Precipitation and Salmonellosis Incidence in Georgia, USA: Interactions between Extreme Rainfall Events and Antecedent Rainfall Conditions.

BACKGROUND: The southeastern United States consistently has high salmonellosis incidence, but disease drivers remain unknown. Salmonella is regularly detected in this region's natural environment, leading to numerous exposure opportunities. Rainfall patterns may impact the survival/transport of environmental Salmonella in ways that can affect disease transmission. OBJECTIVES: This study investigated associations between short-term precipitation (extreme rainfall events) and longer-term precipitation (rainfall conditions antecedent to these extreme events) on salmonellosis counts in the state of Georgia in the United States. METHODS: For the period 1997-2016, negative binomial models estimated associations between weekly county-level extreme rainfall events (≥90th percentile of daily rainfall) and antecedent conditions (8-week precipitation sums, categorized into tertiles) and weekly county-level salmonellosis counts. RESULTS: In Georgia's Coastal Plain counties, extreme and antecedent rainfall were associated with significant differences in salmonellosis counts. In these counties, extreme rainfall was associated with a 5% increase in salmonellosis risk (95% CI: 1%, 10%) compared with weeks with no extreme rainfall. Antecedent dry periods were associated with a 9% risk decrease (95% CI: 5%, 12%), whereas wet periods were associated with a 5% increase (95% CI: 1%, 9%), compared with periods of moderate rainfall. In models considering the interaction between extreme and antecedent rainfall conditions, wet periods were associated with a 13% risk increase (95% CI: 6%, 19%), whereas wet periods followed by extreme events were associated with an 11% increase (95% CI: 5%, 18%). Associations were substantially magnified when analyses were restricted to cases attributed to serovars commonly isolated from wildlife/environment (e.g., Javiana). For example, wet periods followed by extreme rainfall were associated with a 34% risk increase (95% CI: 20%, 49%) in environmental serovar infection. CONCLUSIONS: Given the associations of short-term extreme rainfall events and longer-term rainfall conditions on salmonellosis incidence, our findings suggest that avoiding contact with environmental reservoirs of Salmonella following heavy rainfall events, especially during the rainy season, may reduce the risk of salmonellosis. https://doi.org/10.1289/EHP4621.

Source-Apportioned PM2.5 and Cardiorespiratory Emergency Department Visits: Accounting for Source Contribution Uncertainty.

BACKGROUND: Despite evidence suggesting that air pollution-related health effects differ by emissions source, epidemiologic studies on fine particulate matter (PM2.5) infrequently differentiate between particles from different sources. Those that do rarely account for the uncertainty of source apportionment methods. METHODS: For each day in a 12-year period (1998-2010) in Atlanta, GA, we estimated daily PM2.5 source contributions from a Bayesian ensemble model that combined four source apportionment methods including chemical transport and receptor-based models. We fit Poisson generalized linear models to estimate associations between source-specific PM2.5 concentrations and cardiorespiratory emergency department visits (n = 1,598,117). We propagated uncertainty in the source contribution estimates through analyses using multiple imputation. RESULTS: Respiratory emergency department visits were positively associated with biomass burning and secondary organic carbon. For a 1 µg/m increase in PM2.5 from biomass burning during the past 3 days, the rate of visits for all respiratory outcomes increased by 0.4% (95% CI 0.0%, 0.7%). There was less evidence for associations between PM2.5 sources and cardiovascular outcomes, with the exception of ischemic stroke, which was positively associated with most PM2.5 sources. Accounting for the uncertainty of source apportionment estimates resulted, on average, in an 18% increase in the standard error for rate ratio estimates for all respiratory and cardiovascular emergency department visits, but inflation varied across specific sources and outcomes, ranging from 2% to 39%. CONCLUSIONS: This study provides evidence of associations between PM2.5 sources and some cardiorespiratory outcomes and quantifies the impact of accounting for variability in source apportionment approaches.

On the accuracy and potential of Google Maps location history data to characterize individual mobility for air pollution health studies.

