Reconstructing population exposures to environmental chemicals from biomarkers: challenges and opportunities.

PubMed

Georgopoulos, Panos G; Sasso, Alan F; Isukapalli, Sastry S; Lioy, Paul J; Vallero, Daniel A; Okino, Miles; Reiter, Larry

2009-02-01

A conceptual/computational framework for exposure reconstruction from biomarker data combined with auxiliary exposure-related data is presented, evaluated with example applications, and examined in the context of future needs and opportunities. This framework employs physiologically based toxicokinetic (PBTK) modeling in conjunction with numerical "inversion" techniques. To quantify the value of different types of exposure data "accompanying" biomarker data, a study was conducted focusing on reconstructing exposures to chlorpyrifos, from measurements of its metabolite levels in urine. The study employed biomarker data as well as supporting exposure-related information from the National Human Exposure Assessment Survey (NHEXAS), Maryland, while the MENTOR-3P system (Modeling ENvironment for TOtal Risk with Physiologically based Pharmacokinetic modeling for Populations) was used for PBTK modeling. Recently proposed, simple numerical reconstruction methods were applied in this study, in conjunction with PBTK models. Two types of reconstructions were studied using (a) just the available biomarker and supporting exposure data and (b) synthetic data developed via augmenting available observations. Reconstruction using only available data resulted in a wide range of variation in estimated exposures. Reconstruction using synthetic data facilitated evaluation of numerical inversion methods and characterization of the value of additional information, such as study-specific data that can be collected in conjunction with the biomarker data. Although the NHEXAS data set provides a significant amount of supporting exposure-related information, especially when compared to national studies such as the National Health and Nutrition Examination Survey (NHANES), this information is still not adequate for detailed reconstruction of exposures under several conditions, as demonstrated here. The analysis presented here provides a starting point for introducing improved designs for future biomonitoring studies, from the perspective of exposure reconstruction; identifies specific limitations in existing exposure reconstruction methods that can be applied to population biomarker data; and suggests potential approaches for addressing exposure reconstruction from such data.

Consequences of kriging and land use regression for PM2.5 predictions in epidemiologic analyses: Insights into spatial variability using high-resolution satellite data

PubMed Central

Alexeeff, Stacey E.; Schwartz, Joel; Kloog, Itai; Chudnovsky, Alexandra; Koutrakis, Petros; Coull, Brent A.

2016-01-01

Many epidemiological studies use predicted air pollution exposures as surrogates for true air pollution levels. These predicted exposures contain exposure measurement error, yet simulation studies have typically found negligible bias in resulting health effect estimates. However, previous studies typically assumed a statistical spatial model for air pollution exposure, which may be oversimplified. We address this shortcoming by assuming a realistic, complex exposure surface derived from fine-scale (1km x 1km) remote-sensing satellite data. Using simulation, we evaluate the accuracy of epidemiological health effect estimates in linear and logistic regression when using spatial air pollution predictions from kriging and land use regression models. We examined chronic (long-term) and acute (short-term) exposure to air pollution. Results varied substantially across different scenarios. Exposure models with low out-of-sample R2 yielded severe biases in the health effect estimates of some models, ranging from 60% upward bias to 70% downward bias. One land use regression exposure model with greater than 0.9 out-of-sample R2 yielded upward biases up to 13% for acute health effect estimates. Almost all models drastically underestimated the standard errors. Land use regression models performed better in chronic effects simulations. These results can help researchers when interpreting health effect estimates in these types of studies. PMID:24896768

Hybrid Air Quality Modeling Approach for use in the Hear-road Exposures to Urban air pollutant Study(NEXUS)

EPA Science Inventory

The paper presents a hybrid air quality modeling approach and its application in NEXUS in order to provide spatial and temporally varying exposure estimates and identification of the mobile source contribution to the total pollutant exposure. Model-based exposure metrics, associa...

Comparison of experimental models for predicting laser-tissue interaction from 3.8-micron lasers

NASA Astrophysics Data System (ADS)

Williams, Piper C. M.; Winston, Golda C. H.; Randolph, Don Q.; Neal, Thomas A.; Eurell, Thomas E.; Johnson, Thomas E.

2004-07-01

The purpose of this study was to evaluate the laser-tissue interactions of engineered human skin and in-vivo pig skin following exposure to a single 3.8 micron laser light pulse. The goal of the study was to determine if these tissues shared common histologic features following laser exposure that might prove useful in developing in-vitro and in-vivo experimental models to predict the bioeffects of human laser exposure. The minimum exposure required to produce gross morphologic changes following a four microsecond, pulsed skin exposure for both models was determined. Histology was used to compare the cellular responses of the experimental models following laser exposure. Eighteen engineered skin equivalents (in-vitro model), were exposed to 3.8 micron laser light and the tissue responses compared to equivalent exposures made on five Yorkshire pigs (in-vivo model). Representative biopsies of pig skin were taken for histologic evaluation from various body locations immediately, one hour, and 24 hours following exposure. The pattern of epithelial changes seen following in-vitro laser exposure of the engineered human skin and in-vivo exposure of pig skin indicated a common histologic response for this particular combination of laser parameters.

Air Quality Modeling of Traffic-related Air Pollutants for the NEXUS Study

EPA Science Inventory

The paper presents the results of the model applications to estimate exposure metrics in support of an epidemiologic study in Detroit, Michigan. A major challenge in traffic-related air pollution exposure studies is the lack of information regarding pollutant exposure characteriz...

Incorporating High-Dimensional Exposure Modelling into Studies of Air Pollution and Health.

PubMed

Liu, Yi; Shaddick, Gavin; Zidek, James V

2017-01-01

Performing studies on the risks of environmental hazards on human health requires accurate estimates of exposures that might be experienced by the populations at risk. Often there will be missing data and in many epidemiological studies, the locations and times of exposure measurements and health data do not match. To a large extent this will be due to the health and exposure data having arisen from completely different data sources and not as the result of a carefully designed study, leading to problems of both 'change of support' and 'misaligned data'. In such cases, a direct comparison of the exposure and health outcome is often not possible without an underlying model to align the two in the spatial and temporal domains. The Bayesian approach provides the natural framework for such models; however, the large amounts of data that can arise from environmental networks means that inference using Markov Chain Monte Carlo might not be computationally feasible in this setting. Here we adapt the integrated nested Laplace approximation to implement spatio-temporal exposure models. We also propose methods for the integration of large-scale exposure models and health analyses. It is important that any model structure allows the correct propagation of uncertainty from the predictions of the exposure model through to the estimates of risk and associated confidence intervals. The methods are demonstrated using a case study of the levels of black smoke in the UK, measured over several decades, and respiratory mortality.

Evaluating environmental modeling and sampling data with biomarker data to identify sources and routes of exposure

NASA Astrophysics Data System (ADS)

Shin, Hyeong-Moo; McKone, Thomas E.; Bennett, Deborah H.

2013-04-01

Exposure to environmental chemicals results from multiple sources, environmental media, and exposure routes. Ideally, modeled exposures should be compared to biomonitoring data. This study compares the magnitude and variation of modeled polycyclic aromatic hydrocarbons (PAHs) exposures resulting from emissions to outdoor and indoor air and estimated exposure inferred from biomarker levels. Outdoor emissions result in both inhalation and food-based exposures. We modeled PAH intake doses using U.S. EPA's 2002 National Air Toxics Assessment (NATA) county-level emissions data for outdoor inhalation, the CalTOX model for food ingestion (based on NATA emissions), and indoor air concentrations from field studies for indoor inhalation. We then compared the modeled intake with the measured urine levels of hydroxy-PAH metabolites from the 2001-2002 National Health and Nutrition Examination Survey (NHANES) survey as quantifiable human intake of PAH parent-compounds. Lognormal probability plots of modeled intakes and estimated intakes inferred from biomarkers suggest that a primary route of exposure to naphthalene, fluorene, and phenanthrene for the U.S. population is likely inhalation from indoor sources. For benzo(a)pyrene, the predominant exposure route is likely from food ingestion resulting from multi-pathway transport and bioaccumulation due to outdoor emissions. Multiple routes of exposure are important for pyrene. We also considered the sensitivity of the predicted exposure to the proportion of the total naphthalene production volume emitted to the indoor environment. The comparison of PAH biomarkers with exposure variability estimated from models and sample data for various exposure pathways supports that both indoor and outdoor models are needed to capture the sources and routes of exposure to environmental contaminants.

The Validity and Applicability of Using a Generic Exposure Assessment Model for Occupational Exposure to Nano-Objects and Their Aggregates and Agglomerates.

PubMed

Bekker, Cindy; Voogd, Eef; Fransman, Wouter; Vermeulen, Roel

2016-11-01

Control banding can be used as a first-tier assessment to control worker exposure to nano-objects and their aggregates and agglomerates (NOAA). In a second tier, more advanced modelling approaches are needed to produce quantitative exposure estimates. As currently no general quantitative nano-specific exposure models are available, this study evaluated the validity and applicability of using a generic exposure assessment model (the Advanced REACH Tool-ART) for occupational exposure to NOAA. The predictive capability of ART for occupational exposure to NOAA was tested by calculating the relative bias and correlations (Pearson) between the model estimates and measured concentrations using a dataset of 102 NOAA exposure measurements collected during experimental and workplace exposure studies. Moderate to (very) strong correlations between the ART estimates and measured concentrations were found. Estimates correlated better to measured concentration levels of dust (r = 0.76, P < 0.01) than liquid aerosols (r = 0.51, P = 0.19). However, ART overestimated the measured NOAA concentrations for both the experimental and field measurements (factor 2-127). Overestimation was highest at low concentrations and decreased with increasing concentration. Correlations seemed to be better when looking at the nanomaterials individually compared to combined scenarios, indicating that nanomaterial-specific characteristics are not well captured within the mechanistic model of the ART. Although ART in its current state is not capable to estimate occupational exposure to NOAA, the strong correlations for the individual nanomaterials indicate that the ART (and potentially other generic exposure models) have the potential to be extended or adapted for exposure to NOAA. In the future, studies investigating the potential to estimate exposure to NOAA should incorporate more explicitly nanomaterial-specific characteristics in their models. © The Author 2016. Published by Oxford University Press on behalf of the British Occupational Hygiene Society.

ISSUES AND CHALLENGES IN MODELING CHILDREN'S LONGITUDINAL EXPOSURES: AN OZONE STUDY

EPA Science Inventory

Modeling children's exposures is a complicated, data-intensive process. Modeling longitudinal exposures, which are important for regulatory decision making, especially for most air toxics, adds another level of complexity and data requirements. Because it is difficult to model in...

Sensitivity analysis of the near-road dispersion model RLINE - An evaluation at Detroit, Michigan

NASA Astrophysics Data System (ADS)

Milando, Chad W.; Batterman, Stuart A.

2018-05-01

The development of accurate and appropriate exposure metrics for health effect studies of traffic-related air pollutants (TRAPs) remains challenging and important given that traffic has become the dominant urban exposure source and that exposure estimates can affect estimates of associated health risk. Exposure estimates obtained using dispersion models can overcome many of the limitations of monitoring data, and such estimates have been used in several recent health studies. This study examines the sensitivity of exposure estimates produced by dispersion models to meteorological, emission and traffic allocation inputs, focusing on applications to health studies examining near-road exposures to TRAP. Daily average concentrations of CO and NOx predicted using the Research Line source model (RLINE) and a spatially and temporally resolved mobile source emissions inventory are compared to ambient measurements at near-road monitoring sites in Detroit, MI, and are used to assess the potential for exposure measurement error in cohort and population-based studies. Sensitivity of exposure estimates is assessed by comparing nominal and alternative model inputs using statistical performance evaluation metrics and three sets of receptors. The analysis shows considerable sensitivity to meteorological inputs; generally the best performance was obtained using data specific to each monitoring site. An updated emission factor database provided some improvement, particularly at near-road sites, while the use of site-specific diurnal traffic allocations did not improve performance compared to simpler default profiles. Overall, this study highlights the need for appropriate inputs, especially meteorological inputs, to dispersion models aimed at estimating near-road concentrations of TRAPs. It also highlights the potential for systematic biases that might affect analyses that use concentration predictions as exposure measures in health studies.

Modelling ecological and human exposure to POPs in Venice lagoon - Part II: Quantitative uncertainty and sensitivity analysis in coupled exposure models.

PubMed

Radomyski, Artur; Giubilato, Elisa; Ciffroy, Philippe; Critto, Andrea; Brochot, Céline; Marcomini, Antonio

2016-11-01

The study is focused on applying uncertainty and sensitivity analysis to support the application and evaluation of large exposure models where a significant number of parameters and complex exposure scenarios might be involved. The recently developed MERLIN-Expo exposure modelling tool was applied to probabilistically assess the ecological and human exposure to PCB 126 and 2,3,7,8-TCDD in the Venice lagoon (Italy). The 'Phytoplankton', 'Aquatic Invertebrate', 'Fish', 'Human intake' and PBPK models available in MERLIN-Expo library were integrated to create a specific food web to dynamically simulate bioaccumulation in various aquatic species and in the human body over individual lifetimes from 1932 until 1998. MERLIN-Expo is a high tier exposure modelling tool allowing propagation of uncertainty on the model predictions through Monte Carlo simulation. Uncertainty in model output can be further apportioned between parameters by applying built-in sensitivity analysis tools. In this study, uncertainty has been extensively addressed in the distribution functions to describe the data input and the effect on model results by applying sensitivity analysis techniques (screening Morris method, regression analysis, and variance-based method EFAST). In the exposure scenario developed for the Lagoon of Venice, the concentrations of 2,3,7,8-TCDD and PCB 126 in human blood turned out to be mainly influenced by a combination of parameters (half-lives of the chemicals, body weight variability, lipid fraction, food assimilation efficiency), physiological processes (uptake/elimination rates), environmental exposure concentrations (sediment, water, food) and eating behaviours (amount of food eaten). In conclusion, this case study demonstrated feasibility of MERLIN-Expo to be successfully employed in integrated, high tier exposure assessment. Copyright © 2016 Elsevier B.V. All rights reserved.

A Comparison of Exposure Control Procedures in CAT Systems Based on Different Measurement Models for Testlets

ERIC Educational Resources Information Center

Boyd, Aimee M.; Dodd, Barbara; Fitzpatrick, Steven

2013-01-01

This study compared several exposure control procedures for CAT systems based on the three-parameter logistic testlet response theory model (Wang, Bradlow, & Wainer, 2002) and Masters' (1982) partial credit model when applied to a pool consisting entirely of testlets. The exposure control procedures studied were the modified within 0.10 logits…

Integration of Air Quality & Exposure Models for Health Studies

EPA Science Inventory

The presentation describes a new community-scale tool called exposure model for individuals (EMI), which predicts five tiers of individual-level exposure metrics for ambient PM using outdoor concentrations, questionnaires, weather, and time-location information. In this modeling ...

Simulation of population-based commuter exposure to NO₂ using different air pollution models.

PubMed

Ragettli, Martina S; Tsai, Ming-Yi; Braun-Fahrländer, Charlotte; de Nazelle, Audrey; Schindler, Christian; Ineichen, Alex; Ducret-Stich, Regina E; Perez, Laura; Probst-Hensch, Nicole; Künzli, Nino; Phuleria, Harish C

2014-05-12

We simulated commuter routes and long-term exposure to traffic-related air pollution during commute in a representative population sample in Basel (Switzerland), and evaluated three air pollution models with different spatial resolution for estimating commute exposures to nitrogen dioxide (NO2) as a marker of long-term exposure to traffic-related air pollution. Our approach includes spatially and temporally resolved data on actual commuter routes, travel modes and three air pollution models. Annual mean NO2 commuter exposures were similar between models. However, we found more within-city and within-subject variability in annual mean (±SD) NO2 commuter exposure with a high resolution dispersion model (40 ± 7 µg m(-3), range: 21-61) than with a dispersion model with a lower resolution (39 ± 5 µg m(-3); range: 24-51), and a land use regression model (41 ± 5 µg m(-3); range: 24-54). Highest median cumulative exposures were calculated along motorized transport and bicycle routes, and the lowest for walking. For estimating commuter exposure within a city and being interested also in small-scale variability between roads, a model with a high resolution is recommended. For larger scale epidemiological health assessment studies, models with a coarser spatial resolution are likely sufficient, especially when study areas include suburban and rural areas.

Validity of empirical models of exposure in asphalt paving

PubMed Central

Burstyn, I; Boffetta, P; Burr, G; Cenni, A; Knecht, U; Sciarra, G; Kromhout, H

2002-01-01

Aims: To investigate the validity of empirical models of exposure to bitumen fume and benzo(a)pyrene, developed for a historical cohort study of asphalt paving in Western Europe. Methods: Validity was evaluated using data from the USA, Italy, and Germany not used to develop the original models. Correlation between observed and predicted exposures was examined. Bias and precision were estimated. Results: Models were imprecise. Furthermore, predicted bitumen fume exposures tended to be lower (-70%) than concentrations found during paving in the USA. This apparent bias might be attributed to differences between Western European and USA paving practices. Evaluation of the validity of the benzo(a)pyrene exposure model revealed a similar to expected effect of re-paving and a larger than expected effect of tar use. Overall, benzo(a)pyrene models underestimated exposures by 51%. Conclusions: Possible bias as a result of underestimation of the impact of coal tar on benzo(a)pyrene exposure levels must be explored in sensitivity analysis of the exposure–response relation. Validation of the models, albeit limited, increased our confidence in their applicability to exposure assessment in the historical cohort study of cancer risk among asphalt workers. PMID:12205236

A latent variable approach to study gene-environment interactions in the presence of multiple correlated exposures.

PubMed

Sánchez, Brisa N; Kang, Shan; Mukherjee, Bhramar

2012-06-01

Many existing cohort studies initially designed to investigate disease risk as a function of environmental exposures have collected genomic data in recent years with the objective of testing for gene-environment interaction (G × E) effects. In environmental epidemiology, interest in G × E arises primarily after a significant effect of the environmental exposure has been documented. Cohort studies often collect rich exposure data; as a result, assessing G × E effects in the presence of multiple exposure markers further increases the burden of multiple testing, an issue already present in both genetic and environment health studies. Latent variable (LV) models have been used in environmental epidemiology to reduce dimensionality of the exposure data, gain power by reducing multiplicity issues via condensing exposure data, and avoid collinearity problems due to presence of multiple correlated exposures. We extend the LV framework to characterize gene-environment interaction in presence of multiple correlated exposures and genotype categories. Further, similar to what has been done in case-control G × E studies, we use the assumption of gene-environment (G-E) independence to boost the power of tests for interaction. The consequences of making this assumption, or the issue of how to explicitly model G-E association has not been previously investigated in LV models. We postulate a hierarchy of assumptions about the LV model regarding the different forms of G-E dependence and show that making such assumptions may influence inferential results on the G, E, and G × E parameters. We implement a class of shrinkage estimators to data adaptively trade-off between the most restrictive to most flexible form of G-E dependence assumption and note that such class of compromise estimators can serve as a benchmark of model adequacy in LV models. We demonstrate the methods with an example from the Early Life Exposures in Mexico City to Neuro-Toxicants Study of lead exposure, iron metabolism genes, and birth weight. © 2011, The International Biometric Society.

Shared and unshared exposure measurement error in occupational cohort studies and their effects on statistical inference in proportional hazards models.

PubMed

Hoffmann, Sabine; Laurier, Dominique; Rage, Estelle; Guihenneuc, Chantal; Ancelet, Sophie

2018-01-01

Exposure measurement error represents one of the most important sources of uncertainty in epidemiology. When exposure uncertainty is not or only poorly accounted for, it can lead to biased risk estimates and a distortion of the shape of the exposure-response relationship. In occupational cohort studies, the time-dependent nature of exposure and changes in the method of exposure assessment may create complex error structures. When a method of group-level exposure assessment is used, individual worker practices and the imprecision of the instrument used to measure the average exposure for a group of workers may give rise to errors that are shared between workers, within workers or both. In contrast to unshared measurement error, the effects of shared errors remain largely unknown. Moreover, exposure uncertainty and magnitude of exposure are typically highest for the earliest years of exposure. We conduct a simulation study based on exposure data of the French cohort of uranium miners to compare the effects of shared and unshared exposure uncertainty on risk estimation and on the shape of the exposure-response curve in proportional hazards models. Our results indicate that uncertainty components shared within workers cause more bias in risk estimation and a more severe attenuation of the exposure-response relationship than unshared exposure uncertainty or exposure uncertainty shared between individuals. These findings underline the importance of careful characterisation and modeling of exposure uncertainty in observational studies.

Shared and unshared exposure measurement error in occupational cohort studies and their effects on statistical inference in proportional hazards models

PubMed Central

Laurier, Dominique; Rage, Estelle

2018-01-01

Exposure measurement error represents one of the most important sources of uncertainty in epidemiology. When exposure uncertainty is not or only poorly accounted for, it can lead to biased risk estimates and a distortion of the shape of the exposure-response relationship. In occupational cohort studies, the time-dependent nature of exposure and changes in the method of exposure assessment may create complex error structures. When a method of group-level exposure assessment is used, individual worker practices and the imprecision of the instrument used to measure the average exposure for a group of workers may give rise to errors that are shared between workers, within workers or both. In contrast to unshared measurement error, the effects of shared errors remain largely unknown. Moreover, exposure uncertainty and magnitude of exposure are typically highest for the earliest years of exposure. We conduct a simulation study based on exposure data of the French cohort of uranium miners to compare the effects of shared and unshared exposure uncertainty on risk estimation and on the shape of the exposure-response curve in proportional hazards models. Our results indicate that uncertainty components shared within workers cause more bias in risk estimation and a more severe attenuation of the exposure-response relationship than unshared exposure uncertainty or exposure uncertainty shared between individuals. These findings underline the importance of careful characterisation and modeling of exposure uncertainty in observational studies. PMID:29408862

Local-Scale Air Quality Modeling in Support of Human Health and Exposure Research (Invited)

NASA Astrophysics Data System (ADS)

Isakov, V.

2010-12-01

Spatially- and temporally-sparse information on air quality is a key concern for air-pollution-related environmental health studies. Monitor networks are sparse in both space and time, are costly to maintain, and are often designed purposely to avoid detecting highly localized sources. Recent studies have shown that more narrowly defining the geographic domain of the study populations and improvements in the measured/estimated ambient concentrations can lead to stronger associations between air pollution and hospital admissions and mortality records. Traditionally, ambient air quality measurements have been used as a primary input to support human health and exposure research. However, there is increasing evidence that the current ambient monitoring network is not capturing sharp gradients in exposure due to the presence of high concentration levels near, for example, major roadways. Many air pollutants exhibit large concentration gradients near large emitters such as major roadways, factories, ports, etc. To overcome these limitations, researchers are now beginning to use air quality models to support air pollution exposure and health studies. There are many advantages to using air quality models over traditional approaches based on existing ambient measurements alone. First, models can provide spatially- and temporally-resolved concentrations as direct input to exposure and health studies and thus better defining the concentration levels for the population in the geographic domain. Air quality models have a long history of use in air pollution regulations, and supported by regulatory agencies and a large user community. Also, models can provide bidirectional linkages between sources of emissions and ambient concentrations, thus allowing exploration of various mitigation strategies to reduce risk to exposure. In order to provide best estimates of air concentrations to support human health and exposure studies, model estimates should consider local-scale features, regional-scale transport, and photochemical transformations. Since these needs are currently not met by a single model, hybrid air quality modeling has recently been developed to combine these capabilities. In this paper, we present the results of two studies where we applied the hybrid modeling approach to provide spatial and temporal details in air quality concentrations to support exposure and health studies: a) an urban-scale air quality accountability study involving near-source exposures to multiple ambient air pollutants, and b) an urban-scale epidemiological study involving human health data based on emergency department visits.

Air Quality Modeling in Support of the Near-road EXposures and effects of Urban air pollutants Study (NEXUS)

EPA Science Inventory

The paper presents the results of the model applications to estimate exposure metrics in support of an epidemiologic study in Detroit, Michigan. The Near-road Exposures to Urban air pollutant Study (NEXUS) design includes determining if children in Detroit, MI with asthma living ...

Indicators of residential traffic exposure: Modelled NOX, traffic proximity, and self-reported exposure in RHINE III

NASA Astrophysics Data System (ADS)

Carlsen, Hanne Krage; Bäck, Erik; Eneroth, Kristina; Gislason, Thorarinn; Holm, Mathias; Janson, Christer; Jensen, Steen Solvang; Johannessen, Ane; Kaasik, Marko; Modig, Lars; Segersson, David; Sigsgaard, Torben; Forsberg, Bertil; Olsson, David; Orru, Hans

2017-10-01

Few studies have investigated associations between self-reported and modelled exposure to traffic pollution. The objective of this study was to examine correlations between self-reported traffic exposure and modelled (a) NOX and (b) traffic proximity in seven different northern European cities; Aarhus (Denmark), Bergen (Norway), Gothenburg, Umeå, and Uppsala (Sweden), Reykjavik (Iceland), and Tartu (Estonia). We analysed data from the RHINE III (Respiratory Health in Northern Europe, http://www.rhine.nu)

Spatio-temporal modelling of residential exposure to particulate matter and gaseous pollutants for the Heinz Nixdorf Recall Cohort

NASA Astrophysics Data System (ADS)

Nonnemacher, Michael; Jakobs, Hermann; Viehmann, Anja; Vanberg, Irene; Kessler, Christoph; Moebus, Susanne; Möhlenkamp, Stefan; Erbel, Raimund; Hoffmann, Barbara; Memmesheimer, Michael

2014-07-01

For the simultaneous analysis of short- and long-term effects of air pollution in the Heinz Nixdorf Recall Cohort a sophisticated exposure modelling was performed. The dispersion and chemistry transport model EURAD (European Air Pollution Dispersion) was used for the estimation of hourly concentrations of a number of pollutants for a horizontal grid with a cell size of 1 km² covering the whole study area (three large adjacent cities in a highly urbanized region in Western Germany) for the years 2000-2003 and 2006-2008. For each 1 km² cell we estimated the mean concentration by calculating daily means from the hourly concentrations modelled by the EURAD process. The modelled concentrations showed an overall tendency to decrease from 2001 to 2008 whereas the trend in the single grid cells and study period was inhomogeneous. Participant-related exposure slightly increased from 2001 to 2003 followed by a decrease from 2006 to 2008. The exposure modelling enables a very flexible exposure assessment compared to conventional modelling approaches which either use central monitoring or temporally static spatial contrasts. The modelling allows the calculation of an average exposure concentration for any place and time within the study region and study period with a high spatial and temporal resolution. This is important for the assessment of short-, medium and long-term effects of air pollution on human health in epidemiological studies.

Exposure Modeling of Residential Air Exchange Rates for NEXUS Participants

EPA Science Inventory

Due to cost and participant burden of personal measurements, air pollution health studies often estimate exposures using local ambient air monitors. Since outdoor levels do not necessarily reflect personal exposures, we developed the Exposure Model for Individuals (EMI) to improv...

Exposure Modeling of Residential Air Exchange Rates for NEXUS Participants.

EPA Science Inventory

Due to cost and participant burden of personal measurements, air pollution health studies often estimate exposures using local ambient air monitors. Since outdoor levels do not necessarily reflect personal exposures, we developed the Exposure Model for Individuals (EMI) to improv...

Exposure Modeling of Residential Infiltration of Black Carbon for NEXUS Participants

EPA Science Inventory

Due to cost and participant burden of personal measurements, air pollution health studies often estimate exposures using outdoor concentrations. Since outdoor levels do not necessarily reflect personal exposures, we developed the Exposure Model for Individuals (EMI) to improve ex...

Hybrid Air Quality Modeling Approach For Use in the Near ...

EPA Pesticide Factsheets

The Near-road EXposures to Urban air pollutant Study (NEXUS) investigated whether children with asthma living in close proximity to major roadways in Detroit, MI, (particularly near roadways with high diesel traffic) have greater health impacts associated with exposure to air pollutants than those living farther away. A major challenge in such health and exposure studies is the lack of information regarding pollutant exposure characterization. Air quality modeling can provide spatially and temporally varying exposure estimates for examining relationships between traffic-related air pollutants and adverse health outcomes. This paper presents a hybrid air quality modeling approach and its application in NEXUS in order to provide spatial and temporally varying exposure estimates and identification of the mobile source contribution to the total pollutant exposure. Model-based exposure metrics, associated with local variations of emissions and meteorology, were estimated using a combination of the AERMOD and R-LINE dispersion models, local emission source information from the National Emissions Inventory, detailed road network locations and traffic activity, and meteorological data from the Detroit City Airport. The regional background contribution was estimated using a combination of the Community Multiscale Air Quality (CMAQ) model and the Space/Time Ordinary Kriging (STOK) model. To capture the near-road pollutant gradients, refined “mini-grids” of model recep

Bayesian multinomial probit modeling of daily windows of ...

EPA Pesticide Factsheets

Past epidemiologic studies suggest maternal ambient air pollution exposure during critical periods of the pregnancy is associated with fetal development. We introduce a multinomial probit model that allows for the joint identification of susceptible daily periods during the pregnancy for 12 individual types of CHDs with respect to maternal PM2.5 exposure. We apply the model to a dataset of mothers from the National Birth Defect Prevention Study where daily PM2.5 exposures from weeks 2-8 of pregnancy are assigned (specific to each location and pregnancy date) using predictions from the downscaler pollution model. Results are compared to an aggregated exposure model which defines exposure as the average value over pregnancy weeks 2-8. Increased PM2.5 exposure during pregnancy days 53 and 50-51 for pulmonary valve stenosis and tetralogy of Fallot, respectively, are associated with an increased probability of development of each CHD. The largest estimated effect is seen for atrioventricular septal defects on pregnancy day 14. The aggregated exposure model fails to identify any significant windows of susceptibility during pregnancy weeks 2-8 for the considered CHDs. Considering daily PM2.5 exposures in a new modeling framework revealed positive associations for defects that the standard aggregated exposure model was unable to identify. Disclaimer: The views expressed in this manuscript are those of the authors and do not necessarily represent the views or policie

Reducing attenuation in exposure-response relationships by exposure modeling and grouping: the relationship between wood dust exposure and lung function.

PubMed

Teschke, Kay; Spierings, Judith; Marion, Stephen A; Demers, Paul A; Davies, Hugh W; Kennedy, Susan M

2004-12-01

In a study of wood dust exposure and lung function, we tested the effect on the exposure-response relationship of six different exposure metrics using the mean measured exposure of each subject versus the mean exposure based on various methods of grouping subjects, including job-based groups and groups based on an empirical model of the determinants of exposure. Multiple linear regression was used to examine the association between wood dust concentration and forced expiratory volume in 1s (FEV(1)), adjusting for age, sex, height, race, pediatric asthma, and smoking. Stronger point estimates of the exposure-response relationships were observed when exposures were based on increasing levels of aggregation, allowing the relationships to be found statistically significant in four of the six metrics. The strongest point estimates were found when exposures were based on the determinants of exposure model. Determinants of exposure modeling offers the potential for improvement in risk estimation equivalent to or beyond that from job-based exposure grouping.

Application of a dynamic population-based model for evaluation of exposure reduction strategies in the baking industry

NASA Astrophysics Data System (ADS)

Meijster, Tim; Warren, Nick; Heederik, Dick; Tielemans, Erik

2009-02-01

Recently a dynamic population model was developed that simulates a population of bakery workers longitudinally through time and tracks the development of work-related sensitisation and respiratory symptoms in each worker. Input for this model comes from cross-sectional and longitudinal epidemiological studies which allowed estimation of exposure response relationships and disease transition probabilities This model allows us to study the development of diseases and transitions between disease states over time in relation to determinants of disease including flour dust and/or allergen exposure. Furthermore it enables more realistic modelling of the health impact of different intervention strategies at the workplace (e.g. changes in exposure may take several years to impact on ill-health and often occur as a gradual trend). A large dataset of individual full-shift exposure measurements and real-time exposure measurements were used to obtain detailed insight into the effectiveness of control measures and other determinants of exposure. Given this information a population wide reduction of the median exposure with 50% was evaluated in this paper.

Zebrafish as a model for acetylcholinesterase-inhibiting organophosphorus agent exposure and oxime reactivation

PubMed Central

Koenig, Jeffrey A.; Dao, Thuy L.; Kan, Robert K.; Shih, Tsung-Ming

2016-01-01

The current research progression efforts for investigating novel treatments for exposure to organophosphorus (OP) compounds that inhibit acetylcholinesterase (AChE), including pesticides and chemical warfare nerve agents (CWNAs), rely solely on in vitro cell assays and in vivo rodent models. The zebrafish (Danio rerio) is a popular, well-established vertebrate model in biomedical research that offers high-throughput capabilities and genetic manipulation not readily available with rodents. A number of research studies have investigated the effects of subacute developmental exposure to OP pesticides in zebrafish, observing detrimental effects on gross morphology, neuronal development, and behavior. Few studies, however, have utilized this model to evaluate treatments, such as oxime reactivators, anticholinergics, or anticonvulsants, following acute exposure. Preliminary work has investigated the effects of CWNA exposure. The results clearly demonstrated relative toxicity and oxime efficacy similar to that reported for the rodent model. This review surveys the current literature utilizing zebrafish as a model for OP exposure and highlights its potential use as a high-throughput system for evaluating AChE reactivator antidotal treatments to acute pesticide and CWNA exposure. PMID:27123828

Estimation of Particulate Mass and Manganese Exposure Levels among Welders

PubMed Central

Hobson, Angela; Seixas, Noah; Sterling, David; Racette, Brad A.

2011-01-01

Background: Welders are frequently exposed to Manganese (Mn), which may increase the risk of neurological impairment. Historical exposure estimates for welding-exposed workers are needed for epidemiological studies evaluating the relationship between welding and neurological or other health outcomes. The objective of this study was to develop and validate a multivariate model to estimate quantitative levels of welding fume exposures based on welding particulate mass and Mn concentrations reported in the published literature. Methods: Articles that described welding particulate and Mn exposures during field welding activities were identified through a comprehensive literature search. Summary measures of exposure and related determinants such as year of sampling, welding process performed, type of ventilation used, degree of enclosure, base metal, and location of sampling filter were extracted from each article. The natural log of the reported arithmetic mean exposure level was used as the dependent variable in model building, while the independent variables included the exposure determinants. Cross-validation was performed to aid in model selection and to evaluate the generalizability of the models. Results: A total of 33 particulate and 27 Mn means were included in the regression analysis. The final model explained 76% of the variability in the mean exposures and included welding process and degree of enclosure as predictors. There was very little change in the explained variability and root mean squared error between the final model and its cross-validation model indicating the final model is robust given the available data. Conclusions: This model may be improved with more detailed exposure determinants; however, the relatively large amount of variance explained by the final model along with the positive generalizability results of the cross-validation increases the confidence that the estimates derived from this model can be used for estimating welder exposures in absence of individual measurement data. PMID:20870928

ASSESSING MULTIMEDIA/MULTIPATHWAY EXPOSURE TO ARSENIC USING A MECHANISTIC SOURCE-TO-DOSE MODELING FRAMEWORK

EPA Science Inventory

A series of case studies is presented focusing on multimedia/multipathway population exposures to arsenic, employing the Population Based Modeling approach of the MENTOR (Modeling Environment for Total Risks) framework. This framework considers currently five exposure routes: i...

PM POPULATION EXPOSURE AND DOSE MODELS

EPA Science Inventory

The overall objective of this study is the development of a refined probabilistic exposure and dose model for particulate matter (PM) suitable for predicting PM10 and PM2.5 population exposures. This modeling research will be conducted both in-house by EPA scientists and through...

Simulation of Population-Based Commuter Exposure to NO2 Using Different Air Pollution Models

PubMed Central

Ragettli, Martina S.; Tsai, Ming-Yi; Braun-Fahrländer, Charlotte; de Nazelle, Audrey; Schindler, Christian; Ineichen, Alex; Ducret-Stich, Regina E.; Perez, Laura; Probst-Hensch, Nicole; Künzli, Nino; Phuleria, Harish C.

2014-01-01

We simulated commuter routes and long-term exposure to traffic-related air pollution during commute in a representative population sample in Basel (Switzerland), and evaluated three air pollution models with different spatial resolution for estimating commute exposures to nitrogen dioxide (NO2) as a marker of long-term exposure to traffic-related air pollution. Our approach includes spatially and temporally resolved data on actual commuter routes, travel modes and three air pollution models. Annual mean NO2 commuter exposures were similar between models. However, we found more within-city and within-subject variability in annual mean (±SD) NO2 commuter exposure with a high resolution dispersion model (40 ± 7 µg m−3, range: 21–61) than with a dispersion model with a lower resolution (39 ± 5 µg m−3; range: 24–51), and a land use regression model (41 ± 5 µg m−3; range: 24–54). Highest median cumulative exposures were calculated along motorized transport and bicycle routes, and the lowest for walking. For estimating commuter exposure within a city and being interested also in small-scale variability between roads, a model with a high resolution is recommended. For larger scale epidemiological health assessment studies, models with a coarser spatial resolution are likely sufficient, especially when study areas include suburban and rural areas. PMID:24823664

New developments in exposure assessment: the impact on the practice of health risk assessment and epidemiological studies.

PubMed

Nieuwenhuijsen, Mark; Paustenbach, Dennis; Duarte-Davidson, Raquel

2006-12-01

The field of exposure assessment has matured significantly over the past 10-15 years. Dozens of studies have measured the concentrations of numerous chemicals in many media to which humans are exposed. Others have catalogued the various exposure pathways and identified typical values which can be used in the exposure calculations for the general population such as amount of water or soil ingested per day or the percent of a chemical than can pass through the skin. In addition, studies of the duration of exposure for many tasks (e.g. showering, jogging, working in the office) have been conducted which allow for more general descriptions of the likely range of exposures. All of this information, as well as the development of new and better models (e.g. air dispersion or groundwater models), allow for better estimates of exposure. In addition to identifying better exposure factors, and better mathematical models for predicting the aerial distribution of chemicals, the conduct of simulation studies and dose-reconstruction studies can offer extraordinary opportunities for filling in data gaps regarding historical exposures which are critical to improving the power of epidemiology studies. The use of probabilistic techniques such as Monte Carlo analysis and Bayesian statistics have revolutionized the practice of exposure assessment and has greatly enhanced the quality of the risk characterization. Lastly, the field of epidemiology is about to undergo a sea change with respect to the exposure component because each year better environmental and exposure models, statistical techniques and new biological monitoring techniques are being introduced. This paper reviews these techniques and discusses where additional research is likely to pay a significant dividend. Exposure assessment techniques are now available which can significantly improve the quality of epidemiology and health risk assessment studies and vastly improve their usefulness. As more quantitative exposure components can now be incorporated into these studies, they can be better used to identify safe levels of exposure using customary risk assessment methodologies. Examples are drawn from both environmental and occupational studies illustrating how these techniques have been used to better understand exposure to specific chemicals. Some thoughts are also presented on what lessons have been learned about conducting exposure assessment for health risk assessments and epidemiological studies.

An Exploratory Study: Assessment of Modeled Dioxin Exposure in Ceramic Art Studios (Final Report, 2008)

EPA Science Inventory

EPA announced the availability of the final report, An Exploratory Study: Assessment of Modeled Dioxin Exposure in Ceramic Art Studios. This report investigates the potential dioxin exposure to artists/hobbyists who use ball clay to make pottery and related products. Derm...

SOURCE APPORTIONMENT OF EXPOSURES TO VOLATILE ORGANIC COMPOUNDS: II. APPLICATION OF RECEPTOR MODELS TO TEAM STUDY DATA. (R826788)

EPA Science Inventory

Four receptor-oriented source apportionment models were applied to personal exposure measurements for toxic volatile organic compounds (VOCs). The measurements are from the total exposure assessment methodology studies conducted from 1980 to 1984 in New Jersey (NJ) and Califor...

Latent variable models for gene-environment interactions in longitudinal studies with multiple correlated exposures.

PubMed

Tao, Yebin; Sánchez, Brisa N; Mukherjee, Bhramar

2015-03-30

Many existing cohort studies designed to investigate health effects of environmental exposures also collect data on genetic markers. The Early Life Exposures in Mexico to Environmental Toxicants project, for instance, has been genotyping single nucleotide polymorphisms on candidate genes involved in mental and nutrient metabolism and also in potentially shared metabolic pathways with the environmental exposures. Given the longitudinal nature of these cohort studies, rich exposure and outcome data are available to address novel questions regarding gene-environment interaction (G × E). Latent variable (LV) models have been effectively used for dimension reduction, helping with multiple testing and multicollinearity issues in the presence of correlated multivariate exposures and outcomes. In this paper, we first propose a modeling strategy, based on LV models, to examine the association between repeated outcome measures (e.g., child weight) and a set of correlated exposure biomarkers (e.g., prenatal lead exposure). We then construct novel tests for G × E effects within the LV framework to examine effect modification of outcome-exposure association by genetic factors (e.g., the hemochromatosis gene). We consider two scenarios: one allowing dependence of the LV models on genes and the other assuming independence between the LV models and genes. We combine the two sets of estimates by shrinkage estimation to trade off bias and efficiency in a data-adaptive way. Using simulations, we evaluate the properties of the shrinkage estimates, and in particular, we demonstrate the need for this data-adaptive shrinkage given repeated outcome measures, exposure measures possibly repeated and time-varying gene-environment association. Copyright © 2014 John Wiley & Sons, Ltd.

Biologically based modeling of multimedia, multipathway, multiroute population exposures to arsenic

PubMed Central

Georgopoulos, Panos G.; Wang, Sheng-Wei; Yang, Yu-Ching; Xue, Jianping; Zartarian, Valerie G.; Mccurdy, Thomas; Özkaynak, Halûk

2011-01-01

This article presents an integrated, biologically based, source-to-dose assessment framework for modeling multimedia/multipathway/multiroute exposures to arsenic. Case studies demonstrating this framework are presented for three US counties (Hunderton County, NJ; Pima County, AZ; and Franklin County, OH), representing substantially different conditions of exposure. The approach taken utilizes the Modeling ENvironment for TOtal Risk studies (MENTOR) in an implementation that incorporates and extends the approach pioneered by Stochastic Human Exposure and Dose Simulation (SHEDS), in conjunction with a number of available databases, including NATA, NHEXAS, CSFII, and CHAD, and extends modeling techniques that have been developed in recent years. Model results indicate that, in most cases, the food intake pathway is the dominant contributor to total exposure and dose to arsenic. Model predictions are evaluated qualitatively by comparing distributions of predicted total arsenic amounts in urine with those derived using biomarker measurements from the NHEXAS — Region V study: the population distributions of urinary total arsenic levels calculated through MENTOR and from the NHEXAS measurements are in general qualitative agreement. Observed differences are due to various factors, such as interindividual variation in arsenic metabolism in humans, that are not fully accounted for in the current model implementation but can be incorporated in the future, in the open framework of MENTOR. The present study demonstrates that integrated source-to-dose modeling for arsenic can not only provide estimates of the relative contributions of multipathway exposure routes to the total exposure estimates, but can also estimate internal target tissue doses for speciated organic and inorganic arsenic, which can eventually be used to improve evaluation of health risks associated with exposures to arsenic from multiple sources, routes, and pathways. PMID:18073786

A Pilot Study on Integrating Videography and Environmental Microbial Sampling to Model Fecal Bacterial Exposures in Peri-Urban Tanzania.

PubMed

Julian, Timothy R; Pickering, Amy J

2015-01-01

Diarrheal diseases are a leading cause of under-five mortality and morbidity in sub-Saharan Africa. Quantitative exposure modeling provides opportunities to investigate the relative importance of fecal-oral transmission routes (e.g. hands, water, food) responsible for diarrheal disease. Modeling, however, requires accurate descriptions of individuals' interactions with the environment (i.e., activity data). Such activity data are largely lacking for people in low-income settings. In the present study, we collected activity data and microbiological sampling data to develop a quantitative microbial exposure model for two female caretakers in peri-urban Tanzania. Activity data were combined with microbiological data of contacted surfaces and fomites (e.g. broom handle, soil, clothing) to develop example exposure profiles describing second-by-second estimates of fecal indicator bacteria (E. coli and enterococci) concentrations on the caretaker's hands. The study demonstrates the application and utility of video activity data to quantify exposure factors for people in low-income countries and apply these factors to understand fecal contamination exposure pathways. This study provides both a methodological approach for the design and implementation of larger studies, and preliminary data suggesting contacts with dirt and sand may be important mechanisms of hand contamination. Increasing the scale of activity data collection and modeling to investigate individual-level exposure profiles within target populations for specific exposure scenarios would provide opportunities to identify the relative importance of fecal-oral disease transmission routes.

Studying permethrin exposure in flight attendants using a physiologically based pharmacokinetic model

PubMed Central

Wei, Binnian; Isukapalli, Sastry S.; Weisel, Clifford P.

2014-01-01

Assessment of potential health risks to flight attendants from exposure to pyrethroid insecticides, used for aircraft disinsection, is limited because of (a) lack of information on exposures to these insecticides, and (b) lack of tools for linking these exposures to biomarker data. We developed and evaluated a physiologically based pharmacokinetic (PBPK) model to assess the exposure of flight attendants to the pyrethroid insecticide permethrin attributable to aircraft disinsection. The permethrin PBPK model was developed by adapting previous models for pyrethroids, and was parameterized using currently available metabolic parameters for permethrin. The human permethrin model was first evaluated with data from published human studies. Then, it was used to estimate urinary metabolite concentrations of permethrin in flight attendants who worked in aircrafts, which underwent residual and pre-flight spray treatments. The human model was also applied to analyze the toxicokinetics following permethrin exposures attributable to other aircraft disinsection scenarios. Predicted levels of urinary 3-phenoxybenzoic acid (3-PBA), a metabolite of permethrin, following residual disinsection treatment were comparable to the measurements made for flight attendants. Simulations showed that the median contributions of the dermal, oral and inhalation routes to permethrin exposure in flight attendants were 83.5%, 16.1% and 0.4% under residual treatment scenario, respectively, and were 5.3%, 5.0% and 89.7% under pre-flight spray scenario, respectively. The PBPK model provides the capability to simulate the toxicokinetic profiles of permethrin, and can be used in the studies on human exposure to permethrin. PMID:23462847

CHILDREN'S RESIDENTIAL EXPOSURE TO CHLORPYRIFOS: APPLICATION OF CPPAES FIELD MEASUREMENTS OF CHLORPYRIFOS AND TCPY WITHIN MENTOR/SHEDS PESTICIDES MODEL

EPA Science Inventory

The comprehensive individual field-measurements on non-dietary exposure collected in the Children's-Post-Pesticide-Application-Exposure-Study (CPPAES) were used within MENTOR/SHEDS-Pesticides, a physically based stochastic human exposure and dose model. In this application, howev...

Comparison of stationary and personal air sampling with an ...

