upper Midwest regions [0.22 % (95 % CI, 0.11-0.33 %) and 0.22 % (95 % CI, 0.07-0.37 %), respectively]. These results indicate a statistically significant association between DTR and mortality on average for 95 large US communities. The findings indicate that DTR impacts on nonaccidental and cardiovascular-related mortality in most US regions and the elderly population was most vulnerable to the effects of DTR.

Spatiotemporal prediction of fine particulate matter during the 2008 northern California wildfires using machine learning

Estimating population exposure to particulate matter during wildfires can be difficult because of insufficient monitoring data to capture the spatiotemporal variability of smoke plumes. Chemical transport models (CTMs) and satellite retrievals provide spatiotemporal data that may be useful in predicting PM2.5 during wildfires. We estimated PM2.5 concentrations during the 2008 northern California wildfires using 10-fold cross-validation (CV) to select an optimal prediction model from a set of 11 statistical algorithms and 29 predictor variables. The variables included CTM output, three measures of satellite aerosol optical depth, distance to the nearest fires, meteorological data, and land use, traffic, spatial location, and temporal characteristics. The generalized boosting model (GBM) with 29 predictor variables had the lowest CV root mean squared error and a CV-R2 of 0.803. The most important predictor variable was the Geostationary Operational Environmental Satellite Aerosol/Smoke Product (GASP) Aerosol Optical Depth (AOD), followed by the CTM output and distance to the nearest fire cluster. Parsimonious models with various combinations of fewer variables also predicted PM2.5 well. Using machine learning algorithms to combine spatiotemporal data from satellites and CTMs can reliably predict PM2.5 concentrations during a major wildfire event.

The role of ambient ozone in epidemiologic studies of heat-related mortality

BACKGROUND

A large and growing literature investigating the role of extreme heat on mortality has conceptualized the role of ambient ozone in various ways, sometimes treating it as a confounder, sometimes as an effect modifier, and sometimes as a co-exposure. Thus, there is a lack of consensus about the roles that temperature and ozone together play in causing mortality.

OBJECTIVES

We applied directed acyclic graphs (DAGs) to the topic of heat-related mortality to graphically represent the subject matter behind the research questions and to provide insight on the analytical options available.

DISCUSSION

On the basis of the subject matter encoded in the graphs, we assert that the role of ozone in studies of temperature and mortality is a causal intermediate that is affected by temperature and that can also affect mortality, rather than a confounder.

CONCLUSIONS

We discuss possible questions of interest implied by this causal structure and propose areas of future work to further clarify the role of air pollutants in epidemiologic studies of extreme temperature.

Birth weight following pregnancy during the 2003 Southern California wildfires

BACKGROUND

In late October 2003, a series of wildfires exposed urban populations in Southern California to elevated levels of air pollution over several weeks. Previous research suggests that short-term hospital admissions for respiratory outcomes increased specifically as a result of these fires.

OBJECTIVE

We assessed the impact of a wildfire event during pregnancy on birth weight among term infants.

METHODS

Using records for singleton term births delivered to mothers residing in California's South Coast Air Basin (SoCAB) during 2001-2005 (n = 886,034), we compared birth weights from pregnancies that took place entirely before or after the wildfire event (n = 747,590) with those where wildfires occurred during the first (n = 60,270), second (n = 39,435), or third (n = 38,739) trimester. The trimester-specific effects of wildfire exposure were estimated using a fixed-effects regression model with several maternal characteristics included as covariates.

RESULTS

Compared with pregnancies before and after the wildfires, mean birth weight was estimated to be 7.0 g lower [95% confidence interval (CI): -11.8, -2.2] when the wildfire occurred during the third trimester, 9.7 g lower when it occurred during the second trimester (95% CI: -14.5, -4.8), and 3.3 g lower when it occurred during the first trimester (95% CI: -7.2, 0.6).

CONCLUSIONS

Pregnancy during the 2003 Southern California wildfires was associated with slightly reduced average birth weight among infants exposed in utero. The extent and increasing frequency of wildfire events may have implications for infant health and development.