Appropriately characterizing spatiotemporal individual mobility is important in many research areas, including epidemiological studies focusing on air pollution. However, in many retrospective air pollution health studies, exposure to air pollution is typically estimated at the subjects' residential addresses. Individual mobility is often neglected due to lack of data, and exposure misclassification errors are expected. In this study, we demonstrate the potential of using location history data collected from smartphones by the Google Maps application for characterizing historical individual mobility and exposure. Here, one subject carried a smartphone installed with Google Maps, and a reference GPS data logger which was configured to record location every 10 s, for a period of one week. The retrieved Google Maps Location History (GMLH) data were then compared with the GPS data to evaluate their effectiveness and accuracy of the GMLH data to capture individual mobility. We also conducted an online survey (n = 284) to assess the availability of GMLH data among smartphone users in the US. We found the GMLH data reasonably captured the spatial movement of the subject during the one-week time period at up to 200 m resolution. We were able to accurately estimate the time the subject spent in different microenvironments, as well as the time the subject spent driving during the week. The estimated time-weighted daily exposures to ambient particulate matter using GMLH and the GPS data logger were also similar (error less than 1.2%). Survey results showed that GMLH data may be available for 61% of the survey sample. Considering the popularity of smartphones and the Google Maps application, detailed historical location data are expected to be available for large portion of the population, and results from this study highlight the potential of these location history data to improve exposure estimation for retrospective epidemiological studies.

Characterization of the concentration-response curve for ambient ozone and acute respiratory morbidity in 5 US cities.

Although short-term exposure to ambient ozone (O3) can cause poor respiratory health outcomes, the shape of the concentration-response (C-R) between O3 and respiratory morbidity has not been widely investigated. We estimated the effect of daily O3 on emergency department (ED) visits for selected respiratory outcomes in 5 US cities under various model assumptions and assessed model fit. Population-weighted average 8-h maximum O3 concentrations were estimated in each city. Individual-level data on ED visits were obtained from hospitals or hospital associations. Poisson log-linear models were used to estimate city-specific associations between the daily number of respiratory ED visits and 3-day moving average O3 levels controlling for long-term trends and meteorology. Linear, linear-threshold, quadratic, cubic, categorical, and cubic spline O3 C-R models were considered. Using linear C-R models, O3 was significantly and positively associated with respiratory ED visits in each city with rate ratios of 1.02-1.07 per 25 ppb. Models suggested that O3-ED C-R shapes were linear until O3 concentrations of roughly 60 ppb at which point risk continued to increase linearly in some cities for certain outcomes while risk flattened in others. Assessing C-R shape is necessary to identify the most appropriate form of the exposure for each given study setting.

A multicity study of air pollution and cardiorespiratory emergency department visits: Comparing approaches for combining estimates across cities.

Determining how associations between ambient air pollution and health vary by specific outcome is important for developing public health interventions. We estimated associations between twelve ambient air pollutants of both primary (e.g. nitrogen oxides) and secondary (e.g. ozone and sulfate) origin and cardiorespiratory emergency department (ED) visits for 8 specific outcomes in five U.S. cities including Atlanta, GA; Birmingham, AL; Dallas, TX; Pittsburgh, PA; St. Louis, MO. For each city, we fitted overdispersed Poisson time-series models to estimate associations between each pollutant and specific outcome. To estimate multicity and posterior city-specific associations, we developed a Bayesian multicity multi-outcome (MCM) model that pools information across cities using data from all specific outcomes. We fitted single pollutant models as well as models with multipollutant components using a two-stage chemical mixtures approach. Posterior city-specific associations from the MCM models were somewhat attenuated, with smaller standard errors, compared to associations from time-series regression models. We found positive associations of both primary and secondary pollutants with respiratory disease ED visits. There was some indication that primary pollutants, particularly nitrogen oxides, were also associated with cardiovascular disease ED visits. Bayesian models can help to synthesize findings across multiple outcomes and cities by providing posterior city-specific associations building on variation and similarities across the multiple sources of available information.

Time-Series Analysis of Air Pollution and Health Accounting for Covariate-Dependent Overdispersion.

Time-series studies are routinely used to estimate associations between adverse health outcomes and short-term exposures to ambient air pollutants. Use of the Poisson log-linear model with the assumption of constant overdispersion is the most common approach, particularly when estimating associations between daily air pollution concentrations and aggregated counts of adverse health events throughout a geographical region. We examined how the assumption of constant overdispersion plays a role in estimation of air pollution effects by comparing estimates derived from the standard approach with those estimated from covariate-dependent Bayesian generalized Poisson and negative binomial models that accounted for potential time-varying overdispersion. Through simulation studies, we found that while there was negligible bias in effect estimates, the standard quasi-Poisson approach can result in a larger standard error when the constant overdispersion assumption is violated. This was also observed in a time-series study of daily emergency department visits for respiratory diseases and ozone concentration in Atlanta, Georgia (1999-2009). Allowing for covariate-dependent overdispersion resulted in a reduction in the ozone effect standard error, while the ozone-associated relative risk remained robust to different model specifications. Our findings suggest that improved characterization of overdispersion in time-series modeling can result in more precise health effect estimates in studies of short-term environmental exposures.

