Fine particulate matter (PM2.5) is associated with cardiovascular morbidity, especially among individuals with pre-existing cardiovascular conditions, such as heart failure (HF). Medical management with β-blockers may modify the association between PM2.5 and heart rate (HR) as β-blockers act on similar neurophysiologic pathways as PM2.5. To examine potential medication-PM2.5 interactions, we utilized electronic health records (EHRs) from 26,653 individuals with HF in North Carolina observed from 2014 to 2016. Linear mixed effect models with a random intercept for individual were adjusted for individual and census level demographics and socioeconomic confounders. We examined 0-4-daily PM2.5 lags as well as the 5-day moving average. We stratified observations based β-blocker prescription status and quantified differences using a multiplicative interaction model. We also utilized data from an in vivo study of diesel exhaust exposure and β-blocker usage in HF prone rats to validate results and examine additional outcomes unavailable in the EHR data. Stratified analyses and the multiplicative interaction model revealed a significant difference in the association between PM2.5 and HR based on β-blocker prescription status. For 5-day average PM2.5 we observed a significant interaction (βinteraction = -0.68, 95

Accurate exposure assessment is crucial to understand linkages between ambient air pollution and cardiopulmonary disease. Air quality monitors (AQM) are widely used, but do not account for personal behaviors. We compare the exposure-response relationships between ambient air pollution (PM2.5 and O3) and cardiopulmonary biomarkers in a panel study using both stationary AQM and Exposure Model for Individuals (EMI). Participants (n = 28) underwent 3-5 sessions totaling 134 visits. Participants underwent spirometry and blood sampling. PM2.5 and O3 concentrations were calculated for each visit (lag0) and 4 preceding days (lag1-4) using AQM and EMI. A mixed-effects model was applied to examine the associations between exposure and outcomes. AQM and EMI were strongly correlated for PM2.5 (ρ = 0.89) and moderately correlated for O3 (ρ = 0.46). Exposure-response relationships for PM2.5 were similar, with PM2.5 associated with increased oxLDL at lag1 (12.2 % (95 %CI: 4.5, 20.2) AQM, 17.9 % (95 %CI: 8.1, 27.8) EMI), increased vWF at lag0 (4.27 % (95 %CI: 0.15, 8.39) AQM, 7.12 % (95 %CI: 2.57, 11.67) EMI) and decreased vWF at lag3 -6.5 % (95 %CI: -11.4, -1.6) AQM, -5.6 % (95 %CI: -10.6, -0.7) EMI) and lag4 (-5.4 % (95 %CI: -10.2, -0.7) AQM, -6.7 % (95 %CI: -12.1, -1.3) EMI). O3 showed more variability, with positive associations with vWF at lag0 (12.9 % (95 %CI: 6.1, 19.7) AQM, -2.77 % (95 %CI: -8.1, 2.6) EMI) and D-dimer at lag1 27.0 % (95 %CI: 0.9, 53.0) AQM, -6.86 % (95 %CI: -26.3, 12.6) EMI), for AQM only, and negative associations with tPA at lag3 for EMI only (-10.0 % (95 %CI: -21.5, 1.4) AQM, -11.2 % (95 %CI: -19.6, -2.8) EMI). Our findings suggest that exposure-response associations to short-term PM2.5 and oxLDL and markers of coagulation are consistent between the AMQ and EMI methods, implying increased risk for cardiovascular disease. For O3, AQM and EMI were less consistent, highlighting the challenges of estimating and modeling O3 exposure.

Toxicokinetic modeling describes the absorption, distribution, metabolism, and elimination of chemicals by the body. Chemical-specific in vivo toxicokinetic data is often unavailable for the thousands of chemicals in commerce. However, predictions from generalized toxicokinetic models allow for extrapolation from in vitro toxicological data, obtained via new approach methods (NAMs), to predict in vivo human health outcomes and provide key information on chemicals for public health risk assessment. The httk R package provides an open-source software tool containing a suite of generalized toxicokinetic models covering various exposure scenarios, a library of chemical-specific data from peer-reviewed high-throughput toxicokinetic (HTTK) studies, and other utility functions to parameterize and evaluate toxicokinetic models. Generalized HTTK models in httk use the open-source language MCSim to describe the compartmental and physiologically based toxicokinetics (PBTK). New HTTK models may be integrated into httk with a model description code file (C script generated via MCSim) and a model documentation file (R script). httk provides a series of functionalities such as model parameterization, in vivo-derived data for evaluating model predictions, unit conversion, Monte Carlo simulations for uncertainty propagation and biological variability, and other model utilities. Here, we describe in detail how to add new HTTK models into the httk package to leverage its pre-existing data and functionality. As a demonstration, we describe the integration of a gas inhalation PBTK model. The intention of httk is to provide a transparent, open-source tool for toxicokinetics, bioinformatics, and public health risk assessment that makes use of publicly available data on more than one thousand chemicals.

