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Background: High mammographic density is a strong, well-established breast cancer risk factor. Three studies conducted in various smaller geographic settings reported inconsistent findings between air pollution and mammographic density. We assessed whether particulate matter (PM) exposures (PM2.5, PM2.5-10, and PM10) and distance to roadways were associated with mammographic density among women residing across the United States.Methods: The Nurses' Health Studies are prospective cohorts for whom a subset has screening mammograms from the 1990s (interquartile range 1990-1999). PM was estimated using spatio-temporal models linked to residential addresses. Among 3258 women (average age at mammogram 52.7 years), we performed multivariable linear regression to assess associations between square-root-transformed mammographic density and PM within 1 and 3 years before the mammogram. For linear regression estimates of PM in relation to untransformed mammographic density outcomes, bootstrapped robust standard errors are used to calculate 95% confidence intervals (CIs). Analyses were stratified by menopausal status and region of residence.Results: Recent PM and distance to roadways were not associated with mammographic density in premenopausal women (PM2.5 within 3 years before mammogram β = 0.05, 95% CI -0.16, 0.27; PM2.5-10 β = 0, 95%, CI -0.15, 0.16; PM10 β = 0.02, 95% CI -0.10, 0.13) and postmenopausal women (PM2.5 within 3 years before mammogram β = -0.05, 95% CI -0.27, 0.17; PM2.5-10 β = -0.01, 95% CI -0.16, 0.14; PM10 β = -0.02, 95% CI -0.13, 0.09). Largely null associations were observed within regions. Suggestive associations were observed among postmenopausal women in the Northeast (n = 745), where a 10-μg/m3 increase in PM2.5 within 3 years before the mammogram was associated with 3.4 percentage points higher percent mammographic density (95% CI -0.5, 7.3).Conclusions: These findings do not support that recent PM or roadway exposures influence mammographic density. Although PM was largely not associated with mammographic density, we cannot rule out the role of PM during earlier exposure time windows and possible associations among northeastern postmenopausal women. [ABSTRACT FROM AUTHOR]

Background: Radon and its decay products, a source of ionizing radiation, are primarily inhaled and can deliver a radiation dose to breast tissue, where they may continue to decay and emit DNA damage-inducing particles. Few studies have examined the relationship between radon and breast cancer.Methods: The Nurses' Health Study II (NHSII) includes U.S. female registered nurses who completed biennial questionnaires since 1989. Self-reported breast cancer was confirmed from medical records. County-level radon exposures were linked with geocoded residential addresses updated throughout follow-up. Time-varying Cox regression models adjusted for established breast cancer risk factors were used to calculate hazard ratios (HRs) and 95% confidence intervals (CIs).Results: From 1989 to 2013, 3966 invasive breast cancer cases occurred among 112,639 participants. Increasing radon exposure was not associated with breast cancer risk overall (adjusted HR comparing highest to lowest quintile = 1.06, 95% CI: 0.94, 1.21, p for trend = 0.30). However, women in the highest quintile of exposure (≥74.9 Bq/m3) had a suggested elevated risk of ER-/PR- breast cancer compared to women in the lowest quintile (<27.0 Bq/m3) (adjusted HR = 1.38, 95% CI: 0.97, 1.96, p for trend = 0.05). No association was observed for ER+/PR+ breast cancer.Conclusions: Although we did not find an association between radon exposure and risk of overall or ER+/PR+ breast cancer, we observed a suggestive association with risk of ER-/PR- breast cancer. [ABSTRACT FROM AUTHOR]

Background: Hepatocellular carcinoma (HCC), the most commonly occurring type of primary liver cancer, has been increasing in incidence worldwide. Vitamin D, acquired from sunlight exposure, diet, and dietary supplements, has been hypothesized to impact hepatocarcinogenesis. However, previous epidemiologic studies examining the associations between dietary and serum vitamin D reported mixed results. The purpose of this study was to examine the association between ambient ultraviolet (UV) radiation exposure and HCC risk in the U.S.Methods: The Surveillance, Epidemiology, and End Results (SEER) database provided information on HCC cases diagnosed between 2000 and 2014 from 16 population-based cancer registries across the U.S. Ambient UV exposure was estimated by linking the SEER county with a spatiotemporal UV exposure model using a geographic information system. Poisson regression with robust variance estimation was used to calculate incidence rate ratios (IRRs) and 95% confidence intervals (CIs) for the association between ambient UV exposure per interquartile range (IQR) increase (32.4 mW/m2) and HCC risk adjusting for age at diagnosis, sex, race, year of diagnosis, SEER registry, and county-level information on prevalence of health conditions, lifestyle, socioeconomic, and environmental factors.Results: Higher levels of ambient UV exposure were associated with statistically significant lower HCC risk (n = 56,245 cases; adjusted IRR per IQR increase: 0.83, 95% CI 0.77, 0.90; p < 0.01). A statistically significant inverse association between ambient UV and HCC risk was observed among males (p for interaction = 0.01) and whites (p for interaction = 0.01).Conclusions: Higher ambient UV exposure was associated with a decreased risk of HCC in the U.S. UV exposure may be a potential modifiable risk factor for HCC that should be explored in future research. [ABSTRACT FROM AUTHOR]

