Importance Dust storms are projected to increase with climate change. The short-term health outcomes associated with dust storms in the US are not well characterized, especially for morbidity outcomes. Objective To estimate associations between dust storms and diagnosis-specific emergency department (ED) visits during 2005 to 2018. Design, Setting, and Participants In this cross-sectional study using a time-stratified case-crossover design, short-term associations between dust storms and ED visits were estimated at the zip code level using conditional Poisson analysis with adjustment for meteorology and within-month trends. Same-day dust storm events and storm events within a lag period of up to 7 days were considered. State-wide patient-level ED visit records acquired from 3 state health departments (Arizona, California, and Utah) were analyzed. Data were analyzed between April 21 and November 12, 2024. Exposures Dust storm events were reported by the US National Weather Service and assigned to each patient zip code that had at least a 5% areal overlap with the National Weather Service forecast zone. Main Outcomes and Measures Patient-level ED visits for asthma, chronic obstructive pulmonary disease, culture-negative pneumonia, congestive heart failure (CHF), cerebrovascular disease, ischemic heart disease, and visits due to motor vehicle accidents. Results The analysis included 33 500 ED visits among the outcomes of interest (5717 children aged 0-17 years [17.1%] and 11 150 adults aged >65 years [33.3%]; 17 394 male [51.9%] and 16 104 female [48.1%]; 2829 Black [8.4%] and 22 537 White [67.2%]; 9256 Hispanic [27.6%]) and 206 dust-impacted zip codes. The strongest associations between dust storms and ED visits were found for asthma (lag 0-2 relative risk [RR], 1.06; 95% CI, 1.01-1.11; P = .03), culture-negative pneumonia (lag 0-7 RR, 1.06; 95% CI, 1.02-1.10; P = .002), CHF (lag 0-7 RR, 1.06; 95% CI, 1.01-1.10; P = .01), and motor vehicle accidents (lag 0 RR, 1.13; 95% CI, 1.04-1.23; P = .003). Associations of dust storm exposure with ischemic heart disease were mostly protective (eg, lag 0-2 RR, 0.89; 95% CI, 0.84-0.95; P < .001). Associations of dust storm exposure with risk of ED visits for CHF and motor vehicle accidents were robust against adjustment for ambient ozone (eg, CHF: RR, 1.08; 95% CI, 1.03-1.13; P = .003) and nitrogen dioxide (eg, CHF: RR, 1.08; 95% CI, 1.03-1.13; P = .003) air pollution. Conclusions and Relevance In this study, dust storms were positively associated with ED visits for asthma, pneumonia, heart failure, and motor vehicle accidents. These findings contribute to our understanding of the association of dust storms with morbidity in the US and potential outcomes under a changing climate.

Cognitive impairment and dementia have long been recognized as growing public health threats. Studies have found that air pollution is a potential risk factor for dementia, but the literature remains inconclusive. This study aimed to evaluate the association between three major air pollutants (i.e., PM2.5, O3, and NO2) and cognitive impairment among the Chinese elderly population. Study participants were selected from the Chinese Longitudinal Health Longevity Survey (CLHLS) after 2005. We define cognitive impairment as a Chinese Mini-Mental-State Exam (CMMSE) score <24. The associations of air pollution with cognitive impairment and CMMSE score were evaluated with a logistic regression model and a linear mixed-effect model with random intercepts, respectively. A total of 3,887 participants were enrolled in this study. Of the 2,882 participants who completed at least one follow-up visit, 931 eventually developed cognitive impairment. In single-pollutant models, we found that yearly average PM2.5 and NO2 as well as warm season O3, were positively associated with cognitive impairment. NO2 remained positively associated with cognitive impairment in the multi-pollutant model. The linear mixed-effect models revealed that warm season O3 and yearly average NO2 were significantly associated with decreased CMMSE scores. Our research has established a positive association between cognitive impairment and air pollution in China. These findings underscore the imperative for the next iteration of China's Air Pollution Prevention and Control Action Plan to broaden its focus to encompass gaseous air pollutants since mitigating single air pollutant is insufficient to protect the aging population.