EPA Pesticide Factsheets

Manganese (Mn) is ubiquitous in the environment and essential for normal growth and development, yet excessive exposure can lead to impairments in neurological function. This study modeled ambient Mn concentrations as an alternative to stationary and personal air sampling to assess exposure for children enrolled in the Communities Actively Researching Exposure Study in Marietta, OH. Ambient air Mn concentration values were modeled using US Environmental Protection Agency’s Air Dispersion Model AERMOD based on emissions from the ferromanganese refinery located in Marietta. Modeled Mn concentrations were compared with Mn concentrations from a nearby stationary air monitor. The Index of Agreement for modeled versus monitored data was 0.34 (48 h levels) and 0.79 (monthly levels). Fractional bias was 0.026 for 48 h levels and −0.019 for monthly levels. The ratio of modeled ambient air Mn to measured ambient air Mn at the annual time scale was 0.94. Modeled values were also time matched to personal air samples for 19 children. The modeled values explained a greater degree of variability in personal exposures compared with time-weighted distance from the emission source. Based on these results modeled Mn concentrations provided a suitable approach for assessing airborne Mn exposure in this cohort. The purpose of the study was to investigate the use of air-dispersion modeling as an approach to exposure assessment for ambient manganese.

Classification and Clustering Methods for Multiple Environmental Factors in Gene-Environment Interaction: Application to the Multi-Ethnic Study of Atherosclerosis.

PubMed

Ko, Yi-An; Mukherjee, Bhramar; Smith, Jennifer A; Kardia, Sharon L R; Allison, Matthew; Diez Roux, Ana V

2016-11-01

There has been an increased interest in identifying gene-environment interaction (G × E) in the context of multiple environmental exposures. Most G × E studies analyze one exposure at a time, but we are exposed to multiple exposures in reality. Efficient analysis strategies for complex G × E with multiple environmental factors in a single model are still lacking. Using the data from the Multiethnic Study of Atherosclerosis, we illustrate a two-step approach for modeling G × E with multiple environmental factors. First, we utilize common clustering and classification strategies (e.g., k-means, latent class analysis, classification and regression trees, Bayesian clustering using Dirichlet Process) to define subgroups corresponding to distinct environmental exposure profiles. Second, we illustrate the use of an additive main effects and multiplicative interaction model, instead of the conventional saturated interaction model using product terms of factors, to study G × E with the data-driven exposure subgroups defined in the first step. We demonstrate useful analytical approaches to translate multiple environmental exposures into one summary class. These tools not only allow researchers to consider several environmental exposures in G × E analysis but also provide some insight into how genes modify the effect of a comprehensive exposure profile instead of examining effect modification for each exposure in isolation.

Bayesian adjustment for measurement error in continuous exposures in an individually matched case-control study.

PubMed

Espino-Hernandez, Gabriela; Gustafson, Paul; Burstyn, Igor

2011-05-14

In epidemiological studies explanatory variables are frequently subject to measurement error. The aim of this paper is to develop a Bayesian method to correct for measurement error in multiple continuous exposures in individually matched case-control studies. This is a topic that has not been widely investigated. The new method is illustrated using data from an individually matched case-control study of the association between thyroid hormone levels during pregnancy and exposure to perfluorinated acids. The objective of the motivating study was to examine the risk of maternal hypothyroxinemia due to exposure to three perfluorinated acids measured on a continuous scale. Results from the proposed method are compared with those obtained from a naive analysis. Using a Bayesian approach, the developed method considers a classical measurement error model for the exposures, as well as the conditional logistic regression likelihood as the disease model, together with a random-effect exposure model. Proper and diffuse prior distributions are assigned, and results from a quality control experiment are used to estimate the perfluorinated acids' measurement error variability. As a result, posterior distributions and 95% credible intervals of the odds ratios are computed. A sensitivity analysis of method's performance in this particular application with different measurement error variability was performed. The proposed Bayesian method to correct for measurement error is feasible and can be implemented using statistical software. For the study on perfluorinated acids, a comparison of the inferences which are corrected for measurement error to those which ignore it indicates that little adjustment is manifested for the level of measurement error actually exhibited in the exposures. Nevertheless, a sensitivity analysis shows that more substantial adjustments arise if larger measurement errors are assumed. In individually matched case-control studies, the use of conditional logistic regression likelihood as a disease model in the presence of measurement error in multiple continuous exposures can be justified by having a random-effect exposure model. The proposed method can be successfully implemented in WinBUGS to correct individually matched case-control studies for several mismeasured continuous exposures under a classical measurement error model.

Combining Regional- and Local-Scale Air Quality Models with Exposure Models for Use in Environmental Health Studies

EPA Science Inventory

Population-based human exposure models predict the distribution of personal exposures to pollutants of outdoor origin using a variety of inputs, including: air pollution concentrations; human activity patterns, such as the amount of time spent outdoors vs. indoors, commuting, wal...

Modeling population exposures to outdoor sources of hazardous air pollutants.

PubMed

Ozkaynak, Halûk; Palma, Ted; Touma, Jawad S; Thurman, James

2008-01-01

Accurate assessment of human exposures is an important part of environmental health effects research. However, most air pollution epidemiology studies rely upon imperfect surrogates of personal exposures, such as information based on available central-site outdoor concentration monitoring or modeling data. In this paper, we examine the limitations of using outdoor concentration predictions instead of modeled personal exposures for over 30 gaseous and particulate hazardous air pollutants (HAPs) in the US. The analysis uses the results from an air quality dispersion model (the ASPEN or Assessment System for Population Exposure Nationwide model) and an inhalation exposure model (the HAPEM or Hazardous Air Pollutant Exposure Model, Version 5), applied by the US. Environmental protection Agency during the 1999 National Air Toxic Assessment (NATA) in the US. Our results show that the total predicted chronic exposure concentrations of outdoor HAPs from all sources are lower than the modeled ambient concentrations by about 20% on average for most gaseous HAPs and by about 60% on average for most particulate HAPs (mainly, due to the exclusion of indoor sources from our modeling analysis and lower infiltration of particles indoors). On the other hand, the HAPEM/ASPEN concentration ratio averages for onroad mobile source exposures were found to be greater than 1 (around 1.20) for most mobile-source related HAPs (e.g. 1, 3-butadiene, acetaldehyde, benzene, formaldehyde) reflecting the importance of near-roadway and commuting environments on personal exposures to HAPs. The distribution of the ratios of personal to ambient concentrations was found to be skewed for a number of the VOCs and reactive HAPs associated with major source emissions, indicating the importance of personal mobility factors. We conclude that the increase in personal exposures from the corresponding predicted ambient levels tends to occur near locations where there are either major emission sources of HAPs or when individuals are exposed to either on- or nonroad sources of HAPs during their daily activities. These findings underscore the importance of applying exposure-modeling methods, which incorporate information on time-activity, commuting, and exposure factors data, for the purposes of assigning exposures in air pollution health studies.

A Comparison of Exposure Metrics for Traffic-Related Air Pollutants: Application to Epidemiology Studies in Detroit, Michigan

PubMed Central

Batterman, Stuart; Burke, Janet; Isakov, Vlad; Lewis, Toby; Mukherjee, Bhramar; Robins, Thomas

2014-01-01

Vehicles are major sources of air pollutant emissions, and individuals living near large roads endure high exposures and health risks associated with traffic-related air pollutants. Air pollution epidemiology, health risk, environmental justice, and transportation planning studies would all benefit from an improved understanding of the key information and metrics needed to assess exposures, as well as the strengths and limitations of alternate exposure metrics. This study develops and evaluates several metrics for characterizing exposure to traffic-related air pollutants for the 218 residential locations of participants in the NEXUS epidemiology study conducted in Detroit (MI, USA). Exposure metrics included proximity to major roads, traffic volume, vehicle mix, traffic density, vehicle exhaust emissions density, and pollutant concentrations predicted by dispersion models. Results presented for each metric include comparisons of exposure distributions, spatial variability, intraclass correlation, concordance and discordance rates, and overall strengths and limitations. While showing some agreement, the simple categorical and proximity classifications (e.g., high diesel/low diesel traffic roads and distance from major roads) do not reflect the range and overlap of exposures seen in the other metrics. Information provided by the traffic density metric, defined as the number of kilometers traveled (VKT) per day within a 300 m buffer around each home, was reasonably consistent with the more sophisticated metrics. Dispersion modeling provided spatially- and temporally-resolved concentrations, along with apportionments that separated concentrations due to traffic emissions and other sources. While several of the exposure metrics showed broad agreement, including traffic density, emissions density and modeled concentrations, these alternatives still produced exposure classifications that differed for a substantial fraction of study participants, e.g., from 20% to 50% of homes, depending on the metric, would be incorrectly classified into “low”, “medium” or “high” traffic exposure classes. These and other results suggest the potential for exposure misclassification and the need for refined and validated exposure metrics. While data and computational demands for dispersion modeling of traffic emissions are non-trivial concerns, once established, dispersion modeling systems can provide exposure information for both on- and near-road environments that would benefit future traffic-related assessments. PMID:25226412

Air Pollution Exposure Model for Individuals (EMI) in Health Studies: Evaluation for Ambient PM2.5

EPA Science Inventory

Health studies of fine particulate matter (PM2.5) often use outdoor concentrations as exposure surrogates, which fail to account for indoor attenuation of ambient PM2.5 and time indoors. To address these limitations, we developed an air pollution exposure model for individuals (E...

The effects of post-exposure smallpox vaccination on clinical disease presentation: addressing the data gaps between historical epidemiology and modern surrogate model data.

PubMed

Keckler, M Shannon; Reynolds, Mary G; Damon, Inger K; Karem, Kevin L

2013-10-25

Decades after public health interventions - including pre- and post-exposure vaccination - were used to eradicate smallpox, zoonotic orthopoxvirus outbreaks and the potential threat of a release of variola virus remain public health concerns. Routine prophylactic smallpox vaccination of the public ceased worldwide in 1980, and the adverse event rate associated with the currently licensed live vaccinia virus vaccine makes reinstatement of policies recommending routine pre-exposure vaccination unlikely in the absence of an orthopoxvirus outbreak. Consequently, licensing of safer vaccines and therapeutics that can be used post-orthopoxvirus exposure is necessary to protect the global population from these threats. Variola virus is a solely human pathogen that does not naturally infect any other known animal species. Therefore, the use of surrogate viruses in animal models of orthopoxvirus infection is important for the development of novel vaccines and therapeutics. Major complications involved with the use of surrogate models include both the absence of a model that accurately mimics all aspects of human smallpox disease and a lack of reproducibility across model species. These complications limit our ability to model post-exposure vaccination with newer vaccines for application to human orthopoxvirus outbreaks. This review seeks to (1) summarize conclusions about the efficacy of post-exposure smallpox vaccination from historic epidemiological reports and modern animal studies; (2) identify data gaps in these studies; and (3) summarize the clinical features of orthopoxvirus-associated infections in various animal models to identify those models that are most useful for post-exposure vaccination studies. The ultimate purpose of this review is to provide observations and comments regarding available model systems and data gaps for use in improving post-exposure medical countermeasures against orthopoxviruses. Copyright © 2013 Elsevier Ltd. All rights reserved.

The effects of post-exposure smallpox vaccination on clinical disease presentation: Addressing the data gaps between historical epidemiology and modern surrogate model data

PubMed Central

Keckler, M. Shannon; Reynolds, Mary G.; Damon, Inger K.; Karem, Kevin L.

2015-01-01

Decades after public health interventions – including pre- and post-exposure vaccination – were used to eradicate smallpox, zoonotic orthopoxvirus outbreaks and the potential threat of a release of variola virus remain public health concerns. Routine prophylactic smallpox vaccination of the public ceased worldwide in 1980, and the adverse event rate associated with the currently licensed live vaccinia virus vaccine makes reinstatement of policies recommending routine pre-exposure vaccination unlikely in the absence of an orthopoxvirus outbreak. Consequently, licensing of safer vaccines and therapeutics that can be used post-orthopoxvirus exposure is necessary to protect the global population from these threats. Variola virus is a solely human pathogen that does not naturally infect any other known animal species. Therefore, the use of surrogate viruses in animal models of orthopoxvirus infection is important for the development of novel vaccines and therapeutics. Major complications involved with the use of surrogate models include both the absence of a model that accurately mimics all aspects of human smallpox disease and a lack of reproducibility across model species. These complications limit our ability to model post-exposure vaccination with newer vaccines for application to human orthopoxvirus outbreaks. This review seeks to (1) summarize conclusions about the efficacy of post-exposure smallpox vaccination from historic epidemiological reports and modern animal studies; (2) identify data gaps in these studies; and (3) summarize the clinical features of orthopoxvirus-associated infections in various animal models to identify those models that are most useful for post-exposure vaccination studies. The ultimate purpose of this review is to provide observations and comments regarding available model systems and data gaps for use in improving post-exposure medical countermeasures against orthopoxviruses. PMID:23994378

Evaluating indoor exposure modeling alternatives for LCA: A case study in the vehicle repair industry

DOE Office of Scientific and Technical Information (OSTI.GOV)

Demou, Evangelia; Hellweg, Stefanie; Wilson, Michael P.

2009-05-01

We evaluated three exposure models with data obtained from measurements among workers who use"aerosol" solvent products in the vehicle repair industry and with field experiments using these products to simulate the same exposure conditions. The three exposure models were the: 1) homogeneously-mixed-one-box model, 2) multi-zone model, and 3) eddy-diffusion model. Temporally differentiated real-time breathing zone volatile organic compound (VOC) concentration measurements, integrated far-field area samples, and simulated experiments were used in estimating parameters, such as emission rates, diffusivity, and near-field dimensions. We assessed differences in model input requirements and their efficacy for predictive modeling. The One-box model was not ablemore » to resemble the temporal profile of exposure concentrations, but it performed well concerning time-weighted exposure over extended time periods. However, this model required an adjustment for spatial concentration gradients. Multi-zone models and diffusion-models may solve this problem. However, we found that the reliable use of both these models requires extensive field data to appropriately define pivotal parameters such as diffusivity or near-field dimensions. We conclude that it is difficult to apply these models for predicting VOC exposures in the workplace. However, for comparative exposure scenarios in life-cycle assessment they may be useful.« less

Exposure-lag-response in Longitudinal Studies: Application of Distributed Lag Non-linear Models in an Occupational Cohort.

PubMed

Neophytou, Andreas M; Picciotto, Sally; Brown, Daniel M; Gallagher, Lisa E; Checkoway, Harvey; Eisen, Ellen A; Costello, Sadie

2018-02-13

Prolonged exposures can have complex relationships with health outcomes, as timing, duration, and intensity of exposure are all potentially relevant. Summary measures such as cumulative exposure or average intensity of exposure may not fully capture these relationships. We applied penalized and unpenalized distributed lag non-linear models (DLNMs) with flexible exposure-response and lag-response functions in order to examine the association between crystalline silica exposure and mortality from lung cancer and non-malignant respiratory disease in a cohort study of 2,342 California diatomaceous earth workers, followed 1942-2011. We also assessed associations using simple measures of cumulative exposure assuming linear exposure-response and constant lag-response. Measures of association from DLNMs were generally higher than from simpler models. Rate ratios from penalized DLNMs corresponding to average daily exposures of 0.4 mg/m3 during lag years 31-50 prior to the age of observed cases were 1.47 (95% confidence interval (CI) 0.92, 2.35) for lung cancer and 1.80 (95% CI: 1.14, 2.85) for non-malignant respiratory disease. Rate ratios from the simpler models for the same exposure scenario were 1.15 (95% CI: 0.89-1.48) and 1.23 (95% CI: 1.03-1.46) respectively. Longitudinal cohort studies of prolonged exposures and chronic health outcomes should explore methods allowing for flexibility and non-linearities in the exposure-lag-response. © The Author(s) 2018. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health.

Neurotoxicity in Preclinical Models of Occupational Exposure to Organophosphorus Compounds.

PubMed

Voorhees, Jaymie R; Rohlman, Diane S; Lein, Pamela J; Pieper, Andrew A

2016-01-01

Organophosphorus (OPs) compounds are widely used as insecticides, plasticizers, and fuel additives. These compounds potently inhibit acetylcholinesterase (AChE), the enzyme that inactivates acetylcholine at neuronal synapses, and acute exposure to high OP levels can cause cholinergic crisis in humans and animals. Evidence further suggests that repeated exposure to lower OP levels insufficient to cause cholinergic crisis, frequently encountered in the occupational setting, also pose serious risks to people. For example, multiple epidemiological studies have identified associations between occupational OP exposure and neurodegenerative disease, psychiatric illness, and sensorimotor deficits. Rigorous scientific investigation of the basic science mechanisms underlying these epidemiological findings requires valid preclinical models in which tightly-regulated exposure paradigms can be correlated with neurotoxicity. Here, we review the experimental models of occupational OP exposure currently used in the field. We found that animal studies simulating occupational OP exposures do indeed show evidence of neurotoxicity, and that utilization of these models is helping illuminate the mechanisms underlying OP-induced neurological sequelae. Still, further work is necessary to evaluate exposure levels, protection methods, and treatment strategies, which taken together could serve to modify guidelines for improving workplace conditions globally.

A Framework for Widespread Replication of a Highly Spatially Resolved Childhood Lead Exposure Risk Model

PubMed Central

Kim, Dohyeong; Galeano, M. Alicia Overstreet; Hull, Andrew; Miranda, Marie Lynn

2008-01-01

Background Preventive approaches to childhood lead poisoning are critical for addressing this longstanding environmental health concern. Moreover, increasing evidence of cognitive effects of blood lead levels < 10 μg/dL highlights the need for improved exposure prevention interventions. Objectives Geographic information system–based childhood lead exposure risk models, especially if executed at highly resolved spatial scales, can help identify children most at risk of lead exposure, as well as prioritize and direct housing and health-protective intervention programs. However, developing highly resolved spatial data requires labor-and time-intensive geocoding and analytical processes. In this study we evaluated the benefit of increased effort spent geocoding in terms of improved performance of lead exposure risk models. Methods We constructed three childhood lead exposure risk models based on established methods but using different levels of geocoded data from blood lead surveillance, county tax assessors, and the 2000 U.S. Census for 18 counties in North Carolina. We used the results to predict lead exposure risk levels mapped at the individual tax parcel unit. Results The models performed well enough to identify high-risk areas for targeted intervention, even with a relatively low level of effort on geocoding. Conclusions This study demonstrates the feasibility of widespread replication of highly spatially resolved childhood lead exposure risk models. The models guide resource-constrained local health and housing departments and community-based organizations on how best to expend their efforts in preventing and mitigating lead exposure risk in their communities. PMID:19079729

77 FR 61604 - Exposure Modeling Public Meeting; Notice of Public Meeting

Federal Register 2010, 2011, 2012, 2013, 2014

2012-10-10

..., birds, reptiles, and amphibians: Model Parameterization and Knowledge base Development. 4. Standard Operating Procedure for calculating degradation kinetics. 5. Aquatic exposure modeling using field studies...

Estimators for longitudinal latent exposure models: examining measurement model assumptions.

PubMed

Sánchez, Brisa N; Kim, Sehee; Sammel, Mary D

2017-06-15

Latent variable (LV) models are increasingly being used in environmental epidemiology as a way to summarize multiple environmental exposures and thus minimize statistical concerns that arise in multiple regression. LV models may be especially useful when multivariate exposures are collected repeatedly over time. LV models can accommodate a variety of assumptions but, at the same time, present the user with many choices for model specification particularly in the case of exposure data collected repeatedly over time. For instance, the user could assume conditional independence of observed exposure biomarkers given the latent exposure and, in the case of longitudinal latent exposure variables, time invariance of the measurement model. Choosing which assumptions to relax is not always straightforward. We were motivated by a study of prenatal lead exposure and mental development, where assumptions of the measurement model for the time-changing longitudinal exposure have appreciable impact on (maximum-likelihood) inferences about the health effects of lead exposure. Although we were not particularly interested in characterizing the change of the LV itself, imposing a longitudinal LV structure on the repeated multivariate exposure measures could result in high efficiency gains for the exposure-disease association. We examine the biases of maximum likelihood estimators when assumptions about the measurement model for the longitudinal latent exposure variable are violated. We adapt existing instrumental variable estimators to the case of longitudinal exposures and propose them as an alternative to estimate the health effects of a time-changing latent predictor. We show that instrumental variable estimators remain unbiased for a wide range of data generating models and have advantages in terms of mean squared error. Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.

AC field exposure study: human exposure to 60-Hz electric fields

DOE Office of Scientific and Technical Information (OSTI.GOV)

Silva, J.M.

1985-04-01

The objective of this study was to develop a method of estimating human exposure to the 60 Hz electric fields created by transmission lines. The Activity Systems Model simulates human activities in a variety of situations where exposure to electric fields is possible. The model combines maps of electric fields, activity maps, and experimentally determined activity factors to provide histograms of time spent in electric fields of various strengths in the course of agricultural, recreational, and domestic activities. For corroboration, the study team measured actual human exposure at locations across the United States near transmission lines ranging in voltage frommore » 115 to 1200 kV. The data were collected with a specially designed vest that measures exposure. These data demonstrate the accuracy of the exposure model presented in this report and revealed that most exposure time is spent in fields of magnitudes similar to many household situations. The report provides annual exposure estimates for human activities near transmission lines and in the home and compares them with exposure data from typical laboratory animal experiments. For one exposure index, the cumulative product of time and electric field, exposure during some of the laboratory animal experiments is two to four orders of magnitude greater than cumulative exposure for a human during one year of outdoor work on a farm crossed by a transmission line.« less

Risk assessments using the Strain Index and the TLV for HAL, Part II: Multi-task jobs and prevalence of CTS.

PubMed

Kapellusch, Jay M; Silverstein, Barbara A; Bao, Stephen S; Thiese, Mathew S; Merryweather, Andrew S; Hegmann, Kurt T; Garg, Arun

2018-02-01

The Strain Index (SI) and the American Conference of Governmental Industrial Hygienists (ACGIH) threshold limit value for hand activity level (TLV for HAL) have been shown to be associated with prevalence of distal upper-limb musculoskeletal disorders such as carpal tunnel syndrome (CTS). The SI and TLV for HAL disagree on more than half of task exposure classifications. Similarly, time-weighted average (TWA), peak, and typical exposure techniques used to quantity physical exposure from multi-task jobs have shown between-technique agreement ranging from 61% to 93%, depending upon whether the SI or TLV for HAL model was used. This study compared exposure-response relationships between each model-technique combination and prevalence of CTS. Physical exposure data from 1,834 workers (710 with multi-task jobs) were analyzed using the SI and TLV for HAL and the TWA, typical, and peak multi-task job exposure techniques. Additionally, exposure classifications from the SI and TLV for HAL were combined into a single measure and evaluated. Prevalent CTS cases were identified using symptoms and nerve-conduction studies. Mixed effects logistic regression was used to quantify exposure-response relationships between categorized (i.e., low, medium, and high) physical exposure and CTS prevalence for all model-technique combinations, and for multi-task workers, mono-task workers, and all workers combined. Except for TWA TLV for HAL, all model-technique combinations showed monotonic increases in risk of CTS with increased physical exposure. The combined-models approach showed stronger association than the SI or TLV for HAL for multi-task workers. Despite differences in exposure classifications, nearly all model-technique combinations showed exposure-response relationships with prevalence of CTS for the combined sample of mono-task and multi-task workers. Both the TLV for HAL and the SI, with the TWA or typical techniques, appear useful for epidemiological studies and surveillance. However, the utility of TWA, typical, and peak techniques for job design and intervention is dubious.

A European model and case studies for aggregate exposure assessment of pesticides.

PubMed

Kennedy, Marc C; Glass, C Richard; Bokkers, Bas; Hart, Andy D M; Hamey, Paul Y; Kruisselbrink, Johannes W; de Boer, Waldo J; van der Voet, Hilko; Garthwaite, David G; van Klaveren, Jacob D

2015-05-01

Exposures to plant protection products (PPPs) are assessed using risk analysis methods to protect public health. Traditionally, single sources, such as food or individual occupational sources, have been addressed. In reality, individuals can be exposed simultaneously to multiple sources. Improved regulation therefore requires the development of new tools for estimating the population distribution of exposures aggregated within an individual. A new aggregate model is described, which allows individual users to include as much, or as little, information as is available or relevant for their particular scenario. Depending on the inputs provided by the user, the outputs can range from simple deterministic values through to probabilistic analyses including characterisations of variability and uncertainty. Exposures can be calculated for multiple compounds, routes and sources of exposure. The aggregate model links to the cumulative dietary exposure model developed in parallel and is implemented in the web-based software tool MCRA. Case studies are presented to illustrate the potential of this model, with inputs drawn from existing European data sources and models. These cover exposures to UK arable spray operators, Italian vineyard spray operators, Netherlands users of a consumer spray and UK bystanders/residents. The model could also be adapted to handle non-PPP compounds. Crown Copyright © 2014. Published by Elsevier Ltd. All rights reserved.

Advances in Understanding Air Pollution and Cardiovascular Diseases: The Multi-Ethnic Study of Atherosclerosis and Air Pollution (MESA Air)

PubMed Central

Kaufman, Joel D.; Spalt, Elizabeth W.; Curl, Cynthia L.; Hajat, Anjum; Jones, Miranda R.; Kim, Sun-Young; Vedal, Sverre; Szpiro, Adam A.; Gassett, Amanda; Sheppard, Lianne; Daviglus, Martha L.; Adar, Sara D.

2016-01-01

The Multi-Ethnic Study of Atherosclerosis and Air Pollution (MESA Air) leveraged the platform of the MESA cohort into a prospective longitudinal study of relationships between air pollution and cardiovascular health. MESA Air researchers developed fine-scale, state-of-the-art air pollution exposure models for the MESA Air communities, creating individual exposure estimates for each participant. These models combine cohort-specific exposure monitoring, existing monitoring systems, and an extensive database of geographic and meteorological information. Together with extensive phenotyping in MESA—and adding participants and health measurements to the cohort—MESA Air investigated environmental exposures on a wide range of outcomes. Advances by the MESA Air team included not only a new approach to exposure modeling but also biostatistical advances in addressing exposure measurement error and temporal confounding. The MESA Air study advanced our understanding of the impact of air pollutants on cardiovascular disease and provided a research platform for advances in environmental epidemiology. PMID:27741981

Modeling individual exposures to ambient PM2.5 in the diabetes and the environment panel study (DEPS)

EPA Science Inventory

Air pollution epidemiology studies of ambient fine particulate matter (PM2.5) often use outdoor concentrations as exposure surrogates, which can induce exposure error. The goal of this study was to improve ambient PM2.5 exposure assessments for a repeated measurements study with ...

LIFETIME LUNG CANCER RISKS ASSOCIATED WITH INDOOR RADON EXPOSURE BASED ON VARIOUS RADON RISK MODELS FOR CANADIAN POPULATION.

PubMed

Chen, Jing

2017-04-01

This study calculates and compares the lifetime lung cancer risks associated with indoor radon exposure based on well-known risk models in the literature; two risk models are from joint studies among miners and the other three models were developed from pooling studies on residential radon exposure from China, Europe and North America respectively. The aim of this article is to make clear that the various models are mathematical descriptions of epidemiologically observed real risks in different environmental settings. The risk from exposure to indoor radon is real and it is normal that variations could exist among different risk models even when they were applied to the same dataset. The results show that lifetime risk estimates vary significantly between the various risk models considered here: the model based on the European residential data provides the lowest risk estimates, while models based on the European miners and Chinese residential pooling with complete dosimetry give the highest values. The lifetime risk estimates based on the EPA/BEIR-VI model lie within this range and agree reasonably well with the averages of risk estimates from the five risk models considered in this study. © Crown copyright 2016.

European consumer exposure to cosmetic products, a framework for conducting population exposure assessments.

PubMed

Hall, B; Tozer, S; Safford, B; Coroama, M; Steiling, W; Leneveu-Duchemin, M C; McNamara, C; Gibney, M

2007-11-01

Access to reliable exposure data is essential to evaluate the toxicological safety of ingredients in cosmetic products. This study was carried out by European cosmetic manufacturers acting within the trade association Colipa, with the aim to construct a probabilistic European population model of exposure. The study updates, in distribution form, the current exposure data on daily quantities of six cosmetic products. Data were collected using a combination of market information databases and a controlled product use study. In total 44,100 households and 18,057 individual consumers in five European countries provided data using their own products. All product use occasions were recorded, including those outside of home. The raw data were analysed using Monte Carlo simulation and a European Statistical Population Model of exposure was constructed. A significant finding was an inverse correlation between frequency of product use and quantity used per application for body lotion, facial moisturiser, toothpaste and shampoo. Thus it is not appropriate to calculate daily exposure to these products by multiplying the maximum frequency value by the maximum quantity per event value. The results largely confirm the exposure parameters currently used by the cosmetic industry. Design of this study could serve as a model for future assessments of population exposure to chemicals in products other than cosmetics.

Comparison of task-based exposure metrics for an epidemiologic study of isocyanate inhalation exposures among autobody shop workers.

PubMed

Woskie, Susan R; Bello, Dhimiter; Gore, Rebecca J; Stowe, Meredith H; Eisen, Ellen A; Liu, Youcheng; Sparer, Judy A; Redlich, Carrie A; Cullen, Mark R

2008-09-01

Because many occupational epidemiologic studies use exposure surrogates rather than quantitative exposure metrics, the UMass Lowell and Yale study of autobody shop workers provided an opportunity to evaluate the relative utility of surrogates and quantitative exposure metrics in an exposure response analysis of cross-week change in respiratory function. A task-based exposure assessment was used to develop several metrics of inhalation exposure to isocyanates. The metrics included the surrogates, job title, counts of spray painting events during the day, counts of spray and bystander exposure events, and a quantitative exposure metric that incorporated exposure determinant models based on task sampling and a personal workplace protection factor for respirator use, combined with a daily task checklist. The result of the quantitative exposure algorithm was an estimate of the daily time-weighted average respirator-corrected total NCO exposure (microg/m(3)). In general, these four metrics were found to be variable in agreement using measures such as weighted kappa and Spearman correlation. A logistic model for 10% drop in FEV(1) from Monday morning to Thursday morning was used to evaluate the utility of each exposure metric. The quantitative exposure metric was the most favorable, producing the best model fit, as well as the greatest strength and magnitude of association. This finding supports the reports of others that reducing exposure misclassification can improve risk estimates that otherwise would be biased toward the null. Although detailed and quantitative exposure assessment can be more time consuming and costly, it can improve exposure-disease evaluations and is more useful for risk assessment purposes. The task-based exposure modeling method successfully produced estimates of daily time-weighted average exposures in the complex and changing autobody shop work environment. The ambient TWA exposures of all of the office workers and technicians and 57% of the painters were found to be below the current U.K. Health and Safety Executive occupational exposure limit (OEL) for total NCO of 20 microg/m(3). When respirator use was incorporated, all personal daily exposures were below the U.K. OEL.

Validation of an aggregate exposure model for substances in consumer products: a case study of diethyl phthalate in personal care products

PubMed Central

Delmaar, Christiaan; Bokkers, Bas; ter Burg, Wouter; Schuur, Gerlienke

2015-01-01

As personal care products (PCPs) are used in close contact with a person, they are a major source of consumer exposure to chemical substances contained in these products. The estimation of realistic consumer exposure to substances in PCPs is currently hampered by the lack of appropriate data and methods. To estimate aggregate exposure of consumers to substances contained in PCPs, a person-oriented consumer exposure model has been developed (the Probabilistic Aggregate Consumer Exposure Model, PACEM). The model simulates daily exposure in a population based on product use data collected from a survey among the Dutch population. The model is validated by comparing diethyl phthalate (DEP) dose estimates to dose estimates based on biomonitoring data. It was found that the model's estimates compared well with the estimates based on biomonitoring data. This suggests that the person-oriented PACEM model is a practical tool for assessing realistic aggregate exposures to substances in PCPs. In the future, PACEM will be extended with use pattern data on other product groups. This will allow for assessing aggregate exposure to substances in consumer products across different product groups. PMID:25352161

Strategies for Controlling Item Exposure in Computerized Adaptive Testing with the Generalized Partial Credit Model

ERIC Educational Resources Information Center

Davis, Laurie Laughlin

2004-01-01

Choosing a strategy for controlling item exposure has become an integral part of test development for computerized adaptive testing (CAT). This study investigated the performance of six procedures for controlling item exposure in a series of simulated CATs under the generalized partial credit model. In addition to a no-exposure control baseline…

Mathematical modeling of inhalation exposure

NASA Technical Reports Server (NTRS)

Fiserova-Bergerova, V.

1976-01-01

The paper presents a mathematical model of inhalation exposure in which uptake, distribution and excretion are described by exponential functions, while rate constants are determined by tissue volumes, blood perfusion and by the solubility of vapors (partition coefficients). In the model, tissues are grouped into four pharmokinetic compartments. The model is used to study continuous and interrupted chronic exposures and is applied to the inhalation of Forane and methylene chloride.

Assessing residential exposure to urban noise using environmental models: does the size of the local living neighborhood matter?

PubMed

Tenailleau, Quentin M; Bernard, Nadine; Pujol, Sophie; Houot, Hélène; Joly, Daniel; Mauny, Frédéric

2015-01-01

Environmental epidemiological studies rely on the quantification of the exposure level in a surface defined as the subject's exposure area. For residential exposure, this area is often the subject's neighborhood. However, the variability of the size and nature of the neighborhoods makes comparison of the findings across studies difficult. This article examines the impact of the neighborhood's definition on environmental noise exposure levels obtained from four commonly used sampling techniques: address point, façade, buffers, and official zoning. A high-definition noise model, built on a middle-sized French city, has been used to estimate LAeq,24 h exposure in the vicinity of 10,825 residential buildings. Twelve noise exposure indicators have been used to assess inhabitants' exposure. Influence of urban environmental factors was analyzed using multilevel modeling. When the sampled area increases, the average exposure increases (+3.9 dB), whereas the SD decreases (-1.6 dB) (P<0.01). Most of the indicators differ statistically. When comparing indicators from the 50-m and 400-m radius buffers, the assigned LAeq,24 h level varies across buildings from -9.4 to +22.3 dB. This variation is influenced by urban environmental characteristics (P<0.01). On the basis of this study's findings, sampling technique, neighborhood size, and environmental composition should be carefully considered in further exposure studies.

Predicting Forearm Physical Exposures During Computer Work Using Self-Reports, Software-Recorded Computer Usage Patterns, and Anthropometric and Workstation Measurements.

PubMed

Huysmans, Maaike A; Eijckelhof, Belinda H W; Garza, Jennifer L Bruno; Coenen, Pieter; Blatter, Birgitte M; Johnson, Peter W; van Dieën, Jaap H; van der Beek, Allard J; Dennerlein, Jack T

2017-12-15

Alternative techniques to assess physical exposures, such as prediction models, could facilitate more efficient epidemiological assessments in future large cohort studies examining physical exposures in relation to work-related musculoskeletal symptoms. The aim of this study was to evaluate two types of models that predict arm-wrist-hand physical exposures (i.e. muscle activity, wrist postures and kinematics, and keyboard and mouse forces) during computer use, which only differed with respect to the candidate predicting variables; (i) a full set of predicting variables, including self-reported factors, software-recorded computer usage patterns, and worksite measurements of anthropometrics and workstation set-up (full models); and (ii) a practical set of predicting variables, only including the self-reported factors and software-recorded computer usage patterns, that are relatively easy to assess (practical models). Prediction models were build using data from a field study among 117 office workers who were symptom-free at the time of measurement. Arm-wrist-hand physical exposures were measured for approximately two hours while workers performed their own computer work. Each worker's anthropometry and workstation set-up were measured by an experimenter, computer usage patterns were recorded using software and self-reported factors (including individual factors, job characteristics, computer work behaviours, psychosocial factors, workstation set-up characteristics, and leisure-time activities) were collected by an online questionnaire. We determined the predictive quality of the models in terms of R2 and root mean squared (RMS) values and exposure classification agreement to low-, medium-, and high-exposure categories (in the practical model only). The full models had R2 values that ranged from 0.16 to 0.80, whereas for the practical models values ranged from 0.05 to 0.43. Interquartile ranges were not that different for the two models, indicating that only for some physical exposures the full models performed better. Relative RMS errors ranged between 5% and 19% for the full models, and between 10% and 19% for the practical model. When the predicted physical exposures were classified into low, medium, and high, classification agreement ranged from 26% to 71%. The full prediction models, based on self-reported factors, software-recorded computer usage patterns, and additional measurements of anthropometrics and workstation set-up, show a better predictive quality as compared to the practical models based on self-reported factors and recorded computer usage patterns only. However, predictive quality varied largely across different arm-wrist-hand exposure parameters. Future exploration of the relation between predicted physical exposure and symptoms is therefore only recommended for physical exposures that can be reasonably well predicted. © The Author 2017. Published by Oxford University Press on behalf of the British Occupational Hygiene Society.

Endotoxin and gender modify lung function recovery after occupational organic dust exposure: a 30-year study.

PubMed

Lai, Peggy S; Hang, Jing-Qing; Valeri, Linda; Zhang, Feng-Ying; Zheng, Bu-Yong; Mehta, Amar J; Shi, Jing; Su, Li; Brown, Dan; Eisen, Ellen A; Christiani, David C

2015-08-01

The purpose of this study is to determine the trajectory of lung function change after exposure cessation to occupational organic dust exposure, and to identify factors that modify improvement. The Shanghai Textile Worker Study is a longitudinal study of 447 cotton workers exposed to endotoxin-containing dust and 472 silk workers exposed to non-endotoxin-containing dust. Spirometry was performed at 5-year intervals. Air sampling was performed to estimate individual cumulative exposures. The effect of work cessation on forced expiratory volume in 1 s (FEV1) was modelled using generalised additive mixed effects models to identify the trajectory of FEV1 recovery. Linear mixed effects models incorporating interaction terms were used to identify modifiers of FEV1 recovery. Loss to follow-up was accounted for with inverse probability of censoring weights. 74.2% of the original cohort still alive participated in 2011. Generalised additive mixed models identified a non-linear improvement in FEV1 for all workers after exposure cessation, with no plateau noted 25 years after retirement. Linear mixed effects models incorporating interaction terms identified prior endotoxin exposure (p=0.01) and male gender (p=0.002) as risk factors for impaired FEV1 improvement after exposure cessation. After adjusting for gender, smoking delayed the onset of FEV1 gain but did not affect the overall magnitude of change. Lung function improvement after cessation of exposure to organic dust is sustained. Endotoxin exposure and male gender are risk factors for less FEV1 improvement. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

Time series analysis of personal exposure to ambient air pollution and mortality using an exposure simulator.

PubMed

Chang, Howard H; Fuentes, Montserrat; Frey, H Christopher

2012-09-01

This paper describes a modeling framework for estimating the acute effects of personal exposure to ambient air pollution in a time series design. First, a spatial hierarchical model is used to relate Census tract-level daily ambient concentrations and simulated exposures for a subset of the study period. The complete exposure time series is then imputed for risk estimation. Modeling exposure via a statistical model reduces the computational burden associated with simulating personal exposures considerably. This allows us to consider personal exposures at a finer spatial resolution to improve exposure assessment and for a longer study period. The proposed approach is applied to an analysis of fine particulate matter of <2.5 μm in aerodynamic diameter (PM(2.5)) and daily mortality in the New York City metropolitan area during the period 2001-2005. Personal PM(2.5) exposures were simulated from the Stochastic Human Exposure and Dose Simulation. Accounting for exposure uncertainty, the authors estimated a 2.32% (95% posterior interval: 0.68, 3.94) increase in mortality per a 10 μg/m(3) increase in personal exposure to PM(2.5) from outdoor sources on the previous day. The corresponding estimates per a 10 μg/m(3) increase in PM(2.5) ambient concentration was 1.13% (95% confidence interval: 0.27, 2.00). The risks of mortality associated with PM(2.5) were also higher during the summer months.

Inside the black box: starting to uncover the underlying decision rules used in one-by-one expert assessment of occupational exposure in case-control studies

PubMed Central

Wheeler, David C.; Burstyn, Igor; Vermeulen, Roel; Yu, Kai; Shortreed, Susan M.; Pronk, Anjoeka; Stewart, Patricia A.; Colt, Joanne S.; Baris, Dalsu; Karagas, Margaret R.; Schwenn, Molly; Johnson, Alison; Silverman, Debra T.; Friesen, Melissa C.

2014-01-01

Objectives Evaluating occupational exposures in population-based case-control studies often requires exposure assessors to review each study participants' reported occupational information job-by-job to derive exposure estimates. Although such assessments likely have underlying decision rules, they usually lack transparency, are time-consuming and have uncertain reliability and validity. We aimed to identify the underlying rules to enable documentation, review, and future use of these expert-based exposure decisions. Methods Classification and regression trees (CART, predictions from a single tree) and random forests (predictions from many trees) were used to identify the underlying rules from the questionnaire responses and an expert's exposure assignments for occupational diesel exhaust exposure for several metrics: binary exposure probability and ordinal exposure probability, intensity, and frequency. Data were split into training (n=10,488 jobs), testing (n=2,247), and validation (n=2,248) data sets. Results The CART and random forest models' predictions agreed with 92–94% of the expert's binary probability assignments. For ordinal probability, intensity, and frequency metrics, the two models extracted decision rules more successfully for unexposed and highly exposed jobs (86–90% and 57–85%, respectively) than for low or medium exposed jobs (7–71%). Conclusions CART and random forest models extracted decision rules and accurately predicted an expert's exposure decisions for the majority of jobs and identified questionnaire response patterns that would require further expert review if the rules were applied to other jobs in the same or different study. This approach makes the exposure assessment process in case-control studies more transparent and creates a mechanism to efficiently replicate exposure decisions in future studies. PMID:23155187

The Potential Neurotoxic Effects of Low-Dose Sarin Exposure in a Guinea Pig Model

DTIC Science & Technology

2002-01-01

1 THE POTENTIAL NEUROTOXIC EFFECTS OF LOW-DOSE SARIN EXPOSURE IN A GUINEA PIG MODEL Melinda R. Roberson, PhD, Michelle B. Schmidt...Proving Ground, MD 21010 USA ABSTRACT This study is assessing the effects in guinea pigs of repeated low-dose exposure to the nerve...COVERED - 4. TITLE AND SUBTITLE The Potential Neurotoxic Effects Of Low-Dose Sarin Exposure In A Guinea Pig Model 5a. CONTRACT NUMBER 5b

Pathways of inhalation exposure to manganese in children ...

EPA Pesticide Factsheets

Manganese (Mn) is both essential element and neurotoxicant. Exposure to Mn can occur from various sources and routes. Structural equation modeling was used to examine routes of exposure to Mn among children residing near a ferromanganese refinery in Marietta, Ohio. An inhalation pathway model to ambient air Mn was hypothesized. Data for model evaluation were obtained from participants in the Communities Actively Researching Exposure Study (CARES). These data were collected in 2009 and included levels of Mn in residential soil and dust, levels of Mn in children's hair, information on the amount of time the child spent outside, heat and air conditioning in the home and level of parent education. Hair Mn concentration was the primary endogenous variable used to assess the theoretical inhalation exposure pathways. The model indicated that household dust Mn was a significant contributor to child hair Mn (0.37). Annual ambient air Mn concentration (0.26), time children spent outside (0.24) and soil Mn (0.24) significantly contributed to the amount of Mn in household dust. These results provide a potential framework for understanding the inhalation exposure pathway for children exposed to ambient air Mn who live in proximity to an industrial emission source. The purpose of this study was to use a structural equations modeling approach combined with exposure estimates derived from air-dispersion modeling to assess potential inhalation exposure pathways for children to a

Calculating Formulae of Proportion Factor and Mean Neutron Exposure in the Exponential Expression of Neutron Exposure Distribution

NASA Astrophysics Data System (ADS)

Feng-Hua, Zhang; Gui-De, Zhou; Kun, Ma; Wen-Juan, Ma; Wen-Yuan, Cui; Bo, Zhang

2016-07-01

Previous studies have shown that, for the three main stages of the development and evolution of asymptotic giant branch (AGB) star s-process models, the neutron exposure distribution (DNE) in the nucleosynthesis region can always be considered as an exponential function, i.e., ρAGB(τ) = C/τ0 exp(-τ/τ0) in an effective range of the neutron exposure values. However, the specific expressions of the proportion factor C and the mean neutron exposure τ0 in the exponential distribution function for different models are not completely determined in the related literature. Through dissecting the basic method to obtain the exponential DNE, and systematically analyzing the solution procedures of neutron exposure distribution functions in different stellar models, the general formulae, as well as their auxiliary equations, for calculating C and τ0 are derived. Given the discrete neutron exposure distribution Pk, the relationships of C and τ0 with the model parameters can be determined. The result of this study has effectively solved the problem to analytically calculate the DNE in the current low-mass AGB star s-process nucleosynthesis model of 13C-pocket radiative burning.

APPROACHES TO ECOSYSTEM AND HUMAN EXPOSURE TO MERCURY FOR SENSITIVE POPULATIONS

EPA Science Inventory

Both human and ecosystem exposure studies evaluate exposure of sensitive and vulnerable populations. We will discuss how ecosystem exposure modeling studies completed for input into the US Clean Air Mercury Rule (CAMR) to evaluate the response of aquatic ecosystems to changes in ...

ASSESSING POPULATION EXPOSURES TO MULTIPLE AIR POLLUTANTS USING A MECHANISTIC SOURCE-TO-DOSE MODELING FRAMEWORK

EPA Science Inventory

The Modeling Environment for Total Risks studies (MENTOR) system, combined with an extension of the SHEDS (Stochastic Human Exposure and Dose Simulation) methodology, provide a mechanistically consistent framework for conducting source-to-dose exposure assessments of multiple pol...

Altered exposure-related reshaping of body appreciation in adolescent patients with anorexia nervosa.

PubMed

Mele, Sonia; Cazzato, Valentina; Di Taranto, Francesca; Maestro, Sandra; Fabbro, Franco; Muratori, Filippo; Urgesi, Cosimo

2016-12-01

Several studies suggest a relation between repeated exposure to extremely thin bodies in media and the perceptual and emotional disturbances of body representation in anorexia nervosa (AN). In this study, we utilized an exposure paradigm to investigate how perceptual experience modulates body appreciation in adolescents with AN as compared to healthy adolescents. Twenty AN patients and 20 healthy controls were exposed to pictures of thin or round models and were then required to express liking judgments about bodies of variable weight. Brief exposure to round models increased the liking judgments of round bodies but not those of thin bodies in healthy adolescents. Furthermore, exposure to round models increased the liking judgments of both thin and round bodies in adolescents with AN. Patients did not show any change of liking judgments after exposure to thin models. These results point to weak norm-based reshaping of body appreciation in AN patients. Copyright © 2016 Elsevier Ltd. All rights reserved.

Operational and environmental determinants of in-vehicle CO and PM2.5 exposure.