Epidemiologic studies of ambient fine particulate matter (PM2.5) and ozone (O3) often use outdoor concentrations from central-site monitors or air quality model estimates as exposure surrogates, which can result in exposure errors. We previously developed an exposure model called TracMyAir, which is an iPhone application that determines seven tiers of individual-level exposure metrics for ambient PM2.5 and O3 using outdoor concentrations, home building characteristics, weather, time-activities. The exposure metrics with increasing information needs and complexity include: outdoor concentration (Cout, Tier 1), building infiltration factor (Finf, Tier 2), indoor concentration (Cin, Tier 3), time spent in microenvironments (ME) (TME, Tier 4), personal exposure factor (Fpex, Tier 5), exposure (E, Tier 6), and inhaled dose (D, Tier 7). In this study, we extended TracMyAir with two sets of additional features: (1) time-resolved exposures using smartphone geolocations with a ME classification model (MicroTrac) and official PM2.5 and O3 monitoring network, and (2) exposures based on low-cost outdoor PurpleAir (PA) PM2.5 monitoring network, non-ambient indoor PM2.5 using indoor-outdoor PA monitors, and inhaled dose based on physical activity data from smartphone and smartwatch. To demonstrate the two sets of extended features, we applied TracMyAir to estimate hourly PM2.5 and O3 exposure metrics for two corresponding panel studies with participants living in central North Carolina, USA. For Tier 4, the MicroTrac estimates were compared with 24-h diary data, and correctly classified the ME for 97 % of the daily time spent by the participants. Overall, the TracMyAir estimates showed considerable temporal and building-to-building variability of Finf, and Cin (Tiers 2–3), and person-to-person variability of Cout, TME, Fpex, E, and D (Tiers 1, 4–7). Our study demonstrates the capability of extending TracMyAir with air quality monitors, location-activity sensors, and models to determine fine-scale exposures, in support of epidemiologic studies and public health strategies to help reduce exposures to air pollutants.

Background: Global urbanization is leading to increased exposure to traffic-related air pollution (TRAP), which is associated with adverse health events. While individuals with cardiovascular disease (CVD) are known to have elevated susceptibility to air pollution exposure, no studies have evaluated how mortality risks associated with TRAP exposure differ based on the presence of CVD. Methods: We used three electronic health record-based cohorts to examine associations between proximity to major roadways and all-cause mortality. The three cohorts were a random sample of the hospital population, individuals with a prior myocardial infarction, and individuals with diagnosed heart failure (HF). We used Cox proportional hazards models to evaluate associations while adjusting for age, race, sex, and census block group socioeconomic status. Results: Residing <250 m from a major roadway was associated with a hazard ratio (HR) of 1.13 (95% confidence interval = 1.05, 1.23) for individuals with HF, an HR of 1.07 (95% confidence interval = 0.96, 1.20) for those with a prior myocardial infarction, and an HR of 1.03 (95% confidence interval = 0.89, 1.20) for a random sample of hospital patients. This pattern persisted across several sensitivity analyses including alternative definitions of proximity to major roadways and matching the cohorts on demographics. Conclusion: These results highlight the differences in air quality-related health risks based on underlying CVD. Individuals with HF consistently had the highest environmental health risks. These results may better inform risks related to TRAP exposure in populations with differing underlying CVD.