Multiple regulations have been promulgated to improve air quality, and previous studies have used an accountability chain to evaluate the effects of these regulations on emission levels, air quality, and human health. However, quantifying these impacts through the accountability chain is complex due to interactions between multiple factors that can influence the efficacy of control policies and introduce uncertainties at each step. We evaluated and quantified the impact of emission controls on electricity generating units (EGU) and motor vehicle sources on emissions and air quality via Generalized Additive Models. These GAMs have minimal bias (around 10-5 to 10- 2 mu g/m3 or ppbV) and r2 values for daily concentrations ranging from 0.4 to 0.7 Counterfactual air pollutant concentrations, in the absence of EGU and mobile source regulations, were calculated using estimated counterfactual emissions for the period 2005 to 2019 in Atlanta, New York City, and California's South Coast Air Basin. Counterfactual air pollutant concentrations indicated that the effects of regulations on air pollutants varied depending on the season and location. Predicted counterfactual air pollutant concentrations were generally 2-12 times higher than the measured concentrations at these sites, except for ozone. The impact of regulations on ozone concentrations typically resulted in reduced peak ozone values in the summer, but increased concentrations in the winter. Monte Carlo modeling found small to modest uncertainties, depending on the pollutant, location and regulations assessed. Counterfactual concentrations predicted in this project will be used in the assessment of the trends of toxicity in PM2.5.

Studying the association between mixtures of environmental exposures and health outcomes can be challenging due to issues such as correlation among the exposures and non-linearities or interactions in the exposure-response function. For this reason, one common strategy is to fit flexible nonparametric models to capture the true exposure-response surface. However, once such a model is fit, further decisions are required when it comes to summarizing the marginal and joint effects of the mixture on the outcome. In this work, we describe the use of soft Bayesian additive regression trees (BART) to estimate the exposure-risk surface describing the effect of mixtures of chemical air pollutants and temperature on asthma-related emergency department (ED) visits during the warm season in Atlanta, Georgia from 2011-2018. BART is chosen for its ability to handle large datasets and for its flexibility to be incorporated as a single component of a larger model. We then summarize the results using a strategy known as accumulated local effects to extract meaningful insights into the mixture effects on asthma-related morbidity. Notably, we observe negative associations between nitrogen dioxide and asthma ED visits and harmful associations between ozone and asthma ED visits, both of which are particularly strong on lower temperature days.

Epidemiological approaches for examining human health responses to environmental exposures in observational studies often control for confounding by implementing clever matching schemes and using statistical methods based on conditional likelihood. Nonparametric regression models have surged in popularity in recent years as a tool for estimating individual-level heterogeneous effects, which provide a more detailed picture of the exposure-response relationship but can also be aggregated to obtain improved marginal estimates at the population level. In this work we incorporate Bayesian additive regression trees (BART) into the conditional logistic regression model to identify heterogeneous exposure effects in a case-crossover design. Conditional logistic BART (CL-BART) utilizes reversible jump Markov chain Monte Carlo to bypass the conditional conjugacy requirement of the original BART algorithm. Our work is motivated by the growing interest in identifying subpopulations more vulnerable to environmental exposures. We apply CL-BART to a study of the impact of heat waves on people with Alzheimer's disease in California and effect modification by other chronic conditions. Through this application, we also describe strategies to examine heterogeneous odds ratios through variable importance, partial dependence, and lower-dimensional summaries.

BACKGROUND:Time-series models for count outcomes are routinely used to estimate short-term health effects of environmental exposures. The dispersion parameter is universally assumed to be constant over the study period. OBJECTIVE:The aim is to examine whether dispersion depends on time-varying covariates in a case study of emergency department visits in Atlanta during 1999-2009 and to evaluate approaches for addressing time-varying dispersion. METHODS:Using the double generalized linear model framework, we jointly modeled the Poisson log-linear mean and dispersion to estimate associations between emergency department visits for respiratory diseases and daily ozone concentrations. We conducted a simulation study to evaluate the impact of time-varying overdispersion on health effect estimation when constant overdispersion is assumed and developed an analytic code for implementing double generalized linear model using R. RESULTS:We found dispersion to depend on calendar date and meteorology. Assuming constant dispersion, the relative risk (RR) per interquartile range increase in 3-day moving ozone exposure was 1.037 (95% confidence interval: 1.024, 1.050). In the multivariable dispersion model, the RR was reduced to 1.029 (95% confidence interval: 1.020, 1.039), but with a large (26%) reduction in log RR standard error. The positive associations for ozone were robust against different dispersion model specifications. Simulation study results also demonstrated that when time-varying dispersion is present, it can lead to a larger standard error assuming constant dispersion. CONCLUSION:When the outcome exhibits large dispersion in a time-series analysis, allowing for covariate-dependent time-varying dispersion can improve inference, particularly by increasing estimation precision.

