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Rationale: Extreme heat exposure is a well-known cause of mortality among older adults. However, the impacts of exposure on respiratory morbidity across U.S. cities and population subgroups are not well understood. Objectives: A nationwide study was conducted to determine the impact of high heat on respiratory disease hospitalizations among older adults (≥65 yr of age) living in the 120 largest U.S. cities between 2000 and 2017. Methods: Daily rates of inpatient respiratory hospitalizations were examined with respect to variations in ZIP code-level daily mean temperature or heat index. For each city, we estimated cumulative associations (lag days 0-6) between warm-season heat (June to September) and cause-specific respiratory hospitalizations using time-stratified conditional quasi-Poisson regression with distributed lag nonlinear models. We estimated nationwide associations using multivariate meta-regression and updated city-specific associations via best linear unbiased prediction. With stratified models, we explored effect modification by age, sex, and race (Black or White). Results are reported as percentage change in hospitalizations at high temperatures (95th percentile) compared with median temperatures for each outcome, demographic group, and metropolitan area. Results: We identified 3,275,033 respiratory hospitalizations among Medicare beneficiaries across 120 large U.S. cites between 2000 and 2017. Nationwide, 7-day cumulative associations at high temperatures resulted in a 1.2% (95% confidence interval, 0.4-2.0%) increase in hospitalizations for primary diagnoses of all-cause respiratory disease, driven primarily by increases in respiratory tract infections (1.8% [95% confidence interval, 0.6-3.0%]) and chronic respiratory diseases and/or respiratory failure (1.2% [95% confidence interval, 0.0-2.4%]). Stronger associations were observed when exposure was defined using the heat index instead of mean temperature. Across the 120 cities, we observed considerable geographic variation in the relative risk of heat-related respiratory hospitalizations, and we observed disproportionate burdens of heat-related respiratory hospitalizations among the oldest beneficiaries (≥85 yr of age) and among Black beneficiaries living in South Atlantic cities. During the 18-year study period, there were an estimated 11,710 excess respiratory hospitalizations due to heat exposure. Conclusions: Results suggest that high temperature and humidity contribute to exacerbation of respiratory tract infections and chronic lung diseases among older adults. Geographic variation in heat-related hospitalization rates suggests that contextual factors largely account for disproportionate burdens, and area-level influences should be further investigated in multicity studies.

Background: Climate change is increasing the frequency of high heat and high humidity days. Whether these conditions can trigger ventricular arrhythmias [ventricular tachycardia/ventricular fibrillation, VT/VF] in susceptible persons is unknown., Objectives: The purpose of this study was to determine the relationship between warm-season weather conditions and risk of VT/VF in individuals with pacemakers and defibrillators., Methods: Baseline clinical and device data from 5,944 patients in North Carolina (2010-2021) were linked to daily weather data geocoded to individuals' residential addresses. Associations between extreme humidity, temperature, and VT/VF overall and by patient, community, and built environment factors were estimated using a case time-series design with distributed lag nonlinear models, adjusting for temporal trends and individual factors., Results: VT/VF events occurred on 4,486 of the 484,988 person-days. Extreme humidity (95th percentile: 90% relative humidity) increased odds of VT/VF in the 7 days following exposure (aOR 1.23 [95% CI: 1.00-1.51]). Humidity-associated VT/VF risk was highest among those who were male (aOR: 1.38 [95% CI: 1.08-1.76]), age 67 to 75 years (aOR: 1.65 [95% CI: 1.16-2.35]) with coronary artery disease (aOR: 1.79 [95% CI: 1.25-2.57]), heart failure (aOR: 1.72 [95% CI: 1.2-2.46]), diabetes (aOR: 3.01 [95% CI: 1.99-4.56]), hypertension (aOR: 2.06 [95% CI: 1.48-2.88]), and prior myocardial infarction (aOR: 1.75 [95% CI: 1.23-2.48]). Communities with high socioeconomic deprivation (aOR: 1.83 [95% CI: 1.28-2.62]), high income inequality (aOR: 1.56 [95% CI: 1.19-2.04]), and urban areas with less greenspace (aOR: 1.29 [95% CI: 0.93-1.78]) also had increased VT/VF risk. High temperatures were not associated with VT/VF., Conclusions: In patients with preexisting cardiovascular disease, exposure to extreme humidity increased VT/VF risk, especially among vulnerable individuals, disadvantaged communities, and urban areas with less green space. These findings emphasize the need for policies that address environmental risks in susceptible individuals and communities., Competing Interests: Dr Rosman’s effort on this study was supported by a grant from the 10.13039/100000050National Heart, Lung, and Blood Institute of the 10.13039/100000002National Institutes of Health (K23HL141644). Support for the data linkage and database management were provided by the Translational and Clinical Sciences Institute (UL1TR002489 from the Clinical and Translational Science Award program of the 10.13039/100006108National Center for Advancing Translational Sciences, 10.13039/100000002National Institutes of Health). Dr Rosman has received research grants from Boston Scientific and served on a Biotronik advisory board; and reports consultancy fees from Pfizer and Biotronik. The device manufacturers were not involved in the study design, analysis, and interpretation of data, nor writing or publishing this study. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose., (© 2024 The Authors.)

