Objectives. To describe demographic and social characteristics of US communities exposed to wildfire smoke. Methods. Using satellite-collected data on wildfire smoke with the locations of population centers in the coterminous United States, we identified communities potentially exposed to light-, medium-, and heavy-density smoke plumes for each day from 2011 to 2021. We linked days of exposure to smoke in each category of smoke plume density with 2010 US Census data and community characteristics from the Centers for Disease Control and Prevention's Social Vulnerability Index to describe the co-occurrence of smoke exposure and social disadvantage. Results. During the 2011-to-2021 study period, increases in the number of days of heavy smoke were observed in communities representing 87.3% of the US population, with notably large increases in communities characterized by racial or ethnic minority status, limited English proficiency, lower educational attainment, and crowded housing conditions. Conclusions. From 2011 to 2021, wildfire smoke exposures in the United States increased. As smoke exposure becomes more frequent and intense, interventions that address communities with social disadvantages might maximize their public health impact. (Am J Public Health. 2023;113(7):759-767. https://doi.org/10.2105/AJPH.2023.307286).

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.

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.

There is interest among agencies and public health practitioners in the United States (USA) to estimate the future burden of climate-related health outcomes. Calculating disease burden projections can be especially daunting, given the complexities of climate modeling and the multiple pathways by which climate influences public health. Interdisciplinary coordination between public health practitioners and climate scientists is necessary for scientifically derived estimates. We describe a unique partnership of state and regional climate scientists and public health practitioners assembled by the Florida Building Resilience Against Climate Effects (BRACE) program. We provide a background on climate modeling and projections that has been developed specifically for public health practitioners, describe methodologies for combining climate and health data to project disease burden, and demonstrate three examples of this process used in Florida.