<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Riley, Kylie W</style></author><author><style face="normal" font="default" size="100%">Burke, Kimberly</style></author><author><style face="normal" font="default" size="100%">Dixon, Holly</style></author><author><style face="normal" font="default" size="100%">Holmes, Darrell</style></author><author><style face="normal" font="default" size="100%">Calero, Lehyla</style></author><author><style face="normal" font="default" size="100%">Michael L Barton</style></author><author><style face="normal" font="default" size="100%">Miller, Rachel L</style></author><author><style face="normal" font="default" size="100%">Bramer, Lisa M</style></author><author><style face="normal" font="default" size="100%">Waters, Katrina M</style></author><author><style face="normal" font="default" size="100%">Kim A Anderson</style></author><author><style face="normal" font="default" size="100%">Herbstman, Julie</style></author><author><style face="normal" font="default" size="100%">Rohlman, Diana</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Development and Outcomes of Returning Polycyclic Aromatic Hydrocarbon Exposure Results in the Washington Heights, NYC Community.</style></title><secondary-title><style face="normal" font="default" size="100%">Environ Health Insights</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Environ Health Insights</style></alt-title></titles><dates><year><style  face="normal" font="default" size="100%">2024</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2024</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">18</style></volume><pages><style face="normal" font="default" size="100%">11786302241262604</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Report-back of research results (RBRR) is becoming standard practice for environmental health research studies. RBRR is thought to increase environmental health literacy (EHL), although standardized measurements are limited. For this study, we developed a report back document on exposure to air pollutants, Polycyclic Aromatic Hydrocarbons, during pregnancy through community engaged research and evaluated whether the report increased EHL. We used focus groups and surveys to gather feedback on the report document from an initial group of study participants (Group 1, n = 22) and then sent the revised report to a larger number of participants (Group 2, n = 168). We conducted focus groups among participants in Group 1 and discussed their suggested changes to the report and how those changes could be implemented. Participants in focus groups demonstrated multiple levels of EHL. While participant engagement critically informed report development, a survey comparing feedback from Group 1 (initial report) and Group 2 (revised report) did not show a significant difference in the ease of reading the report or knowledge gained about air pollutants. We acknowledge that our approach was limited by a lack of EHL tools that assess knowledge and behavior change, and a reliance on quantitative methodologies. Future approaches that merge qualitative and quantitative methodologies to evaluate RBRR and methodologies for assessing RBRR materials and subsequent changes in knowledge, attitudes, and behavior, may be necessary.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Lisa M Bramer</style></author><author><style face="normal" font="default" size="100%">Holly Dixon</style></author><author><style face="normal" font="default" size="100%">Diana Rohlman</style></author><author><style face="normal" font="default" size="100%">Richard P Scott</style></author><author><style face="normal" font="default" size="100%">Miller, Rachel L</style></author><author><style face="normal" font="default" size="100%">Laurel D Kincl</style></author><author><style face="normal" font="default" size="100%">Julie Herbstman</style></author><author><style face="normal" font="default" size="100%">Katrina M Waters</style></author><author><style face="normal" font="default" size="100%">Kim A Anderson</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">PM Is Insufficient to Explain Personal PAH Exposure.</style></title><secondary-title><style face="normal" font="default" size="100%">Geohealth</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Geohealth</style></alt-title></titles><dates><year><style  face="normal" font="default" size="100%">2024</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2024 Feb</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">8</style></volume><pages><style face="normal" font="default" size="100%">e2023GH000937</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;To understand how chemical exposure can impact health, researchers need tools that capture the complexities of personal chemical exposure. In practice, fine particulate matter (PM) air quality index (AQI) data from outdoor stationary monitors and Hazard Mapping System (HMS) smoke density data from satellites are often used as proxies for personal chemical exposure, but do not capture total chemical exposure. Silicone wristbands can quantify more individualized exposure data than stationary air monitors or smoke satellites. However, it is not understood how these proxy measurements compare to chemical data measured from wristbands. In this study, participants wore daily wristbands, carried a phone that recorded locations, and answered daily questionnaires for a 7-day period in multiple seasons. We gathered publicly available daily PM AQI data and HMS data. We analyzed wristbands for 94 organic chemicals, including 53 polycyclic aromatic hydrocarbons. Wristband chemical detections and concentrations, behavioral variables (e.g., time spent indoors), and environmental conditions (e.g., PM AQI) significantly differed between seasons. Machine learning models were fit to predict personal chemical exposure using PM AQI only, HMS only, and a multivariate feature set including PM AQI, HMS, and other environmental and behavioral information. On average, the multivariate models increased predictive accuracy by approximately 70% compared to either the AQI model or the HMS model for all chemicals modeled. This study provides evidence that PM AQI data alone or HMS data alone is insufficient to explain personal chemical exposures. Our results identify additional key predictors of personal chemical exposure.&lt;/p&gt;