Many United States (US) cities are experiencing urban heat islands (UHIs) and climate change-driven temperature increases. Extreme heat increases cardiovascular disease (CVD) risk, yet little is known about how this association varies with UHI intensity (UHII) within and between cities. We aimed to identify the urban populations most at-risk of and burdened by heat-related CVD morbidity in UHI-affected areas compared to unaffected areas. ZIP code-level daily counts of CVD hospitalizations among Medicare enrollees, aged 65-114, were obtained for 120 US metropolitan statistical areas (MSAs) between 2000 and 2017. Mean ambient temperature exposure was estimated by interpolating daily weather station observations. ZIP codes were classified as low and high UHII using the first and fourth quartiles of an existing surface UHII metric, weighted to each have 25% of all CVD hospitalizations. MSA-specific associations between ambient temperature and CVD hospitalization were estimated using quasi-Poisson regression with distributed lag non-linear models and pooled via multivariate meta-analyses. Across the US, extreme heat (MSA-specific 99th percentile, on average 28.6 °C) increased the risk of CVD hospitalization by 1.5% (95% CI: 0.4%, 2.6%), with considerable variation among MSAs. Extreme heat-related CVD hospitalization risk in high UHII areas (2.4% [95% CI: 0.4%, 4.3%]) exceeded that in low UHII areas (1.0% [95% CI: -0.8%, 2.8%]), with upwards of a 10% difference in some MSAs. During the 18-year study period, there were an estimated 37,028 (95% CI: 35,741, 37,988) heat-attributable CVD admissions. High UHII areas accounted for 35% of the total heat-related CVD burden, while low UHII areas accounted for 4%. High UHII disproportionately impacted already heat-vulnerable populations; females, individuals aged 75-114, and those with chronic conditions living in high UHII areas experienced the largest heat-related CVD impacts. Overall, extreme heat increased cardiovascular morbidity risk and burden in older urban populations, with UHIs exacerbating these impacts among those with existing vulnerabilities., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Published by Elsevier Ltd.)

A growing body of evidence indicates that exposure to air pollution affects cognitive performance; however, few studies have assessed this in the context of repeated measures within a large group of individuals or in a population with a large age range. In this study, we evaluated the associations between long-term exposure to fine particulate matter (PM 2.5 ) and ozone (O 3 ) in large cohort of adults aged 18-90 years. The study cohort included 29,091 Lumosity users in the contiguous US who completed 20 repetitions of the Lost in Migration game between 2017 and 2018. Game scores reflect the ability to filter information and avoid distracting information. Long-term air pollution data included ambient PM 2.5 and O 3 averaged for the 365-day period before each gameplay date. Generalized linear models were used to examine the associations between long-term PM 2.5 and O 3 and game score percentile. Co-pollutant models were adjusted for meteorology, time trend, age, gender, device, education, local socioeconomic factors, and urbanicity. Results represent the change in attention game score percentile per 1 μg/m 3 increase in PM 2.5 or 0.01 ppm increase in O 3 . In the entire cohort, a -0.10 (95% CI: -0.16, -0.04) change in score percentile was associated with PM 2.5 , while no significant association was observed with O 3 . Modification of these associations by age was observed for both PM 2.5 and O 3 , with stronger associations observed in younger users. In users aged 18-29, a -0.25 (-0.45, -0.05) change in score percentile was associated with PM 2.5 , while no associations were observed in other age groups. With O 3 , there was a -2.92 (-4.63, -1.19) and -2.81 (-4.29, -1.25) change in score percentile for users aged 18-29 and 30-39, respectively. We observed that elevated long-term PM 2.5 and O 3 were associated with decreased focus scores in young adults, but follow-up research is necessary to further illuminate these associations., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Dr. Cavin Ward-Caviness is a paid advisor for the Clock Foundation. The Clock Foundation had no involvement in any aspect of this work. The remaining authors have nothing to disclose., (Published by Elsevier Ltd.)

