BACKGROUND: Studies of thunderstorm asthma to understand risk factors using high-resolution climate data and asthma outcomes on a large scale are scarce. Moreover, thunderstorm asthma is not well studied in the United States. OBJECTIVES: We examined whether climate parameters involved in thunderstorms are associated with emergency department (ED) visits for acute asthma attacks in the United States. METHODS: We analyzed 63,789 asthma-related, daily ED visits for all age groups, and thunderstorm-associated climate data in Louisiana during 2010 through 2012. We performed time-series analyses using quasi-Poisson regression models with natural cubic splines of date, parish, holiday, day of week, season, daily maximum concentrations of ozone (O3) and fine particulate matter [PM 2.5 m in aerodynamic diameter (PM2.5)], and daily mean pressure, precipitation, and temperature. Because of a significant interaction effect between temperature and lightning days on asthma-related visits, we performed stratified analyses by days with/without lightning or thunderstorm (defined by any lightning and precipitation). RESULTS: On thunderstorm days, higher asthma-related ED visits were associated with higher daily mean precipitation [relative risk(RR) =1.145per1g/m2/s (95% CI: 1.009, 1.300)] and lower daily mean temperature [RR =1.011 per 1C change (1.000-1.021)] without carry-over effect to the next non-thunderstorm day. These higher risks were found mainly among children and adults <65years of age. We observed similar results on lightning days. However, we did not find similar associations for non-thunderstorm or non-lightning days. Daily maximum O3 and PM2.5 levels were not significantly associated with asthma ED visits on thunderstorm days. DISCUSSION: Higher precipitation and lower temperature on thunderstorm days appear to contribute to asthma attacks among people with asthma, suggesting they should consider taking precautions during thunderstorms. EDs should consider preparing for a potential increase of asthma-related visits and ensuring sufficient stock of emergency medication and supplies for forecasted severe thunderstorm days. https://doi.org/10.1289/EHP10440.

Climate and weather directly impact plant phenology, affecting airborne pollen. The objective of this systematic review is to examine the impacts of meteorological variables on airborne pollen concentrations and pollen season timing. Using PRISMA methodology, we reviewed literature that assessed whether there was a relationship between local temperature and precipitation and measured airborne pollen. The search strategy included terms related to pollen, trends or measurements, and season timing. For inclusion, studies must have conducted a correlation analysis of at least 5 years of airborne pollen data to local meteorological data and report quantitative results. Data from peer-reviewed articles were extracted on the correlations between seven pollen indicators (main pollen season start date, end date, peak date, and length, annual pollen integral, average daily pollen concentration, and peak pollen concentration), and two meteorological variables (temperature and precipitation). Ninety-three articles were included in the analysis out of 9,679 articles screened. Overall, warmer temperatures correlated with earlier and longer pollen seasons and higher pollen concentrations. Precipitation had varying effects on pollen concentration and pollen season timing indicators. Increased precipitation may have a short-term effect causing low pollen concentrations potentially due to "wash out" effect. Long-term effects of precipitation varied for trees and weeds and had a positive correlation with grass pollen levels. With increases in temperature due to climate change, pollen seasons for some taxa in some regions may start earlier, last longer, and be more intense, which may be associated with adverse health impacts, as pollen exposure has well-known health effects in sensitized individuals.

A wide range of research has promised new tools for forecasting infectious disease dynamics, but little of that research is currently being applied in practice, because tools do not address key public health needs, do not produce probabilistic forecasts, have not been evaluated on external data, or do not provide sufficient forecast skill to be useful. We developed an open collaborative forecasting challenge to assess probabilistic forecasts for seasonal epidemics of dengue, a major global public health problem. Sixteen teams used a variety of methods and data to generate forecasts for 3 epidemiological targets (peak incidence, the week of the peak, and total incidence) over 8 dengue seasons in Iquitos, Peru and San Juan, Puerto Rico. Forecast skill was highly variable across teams and targets. While numerous forecasts showed high skill for midseason situational awareness, early season skill was low, and skill was generally lowest for high incidence seasons, those for which forecasts would be most valuable. A comparison of modeling approaches revealed that average forecast skill was lower for models including biologically meaningful data and mechanisms and that both multimodel and multiteam ensemble forecasts consistently outperformed individual model forecasts. Leveraging these insights, data, and the forecasting framework will be critical to improve forecast skill and the application of forecasts in real time for epidemic preparedness and response. Moreover, key components of this project-integration with public health needs, a common forecasting framework, shared and standardized data, and open participation-can help advance infectious disease forecasting beyond dengue.