Toxicokinetics describes the absorption, distribution, metabolism, and elimination of chemicals by the body. Predictions from toxicokinetic models provide key information for chemical risk assessment. Traditionally, these predictions extrapolate from experimental animal species data (for example, in rats) to humans. More recently, toxicokinetics has been used for extrapolation from in vitro new approach methods (NAMs) for toxicology to in vivo. Chemical-specific in vivo toxicokinetic data are often unavailable for the thousands of chemicals in commerce. Therefore, large amounts of in vitro data measuring chemical-specific toxicokinetics have been collected. These data enable high-throughput toxicokinetic or HTTK modeling. The httk R package provides a library of chemical-specific data from peer-reviewed HTTK studies. httk further provides a suite of tools for parameterizing and evaluating toxicokinetic models. httk uses the open-source language MCSim to describe models for compartmental and physiologically based toxicokinetics (PBTK), MCSim can convert the model descriptions into a high-speed C code script. New models are integrated into httk using the open-source package development functionality in R, a model documentation file (R script), and the HTTK model description code file (C script). In addition to HTTK models, httk provides a series of functionalities such as unit conversion, model parameterization, Monte Carlo simulations for uncertainty propagation and biological variability, in vivo-derived data for evaluating model predictions, and other model utility functions. Here, we describe in detail how to add new HTTK models to httk and take advantage of the pre-existing data and functionality in the package. As a demonstration, we describe the integration of the gas inhalation PBTK model into httk. Modern modeling approaches, as exemplified by httk, allow for clear communication, reproducibility, and public scrutiny. The intention of httk is to provide a transparent, open-source tool for toxicokinetics, bioinformatics, and public health risk assessment. ### Competing Interest Statement The authors have declared no competing interest.

BACKGROUND:A critical aspect of air pollution exposure assessments is determining the time spent in various microenvironments (ME), which can have substantially different pollutant concentrations. We previously developed and evaluated a ME classification model, called Microenvironment Tracker (MicroTrac), to estimate time of day and duration spent in eight MEs (indoors and outdoors at home, work, school; inside vehicles; other locations) based on input data from global positioning system (GPS) loggers.OBJECTIVE:In this study, we extended MicroTrac and evaluated the ability of using geolocation data from smartphones to determine the time spent in the MEs.METHOD:We performed a panel study, and the MicroTrac estimates based on data from smartphones and GPS loggers were compared to 37 days of diary data across five participants.RESULTS:The MEs were correctly classified for 98.1% and 98.3% of the time spent by the participants using smartphones and GPS loggers, respectively.SIGNIFICANCE:Our study demonstrates the extended capability of using ubiquitous smartphone data with MicroTrac to help reduce time-location uncertainty in air pollution exposure models for epidemiologic and exposure field studies.

Rock climbing has evolved from a method for alpine mountaineering into a popular recreational activity and competitive sport. Advances in safety equipment and the rapid growth of indoor climbing facilities has enabled climbers to focus on the physical and technical movements needed to elevate performance. Through improved training methods, climbers can now achieve ascents of extreme difficulty. A critical aspect to further improve performance is the ability to continuously measure body movement and physiologic responses while ascending the climbing wall. However, traditional measurement devices (e.g., dynamometer) limit data collection during climbing. Advances in wearable and non-invasive sensor technologies have enabled new applications for climbing. This paper presents an overview and critical analysis of the scientific literature on sensors used during climbing. We focus on the several highlighted sensors with the ability to provide continuous measurements during climbing. These selected sensors consist of five main types (body movement, respiration, heart activity, eye gazing, skeletal muscle characterization) that demonstrate their capabilities and potential climbing applications. This review will facilitate the selection of these types of sensors in support of climbing training and strategies.

Background Toxicokinetic (TK) data needed for chemical risk assessment are not available for most chemicals. To support a greater number of chemicals, the U.S. Environmental Protection Agency (EPA) created the open-source R package "httk" (High Throughput ToxicoKinetics). The "httk" package provides functions and data tables for simulation and statistical analysis of chemical TK, including a population variability simulator that uses biometrics data from the National Health and Nutrition Examination Survey (NHANES). Objective Here we modernize the "HTTK-Pop" population variability simulator based on the currently available data and literature. We provide explanations of the algorithms used by "httk" for variability simulation and uncertainty propagation. Methods We updated and revised the population variability simulator in the "httk" package with the most recent NHANES biometrics (up to the 2017-18 NHANES cohort). Model equations describing glomerular filtration rate (GFR) were revised to more accurately represent physiology and population variability. The model output from the updated "httk" package was compared with the current version. Results The revised population variability simulator in the "httk" package now provides refined, more relevant, and better justified estimations. Significance Fulfilling the U.S. EPA's mission to provide open-source data and models for evaluations and applications by the broader scientific community, and continuously improving the accuracy of the "httk" package based on the currently available data and literature.