Background: There is increasing evidence that long-term exposure to fine particulate matter [PM ≤ 2.5 μ m in aerodynamic diameter ( PM 2.5 )] may adversely impact cognitive performance. Wildfire smoke is one of the biggest sources of PM 2.5 and concentrations are likely to increase under climate change. However, little is known about how short-term exposure impacts cognitive function., Objectives: We aimed to evaluate the associations between daily and subdaily (hourly) PM 2.5 and wildfire smoke exposure and cognitive performance in adults., Methods: Scores from 20 plays of an attention-oriented brain-training game were obtained for 10,228 adults in the United States (U.S.). We estimated daily and hourly PM 2.5 exposure through a data fusion of observations from multiple monitoring networks. Daily smoke exposure in the western U.S. was obtained from satellite-derived estimates of smoke plume density. We used a longitudinal repeated measures design with linear mixed effects models to test for associations between short-term exposure and attention score. Results were also stratified by age, gender, user behavior, and region., Results: Daily and subdaily PM 2.5 were negatively associated with attention score. A 10   μ g / m 3 increase in PM 2.5 in the 3 h prior to gameplay was associated with a 21.0 [95% confidence interval (CI): 3.3, 38.7]-point decrease in score. PM 2.5 exposure over 20 plays accounted for an estimated average 3.7% (95% CI: 0.7%, 6.7%) reduction in final score. Associations were more pronounced in the wildfire-impacted western U.S. Medium and heavy smoke density were also negatively associated with score. Heavy smoke density the day prior to gameplay was associated with a 117.0 (95% CI: 1.7, 232.3)-point decrease in score relative to no smoke. Although differences between subgroups were not statistically significant, associations were most pronounced for younger (18-29 y), older ( ≥ 70 y ), habitual, and male users., Discussion: Our results indicate that PM 2.5 and wildfire smoke were associated with reduced attention in adults within hours and days of exposure, but further research is needed to elucidate these relationships. https://doi.org/10.1289/EHP10498.

Bacteria live in spatially organized aggregates during chronic infections, where they adapt to the host environment, evade immune responses, and resist therapeutic interventions. Although it is known that environmental factors such as polymers influence bacterial aggregation, it is not clear how bacterial adaptation during chronic infection impacts the formation and spatial organization of aggregates in the presence of polymers. Here, we show that in an in vitro model of cystic fibrosis (CF) containing the polymers extracellular DNA (eDNA) and mucin, O-specific antigen is a major factor determining the formation of two distinct aggregate assembly types of Pseudomonas aeruginosa due to alterations in cell surface hydrophobicity. Our findings suggest that during chronic infection, the interplay between cell surface properties and polymers in the environment may influence the formation and structure of bacterial aggregates, which would shed new light on the fitness costs and benefits of O-antigen production in environments such as CF lungs. IMPORTANCE During chronic infection, several factors contribute to the biogeography of microbial communities. Heterogeneous populations of Pseudomonas aeruginosa form aggregates in cystic fibrosis airways; however, the impact of this population heterogeneity on spatial organization and aggregate assembly is not well understood. In this study, we found that changes in O-specific antigen determine the spatial organization of P. aeruginosa cells by altering the relative cell surface hydrophobicity. This finding suggests a role for O-antigen in regulating P. aeruginosa aggregate size and shape in cystic fibrosis airways.