Use of high-resolution metabolomics for the identification of metabolic signals associated with traffic-related air pollution.

BACKGROUND: High-resolution metabolomics (HRM) is emerging as a sensitive tool for measuring environmental exposures and biological responses. The aim of this analysis is to assess the ability of high-resolution metabolomics (HRM) to reflect internal exposures to complex traffic-related air pollution mixtures. METHODS: We used untargeted HRM profiling to characterize plasma and saliva collected from participants in the Dorm Room Inhalation to Vehicle Emission (DRIVE) study to identify metabolic pathways associated with traffic emission exposures. We measured a suite of traffic-related pollutants at multiple ambient and indoor sites at varying distances from a major highway artery for 12 weeks in 2014. In parallel, 54 students living in dormitories near (20 m) or far (1.4 km) from the highway contributed plasma and saliva samples. Untargeted HRM profiling was completed for both plasma and saliva samples; metabolite and metabolic pathway alternations were evaluated using a metabolome-wide association study (MWAS) framework with pathway analyses. RESULTS: Weekly levels of traffic pollutants were significantly higher at the near dorm when compared to the far dorm (p < 0.05 for all pollutants). In total, 20,766 metabolic features were extracted from plasma samples and 29,013 from saliva samples. 45% of features were detected and shared in both plasma and saliva samples. 1291 unique metabolic features were significantly associated with at least one or more traffic indicator, including black carbon, carbon monoxide, nitrogen oxides and fine particulate matter (p < 0.05 for all significant features), after controlling for confounding and false discovery rate. Pathway analysis of metabolic features associated with traffic exposure indicated elicitation of inflammatory and oxidative stress related pathways, including leukotriene and vitamin E metabolism. We confirmed the chemical identities of 10 metabolites associated with traffic pollutants, including arginine, histidine, γ­linolenic acid, and hypoxanthine. CONCLUSIONS: Using HRM, we identified and verified biological perturbations associated with primary traffic pollutant in panel-based setting with repeated measurement. Observed response was consistent with endogenous metabolic signaling related to oxidative stress, inflammation, and nucleic acid damage and repair. Collectively, the current findings provide support for the use of untargeted HRM in the development of metabolic biomarkers of traffic pollution exposure and response.

Metabolomic profiles of plasma, exhaled breath condensate, and saliva are correlated with potential for air toxics detection.

INTRODUCTION: Advances in the development of high-resolution metabolomics (HRM) have provided new opportunities for their use in characterizing exposures to environmental air pollutants and air pollution-related disease etiologies. Exposure assessment studies have considered blood, breath, and saliva as biological matrices suitable for measuring responses to air pollution exposures. The current study examines comparability among these three matrices using HRM and explores their potential for measuring mobile-source air toxics. METHODS: Four participants provided saliva, exhaled breath concentrate (EBC), and plasma before and after a 2 h road traffic exposure. Samples were analyzed on a Thermo Scientific QExactive MS system in positive electrospray ionization mode and resolution of 70 000 full-width at half-maximum with C18 chromatography. Data were processed using an apLCMS and xMSanalyzer on the R statistical platform. RESULTS: The analysis yielded 7110, 6019, and 7747 reproducible features in plasma, EBC, and saliva, respectively. Correlations were moderate-to-strong (R = 0.41-0.80) across all pairwise comparisons of feature intensity within profiles, with the strongest between EBC and saliva. The associations of mean intensities between matrix pairs were positive and significant, controlling for subject and sampling time effects. Six out of 20 features shared in all three matrices putatively matched a list of known mobile-source air toxics. CONCLUSIONS: Plasma, saliva, and EBC have largely comparable metabolic profiles measurable through HRM. These matrices have the potential to be used in identification and measurement of exposures to mobile-source air toxics, though further, targeted study is needed.

Evaluation of VOC concentrations in indoor and outdoor microenvironments at near-road schools.