PubMed

Alameddine, I; Abi Esber, L; Bou Zeid, E; Hatzopoulou, M; El-Fadel, M

2016-05-01

This study presents a modeling framework to quantify the complex roles that traffic, seasonality, vehicle characteristics, ventilation, meteorology, and ambient air quality play in dictating in-vehicle commuter exposure to CO and PM2.5. For this purpose, a comprehensive one-year monitoring program of 25 different variables was coupled with a multivariate regression analysis to develop models to predict in-vehicle CO and PM2.5 exposure using a database of 119 mobile tests and 120 fume leakage tests. The study aims to improve the understanding of in-cabin exposure, as well as interior-exterior pollutant exchange. Model results highlighted the strong correlation between out-vehicle and in-vehicle concentrations, with the effect of ventilation type only discerned for PM2.5 levels. Car type, road conditions, as well as meteorological conditions all played a significant role in modulating in-vehicle exposure. The CO and PM2.5 exposure models were able to explain 72 and 92% of the variability in measured concentrations, respectively. Both models exhibited robustness and no-evidence of over-fitting. Copyright © 2016 Elsevier B.V. All rights reserved.

Neurotoxicity in Preclinical Models of Occupational Exposure to Organophosphorus Compounds

PubMed Central

Voorhees, Jaymie R.; Rohlman, Diane S.; Lein, Pamela J.; Pieper, Andrew A.

2017-01-01

Organophosphorus (OPs) compounds are widely used as insecticides, plasticizers, and fuel additives. These compounds potently inhibit acetylcholinesterase (AChE), the enzyme that inactivates acetylcholine at neuronal synapses, and acute exposure to high OP levels can cause cholinergic crisis in humans and animals. Evidence further suggests that repeated exposure to lower OP levels insufficient to cause cholinergic crisis, frequently encountered in the occupational setting, also pose serious risks to people. For example, multiple epidemiological studies have identified associations between occupational OP exposure and neurodegenerative disease, psychiatric illness, and sensorimotor deficits. Rigorous scientific investigation of the basic science mechanisms underlying these epidemiological findings requires valid preclinical models in which tightly-regulated exposure paradigms can be correlated with neurotoxicity. Here, we review the experimental models of occupational OP exposure currently used in the field. We found that animal studies simulating occupational OP exposures do indeed show evidence of neurotoxicity, and that utilization of these models is helping illuminate the mechanisms underlying OP-induced neurological sequelae. Still, further work is necessary to evaluate exposure levels, protection methods, and treatment strategies, which taken together could serve to modify guidelines for improving workplace conditions globally. PMID:28149268

Development of an RF-EMF Exposure Surrogate for Epidemiologic Research.

PubMed

Roser, Katharina; Schoeni, Anna; Bürgi, Alfred; Röösli, Martin

2015-05-22

Exposure assessment is a crucial part in studying potential effects of RF-EMF. Using data from the HERMES study on adolescents, we developed an integrative exposure surrogate combining near-field and far-field RF-EMF exposure in a single brain and whole-body exposure measure. Contributions from far-field sources were modelled by propagation modelling and multivariable regression modelling using personal measurements. Contributions from near-field sources were assessed from both, questionnaires and mobile phone operator records. Mean cumulative brain and whole-body doses were 1559.7 mJ/kg and 339.9 mJ/kg per day, respectively. 98.4% of the brain dose originated from near-field sources, mainly from GSM mobile phone calls (93.1%) and from DECT phone calls (4.8%). Main contributors to the whole-body dose were GSM mobile phone calls (69.0%), use of computer, laptop and tablet connected to WLAN (12.2%) and data traffic on the mobile phone via WLAN (6.5%). The exposure from mobile phone base stations contributed 1.8% to the whole-body dose, while uplink exposure from other people's mobile phones contributed 3.6%. In conclusion, the proposed approach is considered useful to combine near-field and far-field exposure to an integrative exposure surrogate for exposure assessment in epidemiologic studies. However, substantial uncertainties remain about exposure contributions from various near-field and far-field sources.

Development of an RF-EMF Exposure Surrogate for Epidemiologic Research

PubMed Central

Roser, Katharina; Schoeni, Anna; Bürgi, Alfred; Röösli, Martin

2015-01-01

Exposure assessment is a crucial part in studying potential effects of RF-EMF. Using data from the HERMES study on adolescents, we developed an integrative exposure surrogate combining near-field and far-field RF-EMF exposure in a single brain and whole-body exposure measure. Contributions from far-field sources were modelled by propagation modelling and multivariable regression modelling using personal measurements. Contributions from near-field sources were assessed from both, questionnaires and mobile phone operator records. Mean cumulative brain and whole-body doses were 1559.7 mJ/kg and 339.9 mJ/kg per day, respectively. 98.4% of the brain dose originated from near-field sources, mainly from GSM mobile phone calls (93.1%) and from DECT phone calls (4.8%). Main contributors to the whole-body dose were GSM mobile phone calls (69.0%), use of computer, laptop and tablet connected to WLAN (12.2%) and data traffic on the mobile phone via WLAN (6.5%). The exposure from mobile phone base stations contributed 1.8% to the whole-body dose, while uplink exposure from other people’s mobile phones contributed 3.6%. In conclusion, the proposed approach is considered useful to combine near-field and far-field exposure to an integrative exposure surrogate for exposure assessment in epidemiologic studies. However, substantial uncertainties remain about exposure contributions from various near-field and far-field sources. PMID:26006132

Design considerations for case series models with exposure onset measurement error.

PubMed

Mohammed, Sandra M; Dalrymple, Lorien S; Sentürk, Damla; Nguyen, Danh V

2013-02-28

The case series model allows for estimation of the relative incidence of events, such as cardiovascular events, within a pre-specified time window after an exposure, such as an infection. The method requires only cases (individuals with events) and controls for all fixed/time-invariant confounders. The measurement error case series model extends the original case series model to handle imperfect data, where the timing of an infection (exposure) is not known precisely. In this work, we propose a method for power/sample size determination for the measurement error case series model. Extensive simulation studies are used to assess the accuracy of the proposed sample size formulas. We also examine the magnitude of the relative loss of power due to exposure onset measurement error, compared with the ideal situation where the time of exposure is measured precisely. To facilitate the design of case series studies, we provide publicly available web-based tools for determining power/sample size for both the measurement error case series model as well as the standard case series model. Copyright © 2012 John Wiley & Sons, Ltd.

PBTK Modeling Demonstrates Contribution of Dermal and Inhalation Exposure Components to End-Exhaled Breath Concentrations of Naphthalene

PubMed Central

Kim, David; Andersen, Melvin E.; Chao, Yi-Chun E.; Egeghy, Peter P.; Rappaport, Stephen M.; Nylander-French, Leena A.

2007-01-01

Background Dermal and inhalation exposure to jet propulsion fuel 8 (JP-8) have been measured in a few occupational exposure studies. However, a quantitative understanding of the relationship between external exposures and end-exhaled air concentrations has not been described for occupational and environmental exposure scenarios. Objective Our goal was to construct a physiologically based toxicokinetic (PBTK) model that quantitatively describes the relative contribution of dermal and inhalation exposures to the end-exhaled air concentrations of naphthalene among U.S. Air Force personnel. Methods The PBTK model comprised five compartments representing the stratum corneum, viable epidermis, blood, fat, and other tissues. The parameters were optimized using exclusively human exposure and biological monitoring data. Results The optimized values of parameters for naphthalene were a) permeability coefficient for the stratum corneum 6.8 × 10−5 cm/hr, b) permeability coefficient for the viable epidermis 3.0 × 10−3 cm/hr, c) fat:blood partition coefficient 25.6, and d) other tissue:blood partition coefficient 5.2. The skin permeability coefficient was comparable to the values estimated from in vitro studies. Based on simulations of workers’ exposures to JP-8 during aircraft fuel-cell maintenance operations, the median relative contribution of dermal exposure to the end-exhaled breath concentration of naphthalene was 4% (10th percentile 1% and 90th percentile 11%). Conclusions PBTK modeling allowed contributions of the end-exhaled air concentration of naphthalene to be partitioned between dermal and inhalation routes of exposure. Further study of inter- and intraindividual variations in exposure assessment is required to better characterize the toxicokinetic behavior of JP-8 components after occupational and/or environmental exposures. PMID:17589597

PBTK modeling demonstrates contribution of dermal and inhalation exposure components to end-exhaled breath concentrations of naphthalene.

PubMed

Kim, David; Andersen, Melvin E; Chao, Yi-Chun E; Egeghy, Peter P; Rappaport, Stephen M; Nylander-French, Leena A

2007-06-01

Dermal and inhalation exposure to jet propulsion fuel 8 (JP-8) have been measured in a few occupational exposure studies. However, a quantitative understanding of the relationship between external exposures and end-exhaled air concentrations has not been described for occupational and environmental exposure scenarios. Our goal was to construct a physiologically based toxicokinetic (PBTK) model that quantitatively describes the relative contribution of dermal and inhalation exposures to the end-exhaled air concentrations of naphthalene among U.S. Air Force personnel. The PBTK model comprised five compartments representing the stratum corneum, viable epidermis, blood, fat, and other tissues. The parameters were optimized using exclusively human exposure and biological monitoring data. The optimized values of parameters for naphthalene were a) permeability coefficient for the stratum corneum 6.8 x 10(-5) cm/hr, b) permeability coefficient for the viable epidermis 3.0 x 10(-3) cm/hr, c) fat:blood partition coefficient 25.6, and d) other tissue:blood partition coefficient 5.2. The skin permeability coefficient was comparable to the values estimated from in vitro studies. Based on simulations of workers' exposures to JP-8 during aircraft fuel-cell maintenance operations, the median relative contribution of dermal exposure to the end-exhaled breath concentration of naphthalene was 4% (10th percentile 1% and 90th percentile 11%). PBTK modeling allowed contributions of the end-exhaled air concentration of naphthalene to be partitioned between dermal and inhalation routes of exposure. Further study of inter- and intraindividual variations in exposure assessment is required to better characterize the toxicokinetic behavior of JP-8 components after occupational and/or environmental exposures.

Nitrite therapy prevents chlorine gas toxicity in rabbits

PubMed Central

Honavar, Jaideep; Doran, Stephen; Ricart, Karina; Matalon, Sadis; Patel, Rakesh P.

2017-01-01

Chlorine (Cl2) gas exposure and toxicity remains a concern in military and industrial sectors. While post-Cl2 exposure damage to the lungs and other tissues has been documented and major underlying mechanisms elucidated, no targeted therapeutics that are effective when administered post-exposure, and which are amenable to mass-casualty scenarios have been developed. Our recent studies show nitrite administered by intramuscular (IM) injection post-Cl2 exposure is effective in preventing acute lung injury and improving survival in rodent models. Our goal in this study was to develop a rabbit model of Cl2 toxicity and test whether nitrite affords protection in a non-rodent model. Exposure of New Zealand White rabbits to Cl2 gas (600ppm, 45min) caused significant increases in protein and neutrophil accumulation in the airways and ~35% mortality over 18h. Nitrite administered 30min post Cl2 exposure by a single IM injection, at 1mg/Kg or 10mg/Kg, prevented indices of acute lung injury at 6h by up to 50%. Moreover, all rabbits that received nitrite survived over the study period. These data provide further rationale for developing nitrite as post-exposure therapeutic to mitigate against Cl2 gas exposure injury. PMID:28237808

Exposure information in environmental health research: Current opportunities and future directions for particulate matter, ozone, and toxic air pollutants

DOE Office of Scientific and Technical Information (OSTI.GOV)

McKone, Thomas E.; Ryan, P. Barry; Ozkaynak, Haluk

2007-02-01

Understanding and quantifying outdoor and indoor sources of human exposure are essential but often not adequately addressed in health-effects studies for air pollution. Air pollution epidemiology, risk assessment, health tracking and accountability assessments are examples of health-effects studies that require but often lack adequate exposure information. Recent advances in exposure modeling along with better information on time-activity and exposure factors data provide us with unique opportunities to improve the assignment of exposures for both future and ongoing studies linking air pollution to health impacts. In September 2006, scientists from the US Environmental Protection Agency (EPA) and the Centers for Diseasemore » Control and Prevention (CDC) along with scientists from the academic community and state health departments convened a symposium on air pollution exposure and health in order to identify, evaluate, and improve current approaches for linking air pollution exposures to disease. This manuscript presents the key issues, challenges and recommendations identified by the exposure working group, who used cases studies of particulate matter, ozone, and toxic air pollutant exposure to evaluate health-effects for air pollution. One of the over-arching lessons of this workshop is that obtaining better exposure information for these different health-effects studies requires both goal-setting for what is needed and mapping out the transition pathway from current capabilities to meeting these goals. Meeting our long-term goals requires definition of incremental steps that provide useful information for the interim and move us toward our long-term goals. Another over-arching theme among the three different pollutants and the different health study approaches is the need for integration among alternate exposure assessment approaches. For example, different groups may advocate exposure indicators, biomonitoring, mapping methods (GIS), modeling, environmental media monitoring, and/or personal exposure modeling. However, emerging research reveals that the greatest progress comes from integration among two or more of these efforts.« less

Traffic-Related Air Pollution and Childhood Asthma: Recent Advances and Remaining Gaps in the Exposure Assessment Methods.

PubMed

Khreis, Haneen; Nieuwenhuijsen, Mark J

2017-03-17

Background : Current levels of traffic-related air pollution (TRAP) are associated with the development of childhood asthma, although some inconsistencies and heterogeneity remain. An important part of the uncertainty in studies of TRAP-associated asthma originates from uncertainties in the TRAP exposure assessment and assignment methods. In this work, we aim to systematically review the exposure assessment methods used in the epidemiology of TRAP and childhood asthma, highlight recent advances, remaining research gaps and make suggestions for further research. Methods : We systematically reviewed epidemiological studies published up until 8 September 2016 and available in Embase, Ovid MEDLINE (R), and "Transport database". We included studies which examined the association between children's exposure to TRAP metrics and their risk of "asthma" incidence or lifetime prevalence, from birth to the age of 18 years old. Results : We found 42 studies which examined the associations between TRAP and subsequent childhood asthma incidence or lifetime prevalence, published since 1999. Land-use regression modelling was the most commonly used method and nitrogen dioxide (NO₂) was the most commonly used pollutant in the exposure assessments. Most studies estimated TRAP exposure at the residential address and only a few considered the participants' mobility. TRAP exposure was mostly assessed at the birth year and only a few studies considered different and/or multiple exposure time windows. We recommend that further work is needed including e.g., the use of new exposure metrics such as the composition of particulate matter, oxidative potential and ultra-fine particles, improved modelling e.g., by combining different exposure assessment models, including mobility of the participants, and systematically investigating different exposure time windows. Conclusions : Although our previous meta-analysis found statistically significant associations for various TRAP exposures and subsequent childhood asthma, further refinement of the exposure assessment may improve the risk estimates, and shed light on critical exposure time windows, putative agents, underlying mechanisms and drivers of heterogeneity.

Traffic-Related Air Pollution and Childhood Asthma: Recent Advances and Remaining Gaps in the Exposure Assessment Methods

PubMed Central

Khreis, Haneen; Nieuwenhuijsen, Mark J.

2017-01-01

Background: Current levels of traffic-related air pollution (TRAP) are associated with the development of childhood asthma, although some inconsistencies and heterogeneity remain. An important part of the uncertainty in studies of TRAP-associated asthma originates from uncertainties in the TRAP exposure assessment and assignment methods. In this work, we aim to systematically review the exposure assessment methods used in the epidemiology of TRAP and childhood asthma, highlight recent advances, remaining research gaps and make suggestions for further research. Methods: We systematically reviewed epidemiological studies published up until 8 September 2016 and available in Embase, Ovid MEDLINE (R), and “Transport database”. We included studies which examined the association between children’s exposure to TRAP metrics and their risk of “asthma” incidence or lifetime prevalence, from birth to the age of 18 years old. Results: We found 42 studies which examined the associations between TRAP and subsequent childhood asthma incidence or lifetime prevalence, published since 1999. Land-use regression modelling was the most commonly used method and nitrogen dioxide (NO2) was the most commonly used pollutant in the exposure assessments. Most studies estimated TRAP exposure at the residential address and only a few considered the participants’ mobility. TRAP exposure was mostly assessed at the birth year and only a few studies considered different and/or multiple exposure time windows. We recommend that further work is needed including e.g., the use of new exposure metrics such as the composition of particulate matter, oxidative potential and ultra-fine particles, improved modelling e.g., by combining different exposure assessment models, including mobility of the participants, and systematically investigating different exposure time windows. Conclusions: Although our previous meta-analysis found statistically significant associations for various TRAP exposures and subsequent childhood asthma, further refinement of the exposure assessment may improve the risk estimates, and shed light on critical exposure time windows, putative agents, underlying mechanisms and drivers of heterogeneity. PMID:28304360

Comparative pharmacokinetic study of the role of gender and developmental differences in occupational and environmental exposure to benzene. Master's thesis

DOE Office of Scientific and Technical Information (OSTI.GOV)

Brown, E.A.

The purpose of this study is two-fold. First, it shows that physiological differences between men and women result in gender-specific exposures with respect to benzene. Second, it assesses the potential for a lactating woman's occupational and personal benzene exposure to impact a nursing infant's exposure, highlighting the possibility of subjecting an infant to the effects of industrial chemicals via breast feeding. This study employs physiologically based pharmacokinetic (PBPK) modeling to investigate the influence of physiological parameters and to evaluate the ability of inhaled benzene to transfer from mother to infant through breastmilk. The models are run through scenarios that simulatemore » occupational, smoking, and background exposures. The gender comparison is facilitated by a sensitivity analysis. The blood/air partition coefficient and maximum velocity of metabolism were found to substantially impact model output. These values were both higher in women and caused an increase in the percentage of benzene metabolized in all of the exposure scenarios. The study of lactating women and infants is essentially theoretical. There is evidence that over 65% of an infant's benzene exposure can be attributed to contaminated breastmilk. A large portion of the ingested exposure can be eliminated by adjusting the mother's working or nursing schedule. Benzene, Physiologically based pharmacokinetics, PBPK.« less

Spatial measurement error and correction by spatial SIMEX in linear regression models when using predicted air pollution exposures.

PubMed

Alexeeff, Stacey E; Carroll, Raymond J; Coull, Brent

2016-04-01

Spatial modeling of air pollution exposures is widespread in air pollution epidemiology research as a way to improve exposure assessment. However, there are key sources of exposure model uncertainty when air pollution is modeled, including estimation error and model misspecification. We examine the use of predicted air pollution levels in linear health effect models under a measurement error framework. For the prediction of air pollution exposures, we consider a universal Kriging framework, which may include land-use regression terms in the mean function and a spatial covariance structure for the residuals. We derive the bias induced by estimation error and by model misspecification in the exposure model, and we find that a misspecified exposure model can induce asymptotic bias in the effect estimate of air pollution on health. We propose a new spatial simulation extrapolation (SIMEX) procedure, and we demonstrate that the procedure has good performance in correcting this asymptotic bias. We illustrate spatial SIMEX in a study of air pollution and birthweight in Massachusetts. © The Author 2015. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

General Population Job Exposure Matrix Applied to a Pooled Study of Prevalent Carpal Tunnel Syndrome

PubMed Central

Dale, Ann Marie; Zeringue, Angelique; Harris-Adamson, Carisa; Rempel, David; Bao, Stephen; Thiese, Matthew S.; Merlino, Linda; Burt, Susan; Kapellusch, Jay; Garg, Arun; Gerr, Fred; Hegmann, Kurt T.; Eisen, Ellen A.; Evanoff, Bradley

2015-01-01

A job exposure matrix may be useful for the study of biomechanical workplace risk factors when individual-level exposure data are unavailable. We used job title–based exposure data from a public data source to construct a job exposure matrix and test exposure-response relationships with prevalent carpal tunnel syndrome (CTS). Exposures of repetitive motion and force from the Occupational Information Network were assigned to 3,452 active workers from several industries, enrolled between 2001 and 2008 from 6 studies. Repetitive motion and force exposures were combined into high/high, high/low, and low/low exposure groupings in each of 4 multivariable logistic regression models, adjusted for personal factors. Although force measures alone were not independent predictors of CTS in these data, strong associations between combined physical exposures of force and repetition and CTS were observed in all models. Consistent with previous literature, this report shows that workers with high force/high repetition jobs had the highest prevalence of CTS (odds ratio = 2.14–2.95) followed by intermediate values (odds ratio = 1.09–2.27) in mixed exposed jobs relative to the lowest exposed workers. This study supports the use of a general population job exposure matrix to estimate workplace physical exposures in epidemiologic studies of musculoskeletal disorders when measures of individual exposures are unavailable. PMID:25700886

Disaggregation of nation-wide dynamic population exposure estimates in The Netherlands: Applications of activity-based transport models

NASA Astrophysics Data System (ADS)

Beckx, Carolien; Int Panis, Luc; Uljee, Inge; Arentze, Theo; Janssens, Davy; Wets, Geert

Traditional exposure studies that link concentrations with population data do not always take into account the temporal and spatial variations in both concentrations and population density. In this paper we present an integrated model chain for the determination of nation-wide exposure estimates that incorporates temporally and spatially resolved information about people's location and activities (obtained from an activity-based transport model) and about ambient pollutant concentrations (obtained from a dispersion model). To the best of our knowledge, it is the first time that such an integrated exercise was successfully carried out in a fully operational modus for all models under consideration. The evaluation of population level exposure in The Netherlands to NO 2 at different time-periods, locations, for different subpopulations (gender, socio-economic status) and during different activities (residential, work, transport, shopping) is chosen as a case-study to point out the new features of this methodology. Results demonstrate that, by neglecting people's travel behaviour, total average exposure to NO 2 will be underestimated by 4% and hourly exposure results can be underestimated by more than 30%. A more detailed exposure analysis reveals the intra-day variations in exposure estimates and the presence of large exposure differences between different activities (traffic > work > shopping > home) and between subpopulations (men > women, low socio-economic class > high socio-economic class). This kind of exposure analysis, disaggregated by activities or by subpopulations, per time of day, provides useful insight and information for scientific and policy purposes. It demonstrates that policy measures, aimed at reducing the overall (average) exposure concentration of the population may impact in a different way depending on the time of day or the subgroup considered. From a scientific point of view, this new approach can be used to reduce exposure misclassification.

Validity of at home model predictions as a proxy for personal exposure to radiofrequency electromagnetic fields from mobile phone base stations.

PubMed

Martens, Astrid L; Bolte, John F B; Beekhuizen, Johan; Kromhout, Hans; Smid, Tjabe; Vermeulen, Roel C H

2015-10-01

Epidemiological studies on the potential health effects of RF-EMF from mobile phone base stations require efficient and accurate exposure assessment methods. Previous studies have demonstrated that the 3D geospatial model NISMap is able to rank locations by indoor and outdoor RF-EMF exposure levels. This study extends on previous work by evaluating the suitability of using NISMap to estimate indoor RF-EMF exposure levels at home as a proxy for personal exposure to RF-EMF from mobile phone base stations. For 93 individuals in the Netherlands we measured personal exposure to RF-EMF from mobile phone base stations during a 24h period using an EME-SPY 121 exposimeter. Each individual kept a diary from which we extracted the time spent at home and in the bedroom. We used NISMap to model exposure at the home address of the participant (at bedroom height). We then compared model predictions with measurements for the 24h period, when at home, and in the bedroom by the Spearman correlation coefficient (rsp) and by calculating specificity and sensitivity using the 90th percentile of the exposure distribution as a cutpoint for high exposure. We found a low to moderate rsp of 0.36 for the 24h period, 0.51 for measurements at home, and 0.41 for measurements in the bedroom. The specificity was high (0.9) but with a low sensitivity (0.3). These results indicate that a meaningful ranking of personal RF-EMF can be achieved, even though the correlation between model predictions and 24h personal RF-EMF measurements is lower than with at home measurements. However, the use of at home RF-EMF field predictions from mobile phone base stations in epidemiological studies leads to significant exposure misclassification that will result in a loss of statistical power to detect health effects. Copyright © 2015 Elsevier Inc. All rights reserved.

Biomarker Utility Analysis Using an Exposure-PBPK/PD Model: A Carbaryl Case Study

EPA Science Inventory

There are two common biomarkers: markers of exposure and markers of health effects. The strength of the correlation between exposure or effect and a biomarker measurement determines the utility of a biomarker for assessing exposures or risks. In the current study, a linked expo...

Modelling of individual subject ozone exposure response kinetics.

PubMed

Schelegle, Edward S; Adams, William C; Walby, William F; Marion, M Susan

2012-06-01

A better understanding of individual subject ozone (O(3)) exposure response kinetics will provide insight into how to improve models used in the risk assessment of ambient ozone exposure. To develop a simple two compartment exposure-response model that describes individual subject decrements in forced expiratory volume in one second (FEV(1)) induced by the acute inhalation of O(3) lasting up to 8 h. FEV(1) measurements of 220 subjects who participated in 14 previously completed studies were fit to the model using both particle swarm and nonlinear least squares optimization techniques to identify three subject-specific coefficients producing minimum "global" and local errors, respectively. Observed and predicted decrements in FEV(1) of the 220 subjects were used for validation of the model. Further validation was provided by comparing the observed O(3)-induced FEV(1) decrements in an additional eight studies with predicted values obtained using model coefficients estimated from the 220 subjects used in cross validation. Overall the individual subject measured and modeled FEV(1) decrements were highly correlated (mean R(2) of 0.69 ± 0.24). In addition, it was shown that a matrix of individual subject model coefficients can be used to predict the mean and variance of group decrements in FEV(1). This modeling approach provides insight into individual subject O(3) exposure response kinetics and provides a potential starting point for improving the risk assessment of environmental O(3) exposure.

The relation between modeled odor exposure from livestock farming and odor annoyance among neighboring residents.

PubMed

Boers, D; Geelen, L; Erbrink, H; Smit, L A M; Heederik, D; Hooiveld, M; Yzermans, C J; Huijbregts, M; Wouters, I M

2016-04-01

Odor annoyance is an important environmental stressor for neighboring residents of livestock farms and may affect their quality of life and health. However, little is known about the relation between odor exposure due to livestock farming and odor annoyance. Even more, the relation between odor exposure and odor annoyance is rather complicated due to variable responses among individuals to comparable exposure levels and a large number of factors (such as age, gender, education) that may affect the relation. In this study, we (1) investigated the relation between modeled odor exposure and odor annoyance; (2) investigated whether other factors can affect this relation; and (3) compared our dose-response relation to a dose-response relation established in a previous study carried out in the Netherlands, more than 10 years ago, in order to investigate changes in odor perception and appreciation over time. We used data from 582 respondents who participated in a questionnaire survey among neighboring residents of livestock farms in the south of the Netherlands. Odor annoyance was established by two close-ended questions in a questionnaire; odor exposure was estimated using the Stacks dispersion model. The results of our study indicate a statistically significant and positive relation between modeled odor exposure and reported odor annoyance from livestock farming (OR 1.92; 95 % CI 1.53-2.41). Furthermore, age, asthma, education and perceived air pollution in the environment are all related to odor annoyance, although they hardly affect the relation between estimated livestock odor exposure and reported odor annoyance. We also found relatively more odor annoyance reported among neighboring residents than in a previous study conducted in the Netherlands. We found a strong relation between modeled odor exposure and odor annoyance. However, due to some uncertainties and small number of studies on this topic, further research and replication of results is recommended.

A Comparison of Item Selection Techniques and Exposure Control Mechanisms in CATs Using the Generalized Partial Credit Model.

ERIC Educational Resources Information Center

Pastor, Dena A.; Dodd, Barbara G.; Chang, Hua-Hua

2002-01-01

Studied the impact of using five different exposure control algorithms in two sizes of item pool calibrated using the generalized partial credit model. Simulation results show that the a-stratified design, in comparison to a no-exposure control condition, could be used to reduce item exposure and overlap and increase pool use, while degrading…

RECEPTOR MODELING OF AMBIENT AND PERSONAL EXPOSURE SAMPLES: 1998 BALTIMORE PARTICULATE MATTER EPIDEMIOLOGY-EXPOSURE STUDY

EPA Science Inventory

Sources of particulate matter exposure for an elderly population in a city north of Baltimore, MD were evaluated using advanced factor analysis models. Data collected with Versatile Air Pollutant Samplers (VAPS) positioned at a community site, outside and inside of an elderly ...

OZONE-INDUCED RESPIRATORY SYMPTOMS: EXPOSURE-RESPONSE MODELS AND ASSOCIATION WITH LUNG FUNCTION

EPA Science Inventory

Ozone-induced respiratory symptoms are known to be functions of concentration, minute ventilation, and duration of exposure. The purposes of this study were to identify an exposure-response model for symptoms, to determine whether response was related to age, and to assess the re...

DEVELOMENT AND EVALUATION OF A MODEL FOR ESTIMATING LONG-TERM AVERAGE OZONE EXPOSURES TO CHILDREN

EPA Science Inventory

Long-term average exposures of school-age children can be modelled using longitudinal measurements collected during the Harvard Southern California Chronic Ozone Exposure Study over a 12-month period: June, 1995-May, 1996. The data base contains over 200 young children with perso...

Contribution of inorganic arsenic sources to population exposure risk on a regional scale.

PubMed

Chou, Wei-Chun; Chen, Jein-Wen; Liao, Chung-Min

2016-07-01

Chronic exposure to inorganic arsenic (iAs) in the human population is associated with various internal cancers and other adverse outcomes. The purpose of this study was to estimate a population-scale exposure risk attributable to iAs consumptions by linking a stochastic physiological-based pharmacokinetic (PBPK) model and biomonitoring data of iAs in urine. The urinary As concentrations were obtained from a total of 1,043 subjects living in an industrial area of Taiwan. The results showed that the study subjects had an iAs exposure risk of 27 % (the daily iAs intake for 27 % study subjects exceeded the WHO-recommended value, 2.1 μg iAs day(-1) kg(-1) body weight). Moreover, drinking water and cooked rice contributed to the iAs exposure risk by 10 and 41 %, respectively. The predicted risks in the current study were 4.82, 27.21, 34.69, and 64.17 %, respectively, among the mid-range of Mexico, Taiwan (this study), Korea, and Bangladesh reported in the literature. In conclusion, we developed a population-scale-based risk model that covered the broad range of iAS exposure by integrating stochastic PBPK modeling and reverse dosimetry to generate probabilistic distribution of As intake corresponding to urinary As measured from the cohort study. The model can also be updated as new urinary As information becomes available.

The Association between Road Traffic Noise Exposure, Annoyance and Health-Related Quality of Life (HRQOL)

PubMed Central

Héritier, Harris; Vienneau, Danielle; Frei, Patrizia; Eze, Ikenna C.; Brink, Mark; Probst-Hensch, Nicole; Röösli, Martin

2014-01-01

The aim of this study is to investigate the relationships between road traffic noise exposure, annoyance caused by different noise sources and validated health indicators in a cohort of 1375 adults from the region of Basel, Switzerland. Road traffic noise exposure for each study participant was determined using modelling, and annoyance from various noise sources was inquired by means of a four-point Likert scale. Regression parameters from multivariable regression models for the von Zerssen score of somatic symptoms (point symptom score increase per annoyance category) showed strongest associations with annoyance from industry noise (2.36, 95% CI: 1.54, 3.17), neighbour noise (1.62, 95% CI: 1.17, 2.06) and road traffic noise (1.53, 95% CI: 1.09, 1.96). Increase in modelled noise exposure by 10 dB(A) resulted in a von Zerssen symptom score increase of 0.47 (95% CI: −0.01, 0.95) units. Subsequent structural equation modelling revealed that the association between physical noise exposure and health-related quality of life (HRQOL) is strongly mediated by annoyance and sleep disturbance. This study elucidates the complex interplay of different factors for the association between physical noise exposure and HRQOL. PMID:25489999

Measurement Error Correction for Predicted Spatiotemporal Air Pollution Exposures.

PubMed

Keller, Joshua P; Chang, Howard H; Strickland, Matthew J; Szpiro, Adam A

2017-05-01

Air pollution cohort studies are frequently analyzed in two stages, first modeling exposure then using predicted exposures to estimate health effects in a second regression model. The difference between predicted and unobserved true exposures introduces a form of measurement error in the second stage health model. Recent methods for spatial data correct for measurement error with a bootstrap and by requiring the study design ensure spatial compatibility, that is, monitor and subject locations are drawn from the same spatial distribution. These methods have not previously been applied to spatiotemporal exposure data. We analyzed the association between fine particulate matter (PM2.5) and birth weight in the US state of Georgia using records with estimated date of conception during 2002-2005 (n = 403,881). We predicted trimester-specific PM2.5 exposure using a complex spatiotemporal exposure model. To improve spatial compatibility, we restricted to mothers residing in counties with a PM2.5 monitor (n = 180,440). We accounted for additional measurement error via a nonparametric bootstrap. Third trimester PM2.5 exposure was associated with lower birth weight in the uncorrected (-2.4 g per 1 μg/m difference in exposure; 95% confidence interval [CI]: -3.9, -0.8) and bootstrap-corrected (-2.5 g, 95% CI: -4.2, -0.8) analyses. Results for the unrestricted analysis were attenuated (-0.66 g, 95% CI: -1.7, 0.35). This study presents a novel application of measurement error correction for spatiotemporal air pollution exposures. Our results demonstrate the importance of spatial compatibility between monitor and subject locations and provide evidence of the association between air pollution exposure and birth weight.

Dermal Exposure Assessment to Pesticides in Farming Systems in Developing Countries: Comparison of Models

PubMed Central

Lesmes Fabian, Camilo; Binder, Claudia R.

2015-01-01

In the field of occupational hygiene, researchers have been working on developing appropriate methods to estimate human exposure to pesticides in order to assess the risk and therefore to take the due decisions to improve the pesticide management process and reduce the health risks. This paper evaluates dermal exposure models to find the most appropriate. Eight models (i.e., COSHH, DERM, DREAM, EASE, PHED, RISKOFDERM, STOFFENMANAGER and PFAM) were evaluated according to a multi-criteria analysis and from these results five models (i.e., DERM, DREAM, PHED, RISKOFDERM and PFAM) were selected for the assessment of dermal exposure in the case study of the potato farming system in the Andean highlands of Vereda La Hoya, Colombia. The results show that the models provide different dermal exposure estimations which are not comparable. However, because of the simplicity of the algorithm and the specificity of the determinants, the DERM, DREAM and PFAM models were found to be the most appropriate although their estimations might be more accurate if specific determinants are included for the case studies in developing countries. PMID:25938911

Low Dose Radiation Cancer Risks: Epidemiological and Toxicological Models

DOE Office of Scientific and Technical Information (OSTI.GOV)

David G. Hoel, PhD

2012-04-19

The basic purpose of this one year research grant was to extend the two stage clonal expansion model (TSCE) of carcinogenesis to exposures other than the usual single acute exposure. The two-stage clonal expansion model of carcinogenesis incorporates the biological process of carcinogenesis, which involves two mutations and the clonal proliferation of the intermediate cells, in a stochastic, mathematical way. The current TSCE model serves a general purpose of acute exposure models but requires numerical computation of both the survival and hazard functions. The primary objective of this research project was to develop the analytical expressions for the survival functionmore » and the hazard function of the occurrence of the first cancer cell for acute, continuous and multiple exposure cases within the framework of the piece-wise constant parameter two-stage clonal expansion model of carcinogenesis. For acute exposure and multiple exposures of acute series, it is either only allowed to have the first mutation rate vary with the dose, or to have all the parameters be dose dependent; for multiple exposures of continuous exposures, all the parameters are allowed to vary with the dose. With these analytical functions, it becomes easy to evaluate the risks of cancer and allows one to deal with the various exposure patterns in cancer risk assessment. A second objective was to apply the TSCE model with varing continuous exposures from the cancer studies of inhaled plutonium in beagle dogs. Using step functions to estimate the retention functions of the pulmonary exposure of plutonium the multiple exposure versions of the TSCE model was to be used to estimate the beagle dog lung cancer risks. The mathematical equations of the multiple exposure versions of the TSCE model were developed. A draft manuscript which is attached provides the results of this mathematical work. The application work using the beagle dog data from plutonium exposure has not been completed due to the fact that the research project did not continue beyond its first year.« less

Heavy Metal Exposure and Metabolic Syndrome: Evidence from Human and Model System Studies.

PubMed

Planchart, Antonio; Green, Adrian; Hoyo, Cathrine; Mattingly, Carolyn J

2018-03-01

Metabolic syndrome (MS) describes the co-occurrence of conditions that increase one's risk for heart disease and other disorders such as diabetes and stroke. The worldwide increase in the prevalence of MS cannot be fully explained by lifestyle factors such as sedentary behavior and caloric intake alone. Environmental exposures, such as heavy metals, have been implicated, but results are conflicting and possible mechanisms remain unclear. To assess recent progress in determining a possible role between heavy metal exposure and MS, we reviewed epidemiological and model system data for cadmium (Cd), lead (Pb), and mercury (Hg) from the last decade. Data from 36 epidemiological studies involving 17 unique countries/regions and 13 studies leveraging model systems are included in this review. Epidemiological and model system studies support a possible association between heavy metal exposure and MS or comorbid conditions; however, results remain conflicting. Epidemiological studies were predominantly cross-sectional and collectively, they highlight a global interest in this question and reveal evidence of differential susceptibility by sex and age to heavy metal exposures. In vivo studies in rats and mice and in vitro cell-based assays provide insights into potential mechanisms of action relevant to MS including altered regulation of lipid and glucose homeostasis, adipogenesis, and oxidative stress. Heavy metal exposure may contribute to MS or comorbid conditions; however, available data are conflicting. Causal inference remains challenging as epidemiological data are largely cross-sectional; and variation in study design, including samples used for heavy metal measurements, age of subjects at which MS outcomes are measured; the scope and treatment of confounding factors; and the population demographics vary widely. Prospective studies, standardization or increased consistency across study designs and reporting, and consideration of molecular mechanisms informed by model system studies are needed to better assess potential causal links between heavy metal exposure and MS.

Modeling spatial and temporal variability of residential air exchange rates for the Near-Road Exposures and Effects of Urban Air Pollutants Study (NEXUS).

PubMed

Breen, Michael S; Burke, Janet M; Batterman, Stuart A; Vette, Alan F; Godwin, Christopher; Croghan, Carry W; Schultz, Bradley D; Long, Thomas C

2014-11-07

Air pollution health studies often use outdoor concentrations as exposure surrogates. Failure to account for variability of residential infiltration of outdoor pollutants can induce exposure errors and lead to bias and incorrect confidence intervals in health effect estimates. The residential air exchange rate (AER), which is the rate of exchange of indoor air with outdoor air, is an important determinant for house-to-house (spatial) and temporal variations of air pollution infiltration. Our goal was to evaluate and apply mechanistic models to predict AERs for 213 homes in the Near-Road Exposures and Effects of Urban Air Pollutants Study (NEXUS), a cohort study of traffic-related air pollution exposures and respiratory effects in asthmatic children living near major roads in Detroit, Michigan. We used a previously developed model (LBL), which predicts AER from meteorology and questionnaire data on building characteristics related to air leakage, and an extended version of this model (LBLX) that includes natural ventilation from open windows. As a critical and novel aspect of our AER modeling approach, we performed a cross validation, which included both parameter estimation (i.e., model calibration) and model evaluation, based on daily AER measurements from a subset of 24 study homes on five consecutive days during two seasons. The measured AER varied between 0.09 and 3.48 h(-1) with a median of 0.64 h(-1). For the individual model-predicted and measured AER, the median absolute difference was 29% (0.19 h‑1) for both the LBL and LBLX models. The LBL and LBLX models predicted 59% and 61% of the variance in the AER, respectively. Daily AER predictions for all 213 homes during the three year study (2010-2012) showed considerable house-to-house variations from building leakage differences, and temporal variations from outdoor temperature and wind speed fluctuations. Using this novel approach, NEXUS will be one of the first epidemiology studies to apply calibrated and home-specific AER models, and to include the spatial and temporal variations of AER for over 200 individual homes across multiple years into an exposure assessment in support of improving risk estimates.

ART, Stoffenmanager, and TRA: A Systematic Comparison of Exposure Estimates Using the TREXMO Translation System.

PubMed

Savic, Nenad; Gasic, Bojan; Vernez, David

2017-12-15

Several occupational exposure models are recommended under the EU's REACH legislation. Due to limited availability of high-quality exposure data, their validation is an ongoing process. It was shown, however, that different models may calculate significantly different estimates and thus lead to potentially dangerous conclusions about chemical risk. In this paper, the between-model translation rules defined in TREXMO were used to generate 319000 different in silico exposure situations in ART, Stoffenmanager, and ECETOC TRA v3. The three models' estimates were computed and the correlation and consistency between them were investigated. The best correlated pair was Stoffenmanager-ART (R, 0.52-0.90), whereas the ART-TRA and Stoffenmanager-TRA correlations were either lower (R, 0.36-0.69) or no correlation was found. Consistency varied significantly according to different exposure types (e.g. vapour versus dust) or settings (near-field versus far-field and indoors versus outdoors). The percentages of generated situations for which estimates differed by more than a factor of 100 ranged from 14 to 97%, 37 to 99%, and 1 to 68% for Stoffenmanager-ART, TRA-ART, and TRA-Stoffenmanager, respectively. Overall, the models were more consistent for vapours than for dusts and solids, near-fields than for far-fields, and indoor than for outdoor exposure. Multiple linear regression analyses evidenced the relationship between the models' parameters and the relative differences between the models' predictions. The relative difference can be used to estimate the consistency between the models. Furthermore, the study showed that the tiered approach is not generally applicable to all exposure situations. These findings emphasize the need for a multiple-model approach to assessing critical exposure scenarios under REACH. Moreover, in combination with occupational exposure measurements, they might also be used for future studies to improve prediction accuracy. © The Author(s) 2017. Published by Oxford University Press on behalf of the British Occupational Hygiene Society.

Using exposure prediction tools to link exposure and dosimetry for risk based decisions: a case study with phthalates

EPA Science Inventory

The Population Life-course Exposure to Health Effects Modeling (PLETHEM) platform being developed provides a tool that links results from emerging toxicity testing tools to exposure estimates for humans as defined by the USEPA. A reverse dosimetry case study using phthalates was ...

HUMAN EXPOSURE ACTIVITY PATTERNS

EPA Science Inventory

Human activity/uptake rate data are necessary to estimate potential human exposure and intake dose to environmental pollutants and to refine human exposure models. Personal exposure monitoring studies have demonstrated the critical role that activities play in explaining and pre...

PBPK and population modelling to interpret urine cadmium concentrations of the French population

DOE Office of Scientific and Technical Information (OSTI.GOV)

Béchaux, Camille, E-mail: Camille.bechaux@anses.fr; Bodin, Laurent; Clémençon, Stéphan

As cadmium accumulates mainly in kidney, urinary concentrations are considered as relevant data to assess the risk related to cadmium. The French Nutrition and Health Survey (ENNS) recorded the concentration of cadmium in the urine of the French population. However, as with all biomonitoring data, it needs to be linked to external exposure for it to be interpreted in term of sources of exposure and for risk management purposes. The objective of this work is thus to interpret the cadmium biomonitoring data of the French population in terms of dietary and cigarette smoke exposures. Dietary and smoking habits recorded inmore » the ENNS study were combined with contamination levels in food and cigarettes to assess individual exposures. A PBPK model was used in a Bayesian population model to link this external exposure with the measured urinary concentrations. In this model, the level of the past exposure was corrected thanks to a scaling function which account for a trend in the French dietary exposure. It resulted in a modelling which was able to explain the current urinary concentrations measured in the French population through current and past exposure levels. Risk related to cadmium exposure in the general French population was then assessed from external and internal critical values corresponding to kidney effects. The model was also applied to predict the possible urinary concentrations of the French population in 2030 assuming there will be no more changes in the exposures levels. This scenario leads to significantly lower concentrations and consequently lower related risk. - Highlights: • Interpretation of urine cadmium concentrations in France • PBPK and Bayesian population modelling of cadmium exposure • Assessment of the historic time-trend of the cadmium exposure in France • Risk assessment from current and future external and internal exposure.« less

A simulation study to determine the attenuation and bias in health risk estimates due to exposure measurement error in bi-pollutant models

EPA Science Inventory

To understand the combined health effects of exposure to ambient air pollutant mixtures, it is becoming more common to include multiple pollutants in epidemiologic models. However, the complex spatial and temporal pattern of ambient pollutant concentrations and related exposures ...

A Comparison of Exposure Control Procedures in CATS Using the GPC Model

ERIC Educational Resources Information Center

Leroux, Audrey J.; Dodd, Barbara G.

2016-01-01

The current study compares the progressive-restricted standard error (PR-SE) exposure control method with the Sympson-Hetter, randomesque, and no exposure control (maximum information) procedures using the generalized partial credit model with fixed- and variable-length CATs and two item pools. The PR-SE method administered the entire item pool…

SUBCHRONIC TOXICITY OF INHALED TOLUENE IN RATS: IMMUNOLOGY, CARDIAC GENE EXPRESSION AND MARKERS OF OXIDATIVE STRESS.

EPA Science Inventory

The health effects of long-term exposure to volatile organic compounds (VOCs) are poorly understood, due primarily to insufficient human exposure data and inconsistent animal models. To develop a rodent model of long-term exposure to VOCs, a sub-chronic inhalation study with mult...

A Comparison of Exposure Control Procedures in CATs Using the 3PL Model

ERIC Educational Resources Information Center

Leroux, Audrey J.; Lopez, Myriam; Hembry, Ian; Dodd, Barbara G.

2013-01-01

This study compares the progressive-restricted standard error (PR-SE) exposure control procedure to three commonly used procedures in computerized adaptive testing, the randomesque, Sympson-Hetter (SH), and no exposure control methods. The performance of these four procedures is evaluated using the three-parameter logistic model under the…

Human Exposure Assessment for Air Pollution.

PubMed

Han, Bin; Hu, Li-Wen; Bai, Zhipeng

2017-01-01

Assessment of human exposure to air pollution is a fundamental part of the more general process of health risk assessment. The measurement methods for exposure assessment now include personal exposure monitoring, indoor-outdoor sampling, mobile monitoring, and exposure assessment modeling (such as proximity models, interpolation model, air dispersion models, and land-use regression (LUR) models). Among these methods, personal exposure measurement is considered to be the most accurate method of pollutant exposure assessment until now, since it can better quantify observed differences and better reflect exposure among smaller groups of people at ground level. And since the great differences of geographical environment, source distribution, pollution characteristics, economic conditions, and living habits, there is a wide range of differences between indoor, outdoor, and individual air pollution exposure in different regions of China. In general, the indoor particles in most Chinese families comprise infiltrated outdoor particles, particles generated indoors, and a few secondary organic aerosol particles, and in most cases, outdoor particle pollution concentrations are a major contributor to indoor concentrations in China. Furthermore, since the time, energy, and expense are limited, it is difficult to measure the concentration of pollutants for each individual. In recent years, obtaining the concentration of air pollutants by using a variety of exposure assessment models is becoming a main method which could solve the problem of the increasing number of individuals in epidemiology studies.