Evaluation of a heat vulnerability index on abnormally hot days: an environmental public health tracking study

BACKGROUND

Extreme hot weather conditions have been associated with increased morbidity and mortality, but risks are not evenly distributed throughout the population. Previously, a heat vulnerability index (HVI) was created to geographically locate populations with increased vulnerability to heat in metropolitan areas throughout the United States.

OBJECTIVES

We sought to determine whether areas with higher heat vulnerability, as characterized by the HVI, experienced higher rates of morbidity and mortality on abnormally hot days.

METHODS

We used Poisson regression to model the interaction of HVI and deviant days (days whose deviation of maximum temperature from the 30-year normal maximum temperature is at or above the 95th percentile) on hospitalization and mortality counts in five states participating in the Environmental Public Health Tracking Network for the years 2000 through 2007.

RESULTS

The HVI was associated with higher hospitalization and mortality rates in all states on both normal days and deviant days. However, associations were significantly stronger (interaction p-value < 0.05) on deviant days for heat-related illness, acute renal failure, electrolyte imbalance, and nephritis in California, heat-related illness in Washington, all-cause mortality in New Mexico, and respiratory hospitalizations in Massachusetts.

CONCLUSION

Our results suggest that the HVI may be a marker of health vulnerability in general, although it may indicate greater vulnerability to heat in some cases.

The long-term effectiveness of clozapine and lamotrigine in a patient with treatment-resistant rapid-cycling bipolar disorder

The challenges in the management of treatment-resistant rapid-cycling bipolar disorder are multifaceted and represent a significant burden to the patient. There is a need for more exploration into the potential utility of various combination therapies in the setting of severe affective states. A 52-year-old woman with a history of severe treatment-resistant rapid-cycling bipolar affective disorder (BPAD) was hospitalized for the treatment of a severe mixed episode. The introduction of lamotrigine and clozapine in combination proved remarkably effective and well tolerated in both the acute management and in subsequent maintenance. The patient has remained asymptomatic during the 5-year follow-up without any further mood disturbance. Lamotrigine and clozapine are among the less-prescribed agents for BPAD and there is as yet little research into their use in combination. It is possible that these agents have complementary modes of action on various facets of the affective pathology, resulting in superior mood stabilization in this patient.

Mapping community determinants of heat vulnerability

BACKGROUND

The evidence that heat waves can result in both increased deaths and illness is substantial, and concern over this issue is rising because of climate change. Adverse health impacts from heat waves can be avoided, and epidemiologic studies have identified specific population and community characteristics that mark vulnerability to heat waves.

OBJECTIVES

We situated vulnerability to heat in geographic space and identified potential areas for intervention and further research.

METHODS

We mapped and analyzed 10 vulnerability factors for heat-related morbidity/mortality in the United States: six demographic characteristics and two household air conditioning variables from the U.S. Census Bureau, vegetation cover from satellite images, and diabetes prevalence from a national survey. We performed a factor analysis of these 10 variables and assigned values of increasing vulnerability for the four resulting factors to each of 39,794 census tracts. We added the four factor scores to obtain a cumulative heat vulnerability index value.

RESULTS

Four factors explained > 75% of the total variance in the original 10 vulnerability variables: a) social/environmental vulnerability (combined education/poverty/race/green space), b) social isolation, c) air conditioning prevalence, and d) proportion elderly/diabetes. We found substantial spatial variability of heat vulnerability nationally, with generally higher vulnerability in the Northeast and Pacific Coast and the lowest in the Southeast. In urban areas, inner cities showed the highest vulnerability to heat.

CONCLUSIONS

These methods provide a template for making local and regional heat vulnerability maps. After validation using health outcome data, interventions can be targeted at the most vulnerable populations.