A 14-week air quality study, characterizing the indoor and outdoor concentrations of 18 VOCs at four El Paso, Texas elementary schools, was conducted in Spring 2010. Three schools were in an area of high traffic density and the fourth school, considered as a background school, was situated in an area affected minimally by stationary and mobile sources of air pollution. Passive samplers were deployed for monitoring and analyzed by GC/MS. Differences in the concentration profiles of the BTEX species between the high and low traffic density schools confirmed the pre-defined exposure patterns. Toluene was the predominant compound within the BTEX group and the 96-hr average outdoor concentrations varied from 1.16 to 4.25 µg/m3 across the four schools. Outdoor BTEX species were strongly correlated with each other (0.63 < r < 1.00, p < 0.05) suggesting a common source: vehicular traffic emissions. As expected, the strength of the associations between these compounds was more intense at each of the three high-exposure schools in contrast to the low-exposure school. This was further corroborated by the results obtained from the BTEX inter-species ratios (toluene: benzene and m, p- xylenes: ethylbenzene). Certain episodic events during the study period resulted in very elevated concentrations of some VOCs such as n-pentane. Indoor concentration of compounds with known indoor sources such as α -pinene, d-limonene, p-dichlorobenzene, and chloroform were generally higher than their corresponding outdoor concentrations. Cleaning agents, furniture polishes, materials used in arts and crafts activities, hot-water usage, and deodorizing cakes used in urinal pots were the likely major sources for these high indoor concentrations. Finally, retrospective assessment of average ambient BTEX concentrations over the last twenty years suggest a gradual decrement in this border region.

Ozone and childhood respiratory disease in three US cities: evaluation of effect measure modification by neighborhood socioeconomic status using a Bayesian hierarchical approach.

BACKGROUND: Ground-level ozone is a potent airway irritant and a determinant of respiratory morbidity. Susceptibility to the health effects of ambient ozone may be influenced by both intrinsic and extrinsic factors, such as neighborhood socioeconomic status (SES). Questions remain regarding the manner and extent that factors such as SES influence ozone-related health effects, particularly across different study areas. METHODS: Using a 2-stage modeling approach we evaluated neighborhood SES as a modifier of ozone-related pediatric respiratory morbidity in Atlanta, Dallas, & St. Louis. We acquired multi-year data on emergency department (ED) visits among 5-18 year olds with a primary diagnosis of respiratory disease in each city. Daily concentrations of 8-h maximum ambient ozone were estimated for all ZIP Code Tabulation Areas (ZCTA) in each city by fusing observed concentration data from available network monitors with simulations from an emissions-based chemical transport model. In the first stage, we used conditional logistic regression to estimate ZCTA-specific odds ratios (OR) between ozone and respiratory ED visits, controlling for temporal trends and meteorology. In the second stage, we combined ZCTA-level estimates in a Bayesian hierarchical model to assess overall associations and effect modification by neighborhood SES considering categorical and continuous SES indicators (e.g., ZCTA-specific levels of poverty). We estimated ORs and 95% posterior intervals (PI) for a 25 ppb increase in ozone. RESULTS: The hierarchical model combined effect estimates from 179 ZCTAs in Atlanta, 205 ZCTAs in Dallas, and 151 ZCTAs in St. Louis. The strongest overall association of ozone and pediatric respiratory disease was in Atlanta (OR = 1.08, 95% PI: 1.06, 1.11), followed by Dallas (OR = 1.04, 95% PI: 1.01, 1.07) and St. Louis (OR = 1.03, 95% PI: 0.99, 1.07). Patterns of association across levels of neighborhood SES in each city suggested stronger ORs in low compared to high SES areas, with some evidence of non-linear effect modification. CONCLUSIONS: Results suggest that ozone is associated with pediatric respiratory morbidity in multiple US cities; neighborhood SES may modify this association in a non-linear manner. In each city, children living in low SES environments appear to be especially vulnerable given positive ORs and high underlying rates of respiratory morbidity.

Evaluation of individual and area-level factors as modifiers of the association between warm-season temperature and pediatric asthma morbidity in Atlanta, GA.