GPS-based microenvironment tracker (MicroTrac) model to estimate time–location of individuals for air pollution exposure assessments: Model evaluation in central North Carolina

PubMed Central

Breen, Michael S.; Long, Thomas C.; Schultz, Bradley D.; Crooks, James; Breen, Miyuki; Langstaff, John E.; Isaacs, Kristin K.; Tan, Yu-Mei; Williams, Ronald W.; Cao, Ye; Geller, Andrew M.; Devlin, Robert B.; Batterman, Stuart A.; Buckley, Timothy J.

2014-01-01

A critical aspect of air pollution exposure assessment is the estimation of the time spent by individuals in various microenvironments (ME). Accounting for the time spent in different ME with different pollutant concentrations can reduce exposure misclassifications, while failure to do so can add uncertainty and bias to risk estimates. In this study, a classification model, called MicroTrac, was developed to estimate time of day and duration spent in eight ME (indoors and outdoors at home, work, school; inside vehicles; other locations) from global positioning system (GPS) data and geocoded building boundaries. Based on a panel study, MicroTrac estimates were compared with 24-h diary data from nine participants, with corresponding GPS data and building boundaries of home, school, and work. MicroTrac correctly classified the ME for 99.5% of the daily time spent by the participants. The capability of MicroTrac could help to reduce the time–location uncertainty in air pollution exposure models and exposure metrics for individuals in health studies. PMID:24619294

Synergistic effects on dopamine cell death in a Drosophila model of chronic toxin exposure

PubMed Central

Martin, Ciara A.; Barajas, Angel; Lawless, George; Lawal, Hakeem O.; Assani, Khadij; Lumintang, Yosephine P.; Nunez, Vanessa; Krantz, David E.

2014-01-01

The neurodegenerative effects of Parkinson’s disease (PD) are marked by a selective loss of dopaminergic (DA) neurons. Epidemiological studies suggest that chronic exposure to the pesticide paraquat may increase the risk for PD and DA cell loss. However, combined exposure with additional fungicide(s) including maneb and/or ziram may be required for pathogenesis. To explore potential pathogenic mechanisms, we have developed a Drosophila model of chronic paraquat exposure. We find that while chronic paraquat exposure alone decreased organismal survival and motor function, combined chronic exposure to both paraquat and maneb was required for DA cell death in the fly. To initiate mechanistic studies of this interaction, we used additional genetic reagents to target the ubiquitin proteasome system, implicated in some rare familial forms of PD and the toxic effects of ziram. Genetic inhibition of E1 ubiquitin ligase, but not the proteasome itself, increased DA cell death in combination with maneb but not paraquat. These studies establish a model for long-term exposure to multiple pesticides, and support the idea that pesticide interactions relevant to PD may involve inhibition of protein ubiquitination. PMID:25160001

Characterization of air manganese exposure estimates for residents in two Ohio towns

PubMed Central

Colledge, Michelle A.; Julian, Jaime R.; Gocheva, Vihra V.; Beseler, Cheryl L.; Roels, Harry A.; Lobdell, Danelle T.; Bowler, Rosemarie M.

2016-01-01

This study was conducted to derive receptor-specific outdoor exposure concentrations of total suspended particulate (TSP) and respirable (dae ≤ 10 μm) air manganese (air-Mn) for East Liverpool and Marietta (Ohio) in the absence of facility emissions data, but where long-term air measurements were available. Our “site-surface area emissions method” used U.S. Environmental Protection Agency’s (EPA) AERMOD (AMS/EPA Regulatory Model) dispersion model and air measurement data to estimate concentrations for residential receptor sites in the two communities. Modeled concentrations were used to create ratios between receptor points and calibrated using measured data from local air monitoring stations. Estimated outdoor air-Mn concentrations were derived for individual study subjects in both towns. The mean estimated long-term air-Mn exposure levels for total suspended particulate were 0.35 μg/m3 (geometric mean [GM]) and 0.88 μg/m3 (arithmetic mean [AM]) in East Liverpool (range: 0.014–6.32 μg/m3) and 0.17 μg/m3 (GM) and 0.21 μg/m3 (AM) in Marietta (range: 0.03–1.61 μg/m3). Modeled results compared well with averaged ambient air measurements from local air monitoring stations. Exposure to respirable Mn particulate matter (PM10; PM <10 μm) was higher in Marietta residents. Implications Few available studies evaluate long-term health outcomes from inhalational manganese (Mn) exposure in residential populations, due in part to challenges in measuring individual exposures. Local long-term air measurements provide the means to calibrate models used in estimating long-term exposures. Furthermore, this combination of modeling and ambient air sampling can be used to derive receptor-specific exposure estimates even in the absence of source emissions data for use in human health outcome studies. PMID:26211636

Local-Scale Exposure Assessment of Air Pollutants in Source-Impacted Neighborhoods in Detroit, MI (Invited)

NASA Astrophysics Data System (ADS)

Vette, A. F.; Bereznicki, S.; Sobus, J.; Norris, G.; Williams, R.; Batterman, S.; Breen, M.; Isakov, V.; Perry, S.; Heist, D.; Community Action Against Asthma Steering Committee

2010-12-01

There has been growing interest in improving local-scale (< 1-km) exposure assessments to better understand the impact of local sources of air pollutants on adverse health outcomes. This paper describes two research studies aimed at understanding the impact of local sources contributing to spatial gradients at the neighborhood-scale in Detroit, MI. The first study, the Detroit Exposure and Aerosol Research Study (DEARS), was designed to assess the variability in concentrations of air pollutants derived from local and regional sources on community, neighborhood and personal exposures to air pollutants. Homes were identified at random in six different neighborhoods throughout Wayne County, MI that varied proximally to local industrial and mobile sources. Data were collected in summer (July-August) and winter (January-March) at a total of 135 homes over a three-year period (2004-2007). For five consecutive days at each home in summer and winter concurrent samples were collected of personal exposures, residential indoor and outdoor concentrations, and at a community monitoring site. The samples were analyzed for PM2.5 (mass and composition), air toxics, O3 and NO2. The second study is on-going and focuses on characterizing the impacts of mobile sources on near-road air quality and exposures among a cohort of asthmatic children. The Near-road EXposures and effects from Urban air pollutants Study (NEXUS) is designed to examine the relationship between near-road exposures to traffic-related air pollutants (BC, CO, NOx and PM components) and respiratory health of asthmatic children who live close to major roadways. The study will investigate the effects of traffic-associated exposures on exaggerated airway responses, biomolecular responses of inflammatory and oxidative stress, and how these exposures affect the frequency and severity of adverse respiratory outcomes. The study will also examine different near-road exposure assessment metrics, including monitoring and modeling techniques. Concentrations of traffic-related air pollutants will be measured and modeled indoors and outdoors of the children’s homes. Measurements will be made in a subset of homes each during fall 2010 and early spring 2011. High-time resolution measurements will be made of the chemical composition of traffic-related pollutants in the gas and particle phases adjacent to selected roadways. These data will be used to quantify the impact of traffic on the observed air quality data. Air pollutant dispersion and exposure models will be used in combination with measured data to estimate indoor/outdoor concentrations and personal exposures. Near-road spatial concentration patterns will be estimated at the children’s residences and schools across the study domain using dispersion modeling. These data will be used as input for an individual-level exposure model to estimate personal exposures from meteorology and questionnaire data on indoor sources, residential characteristics and operation, and time-location-activity patterns.

Nitrite therapy prevents chlorine gas toxicity in rabbits.

PubMed

Honavar, Jaideep; Doran, Stephen; Ricart, Karina; Matalon, Sadis; Patel, Rakesh P

2017-04-05

Chlorine (Cl 2 ) gas exposure and toxicity remains a concern in military and industrial sectors. While post-Cl 2 exposure damage to the lungs and other tissues has been documented and major underlying mechanisms elucidated, no targeted therapeutics that are effective when administered post-exposure, and which are amenable to mass-casualty scenarios have been developed. Our recent studies show nitrite administered by intramuscular (IM) injection post-Cl 2 exposure is effective in preventing acute lung injury and improving survival in rodent models. Our goal in this study was to develop a rabbit model of Cl 2 toxicity and test whether nitrite affords protection in a non-rodent model. Exposure of New Zealand White rabbits to Cl 2 gas (600ppm, 45min) caused significant increases in protein and neutrophil accumulation in the airways and ∼35% mortality over 18h. Nitrite administered 30min post Cl 2 exposure by a single IM injection, at 1mg/kg or 10mg/kg, prevented indices of acute lung injury at 6h by up to 50%. Moreover, all rabbits that received nitrite survived over the study period. These data provide further rationale for developing nitrite as post-exposure therapeutic to mitigate against Cl 2 gas exposure injury. Copyright © 2017 Elsevier B.V. All rights reserved.

How Does Exposure to Cigarette Advertising Contribute to Smoking in Adolescents? The Role of the Developing Self-Concept and Identification with Advertising Models

PubMed Central

Shadel, William G.; Tharp-Taylor, Shannah; Fryer, Craig S.

2009-01-01

Increased exposure to cigarette advertisements is associated with increases in adolescent smoking but the reasons for this association are not well established. This study evaluated whether self-concept development (operationalized as level of self-conflict) and identifying with the models used in cigarette print advertising contributed to smoking intentions among adolescents. Ninety-five adolescents (ages 11-17) participated in this two session study. In session 1, they rated the extent to which they identified with the models used in 10 current cigarette print ads (the models were isolated digitally from the cigarette advertisements) and their level of self-conflict was assessed. In session 2, participants viewed each of the 10 cigarette advertisements from which the models were drawn and rated their intentions to smoke following exposure to each ad. Model identification was associated with similar levels of post ad exposure smoking intentions for both younger and older adolescents when they also exhibited no self-conflict. A contrasting set of findings emerged for younger and older adolescents when they exhibited high levels of self-conflict: Young adolescents who strongly identified with the models used in cigarette advertisements had higher post ad exposure smoking intentions compared to younger adolescents who weakly identified with the models used in the advertisements; in contrast, older adolescents who weakly identified with the models used in cigarette advertisements had stronger post ad exposure smoking intentions compared to older adolescents who strongly identified with the models used in the advertisements. These results point to the importance of examining developmentally-relevant moderators for the effects of cigarette advertising exposure. PMID:19505768

How does exposure to cigarette advertising contribute to smoking in adolescents? The role of the developing self-concept and identification with advertising models.

PubMed

Shadel, William G; Tharp-Taylor, Shannah; Fryer, Craig S

2009-11-01

Increased exposure to cigarette advertisements is associated with increases in adolescent smoking but the reasons for this association are not well established. This study evaluated whether self-concept development (operationalized as level of self-conflict) and identifying with the models used in cigarette print advertising contributed to smoking intentions among adolescents. Ninety-five adolescents (ages 11-17) participated in this two session study. In session 1, they rated the extent to which they identified with the models used in 10 current cigarette print ads (the models were isolated digitally from the cigarette advertisements) and their level of self-conflict was assessed. In session 2, participants viewed each of the 10 cigarette advertisements from which the models were drawn and rated their intentions to smoke following exposure to each ad. Model identification was associated with similar levels of post ad exposure smoking intentions for both younger and older adolescents when they also exhibited no self-conflict. A contrasting set of findings emerged for younger and older adolescents when they exhibited high levels of self-conflict: Young adolescents who strongly identified with the models used in cigarette advertisements had higher post ad exposure smoking intentions compared to younger adolescents who weakly identified with the models used in the advertisements; in contrast, older adolescents who weakly identified with the models used in cigarette advertisements had stronger post ad exposure smoking intentions compared to older adolescents who strongly identified with the models used in the advertisements. These results point to the importance of examining developmentally-relevant moderators for the effects of cigarette advertising exposure.

Silica exposure during construction activities: statistical modeling of task-based measurements from the literature.

PubMed

Sauvé, Jean-François; Beaudry, Charles; Bégin, Denis; Dion, Chantal; Gérin, Michel; Lavoué, Jérôme

2013-05-01

Many construction activities can put workers at risk of breathing silica containing dusts, and there is an important body of literature documenting exposure levels using a task-based strategy. In this study, statistical modeling was used to analyze a data set containing 1466 task-based, personal respirable crystalline silica (RCS) measurements gathered from 46 sources to estimate exposure levels during construction tasks and the effects of determinants of exposure. Monte-Carlo simulation was used to recreate individual exposures from summary parameters, and the statistical modeling involved multimodel inference with Tobit models containing combinations of the following exposure variables: sampling year, sampling duration, construction sector, project type, workspace, ventilation, and controls. Exposure levels by task were predicted based on the median reported duration by activity, the year 1998, absence of source control methods, and an equal distribution of the other determinants of exposure. The model containing all the variables explained 60% of the variability and was identified as the best approximating model. Of the 27 tasks contained in the data set, abrasive blasting, masonry chipping, scabbling concrete, tuck pointing, and tunnel boring had estimated geometric means above 0.1mg m(-3) based on the exposure scenario developed. Water-fed tools and local exhaust ventilation were associated with a reduction of 71 and 69% in exposure levels compared with no controls, respectively. The predictive model developed can be used to estimate RCS concentrations for many construction activities in a wide range of circumstances.

A statistical framework for the validation of a population exposure model based on personal exposure data

NASA Astrophysics Data System (ADS)

Rodriguez, Delphy; Valari, Myrto; Markakis, Konstantinos; Payan, Sébastien

2016-04-01

Currently, ambient pollutant concentrations at monitoring sites are routinely measured by local networks, such as AIRPARIF in Paris, France. Pollutant concentration fields are also simulated with regional-scale chemistry transport models such as CHIMERE (http://www.lmd.polytechnique.fr/chimere) under air-quality forecasting platforms (e.g. Prev'Air http://www.prevair.org) or research projects. These data may be combined with more or less sophisticated techniques to provide a fairly good representation of pollutant concentration spatial gradients over urban areas. Here we focus on human exposure to atmospheric contaminants. Based on census data on population dynamics and demographics, modeled outdoor concentrations and infiltration of outdoor air-pollution indoors we have developed a population exposure model for ozone and PM2.5. A critical challenge in the field of population exposure modeling is model validation since personal exposure data are expensive and therefore, rare. However, recent research has made low cost mobile sensors fairly common and therefore personal exposure data should become more and more accessible. In view of planned cohort field-campaigns where such data will be available over the Paris region, we propose in the present study a statistical framework that makes the comparison between modeled and measured exposures meaningful. Our ultimate goal is to evaluate the exposure model by comparing modeled exposures to monitor data. The scientific question we address here is how to downscale modeled data that are estimated on the county population scale at the individual scale which is appropriate to the available measurements. To assess this question we developed a Bayesian hierarchical framework that assimilates actual individual data into population statistics and updates the probability estimate.

HUMAN-ECOSYSTEM INTERACTIONS: THE CASE OF MERCURY

EPA Science Inventory

Human and ecosystem exposure studies evaluate exposure of sensitive and vulnerable populations. We will discuss how ecosystem exposure modeling studies completed for input into the US Clean Air Mercury Rule (CAMR) to evaluate the response of aquatic ecosystems to changes in mercu...

Human - Ecosystem Interactions: The Case of Mercury

EPA Science Inventory

Human and ecosystem exposure studies evaluate exposure of sensitive and vulnerable populations. We will discuss how ecosystem exposure modeling studies completed for input into the US Clean Air Mercury Rule (CAMR) to evaluate the response of aquatic ecosystems to changes in mercu...

Cumulative Risk and Impact Modeling on Environmental Chemical and Social Stressors.

PubMed

Huang, Hongtai; Wang, Aolin; Morello-Frosch, Rachel; Lam, Juleen; Sirota, Marina; Padula, Amy; Woodruff, Tracey J

2018-03-01

The goal of this review is to identify cumulative modeling methods used to evaluate combined effects of exposures to environmental chemicals and social stressors. The specific review question is: What are the existing quantitative methods used to examine the cumulative impacts of exposures to environmental chemical and social stressors on health? There has been an increase in literature that evaluates combined effects of exposures to environmental chemicals and social stressors on health using regression models; very few studies applied other data mining and machine learning techniques to this problem. The majority of studies we identified used regression models to evaluate combined effects of multiple environmental and social stressors. With proper study design and appropriate modeling assumptions, additional data mining methods may be useful to examine combined effects of environmental and social stressors.

Development of improved wildfire smoke exposure estimates for health studies in the western U.S.

NASA Astrophysics Data System (ADS)

Ivey, C.; Holmes, H.; Loria Salazar, S. M.; Pierce, A.; Liu, C.

2016-12-01

Wildfire smoke exposure is a significant health concern in the western U.S. because large wildfires have increased in size and frequency over the past four years due to drought conditions. The transport phenomena in complex terrain and timing of the wildfire emissions make the smoke plumes difficult to simulate using conventional air quality models. Monitoring data can be used to estimate exposure metrics, but in rural areas the monitoring networks are too sparse to calculate wildfire exposure metrics for the entire population in a region. Satellite retrievals provide global, spatiotemporal air quality information and are used to track pollution plumes, estimate human exposures, model emissions, and determine sources (i.e., natural versus anthropogenic) in regulatory applications. Particulate matter (PM) exposures can be estimated using columnar aerosol optical depth (AOD), where satellite AOD retrievals serve as a spatial surrogate to simulate surface PM gradients. These exposure models have been successfully used in health effects studies in the eastern U.S. where complex mountainous terrain and surface reflectance do not limit AOD retrival from satellites. Using results from a chemical transport model (CTM) is another effective method to determine spatial gradients of pollutants. However, the CTM does not adequately capture the temporal and spatial distribution of wildfire smoke plumes. By combining the spatiotemporal pollutant fields from both satellite retrievals and CTM results with ground based pollutant observations the spatial wildfire smoke exposure model can be improved. This work will address the challenge of understanding the spatiotemporal distributions of pollutant concentrations to model human exposures of wildfire smoke in regions with complex terrain, where meteorological conditions as well as emission sources significantly influence the spatial distribution of pollutants. The focus will be on developing models to enhance exposure estimates of elevated PM and ozone concentrations from wildfire smoke plumes in the western U.S.

A simulation study to quantify the impacts of exposure measurement error on air pollution health risk estimates in copollutant time-series models.

PubMed

Dionisio, Kathie L; Chang, Howard H; Baxter, Lisa K

2016-11-25

Exposure measurement error in copollutant epidemiologic models has the potential to introduce bias in relative risk (RR) estimates. A simulation study was conducted using empirical data to quantify the impact of correlated measurement errors in time-series analyses of air pollution and health. ZIP-code level estimates of exposure for six pollutants (CO, NO x , EC, PM 2.5 , SO 4 , O 3 ) from 1999 to 2002 in the Atlanta metropolitan area were used to calculate spatial, population (i.e. ambient versus personal), and total exposure measurement error. Empirically determined covariance of pollutant concentration pairs and the associated measurement errors were used to simulate true exposure (exposure without error) from observed exposure. Daily emergency department visits for respiratory diseases were simulated using a Poisson time-series model with a main pollutant RR = 1.05 per interquartile range, and a null association for the copollutant (RR = 1). Monte Carlo experiments were used to evaluate the impacts of correlated exposure errors of different copollutant pairs. Substantial attenuation of RRs due to exposure error was evident in nearly all copollutant pairs studied, ranging from 10 to 40% attenuation for spatial error, 3-85% for population error, and 31-85% for total error. When CO, NO x or EC is the main pollutant, we demonstrated the possibility of false positives, specifically identifying significant, positive associations for copollutants based on the estimated type I error rate. The impact of exposure error must be considered when interpreting results of copollutant epidemiologic models, due to the possibility of attenuation of main pollutant RRs and the increased probability of false positives when measurement error is present.

USE OF PHARMACOKINETIC MODELS TO ASSESS OCCUPATIONAL AND RESIDENTIAL PESTICIDE EXPOSURE

EPA Science Inventory

Urinary biomarker measurements were analyzed using a dynamic pharmacokinetic model. The dynamic model provided the structure to link spot urine samples with corresponding exposure and absorbed dose. Data from both occupational and residential studies were analyzed. In the Agri...

Genetically modified crops and aquatic ecosystems: considerations for environmental risk assessment and non-target organism testing.

PubMed

Carstens, Keri; Anderson, Jennifer; Bachman, Pamela; De Schrijver, Adinda; Dively, Galen; Federici, Brian; Hamer, Mick; Gielkens, Marco; Jensen, Peter; Lamp, William; Rauschen, Stefan; Ridley, Geoff; Romeis, Jörg; Waggoner, Annabel

2012-08-01

Environmental risk assessments (ERA) support regulatory decisions for the commercial cultivation of genetically modified (GM) crops. The ERA for terrestrial agroecosystems is well-developed, whereas guidance for ERA of GM crops in aquatic ecosystems is not as well-defined. The purpose of this document is to demonstrate how comprehensive problem formulation can be used to develop a conceptual model and to identify potential exposure pathways, using Bacillus thuringiensis (Bt) maize as a case study. Within problem formulation, the insecticidal trait, the crop, the receiving environment, and protection goals were characterized, and a conceptual model was developed to identify routes through which aquatic organisms may be exposed to insecticidal proteins in maize tissue. Following a tiered approach for exposure assessment, worst-case exposures were estimated using standardized models, and factors mitigating exposure were described. Based on exposure estimates, shredders were identified as the functional group most likely to be exposed to insecticidal proteins. However, even using worst-case assumptions, the exposure of shredders to Bt maize was low and studies supporting the current risk assessments were deemed adequate. Determining if early tier toxicity studies are necessary to inform the risk assessment for a specific GM crop should be done on a case by case basis, and should be guided by thorough problem formulation and exposure assessment. The processes used to develop the Bt maize case study are intended to serve as a model for performing risk assessments on future traits and crops.

Factors affecting measured, modeled and reconstructed estimates of personal exposure to ambient ozone in southern California

NASA Astrophysics Data System (ADS)

Gonzales, Melissa

To evaluate those factors which influence the assignment of ozone ( O3) exposures in an epidemiologic context a field study was conducted in the South Coast Air Basin (SoCAB) during the summer of 19% in which time, location, activity (TLA) information and direct measurements of personal O3 exposure were concurrently collected on a group of college students. Current and past O3 exposures were modeled and evaluated as a function of ambient O 3, activity and mobility patterns, indoor ventilation, and recalled TLA information collected one year later. The effect of these factors on the within- and between-subject exposure variability assigned by ecologic (EC) and microenvironment (MEV) models were examined by two-hour intervals, on weekends and weekdays, and by monitoring week compared to personal exposures measured with a passive sampling device (PSD). The students reported spending 85% of their time inside, 7% outside and 8% in- transit. More time was spent outdoors on weekends than on weekdays. Ambient O3 levels were also higher on weekends. In the study area, where a dense O3 monitoring network and the appropriate topography exist fixed-site O3 accurately assigned ambient O3 levels within a 10 mile radius. The variation in the ecologic exposure assignments was low compared to the estimated variation among PSD-measured and MEV-modeled estimates due to the low spatial variation of ambient O3 levels across the SoCAB areas visited by the students. MEV and PSD exposure estimates better captured the variability of personal exposure in any given ambient spatial regimen compared to ecologic exposure assignments. MEV exposure estimates based on recalled TLA patterns, were similar to the MEV estimates based on diary-recorded TLA patterns. For this study population, PSD-measured O3 exposures were estimated to average 32% lower than ``true'' exposure levels due to indoor/outdoor differences in the PSD collection rate. The level of detail obtained from the TLA diary is not necessary for the assignment of current of past O3 exposures in epidemiologic studies. It may be more adventitious to characterize the locations visited, and indoor and outdoor time with the greatest accuracy possible and to use these data to estimate exposure from nearest-monitor ambient O 3 measurements and sets of indoor/outdoor O3 ratios validated to reflect personal exposure within indoor microenvironments.

Modeling exposure–lag–response associations with distributed lag non-linear models

PubMed Central

Gasparrini, Antonio

2014-01-01

In biomedical research, a health effect is frequently associated with protracted exposures of varying intensity sustained in the past. The main complexity of modeling and interpreting such phenomena lies in the additional temporal dimension needed to express the association, as the risk depends on both intensity and timing of past exposures. This type of dependency is defined here as exposure–lag–response association. In this contribution, I illustrate a general statistical framework for such associations, established through the extension of distributed lag non-linear models, originally developed in time series analysis. This modeling class is based on the definition of a cross-basis, obtained by the combination of two functions to flexibly model linear or nonlinear exposure-responses and the lag structure of the relationship, respectively. The methodology is illustrated with an example application to cohort data and validated through a simulation study. This modeling framework generalizes to various study designs and regression models, and can be applied to study the health effects of protracted exposures to environmental factors, drugs or carcinogenic agents, among others. © 2013 The Authors. Statistics in Medicine published by John Wiley & Sons, Ltd. PMID:24027094

Exposure to traffic noise and air pollution and risk for febrile seizure: a cohort study.

PubMed

Hjortebjerg, Dorrit; Nybo Andersen, Anne-Marie; Ketzel, Matthias; Raaschou-Nielsen, Ole; Sørensen, Mette

2018-03-25

Objectives Exposure to traffic noise and air pollution is suspected to increase susceptibility to viral infections - the main triggering factor for febrile seizures. No studies have examined these two exposures in relation to febrile seizures. We aimed to investigate whether exposure to road traffic noise and air pollution are associated with risk of febrile seizures in childhood. Methods From our study base of 51 465 singletons from a national birth cohort, we identified 2175 cases with febrile seizures using a nationwide registry. Residential address history from conception to six years of age were found in national registers, and road traffic noise (L den ) and air pollution (NO 2 ) were modeled for all addresses. Analyses were done using Cox proportional hazard model with adjustment for potential confounders, including mutual exposure adjustment. Results An interquartile range (IQR) increase in childhood exposure to road traffic noise and air pollution was associated with an 11% [incidence rate ratio (IRR) 1.11, 95% confidence interval (CI) 1.04-1.19) and 5% (IRR 1.05, 95% CI 1.02-1.07) higher risk for febrile seizures, respectively, after adjustment for potential confounders. Weaker tendencies were seen for pregnancy exposure. In models with mutual exposure adjustment, the estimates were slightly lower, with IRR of 1.08 (95% CI 1.00-1.16) and 1.03 (95% CI 0.99-1.06) per IQR increase in childhood exposure to road traffic noise and air pollution, respectively. Conclusions This study suggests that residential exposure to road traffic noise and air pollution is associated with higher risk for febrile seizures.

Modeling cumulative dose and exposure duration provided insights regarding the associations between benzodiazepines and injuries.

PubMed

Abrahamowicz, Michal; Bartlett, Gillian; Tamblyn, Robyn; du Berger, Roxane

2006-04-01

Accurate assessment of medication impact requires modeling cumulative effects of exposure duration and dose; however, postmarketing studies usually represent medication exposure by baseline or current use only. We propose new methods for modeling various aspects of medication use history and employment of them to assess the adverse effects of selected benzodiazepines. Time-dependent measures of cumulative dose or duration of use, with weighting of past exposures by recency, were proposed. These measures were then included in alternative versions of the multivariable Cox model to analyze the risk of fall related injuries among the elderly new users of three benzodiazepines (nitrazepam, temazepam, and flurazepam) in Quebec. Akaike's information criterion (AIC) was used to select the most predictive model for a given benzodiazepine. The best-fitting model included a combination of cumulative duration and current dose for temazepam, and cumulative dose for flurazepam and nitrazepam, with different weighting functions. The window of clinically relevant exposure was shorter for flurazepam than for the two other products. Careful modeling of the medication exposure history may enhance our understanding of the mechanisms underlying their adverse effects.

Comparing exposure assessment methods for traffic-related air pollution in an adverse pregnancy outcome study

PubMed Central

Wu, Jun; Wilhelm, Michelle; Chung, Judith; Ritz, Beate

2011-01-01

Background Previous studies reported adverse impacts of traffic-related air pollution exposure on pregnancy outcomes. Yet, little information exists on how effect estimates are impacted by the different exposure assessment methods employed in these studies. Objectives To compare effect estimates for traffic-related air pollution exposure and preeclampsia, preterm birth (gestational age less than 37 weeks), and very preterm birth (gestational age less than 30 weeks) based on four commonly-used exposure assessment methods. Methods We identified 81,186 singleton births during 1997–2006 at four hospitals in Los Angeles and Orange Counties, California. Exposures were assigned to individual subjects based on residential address at delivery using the nearest ambient monitoring station data [carbon monoxide (CO), nitrogen dioxide (NO2), nitric oxide (NO), nitrogen oxides (NOx), ozone (O3), and particulate matter less than 2.5 (PM2.5) or less than 10 (PM10) μm in aerodynamic diameter], both unadjusted and temporally-adjusted land-use regression (LUR) model estimates (NO, NO2, and NOx), CALINE4 line-source air dispersion model estimates (NOx and PM2.5), and a simple traffic-density measure. We employed unconditional logistic regression to analyze preeclampsia in our birth cohort, while for gestational age-matched risk sets with preterm and very preterm birth we employed conditional logistic regression. Results We observed elevated risks for preeclampsia, preterm birth, and very preterm birth from maternal exposures to traffic air pollutants measured at ambient stations (CO, NO, NO2, and NOx) and modeled through CALINE4 (NOx and PM2.5) and LUR (NO2 and NOx). Increased risk of preterm birth and very preterm birth were also positively associated with PM10 and PM2.5 air pollution measured at ambient stations. For LUR-modeled NO2 and NOx exposures, elevated risks for all the outcomes were observed in Los Angeles only – the region for which the LUR models were initially developed. Unadjusted LUR models often produced odds ratios somewhat larger in size than temporally-adjusted models. The size of effect estimates was smaller for exposures based on simpler traffic density measures than the other exposure assessment methods. Conclusion We generally confirmed that traffic-related air pollution was associated with adverse reproductive outcomes regardless of the exposure assessment method employed, yet the size of the estimated effect depended on how both temporal and spatial variations were incorporated into exposure assessment. The LUR model was not transferable even between two contiguous areas within the same large metropolitan area in Southern California. PMID:21453913

An Exploratory Study: Assessment of Modeled Dioxin ...

EPA Pesticide Factsheets

EPA announced the availability of the final report, An Exploratory Study: Assessment of Modeled Dioxin Exposure in Ceramic Art Studios. This report investigates the potential dioxin exposure to artists/hobbyists who use ball clay to make pottery and related products. Dermal, inhalation, and ingestion exposures to clay were measured at the ceramics art department of Ohio State University in Columbus, OH. The exposure estimates were based on measured levels of clay in the studio air, deposited on surrogate food samples and on the skin of the artists. The purpose of this report is to describe an exploratory investigation of potential dioxin exposures to artists/hobbyists who use ball clay to make pottery and related products.

Combination of a higher-tier flow-through system and population modeling to assess the effects of time-variable exposure of isoproturon on the green algae Desmodesmus subspicatus and Pseudokirchneriella subcapitata.

PubMed

Weber, Denis; Schaefer, Dieter; Dorgerloh, Michael; Bruns, Eric; Goerlitz, Gerhard; Hammel, Klaus; Preuss, Thomas G; Ratte, Hans Toni

2012-04-01

A flow-through system was developed to investigate the effects of time-variable exposure of pesticides on algae. A recently developed algae population model was used for simulations supported and verified by laboratory experiments. Flow-through studies with Desmodesmus subspicatus and Pseudokirchneriella subcapitata under time-variable exposure to isoproturon were performed, in which the exposure patterns were based on the results of FOrum for Co-ordination of pesticide fate models and their USe (FOCUS) model calculations for typical exposure situations via runoff or drain flow. Different types of pulsed exposure events were realized, including a whole range of repeated pulsed and steep peaks as well as periods of constant exposure. Both species recovered quickly in terms of growth from short-term exposure and according to substance dissipation from the system. Even at a peak 10 times the maximum predicted environmental concentration of isoproturon, only transient effects occurred on algae populations. No modified sensitivity or reduced growth was observed after repeated exposure. Model predictions of algal growth in the flow-through tests agreed well with the experimental data. The experimental boundary conditions and the physiological properties of the algae were used as the only model input. No calibration or parameter fitting was necessary. The combination of the flow-through experiments with the algae population model was revealed to be a powerful tool for the assessment of pulsed exposure on algae. It allowed investigating the growth reduction and recovery potential of algae after complex exposure, which is not possible with standard laboratory experiments alone. The results of the combined approach confirm the beneficial use of population models as supporting tools in higher-tier risk assessments of pesticides. Copyright © 2012 SETAC.

Is the perception of clean, humid air indeed affected by cooling the respiratory tract?

NASA Astrophysics Data System (ADS)

Burek, Rudolf; Polednik, Bernard; Guz, Łukasz

2017-07-01

The study aims at determining exposure-response relationships after short exposure to clean air and long exposure to air polluted by people. The impact of water vapor content in the indoor air on its acceptability (ACC) was assessed by the occupants after a short exposure to clean air and an hour-long exposure to increasingly polluted air. The study presents a critical analysis pertaining to the stimulation of olfactory sensations by the air enthalpy suggested in previous models and proposes a new model based on the Weber-Fechner law. Our assumption was that water vapor is the stimulus of olfactory sensations. The model was calibrated and verified in field conditions, in a mechanically ventilated and air conditioned auditorium. Measurements of the air temperature, relative humidity, velocity and CO2 content were carried out; the acceptability of air quality was assessed by 162 untrained students. The subjective assessments and the measurements of the environmental qualities allowed for determining the Weber coefficients and the threshold concentrations of water vapor, as well as for establishing the limitations of the model at short and long exposure to polluted indoor air. The results are in agreement with previous studies. The standard error equals 0.07 for immediate assessments and 0.17 for assessments after adaptation. Based on the model one can predict the ACC assessments of trained and untrained participants.

Modelling hurricane exposure and wind speed on a mesoclimate scale: a case study from Cusuco NP, Honduras.

PubMed

Batke, Sven P; Jocque, Merlijn; Kelly, Daniel L

2014-01-01

High energy weather events are often expected to play a substantial role in biotic community dynamics and large scale diversity patterns but their contribution is hard to prove. Currently, observations are limited to the documentation of accidental records after the passing of such events. A more comprehensive approach is synthesising weather events in a location over a long time period, ideally at a high spatial resolution and on a large geographic scale. We provide a detailed overview on how to generate hurricane exposure data at a meso-climate level for a specific region. As a case study we modelled landscape hurricane exposure in Cusuco National Park (CNP), Honduras with a resolution of 50 m×50 m patches. We calculated actual hurricane exposure vulnerability site scores (EVVS) through the combination of a wind pressure model, an exposure model that can incorporate simple wind dynamics within a 3-dimensional landscape and the integration of historical hurricanes data. The EVSS was calculated as a weighted function of sites exposure, hurricane frequency and maximum wind velocity. Eleven hurricanes were found to have affected CNP between 1995 and 2010. The highest EVSS's were predicted to be on South and South-East facing sites of the park. Ground validation demonstrated that the South-solution (i.e. the South wind inflow direction) explained most of the observed tree damage (90% of the observed tree damage in the field). Incorporating historical data to the model to calculate actual hurricane exposure values, instead of potential exposure values, increased the model fit by 50%.

Modelling Hurricane Exposure and Wind Speed on a Mesoclimate Scale: A Case Study from Cusuco NP, Honduras

PubMed Central

Batke, Sven P.; Jocque, Merlijn; Kelly, Daniel L.

2014-01-01

High energy weather events are often expected to play a substantial role in biotic community dynamics and large scale diversity patterns but their contribution is hard to prove. Currently, observations are limited to the documentation of accidental records after the passing of such events. A more comprehensive approach is synthesising weather events in a location over a long time period, ideally at a high spatial resolution and on a large geographic scale. We provide a detailed overview on how to generate hurricane exposure data at a meso-climate level for a specific region. As a case study we modelled landscape hurricane exposure in Cusuco National Park (CNP), Honduras with a resolution of 50 m×50 m patches. We calculated actual hurricane exposure vulnerability site scores (EVVS) through the combination of a wind pressure model, an exposure model that can incorporate simple wind dynamics within a 3-dimensional landscape and the integration of historical hurricanes data. The EVSS was calculated as a weighted function of sites exposure, hurricane frequency and maximum wind velocity. Eleven hurricanes were found to have affected CNP between 1995 and 2010. The highest EVSS’s were predicted to be on South and South-East facing sites of the park. Ground validation demonstrated that the South-solution (i.e. the South wind inflow direction) explained most of the observed tree damage (90% of the observed tree damage in the field). Incorporating historical data to the model to calculate actual hurricane exposure values, instead of potential exposure values, increased the model fit by 50%. PMID:24614168

Effects of glyphosate exposure on sperm concentration in rodents: A systematic review and meta-analysis.

PubMed

Cai, Wenyan; Ji, Ying; Song, Xianping; Guo, Haoran; Han, Lei; Zhang, Feng; Liu, Xin; Zhang, Hengdong; Zhu, Baoli; Xu, Ming

2017-10-01

Correlation between exposure to glyphosate and sperm concentrations is important in reproductive toxicity risk assessment for male reproductive functions. Many studies have focused on reproductive toxicity on glyphosate, however, results are still controversial. We conducted a systematic review of epidemiological studies on the association between glyphosate exposure and sperm concentrations of rodents. The aim of this study is to explore the potential adverse effects of glyphosate on reproductive function of male rodents. Systematic and comprehensive literature search was performed in MEDLINE, TOXLINE, Embase, WANFANG and CNKI databases with different combinations of glyphosate exposure and sperm concentration. 8 studies were eventually identified and random-effect model was conducted. Heterogeneity among study results was calculated via chi-square tests. Ten independent experimental datasets from these eight studies were acquired to synthesize the random-effect model. A decrease in sperm concentrations was found with mean difference of sperm concentrations(MDsperm)=-2.774×10 6 /sperm/g/testis(95%CI=-0.969 to -4.579) in random-effect model after glyphosate exposure. There was also a significant decrease after fitting the random-effect model: MDsperm=-1.632×10 6 /sperm/g/testis (95%CI=-0.662 to -2.601). The results of meta-analysis support the hypothesis that glyphosate exposure decreased sperm concentration in rodents. Therefore, we conclude that glyphosate is toxic to male rodent's reproductive system. Copyright © 2017. Published by Elsevier B.V.

The role of perceived air pollution and health risk perception in health symptoms and disease: a population-based study combined with modelled levels of PM10.

PubMed

Orru, Kati; Nordin, Steven; Harzia, Hedi; Orru, Hans

2018-07-01

Adverse health impact of air pollution on health may not only be associated with the level of exposure, but rather mediated by perception of the pollution and by top-down processing (e.g. beliefs of the exposure being hazardous), especially in areas with relatively low levels of pollutants. The aim of this study was to test a model that describes interrelations between air pollution (particles < 10 [Formula: see text]m, PM 10 ), perceived pollution, health risk perception, health symptoms and diseases. A population-based questionnaire study was conducted among 1000 Estonian residents (sample was stratified by age, sex, and geographical location) about health risk perception and coping. The PM 10 levels were modelled in 1 × 1 km grids using a Eulerian air quality dispersion model. Respondents were ascribed their annual mean PM 10 exposure according to their home address. Path analysis was performed to test the validity of the model. The data refute the model proposing that exposure level significantly influences symptoms and disease. Instead, the perceived exposure influences symptoms and the effect of perceived exposure on disease is mediated by health risk perception. This relationship is more pronounced in large cities compared to smaller towns or rural areas. Perceived pollution and health risk perception, in particular in large cities, play important roles in understanding and predicting environmentally induced symptoms and diseases at relatively low levels of air pollution.

Multiphysics and Thermal Response Models to Improve Accuracy of Local Temperature Estimation in Rat Cortex under Microwave Exposure

PubMed Central

Kodera, Sachiko; Gomez-Tames, Jose; Hirata, Akimasa; Masuda, Hiroshi; Arima, Takuji; Watanabe, Soichi

2017-01-01

The rapid development of wireless technology has led to widespread concerns regarding adverse human health effects caused by exposure to electromagnetic fields. Temperature elevation in biological bodies is an important factor that can adversely affect health. A thermophysiological model is desired to quantify microwave (MW) induced temperature elevations. In this study, parameters related to thermophysiological responses for MW exposures were estimated using an electromagnetic-thermodynamics simulation technique. To the authors’ knowledge, this is the first study in which parameters related to regional cerebral blood flow in a rat model were extracted at a high degree of accuracy through experimental measurements for localized MW exposure at frequencies exceeding 6 GHz. The findings indicate that the improved modeling parameters yield computed results that match well with the measured quantities during and after exposure in rats. It is expected that the computational model will be helpful in estimating the temperature elevation in the rat brain at multiple observation points (that are difficult to measure simultaneously) and in explaining the physiological changes in the local cortex region. PMID:28358345

Determinants of dermal exposure among Nicaraguan subsistence farmers during pesticide applications with backpack sprayers.

PubMed

Blanco, Luis E; Aragón, Aurora; Lundberg, Ingvar; Lidén, Carola; Wesseling, Catharina; Nise, Gun

2005-01-01

Identification of pesticide exposure determinants has become an issue in explaining exposure variability and improving control measures. Most studies have been conducted in industrialized countries. The aim of this study was to identify relevant dermal exposure determinants among Nicaraguan subsistence farmers. Field data on possible determinants were collected during 32 pesticide applications through observation and supplementary videorecording. A multistep reduction strategy brought down the 110 potential exposure determinants to 27 variables, which were grouped as worksite, spray equipment, working practices, clothing or hygiene practices related. Dermal exposure was quantified with a modification of Fenske's visual scoring method. Multivariate linear regression modeling within groups and across groups was performed. In the within-group analyses, work practices, spray equipment and worksite related determinants explained 52, 33 and 25% of the exposure variability, respectively. Clothing and hygiene practices were weaker determinants and did not always reduce the exposure. The final model included determinants from all groups except hygiene practices and explained 69% of the exposure variability. A less restricted model increased the explained variability to 75%. Several novel determinants were identified, including spraying on a muddy terrain, dew on plants, sealing the tank lid with a cloth and wiping sweat from the face. This study showed that a combination of observation and visual scoring techniques can provide valuable information on determinants of pesticide exposure and affected body parts under developing country conditions. The results could be used to develop job-specific questionnaires and to design training and preventive programs.

MobRISK: a model for assessing the exposure of road users to flash flood events

NASA Astrophysics Data System (ADS)

Shabou, Saif; Ruin, Isabelle; Lutoff, Céline; Debionne, Samuel; Anquetin, Sandrine; Creutin, Jean-Dominique; Beaufils, Xavier

2017-09-01

Recent flash flood impact studies highlight that road networks are often disrupted due to adverse weather and flash flood events. Road users are thus particularly exposed to road flooding during their daily mobility. Previous exposure studies, however, do not take into consideration population mobility. Recent advances in transportation research provide an appropriate framework for simulating individual travel-activity patterns using an activity-based approach. These activity-based mobility models enable the prediction of the sequence of activities performed by individuals and locating them with a high spatial-temporal resolution. This paper describes the development of the MobRISK microsimulation system: a model for assessing the exposure of road users to extreme hydrometeorological events. MobRISK aims at providing an accurate spatiotemporal exposure assessment by integrating travel-activity behaviors and mobility adaptation with respect to weather disruptions. The model is applied in a flash-flood-prone area in southern France to assess motorists' exposure to the September 2002 flash flood event. The results show that risk of flooding mainly occurs in principal road links with considerable traffic load. However, a lag time between the timing of the road submersion and persons crossing these roads contributes to reducing the potential vehicle-related fatal accidents. It is also found that sociodemographic variables have a significant effect on individual exposure. Thus, the proposed model demonstrates the benefits of considering spatiotemporal dynamics of population exposure to flash floods and presents an important improvement in exposure assessment methods. Such improved characterization of road user exposures can present valuable information for flood risk management services.

Modeling population exposures to silver nanoparticles present in consumer products

NASA Astrophysics Data System (ADS)

Royce, Steven G.; Mukherjee, Dwaipayan; Cai, Ting; Xu, Shu S.; Alexander, Jocelyn A.; Mi, Zhongyuan; Calderon, Leonardo; Mainelis, Gediminas; Lee, KiBum; Lioy, Paul J.; Tetley, Teresa D.; Chung, Kian Fan; Zhang, Junfeng; Georgopoulos, Panos G.

2014-11-01

Exposures of the general population to manufactured nanoparticles (MNPs) are expected to keep rising due to increasing use of MNPs in common consumer products (PEN 2014). The present study focuses on characterizing ambient and indoor population exposures to silver MNPs (nAg). For situations where detailed, case-specific exposure-related data are not available, as in the present study, a novel tiered modeling system, Prioritization/Ranking of Toxic Exposures with GIS (geographic information system) Extension (PRoTEGE), has been developed: it employs a product life cycle analysis (LCA) approach coupled with basic human life stage analysis (LSA) to characterize potential exposures to chemicals of current and emerging concern. The PRoTEGE system has been implemented for ambient and indoor environments, utilizing available MNP production, usage, and properties databases, along with laboratory measurements of potential personal exposures from consumer spray products containing nAg. Modeling of environmental and microenvironmental levels of MNPs employs probabilistic material flow analysis combined with product LCA to account for releases during manufacturing, transport, usage, disposal, etc. Human exposure and dose characterization further employ screening microenvironmental modeling and intake fraction methods combined with LSA for potentially exposed populations, to assess differences associated with gender, age, and demographics. Population distributions of intakes, estimated using the PRoTEGE framework, are consistent with published individual-based intake estimates, demonstrating that PRoTEGE is capable of capturing realistic exposure scenarios for the US population. Distributions of intakes are also used to calculate biologically relevant population distributions of uptakes and target tissue doses through human airway dosimetry modeling that takes into account product MNP size distributions and age-relevant physiological parameters.

Using a chemistry transport model to account for the spatial variability of exposure concentrations in epidemiologic air pollution studies.

PubMed

Valari, Myrto; Menut, Laurent; Chatignoux, Edouard

2011-02-01

Environmental epidemiology and more specifically time-series analysis have traditionally used area-averaged pollutant concentrations measured at central monitors as exposure surrogates to associate health outcomes with air pollution. However, spatial aggregation has been shown to contribute to the overall bias in the estimation of the exposure-response functions. This paper presents the benefit of adding features of the spatial variability of exposure by using concentration fields modeled with a chemistry transport model instead of monitor data and accounting for human activity patterns. On the basis of county-level census data for the city of Paris, France, and a Monte Carlo simulation, a simple activity model was developed accounting for the temporal variability between working and evening hours as well as during transit. By combining activity data with modeled concentrations, the downtown, suburban, and rural spatial patterns in exposure to nitrogen dioxide, ozone, and PM2.5 (particulate matter [PM] < or = 10 microm in aerodynamic diameter) were captured and parametrized. Exposures predicted with this model were used in a time-series study of the short-term effect of air pollution on total nonaccidental mortality for the 4-yr period from 2001 to 2004. It was shown that the time series of the exposure surrogates developed here are less correlated across co-pollutants than in the case of the area-averaged monitor data. This led to less biased exposure-response functions when all three co-pollutants were inserted simultaneously in the same regression model. This finding yields insight into pollutant-specific health effects that are otherwise masked by the high correlation among co-pollutants.

Effect of cumulative exposure to corticosteroid and DMARD on radiographic progression in rheumatoid arthritis: results from the ESPOIR cohort.