</style></abstract><issue><style face="normal" font="default" size="100%">2</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">McLarnan, Sarah M</style></author><author><style face="normal" font="default" size="100%">Lisa M Bramer</style></author><author><style face="normal" font="default" size="100%">Holly Dixon</style></author><author><style face="normal" font="default" size="100%">Richard P Scott</style></author><author><style face="normal" font="default" size="100%">Lehyla Calero</style></author><author><style face="normal" font="default" size="100%">Darrell Holmes</style></author><author><style face="normal" font="default" size="100%">Gibson, Elizabeth A</style></author><author><style face="normal" font="default" size="100%">Cavalier, Haleigh M</style></author><author><style face="normal" font="default" size="100%">Diana Rohlman</style></author><author><style face="normal" font="default" size="100%">Miller, Rachel L</style></author><author><style face="normal" font="default" size="100%">Laurel D Kincl</style></author><author><style face="normal" font="default" size="100%">Katrina M Waters</style></author><author><style face="normal" font="default" size="100%">Kim A Anderson</style></author><author><style face="normal" font="default" size="100%">Julie Herbstman</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Predicting personal PAH exposure using high dimensional questionnaire and wristband data.</style></title><secondary-title><style face="normal" font="default" size="100%">J Expo Sci Environ Epidemiol</style></secondary-title><alt-title><style face="normal" font="default" size="100%">J Expo Sci Environ Epidemiol</style></alt-title></titles><dates><year><style  face="normal" font="default" size="100%">2024</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2024 Jan 05</style></date></pub-dates></dates><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;&lt;strong&gt;BACKGROUND: &lt;/strong&gt;Polycyclic aromatic hydrocarbons (PAHs) are a class of pervasive environmental pollutants with a variety of known health effects. While significant work has been completed to estimate personal exposure to PAHs, less has been done to identify sources of these exposures. Comprehensive characterization of reported sources of personal PAH exposure is a critical step to more easily identify individuals at risk of high levels of exposure and for developing targeted interventions based on source of exposure.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;OBJECTIVE: &lt;/strong&gt;In this study, we leverage data from a New York (NY)-based birth cohort to identify personal characteristics or behaviors associated with personal PAH exposure and develop models for the prediction of PAH exposure.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;METHODS: &lt;/strong&gt;We quantified 61 PAHs measured using silicone wristband samplers in association with 75 questionnaire variables from 177 pregnant individuals. We evaluated univariate associations between each compound and questionnaire variable, conducted regression tree analysis for each PAH compound and completed a principal component analysis of for each participant&#039;s entire PAH exposure profile to determine the predictors of PAH levels.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;RESULTS: &lt;/strong&gt;Regression tree analyses of individual compounds and exposure mixture identified income, time spent outdoors, maternal age, country of birth, transportation type, and season as the variables most frequently predictive of exposure.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Holly Dixon</style></author><author><style face="normal" font="default" size="100%">Lisa M Bramer</style></author><author><style face="normal" font="default" size="100%">Richard P Scott</style></author><author><style face="normal" font="default" size="100%">Lehyla Calero</style></author><author><style face="normal" font="default" size="100%">Darrell Holmes</style></author><author><style face="normal" font="default" size="100%">Gibson, Elizabeth A</style></author><author><style face="normal" font="default" size="100%">Cavalier, Haleigh M</style></author><author><style face="normal" font="default" size="100%">Diana Rohlman</style></author><author><style face="normal" font="default" size="100%">Miller, Rachel L</style></author><author><style face="normal" font="default" size="100%">Antonia M Calafat</style></author><author><style face="normal" font="default" size="100%">Laurel D Kincl</style></author><author><style face="normal" font="default" size="100%">Katrina M Waters</style></author><author><style face="normal" font="default" size="100%">Julie Herbstman</style></author><author><style face="normal" font="default" size="100%">Kim A Anderson</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Evaluating predictive relationships between wristbands and urine for assessment of personal PAH exposure.</style></title><secondary-title><style face="normal" font="default" size="100%">Environ Int</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Environ Int</style></alt-title></titles><dates><year><style  face="normal" font="default" size="100%">2022</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2022 Apr 04</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">163</style></volume><pages><style face="normal" font="default" size="100%">107226</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;During events like the COVID-19 pandemic or a disaster, researchers may need to switch from collecting biological samples to personal exposure samplers that are easy and safe to transport and wear, such as silicone wristbands. Previous studies have demonstrated significant correlations between urine biomarker concentrations and chemical levels in wristbands. We build upon those studies and use a novel combination of descriptive statistics and supervised