Background: Systemic inflammation has been associated with reduced pulmonary function in individuals with and without chronic medical conditions. Individuals with chronic spinal cord injury (SCI) have clinical characteristics that promote systemic inflammation and also have reduced pulmonary function. We sought to assess the associations between biomarkers of systemic inflammation with pulmonary function in a chronic SCI cohort, adjusting for other potential confounding factors.Methods: Participants (n = 311) provided a blood sample, completed a respiratory health questionnaire, and underwent spirometry. Linear regression methods were used to assess cross-sectional associations between plasma C-reactive protein (CRP) and interleukin-6 (IL-6) with forced expiratory volume in one second (FEV1), forced vital capacity (FVC), and FEV1/FVC.Results: There were statistically significant inverse relationships between plasma CRP and IL-6 assessed in quartiles or continuously with FEV1 and FVC. In fully adjusted models, each interquartile range (5.91 mg/L) increase in CRP was associated with a significant decrease in FEV1 (-55.85 ml; 95% CI: -89.21, -22.49) and decrease in FVC (-65.50 ml; 95% CI: -106.61, -24.60). There were similar significant findings for IL-6. There were no statistically significant associations observed with FEV1/FVC.Conclusion: Plasma CRP and IL-6 in individuals with chronic SCI are inversely associated with FEV1 and FVC, independent of SCI level and severity of injury, BMI, and other covariates. This finding suggests that systemic inflammation associated with chronic SCI may contribute to reduced pulmonary function. [ABSTRACT FROM AUTHOR]

Background: Occupational exposures to ultrafine particles in the plume generated during laser hair removal procedures, the most commonly performed light based cosmetic procedure, have not been thoroughly characterized. Acute and chronic exposures to ambient ultrafine particles have been associated with a number of negative respiratory and cardiovascular health effects. Thus, the aim of this study was to measure airborne concentrations of particles in a diameter size range of 10 nm to 1 μm in procedure rooms during laser hair removal procedures.Methods: TSI Model 3007 Condensation Particle Counters were used to quantify the particle count concentrations in the waiting and procedure rooms of a dermatology office. Particle concentrations were sampled before, during, and after laser hair removal procedures, and characteristics of each procedure were noted by the performing dermatologist.Results: Twelve procedures were sampled over 4 days. Mean ultrafine particle concentrations in the waiting and procedure rooms were 14,957.4 particles/cm3 and 22,916.8 particles/cm3 (p < 0.0001), respectively. Compared to background ultrafine particle concentrations before the procedure, the mean concentration in the procedure room was 2.89 times greater during the procedure (p = 0.009) and 2.09 times greater after the procedure (p = 0.007). Duration of procedure (p = 0.006), body part (p = 0.013), and the use of pre-laser lotion/type of laser (p = 0.039), were the most important predictors of ultrafine particle concentrations. Use of a smoke evacuator (a recommended form of local exhaust ventilation) positioned at 30.5 cm from the source, as opposed to the recommended 1-2 in., lowered particle concentrations, but was not a statistically significant predictor (p = 0.49).Conclusions: Laser hair removal procedures can generate high exposures to ultrafine particles for dermatologists and other individuals performing laser hair removal, with exposure varying based on multiple determinants. [ABSTRACT FROM AUTHOR]