PubMed

Louveau, Baptiste; De Rycke, Yann; Lafourcade, Alexandre; Saraux, Alain; Guillemin, Francis; Tubach, Florence; Fautrel, Bruno; Hajage, David

2018-05-22

Several authors have tried to predict the risk of radiographic progression in RA according to baseline characteristics, considering exposure to treatment only as a binary variable (Treated: Yes/No). This study aims to model the risk of 5-year radiographic progression taking into account both baseline characteristics and the cumulative time-varying exposure to corticosteroids or DMARDs. The study population consisted of 403 patients of the Etude et Suivi des Polyarthrites Indifférenciées Récentes cohort meeting the 1987 ACR or 2010 ACR/EULAR criteria for RA at inclusion and having complete radiographic data at baseline and 5 years. Radiographic progression was defined at 5 years as a significant increase of the Sharp/van der Heidje score (smallest detectable difference ⩾5). The best logistic regression model was selected from the following: model including only clinico-biological baseline characteristics; model considering baseline characteristics and treatments as binary variables; and model considering baseline characteristics and treatments as weighted cumulative exposure variables. Radiographic progression occurred in 143 (35.5%) patients. The best model combined anti-citrullinated peptide antibody positivity, ESR, swollen joint count >14 and erosion score at baseline, as well as corticosteroids, MTX/LEF (MTX or LEF) and biologic DMARDs (bDMARDs) as weighted cumulative exposure variables. Recent cumulative exposure to high doses of corticosteroids (⩽ 3months) was significantly associated with the risk of 5-year radiographic progression and a significant protective association was highlighted for a 36-month exposure to bDMARDs. Corticosteroids and bDMARDs play an important role in radiographic progression. Accounting for treatment class and intensity of exposure is a major concern in predictive models of radiographic progression in RA patients.

Determinants of wheat antigen and fungal alpha-amylase exposure in bakeries.

PubMed

Burstyn, I; Teschke, K; Bartlett, K; Kennedy, S M

1998-05-01

The study's objectives were to measure flour antigen exposure in bakeries and define the determinants of exposure. Ninety-six bakery workers, employed in seven different bakeries, participated in the study. Two side-by-side full-shift inhalable dust samples were obtained from each study participant on a single occasion. The flour antigen exposure was measured as wheat antigen and fungal alpha-amylase content of the water-soluble fraction of inhalable dust, assayed via enzyme-linked immunosorbent assays. During the entire sampling period bakers were observed and information on 14 different tasks was recorded at 15-minute intervals. Other production characteristics were also recorded for each sampling day and used in statistical modeling to identify significant predictors of exposure. The mean alpha-amylase antigen exposure was 22.0 ng/m3 (ranging from below the limit of detection of 0.1 ng/m3 to 307.1 ng/m3) and the mean wheat antigen exposure was 109 micrograms/m3 (ranging from below the limit of detection of 1 microgram/m3 to 1018 micrograms/m3). Regression models that explained 74% of variability in wheat antigen and alpha-amylase antigen exposures were constructed. The models indicated that tasks such as weighing, pouring, and operating dough-brakers increased flour antigen exposure, while packing and decorating resulted in lower exposures. Croissant, puff-pastry, and bread/bun production lines were associated with increased exposure, while cake production and substitution of dusting with the use of divider oil were associated with decreased exposure. Exposure levels can be reduced by the automation of forming tasks, alteration of tasks requiring pouring of flour, and changes to the types of products manufactured.

MIXED MODELS ANALYSIS OR URBANIZATION LEVEL ON CHLORPYRIFOS EXPOSURE

EPA Science Inventory

The National Human Exposure Assessment Survey (NHEXAS) pilot studies were conducted from 1995 through 1997 to examine human population exposure to a wide range of environmental contaminants. In one of the studies, NHEXAS-Maryland, a longitudinal design was used to repeatedly m...

The effects of exposure to muscular male models among men: exploring the moderating role of gym use and exercise motivation.

PubMed

Halliwell, Emma; Dittmar, Helga; Orsborn, Amber

2007-09-01

This study examines the effects of exposure to the muscular male body ideal on body-focused negative affect among male gym users and non-exercisers. As hypothesized, the impact of media exposure depended on men's exercise status. Non-exercisers (n = 58) reported greater body-focused negative affect after exposure to images of muscular male models than after neutral images (no model controls), whereas gym users (n = 58) showed a tendency for less body-focused negative affect after the model images than after the control images. Furthermore, the extent to which gym users were motivated to increase strength and muscularity moderated these exposure effects; men who reported stronger strength and muscularity exercise motivation reported a greater degree of self-enhancement after exposure to the muscular ideal. The findings are interpreted with respect to likely differences in motives for social comparisons.

Analysis of Coupled Model Uncertainties in Source to Dose Modeling of Human Exposures to Ambient Air Pollution: a PM2.5 Case-Study

EPA Science Inventory

Quantitative assessment of human exposures and health effects due to air pollution involve detailed characterization of impacts of air quality on exposure and dose. A key challenge is to integrate these three components on a consistent spatial and temporal basis taking into acco...

A DYNAMIC NONLINEAR MODEL OF OZONE-INDUCED FEV1 RESPONSE UNDER CHANGING EXPOSURE CONDITIONS

EPA Science Inventory

A Dynamic Nonlinear Model of Ozone-induced FEV1 Response under Changing Exposure Conditions. 1WF McDonnell, 2PW Stewart, 3MV Smith. 1Human Studies Division, NHEERL, U.S. EPA, RTP, NC. 2University of North Carolina, Chapel Hill, NC. 3ASI, Durham, NC.

Ozone exposure result...

Assessing uncertain human exposure to ambient air pollution using environmental models in the Web

NASA Astrophysics Data System (ADS)

Gerharz, L. E.; Pebesma, E.; Denby, B.

2012-04-01

Ambient air quality can have significant impact on human health by causing respiratory and cardio-vascular diseases. Thereby, the pollutant concentration a person is exposed to can differ considerably between individuals depending on their daily routine and movement patterns. Using a straight forward approach this exposure can be estimated by integration of individual space-time paths and spatio-temporally resolved ambient air quality data. To allow a realistic exposure assessment, it is furthermore important to consider uncertainties due to input and model errors. In this work, we present a generic, web-based approach for estimating individual exposure by integration of uncertain position and air quality information implemented as a web service. Following the Model Web initiative envisioning an infrastructure for deploying, executing and chaining environmental models as services, existing models and data sources for e.g. air quality, can be used to assess exposure. Therefore, the service needs to deal with different formats, resolutions and uncertainty representations provided by model or data services. Potential mismatch can be accounted for by transformation of uncertainties and (dis-)aggregation of data under consideration of changes in the uncertainties using components developed in the UncertWeb project. In UncertWeb, the Model Web vision is extended to an Uncertainty-enabled Model Web, where services can process and communicate uncertainties in the data and models. The propagation of uncertainty to the exposure results is quantified using Monte Carlo simulation by combining different realisations of positions and ambient concentrations. Two case studies were used to evaluate the developed exposure assessment service. In a first study, GPS tracks with a positional uncertainty of a few meters, collected in the urban area of Münster, Germany were used to assess exposure to PM10 (particulate matter smaller 10 µm). Air quality data was provided by an uncertainty-enabled air quality model system which provided realisations of concentrations per hour on a 250 m x 250 m resolved grid over Münster. The second case study uses modelled human trajectories in Rotterdam, The Netherlands. The trajectories were provided as realisations in 15 min resolution per 4 digit postal code from an activity model. Air quality estimates were provided for different pollutants as ensembles by a coupled meteorology and air quality model system on a 1 km x 1 km grid with hourly resolution. Both case studies show the successful application of the service to different resolutions and uncertainty representations.

Evaluation of COSHH essentials: methylene chloride, isopropanol, and acetone exposures in a small printing plant.

PubMed

Lee, Eun Gyung; Harper, Martin; Bowen, Russell B; Slaven, James

2009-07-01

The current study evaluated the Control of Substances Hazardous to Health (COSHH) Essentials model for short-term task-based exposures and full-shift exposures using measured concentrations of three volatile organic chemicals at a small printing plant. A total of 188 exposure measurements of isopropanol and 187 measurements of acetone were collected and each measurement took approximately 60 min. Historically, collected time-weighted average concentrations (seven results) were evaluated for methylene chloride. The COSHH Essentials model recommended general ventilation control for both isopropanol and acetone. There was good agreement between the task-based exposure measurements and the COSHH Essentials predicted exposure range (PER) for cleaning and print preparation with isopropanol and for cleaning with acetone. For the other tasks and for full-shift exposures, agreement between the exposure measurements and the PER was either moderate or poor. However, for both isopropanol and acetone, our findings suggested that the COSHH Essentials model worked reasonably well because the probabilities of short-term exposure measurements exceeding short-term occupational exposure limits (OELs) or full-shift exposures exceeding the corresponding full-shift OELs were <0.05 under the recommended control strategy. For methylene chloride, the COSHH Essentials recommended containment control but a follow-up study was not able to be performed because it had already been replaced with a less hazardous substance (acetone). This was considered a more acceptable alternative to increasing the level of control.

Development of Combining of Human Bronchial Mucosa Models with XposeALI® for Exposure of Air Pollution Nanoparticles.

PubMed

Ji, Jie; Hedelin, Anna; Malmlöf, Maria; Kessler, Vadim; Seisenbaeva, Gulaim; Gerde, Per; Palmberg, Lena

2017-01-01

Exposure to agents via inhalation is of great concerns both in workplace environment and in the daily contact with particles in the ambient air. Reliable human airway exposure systems will most likely replace animal experiment in future toxicity assessment studies of inhaled agents. In this study, we successfully established a combination of an exposure system (XposeALI) with 3D models mimicking both healthy and chronic bronchitis-like mucosa by co-culturing human primary bronchial epithelial cells (PBEC) and fibroblast at air-liquid interface (ALI). Light-, confocal microscopy, scanning- and transmission electron microscopy, transepithelial electrical resistance (TEER) measurement and RT-PCR were performed to identify how the PBEC differentiated under ALI culture condition. Both models were exposed to palladium (Pd) nanoparticles which sized 6-10 nm, analogous to those released from modern car catalysts, at three different concentrations utilizing the XposeALI module of the PreciseInhale® exposure system. Exposing the 3D models to Pd nanoparticles induced increased secretion of IL-8, yet the chronic bronchitis-like model released significantly more IL-8 than the normal model. The levels of IL-8 in basal medium (BM) and apical lavage medium (AM) were in the same ranges, but the secretion of MMP-9 was significantly higher in the AM compared to the BM. This combination of relevant human bronchial mucosa models and sophisticated exposure system can mimic in vivo conditions and serve as a useful alternative animal testing tool when studying adverse effects in humans exposed to aerosols, air pollutants or particles in an occupational setting.

Acute Lung Injury and Persistent Small Airway Disease in a Rabbit Model of Chlorine Inhalation

PubMed Central

Musah, Sadiatu; Schlueter, Connie F.; Humphrey, David M.; Powell, Karen S.; Roberts, Andrew M.; Hoyle, Gary W.

2016-01-01

Chlorine is a pulmonary toxicant to which humans can be exposed through accidents or intentional releases. Acute effects of chlorine inhalation in humans and animal models have been well characterized, but less is known about persistent effects of acute, high-level chlorine exposures. In particular, animal models that reproduce the long-term effects suggested to occur in humans are lacking. Here, we report the development of a rabbit model in which both acute and persistent effects of chlorine inhalation can be assessed. Male New Zealand White rabbits were exposed to chlorine while the lungs were mechanically ventilated. After chlorine exposure, the rabbits were extubated and were allowed to survive for up to 24 h after exposure to 800 ppm chlorine for 4 min to study acute effects or up to 7 days after exposure to 400 ppm for 8 min to study longer term effects. Acute effects observed 6 or 24 h after inhalation of 800 ppm chlorine for 4 min included hypoxemia, pulmonary edema, airway epithelial injury, inflammation, altered baseline lung mechanics, and airway hyperreactivity to inhaled methacholine. Seven days after recovery from inhalation of 400 ppm chlorine for 8 min, rabbits exhibited mild hypoxemia, increased area of pressure-volume loops, and airway hyperreactivity. Lung histology 7 days after chlorine exposure revealed abnormalities in the small airways, including inflammation and sporadic bronchiolitis obliterans lesions. Immunostaining showed a paucity of club and ciliated cells in the epithelium at these sites. These results suggest that small airway disease may be an important component of persistent respiratory abnormalities that occur following acute chlorine exposure. This non-rodent chlorine exposure model should prove useful for studying persistent effects of acute chlorine exposure and for assessing efficacy of countermeasures for chlorine-induced lung injury. PMID:27913141

Reconstruction of Exposure to m-Xylene from Human Biomonitoring Data Using PBPK Modelling, Bayesian Inference, and Markov Chain Monte Carlo Simulation

PubMed Central

McNally, Kevin; Cotton, Richard; Cocker, John; Jones, Kate; Bartels, Mike; Rick, David; Price, Paul; Loizou, George

2012-01-01

There are numerous biomonitoring programs, both recent and ongoing, to evaluate environmental exposure of humans to chemicals. Due to the lack of exposure and kinetic data, the correlation of biomarker levels with exposure concentrations leads to difficulty in utilizing biomonitoring data for biological guidance values. Exposure reconstruction or reverse dosimetry is the retrospective interpretation of external exposure consistent with biomonitoring data. We investigated the integration of physiologically based pharmacokinetic modelling, global sensitivity analysis, Bayesian inference, and Markov chain Monte Carlo simulation to obtain a population estimate of inhalation exposure to m-xylene. We used exhaled breath and venous blood m-xylene and urinary 3-methylhippuric acid measurements from a controlled human volunteer study in order to evaluate the ability of our computational framework to predict known inhalation exposures. We also investigated the importance of model structure and dimensionality with respect to its ability to reconstruct exposure. PMID:22719759

Modeling heat and moisture transport in firefighter protective clothing during flash fire exposure

NASA Astrophysics Data System (ADS)

Chitrphiromsri, Patirop; Kuznetsov, Andrey V.

2005-01-01

In this paper, a model of heat and moisture transport in firefighter protective clothing during a flash fire exposure is presented. The aim of this study is to investigate the effect of coupled heat and moisture transport on the protective performance of the garment. Computational results show the distribution of temperature and moisture content in the fabric during the exposure to the flash fire as well as during the cool-down period. Moreover, the duration of the exposure during which the garment protects the firefighter from getting second and third degree burns from the flash fire exposure is numerically predicted. A complete model for the fire-fabric-air gap-skin system is presented.

Improvements in Modelling Bystander and Resident Exposure to Pesticide Spray Drift: Investigations into New Approaches for Characterizing the 'Collection Efficiency' of the Human Body.

PubMed

Butler Ellis, M Clare; Kennedy, Marc C; Kuster, Christian J; Alanis, Rafael; Tuck, Clive R

2018-05-28

The BREAM (Bystander and Resident Exposure Assessment Model) (Kennedy et al. in BREAM: A probabilistic bystander and resident exposure assessment model of spray drift from an agricultural boom sprayer. Comput Electron Agric 2012;88:63-71) for bystander and resident exposure to spray drift from boom sprayers has recently been incorporated into the European Food Safety Authority (EFSA) guidance for determining non-dietary exposures of humans to plant protection products. The component of BREAM, which relates airborne spray concentrations to bystander and resident dermal exposure, has been reviewed to identify whether it is possible to improve this and its description of variability captured in the model. Two approaches have been explored: a more rigorous statistical analysis of the empirical data and a semi-mechanistic model based on established studies combined with new data obtained in a wind tunnel. A statistical comparison between field data and model outputs was used to determine which approach gave the better prediction of exposures. The semi-mechanistic approach gave the better prediction of experimental data and resulted in a reduction in the proposed regulatory values for the 75th and 95th percentiles of the exposure distribution.

An Exploratory Study: Assessment of Modeled Dioxin Exposure in Ceramic Art Studios (External Review Draft)

EPA Science Inventory

EPA has released an external review draft entitled, An Exploratory Study: Assessment of Modeled Dioxin Exposure in Ceramic Art Studios(External Review Draft). The public comment period and the external peer-review workshop are separate processes that provide opportunities ...

Sensitivity analyses of exposure estimates from a quantitative job-exposure matrix (SYN-JEM) for use in community-based studies.

PubMed

Peters, Susan; Kromhout, Hans; Portengen, Lützen; Olsson, Ann; Kendzia, Benjamin; Vincent, Raymond; Savary, Barbara; Lavoué, Jérôme; Cavallo, Domenico; Cattaneo, Andrea; Mirabelli, Dario; Plato, Nils; Fevotte, Joelle; Pesch, Beate; Brüning, Thomas; Straif, Kurt; Vermeulen, Roel

2013-01-01

We describe the elaboration and sensitivity analyses of a quantitative job-exposure matrix (SYN-JEM) for respirable crystalline silica (RCS). The aim was to gain insight into the robustness of the SYN-JEM RCS estimates based on critical decisions taken in the elaboration process. SYN-JEM for RCS exposure consists of three axes (job, region, and year) based on estimates derived from a previously developed statistical model. To elaborate SYN-JEM, several decisions were taken: i.e. the application of (i) a single time trend; (ii) region-specific adjustments in RCS exposure; and (iii) a prior job-specific exposure level (by the semi-quantitative DOM-JEM), with an override of 0 mg/m(3) for jobs a priori defined as non-exposed. Furthermore, we assumed that exposure levels reached a ceiling in 1960 and remained constant prior to this date. We applied SYN-JEM to the occupational histories of subjects from a large international pooled community-based case-control study. Cumulative exposure levels derived with SYN-JEM were compared with those from alternative models, described by Pearson correlation ((Rp)) and differences in unit of exposure (mg/m(3)-year). Alternative models concerned changes in application of job- and region-specific estimates and exposure ceiling, and omitting the a priori exposure ranking. Cumulative exposure levels for the study subjects ranged from 0.01 to 60 mg/m(3)-years, with a median of 1.76 mg/m(3)-years. Exposure levels derived from SYN-JEM and alternative models were overall highly correlated (R(p) > 0.90), although somewhat lower when omitting the region estimate ((Rp) = 0.80) or not taking into account the assigned semi-quantitative exposure level (R(p) = 0.65). Modification of the time trend (i.e. exposure ceiling at 1950 or 1970, or assuming a decline before 1960) caused the largest changes in absolute exposure levels (26-33% difference), but without changing the relative ranking ((Rp) = 0.99). Exposure estimates derived from SYN-JEM appeared to be plausible compared with (historical) levels described in the literature. Decisions taken in the development of SYN-JEM did not critically change the cumulative exposure levels. The influence of region-specific estimates needs to be explored in future risk analyses.

Acute lung injury and persistent small airway disease in a rabbit model of chlorine inhalation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Musah, Sadiatu; Schlueter, Connie F.; Humphrey, Da

Chlorine is a pulmonary toxicant to which humans can be exposed through accidents or intentional releases. Acute effects of chlorine inhalation in humans and animal models have been well characterized, but less is known about persistent effects of acute, high-level chlorine exposures. In particular, animal models that reproduce the long-term effects suggested to occur in humans are lacking. Here, we report the development of a rabbit model in which both acute and persistent effects of chlorine inhalation can be assessed. Male New Zealand White rabbits were exposed to chlorine while the lungs were mechanically ventilated. After chlorine exposure, the rabbitsmore » were extubated and were allowed to survive for up to 24 h after exposure to 800 ppm chlorine for 4 min to study acute effects or up to 7 days after exposure to 400 ppm for 8 min to study longer term effects. Acute effects observed 6 or 24 h after inhalation of 800 ppm chlorine for 4 min included hypoxemia, pulmonary edema, airway epithelial injury, inflammation, altered baseline lung mechanics, and airway hyperreactivity to inhaled methacholine. Seven days after recovery from inhalation of 400 ppm chlorine for 8 min, rabbits exhibited mild hypoxemia, increased area of pressure–volume loops, and airway hyperreactivity. Lung histology 7 days after chlorine exposure revealed abnormalities in the small airways, including inflammation and sporadic bronchiolitis obliterans lesions. Immunostaining showed a paucity of club and ciliated cells in the epithelium at these sites. These results suggest that small airway disease may be an important component of persistent respiratory abnormalities that occur following acute chlorine exposure. This non-rodent chlorine exposure model should prove useful for studying persistent effects of acute chlorine exposure and for assessing efficacy of countermeasures for chlorine-induced lung injury. - Highlights: • A novel rabbit model of chlorine-induced lung disease was developed. • Acute effects of chlorine were pulmonary edema, hypoxemia and impaired lung function. • Persistent small airway disease developed following recovery from acute injury. • Small airway disease included inflammation and bronchiolitis obliterans lesions. • The model should be useful for studying chlorine lung injury and testing treatments.« less

A quantitative assessment of risks of heavy metal residues in laundered shop towels and their use by workers.

PubMed

Connor, Kevin; Magee, Brian

2014-10-01

This paper presents a risk assessment of exposure to metal residues in laundered shop towels by workers. The concentrations of 27 metals measured in a synthetic sweat leachate were used to estimate the releasable quantity of metals which could be transferred to workers' skin. Worker exposure was evaluated quantitatively with an exposure model that focused on towel-to-hand transfer and subsequent hand-to-food or -mouth transfers. The exposure model was based on conservative, but reasonable assumptions regarding towel use and default exposure factor values from the published literature or regulatory guidance. Transfer coefficients were derived from studies representative of the exposures to towel users. Contact frequencies were based on assumed high-end use of shop towels, but constrained by a theoretical maximum dermal loading. The risk estimates for workers developed for all metals were below applicable regulatory risk benchmarks. The risk assessment for lead utilized the Adult Lead Model and concluded that predicted lead intakes do not constitute a significant health hazard based on potential worker exposures. Uncertainties are discussed in relation to the overall confidence in the exposure estimates developed for each exposure pathway and the likelihood that the exposure model is under- or overestimating worker exposures and risk. Copyright © 2014 Elsevier Inc. All rights reserved.

Wavelet-based functional linear mixed models: an application to measurement error-corrected distributed lag models.

PubMed

Malloy, Elizabeth J; Morris, Jeffrey S; Adar, Sara D; Suh, Helen; Gold, Diane R; Coull, Brent A

2010-07-01

Frequently, exposure data are measured over time on a grid of discrete values that collectively define a functional observation. In many applications, researchers are interested in using these measurements as covariates to predict a scalar response in a regression setting, with interest focusing on the most biologically relevant time window of exposure. One example is in panel studies of the health effects of particulate matter (PM), where particle levels are measured over time. In such studies, there are many more values of the functional data than observations in the data set so that regularization of the corresponding functional regression coefficient is necessary for estimation. Additional issues in this setting are the possibility of exposure measurement error and the need to incorporate additional potential confounders, such as meteorological or co-pollutant measures, that themselves may have effects that vary over time. To accommodate all these features, we develop wavelet-based linear mixed distributed lag models that incorporate repeated measures of functional data as covariates into a linear mixed model. A Bayesian approach to model fitting uses wavelet shrinkage to regularize functional coefficients. We show that, as long as the exposure error induces fine-scale variability in the functional exposure profile and the distributed lag function representing the exposure effect varies smoothly in time, the model corrects for the exposure measurement error without further adjustment. Both these conditions are likely to hold in the environmental applications we consider. We examine properties of the method using simulations and apply the method to data from a study examining the association between PM, measured as hourly averages for 1-7 days, and markers of acute systemic inflammation. We use the method to fully control for the effects of confounding by other time-varying predictors, such as temperature and co-pollutants.

Examining Exposure Assessment in Shift Work Research: A Study on Depression Among Nurses.

PubMed

Hall, Amy L; Franche, Renée-Louise; Koehoorn, Mieke

2018-02-13

Coarse exposure assessment and assignment is a common issue facing epidemiological studies of shift work. Such measures ignore a number of exposure characteristics that may impact on health, increasing the likelihood of biased effect estimates and masked exposure-response relationships. To demonstrate the impacts of exposure assessment precision in shift work research, this study investigated relationships between work schedule and depression in a large survey of Canadian nurses. The Canadian 2005 National Survey of the Work and Health of Nurses provided the analytic sample (n = 11450). Relationships between work schedule and depression were assessed using logistic regression models with high, moderate, and low-precision exposure groupings. The high-precision grouping described shift timing and rotation frequency, the moderate-precision grouping described shift timing, and the low-precision grouping described the presence/absence of shift work. Final model estimates were adjusted for the potential confounding effects of demographic and work variables, and bootstrap weights were used to generate sampling variances that accounted for the survey sample design. The high-precision exposure grouping model showed the strongest relationships between work schedule and depression, with increased odds ratios [ORs] for rapidly rotating (OR = 1.51, 95% confidence interval [CI] = 0.91-2.51) and undefined rotating (OR = 1.67, 95% CI = 0.92-3.02) shift workers, and a decreased OR for depression in slow rotating (OR = 0.79, 95% CI = 0.57-1.08) shift workers. For the low- and moderate-precision exposure grouping models, weak relationships were observed for all work schedule categories (OR range 0.95 to 0.99). Findings from this study support the need to consider and collect the data required for precise and conceptually driven exposure assessment and assignment in future studies of shift work and health. Further research into the effects of shift rotation frequency on depression is also recommended. © The Author(s) 2018. Published by Oxford University Press on behalf of the British Occupational Hygiene Society.

DATA REQUIREMENTS FOR ASSESSING CHILDREN'S EXPOSURE TO PESTICIDES

EPA Science Inventory

Several multimedia, multipathway exposure monitoring studies are currently being planned within EPA/NERL. The overall objectives of these studies are (1) to develop the data and models that can be used to estimate exposure and dose for young children to pesticides and (2) to i...

Modeling of exposure to carbon monoxide in fires

NASA Technical Reports Server (NTRS)

Cagliostro, D. E.

1980-01-01

A mathematical model is developed to predict carboxyhemoglobin concentrations in regions of the body for short exposures to carbon monoxide levels expected during escape from aircraft fires. The model includes the respiratory and circulatory dynamics of absorption and distribution of carbon monoxide and carboxyhemoglobin. Predictions of carboxyhemoglobin concentrations are compared to experimental values obtained for human exposures to constant high carbon monoxide levels. Predictions are within 20% of experimental values. For short exposure times, transient concentration effects are predicted. The effect of stress is studied and found to increase carboxyhemoglobin levels substantially compared to a rest state.

Chronic escalating cocaine exposure, abstinence/withdrawal, and chronic re-exposure: Effects on striatal dopamine and opioid systems in C57BL/6J mice

PubMed Central

Zhang, Yong; Schlussman, Stefan D.; Rabkin, Jacqui; Butelman, Eduardo R.; Ho, Ann; Kreek, Mary Jeanne

2013-01-01

Cocaine addiction is a chronic relapsing disease with periods of chronic escalating self-exposure, separated by periods of abstinence/withdrawal of varying duration. Few studies compare such cycles in preclinical models. This study models an “addiction-like cycle” in mice to determine neurochemical/molecular alterations that underlie the chronic, relapsing nature of this disease. Groups of male C57BL/6J mice received acute cocaine exposure (14-day saline/14-day withdrawal /13-day saline + 1-day cocaine), chronic cocaine exposure (14 day cocaine) or chronic re-exposure (14-day cocaine/14-day withdrawal /14-day cocaine). Escalating-dose binge cocaine (15-30 mg/kg/injection x 3/day, i.p. at hourly intervals) or saline (14-day saline) was administered, modeling initial exposure. In “re-exposure” groups, after a 14-day injection-free period (modeling abstinence/withdrawal), mice that had received cocaine were re-injected with 14-day escalating-dose binge cocaine, whereas controls received saline. Microdialysis was conducted on the 14th day of exposure or re-exposure to determine striatal dopamine content. Messenger RNA levels of preprodynorphin (Pdyn), dopamine D1 (Drd1) and D2 (Drd2) in the caudate putamen were determined by real-time PCR. Basal striatal dopamine levels were lower in mice after 14-day escalating exposure or re-exposure than in those in the acute cocaine group and controls. Pdyn mRNA levels were higher in the cocaine groups than in controls. Long-term adaptation was observed across the stages of this addiction-like cycle, in that the effects of cocaine on dopamine levels were increased after re-exposure compared to exposure. Changes in striatal dopaminergic responses across chronic escalating cocaine exposure and re-exposure are a central feature of the neurobiology of relapsing addictive states. PMID:23164614

Inferring Population Exposure from Biomonitoring Data on Urinary Concentrations (SOT)

EPA Science Inventory

Biomonitoring studies such as the National Health and Nutrition Examination Survey (NHANES) are valuable to exposure assessment both as sources of data to evaluate exposure models and as training sets to develop heuristics for rapid-exposure-assessment tools. However, linking in...

Modeling long-term effects attributed to nitrogen dioxide (NO2) and sulfur dioxide (SO2) exposure on asthma morbidity in a nationwide cohort in Israel.

PubMed

Greenberg, N; Carel, R S; Derazne, E; Tiktinsky, A; Tzur, D; Portnov, B A

2017-01-01

Studies have provided extensive documentation that acutely elevated environmental exposures contribute to chronic health problems. However, only attention has been paid to the effects of modificate of exposure assessment methods in environmental health investigations, leading to uncertainty and gaps in our understanding of exposure- and dose-response relationships. The goal of the present study was to evaluate whether average or peak concentration exerts a greater influence on asthma outcome, and which of the exposure models may better explain various physiological responses generated by nitrogen dioxide (NO 2 ) or sulfur dioxide (SO 2 ) air pollutants. The effects of annual NO 2 and SO 2 exposures on asthma prevalence were determined in 137,040 17-year-old males in Israel, who underwent standard health examinations before induction to military service during 1999-2008. Three alternative models of cumulative exposure were used: arithmetic mean level (AM), average peak concentration (APC), and total number of air pollution exposure episodes (NEP). Air pollution data for NO 2 and SO 2 levels were linked to the residence of each subject and asthma prevalence was predicted using bivariate logistic regression. There was significant increased risk for asthma occurrence attributed to NO 2 exposure in all models with the highest correlations demonstrated using the APC model. Data suggested that exposure-response is better correlated with NO 2 peak concentration than with average exposure concentration in subjects with asthma. For SO 2 , there was a weaker but still significant exposure response association in all models. These differences may be related to differences in physiological responses including effects on different regions of the airways following exposure to these pollutants. NO 2 , which is poorly soluble in water, penetrates deep into the bronchial tree, producing asthmatic manifestations such as inflammation and increased mucus production as a result of high gaseous concentrations in the lung parenchyma. In contrast, SO 2 , which is highly water soluble, exerts its effects rapidly in the upper airways, leading to similar limited correlations at all levels of exposure with fewer asthmatic manifestations observed. These data indicate that differing exposure assessment methods may be needed to capture specific disease consequences associated with these air pollutants.

Calculating Formulas of Coefficient and Mean Neutron Exposure in the Exponential Expression of Neutron Exposure Distribution

NASA Astrophysics Data System (ADS)

Zhang, F. H.; Zhou, G. D.; Ma, K.; Ma, W. J.; Cui, W. Y.; Zhang, B.

2015-11-01

Present studies have shown that, in the main stages of the development and evolution of asymptotic giant branch (AGB) star s-process models, the distributions of neutron exposures in the nucleosynthesis regions can all be expressed by an exponential function ({ρ_{AGB}}(τ) = C/{τ_0}exp ( - τ/{τ_0})) in the effective range of values. However, the specific expressions of the proportional coefficient C and the mean neutron exposure ({τ_0}) in the formula for different models are not completely determined in the related literatures. Through dissecting the basic solving method of the exponential distribution of neutron exposures, and systematically combing the solution procedure of exposure distribution for different stellar models, the general calculating formulas as well as their auxiliary equations for calculating C and ({τ_0}) are reduced. Given the discrete distribution of neutron exposures ({P_k}), i.e. the mass ratio of the materials which have exposed to neutrons for (k) ((k = 0, 1, 2 \\cdots )) times when reaching the final distribution with respect to the materials of the He intershell, (C = - {P_1}/ln R), and ({τ_0} = - Δ τ /ln R) can be obtained. Here, (R) expresses the probability that the materials can successively experience neutron irradiation for two times in the He intershell. For the convective nucleosynthesis model (including the Ulrich model and the ({}^{13}{C})-pocket convective burning model), (R) is just the overlap factor r, namely the mass ratio of the materials which can undergo two successive thermal pulses in the He intershell. And for the (^{13}{C})-pocket radiative burning model, (R = sumlimits_{k = 1}^∞ {{P_k}} ). This set of formulas practically give the corresponding relationship between C or ({τ_0}) and the model parameters. The results of this study effectively solve the problem of analytically calculating the distribution of neutron exposures in the low-mass AGB star s-process nucleosynthesis model of (^{13}{C})-pocket radiative burning.

Land Use Regression Modeling of Outdoor Noise Exposure in Informal Settlements in Western Cape, South Africa.

PubMed

Sieber, Chloé; Ragettli, Martina S; Brink, Mark; Toyib, Olaniyan; Baatjies, Roslyn; Saucy, Apolline; Probst-Hensch, Nicole; Dalvie, Mohamed Aqiel; Röösli, Martin

2017-10-20

In low- and middle-income countries, noise exposure and its negative health effects have been little explored. The present study aimed to assess the noise exposure situation in adults living in informal settings in the Western Cape Province, South Africa. We conducted continuous one-week outdoor noise measurements at 134 homes in four different areas. These data were used to develop a land use regression (LUR) model to predict A-weighted day-evening-night equivalent sound levels (L den ) from geographic information system (GIS) variables. Mean noise exposure during day (6:00-18:00) was 60.0 A-weighted decibels (dB(A)) (interquartile range 56.9-62.9 dB(A)), during night (22:00-6:00) 52.9 dB(A) (49.3-55.8 dB(A)) and average L den was 63.0 dB(A) (60.1-66.5 dB(A)). Main predictors of the LUR model were related to road traffic and household density. Model performance was low (adjusted R 2 = 0.130) suggesting that other influences than those represented in the geographic predictors are relevant for noise exposure. This is one of the few studies on the noise exposure situation in low- and middle-income countries. It demonstrates that noise exposure levels are high in these settings.

Prenatal and Postnatal Exposure to Persistent Organic Pollutants and Infant Growth: A Pooled Analysis of Seven European Birth Cohorts.

PubMed

Iszatt, Nina; Stigum, Hein; Verner, Marc-André; White, Richard A; Govarts, Eva; Murinova, Lubica Palkovicova; Schoeters, Greet; Trnovec, Tomas; Legler, Juliette; Pelé, Fabienne; Botton, Jérémie; Chevrier, Cécile; Wittsiepe, Jürgen; Ranft, Ulrich; Vandentorren, Stéphanie; Kasper-Sonnenberg, Monika; Klümper, Claudia; Weisglas-Kuperus, Nynke; Polder, Anuschka; Eggesbø, Merete

2015-07-01

Infant exposure to persistent organic pollutants (POPs) may contribute to obesity. However, many studies so far have been small, focused on transplacental exposure, used an inappropriate measure to assess postnatal exposure through breastfeeding if any, or did not discern between prenatal and postnatal effects. We investigated prenatal and postnatal exposure to POPs and infant growth (a predictor of obesity). We pooled data from seven European birth cohorts with biomarker concentrations of polychlorinated biphenyl 153 (PCB-153) (n = 2,487), and p,p'-dichlorodiphenyldichloroethylene (p,p'-DDE) (n = 1,864), estimating prenatal and postnatal POPs exposure using a validated pharmacokinetic model. Growth was change in weight-for-age z-score between birth and 24 months. Per compound, multilevel models were fitted with either POPs total exposure from conception to 24 months or prenatal or postnatal exposure. We found a significant increase in growth associated with p,p'-DDE, seemingly due to prenatal exposure (per interquartile increase in exposure, adjusted β = 0.12; 95% CI: 0.03, 0.22). Due to heterogeneity across cohorts, this estimate cannot be considered precise, but does indicate that an association with infant growth is present on average. In contrast, a significant decrease in growth was associated with postnatal PCB-153 exposure (β = -0.10; 95% CI: -0.19, -0.01). To our knowledge, this is the largest study to date of POPs exposure and infant growth, and it contains state-of-the-art exposure modeling. Prenatal p,p'-DDE was associated with increased infant growth, and postnatal PCB-153 with decreased growth at European exposure levels.

Predicting the hepatocarcinogenic potential of alkenylbenzene flavoring agents using toxicogenomics and machine learning

DOE Office of Scientific and Technical Information (OSTI.GOV)

Auerbach, Scott S.; Shah, Ruchir R.; Mav, Deepak

Identification of carcinogenic activity is the primary goal of the 2-year bioassay. The expense of these studies limits the number of chemicals that can be studied and therefore chemicals need to be prioritized based on a variety of parameters. We have developed an ensemble of support vector machine classification models based on male F344 rat liver gene expression following 2, 14 or 90 days of exposure to a collection of hepatocarcinogens (aflatoxin B1, 1-amino-2,4-dibromoanthraquinone, N-nitrosodimethylamine, methyleugenol) and non-hepatocarcinogens (acetaminophen, ascorbic acid, tryptophan). Seven models were generated based on individual exposure durations (2, 14 or 90 days) or a combination ofmore » exposures (2 + 14, 2 + 90, 14 + 90 and 2 + 14 + 90 days). All sets of data, with the exception of one yielded models with 0% cross-validation error. Independent validation of the models was performed using expression data from the liver of rats exposed at 2 dose levels to a collection of alkenylbenzene flavoring agents. Depending on the model used and the exposure duration of the test data, independent validation error rates ranged from 47% to 10%. The variable with the most notable effect on independent validation accuracy was exposure duration of the alkenylbenzene test data. All models generally exhibited improved performance as the exposure duration of the alkenylbenzene data increased. The models differentiated between hepatocarcinogenic (estragole and safrole) and non-hepatocarcinogenic (anethole, eugenol and isoeugenol) alkenylbenzenes previously studied in a carcinogenicity bioassay. In the case of safrole the models correctly differentiated between carcinogenic and non-carcinogenic dose levels. The models predict that two alkenylbenzenes not previously assessed in a carcinogenicity bioassay, myristicin and isosafrole, would be weakly hepatocarcinogenic if studied at a dose level of 2 mmol/kg bw/day for 2 years in male F344 rats; therefore suggesting that these chemicals should be a higher priority relative to other untested alkenylbenzenes for evaluation in the carcinogenicity bioassay. The results of the study indicate that gene expression-based predictive models are an effective tool for identifying hepatocarcinogens. Furthermore, we find that exposure duration is a critical variable in the success or failure of such an approach, particularly when evaluating chemicals with unknown carcinogenic potency.« less

Predicting the hepatocarcinogenic potential of alkenylbenzene flavoring agents using toxicogenomics and machine learning.

PubMed

Auerbach, Scott S; Shah, Ruchir R; Mav, Deepak; Smith, Cynthia S; Walker, Nigel J; Vallant, Molly K; Boorman, Gary A; Irwin, Richard D

2010-03-15

Identification of carcinogenic activity is the primary goal of the 2-year bioassay. The expense of these studies limits the number of chemicals that can be studied and therefore chemicals need to be prioritized based on a variety of parameters. We have developed an ensemble of support vector machine classification models based on male F344 rat liver gene expression following 2, 14 or 90 days of exposure to a collection of hepatocarcinogens (aflatoxin B1, 1-amino-2,4-dibromoanthraquinone, N-nitrosodimethylamine, methyleugenol) and non-hepatocarcinogens (acetaminophen, ascorbic acid, tryptophan). Seven models were generated based on individual exposure durations (2, 14 or 90 days) or a combination of exposures (2+14, 2+90, 14+90 and 2+14+90 days). All sets of data, with the exception of one yielded models with 0% cross-validation error. Independent validation of the models was performed using expression data from the liver of rats exposed at 2 dose levels to a collection of alkenylbenzene flavoring agents. Depending on the model used and the exposure duration of the test data, independent validation error rates ranged from 47% to 10%. The variable with the most notable effect on independent validation accuracy was exposure duration of the alkenylbenzene test data. All models generally exhibited improved performance as the exposure duration of the alkenylbenzene data increased. The models differentiated between hepatocarcinogenic (estragole and safrole) and non-hepatocarcinogenic (anethole, eugenol and isoeugenol) alkenylbenzenes previously studied in a carcinogenicity bioassay. In the case of safrole the models correctly differentiated between carcinogenic and non-carcinogenic dose levels. The models predict that two alkenylbenzenes not previously assessed in a carcinogenicity bioassay, myristicin and isosafrole, would be weakly hepatocarcinogenic if studied at a dose level of 2 mmol/kg bw/day for 2 years in male F344 rats; therefore suggesting that these chemicals should be a higher priority relative to other untested alkenylbenzenes for evaluation in the carcinogenicity bioassay. The results of the study indicate that gene expression-based predictive models are an effective tool for identifying hepatocarcinogens. Furthermore, we find that exposure duration is a critical variable in the success or failure of such an approach, particularly when evaluating chemicals with unknown carcinogenic potency. Published by Elsevier Inc.

In utero exposure to valproic acid and autism--a current review of clinical and animal studies.

PubMed

Roullet, Florence I; Lai, Jonathan K Y; Foster, Jane A

2013-01-01

Valproic acid (VPA) is both an anti-convulsant and a mood stabilizer. Clinical studies over the past 40 years have shown that exposure to VPA in utero is associated with birth defects, cognitive deficits, and increased risk of autism. Two recent FDA warnings related to use of VPA in pregnancy emphasize the need to reevaluate its use clinically during child-bearing years. The emerging clinical evidence showing a link between VPA exposure and both cognitive function and risk of autism brings to the forefront the importance of understanding how VPA exposure influences neurodevelopment. In the past 10 years, animal studies have investigated anatomical, behavioral, molecular, and physiological outcomes related to in utero VPA exposure. Behavioral studies show that VPA exposure in both rats and mice leads to autistic-like behaviors in the offspring, including social behavior deficits, increased repetitive behaviors, and deficits in communication. Based on this work VPA maternal challenge in rodents has been proposed as an animal model to study autism. This model has both face and construct validity; however, like all animal models there are limitations to its translation to the clinical setting. Here we provide a review of clinical studies that examined pregnancy outcomes of VPA use as well as the related animal studies. Copyright © 2013 Elsevier Inc. All rights reserved.

Risk assessments using the Strain Index and the TLV for HAL, Part I: Task and multi-task job exposure classifications.

PubMed

Kapellusch, Jay M; Bao, Stephen S; Silverstein, Barbara A; Merryweather, Andrew S; Thiese, Mathew S; Hegmann, Kurt T; Garg, Arun

2017-12-01

The Strain Index (SI) and the American Conference of Governmental Industrial Hygienists (ACGIH) Threshold Limit Value for Hand Activity Level (TLV for HAL) use different constituent variables to quantify task physical exposures. Similarly, time-weighted-average (TWA), Peak, and Typical exposure techniques to quantify physical exposure from multi-task jobs make different assumptions about each task's contribution to the whole job exposure. Thus, task and job physical exposure classifications differ depending upon which model and technique are used for quantification. This study examines exposure classification agreement, disagreement, correlation, and magnitude of classification differences between these models and techniques. Data from 710 multi-task job workers performing 3,647 tasks were analyzed using the SI and TLV for HAL models, as well as with the TWA, Typical and Peak job exposure techniques. Physical exposures were classified as low, medium, and high using each model's recommended, or a priori limits. Exposure classification agreement and disagreement between models (SI, TLV for HAL) and between job exposure techniques (TWA, Typical, Peak) were described and analyzed. Regardless of technique, the SI classified more tasks as high exposure than the TLV for HAL, and the TLV for HAL classified more tasks as low exposure. The models agreed on 48.5% of task classifications (kappa = 0.28) with 15.5% of disagreement between low and high exposure categories. Between-technique (i.e., TWA, Typical, Peak) agreement ranged from 61-93% (kappa: 0.16-0.92) depending on whether the SI or TLV for HAL was used. There was disagreement between the SI and TLV for HAL and between the TWA, Typical and Peak techniques. Disagreement creates uncertainty for job design, job analysis, risk assessments, and developing interventions. Task exposure classifications from the SI and TLV for HAL might complement each other. However, TWA, Typical, and Peak job exposure techniques all have limitations. Part II of this article examines whether the observed differences between these models and techniques produce different exposure-response relationships for predicting prevalence of carpal tunnel syndrome.

Effect modification of the association of cumulative exposure and cancer risk by intensity of exposure and time since exposure cessation: a flexible method applied to cigarette smoking and lung cancer in the SYNERGY Study.

PubMed

Vlaanderen, Jelle; Portengen, Lützen; Schüz, Joachim; Olsson, Ann; Pesch, Beate; Kendzia, Benjamin; Stücker, Isabelle; Guida, Florence; Brüske, Irene; Wichmann, Heinz-Erich; Consonni, Dario; Landi, Maria Teresa; Caporaso, Neil; Siemiatycki, Jack; Merletti, Franco; Mirabelli, Dario; Richiardi, Lorenzo; Gustavsson, Per; Plato, Nils; Jöckel, Karl-Heinz; Ahrens, Wolfgang; Pohlabeln, Hermann; Tardón, Adonina; Zaridze, David; Field, John K; 't Mannetje, Andrea; Pearce, Neil; McLaughlin, John; Demers, Paul; Szeszenia-Dabrowska, Neonila; Lissowska, Jolanta; Rudnai, Peter; Fabianova, Eleonora; Stanescu Dumitru, Rodica; Bencko, Vladimir; Foretova, Lenka; Janout, Vladimir; Boffetta, Paolo; Forastiere, Francesco; Bueno-de-Mesquita, Bas; Peters, Susan; Brüning, Thomas; Kromhout, Hans; Straif, Kurt; Vermeulen, Roel

2014-02-01

The indiscriminate use of the cumulative exposure metric (the product of intensity and duration of exposure) might bias reported associations between exposure to hazardous agents and cancer risk. To assess the independent effects of duration and intensity of exposure on cancer risk, we explored effect modification of the association of cumulative exposure and cancer risk by intensity of exposure. We applied a flexible excess odds ratio model that is linear in cumulative exposure but potentially nonlinear in intensity of exposure to 15 case-control studies of cigarette smoking and lung cancer (1985-2009). Our model accommodated modification of the excess odds ratio per pack-year of cigarette smoking by time since smoking cessation among former smokers. We observed negative effect modification of the association of pack-years of cigarette smoking and lung cancer by intensity of cigarette smoke for persons who smoked more than 20-30 cigarettes per day. Patterns of effect modification were similar across individual studies and across major lung cancer subtypes. We observed strong negative effect modification by time since smoking cessation. Application of our method in this example of cigarette smoking and lung cancer demonstrated that reducing a complex exposure history to a metric such as cumulative exposure is too restrictive.