statistical learning to evaluate the relationship between polycyclic aromatic hydrocarbon (PAH) concentrations in silicone wristbands and hydroxy-PAH (OH-PAH) concentrations in urine. In New York City, 109 participants in a longitudinal birth cohort wore one wristband for 48&amp;nbsp;h and provided a spot urine sample at the end of the 48-hour period during their third trimester of pregnancy. We compared four PAHs with the corresponding seven OH-PAHs using descriptive statistics, a linear regression model, and a linear discriminant analysis model. Five of the seven PAH and OH-PAH pairs had significant correlations (Pearson&#039;s r&amp;nbsp;=&amp;nbsp;0.35-0.64, p&amp;nbsp;≤&amp;nbsp;0.003) and significant chi-square tests of independence for exposure categories (p&amp;nbsp;≤&amp;nbsp;0.009). For these five comparisons, the observed PAH or OH-PAH concentration could predict the other concentration within a factor of 1.47 for 50-80% of the measurements (depending on the pair). Prediction accuracies for high exposure categories were at least 1.5 times higher compared to accuracies based on random chance. These results demonstrate that wristbands and urine provide similar PAH exposure assessment information, which is critical for environmental health researchers looking for the flexibility to switch between biological sample and wristband collection.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Evoy, Richard</style></author><author><style face="normal" font="default" size="100%">Laurel D Kincl</style></author><author><style face="normal" font="default" size="100%">Diana Rohlman</style></author><author><style face="normal" font="default" size="100%">Lisa M Bramer</style></author><author><style face="normal" font="default" size="100%">Holly Dixon</style></author><author><style face="normal" font="default" size="100%">Hystad, Perry</style></author><author><style face="normal" font="default" size="100%">Bae, Harold</style></author><author><style face="normal" font="default" size="100%">Michael L Barton</style></author><author><style face="normal" font="default" size="100%">Phillips, Aaron</style></author><author><style face="normal" font="default" size="100%">Miller, Rachel L</style></author><author><style face="normal" font="default" size="100%">Katrina M Waters</style></author><author><style face="normal" font="default" size="100%">Julie Herbstman</style></author><author><style face="normal" font="default" size="100%">Kim A Anderson</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Impact of acute temperature and air pollution exposures on adult lung function: A panel study of asthmatics.</style></title><secondary-title><style face="normal" font="default" size="100%">PLoS One</style></secondary-title><alt-title><style face="normal" font="default" size="100%">PLoS One</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Adult</style></keyword><keyword><style  face="normal" font="default" size="100%">Air Pollution</style></keyword><keyword><style  face="normal" font="default" size="100%">Asthma</style></keyword><keyword><style  face="normal" font="default" size="100%">Bronchodilator Agents</style></keyword><keyword><style  face="normal" font="default" size="100%">Environmental Exposure</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Lung</style></keyword><keyword><style  face="normal" font="default" size="100%">Temperature</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2022</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2022</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">17</style></volume><pages><style face="normal" font="default" size="100%">e0270412</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;&lt;strong&gt;BACKGROUND: &lt;/strong&gt;Individuals with respiratory conditions, such as asthma, are particularly susceptible to adverse health effects associated with higher levels of ambient air pollution and temperature. This study evaluates whether hourly levels of fine particulate matter (PM2.5) and dry bulb globe temperature (DBGT) are associated with the lung function of adult participants with asthma.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;METHODS AND FINDINGS: &lt;/strong&gt;Global positioning system (GPS) location, respiratory function (measured as forced expiratory volume at 1 second (FEV1)), and self-reports of asthma medication usage and symptoms were collected as part of the Exposure, Location, and Lung Function (ELF) study. Hourly ambient PM2.5 and DBGT exposures were estimated by integrating air quality and temperature public records with time-activity patterns using GPS coordinates for each participant (n = 35). The relationships between acute PM2.5, DBGT, rescue bronchodilator use, and lung function collected in one week periods and over two seasons (summer/winter) were analyzed by multivariate regression, using different exposure time frames. In separate models, increasing levels in PM2.5, but not DBGT, were associated with rescue bronchodilator use. Conversely DBGT, but not PM2.5, had a significant association with FEV1. When DBGT and PM2.5 exposures were placed in the same model, the strongest association between cumulative PM2.5 exposures and the use of rescue bronchodilator was identified at the 0-24 hours (OR = 1.030; 95% CI = 1.012-1.049; p-value = 0.001) and 0-48 hours (OR = 1.030; 95% CI = 1.013-1.057; p-value = 0.001) prior to lung function measure. Conversely, DBGT exposure at 0 hours (β = 3.257; SE = 0.879; p-value&amp;gt;0.001) and 0-6 hours (β = 2.885; SE = 0.903; p-value = 0.001) hours before a reading were associated with FEV1. No significant interactions between DBGT and PM2.5 were observed for rescue bronchodilator use or FEV1.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;CONCLUSIONS: &lt;/strong&gt;Short-term increases in PM2.5 were associated with increased rescue bronchodilator use, while DBGT was associated with higher lung function (i.e. FEV1). Further studies are needed to continue to elucidate the mechanisms of acute exposure to PM2.5 and DBGT on lung function in asthmatics.&lt;/p&gt;
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