Background: Ultraviolet B (UV-B) radiation plays a multifaceted role in human health, inducing DNA damage and representing the primary source of vitamin D for most humans; however, current U.S. UV exposure models are limited in spatial, temporal, and/or spectral resolution. Area-to-point (ATP) residual kriging is a geostatistical method that can be used to create a spatiotemporal exposure model by downscaling from an area- to point-level spatial resolution using fine-scale ancillary data. Methods: A stratified ATP residual kriging approach was used to predict average July noon-time erythemal UV (UVEry) (mW/m2) biennially from 1998 to 2012 by downscaling National Aeronautics and Space Administration (NASA) Total Ozone Mapping Spectrometer (TOMS) and Ozone Monitoring Instrument (OMI) gridded remote sensing images to a 1 km spatial resolution. Ancillary data were incorporated in random intercept linear mixedeffects regression models. Modeling was performed separately within nine U.S. regions to satisfy stationarity and account for locally varying associations between UVEry and predictors. Cross-validation was used to compare ATP residual kriging models and NASA grids to UV-B Monitoring and Research Program (UVMRP) measurements (gold standard). Results: Predictors included in the final regional models included surface albedo, aerosol optical depth (AOD), cloud cover, dew point, elevation, latitude, ozone, surface incoming shortwave flux, sulfur dioxide (SO2), year, and interactions between year and surface albedo, AOD, cloud cover, dew point, elevation, latitude, and SO2. ATP residual kriging models more accurately estimated UVEry at UVMRP monitoring stations on average compared to NASA grids across the contiguous U.S. (average mean absolute error [MAE] for ATP, NASA: 15.8, 20.3; average root mean square error [RMSE]: 21.3, 25.5). ATP residual kriging was associated with positive percent relative improvements in MAE (0.6-31.5%) and RMSE (3.6-29.4%) across all regions compared to NASA grids. Conclusions: ATP residual kriging incorporating fine-scale spatial predictors can provide more accurate, high-resolution UVEry estimates compared to using NASA grids and can be used in epidemiologic studies examining the health effects of ambient UV. [ABSTRACT FROM AUTHOR]

Background: Exposure to air pollution, including traffic-related pollutants, has been associated with a variety of adverse health outcomes, including increased cardiopulmonary morbidity and mortality, and increased lung cancer risk. Methods: To better understand the cellular responses induced by air pollution exposures, we performed genomewide gene expression microarray analysis using whole blood RNA sampled at three time-points across the work weeks of 63 non-smoking employees at 10 trucking terminals in the northeastern US. We defined genes and gene networks that were differentially activated in response to PM2.5 (particulate matter ≤ 2.5 microns in diameter) and elemental carbon (EC) and organic carbon (OC). Results: Multiple transcripts were strongly associated (padj < 0.001) with pollutant levels (48, 260, and 49 transcripts for EC, OC, and PM2.5, respectively), including 63 that were statistically significantly correlated with at least two out of the three exposures. These genes included many that have been implicated in ischemic heart disease, chronic obstructive pulmonary disease (COPD), lung cancer, and other pollution-related illnesses. Through the combination of Gene Set Enrichment Analysis and network analysis (using GeneMANIA), we identified a core set of 25 interrelated genes that were common to all three exposure measures and were differentially expressed in two previous studies assessing gene expression attributable to air pollution. Many of these are members of fundamental cancer-related pathways, including those related to DNA and metal binding, and regulation of apoptosis and also but include genes implicated in chronic heart and lung diseases. Conclusions: These data provide a molecular link between the associations of air pollution exposures with health effects. [ABSTRACT FROM AUTHOR]

Background: Long-term exposure to particulate matter less than 2.5 μm in diameter (PM2.5) has been consistently associated with risk of all-cause mortality. The methods used to assess exposure, such as area averages, nearest monitor values, land use regressions, and spatio-temporal models in these studies are subject to measurement error. However, to date, no study has attempted to incorporate adjustment for measurement error into a long-term study of the effects of air pollution on mortality. Methods: We followed 108,767 members of the Nurses' Health Study (NHS) 2000-2006 and identified all deaths. Biennial mailed questionnaires provided a detailed residential address history and updated information on potential confounders. Time-varying average PM2.5 in the previous 12-months was assigned based on residential address and was predicted from either spatio-temporal prediction models or as concentrations measured at the nearest USEPA monitor. Information on the relationships of personal exposure to PM2.5 of ambient origin with spatio-temporal predicted and nearest monitor PM2.5 was available from five previous validation studies. Time-varying Cox proportional hazards models were used to estimate hazard ratios (HRs) and 95 percent confidence intervals (95%CI) for each 10 μg/m3 increase in PM2.5. Risk-set regression calibration was used to adjust estimates for measurement error. Results: Increasing exposure to PM2.5 was associated with an increased risk of mortality, and results were similar regardless of the method chosen for exposure assessment. Specifically, the multivariable adjusted HRs for each 10 μg/m3 increase in 12-month average PM2.5 from spatio-temporal prediction models were 1.13 (95%CI:1.05, 1.22) and 1.12 (95%CI:1.05, 1.21) for concentrations at the nearest EPA monitoring location. Adjustment for measurement error increased the magnitude of the HRs 4-10% and led to wider CIs (HR = 1.18; 95%CI: 1.02, 1.36 for each 10 μg/m3 increase in PM2.5 from the spatio-temporal models and HR = 1.22; 95%CI: 1.02, 1.45 from the nearest monitor estimates). Conclusions: These findings support the large body of literature on the adverse effects of PM2.5, and suggest that adjustment for measurement error be considered in future studies where possible. [ABSTRACT FROM AUTHOR]