INTRODUCTION: Previous studies have found associations between respiratory morbidity and high temperatures; however, few studies have explored associations in potentially sensitive sub-populations. METHODS: We evaluated individual and area-level factors as modifiers of the association between warm-season (May-Sept.) temperature and pediatric respiratory morbidity in Atlanta. Emergency department (ED) visit data were obtained for children, 5-18 years old, with primary diagnoses of asthma or respiratory disease (diagnoses of upper respiratory infections, bronchiolitis, pneumonia, chronic obstructive pulmonary disease, asthma, or wheeze) in 20-county Atlanta during 1993-2012. Daily maximum temperature (Tmax) was acquired from the automated surface observing station at Atlanta Hartsfield International Airport. Poisson generalized linear models were used to estimate rate ratios (RR) between daily Tmax and asthma or respiratory disease ED visits, controlling for time and meteorology. Tmax effects were estimated for single-day lags of 0-6 days, for 3-, 5-, and 7-day moving averages and modeled with cubic terms to allow for non-linear relationships. Effect modification by individual factors (sex, race, insurance status) and area-level socioeconomic status (SES; ZIP code levels of poverty, education, and the neighborhood deprivation index) was examined via stratification. RESULTS: Estimated RRs for Tmax and pediatric asthma ED visits were positive and significant for lag days 1-5, with the strongest single day association observed on lag day 2 (RR=1.06, 95% CI: 1.03, 1.09) for a change in Tmax from 27°C to 32°C (25th to 75th percentile). For the moving average exposure periods, associations increased as moving average periods increased. We observed stronger RRs between Tmax and asthma among males compared to females, non-white children compared to white children, children with private insurance compared to children with Medicaid, and among children living in high compared to low SES areas. Associations between Tmax and respiratory disease ED visits were weak and non-significant (p-value>0.05). CONCLUSIONS: Results suggest socio-demographic factors (race/ethnicity, insurance status, and area-level SES) may confer vulnerability to temperature-related pediatric asthma morbidity. Our findings of weaker associations among children with Medicaid compared to other health insurance types and among children living in low compared to high SES areas run counter to our belief that children from disadvantaged households or ZIP codes would be more vulnerable to the respiratory effects of temperature. The potential reasons for these unexpected results are explored in the discussion.

Assessment of neighbourhood-level socioeconomic status as a modifier of air pollution-asthma associations among children in Atlanta.

BACKGROUND: A broad literature base provides evidence of association between air pollution and paediatric asthma. Socioeconomic status (SES) may modify these associations; however, previous studies have found inconsistent evidence regarding the role of SES. METHODS: Effect modification of air pollution-paediatric asthma morbidity by multiple indicators of neighbourhood SES was examined in Atlanta, Georgia. Emergency department (ED) visit data were obtained for 5-18 years old with a diagnosis of asthma in 20-county Atlanta during 2002-2008. Daily ZIP Code Tabulation Area (ZCTA)-level concentrations of ozone, nitrogen dioxide, fine particulate matter and elemental carbon were estimated using ambient monitoring data and emissions-based chemical transport model simulations. Pollutant-asthma associations were estimated using a case-crossover approach, controlling for temporal trends and meteorology. Effect modification by ZCTA-level (neighbourhood) SES was examined via stratification. RESULTS: We observed stronger air pollution-paediatric asthma associations in 'deprivation areas' (eg, ≥20% of the ZCTA population living in poverty) compared with 'non-deprivation areas'. When stratifying analyses by quartiles of neighbourhood SES, ORs indicated stronger associations in the highest and lowest SES quartiles and weaker associations among the middle quartiles. CONCLUSIONS: Our results suggest that neighbourhood-level SES is a factor contributing vulnerability to air pollution-related paediatric asthma morbidity in Atlanta. Children living in low SES environments appear to be especially vulnerable given positive ORs and high underlying asthma ED rates. Inconsistent findings of effect modification among previous studies may be partially explained by choice of SES stratification criteria, and the use of multiplicative models combined with differing baseline risk across SES populations.

Associations between Source-Specific Fine Particulate Matter and Emergency Department Visits for Respiratory Disease in Four U.S. Cities.