Exposure to wildfire smoke increases the risk of respiratory and cardiovascular hospital admissions. Health impact assessments, used to inform decision-making processes, characterize the health impacts of environmental exposures by combining preexisting epidemiological concentration-response functions (CRFs) with estimates of exposure. These two key inputs influence the magnitude and uncertainty of the health impacts estimated, but for wildfire-related impact assessments the extent of their impact is largely unknown. We first estimated the number of respiratory, cardiovascular, and asthma hospital admissions attributable to fire-originated PM 2.5 exposure in central California during the October 2017 wildfires, using Monte Carlo simulations to quantify uncertainty with respect to the exposure and epidemiological inputs. We next conducted sensitivity analyses, comparing four estimates of fire-originated PM 2.5 and two CRFs, wildfire and nonwildfire specific, to understand their impact on the estimation of excess admissions and sources of uncertainty. We estimate the fires accounted for an excess 240 (95% CI: 114, 404) respiratory, 68 (95% CI: -10, 159) cardiovascular, and 45 (95% CI: 18, 81) asthma hospital admissions, with 56% of admissions occurring in the Bay Area. Although differences between impact assessment methods are not statistically significant, the admissions estimates' magnitude is particularly sensitive to the CRF specified while the uncertainty is most sensitive to estimates of fire-originated PM 2.5 . Not accounting for the exposure surface's uncertainty leads to an underestimation of the uncertainty of the health impacts estimated. Employing context-specific CRFs and using accurate exposure estimates that combine multiple data sets generates more certain estimates of the acute health impacts of wildfires., Competing Interests: The authors declare no conflicts of interest relevant to this study., (© 2021. The Authors. GeoHealth published by Wiley Periodicals LLC on behalf of American Geophysical Union.)

Estimates of ground-level ozone concentrations are necessary to determine the human health burden of ozone. To support the Global Burden of Disease Study, we produce yearly fine resolution global surface ozone estimates from 1990 to 2017 through a data fusion of observations and models. As ozone observations are sparse in many populated regions, we use a novel combination of the M 3 Fusion and Bayesian Maximum Entropy (BME) methods. With M 3 Fusion, we create a multimodel composite by bias-correcting and weighting nine global atmospheric chemistry models based on their ability to predict observations (8834 sites globally) in each region and year. BME is then used to integrate observations, such that estimates match observations at each monitoring site with the observational influence decreasing smoothly across space and time until the output matches the multimodel composite. After estimating at 0.5° resolution using BME, we add fine spatial detail from an additional model, yielding estimates at 0.1° resolution. Observed ozone is predicted more accurately ( R 2 = 0.81 at the test point, 0.63 at 0.1°, and 0.62 at 0.5°) than the multimodel mean ( R 2 = 0.28 at 0.5°). Global ozone exposure is estimated to be increasing, driven by highly populated regions of Asia and Africa, despite decreases in the United States and Russia.

Exposure to wildfire smoke causes adverse health outcomes, suggesting the importance of accurately estimating smoke concentrations. Geostatistical methods can combine observed, modeled, and satellite-derived concentrations to produce accurate estimates. Here, we estimate daily average ground-level PM 2.5 concentrations at a 1 km resolution during the October 2017 California wildfires, using the Constant Air Quality Model Performance (CAMP) and Bayesian Maximum Entropy (BME) methods to bias-correct and fuse three concentration datasets: permanent and temporary monitoring stations, a chemical transport model (CTM), and satellite-derived estimates. Four BME space/time kriging and data fusion methods were evaluated. All BME methods produce more accurate estimates than the standalone CTM and satellite products. Adding temporary station data increases the R 2 by 36%. The data fusion of observations with the CAMP-corrected CTM and satellite-derived concentrations provides the best estimate ( R 2 = 0.713) in fire-impacted regions, emphasizing the importance of combining multiple datasets. We estimate that approximately 65,000 people were exposed to very unhealthy air (daily average PM 2.5 ≥ 150.5 μg/m 3 ).