Background Identifying spatial clusters of chronic diseases has been conducted over the past several decades. More recently these approaches have been applied to physical activity and obesity. However, few studies have investigated built environment characteristics in relation to these spatial clusters. This study's aims were to detect spatial clusters of physical activity and obesity, examine whether the geographic distribution of covariates affects clusters, and compare built environment characteristics inside and outside clusters. Methods In 2004, Nurses' Health Study participants from California, Massachusetts, and Pennsylvania completed survey items on physical activity (N = 22,599) and weight-status (N = 19,448). The spatial scan statistic was utilized to detect spatial clustering of higher and lower likelihood of obesity and meeting physical activity recommendations via walking. Clustering analyses and tests that adjusted for socio-demographic and health-related variables were conducted. Neighborhood built environment characteristics for participants inside and outside spatial clusters were compared. Results Seven clusters of physical activity were identified in California and Massachusetts. Two clusters of obesity were identified in Pennsylvania. Overall, adjusting for socio-demographic and health-related covariates had little effect on the size or location of clusters in the three states with a few exceptions. For instance, adjusting for husband's education fully accounted for physical activity clusters in California. In California and Massachusetts, population density, intersection density, and diversity and density of facilities in two higher physical activity clusters were significantly greater than in neighborhoods outside of clusters. In contrast, in two other higher physical activity clusters in California and Massachusetts, population density, diversity of facilities, and density of facilities were significantly lower than in areas outside of clusters. In Pennsylvania, population density, intersection density, diversity of facilities, and certain types of facility density inside obesity clusters were significantly lower compared to areas outside the clusters. Conclusions Spatial clustering techniques can identify high and low risk areas for physical activity and obesity. Although covariates significantly differed inside and outside the clusters, patterns of differences were mostly inconsistent. The findings from these spatial analyses could eventually facilitate the design and implementation of more resource-efficient, geographically targeted interventions for both physical activity and obesity. [ABSTRACT FROM AUTHOR]

Background: Systemic inflammation has been associated with reduced pulmonary function in individuals with and without chronic medical conditions. Individuals with chronic spinal cord injury (SCI) have clinical characteristics that promote systemic inflammation and also have reduced pulmonary function. We sought to assess the associations between biomarkers of systemic inflammation with pulmonary function in a chronic SCI cohort, adjusting for other potential confounding factors. Methods: Participants (n = 311) provided a blood sample, completed a respiratory health questionnaire, and underwent spirometry. Linear regression methods were used to assess cross-sectional associations between plasma C-reactive protein (CRP) and interleukin-6 (IL-6) with forced expiratory volume in one second (FEV1), forced vital capacity (FVC), and FEV1/FVC. Results: There were statistically significant inverse relationships between plasma CRP and IL-6 assessed in quartiles or continuously with FEV1 and FVC. In fully adjusted models, each interquartile range (5.91 mg/L) increase in CRP was associated with a significant decrease in FEV1 (−55.85 ml; 95% CI: -89.21, −22.49) and decrease in FVC (−65.50 ml; 95% CI: -106.61, −24.60). There were similar significant findings for IL-6. There were no statistically significant associations observed with FEV1/FVC. Conclusion: Plasma CRP and IL-6 in individuals with chronic SCI are inversely associated with FEV1 and FVC, independent of SCI level and severity of injury, BMI, and other covariates. This finding suggests that systemic inflammation associated with chronic SCI may contribute to reduced pulmonary function. Electronic supplementary material The online version of this article (doi:10.1186/s12890-017-0459-6) contains supplementary material, which is available to authorized users.

Background: Ultraviolet B (UV-B) radiation plays a multifaceted role in human health, inducing DNA damage and representing the primary source of vitamin D for most humans; however, current U.S. UV exposure models are limited in spatial, temporal, and/or spectral resolution. Area-to-point (ATP) residual kriging is a geostatistical method that can be used to create a spatiotemporal exposure model by downscaling from an area- to point-level spatial resolution using fine-scale ancillary data. Methods: A stratified ATP residual kriging approach was used to predict average July noon-time erythemal UV (UVEry) (mW/m2) biennially from 1998 to 2012 by downscaling National Aeronautics and Space Administration (NASA) Total Ozone Mapping Spectrometer (TOMS) and Ozone Monitoring Instrument (OMI) gridded remote sensing images to a 1 km spatial resolution. Ancillary data were incorporated in random intercept linear mixed-effects regression models. Modeling was performed separately within nine U.S. regions to satisfy stationarity and account for locally varying associations between UVEry and predictors. Cross-validation was used to compare ATP residual kriging models and NASA grids to UV-B Monitoring and Research Program (UVMRP) measurements (gold standard). Results: Predictors included in the final regional models included surface albedo, aerosol optical depth (AOD), cloud cover, dew point, elevation, latitude, ozone, surface incoming shortwave flux, sulfur dioxide (SO2), year, and interactions between year and surface albedo, AOD, cloud cover, dew point, elevation, latitude, and SO2. ATP residual kriging models more accurately estimated UVEry at UVMRP monitoring stations on average compared to NASA grids across the contiguous U.S. (average mean absolute error [MAE] for ATP, NASA: 15.8, 20.3; average root mean square error [RMSE]: 21.3, 25.5). ATP residual kriging was associated with positive percent relative improvements in MAE (0.6–31.5%) and RMSE (3.6–29.4%) across all regions compared to NASA grids. Conclusions: ATP residual kriging incorporating fine-scale spatial predictors can provide more accurate, high-resolution UVEry estimates compared to using NASA grids and can be used in epidemiologic studies examining the health effects of ambient UV. Electronic supplementary material The online version of this article (doi:10.1186/s12940-016-0197-x) contains supplementary material, which is available to authorized users.