BACKGROUND: Short-term exposure to ambient fine particulate matter (PM2.5) concentrations has been associated with increased mortality and morbidity. Determining which sources of PM2.5 are most toxic can help guide targeted reduction of PM2.5. However, conducting multicity epidemiologic studies of sources is difficult because source-specific PM2.5 is not directly measured, and source chemical compositions can vary between cities. OBJECTIVES: We determined how the chemical composition of primary ambient PM2.5 sources varies across cities. We estimated associations between source-specific PM2.5 and respiratory disease emergency department (ED) visits and examined between-city heterogeneity in estimated associations. METHODS: We used source apportionment to estimate daily concentrations of primary source-specific PM2.5 for four U.S. cities. For sources with similar chemical compositions between cities, we applied Poisson time-series regression models to estimate associations between source-specific PM2.5 and respiratory disease ED visits. RESULTS: We found that PM2.5 from biomass burning, diesel vehicle, gasoline vehicle, and dust sources was similar in chemical composition between cities, but PM2.5 from coal combustion and metal sources varied across cities. We found some evidence of positive associations of respiratory disease ED visits with biomass burning PM2.5; associations with diesel and gasoline PM2.5 were frequently imprecise or consistent with the null. We found little evidence of associations with dust PM2.5. CONCLUSIONS: We introduced an approach for comparing the chemical compositions of PM2.5 sources across cities and conducted one of the first multicity studies of source-specific PM2.5 and ED visits. Across four U.S. cities, among the primary PM2.5 sources assessed, biomass burning PM2.5 was most strongly associated with respiratory health. Citation: Krall JR, Mulholland JA, Russell AG, Balachandran S, Winquist A, Tolbert PE, Waller LA, Sarnat SE. 2017. Associations between source-specific fine particulate matter and emergency department visits for respiratory disease in four U.S. cities. Environ Health Perspect 125:97-103; http://dx.doi.org/10.1289/EHP271.

Estimating Acute Cardiorespiratory Effects of Ambient Volatile Organic Compounds.

BACKGROUND: The health effects of ambient volatile organic compounds (VOCs) have received less attention in epidemiologic studies than other commonly measured ambient pollutants. In this study, we estimated acute cardiorespiratory effects of ambient VOCs in an urban population. METHODS: Daily concentrations of 89 VOCs were measured at a centrally-located ambient monitoring site in Atlanta and daily counts of emergency department visits for cardiovascular diseases and asthma in the five-county Atlanta area were obtained for the 1998-2008 period. To understand the health effects of the large number of species, we grouped these VOCs a priori by chemical structure and estimated the associations between VOC groups and daily counts of emergency department visits in a time-series framework using Poisson regression. We applied three analytic approaches to estimate the VOC group effects: an indicator pollutant approach, a joint effect analysis, and a random effect meta-analysis, each with different assumptions. We performed sensitivity analyses to evaluate copollutant confounding. RESULTS: Hydrocarbon groups, particularly alkenes and alkynes, were associated with emergency department visits for cardiovascular diseases, while the ketone group was associated with emergency department visits for asthma. CONCLUSIONS: The associations observed between emergency department visits for cardiovascular diseases and alkenes and alkynes may reflect the role of traffic exhaust, while the association between asthma visits and ketones may reflect the role of secondary organic compounds. The different patterns of associations we observed for cardiovascular diseases and asthma suggest different modes of action of these pollutants or the mixtures they represent.

Refined assessment of associations between drinking water residence time and emergency department visits for gastrointestinal illness in Metro Atlanta, Georgia.

Recent outbreak investigations suggest that a substantial proportion of waterborne disease outbreaks are attributable to water distribution system issues. In this analysis, we examine the relationship between modeled water residence time (WRT), a proxy for probability of microorganism intrusion into the distribution system, and emergency department visits for gastrointestinal (GI) illness for two water utilities in Metro Atlanta, USA during 1993-2004. We also examine the association between proximity to the nearest distribution system node, based on patients' residential address, and GI illness using logistic regression models. Comparing long (≥90th percentile) with intermediate WRTs (11th to 89th percentile), we observed a modestly increased risk for GI illness for Utility 1 (OR = 1.07, 95% CI: 1.02-1.13), which had substantially higher average WRT than Utility 2, for which we found no increased risk (OR = 0.98, 95% CI: 0.94-1.02). Examining finer, 12-hour increments of WRT, we found that exposures >48 h were associated with increased risk of GI illness, and exposures of >96 h had the strongest associations, although none of these associations was statistically significant. Our results suggest that utilities might consider reducing WRTs to <2-3 days or adding booster disinfection in areas with longer WRT, to minimize risk of GI illness from water consumption.

Method for Fusing Observational Data and Chemical Transport Model Simulations To Estimate Spatiotemporally Resolved Ambient Air Pollution.