Competitive indoor climbing has increased in popularity at the youth, collegiate, and Olympic levels. A critical aspect for improving performance is characterizing the physiologic response to different climbing strategies (e.g., work/rest patterns, pacing) and techniques (e.g., body position and movement) relative to location on climbing wall with spatially varying characteristics (e.g., wall inclinations, position of foot/hand holds). However, this response is not well understood due to the limited capabilities of climbing-specific measurement and assessment tools. In this study, we developed a novel method to examine time-resolved sensor-based measurements of multiple personal biometrics at different microlocations (finely spaced positions; MLs) along a climbing route. For the ML-specific biometric system (MLBS), we integrated continuous data from wearable biometric sensors and smartphone-based video during climbing, with a customized visualization and analysis system to determine three physiologic parameters (heart rate, breathing rate, ventilation rate) and one body movement parameter (hip acceleration), which are automatically time-matched to the corresponding video frame to determine ML-specific biometrics. Key features include: (1) biometric sensors that are seamlessly embedded in the fabric of an athletic compression shirt, and do not interfere with climbing performance, (2) climbing video, and (3) an interactive graphical user interface to rapidly visualize and analyze the time-matched biometrics and climbing video, determine timing sequence between the biometrics at key events, and calculate summary statistics. To demonstrate the capabilities of MLBS, we examined the relationship between changes in ML-specific climbing characteristics and changes in the physiologic parameters. Our study demonstrates the ability of MLBS to determine multiple time-resolved biometrics at different MLs, in support of developing and assessing different climbing strategies and training methods to help improve performance.

Background: Fine particulate matter (PM2.5) is associated with cardiovascular morbidity and mortality. Medications that target similar pathophysiologic pathways as PM2.5 may modify PM2.5-related health risks. Here, we used EPA CARES, a collection of electronic health records (EHRs) linked to environmental data, to examine whether anti-hypertensive medications modify associations between PM2.5 and blood pressure (BP). Methods: For this study, we used EHRs from 27,953 heart failure patients observed from 2014-2016. Daily PM2.5 was measured at the nearest US EPA monitor to each study participant's primary residence. Linear mixed models adjusted for age, sex, race, season, relative humidity, temperature, and a natural spline term for time since study start were used to estimate associations between systolic and diastolic BP and daily PM2.5 on the day of measurement and up to 4 days before measurement as well as the 5-day average. Associations were stratified on use of anti-hypertensive medications and a multiplicative interaction term was used to estimate the interaction between PM2.5 and medication usage. Results are presented as the change in BP (mmHg) per 10 µg/m3 PM2.5 and the associated 95% confidence interval (CI). Results: The pattern of associations was consistent for all time periods examined so we present here just the 5-day average PM2.5 associations. For BP assessed when on anti-hypertensive medications we observed negative associations for systolic (-0.30, CI= -0.42, -0.18) and diastolic (-0.19; CI= -0.27, -0.12) BP. Conversely, for time-periods not on anti-hypertensive medications associations were positive for systolic (0.42, CI= 0.27, 0.57; interaction P= 5.3x10-12) and diastolic (0.13; CI= 0.04, 0.23; interaction P= 2.3x10-9) BP. Conclusions: Anti-hypertensive medication usage likely has interactions with short-term PM2.5 and medication usage in general should be accounted for when possible and explored for its ability to modify PM2.5-related health risks. This abstract does not necessarily reflect the policies of the US EPA.

Introduction Toxicity data are unavailable for many thousands of chemicals in commerce and the environment. Therefore, risk assessors need to rapidly screen these chemicals for potential risk to public health. High-throughput screening (HTS) for in vitro bioactivity, when used with high-throughput toxicokinetic (HTTK) data and models, allows characterization of these thousands of chemicals.Areas covered This review covers generic physiologically based toxicokinetic (PBTK) models and high-throughput PBTK modeling for in vitro-in vivo extrapolation (IVIVE) of HTS data. We focus on ‘httk’, a public, open-source set of computational modeling tools and in vitro toxicokinetic (TK) data.Expert opinion HTTK benefits chemical risk assessors with its ability to support rapid chemical screening/prioritization, perform IVIVE, and provide provisional TK modeling for large numbers of chemicals using only limited chemical-specific data. Although generic TK model design can increase prediction uncertainty, these models provide offsetting benefits by increasing model implementation accuracy. Also, public distribution of the models and data enhances reproducibility. For the httk package, the modular and open-source design can enable the tool to be used and continuously improved by a broad user community in support of the critical need for high-throughput chemical prioritization and rapid dose estimation to facilitate rapid hazard assessments.