Background: Exposure to air pollution, including traffic-related pollutants, has been associated with a variety of adverse health outcomes, including increased cardiopulmonary morbidity and mortality, and increased lung cancer risk. Methods: To better understand the cellular responses induced by air pollution exposures, we performed genome-wide gene expression microarray analysis using whole blood RNA sampled at three time-points across the work weeks of 63 non-smoking employees at 10 trucking terminals in the northeastern US. We defined genes and gene networks that were differentially activated in response to PM2.5 (particulate matter ≤ 2.5 microns in diameter) and elemental carbon (EC) and organic carbon (OC). Results: Multiple transcripts were strongly associated (padj < 0.001) with pollutant levels (48, 260, and 49 transcripts for EC, OC, and PM2.5, respectively), including 63 that were statistically significantly correlated with at least two out of the three exposures. These genes included many that have been implicated in ischemic heart disease, chronic obstructive pulmonary disease (COPD), lung cancer, and other pollution-related illnesses. Through the combination of Gene Set Enrichment Analysis and network analysis (using GeneMANIA), we identified a core set of 25 interrelated genes that were common to all three exposure measures and were differentially expressed in two previous studies assessing gene expression attributable to air pollution. Many of these are members of fundamental cancer-related pathways, including those related to DNA and metal binding, and regulation of apoptosis and also but include genes implicated in chronic heart and lung diseases. Conclusions: These data provide a molecular link between the associations of air pollution exposures with health effects. Electronic supplementary material The online version of this article (doi:10.1186/s12940-016-0187-z) contains supplementary material, which is available to authorized users.

Background: Previous studies have suggested associations of adult exposures to secondhand smoke (SHS) with respiratory symptoms, but no study has focused on blue-collar industrial environments. We assessed the association between SHS and respiratory symptoms in 1,562 non-current smoking U.S. trucking industry workers. Methods: Information on SHS exposure and respiratory health was obtained by questionnaire. Multiple logistic regression analyses were used to assess the associations of recent and lifetime exposures to SHS with chronic phlegm, chronic cough, and any wheeze, defined by American Thoracic Society criteria. Results: In analyses adjusted for age, gender, race, childhood SHS exposure, former smoking, pack-years of smoking and years since quitting, body mass index, job title, region of the country, and urban residence, recent exposures to SHS were associated with all three respiratory symptoms (odds ratio (OR) = 1.46; 95% confidence interval (CI) = 1.00- 2.13) for chronic cough, 1.55 (95% CI = 1.08-2.21) for chronic phlegm, and 1.76 (95% CI = 1.41-2.21) for any wheeze). Workplace exposure was the most important recent exposure. Childhood exposure to SHS was also associated with all three symptoms, but only statistically significantly for chronic phlegm (OR = 1.84; 95% CI = 1.24-2.75). Additional years of living with a smoker were associated with an increased risk, but there was no evidence of a dose-response, except for chronic phlegm. Conclusions: In this group of trucking industry workers, childhood and recent exposures to SHS were related to respiratory symptoms. [ABSTRACT FROM AUTHOR]

Background: Experimental evidence suggests that inhaled particles from vehicle exhaust have systemic effects on inflammation, endothelial activation and oxidative stress. In the present study we assess the relationships of short-term exposures with inflammatory endothelial activation and oxidative stress biomarker levels in a population of trucking industry workers.Methods: Blood and urine samples were collected pre and post-shift, at the beginning and end of a workweek from 67 male non-smoking US trucking industry workers. Concurrent measurements of microenvironment concentrations of elemental and organic carbon (EC & OC), and fine particulate matter (PM2.5) combined with time activity patterns allowed for calculation of individual exposures. Associations between daily and first and last-day average levels of exposures and repeated measures of intercellular and vascular cell adhesion molecule-1 (ICAM-1 & VCAM-1), interleukin 6 (IL-6) and C-reactive protein (CRP) blood levels and urinary 8-Hydroxy-2'-Deoxyguanosine (8-OHdG) were assessed using linear mixed effects models for repeated measures.Results: There was a statistically significant association between first and last-day average PM2.5 and 8-OHdG (21% increase, 95% CI: 2, 42%) and first and last-day average OC and IL-6 levels (18% increase 95% CI: 1, 37%) per IQR in exposure. There were no significant findings associated with EC or associations suggesting acute cross-shift effects.Conclusion: Our findings suggest associations between weekly average exposures of PM2.5 on markers of oxidative stress and OC on IL-6 levels. [ABSTRACT FROM AUTHOR]