Motor vehicle crashes remain a leading cause of accidental death in the United States, and weather is frequently cited as a contributing factor in fatal crashes. Previous studies have investigated the link between these crashes and precipitation typically using station-based observations that, while providing a good estimate of the prevailing conditions on a given day or hour, often fail to capture the conditions present at the actual time and location of a crash. Using a multiyear, high-resolution radar reanalysis and information on 125,012 fatal crashes spanning the entire continental United States over a 6-yr period, we find that the overall risk of a fatal crash increases by approximately 34% during active precipitation. The risk is significant in all regions of the continental United States, and it is highest during the morning rush hour and during the winter months.

Extreme weather and climate-related events affect human health by causing death, injury, and illness, as well as having large socioeconomic impacts. Climate change has caused changes in extreme event frequency, intensity and geographic distribution, and will continue to be a driver for change in the future. Some of these events include heat waves, droughts, wildfires, dust storms, flooding rains, coastal flooding, storm surge, and hurricanes. The pathways connecting extreme events to health outcomes and economic losses can be diverse and complex. The difficulty in predicting these relationships comes from the local societal and environmental factors that affect disease burden. More information is needed about the impacts of climate change on public health and economies to effectively plan for and adapt to climate change. This article describes some of the ways extreme events are changing and provides examples of the potential impacts on human health and infrastructure. It also identifies key research gaps to be addressed to improve the resilience of public health to extreme events in the future. IMPLICATIONS: Extreme weather and climate events affect human health by causing death, injury, and illness, as well as having large socio-economic impacts. Climate change has caused changes in extreme event frequency, intensity and geographic distribution, and will continue to be a driver for change in the future. Some of these events include heat waves, droughts, wildfires, flooding rains, coastal flooding, storm surge, and hurricanes. The pathways connecting extreme events to health outcomes and economic losses can be diverse and complex. The difficulty in predicting these relationships comes from the local societal and environmental factors that affect disease burden.

Coccidioidomycosis (also called Valley fever) is caused by a soilborne fungus, Coccidioides spp., in arid regions of the southwestern United States. Though some who develop infections from this fungus remain asymptomatic, others develop respiratory disease as a consequence. Less commonly, severe illness and death can occur when the infection spreads to other regions of the body. Previous analyses have attempted to connect the incidence of coccidioidomycosis to broadly available climatic measurements, such as precipitation or temperature. However, with the limited availability of long-term, in situ soil moisture data sets, it has not been feasible to perform a direct analysis of the relationships between soil moisture levels and coccidioidomycosis incidence on a larger temporal and spatial scale. Utilizing in situ soil moisture gauges throughout the southwest from the U.S. Climate Reference Network and a model with which to extend those estimates, this work connects periods of higher and lower soil moisture in Arizona and California between 2002 and 2014 to the reported incidence of coccidioidomycosis. The results indicate that in both states, coccidioidomycosis incidence is related to soil moisture levels from previous summers and falls. Stated differently, a higher number of coccidioidomycosis cases are likely to be reported if previous bands of months have been atypically wet or dry, depending on the location.

Coccidioidomycosis is a fungal infection endemic to the southwestern United States, particularly Arizona and California. Its incidence has increased, potentially due in part to the effects of changing climatic variables on fungal growth and spore dissemination. This study aims to quantify the county-level vulnerability to coccidioidomycosis in Arizona and California and to assess the relationships between population vulnerability and climate variability. The variables representing exposure, sensitivity, and adaptive capacity were combined to calculate county level vulnerability indices. Three methods were used: (1) principal components analysis; (2) quartile weighting; and (3) percentile weighting. Two sets of indices, "unsupervised" and "supervised", were created. Each index was correlated with coccidioidomycosis incidence data from 2000-2014. The supervised percentile index had the highest correlation; it was then correlated with variability measures for temperature, precipitation, and drought. The supervised percentile index was significantly correlated (p < 0.05) with coccidioidomycosis incidence in both states. Moderate, positive significant associations (p < 0.05) were found between index scores and climate variability when both states were concurrently analyzed and when California was analyzed separately. This research adds to the body of knowledge that could be used to target interventions to vulnerable counties and provides support for the hypothesis that population vulnerability to coccidioidomycosis is associated with climate variability.

Climate change is anticipated to alter the production, use, release, and fate of environmental chemicals, likely leading to increased uncertainty in exposure and human health risk predictions. Exposure science provides a key connection between changes in climate and associated health outcomes. The theme of the 2015 Annual Meeting of the International Society of Exposure Science-Exposures in an Evolving Environment-brought this issue to the fore. By directing attention to questions that may affect society in profound ways, exposure scientists have an opportunity to conduct "consequential science"-doing science that matters, using our tools for the greater good and to answer key policy questions, and identifying causes leading to implementation of solutions. Understanding the implications of changing exposures on public health may be one of the most consequential areas of study in which exposure scientists could currently be engaged. In this paper, we use a series of case studies to identify exposure data gaps and research paths that will enable us to capture the information necessary for understanding climate change-related human exposures and consequent health impacts. We hope that paper will focus attention on under-developed areas of exposure science that will likely have broad implications for public health.