Chronic obstructive pulmonary disease (COPD) is a frequent diagnosis in older individuals and contributor to global morbidity and mortality. Given the link between lung disease and aging, we need to understand how molecular indicators of aging relate to lung function and disease. Using data from the population-based KORA (Cooperative Health Research in the Region of Augsburg) surveys, we associated baseline epigenetic (DNA methylation) age acceleration with incident COPD and lung function. Models were adjusted for age, sex, smoking, height, weight, and baseline lung disease as appropriate. Associations were replicated in the Normative Aging Study. Of 770 KORA participants, 131 developed incident COPD over 7 years. Baseline accelerated epigenetic aging was significantly associated with incident COPD. The change in age acceleration (follow-up - baseline) was more strongly associated with COPD than baseline aging alone. The association between the change in age acceleration between baseline and follow-up and incident COPD replicated in the Normative Aging Study. Associations with spirometric lung function parameters were weaker than those with COPD, but a meta-analysis of both cohorts provide suggestive evidence of associations. Accelerated epigenetic aging, both baseline measures and changes over time, may be a risk factor for COPD and reduced lung function.

Air pollution epidemiological studies of ambient fine particulate matter (PM2.5) and ozone (O3) often use outdoor concentrations from central-site monitors as exposure surrogates, which can add bias or uncertainty in health effect estimates. The goal of this study was to improve exposure assessments of ambient PM2.5 and O3 for a 10-year epidemiological study with 2,271 participants with coronary artery disease in central North Carolina called the Catheterization Genetics (CATHGEN) study. We developed an exposure modeling approach to estimate three tiers of individual-level exposure metrics for ambient PM2.5 and O3. We used a hybrid outdoor air quality model (based on satellite- and ground-based air pollution measurements, chemical transport and land-use models) linked to a residential air exchange rate model (based on building characteristics, indoor-outdoor temperatures, wind speed) and mass-balance infiltration model to determine residential air exchange rates (AER, Tier 1), infiltration factors (Finf, Tier 2), and indoor concentrations (Cin, Tier 3). For each of the 2,271 participant homes, we applied the exposure model to determine daily house-specific PM2.5 and O3 exposure metrics (Tiers 1-3) for the 365 days before each participant's cardiac catheterization date. The daily modeled exposure metrics for all 828,915 participant days showed considerable temporal and house-to-house variability of AER, Finf and Cout (Tiers 1-3). Our study demonstrates the ability to apply an outdoor air quality model linked to a residential infiltration model to determine individual-level ambient PM2.5 and O3 exposure metrics for a large, long-term epidemiological study, in support of improving risk estimation.

Background: Air pollution, particularly particulate matter less than 2.5 micrometers in diameter (PM2.5), is a significant risk factor for cardiovascular morbidity. Current studies have been primarily based on the general, typically healthy, population and there is limited information for individuals with pre-existing disease. We used the EPA CARES resource to examine the association between short-term PM2.5 exposure and heart rate (HR) in heart failure (HF) patients. Additionally, we examine potential effect modification by beta-blockers, a common medication class that modifies HR. Methods: We analyzed 3,048,856 heart rate (HR) measurements on 26,634 HF patients between January 2014 and December 2016, compiled using electronic health records from University of North Carolina affiliated hospitals. Satellite data, land use, and ground based monitoring were used to estimate daily average concentrations of PM2.5 at 1km resolution, and immediate (lag 0), delayed (lag 1 to 4), and 5 day moving average (5dMA) exposures at each primary address were computed. We used generalized additive mixed models to associate PM2.5 with HR while adjusting for age, sex, race, season, time-trend, daily temperature, and relative humidity, with a random intercept for individual. Results: PM2.5 exposure was associated with HR for lag 2 and 3 (beta = 0.006, CI = 0.002, 0.009; beta = 0.005, CI = 0.002, 0.010). Associations were stronger in individuals not taking beta-blocker medications at any time prior to HR measurement, with associations seen at all lags and strongest for 5dMA (beta = 0.086, CI = 0.073, 0.099). Conclusions: Elevated PM2.5 is associated with increased HR in HF patients. Associations are at best weak for the entire population, but strong and consistent across lags for measurements prior to beginning beta-blockers, suggesting that beta-blocker medication regimes may substantially attenuate effects of PM2.5 on HR. This abstract does not necessarily reflect the policies of the U.S. EPA.