Background: Long-term exposure to particulate matter less than 2.5 μm in diameter (PM2.5) has been consistently associated with risk of all-cause mortality. The methods used to assess exposure, such as area averages, nearest monitor values, land use regressions, and spatio-temporal models in these studies are subject to measurement error. However, to date, no study has attempted to incorporate adjustment for measurement error into a long-term study of the effects of air pollution on mortality. Methods: We followed 108,767 members of the Nurses’ Health Study (NHS) 2000–2006 and identified all deaths. Biennial mailed questionnaires provided a detailed residential address history and updated information on potential confounders. Time-varying average PM2.5 in the previous 12-months was assigned based on residential address and was predicted from either spatio-temporal prediction models or as concentrations measured at the nearest USEPA monitor. Information on the relationships of personal exposure to PM2.5 of ambient origin with spatio-temporal predicted and nearest monitor PM2.5 was available from five previous validation studies. Time-varying Cox proportional hazards models were used to estimate hazard ratios (HRs) and 95 percent confidence intervals (95%CI) for each 10 μg/m3 increase in PM2.5. Risk-set regression calibration was used to adjust estimates for measurement error. Results: Increasing exposure to PM2.5 was associated with an increased risk of mortality, and results were similar regardless of the method chosen for exposure assessment. Specifically, the multivariable adjusted HRs for each 10 μg/m3 increase in 12-month average PM2.5 from spatio-temporal prediction models were 1.13 (95%CI:1.05, 1.22) and 1.12 (95%CI:1.05, 1.21) for concentrations at the nearest EPA monitoring location. Adjustment for measurement error increased the magnitude of the HRs 4-10% and led to wider CIs (HR = 1.18; 95%CI: 1.02, 1.36 for each 10 μg/m3 increase in PM2.5 from the spatio-temporal models and HR = 1.22; 95%CI: 1.02, 1.45 from the nearest monitor estimates). Conclusions: These findings support the large body of literature on the adverse effects of PM2.5, and suggest that adjustment for measurement error be considered in future studies where possible.

Background: Exposure to air pollution has been implicated in a number of adverse health outcomes and the effect of particulate matter (PM) on the brain is beginning to be recognized. Yet, no prospective study has examined the association between PM and risk of Parkinson Disease. Thus, our goal was assess if exposure to particulate matter air pollution is related to risk of Parkinson’s disease (PD) in the Nurses’ Health Study (NHS), a large prospective cohort of women. Methods: Cumulative average exposure to different size fractions of PM up to 2 years before the onset of PD, was estimated using a spatio-temporal model by linking each individual’s places of residence throughout the study with location-specific air pollution levels. We prospectively followed 115,767 women in the NHS, identified 508 incident PD cases and used multivariable Cox proportional hazards models to estimate the risk of PD associated with each size fraction of PM independently. Results: In models adjusted for age in months, smoking, region, population density, caffeine and ibuprofen intake, we observed no statistically significant associations between exposure to air pollution and PD risk. The relative risk (RR) comparing the top quartile to the bottom quartile of PM exposure was 0.99 (95% Confidence Intervals (CI): 0.84,1.16) for PM10 (≤10 microns in diameter), 1.08 (95% CI: 0.81, 1.45) for PM2.5 (≤2.5 microns in diameter), and 0.92 (95% CI: 0.71, 1.19) for PM10–2.5 (2.5 to 10 microns in diameter). Conclusions: In this study, we found no evidence that exposure to air pollution is a risk factor for PD.