Effect Modification of the Association of Cumulative Exposure and Cancer Risk by Intensity of Exposure and Time Since Exposure Cessation: A Flexible Method Applied to Cigarette Smoking and Lung Cancer in the SYNERGY Study

PubMed Central

Vlaanderen, Jelle; Portengen, Lützen; Schüz, Joachim; Olsson, Ann; Pesch, Beate; Kendzia, Benjamin; Stücker, Isabelle; Guida, Florence; Brüske, Irene; Wichmann, Heinz-Erich; Consonni, Dario; Landi, Maria Teresa; Caporaso, Neil; Siemiatycki, Jack; Merletti, Franco; Mirabelli, Dario; Richiardi, Lorenzo; Gustavsson, Per; Plato, Nils; Jöckel, Karl-Heinz; Ahrens, Wolfgang; Pohlabeln, Hermann; Tardón, Adonina; Zaridze, David; Field, John K.; 't Mannetje, Andrea; Pearce, Neil; McLaughlin, John; Demers, Paul; Szeszenia-Dabrowska, Neonila; Lissowska, Jolanta; Rudnai, Peter; Fabianova, Eleonora; Stanescu Dumitru, Rodica; Bencko, Vladimir; Foretova, Lenka; Janout, Vladimir; Boffetta, Paolo; Forastiere, Francesco; Bueno-de-Mesquita, Bas; Peters, Susan; Brüning, Thomas; Kromhout, Hans; Straif, Kurt; Vermeulen, Roel

2014-01-01

The indiscriminate use of the cumulative exposure metric (the product of intensity and duration of exposure) might bias reported associations between exposure to hazardous agents and cancer risk. To assess the independent effects of duration and intensity of exposure on cancer risk, we explored effect modification of the association of cumulative exposure and cancer risk by intensity of exposure. We applied a flexible excess odds ratio model that is linear in cumulative exposure but potentially nonlinear in intensity of exposure to 15 case-control studies of cigarette smoking and lung cancer (1985–2009). Our model accommodated modification of the excess odds ratio per pack-year of cigarette smoking by time since smoking cessation among former smokers. We observed negative effect modification of the association of pack-years of cigarette smoking and lung cancer by intensity of cigarette smoke for persons who smoked more than 20–30 cigarettes per day. Patterns of effect modification were similar across individual studies and across major lung cancer subtypes. We observed strong negative effect modification by time since smoking cessation. Application of our method in this example of cigarette smoking and lung cancer demonstrated that reducing a complex exposure history to a metric such as cumulative exposure is too restrictive. PMID:24355332

Investigating the role of transportation models in epidemiologic studies of traffic related air pollution and health effects.

PubMed

Shekarrizfard, Maryam; Valois, Marie-France; Goldberg, Mark S; Crouse, Dan; Ross, Nancy; Parent, Marie-Elise; Yasmin, Shamsunnahar; Hatzopoulou, Marianne

2015-07-01

In two earlier case-control studies conducted in Montreal, nitrogen dioxide (NO2), a marker for traffic-related air pollution was found to be associated with the incidence of postmenopausal breast cancer and prostate cancer. These studies relied on a land use regression model (LUR) for NO2 that is commonly used in epidemiologic studies for deriving estimates of traffic-related air pollution. Here, we investigate the use of a transportation model developed during the summer season to generate a measure of traffic emissions as an alternative to the LUR model. Our traffic model provides estimates of emissions of nitrogen oxides (NOx) at the level of individual roads, as does the LUR model. Our main objective was to compare the distribution of the spatial estimates of NOx computed from our transportation model to the distribution obtained from the LUR model. A secondary objective was to compare estimates of risk using these two exposure estimates. We observed that the correlation (spearman) between our two measures of exposure (NO2 and NOx) ranged from less than 0.3 to more than 0.9 across Montreal neighborhoods. The most important factor affecting the "agreement" between the two measures in a specific area was found to be the length of roads. Areas affected by a high level of traffic-related air pollution had a far better agreement between the two exposure measures. A comparison of odds ratios (ORs) obtained from NO2 and NOx used in two case-control studies of breast and prostate cancer, showed that the differences between the ORs associated with NO2 exposure vs NOx exposure differed by 5.2-8.8%. Copyright © 2015 Elsevier Inc. All rights reserved.

Population pharmacokinetics and exposure-response of osimertinib in patients with non-small cell lung cancer.

PubMed

Brown, Kathryn; Comisar, Craig; Witjes, Han; Maringwa, John; de Greef, Rik; Vishwanathan, Karthick; Cantarini, Mireille; Cox, Eugène

2017-06-01

To develop a population (pop) pharmacokinetic (PK) model for osimertinib (AZD9291) and its metabolite (AZ5104) and investigate the exposure-response relationships for selected efficacy and safety parameters. PK, safety and efficacy data were collected from two non-small cell lung cancer (NSCLC) patient studies (n = 748) and one healthy volunteer study (n = 32), after single or multiple once-daily dosing of 20-240 mg osimertinib. Nonlinear mixed effects modelling was used to characterise the popPK. Individual exposure values were used to investigate the relationship with response evaluation criteria in solid tumours (RECIST 1.1) efficacy parameters and key safety parameters (rash, diarrhoea, QTcF). A popPK model that adequately described osimertinib and its metabolite AZ5104 in a joint manner was developed. Body weight, serum albumin and ethnicity were identified as significant covariates on PK in the analysis, but were not found to have a clinically relevant impact on osimertinib exposure. No relationship was identified between exposure and efficacy over the dose range studied. A linear relationship was observed between exposure and the occurrence of rash or diarrhoea, and between concentration and QTcF, with a predicted mean (upper 90% confidence interval) increase of 14.2 (15.8) ms at the maximum concentration for an 80 mg once-daily dose at steady state. PopPK and exposure-response models were developed for osimertinib and AZ5104. There was no relationship between exposure and efficacy but a linear relationship between exposure and safety endpoints (rash, diarrhoea and QTcF) was observed. © 2016 The British Pharmacological Society.

Reaching Nutritional Adequacy Does Not Necessarily Increase Exposure to Food Contaminants: Evidence from a Whole-Diet Modeling Approach.

PubMed

Barré, Tangui; Vieux, Florent; Perignon, Marlène; Cravedi, Jean-Pierre; Amiot, Marie-Josèphe; Micard, Valérie; Darmon, Nicole

2016-10-01

Dietary guidelines are designed to help meet nutritional requirements, but they do not explicitly or quantitatively account for food contaminant exposures. In this study, we aimed to test whether dietary changes needed to achieve nutritional adequacy were compatible with acceptable exposure to food contaminants. Data from the French national dietary survey were linked with food contaminant data from the French Total Diet Study to estimate the mean intake of 204 representative food items and mean exposure to 27 contaminants, including pesticides, heavy metals, mycotoxins, nondioxin-like polychlorinated biphenyls (NDL-PCBs) and dioxin-like compounds. For each sex, 2 modeled diets that departed the least from the observed diet were designed: 1) a diet respecting only nutritional recommendations (NUT model), and 2) a diet that met nutritional recommendations without exceeding Toxicological Reference Values (TRVs) and observed contaminant exposures (NUTOX model). Food, nutrient, and contaminant contents in observed diets and NUT and NUTOX diets were compared with the use of paired t tests. Mean observed diets did not meet all nutritional recommendations, but no contaminant was over 48% of its TRV. Achieving all the nutrient recommendations through the NUT model mainly required increases in fruit, vegetable, and fish intake and decreases in meat, cheese, and animal fat intake. These changes were associated with significantly increased dietary exposure to some contaminants, but without exceeding 57% of TRVs. The highest increases were found for NDL-PCBs (from 26% to 57% of TRV for women). Reaching nutritional adequacy without exceeding observed contaminant exposure (NUTOX model) was possible but required further departure from observed food quantities. Based on a broad range of nutrients and contaminants, this first assessment of compatibility between nutritional adequacy and toxicological exposure showed that reaching nutritional adequacy might increase exposure to food contaminants, but within tolerable levels. However, there are some food combinations that can meet nutritional recommendations without exceeding observed exposures. © 2016 American Society for Nutrition.

Estimation of personal PM2.5 and BC exposure by a modeling approach - Results of a panel study in Shanghai, China.

PubMed

Chen, Chen; Cai, Jing; Wang, Cuicui; Shi, Jingjin; Chen, Renjie; Yang, Changyuan; Li, Huichu; Lin, Zhijing; Meng, Xia; Zhao, Ang; Liu, Cong; Niu, Yue; Xia, Yongjie; Peng, Li; Zhao, Zhuohui; Chillrud, Steven; Yan, Beizhan; Kan, Haidong

2018-06-06

Epidemiologic studies of PM 2.5 (particulate matter with aerodynamic diameter ≤2.5 μm) and black carbon (BC) typically use ambient measurements as exposure proxies given that individual measurement is infeasible among large populations. Failure to account for variation in exposure will bias epidemiologic study results. The ability of ambient measurement as a proxy of exposure in regions with heavy pollution is untested. We aimed to investigate effects of potential determinants and to estimate PM 2.5 and BC exposure by a modeling approach. We collected 417 24 h personal PM 2.5 and 130 72 h personal BC measurements from a panel of 36 nonsmoking college students in Shanghai, China. Each participant underwent 4 rounds of three consecutive 24-h sampling sessions through December 2014 to July 2015. We applied backwards regression to construct mixed effect models incorporating all accessible variables of ambient pollution, climate and time-location information for exposure prediction. All models were evaluated by marginal R 2 and root mean square error (RMSE) from a leave-one-out-cross-validation (LOOCV) and a 10-fold cross-validation (10-fold CV). Personal PM 2.5 was 47.6% lower than ambient level, with mean (±Standard Deviation, SD) level of 39.9 (±32.1) μg/m 3 ; whereas personal BC (6.1 (±2.8) μg/m 3 ) was about one-fold higher than the corresponding ambient concentrations. Ambient levels were the most significant determinants of PM 2.5 and BC exposure. Meteorological and season indicators were also important predictors. Our final models predicted 75% of the variance in 24 h personal PM 2.5 and 72 h personal BC. LOOCV analysis showed an R 2 (RMSE) of 0.73 (0.40) for PM 2.5 and 0.66 (0.27) for BC. Ten-fold CV analysis showed a R 2 (RMSE) of 0.73 (0.41) for PM 2.5 and 0.68 (0.26) for BC. We used readily accessible data and established intuitive models that can predict PM 2.5 and BC exposure. This modeling approach can be a feasible solution for PM exposure estimation in epidemiological studies. Copyright © 2018 Elsevier Ltd. All rights reserved.

Vegetation Exposure to Ozone over the Continental United States: Assessment of Exposure Indices by the Eta-CMAQ Air Quality Forecast Model

EPA Science Inventory

This study presents the first evaluation of the performance of the Eta-CMAQ air quality forecast model to predict a variety of widely used seasonal mean and cumulative O₃ exposure indices associated with vegetation using the U.S. AIRNow O₃ observations.

Association of long-term PM2.5 exposure with mortality using different air pollution exposure models: impacts in rural and urban California.

PubMed

Garcia, Cynthia A; Yap, Poh-Sin; Park, Hye-Youn; Weller, Barbara L

2016-01-01

Most PM2.5-associated mortality studies are not conducted in rural areas where mortality rates may differ when population characteristics, health care access, and PM2.5 composition differ. PM2.5-associated mortality was investigated in the elderly residing in rural-urban zip codes. Exposure (2000-2006) was estimated using different models and Poisson regression was performed using 2006 mortality data. PM2.5 models estimated comparable exposures, although subtle differences were observed in rate ratios (RR) within areas by health outcomes. Cardiovascular disease (CVD), ischemic heart disease (IHD), and cardiopulmonary disease (CPD), mortality was significantly associated with rural, urban, and statewide chronic PM2.5 exposures. We observed larger effect sizes in RRs for CVD, CPD, and all-cause (AC) with similar sizes for IHD mortality in rural areas compared to urban areas. PM2.5 was significantly associated with AC mortality in rural areas and statewide; however, in urban areas, only the most restrictive exposure model showed an association. Given the results seen, future mortality studies should consider adjusting for differences with rural-urban variables.

Air Pollution Exposure Modeling for Epidemiology Studies and Public Health

EPA Science Inventory

Air pollution epidemiology studies of ambient fine particulate matter (PM2.5) often use outdoor concentrations as exposure surrogates. These surrogates can induce exposure error since they do not account for (1) time spent indoors with ambient PM2.5 levels attenuated from outdoor...

Sea lice and salmon population dynamics: effects of exposure time for migratory fish.

PubMed

Krkosek, Martin; Morton, Alexandra; Volpe, John P; Lewis, Mark A

2009-08-07

The ecological impact of parasite transmission from fish farms is probably mediated by the migration of wild fishes, which determines the period of exposure to parasites. For Pacific salmon and the parasitic sea louse, Lepeophtheirus salmonis, analysis of the exposure period may resolve conflicting observations of epizootic mortality in field studies and parasite rejection in experiments. This is because exposure periods can differ by 2-3 orders of magnitude, ranging from months in the field to hours in experiments. We developed a mathematical model of salmon-louse population dynamics, parametrized by a study that monitored naturally infected juvenile salmon held in ocean enclosures. Analysis of replicated trials indicates that lice suffer high mortality, particularly during pre-adult stages. The model suggests louse populations rapidly decline following brief exposure of juvenile salmon, similar to laboratory study designs and data. However, when the exposure period lasts for several weeks, as occurs when juvenile salmon migrate past salmon farms, the model predicts that lice accumulate to abundances that can elevate salmon mortality and depress salmon populations. The duration of parasite exposure is probably critical to salmon-louse population dynamics, and should therefore be accommodated in coastal planning and management where fish farms are situated on wild fish migration routes.

A Model of Medical Countermeasures for Vesicant Exposure

DTIC Science & Technology

2015-10-01

measured protease activity that was collected from mouse ear exposures (Powers J. C., 1999) instead of via in vitro human keratinocyte exposure. The...to mice ears with and without treatment. This is a model study in the type of data that can best be used for the GVM, because it considers different...mice were exposed on their backs; however, for the Lomash et al. study the mice were exposed on their ears . This tends to be an issue when working

Road traffic air and noise pollution exposure assessment - A review of tools and techniques.

PubMed

Khan, Jibran; Ketzel, Matthias; Kakosimos, Konstantinos; Sørensen, Mette; Jensen, Steen Solvang

2018-09-01

Road traffic induces air and noise pollution in urban environments having negative impacts on human health. Thus, estimating exposure to road traffic air and noise pollution (hereafter, air and noise pollution) is important in order to improve the understanding of human health outcomes in epidemiological studies. The aims of this review are (i) to summarize current practices of modelling and exposure assessment techniques for road traffic air and noise pollution (ii) to highlight the potential of existing tools and techniques for their combined exposure assessment for air and noise together with associated challenges, research gaps and priorities. The study reviews literature about air and noise pollution from urban road traffic, including other relevant characteristics such as the employed dispersion models, Geographic Information System (GIS)-based tool, spatial scale of exposure assessment, study location, sample size, type of traffic data and building geometry information. Deterministic modelling is the most frequently used assessment technique for both air and noise pollution of short-term and long-term exposure. We observed a larger variety among air pollution models as compared to the applied noise models. Correlations between air and noise pollution vary significantly (0.05-0.74) and are affected by several parameters such as traffic attributes, building attributes and meteorology etc. Buildings act as screens for the dispersion of pollution, but the reduction effect is much larger for noise than for air pollution. While, meteorology has a greater influence on air pollution levels as compared to noise, although also important for noise pollution. There is a significant potential for developing a standard tool to assess combined exposure of traffic related air and noise pollution to facilitate health related studies. GIS, due to its geographic nature, is well established and has a significant capability to simultaneously address both exposures. Copyright © 2018 Elsevier B.V. All rights reserved.

Dose conversion factors for radon: recent developments.

PubMed

Marsh, James W; Harrison, John D; Laurier, Dominique; Blanchardon, Eric; Paquet, François; Tirmarche, Margot

2010-10-01

Epidemiological studies of the occupational exposure of miners and domestic exposures of the public have provided strong and complementary evidence of the risks of lung cancer following inhalation of radon progeny. Recent miner epidemiological studies, which include low levels of exposure, long duration of follow-up, and good quality of individual exposure data, suggest higher risks of lung cancer per unit exposure than assumed previously by the International Commission on Radiological Protection (ICRP). Although risks can be managed by controlling exposures, dose estimates are required for the control of occupational exposures and are also useful for comparing sources of public exposure. Currently, ICRP calculates doses from radon and its progeny using dose conversion factors from exposure (WLM) to dose (mSv) based on miner epidemiological studies, referred to as the epidemiological approach. Revision of these dose conversion factors using risk estimates based on the most recent epidemiological data gives values that are in good agreement with the results of calculations using ICRP biokinetic and dosimetric models, the dosimetric approach. ICRP now proposes to treat radon progeny in the same way as other radionuclides and to publish dose coefficients calculated using models, for use within the ICRP system of protection.

COMPARING THE UTILITY OF MULTIMEDIA MODELS FOR HUMAN AND ECOLOGICAL EXPOSURE ANALYSIS: TWO CASES

EPA Science Inventory

A number of models are available for exposure assessment; however, few are used as tools for both human and ecosystem risks. This discussion will consider two modeling frameworks that have recently been used to support human and ecological decision making. The study will compare ...

Using physiologically based pharmacokinetic modeling and benchmark dose methods to derive an occupational exposure limit for N-methylpyrrolidone.

PubMed

Poet, T S; Schlosser, P M; Rodriguez, C E; Parod, R J; Rodwell, D E; Kirman, C R

2016-04-01

The developmental effects of NMP are well studied in Sprague-Dawley rats following oral, inhalation, and dermal routes of exposure. Short-term and chronic occupational exposure limit (OEL) values were derived using an updated physiologically based pharmacokinetic (PBPK) model for NMP, along with benchmark dose modeling. Two suitable developmental endpoints were evaluated for human health risk assessment: (1) for acute exposures, the increased incidence of skeletal malformations, an effect noted only at oral doses that were toxic to the dam and fetus; and (2) for repeated exposures to NMP, changes in fetal/pup body weight. Where possible, data from multiple studies were pooled to increase the predictive power of the dose-response data sets. For the purposes of internal dose estimation, the window of susceptibility was estimated for each endpoint, and was used in the dose-response modeling. A point of departure value of 390 mg/L (in terms of peak NMP in blood) was calculated for skeletal malformations based on pooled data from oral and inhalation studies. Acceptable dose-response model fits were not obtained using the pooled data for fetal/pup body weight changes. These data sets were also assessed individually, from which the geometric mean value obtained from the inhalation studies (470 mg*hr/L), was used to derive the chronic OEL. A PBPK model for NMP in humans was used to calculate human equivalent concentrations corresponding to the internal dose point of departure values. Application of a net uncertainty factor of 20-21, which incorporates data-derived extrapolation factors, to the point of departure values yields short-term and chronic occupational exposure limit values of 86 and 24 ppm, respectively. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.

Cross-shift changes in FEV1 in relation to wood dust exposure: the implications of different exposure assessment methods

PubMed Central

Schlunssen, V; Sigsgaard, T; Schaumburg, I; Kromhout, H

2004-01-01

Background: Exposure-response analyses in occupational studies rely on the ability to distinguish workers with regard to exposures of interest. Aims: To evaluate different estimates of current average exposure in an exposure-response analysis on dust exposure and cross-shift decline in FEV1 among woodworkers. Methods: Personal dust samples (n = 2181) as well as data on lung function parameters were available for 1560 woodworkers from 54 furniture industries. The exposure to wood dust for each worker was calculated in eight different ways using individual measurements, group based exposure estimates, a weighted estimate of individual and group based exposure estimates, and predicted values from mixed models. Exposure-response relations on cross-shift changes in FEV1 and exposure estimates were explored. Results: A positive exposure-response relation between average dust exposure and cross-shift FEV1 was shown for non-smokers only and appeared to be most pronounced among pine workers. In general, the highest slope and standard error (SE) was revealed for grouping by a combination of task and factory size, the lowest slope and SE was revealed for estimates based on individual measurements, with the weighted estimate and the predicted values in between. Grouping by quintiles of average exposure for task and factory combinations revealed low slopes and high SE, despite a high contrast. Conclusion: For non-smokers, average dust exposure and cross-shift FEV1 were associated in an exposure dependent manner, especially among pine workers. This study confirms the consequences of using different exposure assessment strategies studying exposure-response relations. It is possible to optimise exposure assessment combining information from individual and group based exposure estimates, for instance by applying predicted values from mixed effects models. PMID:15377768

An object-oriented software for fate and exposure assessments.

PubMed

Scheil, S; Baumgarten, G; Reiter, B; Schwartz, S; Wagner, J O; Trapp, S; Matthies, M

1995-07-01

The model system CemoS(1) (Chemical Exposure Model System) was developed for the exposure prediction of hazardous chemicals released to the environment. Eight different models were implemented involving chemicals fate simulation in air, water, soil and plants after continuous or single emissions from point and diffuse sources. Scenario studies are supported by a substance and an environmental data base. All input data are checked on their plausibility. Substance and environmental process estimation functions facilitate generic model calculations. CemoS is implemented in a modular structure using object-oriented programming.

Neurodevelopment in Early Childhood Affected by Prenatal Lead Exposure and Iron Intake.

PubMed

Shah-Kulkarni, Surabhi; Ha, Mina; Kim, Byung-Mi; Kim, Eunjeong; Hong, Yun-Chul; Park, Hyesook; Kim, Yangho; Kim, Bung-Nyun; Chang, Namsoo; Oh, Se-Young; Kim, Young Ju; Kimʼs, Young Ju; Lee, Boeun; Ha, Eun-Hee

2016-01-01

No safe threshold level of lead exposure in children has been recognized. Also, the information on shielding effect of maternal dietary iron intake during pregnancy on the adverse effects of prenatal lead exposure on children's postnatal neurocognitive development is very limited. We examined the association of prenatal lead exposure and neurodevelopment in children at 6, 12, 24, and 36 months and the protective action of maternal dietary iron intake against the impact of lead exposure. The study participants comprise 965 pregnant women and their subsequent offspring of the total participants enrolled in the Mothers and Children's environmental health study: a prospective birth cohort study. Generalized linear model and linear mixed model analysis were performed to analyze the effect of prenatal lead exposure and mother's dietary iron intake on children's cognitive development at 6, 12, 24, and 36 months. Maternal late pregnancy lead was marginally associated with deficits in mental development index (MDI) of children at 6 months. Mothers having less than 75th percentile of dietary iron intake during pregnancy showed significant increase in the harmful effect of late pregnancy lead exposure on MDI at 6 months. Linear mixed model analyses showed the significant detrimental effect of prenatal lead exposure in late pregnancy on cognitive development up to 36 months in children of mothers having less dietary iron intake during pregnancy. Thus, our findings imply importance to reduce prenatal lead exposure and have adequate iron intake for better neurodevelopment in children.

Neurodevelopment in Early Childhood Affected by Prenatal Lead Exposure and Iron Intake

PubMed Central

Shah-Kulkarni, Surabhi; Ha, Mina; Kim, Byung-Mi; Kim, Eunjeong; Hong, Yun-Chul; Park, Hyesook; Kim, Yangho; Kim, Bung-Nyun; Chang, Namsoo; Oh, Se-Young; Kim, Young Ju; Lee, Boeun; Ha, Eun-Hee

2016-01-01

Abstract No safe threshold level of lead exposure in children has been recognized. Also, the information on shielding effect of maternal dietary iron intake during pregnancy on the adverse effects of prenatal lead exposure on children's postnatal neurocognitive development is very limited. We examined the association of prenatal lead exposure and neurodevelopment in children at 6, 12, 24, and 36 months and the protective action of maternal dietary iron intake against the impact of lead exposure. The study participants comprise 965 pregnant women and their subsequent offspring of the total participants enrolled in the Mothers and Children's environmental health study: a prospective birth cohort study. Generalized linear model and linear mixed model analysis were performed to analyze the effect of prenatal lead exposure and mother's dietary iron intake on children's cognitive development at 6, 12, 24, and 36 months. Maternal late pregnancy lead was marginally associated with deficits in mental development index (MDI) of children at 6 months. Mothers having less than 75th percentile of dietary iron intake during pregnancy showed significant increase in the harmful effect of late pregnancy lead exposure on MDI at 6 months. Linear mixed model analyses showed the significant detrimental effect of prenatal lead exposure in late pregnancy on cognitive development up to 36 months in children of mothers having less dietary iron intake during pregnancy. Thus, our findings imply importance to reduce prenatal lead exposure and have adequate iron intake for better neurodevelopment in children. PMID:26825887

Attention and working memory deficits in a perinatal nicotine exposure mouse model.

PubMed

Zhang, Lin; Spencer, Thomas J; Biederman, Joseph; Bhide, Pradeep G

2018-01-01

Cigarette smoking by pregnant women is associated with a significant increase in the risk for cognitive disorders in their children. Preclinical models confirm this risk by showing that exposure of the developing brain to nicotine produces adverse behavioral outcomes. Here we describe behavioral phenotypes resulting from perinatal nicotine exposure in a mouse model, and discuss our findings in the context of findings from previously published studies using preclinical models of developmental nicotine exposure. Female C57Bl/6 mice received drinking water containing nicotine (100μg/ml) + saccharin (2%) starting 3 weeks prior to breeding and continuing throughout pregnancy, and until 3 weeks postpartum. Over the same period, female mice in two control groups received drinking water containing saccharin (2%) or plain drinking water. Offspring from each group were weaned at 3-weeks of age and subjected to behavioral analyses at 3 months of age. We examined spontaneous locomotor activity, anxiety-like behavior, spatial working memory, object based attention, recognition memory and impulsive-like behavior. We found significant deficits in attention and working memory only in male mice, and no significant changes in the other behavioral phenotypes in male or female mice. Exposure to saccharin alone did not produce significant changes in either sex. The perinatal nicotine exposure produced significant deficits in attention and working memory in a sex-dependent manner in that the male but not female offspring displayed these behaviors. These behavioral phenotypes are associated with attention deficit hyperactivity disorder (ADHD) and have been reported in other studies that used pre- or perinatal nicotine exposure. Therefore, we suggest that preclinical models of developmental nicotine exposure could be useful tools for modeling ADHD and related disorders.

Examining the Pathologic Adaptation Model of Community Violence Exposure in Male Adolescents of Color

PubMed Central

Gaylord-Harden, Noni K.; So, Suzanna; Bai, Grace J.; Henry, David B.; Tolan, Patrick H.

2017-01-01

The current study examined a model of desensitization to community violence exposure—the pathologic adaptation model—in male adolescents of color. The current study included 285 African American (61%) and Latino (39%) male adolescents (W1 M age = 12.41) from the Chicago Youth Development Study to examine the longitudinal associations between community violence exposure, depressive symptoms, and violent behavior. Consistent with the pathologic adaptation model, results indicated a linear, positive association between community violence exposure in middle adolescence and violent behavior in late adolescence, as well as a curvilinear association between community violence exposure in middle adolescence and depressive symptoms in late adolescence, suggesting emotional desensitization. Further, these effects were specific to cognitive-affective symptoms of depression and not somatic symptoms. Emotional desensitization outcomes, as assessed by depressive symptoms, can occur in male adolescents of color exposed to community violence and these effects extend from middle adolescence to late adolescence. PMID:27653968

Lead exposure potentiates predatory attack behavior in the cat.

PubMed

Li, Wenjie; Han, Shenggao; Gregg, Thomas R; Kemp, Francis W; Davidow, Amy L; Louria, Donald B; Siegel, Allan; Bogden, John D

2003-07-01

Epidemiologic studies have demonstrated that environmental lead exposure is associated with aggressive behavior in children; however, numerous confounding variables limit the ability of these studies to establish a causal relationship. The study of aggressive behavior using a validated animal model was used to test the hypothesis that there is a causal relationship between lead exposure and aggression in the absence of confounding variables. We studied the effects of lead exposure on a feline model of aggression: predatory (quiet biting) attack of an anesthetized rat. Five cats were stimulated with a precisely controlled electrical current via electrodes inserted into the lateral hypothalamus. The response measure was the predatory attack threshold current (i.e., the current required to elicit an attack response on 50% of the trials). Blocks of trials were administered in which predatory attack threshold currents were measured three times a week for a total of 6-10 weeks, including before, during, and after lead exposure. Lead was incorporated into cat food "treats" at doses of 50-150 mg/kg/day. Two of the five cats received a second period of lead exposure. Blood lead concentrations were measured twice a week and were <1, 21-77, and <20 micro g/dL prior to, during, and after lead exposure, respectively. The predatory attack threshold decreased significantly during initial lead exposure in three of five cats and increased after the cessation of lead exposure in four of the five cats (P<0.01). The predatory attack thresholds and blood lead concentrations for each cat were inversely correlated (r=-0.35 to -0.74). A random-effects mixed model demonstrated a significant (P=0.0019) negative association between threshold current and blood lead concentration. The data of this study demonstrate that lead exposure enhances predatory aggression in the cat and provide experimental support for a causal relationship between lead exposure and aggressive behavior in humans.

Prediction of Drug-Drug Interactions with Crizotinib as the CYP3A Substrate Using a Physiologically Based Pharmacokinetic Model.

PubMed

Yamazaki, Shinji; Johnson, Theodore R; Smith, Bill J

2015-10-01

An orally available multiple tyrosine kinase inhibitor, crizotinib (Xalkori), is a CYP3A substrate, moderate time-dependent inhibitor, and weak inducer. The main objectives of the present study were to: 1) develop and refine a physiologically based pharmacokinetic (PBPK) model of crizotinib on the basis of clinical single- and multiple-dose results, 2) verify the crizotinib PBPK model from crizotinib single-dose drug-drug interaction (DDI) results with multiple-dose coadministration of ketoconazole or rifampin, and 3) apply the crizotinib PBPK model to predict crizotinib multiple-dose DDI outcomes. We also focused on gaining insights into the underlying mechanisms mediating crizotinib DDIs using a dynamic PBPK model, the Simcyp population-based simulator. First, PBPK model-predicted crizotinib exposures adequately matched clinically observed results in the single- and multiple-dose studies. Second, the model-predicted crizotinib exposures sufficiently matched clinically observed results in the crizotinib single-dose DDI studies with ketoconazole or rifampin, resulting in the reasonably predicted fold-increases in crizotinib exposures. Finally, the predicted fold-increases in crizotinib exposures in the multiple-dose DDI studies were roughly comparable to those in the single-dose DDI studies, suggesting that the effects of crizotinib CYP3A time-dependent inhibition (net inhibition) on the multiple-dose DDI outcomes would be negligible. Therefore, crizotinib dose-adjustment in the multiple-dose DDI studies could be made on the basis of currently available single-dose results. Overall, we believe that the crizotinib PBPK model developed, refined, and verified in the present study would adequately predict crizotinib oral exposures in other clinical studies, such as DDIs with weak/moderate CYP3A inhibitors/inducers and drug-disease interactions in patients with hepatic or renal impairment. Copyright © 2015 by The American Society for Pharmacology and Experimental Therapeutics.

On the use of a PM2.5 exposure simulator to explain birthweight

PubMed Central

Berrocal, Veronica J.; Gelfand, Alan E.; Holland, David M.; Burke, Janet; Miranda, Marie Lynn

2010-01-01

In relating pollution to birth outcomes, maternal exposure has usually been described using monitoring data. Such characterization provides a misrepresentation of exposure as it (i) does not take into account the spatial misalignment between an individual’s residence and monitoring sites, and (ii) it ignores the fact that individuals spend most of their time indoors and typically in more than one location. In this paper, we break with previous studies by using a stochastic simulator to describe personal exposure (to particulate matter) and then relate simulated exposures at the individual level to the health outcome (birthweight) rather than aggregating to a selected spatial unit. We propose a hierarchical model that, at the first stage, specifies a linear relationship between birthweight and personal exposure, adjusting for individual risk factors and introduces random spatial effects for the census tract of maternal residence. At the second stage, our hierarchical model specifies the distribution of each individual’s personal exposure using the empirical distribution yielded by the stochastic simulator as well as a model for the spatial random effects. We have applied our framework to analyze birthweight data from 14 counties in North Carolina in years 2001 and 2002. We investigate whether there are certain aspects and time windows of exposure that are more detrimental to birthweight by building different exposure metrics which we incorporate, one by one, in our hierarchical model. To assess the difference in relating ambient exposure to birthweight versus personal exposure to birthweight, we compare estimates of the effect of air pollution obtained from hierarchical models that linearly relate ambient exposure and birthweight versus those obtained from our modeling framework. Our analysis does not show a significant effect of PM2.5 on birthweight for reasons which we discuss. However, our modeling framework serves as a template for analyzing the relationship between personal exposure and longer term health endpoints. PMID:21691413

Survival analysis with error-prone time-varying covariates: a risk set calibration approach

PubMed Central

Liao, Xiaomei; Zucker, David M.; Li, Yi; Spiegelman, Donna

2010-01-01

Summary Occupational, environmental, and nutritional epidemiologists are often interested in estimating the prospective effect of time-varying exposure variables such as cumulative exposure or cumulative updated average exposure, in relation to chronic disease endpoints such as cancer incidence and mortality. From exposure validation studies, it is apparent that many of the variables of interest are measured with moderate to substantial error. Although the ordinary regression calibration approach is approximately valid and efficient for measurement error correction of relative risk estimates from the Cox model with time-independent point exposures when the disease is rare, it is not adaptable for use with time-varying exposures. By re-calibrating the measurement error model within each risk set, a risk set regression calibration method is proposed for this setting. An algorithm for a bias-corrected point estimate of the relative risk using an RRC approach is presented, followed by the derivation of an estimate of its variance, resulting in a sandwich estimator. Emphasis is on methods applicable to the main study/external validation study design, which arises in important applications. Simulation studies under several assumptions about the error model were carried out, which demonstrated the validity and efficiency of the method in finite samples. The method was applied to a study of diet and cancer from Harvard’s Health Professionals Follow-up Study (HPFS). PMID:20486928

Long-term exposure to residential railway and road traffic noise and risk for diabetes in a Danish cohort.

PubMed

Roswall, Nina; Raaschou-Nielsen, Ole; Jensen, Steen Solvang; Tjønneland, Anne; Sørensen, Mette

2018-01-01

Road traffic noise exposure has been found associated with diabetes incidence. Evidence for an association between railway noise exposure is less clear, as large studies with detailed railway noise modelling are lacking. To investigate the association between residential railway noise and diabetes incidence, and to repeat previous analyses on road traffic noise and diabetes with longer follow-up time. Among 50,534 middle-aged Danes enrolled into the Diet, Cancer and Health cohort from 1993 to 97, we identified 5062 cases of incident diabetes during a median follow-up of 15.5 years. Present and historical residential addresses from 1987 to 2012 were found in national registries, and railway and road traffic noise (L den ) were modelled for all addresses, using the Nordic prediction method. We used Cox proportional hazard models to investigate the association between residential traffic noise over 1 and 5 years before diagnosis, and diabetes incidence. Hazard ratios (HRs) were calculated as crude and adjusted for potential confounders. We found no association between railway noise exposure and diabetes incidence among the 9527 persons exposed, regardless of exposure time-window: HR 0.99 (0.94-1.04) per 10dB for 5-year exposure in fully adjusted models. There was no effect modification by sex, road traffic noise, and education. We confirmed the previously found association between road traffic noise exposure and diabetes including 6 additional years of follow-up: HR 1.08 (1.04-1.13) per 10dB for 5-year exposure in fully adjusted models. The study does not suggest an association between residential railway noise exposure and diabetes incidence, but supports the finding of a direct association with residential road traffic noise. Copyright © 2017 Elsevier Inc. All rights reserved.

A simulation study to quantify the impacts of exposure measurement error on air pollution health risk estimates in copollutant time-series models.

EPA Science Inventory

BackgroundExposure measurement error in copollutant epidemiologic models has the potential to introduce bias in relative risk (RR) estimates. A simulation study was conducted using empirical data to quantify the impact of correlated measurement errors in time-series analyses of a...

Community Violence Exposure and Aggression among Urban Adolescents: Testing a Cognitive Mediator Model

ERIC Educational Resources Information Center

McMahon, Susan D.; Felix, Erika D.; Halpert, Jane A.; Petropoulos, Lara A. N.

2009-01-01

Past research has shown that exposure to violence leads to aggressive behavior, but few community-based studies have examined theoretical models illustrating the mediating social cognitive processes that explain this relation with youth exposed to high rates of violence. This study examines the impact of community violence on behavior through…

Longitudinal Effects of Embryonic Exposure to Cocaine on Morphology, Cardiovascular Physiology, and Behavior in Zebrafish.

PubMed

Mersereau, Eric J; Boyle, Cody A; Poitra, Shelby; Espinoza, Ana; Seiler, Joclyn; Longie, Robert; Delvo, Lisa; Szarkowski, Megan; Maliske, Joshua; Chalmers, Sarah; Darland, Diane C; Darland, Tristan

2016-05-31

A sizeable portion of the societal drain from cocaine abuse results from the complications of in utero drug exposure. Because of challenges in using humans and mammalian model organisms as test subjects, much debate remains about the impact of in utero cocaine exposure. Zebrafish offer a number of advantages as a model in longitudinal toxicology studies and are quite sensitive physiologically and behaviorally to cocaine. In this study, we have used zebrafish to model the effects of embryonic pre-exposure to cocaine on development and on subsequent cardiovascular physiology and cocaine-induced conditioned place preference (CPP) in longitudinal adults. Larval fish showed a progressive decrease in telencephalic size with increased doses of cocaine. These treated larvae also showed a dose dependent response in heart rate that persisted 24 h after drug cessation. Embryonic cocaine exposure had little effect on overall health of longitudinal adults, but subtle changes in cardiovascular physiology were seen including decreased sensitivity to isoproterenol and increased sensitivity to cocaine. These longitudinal adult fish also showed an embryonic dose-dependent change in CPP behavior, suggesting an increased sensitivity. These studies clearly show that pre-exposure during embryonic development affects subsequent cocaine sensitivity in longitudinal adults.

Data-driven nonlinear optimisation of a simple air pollution dispersion model generating high resolution spatiotemporal exposure

NASA Astrophysics Data System (ADS)

Yuval; Bekhor, Shlomo; Broday, David M.

2013-11-01

Spatially detailed estimation of exposure to air pollutants in the urban environment is needed for many air pollution epidemiological studies. To benefit studies of acute effects of air pollution such exposure maps are required at high temporal resolution. This study introduces nonlinear optimisation framework that produces high resolution spatiotemporal exposure maps. An extensive traffic model output, serving as proxy for traffic emissions, is fitted via a nonlinear model embodying basic dispersion properties, to high temporal resolution routine observations of traffic-related air pollutant. An optimisation problem is formulated and solved at each time point to recover the unknown model parameters. These parameters are then used to produce a detailed concentration map of the pollutant for the whole area covered by the traffic model. Repeating the process for multiple time points results in the spatiotemporal concentration field. The exposure at any location and for any span of time can then be computed by temporal integration of the concentration time series at selected receptor locations for the durations of desired periods. The methodology is demonstrated for NO2 exposure using the output of a traffic model for the greater Tel Aviv area, Israel, and the half-hourly monitoring and meteorological data from the local air quality network. A leave-one-out cross-validation resulted in simulated half-hourly concentrations that are almost unbiased compared to the observations, with a mean error (ME) of 5.2 ppb, normalised mean error (NME) of 32%, 78% of the simulated values are within a factor of two (FAC2) of the observations, and the coefficient of determination (R2) is 0.6. The whole study period integrated exposure estimations are also unbiased compared with their corresponding observations, with ME of 2.5 ppb, NME of 18%, FAC2 of 100% and R2 that equals 0.62.

Hypertension and Exposure to Noise near Airports (HYENA): study design and noise exposure assessment.

PubMed

Jarup, Lars; Dudley, Marie-Louise; Babisch, Wolfgang; Houthuijs, Danny; Swart, Wim; Pershagen, Göran; Bluhm, Gösta; Katsouyanni, Klea; Velonakis, Manolis; Cadum, Ennio; Vigna-Taglianti, Federica

2005-11-01

An increasing number of people live near airports with considerable noise and air pollution. The Hypertension and Exposure to Noise near Airports (HYENA) project aims to assess the impact of airport-related noise exposure on blood pressure (BP) and cardiovascular disease using a cross-sectional study design. We selected 6,000 persons (45-70 years of age) who had lived at least 5 years near one of six major European airports. We used modeled aircraft noise contours, aiming to maximize exposure contrast. Automated BP instruments are used to reduce observer error. We designed a standardized questionnaire to collect data on annoyance, noise disturbance, and major confounders. Cortisol in saliva was collected in a subsample of the study population (n = 500) stratified by noise exposure level. To investigate short-term noise effects on BP and possible effects on nighttime BP dipping, we measured 24-hr BP and assessed continuous night noise in another subsample (n = 200). To ensure comparability between countries, we used common noise models to assess individual noise exposure, with a resolution of 1 dB(A). Modifiers of individual exposure, such as the orientation of living and bedroom toward roads, window-opening habits, and sound insulation, were assessed by the questionnaire. For four airports, we estimated exposure to air pollution to explore modifying effects of air pollution on cardiovascular disease. The project assesses exposure to traffic-related air pollutants, primarily using data from another project funded by the European Union (APMoSPHERE, Air Pollution Modelling for Support to Policy on Health and Environmental Risks in Europe).

Characterization of exposure in epidemiological studies on air pollution from biodegradable wastes: Misclassification and comparison of exposure assessment strategies.

PubMed

Cantuaria, Manuella Lech; Suh, Helen; Løfstrøm, Per; Blanes-Vidal, Victoria

2016-11-01

The assignment of exposure is one of the main challenges faced by environmental epidemiologists. However, misclassification of exposures has not been explored in population epidemiological studies on air pollution from biodegradable wastes. The objective of this study was to investigate the use of different approaches for assessing exposure to air pollution from biodegradable wastes by analyzing (1) the misclassification of exposure that is committed by using these surrogates, (2) the existence of differential misclassification (3) the effects that misclassification may have on health effect estimates and the interpretation of epidemiological results, and (4) the ability of the exposure measures to predict health outcomes using 10-fold cross validation. Four different exposure assessment approaches were studied: ammonia concentrations at the residence (Metric I), distance to the closest source (Metric II), number of sources within certain distances from the residence (Metric IIIa,b) and location in a specific region (Metric IV). Exposure-response models based on Metric I provided the highest predictive ability (72.3%) and goodness-of-fit, followed by IV, III and II. When compared to Metric I, Metric IV yielded the best results for exposure misclassification analysis and interpretation of health effect estimates, followed by Metric IIIb, IIIa and II. The study showed that modelled NH 3 concentrations provide more accurate estimations of true exposure than distances-based surrogates, and that distance-based surrogates (especially those based on distance to the closest point source) are imprecise methods to identify exposed populations, although they may be useful for initial studies. Copyright © 2016 Elsevier GmbH. All rights reserved.

Using spatio-temporal modeling to predict long-term exposure to black smoke at fine spatial and temporal scale

NASA Astrophysics Data System (ADS)

Dadvand, Payam; Rushton, Stephen; Diggle, Peter J.; Goffe, Louis; Rankin, Judith; Pless-Mulloli, Tanja

2011-01-01

Whilst exposure to air pollution is linked to a wide range of adverse health outcomes, assessing levels of this exposure has remained a challenge. This study reports a modeling approach for the estimation of weekly levels of ambient black smoke (BS) at residential postcodes across Northeast England (2055 km 2) over a 12 year period (1985-1996). A two-stage modeling strategy was developed using monitoring data on BS together with a range of covariates including data on traffic, population density, industrial activity, land cover (remote sensing), and meteorology. The first stage separates the temporal trend in BS for the region as a whole from within-region spatial variation and the second stage is a linear model which predicts BS levels at all locations in the region using spatially referenced covariate data as predictors and the regional predicted temporal trend as an offset. Traffic and land cover predictors were included in the final model, which predicted 70% of the spatio-temporal variation in BS across the study region over the study period. This modeling approach appears to provide a robust way of estimating exposure to BS at an inter-urban scale.

Development of Combining of Human Bronchial Mucosa Models with XposeALI® for Exposure of Air Pollution Nanoparticles

PubMed Central

Ji, Jie; Hedelin, Anna; Malmlöf, Maria; Kessler, Vadim; Seisenbaeva, Gulaim; Gerde, Per; Palmberg, Lena

2017-01-01

Background Exposure to agents via inhalation is of great concerns both in workplace environment and in the daily contact with particles in the ambient air. Reliable human airway exposure systems will most likely replace animal experiment in future toxicity assessment studies of inhaled agents. Methods In this study, we successfully established a combination of an exposure system (XposeALI) with 3D models mimicking both healthy and chronic bronchitis-like mucosa by co-culturing human primary bronchial epithelial cells (PBEC) and fibroblast at air-liquid interface (ALI). Light-, confocal microscopy, scanning- and transmission electron microscopy, transepithelial electrical resistance (TEER) measurement and RT-PCR were performed to identify how the PBEC differentiated under ALI culture condition. Both models were exposed to palladium (Pd) nanoparticles which sized 6–10 nm, analogous to those released from modern car catalysts, at three different concentrations utilizing the XposeALI module of the PreciseInhale® exposure system. Results Exposing the 3D models to Pd nanoparticles induced increased secretion of IL-8, yet the chronic bronchitis-like model released significantly more IL-8 than the normal model. The levels of IL-8 in basal medium (BM) and apical lavage medium (AM) were in the same ranges, but the secretion of MMP-9 was significantly higher in the AM compared to the BM. Conclusion This combination of relevant human bronchial mucosa models and sophisticated exposure system can mimic in vivo conditions and serve as a useful alternative animal testing tool when studying adverse effects in humans exposed to aerosols, air pollutants or particles in an occupational setting. PMID:28107509

Comparing exposure assessment methods for traffic-related air pollution in an adverse pregnancy outcome study.

PubMed

Wu, Jun; Wilhelm, Michelle; Chung, Judith; Ritz, Beate

2011-07-01

Previous studies reported adverse impacts of traffic-related air pollution exposure on pregnancy outcomes. Yet, little information exists on how effect estimates are impacted by the different exposure assessment methods employed in these studies. To compare effect estimates for traffic-related air pollution exposure and preeclampsia, preterm birth (gestational age less than 37 weeks), and very preterm birth (gestational age less than 30 weeks) based on four commonly used exposure assessment methods. We identified 81,186 singleton births during 1997-2006 at four hospitals in Los Angeles and Orange Counties, California. Exposures were assigned to individual subjects based on residential address at delivery using the nearest ambient monitoring station data [carbon monoxide (CO), nitrogen dioxide (NO(2)), nitric oxide (NO), nitrogen oxides (NO(x)), ozone (O(3)), and particulate matter less than 2.5 (PM(2.5)) or less than 10 (PM(10))μm in aerodynamic diameter], both unadjusted and temporally adjusted land-use regression (LUR) model estimates (NO, NO(2), and NO(x)), CALINE4 line-source air dispersion model estimates (NO(x) and PM(2.5)), and a simple traffic-density measure. We employed unconditional logistic regression to analyze preeclampsia in our birth cohort, while for gestational age-matched risk sets with preterm and very preterm birth we employed conditional logistic regression. We observed elevated risks for preeclampsia, preterm birth, and very preterm birth from maternal exposures to traffic air pollutants measured at ambient stations (CO, NO, NO(2), and NO(x)) and modeled through CALINE4 (NO(x) and PM(2.5)) and LUR (NO(2) and NO(x)). Increased risk of preterm birth and very preterm birth were also positively associated with PM(10) and PM(2.5) air pollution measured at ambient stations. For LUR-modeled NO(2) and NO(x) exposures, elevated risks for all the outcomes were observed in Los Angeles only--the region for which the LUR models were initially developed. Unadjusted LUR models often produced odds ratios somewhat larger in size than temporally adjusted models. The size of effect estimates was smaller for exposures based on simpler traffic density measures than the other exposure assessment methods. We generally confirmed that traffic-related air pollution was associated with adverse reproductive outcomes regardless of the exposure assessment method employed, yet the size of the estimated effect depended on how both temporal and spatial variations were incorporated into exposure assessment. The LUR model was not transferable even between two contiguous areas within the same large metropolitan area in Southern California. Copyright © 2011 Elsevier Inc. All rights reserved.