Air pollution epidemiological studies often use outdoor concentrations from central-site monitors as exposure surrogates, which can induce measurement error. The goal of this study was to improve exposure assessments of ambient fine particulate matter (PM2.5), elemental carbon (EC), nitrogen oxides (NOx), and carbon monoxide (CO) for a repeated measurements study with 15 individuals with coronary artery disease in central North Carolina called the Coronary Artery Disease and Environmental Exposure (CADEE) study. We developed a fine-scale exposure modeling approach to determine five tiers of individual-level exposure metrics for PM2.5, EC, NOx, and CO using outdoor concentrations, on-road vehicle emissions, weather, home building characteristics, time-locations, and time-activities. We linked an urban-scale air quality model, residential air exchange rate model, building infiltration model, global positioning system (GPS)-based microenvironment model, and accelerometer-based inhaled ventilation model to determine residential outdoor concentrations (Cout_home, Tier 1), residential indoor concentrations (Cin_home, Tier 2), personal outdoor concentrations (Cout_personal, Tier 3), exposures (E, Tier 4), and inhaled doses (D, Tier 5). We applied the fine-scale exposure model to determine daily 24 h average PM2.5, EC, NOx, and CO exposure metrics (Tiers 1–5) for 720 participant-days across the 25 months of the CADEE study. Daily modeled metrics showed considerable temporal and home-to-home variability of Cout_home and Cin_home (Tiers 1–2) and person-to-person variability of Cout_personal, E, and D (Tiers 3–5). Our study demonstrates the ability to apply an urban-scale air quality model with an individual-level exposure model to determine multiple tiers of exposure metrics for an epidemiological study, in support of improving health risk assessments.

Air pollution epidemiology studies of ambient fine particulate matter (PM2.5) and ozone (O3) often use outdoor concentrations as exposure surrogates. Failure to account for the variability of the indoor infiltration of ambient PM2.5 and O3, and time indoors, can induce exposure errors. We developed an exposure model called TracMyAir, which is an iPhone application ("app") that determines seven tiers of individual-level exposure metrics in real-time for ambient PM2.5 and O3 using outdoor concentrations, weather, home building characteristics, time-locations, and time-activities. We linked a mechanistic air exchange rate (AER) model, a mass-balance PM2.5 and O3 building infiltration model, and an inhaled ventilation model to determine outdoor concentrations (Tier 1), residential AER (Tier 2), infiltration factors (Tier 3), indoor concentrations (Tier 4), personal exposure factors (Tier 5), personal exposures (Tier 6), and inhaled doses (Tier 7). Using the application in central North Carolina, we demonstrated its ability to automatically obtain real-time input data from the nearest air monitors and weather stations, and predict the exposure metrics. A sensitivity analysis showed that the modeled exposure metrics can vary substantially with changes in seasonal indoor-outdoor temperature differences, daily home operating conditions (i.e., opening windows and operating air cleaners), and time spent outdoors. The capability of TracMyAir could help reduce uncertainty of ambient PM2.5 and O3 exposure metrics used in epidemiology studies.

TPS 682: Long-term health effects of air pollutants 2, Exhibition Hall, Ground floor, August 27, 2019, 3:00 PM - 4:30 PM Background: Traffic is a primary source of urban air pollution exposure, and is associated with adverse health outcomes. There is limited information on the impact of exposure to traffic on mortality for individuals with pre-existing disease. We used the EPA CARES resource to examine the relationship between residential proximity to traffic, the distance between the primary residence and the nearest major roadway (DTR), and mortality in heart failure (HF) patients. Methods: The study cohort comprised 30,599 North Carolina (NC) residents diagnosed with HF between 2004 and 2016, who did not reside in a group home or institutional residence. Cox proportional hazards models were used to determine the association between all-cause mortality and DTR while adjusting for age, sex, race, and socioeconomic status indicators measured at the census block group: median household income, median home value, urbanicity, percent households below poverty line, and percent households receiving public assistance. Results are given in as the hazard ratio (HR) per 1 km decrease in DTR and the associated 95% confidence interval (CI). Results: In the central, more urban counties of NC (Durham, Wake, Orange, and Chatham), DTR was associated with mortality in HF patients (HR = 1.17, CI = 1.01, 1.35). The association was weaker in the entire state (HR = 1.05, CI = 0.95, 1.17), and not observed when restricting to the 96 counties outside of central NC (HR = 0.95, CI = 0.82, 1.11). The association was stronger in men than women, and in Caucasians than African-Americans. Conclusions: Residential proximity to traffic may be a significant mortality risk factor for HF patients in urban areas. With increasing urban density and HF prevalence in developed nations, it is important to understand, monitor, and communicate environmental traffic-related environmental health risks. This abstract does not necessarily reflect the policies of the U.S. EPA.