Background: Identifying spatial clusters of chronic diseases has been conducted over the past several decades. More recently these approaches have been applied to physical activity and obesity. However, few studies have investigated built environment characteristics in relation to these spatial clusters. This study’s aims were to detect spatial clusters of physical activity and obesity, examine whether the geographic distribution of covariates affects clusters, and compare built environment characteristics inside and outside clusters. Methods: In 2004, Nurses’ Health Study participants from California, Massachusetts, and Pennsylvania completed survey items on physical activity (N = 22,599) and weight-status (N = 19,448). The spatial scan statistic was utilized to detect spatial clustering of higher and lower likelihood of obesity and meeting physical activity recommendations via walking. Clustering analyses and tests that adjusted for socio-demographic and health-related variables were conducted. Neighborhood built environment characteristics for participants inside and outside spatial clusters were compared. Results: Seven clusters of physical activity were identified in California and Massachusetts. Two clusters of obesity were identified in Pennsylvania. Overall, adjusting for socio-demographic and health-related covariates had little effect on the size or location of clusters in the three states with a few exceptions. For instance, adjusting for husband’s education fully accounted for physical activity clusters in California. In California and Massachusetts, population density, intersection density, and diversity and density of facilities in two higher physical activity clusters were significantly greater than in neighborhoods outside of clusters. In contrast, in two other higher physical activity clusters in California and Massachusetts, population density, diversity of facilities, and density of facilities were significantly lower than in areas outside of clusters. In Pennsylvania, population density, intersection density, diversity of facilities, and certain types of facility density inside obesity clusters were significantly lower compared to areas outside the clusters. Conclusions: Spatial clustering techniques can identify high and low risk areas for physical activity and obesity. Although covariates significantly differed inside and outside the clusters, patterns of differences were mostly inconsistent. The findings from these spatial analyses could eventually facilitate the design and implementation of more resource-efficient, geographically targeted interventions for both physical activity and obesity.

Background: Exposure to atmospheric particulate matter (PM) remains an important public health concern, although it remains difficult to quantify accurately across large geographic areas with sufficiently high spatial resolution. Recent epidemiologic analyses have demonstrated the importance of spatially- and temporally-resolved exposure estimates, which show larger PM-mediated health effects as compared to nearest monitor or county-specific ambient concentrations. Methods: We developed generalized additive mixed models that describe regional and small-scale spatial and temporal gradients (and corresponding uncertainties) in monthly mass concentrations of fine (PM2.5), inhalable (PM10), and coarse mode particle mass (PM2.5–10) for the conterminous United States (U.S.). These models expand our previously developed models for the Northeastern and Midwestern U.S. by virtue of their larger spatial domain, their inclusion of an additional 5 years of PM data to develop predictions through 2007, and their use of refined geographic covariates for population density and point-source PM emissions. Covariate selection and model validation were performed using 10-fold cross-validation (CV). Results: The PM2.5 models had high predictive accuracy (CV R2=0.77 for both 1988–1998 and 1999–2007). While model performance remained strong, the predictive ability of models for PM10 (CV R2=0.58 for both 1988–1998 and 1999–2007) and PM2.5–10 (CV R2=0.46 and 0.52 for 1988–1998 and 1999–2007, respectively) was somewhat lower. Regional variation was found in the effects of geographic and meteorological covariates. Models generally performed well in both urban and rural areas and across seasons, though predictive performance varied somewhat by region (CV R2=0.81, 0.81, 0.83, 0.72, 0.69, 0.50, and 0.60 for the Northeast, Midwest, Southeast, Southcentral, Southwest, Northwest, and Central Plains regions, respectively, for PM2.5 from 1999–2007). Conclusions: Our models provide estimates of monthly-average outdoor concentrations of PM2.5, PM10, and PM2.5–10 with high spatial resolution and low bias. Thus, these models are suitable for estimating chronic exposures of populations living in the conterminous U.S. from 1988 to 2007.

Background: Previous studies have suggested associations of adult exposures to secondhand smoke (SHS) with respiratory symptoms, but no study has focused on blue-collar industrial environments. We assessed the association between SHS and respiratory symptoms in 1,562 non-current smoking U.S. trucking industry workers.Methods: Information on SHS exposure and respiratory health was obtained by questionnaire. Multiple logistic regression analyses were used to assess the associations of recent and lifetime exposures to SHS with chronic phlegm, chronic cough, and any wheeze, defined by American Thoracic Society criteria.Results: In analyses adjusted for age, gender, race, childhood SHS exposure, former smoking, pack-years of smoking and years since quitting, body mass index, job title, region of the country, and urban residence, recent exposures to SHS were associated with all three respiratory symptoms (odds ratio (OR) = 1.46; 95% confidence interval (CI) = 1.00-2.13) for chronic cough, 1.55 (95% CI = 1.08-2.21) for chronic phlegm, and 1.76 (95% CI = 1.41-2.21) for any wheeze). Workplace exposure was the most important recent exposure. Childhood exposure to SHS was also associated with all three symptoms, but only statistically significantly for chronic phlegm (OR = 1.84; 95% CI = 1.24-2.75). Additional years of living with a smoker were associated with an increased risk, but there was no evidence of a dose-response, except for chronic phlegm.Conclusions: In this group of trucking industry workers, childhood and recent exposures to SHS were related to respiratory symptoms. [ABSTRACT FROM AUTHOR]