Aeroallergens, allergic disease, and climate change: impacts and adaptation

Recent research has shown that there are many effects of climate change on aeroallergens and thus allergic diseases in humans. Increased atmospheric carbon dioxide concentration acts as a fertilizer for plant growth. The fertilizing effects of carbon dioxide, as well as increased temperatures from climate change, increase pollen production and the allergen content of pollen grains. In addition, higher temperatures are changing the timing and duration of the pollen season. As regional climates change, plants can move into new areas and changes in atmospheric circulation can blow pollen- and spore-containing dust to new areas, thus introducing people to allergens to which they have not been exposed previously. Climate change also influences the concentrations of airborne pollutants, which alone, and in conjunction with aeroallergens, can exacerbate asthma or other respiratory illnesses. The few epidemiological analyses of meteorological factors, aeroallergens, and allergic diseases demonstrate the pathways through which climate can exert its influence on aeroallergens and allergic diseases. In addition to the need for more research, there is the imperative to take preventive and adaptive actions to address the onset and exacerbation of allergic diseases associated with climate variability and change.

Endotheliotropic elephant herpes virus (EEHV) infection. The first PCR-confirmed fatal case in Asia

Since 1995, 4 suspected cases of Endotheliotropic Elephant Herpes Virus (EEHV) infection, i.e. based on clinical presentation, have occurred in Asia without resulting in epidemic outbreaks as expected. In order to confirm the presence of EEHV on the continent of Asia, viral DNA particles from liver samples of a wild-caught 3-year-old elephant found dead at a Cambodian elephant sanctuary and clinically diagnosed with EEHV, were PCR processed using known EEHV strain primers. The presence of EEHV viral nucleic acids was confirmed and the nucleic acids had a 99% sequence similarity to the U.S.A strain (gene bank locus: AF117265) and 97% sequence similarity to the European strain (gene bank locus: AF354746) assigning this case to the EEHV-1 cluster. More than the confirmation of EEHV on the continent of Asia, is the phylogenic relationship to the USA and European strains with no corresponding contact or transport of USA or European elephants to Asia. Thus, this brings many of the traditional theories into question. Although almost forgotten, this disease is still ramped in captive elephant populations worldwide and continues to devastate particularly the neonatal and weaning-age population. Special attention and continued research are needed specifically in the area of basic virology and epidemiology.

A host-specific function is required for ligation of a wide variety of ribozyme-processed RNAs

Hepatitis delta virus (HDV) replicates its circular RNA genome via a rolling circle mechanism. During this process, cis-acting ribozymes cleave adjacent upstream sequences and thereby resolve replication intermediates to unit-length RNA. The subsequent ligation of these 5'OH and 2',3'-cyclic phosphate termini to form circular RNA is an essential step in the life cycle of the virus. Here we present evidence for the involvement of a host activity in the ligation of HDV RNA. We used both HDV and hammerhead ribozymes to generate a panel of HDV and non-HDV RNA substrates that bear 5' hydroxyl and 2', 3'- cyclic phosphate termini. We found that ligation of these substrates occurred in host cells, but not in vitro or in Escherichia coli. The host-specific ligation activity was capable of joining RNA in both bimolecular and intramolecular reactions and functioned in a sequence-independent manner. We conclude that mammalian cells contain a default pathway that efficiently circularizes ribozyme processed RNAs. This pathway could be exploited in the delivery of stable antisense and decoy RNA to the nucleus.

Interaction of papillomavirus E6 oncoproteins with a putative calcium-binding protein

Human papillomaviruses (HPVs) are associated with the majority of cervical cancers and encode a transforming protein, E6, that interacts with the tumor suppressor protein p53. Because E6 has p53-independent transforming activity, the yeast two-hybrid system was used to search for other E6-binding proteins. One such protein, E6BP, interacted with cancer-associated HPV E6 and with bovine papillomavirus type 1 (BPV-1) E6. The transforming activity of BPV-1 E6 mutants correlated with their E6BP-binding ability. E6BP is identical to a putative calcium-binding protein, ERC-55, that appears to be localized in the endoplasmic reticulum.