Infants and young children modeling method for numerical dosimetry studies: application to plane wave exposure

NASA Astrophysics Data System (ADS)

Dahdouh, S.; Varsier, N.; Nunez Ochoa, M. A.; Wiart, J.; Peyman, A.; Bloch, I.

2016-02-01

Numerical dosimetry studies require the development of accurate numerical 3D models of the human body. This paper proposes a novel method for building 3D heterogeneous young children models combining results obtained from a semi-automatic multi-organ segmentation algorithm and an anatomy deformation method. The data consist of 3D magnetic resonance images, which are first segmented to obtain a set of initial tissues. A deformation procedure guided by the segmentation results is then developed in order to obtain five young children models ranging from the age of 5 to 37 months. By constraining the deformation of an older child model toward a younger one using segmentation results, we assure the anatomical realism of the models. Using the proposed framework, five models, containing thirteen tissues, are built. Three of these models are used in a prospective dosimetry study to analyze young child exposure to radiofrequency electromagnetic fields. The results lean to show the existence of a relationship between age and whole body exposure. The results also highlight the necessity to specifically study and develop measurements of child tissues dielectric properties.

Biomolecular Profiling of Jet Fuel Toxicity Using Proteomics

DTIC Science & Technology

2006-02-28

pulmonary alveolar type II cells and macrophages, and human epidermal keratinocytes in various exposure models. Results strongly suggest an injurious effect ...of exposure on all cells studied. In both pulmonary and skin cells, the protein profiles of JP-8 effect corroborates previous histological findings...potential intervention by Substance P (SP) in the pulmonary effects of JP-8 exposure , studies incorporating SP treatment along with JP-8 exposure

Evaluating uses of data mining techniques in propensity score estimation: a simulation study.

PubMed

Setoguchi, Soko; Schneeweiss, Sebastian; Brookhart, M Alan; Glynn, Robert J; Cook, E Francis

2008-06-01

In propensity score modeling, it is a standard practice to optimize the prediction of exposure status based on the covariate information. In a simulation study, we examined in what situations analyses based on various types of exposure propensity score (EPS) models using data mining techniques such as recursive partitioning (RP) and neural networks (NN) produce unbiased and/or efficient results. We simulated data for a hypothetical cohort study (n = 2000) with a binary exposure/outcome and 10 binary/continuous covariates with seven scenarios differing by non-linear and/or non-additive associations between exposure and covariates. EPS models used logistic regression (LR) (all possible main effects), RP1 (without pruning), RP2 (with pruning), and NN. We calculated c-statistics (C), standard errors (SE), and bias of exposure-effect estimates from outcome models for the PS-matched dataset. Data mining techniques yielded higher C than LR (mean: NN, 0.86; RPI, 0.79; RP2, 0.72; and LR, 0.76). SE tended to be greater in models with higher C. Overall bias was small for each strategy, although NN estimates tended to be the least biased. C was not correlated with the magnitude of bias (correlation coefficient [COR] = -0.3, p = 0.1) but increased SE (COR = 0.7, p < 0.001). Effect estimates from EPS models by simple LR were generally robust. NN models generally provided the least numerically biased estimates. C was not associated with the magnitude of bias but was with the increased SE.

A Bayesian Model and Stochastic Exposure (Dose) Estimation for Relative Exposure Risk Comparison Involving Asbestos-Containing Dropped Ceiling Panel Installation and Maintenance Tasks.

PubMed

Boelter, Fred W; Xia, Yulin; Persky, Jacob D

2017-09-01

Assessing exposures to hazards in order to characterize risk is at the core of occupational hygiene. Our study examined dropped ceiling systems commonly used in schools and commercial buildings and lay-in ceiling panels that may have contained asbestos prior to the mid to late 1970s. However, most ceiling panels and tiles do not contain asbestos. Since asbestos risk relates to dose, we estimated the distribution of eight-hour TWA concentrations and one-year exposures (a one-year dose equivalent) to asbestos fibers (asbestos f/cc-years) for five groups of workers who may encounter dropped ceilings: specialists, generalists, maintenance workers, nonprofessional do-it-yourself (DIY) persons, and other tradespersons who are bystanders to ceiling work. Concentration data (asbestos f/cc) were obtained through two exposure assessment studies in the field and one chamber study. Bayesian and stochastic models were applied to estimate distributions of eight-hour TWAs and annual exposures (dose). The eight-hour TWAs for all work categories were below current and historic occupational exposure limits (OELs). Exposures to asbestos fibers from dropped ceiling work would be categorized as "highly controlled" for maintenance workers and "well controlled" for remaining work categories, according to the American Industrial Hygiene Association exposure control rating system. Annual exposures (dose) were found to be greatest for specialists, followed by maintenance workers, generalists, bystanders, and DIY. On a comparative basis, modeled dose and thus risk from dropped ceilings for all work categories were orders of magnitude lower than published exposures for other sources of banned friable asbestos-containing building material commonly encountered in construction trades. © 2016 The Authors Risk Analysis published by Wiley Periodicals, Inc. on behalf of Society for Risk Analysis.

Urban UV environment in a sub-tropical megacity - A measurement and modelling study

NASA Astrophysics Data System (ADS)

Wai, Ka-Ming; Yu, Peter K. N.; Chan, Pok-Man

The variations of solar total UV (UVA + UVB) exposure rates in a megacity featured with high-rise buildings during summer months were measured and relevant model predictions were evaluated. The maximum pedestrian-level total solar UV exposure rate was less than the un-obstructed exposure rate at any time, attributing to the prevailing reduction in the diffuse solar radiation due to the obstruction effects of distant buildings. Comparing with the measurements, our coupled model well captured the spatial and temporal variations of the reduction of UV exposure rates. By measurements, large reduction in the solar total UV exposure rate down to 12% of un-obstructed exposure rate due to the building obstruction effects was found, agreeing with our previous simulation results and results from an Australian megacity. On the other hand, building reflection from reflective curtain walls could reach 23% of the un-obstructed solar total UV exposure rate at the ground level. This implied improper building design creating additional harmful effects of solar UV radiation on the environment. The coupled model was also applied to predict the urban UV exposure rates during a tropical-cyclone induced aerosol episode. A well-evaluated urban solar UV model is an important tool for sustainable urban design.

Quantifying the potential impact of measurement error in an investigation of autism spectrum disorder (ASD).

PubMed

Heavner, Karyn; Newschaffer, Craig; Hertz-Picciotto, Irva; Bennett, Deborah; Burstyn, Igor

2014-05-01

The Early Autism Risk Longitudinal Investigation (EARLI), an ongoing study of a risk-enriched pregnancy cohort, examines genetic and environmental risk factors for autism spectrum disorders (ASDs). We simulated the potential effects of both measurement error (ME) in exposures and misclassification of ASD-related phenotype (assessed as Autism Observation Scale for Infants (AOSI) scores) on measures of association generated under this study design. We investigated the impact on the power to detect true associations with exposure and the false positive rate (FPR) for a non-causal correlate of exposure (X2, r=0.7) for continuous AOSI score (linear model) versus dichotomised AOSI (logistic regression) when the sample size (n), degree of ME in exposure, and strength of the expected (true) OR (eOR)) between exposure and AOSI varied. Exposure was a continuous variable in all linear models and dichotomised at one SD above the mean in logistic models. Simulations reveal complex patterns and suggest that: (1) There was attenuation of associations that increased with eOR and ME; (2) The FPR was considerable under many scenarios; and (3) The FPR has a complex dependence on the eOR, ME and model choice, but was greater for logistic models. The findings will stimulate work examining cost-effective strategies to reduce the impact of ME in realistic sample sizes and affirm the importance for EARLI of investment in biological samples that help precisely quantify a wide range of environmental exposures.

What can 35 years and over 700,000 measurements tell us about noise exposure in the mining industry?

PubMed Central

Roberts, Benjamin; Sun, Kan; Neitzel, Richard L.

2017-01-01

Objective To analyze over 700,000 cross-sectional measurements from the Mine Safety and Health Administration (MHSA) and develop statistical models to predict noise exposure for a worker. Design Descriptive statistics were used to summarize the data. Two linear regression models were used to predict noise exposure based on MSHA permissible exposure limit (PEL) and action level (AL) respectively. Two-fold cross validation was used to compare the exposure estimates from the models to actual measurements in the hold out data. The mean difference and t-statistic was calculated for each job title to determine if the model exposure predictions were significantly different from the actual data. Study Sample Measurements were acquired from MSHA through a Freedom of Information Act request. Results From 1979 to 2014 the average noise measurement has decreased. Measurements taken before the implementation of MSHA’s revised noise regulation in 2000 were on average 4.5 dBA higher than after the law came in to effect. Both models produced mean exposure predictions that were less than 1 dBA different compared to the holdout data. Conclusion Overall noise levels in mines have been decreasing. However, this decrease has not been uniform across all mining sectors. The exposure predictions from the model will be useful to help predict hearing loss in workers from the mining industry. PMID:27871188

Residential magnetic fields predicted from wiring configurations: I. Exposure model.

PubMed

Bowman, J D; Thomas, D C; Jiang, L; Jiang, F; Peters, J M

1999-10-01

A physically based model for residential magnetic fields from electric transmission and distribution wiring was developed to reanalyze the Los Angeles study of childhood leukemia by London et al. For this exposure model, magnetic field measurements were fitted to a function of wire configuration attributes that was derived from a multipole expansion of the Law of Biot and Savart. The model parameters were determined by nonlinear regression techniques, using wiring data, distances, and the geometric mean of the ELF magnetic field magnitude from 24-h bedroom measurements taken at 288 homes during the epidemiologic study. The best fit to the measurement data was obtained with separate models for the two major utilities serving Los Angeles County. This model's predictions produced a correlation of 0.40 with the measured fields, an improvement on the 0.27 correlation obtained with the Wertheimer-Leeper (WL) wire code. For the leukemia risk analysis in a companion paper, the regression model predicts exposures to the 24-h geometric mean of the ELF magnetic fields in Los Angeles homes where only wiring data and distances have been obtained. Since these input parameters for the exposure model usually do not change for many years, the predicted magnetic fields will be stable over long time periods, just like the WL code. If the geometric mean is not the exposure metric associated with cancer, this regression technique could be used to estimate long-term exposures to temporal variability metrics and other characteristics of the ELF magnetic field which may be cancer risk factors.

Assessing multimedia/multipathway exposures to inorganic arsenic at population and individual level using MERLIN-Expo.

PubMed

Van Holderbeke, Mirja; Fierens, Tine; Standaert, Arnout; Cornelis, Christa; Brochot, Céline; Ciffroy, Philippe; Johansson, Erik; Bierkens, Johan

2016-10-15

In this study, we report on model simulations performed using the newly developed exposure tool, MERLIN-Expo, in order to assess inorganic arsenic (iAs) exposure to adults resulting from past emissions by non-ferrous smelters in Belgium (Northern Campine area). Exposure scenarios were constructed to estimate external iAs exposure as well as the toxicologically relevant As (tAs, i.e., iAs, MMA and DMA) body burden in adults living in the vicinity of the former industrial sites as compared to adults living in adjacent areas and a reference area. Two scenarios are discussed: a first scenario studying exposure to iAs at the aggregated population level and a second scenario studying exposure at the individual level for a random sub-sample of subjects in each of the three different study areas. These two scenarios only differ in the type of human related input data (i.e., time-activity data, ingestion rates and consumption patterns) that were used, namely averages (incl. probability density functions, PDFs) in the simulation at population level and subject-specific values in the simulation at individual level. The model predictions are shown to be lower than the corresponding biomonitoring data from the monitoring campaign. Urinary tAs levels in adults, irrespective of the area they lived in, were under-predicted by MERLIN-Expo by 40% on average. The model predictions for individual adults, by contrast, under-predict the biomonitoring data by 7% on average, but with more important under-predictions for subjects at the upper end of exposure. Still, average predicted urinary tAs levels from the simulations at population level and at individual level overlap, and, at least for the current case, lead to similar conclusions. These results constitute a first and partial verification of the model performance of MERLIN-Expo when dealing with iAs in a complex site-specific exposure scenario, and demonstrate the robustness of the modelling tool for these situations. Copyright © 2016 Elsevier B.V. All rights reserved.

Measurement errors in the assessment of exposure to solar ultraviolet radiation and its impact on risk estimates in epidemiological studies.

PubMed

Dadvand, Payam; Basagaña, Xavier; Barrera-Gómez, Jose; Diffey, Brian; Nieuwenhuijsen, Mark

2011-07-01

To date, many studies addressing long-term effects of ultraviolet radiation (UVR) exposure on human health have relied on a range of surrogates such as the latitude of the city of residence, ambient UVR levels, or time spent outdoors to estimate personal UVR exposure. This study aimed to differentiate the contributions of personal behaviour and ambient UVR levels on facial UVR exposure and to evaluate the impact of using UVR exposure surrogates on detecting exposure-outcome associations. Data on time-activity, holiday behaviour, and ambient UVR levels were obtained for adult (aged 25-55 years old) indoor workers in six European cities: Athens (37°N), Grenoble (45°N), Milan (45°N), Prague (50°N), Oxford (52°N), and Helsinki (60°N). Annual UVR facial exposure levels were simulated for 10,000 subjects for each city, using a behavioural UVR exposure model. Within-city variations of facial UVR exposure were three times larger than the variation between cities, mainly because of time-activity patterns. In univariate models, ambient UVR levels, latitude and time spent outdoors, each accounted for less than one fourth of the variation in facial exposure levels. Use of these surrogates to assess long-term exposure to UVR resulted in requiring more than four times more participants to achieve similar statistical power to the study that applied simulated facial exposure. Our results emphasise the importance of integrating both personal behaviour and ambient UVR levels/latitude in exposure assessment methodologies.

Use-Exposure Relationships of Pesticides for Aquatic Risk Assessment

PubMed Central

Luo, Yuzhou; Spurlock, Frank; Deng, Xin; Gill, Sheryl; Goh, Kean

2011-01-01

Field-scale environmental models have been widely used in aquatic exposure assessments of pesticides. Those models usually require a large set of input parameters and separate simulations for each pesticide in evaluation. In this study, a simple use-exposure relationship is developed based on regression analysis of stochastic simulation results generated from the Pesticide Root-Zone Model (PRZM). The developed mathematical relationship estimates edge-of-field peak concentrations of pesticides from aerobic soil metabolism half-life (AERO), organic carbon-normalized soil sorption coefficient (KOC), and application rate (RATE). In a case study of California crop scenarios, the relationships explained 90–95% of the variances in the peak concentrations of dissolved pesticides as predicted by PRZM simulations for a 30-year period. KOC was identified as the governing parameter in determining the relative magnitudes of pesticide exposures in a given crop scenario. The results of model application also indicated that the effects of chemical fate processes such as partitioning and degradation on pesticide exposure were similar among crop scenarios, while the cross-scenario variations were mainly associated with the landscape characteristics, such as organic carbon contents and curve numbers. With a minimum set of input data, the use-exposure relationships proposed in this study could be used in screening procedures for potential water quality impacts from the off-site movement of pesticides. PMID:21483772

A methodology for the assessment of inhalation exposure to aluminium from antiperspirant sprays.

PubMed

Schwarz, Katharina; Pappa, Gerlinde; Miertsch, Heike; Scheel, Julia; Koch, Wolfgang

2018-04-01

Inhalative exposure can occur accidentally when using cosmetic spray products. Usually, a tiered approach is applied for exposure assessment, starting with rather conservative, simplistic calculation models that may be improved with measured data and more refined modelling. Here we report on an advanced methodology to mimic in-use conditions for antiperspirant spray products to provide a more accurate estimate of the amount of aluminium possibly inhaled and taken up systemically, thus contributing to the overall body burden. Four typical products were sprayed onto a skin surrogate in defined rooms. For aluminium, size-related aerosol release fractions, i.e. inhalable, thoracic and respirable, were determined by a mass balance method taking droplet maturation into account. These data were included into a simple two-box exposure model, allowing calculation of the inhaled aluminium dose over 12 min. Systemic exposure doses were calculated for exposure of the deep lung and the upper respiratory tract using the Multiple Path Particle Deposition Model (MPPD) model. The total systemically available dose of aluminium was in all cases found to be less than 0.5 µg per application. With this study it could be demonstrated that refinement of the input data of the two-box exposure model with measured data of released airborne aluminium is a valuable approach to analyse the contribution of antiperspirant spray inhalation to total aluminium exposure as part of the overall risk assessment. We suggest the methodology which can also be applied to other exposure modelling approaches for spray products, and further is adapted to other similar use scenarios.

Varying coefficient function models to explore interactions between maternal nutritional status and prenatal methylmercury toxicity in the Seychelles Child Development Nutrition Study

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lynch, Miranda L., E-mail: Miranda_Lynch@urmc.rochester.edu; Huang, Li-Shan; Cox, Christopher

Maternal consumption of fish during the gestational period exposes the fetus to both nutrients, especially the long-chain polyunsaturated fatty acids (LCPUFAs), believed to be beneficial for fetal brain development, as well as to the neurotoxicant methylmercury (MeHg). We recently reported that nutrients present in fish may modify MeHg neurotoxicity. Understanding the apparent interaction of MeHg exposure and nutrients present in fish is complicated by the limitations of modeling methods. In this study we fit varying coefficient function models to data from the Seychelles Child Development Nutrition Study (SCDNS) cohort to assess the association of dietary nutrients and children's development. Thismore » cohort of mother-child pairs in the Republic of Seychelles had fish consumption averaging 9 meals per week. Maternal nutritional status was assessed for five different nutritional components known to be present in fish (n-3 LCPUFA, n-6 LCPUFA, iron status, iodine status, and choline) and associated with children's neurological development. We also included prenatal MeHg exposure (measured in maternal hair). We examined two child neurodevelopmental outcomes (Bayley Scales Infant Development-II (BSID-II) Mental Developmental Index (MDI) and Psychomotor Developmental Index (PDI)), each administered at 9 and at 30 months. The varying coefficient models allow the possible interactions between each nutritional component and MeHg to be modeled as a smoothly varying function of MeHg as an effect modifier. Iron, iodine, choline, and n-6 LCPUFA had little or no observable modulation at different MeHg exposures. In contrast the n-3 LCPUFA docosahexaenoic acid (DHA) had beneficial effects on the BSID-II PDI that were reduced or absent at higher MeHg exposures. This study presents a useful modeling method that can be brought to bear on questions involving interactions between covariates, and illustrates the continuing importance of viewing fish consumption during pregnancy as a case of multiple exposures to nutrients and to MeHg. The results encourage more emphasis on a holistic view of the risks and benefits of fish consumption as it relates to infant development. - Research highlights: {yields}Varying coefficient models are tools for examining interactions in exposure settings Associations between MeHg and fish nutrients and developmental outcomes were examined. {yields} Interactions between MeHg exposure and fish-derived nutrients were modeled using VC. {yields} Models show beneficial association of DHA with outcomes were reduced as MeHg increases. {yields} VC models show other measured nutrients unmodulated by increasing MeHg exposure.« less

Stoffenmanager exposure model: company-specific exposure assessments using a Bayesian methodology.

PubMed

van de Ven, Peter; Fransman, Wouter; Schinkel, Jody; Rubingh, Carina; Warren, Nicholas; Tielemans, Erik

2010-04-01

The web-based tool "Stoffenmanager" was initially developed to assist small- and medium-sized enterprises in the Netherlands to make qualitative risk assessments and to provide advice on control at the workplace. The tool uses a mechanistic model to arrive at a "Stoffenmanager score" for exposure. In a recent study it was shown that variability in exposure measurements given a certain Stoffenmanager score is still substantial. This article discusses an extension to the tool that uses a Bayesian methodology for quantitative workplace/scenario-specific exposure assessment. This methodology allows for real exposure data observed in the company of interest to be combined with the prior estimate (based on the Stoffenmanager model). The output of the tool is a company-specific assessment of exposure levels for a scenario for which data is available. The Bayesian approach provides a transparent way of synthesizing different types of information and is especially preferred in situations where available data is sparse, as is often the case in small- and medium sized-enterprises. Real-world examples as well as simulation studies were used to assess how different parameters such as sample size, difference between prior and data, uncertainty in prior, and variance in the data affect the eventual posterior distribution of a Bayesian exposure assessment.

Depression in Intimate Partner Violence Victims in Slovenia: A Crippling Pattern of Factors Identified in Family Practice Attendees.

PubMed

Guček, Nena Kopčavar; Selič, Polona

2018-01-26

This multi-centre cross-sectional study explored associations between prevalence of depression and exposure to intimate partner violence (IPV) at any time in patients' adult life in 471 participants of a previous IPV study. In 2016, 174 interviews were performed, using the Short Form Domestic Violence Exposure Questionnaire, the Zung Scale and questions about behavioural patterns of exposure to IPV. Family doctors reviewed patients' medical charts for period from 2012 to 2016, using the Domestic Violence Exposure Medical Chart Check List, for conditions which persisted for at least three years. Depression was found to be associated with any exposure to IPV in adult life and was more likely to affect women. In multivariable logistic regression modelling, factors associated with self-rated depression were identified (p < 0.05). Exposure to emotional and physical violence was identified as a risk factor in the first model, explaining 23% of the variance. The second model explained 66% of the variance; past divorce, dysfunctional family relationships and a history of incapacity to work increased the likelihood of depression in patients. Family doctors should consider IPV exposure when detecting depression, since lifetime IPV exposure was found to be 40.4% and 36.9% of depressed revealed it.

Truncated Lévy flights and agenda-based mobility are useful for the assessment of personal human exposure.

PubMed

Schlink, Uwe; Ragas, Ad M J

2011-01-01

Receptor-oriented approaches can assess the individual-specific exposure to air pollution. In such an individual-based model we analyse the impact of human mobility to the personal exposure that is perceived by individuals simulated in an exemplified urban area. The mobility models comprise random walk (reference point mobility, RPM), truncated Lévy flights (TLF), and agenda-based walk (RPMA). We describe and review the general concepts and provide an inter-comparison of these concepts. Stationary and ergodic behaviour are explained and applied as well as performance criteria for a comparative evaluation of the investigated algorithms. We find that none of the studied algorithm results in purely random trajectories. TLF and RPMA prove to be suitable for human mobility modelling, because they provide conditions for very individual-specific trajectories and exposure. Suggesting these models we demonstrate the plausibility of their results for exposure to air-borne benzene and the combined exposure to benzene and nonane. Copyright © 2011 Elsevier Ltd. All rights reserved.

Examining the effects of emotional and cognitive desensitization to community violence exposure in male adolescents of color.

PubMed

Gaylord-Harden, Noni K; So, Suzanna; Bai, Grace J; Tolan, Patrick H

2017-01-01

The current study examined pathways in a model of desensitization, the Pathologic Adaptation Model, in adolescent males of color. Specifically, the current study examined depressive symptoms and deviant beliefs as mediators of the association between community violence exposure and subsequent violent behavior. The current study included 250 African-American (67%) and Latino (33%) male adolescents (T1 mean age = 15.32) from the Chicago Youth Development Study. Consistent with the Pathologic Adaptation Model, results demonstrated that depressive symptoms mediated the association between the quadratic violence exposure term in middle adolescence and violent behaviors in late adolescence, but the direction of the mediation effect was dependent upon the levels of violence exposure in middle adolescence. However, deviant beliefs were not found to be a significant mediator. Emotional desensitization effects may increase the likelihood of violence perpetration in adolescent males exposed to community violence, and the implications for future research and intervention efforts are discussed. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Examining the Effects of Emotional and Cognitive Desensitization to Community Violence Exposure in Male Adolescents of Color

PubMed Central

Gaylord-Harden, Noni K.; So, Suzanna; Bai, Grace J.; Tolan, Patrick H.

2016-01-01

Objective The current study examined pathways in a model of desensitization, the Pathologic Adaptation Model, in adolescent males of color. Specifically, the current study examined depressive symptoms and deviant beliefs as mediators of the association between community violence exposure and subsequent violent behavior. Method The current study included 250 African American (67%) and Latino (33%) male adolescents (T1 mean age = 15.32) from the Chicago Youth Development Study. Results Consistent with the Pathologic Adaptation Model, results demonstrated that depressive symptoms mediated the association between the quadratic violence exposure term in middle adolescence and violent behaviors in late adolescence, but the direction of the mediation effect was dependent upon the levels of violence exposure in middle adolescence. However, deviant beliefs were not found to be a significant mediator. Conclusion Emotional desensitization effects may increase the likelihood of violence perpetration in adolescent males exposed to community violence, and the implications for future research and intervention efforts are discussed. PMID:27977283

Zebrafish for the Study of the Biological Effects of Nicotine

PubMed Central

Klee, Eric W.; Schneider, Henning; Hurt, Richard D.; Ekker, Stephen C.

2011-01-01

Introduction: Zebrafish are emerging as a powerful animal model for studying the molecular and physiological effects of nicotine exposure. The zebrafish have many advantageous physical characteristics, including small size, high fecundity rates, and externally developing transparent embryos. When combined with a battery of molecular–genetic tools and behavioral assays, these attributes enable studies to be conducted that are not practical using traditional animal models. Methods: We reviewed the literature on the application of the zebrafish model as a preclinical model to study the biological effects of nicotine exposure. Results: The identified studies used zebrafish to examine the effects of nicotine exposure on early development, addiction, anxiety, and learning. The methods used included green fluorescent protein–labeled proteins to track in vivo nicotine-altered neuron development, nicotine-conditioned place preference, and locomotive sensitization linked with high-throughput molecular and genetic screens and behavioral models of learning and stress response to nicotine. Data are presented on the complete homology of all known human neural nicotinic acetylcholine receptors in zebrafish and on the biological similarity of human and zebrafish dopaminergic signaling. Conclusions: Tobacco dependence remains a major health problem worldwide. Further understanding of the molecular effects of nicotine exposure and genetic contributions to dependence may lead to improvement in patient treatment strategies. While there are limitations to the use of zebrafish as a preclinical model, it should provide a valuable tool to complement existing model systems. The reviewed studies demonstrate the enormous opportunity zebrafish have to advance the science of nicotine and tobacco research. PMID:21385906

Dissecting effects of complex mixtures: who's afraid of informative priors?

PubMed

Thomas, Duncan C; Witte, John S; Greenland, Sander

2007-03-01

Epidemiologic studies commonly investigate multiple correlated exposures, which are difficult to analyze appropriately. Hierarchical modeling provides a promising approach for analyzing such data by adding a higher-level structure or prior model for the exposure effects. This prior model can incorporate additional information on similarities among the correlated exposures and can be parametric, semiparametric, or nonparametric. We discuss the implications of applying these models and argue for their expanded use in epidemiology. While a prior model adds assumptions to the conventional (first-stage) model, all statistical methods (including conventional methods) make strong intrinsic assumptions about the processes that generated the data. One should thus balance prior modeling assumptions against assumptions of validity, and use sensitivity analyses to understand their implications. In doing so - and by directly incorporating into our analyses information from other studies or allied fields - we can improve our ability to distinguish true causes of disease from noise and bias.

BEHAVIORAL ASSESSMENTS OF LONG EVANS RATS FOLLOWING A 13-WEEK SUBCHRONIC TOLUENE EXPOSURE.

EPA Science Inventory

The current study sought to develop an animal model of the neurotoxicity of long-term exposure to volatile organic compounds (VOCs) which may be used to predict the effects of chronic exposure to VOCs on public health. The effects of Subchronic inhalation exposure to toluene (0,...

Air Pollution and Lung Function in Dutch Children: A Comparison of Exposure Estimates and Associations Based on Land Use Regression and Dispersion Exposure Modeling Approaches

PubMed Central

Gehring, Ulrike; Hoek, Gerard; Keuken, Menno; Jonkers, Sander; Beelen, Rob; Eeftens, Marloes; Postma, Dirkje S.; Brunekreef, Bert

2015-01-01

Background There is limited knowledge about the extent to which estimates of air pollution effects on health are affected by the choice for a specific exposure model. Objectives We aimed to evaluate the correlation between long-term air pollution exposure estimates using two commonly used exposure modeling techniques [dispersion and land use regression (LUR) models] and, in addition, to compare the estimates of the association between long-term exposure to air pollution and lung function in children using these exposure modeling techniques. Methods We used data of 1,058 participants of a Dutch birth cohort study with measured forced expiratory volume in 1 sec (FEV1), forced vital capacity (FVC), and peak expiratory flow (PEF) measurements at 8 years of age. For each child, annual average outdoor air pollution exposure [nitrogen dioxide (NO2), mass concentration of particulate matter with diameters ≤ 2.5 and ≤ 10 μm (PM2.5, PM10), and PM2.5 soot] was estimated for the current addresses of the participants by a dispersion and a LUR model. Associations between exposures to air pollution and lung function parameters were estimated using linear regression analysis with confounder adjustment. Results Correlations between LUR- and dispersion-modeled pollution concentrations were high for NO2, PM2.5, and PM2.5 soot (R = 0.86–0.90) but low for PM10 (R = 0.57). Associations with lung function were similar for air pollutant exposures estimated using LUR and dispersion modeling, except for associations of PM2.5 with FEV1 and FVC, which were stronger but less precise for exposures based on LUR compared with dispersion model. Conclusions Predictions from LUR and dispersion models correlated very well for PM2.5, NO2, and PM2.5 soot but not for PM10. Health effect estimates did not depend on the type of model used to estimate exposure in a population of Dutch children. Citation Wang M, Gehring U, Hoek G, Keuken M, Jonkers S, Beelen R, Eeftens M, Postma DS, Brunekreef B. 2015. Air pollution and lung function in Dutch children: a comparison of exposure estimates and associations based on land use regression and dispersion exposure modeling approaches. Environ Health Perspect 123:847–851; http://dx.doi.org/10.1289/ehp.1408541 PMID:25839747

Effects of prenatal cocaine exposure on special education in school-aged children.

PubMed

Levine, Todd P; Liu, Jing; Das, Abhik; Lester, Barry; Lagasse, Linda; Shankaran, Seetha; Bada, Henrietta S; Bauer, Charles R; Higgins, Rosemary

2008-07-01

The objective of this study was to evaluate the effects of prenatal cocaine exposure on special education at age 7 with adjustment for covariates. As part of the prospective, longitudinal, multisite study of children with prenatal cocaine exposure (Maternal Lifestyle Study), school records were reviewed for 943 children at 7 years to determine involvement in special education outcomes: (1) individualized education plan; (2) special education conditions; (3) support services; (4) special education classes; and (5) speech and language services. Logistic regression was used to examine the effect of prenatal cocaine exposure on these outcomes with environmental, maternal, and infant medical variables as covariates, as well as with and without low child IQ. Complete data for each analysis model were available for 737 to 916 children. When controlling for covariates including low child IQ, prenatal cocaine exposure had a significant effect on individualized education plan. When low child IQ was not included in the model, prenatal cocaine exposure had a significant effect on support services. Male gender, low birth weight, white race, and low child IQ also predicted individualized education plan. Low birth weight and low child IQ were significant in all models. White race was also significant in speech and language services. Other covariate effects were model specific. When included in the models, low child IQ accounted for more of the variance and changed the significance of other covariates. Prenatal cocaine exposure increased the likelihood of receiving an individualized education plan and support services, with adjustment for covariates. Low birth weight and low child IQ increased the likelihood of all outcomes. The finding that white children were more likely to get an individualized education plan and speech and language services could indicate a greater advantage in getting educational resources for this population.

COMPARISON OF THE USE OF A PHYSIOLOGICALLY-BASED PHARMACOKINETIC MODEL AND A CLASSICAL PHARMACOKINETIC MODEL FOR DIOXIN EXPOSURE ASSESSMENTS

EPA Science Inventory

In epidemiological studies, exposure assessments to TCDD, known as a possible human carcinogen, assume mono or biphasic elimination rates. Recent data suggests a dose dependent elimination rate for TCDD. A PBPK model, which uses a body burden dependent elimination rate, was dev...

Analysis of real-time mixture cytotoxicity data following repeated exposure using BK/TD models

DOE Office of Scientific and Technical Information (OSTI.GOV)

Teng, S.; Tebby, C.

Cosmetic products generally consist of multiple ingredients. Thus, cosmetic risk assessment has to deal with mixture toxicity on a long-term scale which means it has to be assessed in the context of repeated exposure. Given that animal testing has been banned for cosmetics risk assessment, in vitro assays allowing long-term repeated exposure and adapted for in vitro – in vivo extrapolation need to be developed. However, most in vitro tests only assess short-term effects and consider static endpoints which hinder extrapolation to realistic human exposure scenarios where concentration in target organs is varies over time. Thanks to impedance metrics, real-timemore » cell viability monitoring for repeated exposure has become possible. We recently constructed biokinetic/toxicodynamic models (BK/TD) to analyze such data (Teng et al., 2015) for three hepatotoxic cosmetic ingredients: coumarin, isoeugenol and benzophenone-2. In the present study, we aim to apply these models to analyze the dynamics of mixture impedance data using the concepts of concentration addition and independent action. Metabolic interactions between the mixture components were investigated, characterized and implemented in the models, as they impacted the actual cellular exposure. Indeed, cellular metabolism following mixture exposure induced a quick disappearance of the compounds from the exposure system. We showed that isoeugenol substantially decreased the metabolism of benzophenone-2, reducing the disappearance of this compound and enhancing its in vitro toxicity. Apart from this metabolic interaction, no mixtures showed any interaction, and all binary mixtures were successfully modeled by at least one model based on exposure to the individual compounds. - Highlights: • We could predict cell response over repeated exposure to mixtures of cosmetics. • Compounds acted independently on the cells. • Metabolic interactions impacted exposure concentrations to the compounds.« less

Estimating exposures in the asphalt industry for an international epidemiological cohort study of cancer risk.

PubMed

Burstyn, Igor; Boffetta, Paolo; Kauppinen, Timo; Heikkilä, Pirjo; Svane, Ole; Partanen, Timo; Stücker, Isabelle; Frentzel-Beyme, Rainer; Ahrens, Wolfgang; Merzenich, Hiltrud; Heederik, Dick; Hooiveld, Mariëtte; Langård, Sverre; Randem, Britt G; Järvholm, Bengt; Bergdahl, Ingvar; Shaham, Judith; Ribak, Joseph; Kromhout, Hans

2003-01-01

An exposure matrix (EM) for known and suspected carcinogens was required for a multicenter international cohort study of cancer risk and bitumen among asphalt workers. Production characteristics in companies enrolled in the study were ascertained through use of a company questionnaire (CQ). Exposures to coal tar, bitumen fume, organic vapor, polycyclic aromatic hydrocarbons, diesel fume, silica, and asbestos were assessed semi-quantitatively using information from CQs, expert judgment, and statistical models. Exposures of road paving workers to bitumen fume, organic vapor, and benzo(a)pyrene were estimated quantitatively by applying regression models, based on monitoring data, to exposure scenarios identified by the CQs. Exposures estimates were derived for 217 companies enrolled in the cohort, plus the Swedish asphalt paving industry in general. Most companies were engaged in road paving and asphalt mixing, but some also participated in general construction and roofing. Coal tar use was most common in Denmark and The Netherlands, but the practice is now obsolete. Quantitative estimates of exposure to bitumen fume, organic vapor, and benzo(a)pyrene for pavers, and semi-quantitative estimates of exposure to these agents among all subjects were strongly correlated. Semi-quantitative estimates of exposure to bitumen fume and coal tar exposures were only moderately correlated. EM assessed non-monotonic historical decrease in exposures to all agents assessed except silica and diesel exhaust. We produced a data-driven EM using methodology that can be adapted for other multicenter studies. Copyright 2003 Wiley-Liss, Inc.

Effect of Cumulating Exposure to Abacavir on the Risk of Cardiovascular Disease Events in Patients From the Swiss HIV Cohort Study.

PubMed

Young, Jim; Xiao, Yongling; Moodie, Erica E M; Abrahamowicz, Michal; Klein, Marina B; Bernasconi, Enos; Schmid, Patrick; Calmy, Alexandra; Cavassini, Matthias; Cusini, Alexia; Weber, Rainer; Bucher, Heiner C

2015-08-01

Patients with HIV exposed to the antiretroviral drug abacavir may have an increased risk of cardiovascular disease (CVD). There is concern that this association arises because of a channeling bias. Even if exposure is a risk, it is not clear how that risk changes as exposure cumulates. We assess the effect of exposure to abacavir on the risk of CVD events in the Swiss HIV Cohort Study. We use a new marginal structural Cox model to estimate the effect of abacavir as a flexible function of past exposures while accounting for risk factors that potentially lie on a causal pathway between exposure to abacavir and CVD. A total of 11,856 patients were followed for a median of 6.6 years; 365 patients had a CVD event (4.6 events per 1000 patient-years). In a conventional Cox model, recent--but not cumulative--exposure to abacavir increased the risk of a CVD event. In the new marginal structural Cox model, continued exposure to abacavir during the past 4 years increased the risk of a CVD event (hazard ratio = 2.06; 95% confidence interval: 1.43 to 2.98). The estimated function for the effect of past exposures suggests that exposure during the past 6-36 months caused the greatest increase in risk. Abacavir increases the risk of a CVD event: the effect of exposure is not immediate, rather the risk increases as exposure cumulates over the past few years. This gradual increase in risk is not consistent with a rapidly acting mechanism, such as acute inflammation.

BK/TD models for analyzing in vitro impedance data on cytotoxicity.

PubMed

Teng, S; Barcellini-Couget, S; Beaudouin, R; Brochot, C; Desousa, G; Rahmani, R; Pery, A R R

2015-06-01

The ban of animal testing has enhanced the development of new in vitro technologies for cosmetics safety assessment. Impedance metrics is one such technology which enables monitoring of cell viability in real time. However, analyzing real time data requires moving from static to dynamic toxicity assessment. In the present study, we built mechanistic biokinetic/toxicodynamic (BK/TD) models to analyze the time course of cell viability in cytotoxicity assay using impedance. These models account for the fate of the tested compounds during the assay. BK/TD models were applied to analyze HepaRG cell viability, after single (48 h) and repeated (4 weeks) exposures to three hepatotoxic compounds (coumarin, isoeugenol and benzophenone-2). The BK/TD models properly fit the data used for their calibration that was obtained for single or repeated exposure. Only for one out of the three compounds, the models calibrated with a single exposure were able to predict repeated exposure data. We therefore recommend the use of long-term exposure in vitro data in order to adequately account for chronic hepatotoxic effects. The models we propose here are capable of being coupled with human biokinetic models in order to relate dose exposure and human hepatotoxicity. Copyright © 2015 The Authors. Published by Elsevier Ireland Ltd.. All rights reserved.

Subchronic Arsenic Exposure Induces Anxiety-Like Behaviors in Normal Mice and Enhances Depression-Like Behaviors in the Chemically Induced Mouse Model of Depression

PubMed Central

Chang, Chia-Yu; Guo, How-Ran; Tsai, Wan-Chen; Yang, Kai-Lin; Lin, Li-Chuan

2015-01-01

Accumulating evidence implicates that subchronic arsenic exposure causes cerebral neurodegeneration leading to behavioral disturbances relevant to psychiatric disorders. However, there is still little information regarding the influence of subchronic exposure to arsenic-contaminated drinking water on mood disorders and its underlying mechanisms in the cerebral prefrontal cortex. The aim of this study is to assess the effects of subchronic arsenic exposure (10 mg/LAs2O3 in drinking water) on the anxiety- and depression-like behaviors in normal mice and in the chemically induced mouse model of depression by reserpine pretreatment. Our findings demonstrated that 4 weeks of arsenic exposure enhance anxiety-like behaviors on elevated plus maze (EPM) and open field test (OFT) in normal mice, and 8 weeks of arsenic exposure augment depression-like behaviors on tail suspension test (TST) and forced swimming test (FST) in the reserpine pretreated mice. In summary, in this present study, we demonstrated that subchronic arsenic exposure induces only the anxiety-like behaviors in normal mice and enhances the depression-like behaviors in the reserpine induced mouse model of depression, in which the cerebral prefrontal cortex BDNF-TrkB signaling pathway is involved. We also found that eight weeks of subchronic arsenic exposure are needed to enhance the depression-like behaviors in the mouse model of depression. These findings imply that arsenic could be an enhancer of depressive symptoms for those patients who already had the attribute of depression. PMID:26114099

Subchronic Arsenic Exposure Induces Anxiety-Like Behaviors in Normal Mice and Enhances Depression-Like Behaviors in the Chemically Induced Mouse Model of Depression.

PubMed

Chang, Chia-Yu; Guo, How-Ran; Tsai, Wan-Chen; Yang, Kai-Lin; Lin, Li-Chuan; Cheng, Tain-Junn; Chuu, Jiunn-Jye

2015-01-01

Accumulating evidence implicates that subchronic arsenic exposure causes cerebral neurodegeneration leading to behavioral disturbances relevant to psychiatric disorders. However, there is still little information regarding the influence of subchronic exposure to arsenic-contaminated drinking water on mood disorders and its underlying mechanisms in the cerebral prefrontal cortex. The aim of this study is to assess the effects of subchronic arsenic exposure (10 mg/LAs2O3 in drinking water) on the anxiety- and depression-like behaviors in normal mice and in the chemically induced mouse model of depression by reserpine pretreatment. Our findings demonstrated that 4 weeks of arsenic exposure enhance anxiety-like behaviors on elevated plus maze (EPM) and open field test (OFT) in normal mice, and 8 weeks of arsenic exposure augment depression-like behaviors on tail suspension test (TST) and forced swimming test (FST) in the reserpine pretreated mice. In summary, in this present study, we demonstrated that subchronic arsenic exposure induces only the anxiety-like behaviors in normal mice and enhances the depression-like behaviors in the reserpine induced mouse model of depression, in which the cerebral prefrontal cortex BDNF-TrkB signaling pathway is involved. We also found that eight weeks of subchronic arsenic exposure are needed to enhance the depression-like behaviors in the mouse model of depression. These findings imply that arsenic could be an enhancer of depressive symptoms for those patients who already had the attribute of depression.

A MODEL TO EVALUATE PAST EXPOSURE TO 2,3,7,8 ...

EPA Pesticide Factsheets

Data from several studies suggest that concentrations of dioxins rose in the environment from the 1930s to about the 1960s/70s and have been declining over the last decade or two. The most direct evidence of this trend comes from lake core sediments, which can be used to estimate past atmospheric depositions of dioxins. The primary source of human exposure to dioxins is through the food supply. The pathway relating atmospheric depositions to concentrations in food is quite complex, and accordingly, it is not known to what extent the trend in human exposure mirrors the trend in atmospheric depositions. This paper describes an attempt to statistically reconstruct the pattern of past human exposure to the most toxic dioxin congener, 2,3,7,8-TCDD (abbreviated TCDD), through use of a simple pharmacokinetic (PK) model which included a time-varying TCDD exposure dose. This PK model was fit to TCDD body burden data (i.e., TCDD concentrations in lipid) from five U.S. studies dating from 1972 to 1987 and covering a wide age range. A Bayesian statistical approach was used to fit TCDD exposure; model parameters other than exposure were all previously known or estimated from other data sources. The primary results of the analysis are as follows: 1.) use of a time-varying exposure dose provided a far better fit to the TCDD body burden data than did using a dose that was constant over time; this is strong evidence that exposure to TCDD has, in fact, varied during the

A latent process model for forecasting multiple time series in environmental public health surveillance.

PubMed

Morrison, Kathryn T; Shaddick, Gavin; Henderson, Sarah B; Buckeridge, David L

2016-08-15

This paper outlines a latent process model for forecasting multiple health outcomes arising from a common environmental exposure. Traditionally, surveillance models in environmental health do not link health outcome measures, such as morbidity or mortality counts, to measures of exposure, such as air pollution. Moreover, different measures of health outcomes are treated as independent, while it is known that they are correlated with one another over time as they arise in part from a common underlying exposure. We propose modelling an environmental exposure as a latent process, and we describe the implementation of such a model within a hierarchical Bayesian framework and its efficient computation using integrated nested Laplace approximations. Through a simulation study, we compare distinct univariate models for each health outcome with a bivariate approach. The bivariate model outperforms the univariate models in bias and coverage of parameter estimation, in forecast accuracy and in computational efficiency. The methods are illustrated with a case study using healthcare utilization and air pollution data from British Columbia, Canada, 2003-2011, where seasonal wildfires produce high levels of air pollution, significantly impacting population health. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Modelling survival: exposure pattern, species sensitivity and uncertainty.

PubMed

Ashauer, Roman; Albert, Carlo; Augustine, Starrlight; Cedergreen, Nina; Charles, Sandrine; Ducrot, Virginie; Focks, Andreas; Gabsi, Faten; Gergs, André; Goussen, Benoit; Jager, Tjalling; Kramer, Nynke I; Nyman, Anna-Maija; Poulsen, Veronique; Reichenberger, Stefan; Schäfer, Ralf B; Van den Brink, Paul J; Veltman, Karin; Vogel, Sören; Zimmer, Elke I; Preuss, Thomas G

2016-07-06

The General Unified Threshold model for Survival (GUTS) integrates previously published toxicokinetic-toxicodynamic models and estimates survival with explicitly defined assumptions. Importantly, GUTS accounts for time-variable exposure to the stressor. We performed three studies to test the ability of GUTS to predict survival of aquatic organisms across different pesticide exposure patterns, time scales and species. Firstly, using synthetic data, we identified experimental data requirements which allow for the estimation of all parameters of the GUTS proper model. Secondly, we assessed how well GUTS, calibrated with short-term survival data of Gammarus pulex exposed to four pesticides, can forecast effects of longer-term pulsed exposures. Thirdly, we tested the ability of GUTS to estimate 14-day median effect concentrations of malathion for a range of species and use these estimates to build species sensitivity distributions for different exposure patterns. We find that GUTS adequately predicts survival across exposure patterns that vary over time. When toxicity is assessed for time-variable concentrations species may differ in their responses depending on the exposure profile. This can result in different species sensitivity rankings and safe levels. The interplay of exposure pattern and species sensitivity deserves systematic investigation in order to better understand how organisms respond to stress, including humans.