Investigations of ambient air pollution health effects rely on complete and accurate spatiotemporal air pollutant estimates. Three methods are developed for fusing ambient monitor measurements and 12 km resolution chemical transport model (CMAQ) simulations to estimate daily air pollutant concentrations across Georgia. Temporal variance is determined by observations in one method, with the annual mean CMAQ field providing spatial structure. A second method involves scaling daily CMAQ simulated fields using mean observations to reduce bias. Finally, a weighted average of these results based on prediction of temporal variance provides optimized daily estimates for each 12 × 12 km grid. These methods were applied to daily metrics of 12 pollutants (CO, NO2, NOx, O3, SO2, PM10, PM2.5, and five PM2.5 components) over the state of Georgia for a seven-year period (2002-2008). Cross-validation demonstrates a wide range in optimized model performance across pollutants, with SO2 predicted most poorly due to limitations in coal combustion plume monitoring and modeling. For the other pollutants studied, 54-88% of the spatiotemporal variance (Pearson R(2) from cross-validation) was captured, with ozone and PM2.5 predicted best. The optimized fusion approach developed provides daily spatial field estimates of air pollutant concentrations and uncertainties that are consistent with observations, emissions, and meteorology.

Warm season temperatures and emergency department visits in Atlanta, Georgia.

PURPOSE: Extreme heat events will likely increase in frequency with climate change. Heat-related health effects are better documented among the elderly than among younger age groups. We assessed associations between warm-season ambient temperature and emergency department (ED) visits across ages in Atlanta during 1993-2012. METHODS: We examined daily counts of ED visits with primary diagnoses of heat illness, fluid/electrolyte imbalances, renal disease, cardiorespiratory diseases, and intestinal infections by age group (0-4, 5-18, 19-64, 65+years) in relation to daily maximum temperature (TMX) using Poisson time series models that included cubic terms for TMX at single-day lags of 0-6 days, controlling for maximum dew-point temperature, time trends, week day, holidays, and hospital participation periods. We estimated rate ratios (RRs) and 95% confidence intervals (CI) for TMX changes from 27°C to 32°C (25th to 75th percentile) and conducted extensive sensitivity analyses. RESULTS: We observed associations between TMX and ED visits for all internal causes, heat illness, fluid/electrolyte imbalances, renal diseases, asthma/wheeze, diabetes, and intestinal infections. Age groups with the strongest observed associations were 65+years for all internal causes [lag 0 RR (CI)=1.022 (1.016-1.028)] and diabetes [lag 0 RR=1.050 (1.008-1.095)]; 19-64 years for fluid/electrolyte imbalances [lag 0 RR=1.170 (1.136-1.205)] and renal disease [lag 1 RR=1.082 (1.065-1.099)]; and 5-18 years for asthma/wheeze [lag 2 RR=1.059 (1.030-1.088)] and intestinal infections [lag 1 RR=1.120 (1.041-1.205)]. CONCLUSIONS: Varying strengths of associations between TMX and ED visits by age suggest that optimal interventions and health-impact projections would account for varying heat health impacts across ages.

Ambient air pollution and emergency department visits for asthma: a multi-city assessment of effect modification by age.

Previous studies have found strong associations between asthma morbidity and major ambient air pollutants. Relatively little research has been conducted to assess whether age is a factor conferring susceptibility to air pollution-related asthma morbidity. We investigated the short-term relationships between asthma emergency department (ED) visits and ambient ozone (O3), carbon monoxide (CO), nitrogen dioxide (NO2), sulfur dioxide (SO2), and fine particulate matter (PM2.5) in Atlanta (1993-2009), Dallas (2006-2009), and St. Louis (2001-2007). City-specific daily time-series analyses were conducted to estimate associations by age group (0-4, 5-18, 19-39, 40-64, and 65+ years). Sub-analyses were performed stratified by race and sex. City-specific rate ratios (RRs) were combined by inverse-variance weighting to provide an overall association for each strata. The overall RRs differed across age groups, with associations for all pollutants consistently strongest for children aged 5-18 years. The patterns of association across age groups remained generally consistent when models were stratified by sex and race, although the strong observed associations among 5-18 year olds appeared to be partially driven by non-white and male patients. Our findings suggest that age is a susceptibility factor for asthma exacerbations in response to air pollution, with school-age children having the highest susceptibility.