OBJECTIVE:This study aimed to improve estimates of the association between acute kidney injury (AKI) and ambient heat exposure through better case ascertainment and by isolating community-acquired AKI. METHODS:We conducted a case-crossover study using data on AKI-related emergency department (ED) visits in Atlanta. Daymet meteorology was used to assess exposure. We compared effect estimates for seven approaches (definitions) for identifying AKI. RESULTS:There were between 5868 and 64,269 AKI cases depending on the definition. Higher temperatures were associated with AKI for all case definitions. Associations were strongest for cases with an ICD-coded AKI as a primary diagnosis (OR, 1.15; 95% CI, 1.01-1.31). We also observed significant ( P < 0.05) heat associations among cases identified using laboratory results but without an ICD-coded AKI diagnosis. CONCLUSIONS:Many AKI cases are not diagnosed; these cases are associated with heat.

To synthesize vast amounts of high-throughput biological information, omics-fields like epigenetics have applied risk scores to develop biomarkers for environmental exposures. Extending the risk score analytic tool to the metabolomic data would be highly beneficial. This research aimed to develop and evaluate metabolomic risk score (metRS) approaches reflecting the biological response to traffic-related air pollution (TRAP) exposure (fine particulate matter, black carbon, and nitrogen dioxide). A simulation study compared three metRS methodologies: elastic net regression, which uses penalized regression to select metabolites, and two variations of thresholding, where a p-value cutoff is used to select metabolites. The methods performance was compared to assess 1) ability to correctly select metabolites associated with daily TRAP and 2) ability of the risk score to predict daily TRAP exposure. Power calculations and false discovery rates (FDR) were calculated for each approach. This metRS was applied to two real cohorts, the Center for Health Discovery and Wellbeing (CHDWB, n = 180) and Environment and Reproductive Health (EARTH, n = 200). In simulations, elastic net regression consistently presented inflated FDR for both high and low effect sizes and across all three sample sizes (n = 200; 500; 1000). Power to detect correct metabolites exceeded 0.8 for all three sample sizes in all three methods. In the real data application assessing associations of metabolomics risk scores and TRAP, associations were largely null. While we did not identify strong associations between the risk scores and TRAP in the real data application, metabolites selected by the risk score approaches were enriched in pathways that are well-known for their association with TRAP. These results demonstrate that certain methodologies to construct metabolomics risk scores are statistically robust and valid; however, standardized metabolic profiling and large sample sizes are required.

INTRODUCTION Growing evidence indicates fine particulate matter (PM 2.5 ) as risk factor for Alzheimer’s’ disease (AD), but the underlying mechanisms have been insufficiently investigated. We hypothesized differential DNA methylation (DNAm) in brain tissue as potential mediator of this association. METHODS We assessed genome-wide DNAm (Illumina EPIC BeadChips) in prefrontal cortex tissue and three AD-related neuropathological markers (Braak stage, CERAD, ABC score) for 159 donors, and estimated donors’ residential traffic-related PM 2.5 exposure 1, 3 and 5 years prior to death. We used a combination of the Meet-in-the-Middle approach, high-dimensional mediation analysis, and causal mediation analysis to identify potential mediating CpGs. RESULTS PM 2.5 was significantly associated with differential DNAm at cg25433380 and cg10495669. Twenty-six CpG sites were identified as mediators of the association between PM 2.5 exposure and neuropathology markers, several located in genes related to neuroinflammation. DISCUSSION Our findings suggest differential DNAm related to neuroinflammation mediates the association between traffic-related PM 2.5 and AD.

Regulatory actions and fuel price trends have decreased emissions from electricity generating units (EGUs) in the United States. The objective of this study is to separate coincident regulatory and fuel price impacts on EGU emissions and air pollution in Atlanta and New York City (NYC) between 2006 and 2019. We used observed fuel prices, EGU emissions, and air pollutant concentrations to estimate what air pollutant concentrations would have been under counterfactual fuel price and regulatory scenarios. When the actual and counterfactual scenarios were compared, fuel prices influenced EGU dispatch, which reduced ozone and PM2.5 concentrations in Atlanta beginning in 2009 and PM2.5 concentrations in NYC beginning in 2012. Beginning in 2008, installations of emissions controls due to the Clean Air Interstate Rule and enforcement of the New Source Review reduced PM2.5 concentrations year-round but increased ozone concentrations during the winter months. Coal-fired EGU retirements due to the combined influence of the Cross-State Air Pollution Rule, Mercury and Air Toxics Standards, and other market and policy factors account for the remaining long-term reductions in PM2.5 and ozone concentrations. Such information can be used to estimate the air quality and health benefits of past air quality regulations to aid the development of future emissions reductions strategies.