Background: Radon and its decay products, a source of ionizing radiation, are primarily inhaled and can deliver a radiation dose to breast tissue, where they may continue to decay and emit DNA damage-inducing particles. Few studies have examined the relationship between radon and breast cancer., Methods: The Nurses' Health Study II (NHSII) includes U.S. female registered nurses who completed biennial questionnaires since 1989. Self-reported breast cancer was confirmed from medical records. County-level radon exposures were linked with geocoded residential addresses updated throughout follow-up. Time-varying Cox regression models adjusted for established breast cancer risk factors were used to calculate hazard ratios (HRs) and 95% confidence intervals (CIs)., Results: From 1989 to 2013, 3966 invasive breast cancer cases occurred among 112,639 participants. Increasing radon exposure was not associated with breast cancer risk overall (adjusted HR comparing highest to lowest quintile = 1.06, 95% CI: 0.94, 1.21, p for trend = 0.30). However, women in the highest quintile of exposure (≥74.9 Bq/m 3 ) had a suggested elevated risk of ER-/PR- breast cancer compared to women in the lowest quintile (<27.0 Bq/m 3 ) (adjusted HR = 1.38, 95% CI: 0.97, 1.96, p for trend = 0.05). No association was observed for ER+/PR+ breast cancer., Conclusions: Although we did not find an association between radon exposure and risk of overall or ER+/PR+ breast cancer, we observed a suggestive association with risk of ER-/PR- breast cancer.

Background: Hepatocellular carcinoma (HCC), the most commonly occurring type of primary liver cancer, has been increasing in incidence worldwide. Vitamin D, acquired from sunlight exposure, diet, and dietary supplements, has been hypothesized to impact hepatocarcinogenesis. However, previous epidemiologic studies examining the associations between dietary and serum vitamin D reported mixed results. The purpose of this study was to examine the association between ambient ultraviolet (UV) radiation exposure and HCC risk in the U.S., Methods: The Surveillance, Epidemiology, and End Results (SEER) database provided information on HCC cases diagnosed between 2000 and 2014 from 16 population-based cancer registries across the U.S. Ambient UV exposure was estimated by linking the SEER county with a spatiotemporal UV exposure model using a geographic information system. Poisson regression with robust variance estimation was used to calculate incidence rate ratios (IRRs) and 95% confidence intervals (CIs) for the association between ambient UV exposure per interquartile range (IQR) increase (32.4 mW/m 2 ) and HCC risk adjusting for age at diagnosis, sex, race, year of diagnosis, SEER registry, and county-level information on prevalence of health conditions, lifestyle, socioeconomic, and environmental factors., Results: Higher levels of ambient UV exposure were associated with statistically significant lower HCC risk (n = 56,245 cases; adjusted IRR per IQR increase: 0.83, 95% CI 0.77, 0.90; p < 0.01). A statistically significant inverse association between ambient UV and HCC risk was observed among males (p for interaction = 0.01) and whites (p for interaction = 0.01)., Conclusions: Higher ambient UV exposure was associated with a decreased risk of HCC in the U.S. UV exposure may be a potential modifiable risk factor for HCC that should be explored in future research.

Background: Occupational exposures to ultrafine particles in the plume generated during laser hair removal procedures, the most commonly performed light based cosmetic procedure, have not been thoroughly characterized. Acute and chronic exposures to ambient ultrafine particles have been associated with a number of negative respiratory and cardiovascular health effects. Thus, the aim of this study was to measure airborne concentrations of particles in a diameter size range of 10 nm to 1 μm in procedure rooms during laser hair removal procedures., Methods: TSI Model 3007 Condensation Particle Counters were used to quantify the particle count concentrations in the waiting and procedure rooms of a dermatology office. Particle concentrations were sampled before, during, and after laser hair removal procedures, and characteristics of each procedure were noted by the performing dermatologist., Results: Twelve procedures were sampled over 4 days. Mean ultrafine particle concentrations in the waiting and procedure rooms were 14,957.4 particles/cm 3 and 22,916.8 particles/cm 3 (p < 0.0001), respectively. Compared to background ultrafine particle concentrations before the procedure, the mean concentration in the procedure room was 2.89 times greater during the procedure (p = 0.009) and 2.09 times greater after the procedure (p = 0.007). Duration of procedure (p = 0.006), body part (p = 0.013), and the use of pre-laser lotion/type of laser (p = 0.039), were the most important predictors of ultrafine particle concentrations. Use of a smoke evacuator (a recommended form of local exhaust ventilation) positioned at 30.5 cm from the source, as opposed to the recommended 1-2 in., lowered particle concentrations, but was not a statistically significant predictor (p = 0.49)., Conclusions: Laser hair removal procedures can generate high exposures to ultrafine particles for dermatologists and other individuals performing laser hair removal, with exposure varying based on multiple determinants.