Air pollution and neighborhood socioeconomic status (N-SES) are associated with adverse cardiovascular health and neuropsychiatric functioning in older adults. This study examines the degree to which the joint effects of air pollution and N-SES on the cognitive decline are mediated by high cholesterol levels, high blood pressure (HBP), and depression. In the Emory Healthy Aging Study, 14,390 participants aged 50+ years from Metro Atlanta, GA, were assessed for subjective cognitive decline using the cognitive function instrument (CFI). Information on the prior diagnosis of high cholesterol, HBP, and depression was collected through the Health History Questionnaire. Participants' census tracts were assigned 3-year average concentrations of 12 air pollutants and 16 N-SES characteristics. We used the unsupervised clustering algorithm Self-Organizing Maps (SOM) to create 6 exposure clusters based on the joint distribution of air pollution and N-SES in each census tract. Linear regression analysis was used to estimate the effects of the SOM cluster indicator on CFI, adjusting for age, race/ethnicity, education, and neighborhood residential stability. The proportion of the association mediated by high cholesterol levels, HBP, and depression was calculated by comparing the total and direct effects of SOM clusters on CFI. Depression mediated up to 87 % of the association between SOM clusters and CFI. For example, participants living in the high N-SES and high air pollution cluster had CFI scores 0.05 (95 %-CI:0.01,0.09) points higher on average compared to those from the high N-SES and low air pollution cluster; after adjusting for depression, this association was attenuated to 0.01 (95 %-CI:-0.04,0.05). HBP mediated up to 8 % of the association between SOM clusters and CFI and high cholesterol up to 5 %. Air pollution and N-SES associated cognitive decline was partially mediated by depression. Only a small portion (<10 %) of the association was mediated by HBP and high cholesterol.

Background:Pollen exposure is associated with substantial respiratory morbidity, but its potential impact on cardiovascular disease (CVD) remains less understood. This study aimed to investigate the associations between daily levels of 13 pollen types and emergency department (ED) visits for eight CVD outcomes over a 26-year period in Atlanta, GA.Methods:We acquired pollen data from Atlanta Allergy & Asthma, a nationally certified pollen counting station, and ED visit data from individual hospitals and the Georgia Hospital Association. We performed time-series analyses using quasi-Poisson distributed lag models, with primary analyses assessing 3-day (lag 0-2 days) pollen levels. Models controlled for temporally varying covariates, including air pollutants.Results:During 1993-2018, there were 1,573,968 CVD ED visits. Most pairwise models of the 13 pollen types and eight CVD outcomes showed no association, with a few exceptions potentially due to chance.Conclusion:We found limited evidence of the impact of pollen on cardiovascular morbidity in Atlanta. Further study on pollen exposures in different climactic zones and exploration of pollen-pollution mixture effects is warranted.

INTRODUCTION: Growing evidence indicates that fine particulate matter (PM2.5) is a risk factor for Alzheimer's disease (AD), but the underlying mechanisms have been insufficiently investigated. We hypothesized differential DNA methylation (DNAm) in brain tissue as a potential mediator of this association. METHODS: We assessed genome-wideDNAm(Illumina EPIC BeadChips) in prefrontal cortex tissue and three AD-related neuropathological markers (Braak stage, CERAD, ABC score) for 159 donors, and estimated donors' residential traffic-related PM2.5 exposure 1, 3, and 5 years prior to death. We used a combination of the Meet-in-theMiddle approach, high-dimensional mediation analysis, and causal mediation analysis to identify potential mediating CpGs. RESULTS: PM2.5 was significantly associated with differential DNAm at cg25433380 and cg10495669. Twenty-four CpG sites were identified as mediators of the association between PM(2.)5 exposure and neuropathology markers, several located in genes related to neuroinflammation. DISCUSSION: Our findings suggest differential DNAm related to neuroinflammation mediates the association between traffic-related PM2.5 and AD.