Modelling survival: exposure pattern, species sensitivity and uncertainty

NASA Astrophysics Data System (ADS)

Ashauer, Roman; Albert, Carlo; Augustine, Starrlight; Cedergreen, Nina; Charles, Sandrine; Ducrot, Virginie; Focks, Andreas; Gabsi, Faten; Gergs, André; Goussen, Benoit; Jager, Tjalling; Kramer, Nynke I.; Nyman, Anna-Maija; Poulsen, Veronique; Reichenberger, Stefan; Schäfer, Ralf B.; van den Brink, Paul J.; Veltman, Karin; Vogel, Sören; Zimmer, Elke I.; Preuss, Thomas G.

2016-07-01

The General Unified Threshold model for Survival (GUTS) integrates previously published toxicokinetic-toxicodynamic models and estimates survival with explicitly defined assumptions. Importantly, GUTS accounts for time-variable exposure to the stressor. We performed three studies to test the ability of GUTS to predict survival of aquatic organisms across different pesticide exposure patterns, time scales and species. Firstly, using synthetic data, we identified experimental data requirements which allow for the estimation of all parameters of the GUTS proper model. Secondly, we assessed how well GUTS, calibrated with short-term survival data of Gammarus pulex exposed to four pesticides, can forecast effects of longer-term pulsed exposures. Thirdly, we tested the ability of GUTS to estimate 14-day median effect concentrations of malathion for a range of species and use these estimates to build species sensitivity distributions for different exposure patterns. We find that GUTS adequately predicts survival across exposure patterns that vary over time. When toxicity is assessed for time-variable concentrations species may differ in their responses depending on the exposure profile. This can result in different species sensitivity rankings and safe levels. The interplay of exposure pattern and species sensitivity deserves systematic investigation in order to better understand how organisms respond to stress, including humans.

Physiologically based pharmacokinetic toolkit to evaluate environmental exposures: Applications of the dioxin model to study real life exposures.

PubMed

Emond, Claude; Ruiz, Patricia; Mumtaz, Moiz

2017-01-15

Chlorinated dibenzo-p-dioxins (CDDs) are a series of mono- to octa-chlorinated homologous chemicals commonly referred to as polychlorinated dioxins. One of the most potent, well-known, and persistent member of this family is 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD). As part of translational research to make computerized models accessible to health risk assessors, we present a Berkeley Madonna recoded version of the human physiologically based pharmacokinetic (PBPK) model used by the U.S. Environmental Protection Agency (EPA) in the recent dioxin assessment. This model incorporates CYP1A2 induction, which is an important metabolic vector that drives dioxin distribution in the human body, and it uses a variable elimination half-life that is body burden dependent. To evaluate the model accuracy, the recoded model predictions were compared with those of the original published model. The simulations performed with the recoded model matched well with those of the original model. The recoded model was then applied to available data sets of real life exposure studies. The recoded model can describe acute and chronic exposures and can be useful for interpreting human biomonitoring data as part of an overall dioxin and/or dioxin-like compounds risk assessment. Copyright © 2016. Published by Elsevier Inc.

PM ACTIVITY PATTERN RESEARCH

EPA Science Inventory

Human activity/uptake rate data are necessary to estimate potential human exposure and intake dose to environmental pollutants and to refine human exposure models. Personal exposure monitoring studies have demonstrated the critical role that activities play in explaining and pre...

Youth violence in South Africa: exposure, attitudes, and resilience in Zulu adolescents.

PubMed

Choe, Daniel Ewon; Zimmerman, Marc A; Devnarain, Bashi

2012-01-01

Exposure to violence is common in South Africa. Yet, few studies examine how violence exposure contributes to South African adolescents' participation in youth violence. The aims of this study were to examine effects of different violence exposures on violent attitudes and behavior, to test whether attitudes mediated effects of violence exposures on violent behavior, and to test whether adult involvement had protective or promotive effects. Questionnaires were administered to 424 Zulu adolescents in township high schools around Durban, South Africa. Structural equation modeling (SEM) was used to test associations among violence exposures and both violent attitudes and behavior. Victimization, witnessing violence, and friends' violent behavior contributed directly to violent behavior. Only family conflict and friends' violence influenced violent attitudes. Attitudes mediated effects of friends' violence on violent behavior. Multiple-group SEM indicated that adult involvement fit a protective model of resilience. These findings are discussed regarding their implications for prevention.

Physiologically based pharmacokinetic toolkit to evaluate environmental exposures: Applications of the dioxin model to study real life exposures

DOE Office of Scientific and Technical Information (OSTI.GOV)

Emond, Claude, E-mail: claude.emond@biosmc.com

Chlorinated dibenzo-p-dioxins (CDDs) are a series of mono- to octa-chlorinated homologous chemicals commonly referred to as polychlorinated dioxins. One of the most potent, well-known, and persistent member of this family is 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD). As part of translational research to make computerized models accessible to health risk assessors, we present a Berkeley Madonna recoded version of the human physiologically based pharmacokinetic (PBPK) model used by the U.S. Environmental Protection Agency (EPA) in the recent dioxin assessment. This model incorporates CYP1A2 induction, which is an important metabolic vector that drives dioxin distribution in the human body, and it uses a variable eliminationmore » half-life that is body burden dependent. To evaluate the model accuracy, the recoded model predictions were compared with those of the original published model. The simulations performed with the recoded model matched well with those of the original model. The recoded model was then applied to available data sets of real life exposure studies. The recoded model can describe acute and chronic exposures and can be useful for interpreting human biomonitoring data as part of an overall dioxin and/or dioxin-like compounds risk assessment. - Highlights: • The best available dioxin PBPK model for interpreting human biomonitoring data is presented. • The original PBPK model was recoded from acslX to the Berkeley Madonna (BM) platform. • Comparisons were made of the accuracy of the recoded model with the original model. • The model is a useful addition to the ATSDR's BM based PBPK toolkit that supports risk assessors. • The application of the model to real-life exposure data sets is illustrated.« less

Land Use Regression Modeling of Outdoor Noise Exposure in Informal Settlements in Western Cape, South Africa

PubMed Central

Sieber, Chloé; Ragettli, Martina S.; Toyib, Olaniyan; Baatjies, Roslyn; Saucy, Apolline; Probst-Hensch, Nicole; Dalvie, Mohamed Aqiel; Röösli, Martin

2017-01-01

In low- and middle-income countries, noise exposure and its negative health effects have been little explored. The present study aimed to assess the noise exposure situation in adults living in informal settings in the Western Cape Province, South Africa. We conducted continuous one-week outdoor noise measurements at 134 homes in four different areas. These data were used to develop a land use regression (LUR) model to predict A-weighted day-evening-night equivalent sound levels (Lden) from geographic information system (GIS) variables. Mean noise exposure during day (6:00–18:00) was 60.0 A-weighted decibels (dB(A)) (interquartile range 56.9–62.9 dB(A)), during night (22:00–6:00) 52.9 dB(A) (49.3–55.8 dB(A)) and average Lden was 63.0 dB(A) (60.1–66.5 dB(A)). Main predictors of the LUR model were related to road traffic and household density. Model performance was low (adjusted R2 = 0.130) suggesting that other influences than those represented in the geographic predictors are relevant for noise exposure. This is one of the few studies on the noise exposure situation in low- and middle-income countries. It demonstrates that noise exposure levels are high in these settings. PMID:29053590

Mechanistic modeling of pesticide exposure: The missing keystone of honey bee toxicology.

PubMed

Sponsler, Douglas B; Johnson, Reed M

2017-04-01

The role of pesticides in recent honey bee losses is controversial, partly because field studies often fail to detect effects predicted by laboratory studies. This dissonance highlights a critical gap in the field of honey bee toxicology: there exists little mechanistic understanding of the patterns and processes of exposure that link honey bees to pesticides in their environment. The authors submit that 2 key processes underlie honey bee pesticide exposure: 1) the acquisition of pesticide by foraging bees, and 2) the in-hive distribution of pesticide returned by foragers. The acquisition of pesticide by foraging bees must be understood as the spatiotemporal intersection between environmental contamination and honey bee foraging activity. This implies that exposure is distributional, not discrete, and that a subset of foragers may acquire harmful doses of pesticide while the mean colony exposure would appear safe. The in-hive distribution of pesticide is a complex process driven principally by food transfer interactions between colony members, and this process differs importantly between pollen and nectar. High priority should be placed on applying the extensive literature on honey bee biology to the development of more rigorously mechanistic models of honey bee pesticide exposure. In combination with mechanistic effects modeling, mechanistic exposure modeling has the potential to integrate the field of honey bee toxicology, advancing both risk assessment and basic research. Environ Toxicol Chem 2017;36:871-881. © 2016 SETAC. © 2016 SETAC.

Characterizing Air Pollution Exposure Misclassification Errors Using Detailed Cell Phone Location Data

NASA Astrophysics Data System (ADS)

Yu, H.; Russell, A. G.; Mulholland, J. A.

2017-12-01

In air pollution epidemiologic studies with spatially resolved air pollution data, exposures are often estimated using the home locations of individual subjects. Due primarily to lack of data or logistic difficulties, the spatiotemporal mobility of subjects are mostly neglected, which are expected to result in exposure misclassification errors. In this study, we applied detailed cell phone location data to characterize potential exposure misclassification errors associated with home-based exposure estimation of air pollution. The cell phone data sample consists of 9,886 unique simcard IDs collected on one mid-week day in October, 2013 from Shenzhen, China. The Community Multi-scale Air Quality model was used to simulate hourly ambient concentrations of six chosen pollutants at 3 km spatial resolution, which were then fused with observational data to correct for potential modeling biases and errors. Air pollution exposure for each simcard ID was estimated by matching hourly pollutant concentrations with detailed location data for corresponding IDs. Finally, the results were compared with exposure estimates obtained using the home location method to assess potential exposure misclassification errors. Our results show that the home-based method is likely to have substantial exposure misclassification errors, over-estimating exposures for subjects with higher exposure levels and under-estimating exposures for those with lower exposure levels. This has the potential to lead to a bias-to-the-null in the health effect estimates. Our findings suggest that the use of cell phone data has the potential for improving the characterization of exposure and exposure misclassification in air pollution epidemiology studies.

Comparison of exposure estimation methods for air pollutants: ambient monitoring data and regional air quality simulation.

PubMed

Bravo, Mercedes A; Fuentes, Montserrat; Zhang, Yang; Burr, Michael J; Bell, Michelle L

2012-07-01

Air quality modeling could potentially improve exposure estimates for use in epidemiological studies. We investigated this application of air quality modeling by estimating location-specific (point) and spatially-aggregated (county level) exposure concentrations of particulate matter with an aerodynamic diameter less than or equal to 2.5 μm (PM(2.5)) and ozone (O(3)) for the eastern U.S. in 2002 using the Community Multi-scale Air Quality (CMAQ) modeling system and a traditional approach using ambient monitors. The monitoring approach produced estimates for 370 and 454 counties for PM(2.5) and O(3), respectively. Modeled estimates included 1861 counties, covering 50% more population. The population uncovered by monitors differed from those near monitors (e.g., urbanicity, race, education, age, unemployment, income, modeled pollutant levels). CMAQ overestimated O(3) (annual normalized mean bias=4.30%), while modeled PM(2.5) had an annual normalized mean bias of -2.09%, although bias varied seasonally, from 32% in November to -27% in July. Epidemiology may benefit from air quality modeling, with improved spatial and temporal resolution and the ability to study populations far from monitors that may differ from those near monitors. However, model performance varied by measure of performance, season, and location. Thus, the appropriateness of using such modeled exposures in health studies depends on the pollutant and metric of concern, acceptable level of uncertainty, population of interest, study design, and other factors. Copyright © 2012 Elsevier Inc. All rights reserved.

Spatial variability of the effect of air pollution on term birth weight: evaluating influential factors using Bayesian hierarchical models.

PubMed

Li, Lianfa; Laurent, Olivier; Wu, Jun

2016-02-05

Epidemiological studies suggest that air pollution is adversely associated with pregnancy outcomes. Such associations may be modified by spatially-varying factors including socio-demographic characteristics, land-use patterns and unaccounted exposures. Yet, few studies have systematically investigated the impact of these factors on spatial variability of the air pollution's effects. This study aimed to examine spatial variability of the effects of air pollution on term birth weight across Census tracts and the influence of tract-level factors on such variability. We obtained over 900,000 birth records from 2001 to 2008 in Los Angeles County, California, USA. Air pollution exposure was modeled at individual level for nitrogen dioxide (NO2) and nitrogen oxides (NOx) using spatiotemporal models. Two-stage Bayesian hierarchical non-linear models were developed to (1) quantify the associations between air pollution exposure and term birth weight within each tract; and (2) examine the socio-demographic, land-use, and exposure-related factors contributing to the between-tract variability of the associations between air pollution and term birth weight. Higher air pollution exposure was associated with lower term birth weight (average posterior effects: -14.7 (95 % CI: -19.8, -9.7) g per 10 ppb increment in NO2 and -6.9 (95 % CI: -12.9, -0.9) g per 10 ppb increment in NOx). The variation of the association across Census tracts was significantly influenced by the tract-level socio-demographic, exposure-related and land-use factors. Our models captured the complex non-linear relationship between these factors and the associations between air pollution and term birth weight: we observed the thresholds from which the influence of the tract-level factors was markedly exacerbated or attenuated. Exacerbating factors might reflect additional exposure to environmental insults or lower socio-economic status with higher vulnerability, whereas attenuating factors might indicate reduced exposure or higher socioeconomic status with lower vulnerability. Our Bayesian models effectively combined a priori knowledge with training data to infer the posterior association of air pollution with term birth weight and to evaluate the influence of the tract-level factors on spatial variability of such association. This study contributes new findings about non-linear influences of socio-demographic factors, land-use patterns, and unaccounted exposures on spatial variability of the effects of air pollution.

Modeling personal particle-bound polycyclic aromatic hydrocarbon (pb-pah) exposure in human subjects in Southern California.

PubMed

Wu, Jun; Tjoa, Thomas; Li, Lianfa; Jaimes, Guillermo; Delfino, Ralph J

2012-07-11

Exposure to polycyclic aromatic hydrocarbon (PAH) has been linked to various adverse health outcomes. Personal PAH exposures are usually measured by personal monitoring or biomarkers, which are costly and impractical for a large population. Modeling is a cost-effective alternative to characterize personal PAH exposure although challenges exist because the PAH exposure can be highly variable between locations and individuals in non-occupational settings. In this study we developed models to estimate personal inhalation exposures to particle-bound PAH (PB-PAH) using data from global positioning system (GPS) time-activity tracking data, traffic activity, and questionnaire information. We conducted real-time (1-min interval) personal PB-PAH exposure sampling coupled with GPS tracking in 28 non-smoking women for one to three sessions and one to nine days each session from August 2009 to November 2010 in Los Angeles and Orange Counties, California. Each subject filled out a baseline questionnaire and environmental and behavior questionnaires on their typical activities in the previous three months. A validated model was used to classify major time-activity patterns (indoor, in-vehicle, and other) based on the raw GPS data. Multiple-linear regression and mixed effect models were developed to estimate averaged daily and subject-level PB-PAH exposures. The covariates we examined included day of week and time of day, GPS-based time-activity and GPS speed, traffic- and roadway-related parameters, meteorological variables (i.e. temperature, wind speed, relative humidity), and socio-demographic variables and occupational exposures from the questionnaire. We measured personal PB-PAH exposures for 180 days with more than 6 h of valid data on each day. The adjusted R2 of the model was 0.58 for personal daily exposures, 0.61 for subject-level personal exposures, and 0.75 for subject-level micro-environmental exposures. The amount of time in vehicle (averaging 4.5% of total sampling time) explained 48% of the variance in daily personal PB-PAH exposure and 39% of the variance in subject-level exposure. The other major predictors of PB-PAH exposures included length-weighted traffic count, work-related exposures, and percent of weekday time. We successfully developed regression models to estimate PB-PAH exposures based on GPS-tracking data, traffic data, and simple questionnaire information. Time in vehicle was the most important determinant of personal PB-PAH exposure in this population. We demonstrated the importance of coupling real-time exposure measures with GPS time-activity tracking in personal air pollution exposure assessment.

A modelling exercise to examine variations of NOx concentrations on adjacent footpaths in a street canyon: The importance of accounting for wind conditions and fleet composition.

PubMed

Gallagher, J

2016-04-15

Personal measurement studies and modelling investigations are used to examine pollutant exposure for pedestrians in the urban environment: each presenting various strengths and weaknesses in relation to labour and equipment costs, a sufficient sampling period and the accuracy of results. This modelling exercise considers the potential benefits of modelling results over personal measurement studies and aims to demonstrate how variations in fleet composition affects exposure results (presented as mean concentrations along the centre of both footpaths) in different traffic scenarios. A model of Pearse Street in Dublin, Ireland was developed by combining a computational fluid dynamic (CFD) model and a semi-empirical equation to simulate pollutant dispersion in the street. Using local NOx concentrations, traffic and meteorological data from a two-week period in 2011, the model were validated and a good fit was presented. To explore the long-term variations in personal exposure due to variations in fleet composition, synthesised traffic data was used to compare short-term personal exposure data (over a two-week period) with the results for an extended one-year period. Personal exposure during the two-week period underestimated the one-year results by between 8% and 65% on adjacent footpaths. The findings demonstrate the potential for relative differences in pedestrian exposure to exist between the north and south footpaths due to changing wind conditions in both peak and off-peak traffic scenarios. This modelling approach may help overcome potential under- or over-estimations of concentrations in personal measurement studies on the footpaths. Further research aims to measure pollutant concentrations on adjacent footpaths in different traffic and wind conditions and to develop a simpler modelling system to identify pollutant hotspots on our city footpaths so that urban planners can implement improvement strategies to improve urban air quality. Copyright © 2016 Elsevier B.V. All rights reserved.

Mouse models to unravel the role of inhaled pollutants on allergic sensitization and airway inflammation

PubMed Central

2010-01-01

Air pollutant exposure has been linked to a rise in wheezing illnesses. Clinical data highlight that exposure to mainstream tobacco smoke (MS) and environmental tobacco smoke (ETS) as well as exposure to diesel exhaust particles (DEP) could promote allergic sensitization or aggravate symptoms of asthma, suggesting a role for these inhaled pollutants in the pathogenesis of asthma. Mouse models are a valuable tool to study the potential effects of these pollutants in the pathogenesis of asthma, with the opportunity to investigate their impact during processes leading to sensitization, acute inflammation and chronic disease. Mice allow us to perform mechanistic studies and to evaluate the importance of specific cell types in asthma pathogenesis. In this review, the major clinical effects of tobacco smoke and diesel exhaust exposure regarding to asthma development and progression are described. Clinical data are compared with findings from murine models of asthma and inhalable pollutant exposure. Moreover, the potential mechanisms by which both pollutants could aggravate asthma are discussed. PMID:20092634

Using full-cohort data in nested case-control and case-cohort studies by multiple imputation.

PubMed

Keogh, Ruth H; White, Ian R

2013-10-15

In many large prospective cohorts, expensive exposure measurements cannot be obtained for all individuals. Exposure-disease association studies are therefore often based on nested case-control or case-cohort studies in which complete information is obtained only for sampled individuals. However, in the full cohort, there may be a large amount of information on cheaply available covariates and possibly a surrogate of the main exposure(s), which typically goes unused. We view the nested case-control or case-cohort study plus the remainder of the cohort as a full-cohort study with missing data. Hence, we propose using multiple imputation (MI) to utilise information in the full cohort when data from the sub-studies are analysed. We use the fully observed data to fit the imputation models. We consider using approximate imputation models and also using rejection sampling to draw imputed values from the true distribution of the missing values given the observed data. Simulation studies show that using MI to utilise full-cohort information in the analysis of nested case-control and case-cohort studies can result in important gains in efficiency, particularly when a surrogate of the main exposure is available in the full cohort. In simulations, this method outperforms counter-matching in nested case-control studies and a weighted analysis for case-cohort studies, both of which use some full-cohort information. Approximate imputation models perform well except when there are interactions or non-linear terms in the outcome model, where imputation using rejection sampling works well. Copyright © 2013 John Wiley & Sons, Ltd.

A physiologically based pharmacokinetic model for ionic silver and silver nanoparticles

PubMed Central

Bachler, Gerald; von Goetz, Natalie; Hungerbühler, Konrad

2013-01-01

Silver is a strong antibiotic that is increasingly incorporated into consumer products as a bulk, salt, or nanosilver, thus potentially causing side-effects related to human exposure. However, the fate and behavior of (nano)silver in the human body is presently not well understood. In order to aggregate the existing experimental information, a physiologically based pharmacokinetic model (PBPK) was developed in this study for ionic silver and nanosilver. The structure of the model was established on the basis of toxicokinetic data from intravenous studies. The number of calibrated parameters was minimized in order to enhance the predictive capability of the model. We validated the model structure for both silver forms by reproducing exposure conditions (dermal, oral, and inhalation) of in vivo experiments and comparing simulated and experimentally assessed organ concentrations. Therefore, the percutaneous, intestinal, or pulmonary absorption fraction was estimated based on the blood silver concentration of the respective experimental data set. In all of the cases examined, the model could successfully predict the biodistribution of ionic silver and 15–150 nm silver nanoparticles, which were not coated with substances designed to prolong the circulatory time (eg, polyethylene glycol). Furthermore, the results of our model indicate that: (1) within the application domain of our model, the particle size and coating had a minor influence on the biodistribution; (2) in vivo, it is more likely that silver nanoparticles are directly stored as insoluble salt particles than dissolve into Ag+; and (3) compartments of the mononuclear phagocytic system play a minor role in exposure levels that are relevant for human consumers. We also give an example of how the model can be used in exposure and risk assessments based on five different exposure scenarios, namely dietary intake, use of three separate consumer products, and occupational exposure. PMID:24039420

Modeling Spatial and Temporal Variability of Residential Air Exchange Rates for the Near-Road Exposures and Effects of Urban Air Pollutants Study (NEXUS)

EPA Science Inventory

Air pollution health studies often use outdoor concentrations as exposure surrogates. Failure to account for variability of residential infiltration of outdoor pollutants can induce exposure errors and lead to bias and incorrect confidence intervals in health effect estimates. Th...

Hybrid Air Quality Modeling Approach For Use in the Near-Road Exposures to Urban Air Pollutant Study (NEXUS)

EPA Science Inventory

The Near-road EXposures to Urban air pollutant Study (NEXUS) investigated whether children with asthma living in close proximity to major roadways in Detroit, MI, (particularly near roadways with high diesel traffic) have greater health impacts associated with exposure to air pol...

Modeling exposures to traffic-related air pollutants for the NEXUS respiratory health study of asthmatic children in Detroit, MI

EPA Science Inventory

The Near-Road EXposures and Effects of Urban Air Pollutants Study (NEXUS) was designed to investigate associations between exposure to traffic-related air pollution and the respiratory health of asthmatic children living near major roadways in Detroit, MI. A combination of modeli...

TOWARDS RELIABLE AND COST-EFFECTIVE OZONE EXPOSURE ASSESSMENT: PARAMETER EVALUATION AND MODEL VALIDATION USING THE HARVARD SOUTHERN CALIFORNIA CHRONIC OZONE EXPOSURE STUDY DATA

EPA Science Inventory

Accurate assessment of chronic human exposure to atmospheric criteria pollutants, such as ozone, is critical for understanding human health risks associated with living in environments with elevated ambient pollutant concentrations. In this study, we analyzed a data set from a...

Evaluation of Cox's model and logistic regression for matched case-control data with time-dependent covariates: a simulation study.

PubMed

Leffondré, Karen; Abrahamowicz, Michal; Siemiatycki, Jack

2003-12-30

Case-control studies are typically analysed using the conventional logistic model, which does not directly account for changes in the covariate values over time. Yet, many exposures may vary over time. The most natural alternative to handle such exposures would be to use the Cox model with time-dependent covariates. However, its application to case-control data opens the question of how to manipulate the risk sets. Through a simulation study, we investigate how the accuracy of the estimates of Cox's model depends on the operational definition of risk sets and/or on some aspects of the time-varying exposure. We also assess the estimates obtained from conventional logistic regression. The lifetime experience of a hypothetical population is first generated, and a matched case-control study is then simulated from this population. We control the frequency, the age at initiation, and the total duration of exposure, as well as the strengths of their effects. All models considered include a fixed-in-time covariate and one or two time-dependent covariate(s): the indicator of current exposure and/or the exposure duration. Simulation results show that none of the models always performs well. The discrepancies between the odds ratios yielded by logistic regression and the 'true' hazard ratio depend on both the type of the covariate and the strength of its effect. In addition, it seems that logistic regression has difficulty separating the effects of inter-correlated time-dependent covariates. By contrast, each of the two versions of Cox's model systematically induces either a serious under-estimation or a moderate over-estimation bias. The magnitude of the latter bias is proportional to the true effect, suggesting that an improved manipulation of the risk sets may eliminate, or at least reduce, the bias. Copyright 2003 JohnWiley & Sons, Ltd.

Developmental toxicity of prenatal exposure to toluene.

PubMed

Bowen, Scott E; Hannigan, John H

2006-01-01

Organic solvents have become ubiquitous in our environment and are essential for industry. Many women of reproductive age are increasingly exposed to solvents such as toluene in occupational settings (ie, long-term, low-concentration exposures) or through inhalant abuse (eg, episodic, binge exposures to high concentrations). The risk for teratogenic outcome is much less with low to moderate occupational solvent exposure compared with the greater potential for adverse pregnancy outcomes, developmental delays, and neurobehavioral problems in children born to women exposed to high concentrations of abused organic solvents such as toluene, 1,1,1-trichloroethane, xylenes, and nitrous oxide. Yet the teratogenic effects of abuse patterns of exposure to toluene and other inhalants remain understudied. We briefly review how animal models can aid substantially in clarifying the developmental risk of exposure to solvents for adverse biobehavioral outcomes following abuse patterns of use and in the absence of associated health problems and co-drug abuse (eg, alcohol). Our studies also begin to establish the importance of dose (concentration) and critical perinatal periods of exposure to specific outcomes. The present results with our clinically relevant animal model of repeated, brief, high-concentration binge prenatal toluene exposure demonstrate the dose-dependent effect of toluene on prenatal development, early postnatal maturation, spontaneous exploration, and amphetamine-induced locomotor activity. The results imply that abuse patterns of toluene exposure may be more deleterious than typical occupational exposure on fetal development and suggest that animal models are effective in studying the mechanisms and risk factors of organic solvent teratogenicity.

Cigarette Smoke Exposure during Pregnancy Alters Fetomaternal Cell Trafficking Leading to Retention of Microchimeric Cells in the Maternal Lung

PubMed Central

Vogelgesang, Anja; Scapin, Cristina; Barone, Caroline; Tam, Elaine

2014-01-01

Cigarette smoke exposure causes chronic oxidative lung damage. During pregnancy, fetal microchimeric cells traffic to the mother. Their numbers are increased at the site of acute injury. We hypothesized that milder chronic diffuse smoke injury would attract fetal cells to maternal lungs. We used a green-fluorescent-protein (GFP) mouse model to study the effects of cigarette smoke exposure on fetomaternal cell trafficking. Wild-type female mice were exposed to cigarette smoke for about 4 weeks and bred with homozygote GFP males. Cigarette smoke exposure continued until lungs were harvested and analyzed. Exposure to cigarette smoke led to macrophage accumulation in the maternal lung and significantly lower fetal weights. Cigarette smoke exposure influenced fetomaternal cell trafficking. It was associated with retention of GFP-positive fetal cells in the maternal lung and a significant reduction of fetal cells in maternal livers at gestational day 18, when fetomaternal cell trafficking peaks in the mouse model. Cells quickly clear postpartum, leaving only a few, difficult to detect, persisting microchimeric cells behind. In our study, we confirmed the postpartum clearance of cells in the maternal lungs, with no significant difference in both groups. We conclude that in the mouse model, cigarette smoke exposure during pregnancy leads to a retention of fetal microchimeric cells in the maternal lung, the site of injury. Further studies will be needed to elucidate the effect of cigarette smoke exposure on the phenotypic characteristics and function of these fetal microchimeric cells, and confirm its course in cigarette smoke exposure in humans. PMID:24832066

Research Models in Developmental Behavioral Toxicology.

ERIC Educational Resources Information Center

Dietrich, Kim N.; Pearson, Douglas T.

Developmental models currently used by child behavioral toxicologists and teratologists are inadequate to address current issues in these fields. Both child behavioral teratology and toxicology scientifically study the impact of exposure to toxic agents on behavior development: teratology focuses on prenatal exposure and postnatal behavior…

Trends in photoprotection in American fashion magazines, 1983-1993. will fashion make you look old and ugly?

PubMed

George, P M; Kuskowski, M; Schmidt, C

1996-03-01

During the past 50 years recreational sun exposure has greatly increased in the United States. The purpose of this study was to examine the photoprotecion message of American fashion magazines and to identify recent trends. We evaluated models for tan, skin exposure, and other sun-related criteria in six leading fashion magazines between 1983 and 1993. We also recorded the number of sunscreen advertisements and sun awareness articles. We evaluated 3031 models. Adult models had darker tans and greater skin exposure than adolescents and children. Men had darker tans than women. We noted trends toward lighter tans, more women wearing hats, more sunscreen advertisements, and sun awareness articles. Many sunscreen advertisements glorified tanning. Their models had darker tans and more skin exposure, and fewer wore a hat than did nonadvertisement models. The fashion industry and especially sunsreen manufacturers promote excessive sun exposure. Although we found encouraging trends, gains were modest, especially in men's magazines.

A long term study of pulmonary function among US refractory ceramic fibre workers

PubMed Central

LeMasters, Grace K; Hilbert, Timothy J; Levin, Linda S; Rice, Carol H; Borton, Eric K; Lockey, James E

2010-01-01

Background Cross-sectional studies have shown declines in lung function among refractory ceramic fibre (RCF) workers with increasing fibre exposure. This study followed current and former workers (n=1396) for up to 17 years and collected 5243 pulmonary function tests. Methods Cumulative fibre exposure and production years were categorised into exposure levels at five manufacturing locations. Conventional longitudinal models did not adequately partition age-related changes from other time-dependent variables. Therefore, a restricted cubic spline model was developed to account for the non-linear decline with age. Results Cumulative fibre >60 fibre-months/cc showed a significant loss in lung function at the first test. When results were examined longitudinally, cumulative exposure was confounded with age as workers with the highest cumulative exposure were generally older. A longitudinal model adjusted by age groups was implemented to control for this confounding. No consistent longitudinal loss in lung function was observed with RCF exposure. Smoking, initial weight and weight increase were significant factors. Conclusion No consistent decline was observed longitudinally with exposure to RCF, although cross-sectional and longitudinal findings were discordant. Confounding and accelerated lung function declines with ageing and the correlation of multiple time-dependent variables should be considered in order to minimise error and maximise precision. An innovative statistical methodology for these types of data is described. PMID:20798015

MODELING POPULATION EXPOSURES TO OUTDOOR SOURCES OF HAZARDOUS AIR POLLUTANTS

EPA Science Inventory

Accurate assessment of human exposures is an important part of environmental health effects research. However, most air pollution epidemiology studies rely upon imperfect surrogates of personal exposures, such as information based on available central-site outdoor concentration ...

Modeling flight attendants' exposure to secondhand smoke in commercial aircraft: historical trends from 1955 to 1989.

PubMed

Liu, Ruiling; Dix-Cooper, Linda; Hammond, S Katharine

2015-01-01

Flight attendants were exposed to elevated levels of secondhand smoke (SHS) in commercial aircraft when smoking was allowed on planes. During flight attendants' working years, their occupational SHS exposure was influenced by various factors, including the prevalence of active smokers on planes, fliers' smoking behaviors, airplane flight load factors, and ventilation systems. These factors have likely changed over the past six decades and would affect SHS concentrations in commercial aircraft. However, changes in flight attendants' exposure to SHS have not been examined in the literature. This study estimates the magnitude of the changes and the historic trends of flight attendants' SHS exposure in U.S. domestic commercial aircraft by integrating historical changes of contributing factors. Mass balance models were developed and evaluated to estimate flight attendants' exposure to SHS in passenger cabins, as indicated by two commonly used tracers (airborne nicotine and particulate matter (PM)). Monte Carlo simulations integrating historical trends and distributions of influence factors were used to simulate 10,000 flight attendants' exposure to SHS on commercial flights from 1955 to 1989. These models indicate that annual mean SHS PM concentrations to which flight attendants were exposed in passenger cabins steadily decreased from approximately 265 μg/m(3) in 1955 and 1960 to 93 μg/m(3) by 1989, and airborne nicotine exposure among flight attendants also decreased from 11.1 μg/m(3) in 1955 to 6.5 μg/m(3) in 1989. Using duration of employment as an indicator of flight attendants' cumulative occupational exposure to SHS in epidemiological studies would inaccurately assess their lifetime exposures and thus bias the relationship between the exposure and health effects. This historical trend should be considered in future epidemiological studies.

Conservative Exposure Predictions for Rapid Risk Assessment of Phase-Separated Additives in Medical Device Polymers.

PubMed

Chandrasekar, Vaishnavi; Janes, Dustin W; Saylor, David M; Hood, Alan; Bajaj, Akhil; Duncan, Timothy V; Zheng, Jiwen; Isayeva, Irada S; Forrey, Christopher; Casey, Brendan J

2018-01-01

A novel approach for rapid risk assessment of targeted leachables in medical device polymers is proposed and validated. Risk evaluation involves understanding the potential of these additives to migrate out of the polymer, and comparing their exposure to a toxicological threshold value. In this study, we propose that a simple diffusive transport model can be used to provide conservative exposure estimates for phase separated color additives in device polymers. This model has been illustrated using a representative phthalocyanine color additive (manganese phthalocyanine, MnPC) and polymer (PEBAX 2533) system. Sorption experiments of MnPC into PEBAX were conducted in order to experimentally determine the diffusion coefficient, D = (1.6 ± 0.5) × 10 -11  cm 2 /s, and matrix solubility limit, C s  = 0.089 wt.%, and model predicted exposure values were validated by extraction experiments. Exposure values for the color additive were compared to a toxicological threshold for a sample risk assessment. Results from this study indicate that a diffusion model-based approach to predict exposure has considerable potential for use as a rapid, screening-level tool to assess the risk of color additives and other small molecule additives in medical device polymers.

A Bayesian Semiparametric Approach for Incorporating Longitudinal Information on Exposure History for Inference in Case-Control Studies

PubMed Central

Bhadra, Dhiman; Daniels, Michael J.; Kim, Sungduk; Ghosh, Malay; Mukherjee, Bhramar

2014-01-01

In a typical case-control study, exposure information is collected at a single time-point for the cases and controls. However, case-control studies are often embedded in existing cohort studies containing a wealth of longitudinal exposure history on the participants. Recent medical studies have indicated that incorporating past exposure history, or a constructed summary measure of cumulative exposure derived from the past exposure history, when available, may lead to more precise and clinically meaningful estimates of the disease risk. In this paper, we propose a flexible Bayesian semiparametric approach to model the longitudinal exposure profiles of the cases and controls and then use measures of cumulative exposure based on a weighted integral of this trajectory in the final disease risk model. The estimation is done via a joint likelihood. In the construction of the cumulative exposure summary, we introduce an influence function, a smooth function of time to characterize the association pattern of the exposure profile on the disease status with different time windows potentially having differential influence/weights. This enables us to analyze how the present disease status of a subject is influenced by his/her past exposure history conditional on the current ones. The joint likelihood formulation allows us to properly account for uncertainties associated with both stages of the estimation process in an integrated manner. Analysis is carried out in a hierarchical Bayesian framework using Reversible jump Markov chain Monte Carlo (RJMCMC) algorithms. The proposed methodology is motivated by, and applied to a case-control study of prostate cancer where longitudinal biomarker information is available for the cases and controls. PMID:22313248

A study protocol to evaluate the relationship between outdoor air pollution and pregnancy outcomes

PubMed Central

2010-01-01

Background The present study protocol is designed to assess the relationship between outdoor air pollution and low birth weight and preterm births outcomes performing a semi-ecological analysis. Semi-ecological design studies are widely used to assess effects of air pollution in humans. In this type of analysis, health outcomes and covariates are measured in individuals and exposure assignments are usually based on air quality monitor stations. Therefore, estimating individual exposures are one of the major challenges when investigating these relationships with a semi-ecologic design. Methods/Design Semi-ecologic study consisting of a retrospective cohort study with ecologic assignment of exposure is applied. Health outcomes and covariates are collected at Primary Health Care Center. Data from pregnant registry, clinical record and specific questionnaire administered orally to the mothers of children born in period 2007-2010 in Portuguese Alentejo Litoral region, are collected by the research team. Outdoor air pollution data are collected with a lichen diversity biomonitoring program, and individual pregnancy exposures are assessed with spatial geostatistical simulation, which provides the basis for uncertainty analysis of individual exposures. Awareness of outdoor air pollution uncertainty will improve validity of individual exposures assignments for further statistical analysis with multivariate regression models. Discussion Exposure misclassification is an issue of concern in semi-ecological design. In this study, personal exposures are assigned to each pregnant using geocoded addresses data. A stochastic simulation method is applied to lichen diversity values index measured at biomonitoring survey locations, in order to assess spatial uncertainty of lichen diversity value index at each geocoded address. These methods assume a model for spatial autocorrelation of exposure and provide a distribution of exposures in each study location. We believe that variability of simulated exposure values at geocoded addresses will improve knowledge on variability of exposures, improving therefore validity of individual exposures to input in posterior statistical analysis. PMID:20950449

A study protocol to evaluate the relationship between outdoor air pollution and pregnancy outcomes.

PubMed

Ribeiro, Manuel C; Pereira, Maria J; Soares, Amílcar; Branquinho, Cristina; Augusto, Sofia; Llop, Esteve; Fonseca, Susana; Nave, Joaquim G; Tavares, António B; Dias, Carlos M; Silva, Ana; Selemane, Ismael; de Toro, Joaquin; Santos, Mário J; Santos, Fernanda

2010-10-15

The present study protocol is designed to assess the relationship between outdoor air pollution and low birth weight and preterm births outcomes performing a semi-ecological analysis. Semi-ecological design studies are widely used to assess effects of air pollution in humans. In this type of analysis, health outcomes and covariates are measured in individuals and exposure assignments are usually based on air quality monitor stations. Therefore, estimating individual exposures are one of the major challenges when investigating these relationships with a semi-ecologic design. Semi-ecologic study consisting of a retrospective cohort study with ecologic assignment of exposure is applied. Health outcomes and covariates are collected at Primary Health Care Center. Data from pregnant registry, clinical record and specific questionnaire administered orally to the mothers of children born in period 2007-2010 in Portuguese Alentejo Litoral region, are collected by the research team. Outdoor air pollution data are collected with a lichen diversity biomonitoring program, and individual pregnancy exposures are assessed with spatial geostatistical simulation, which provides the basis for uncertainty analysis of individual exposures. Awareness of outdoor air pollution uncertainty will improve validity of individual exposures assignments for further statistical analysis with multivariate regression models. Exposure misclassification is an issue of concern in semi-ecological design. In this study, personal exposures are assigned to each pregnant using geocoded addresses data. A stochastic simulation method is applied to lichen diversity values index measured at biomonitoring survey locations, in order to assess spatial uncertainty of lichen diversity value index at each geocoded address. These methods assume a model for spatial autocorrelation of exposure and provide a distribution of exposures in each study location. We believe that variability of simulated exposure values at geocoded addresses will improve knowledge on variability of exposures, improving therefore validity of individual exposures to input in posterior statistical analysis.

Long-term air pollution and traffic noise exposures and cognitive function:A cross-sectional analysis of the Heinz Nixdorf Recall study.

PubMed

Tzivian, Lilian; Dlugaj, Martha; Winkler, Angela; Hennig, Frauke; Fuks, Kateryna; Sugiri, Dorothee; Schikowski, Tamara; Jakobs, Hermann; Erbel, Raimund; Jöckel, Karl-Heinz; Moebus, Susanne; Hoffmann, Barbara; Weimar, Christian

2016-01-01

Investigations of adverse effects of air pollution (AP) and ambient noise on cognitive functions are apparently scarce, and findings seem to be inconsistent. The aim of this study was to examine the associations of long-term exposure to AP and traffic noise with cognitive performance. At the second examination of the population-based Heinz Nixdorf Recall study (2006-2008), cognitive performance was evaluated in 4086 participants. Long-term residential exposure to size-specific particulate matter (PM) and nitrogen oxides (NOx) with land use regression, to and traffic noise (weighted 24-h (L DEN ) and nighttime (L NIGHT ) means), was assessed according to the European Union (EU) Directive 2002/49/EC. Multiple regression models were calculated for the relationship of environmental exposures with a global cognitive score (GCS) and in five cognitive subtests, using single- and two-exposure models. In fully adjusted models, several AP metrics were negatively associated with four of five subtests and with GCS. For example, an interquartile range increase in PM 2.5 was correlated with verbal fluency, labyrinth test, and immediate and delayed verbal recall. A 10 dB(A) elevation in L DEN and L NIGHT was associated with GCS. Similar but not significant associations were found for the cognitive subtests. In two-exposure models including noise and air pollution simultaneously, the associations did not change markedly for air pollution, but attenuated numerically for noise. Long-term exposures to AP and traffic noise are negatively correlated with subtests related to memory and executive functions. There appears to be little evidence for mutual confounding.

Channel-Island Connectivity Affects Water Exposure Time Distributions in a Coastal River Delta

NASA Astrophysics Data System (ADS)

Hiatt, Matthew; Castañeda-Moya, Edward; Twilley, Robert; Hodges, Ben R.; Passalacqua, Paola

2018-03-01

The exposure time is a water transport time scale defined as the cumulative amount of time a water parcel spends in the domain of interest regardless of the number of excursions from the domain. Transport time scales are often used to characterize the nutrient removal potential of aquatic systems, but exposure time distribution estimates are scarce for deltaic systems. Here we analyze the controls on exposure time distributions using a hydrodynamic model in two domains: the Wax Lake delta in Louisiana, USA, and an idealized channel-island complex. In particular, we study the effects of river discharge, vegetation, network geometry, and tides and use a simple model for the fractional removal of nitrate. In both domains, we find that channel-island hydrological connectivity significantly affects exposure time distributions and nitrate removal. The relative contributions of the island and channel portions of the delta to the overall exposure time distribution are controlled by island vegetation roughness and network geometry. Tides have a limited effect on the system's exposure time distribution but can introduce significant spatial variability in local exposure times. The median exposure time for the WLD model is 10 h under the conditions tested and water transport within the islands contributes to 37-50% of the network-scale exposure time distribution and 52-73% of the modeled nitrate removal, indicating that islands may account for the majority of nitrate removal in river deltas.

Acute lung injury and persistent small airway disease in a rabbit model of chlorine inhalation.

PubMed

Musah, Sadiatu; Schlueter, Connie F; Humphrey, David M; Powell, Karen S; Roberts, Andrew M; Hoyle, Gary W

2017-01-15

Chlorine is a pulmonary toxicant to which humans can be exposed through accidents or intentional releases. Acute effects of chlorine inhalation in humans and animal models have been well characterized, but less is known about persistent effects of acute, high-level chlorine exposures. In particular, animal models that reproduce the long-term effects suggested to occur in humans are lacking. Here, we report the development of a rabbit model in which both acute and persistent effects of chlorine inhalation can be assessed. Male New Zealand White rabbits were exposed to chlorine while the lungs were mechanically ventilated. After chlorine exposure, the rabbits were extubated and were allowed to survive for up to 24h after exposure to 800ppm chlorine for 4min to study acute effects or up to 7days after exposure to 400ppm for 8min to study longer term effects. Acute effects observed 6 or 24h after inhalation of 800ppm chlorine for 4min included hypoxemia, pulmonary edema, airway epithelial injury, inflammation, altered baseline lung mechanics, and airway hyperreactivity to inhaled methacholine. Seven days after recovery from inhalation of 400ppm chlorine for 8min, rabbits exhibited mild hypoxemia, increased area of pressure-volume loops, and airway hyperreactivity. Lung histology 7days after chlorine exposure revealed abnormalities in the small airways, including inflammation and sporadic bronchiolitis obliterans lesions. Immunostaining showed a paucity of club and ciliated cells in the epithelium at these sites. These results suggest that small airway disease may be an important component of persistent respiratory abnormalities that occur following acute chlorine exposure. This non-rodent chlorine exposure model should prove useful for studying persistent effects of acute chlorine exposure and for assessing efficacy of countermeasures for chlorine-induced lung injury. Copyright © 2016 Elsevier Inc. All rights reserved.

Field Measurements of Inadvertent Ingestion Exposure to Metals.

PubMed

Gorman Ng, Melanie; MacCalman, Laura; Semple, Sean; van Tongeren, Martie

2017-11-10

The determinants of inadvertent occupational ingestion exposure are poorly understood, largely due to a lack of available exposure measurement data. In this study, perioral exposure wipes were used as a surrogate for inadvertent ingestion exposure to measure exposure to eight metals (chromium, nickel, aluminium, cobalt, lead, arsenic, manganese, and tin) among 38 workers at 5 work sites in the UK. This work was done alongside a previously reported observational study of hand/object-to-mouth contact frequency. Systematic wipes of the perioral area, and of both hands were taken with proprietary cellulose wipes pre-moistened with deionized water. Measurements were taken at the beginning, middle and end of the shift. Mixed-effect models of exposure measurements were built with area of skin sampled, time during shift, and job group entered as fixed effects and worker identification as a random effect. Linear regression modelling was used to study the effect of hand/object-to-mouth contact frequency on perioral exposure, adjusting for the measured exposure on the hand and observed respirator use. Hand and perioral exposure measurements were correlated with one another (r = 0.79) but mass per unit area exposure was significantly higher on the perioral area than on the hands for seven of the metals (at P < 0.05). There were no significant differences between measurements taken at the middle or the end of the shift for five of the metals suggesting that dermal loading may remain relatively constant for much of the workday. This applies to both hand and perioral measurements. In linear regression modelling there was no relationship between hand/object-to-mouth contact frequency and perioral exposure, but hand exposure was significantly positively related to perioral exposure and workers who used respirators had significantly higher perioral exposure than those who did not. The results suggest the levels of exposure on the hand and respirator use are important determinants of potential inadvertent ingestion exposure. The results did not demonstrate a relationship between perioral exposure and hand-to-mouth contact frequency. Perioral wipe sampling may be a useful surrogate measure for exposure by the inadvertent ingestion route, but further research is required to confirm the link between perioral levels and actual exposure, measured using biological monitoring. © The Author 2017. Published by Oxford University Press on behalf of the British Occupational Hygiene Society.

Large scale study on the variation of RF energy absorption in the head & brain regions of adults and children and evaluation of the SAM phantom conservativeness.

PubMed

Keshvari, J; Kivento, M; Christ, A; Bit-Babik, G

2016-04-21

This paper presents the results of two computational large scale studies using highly realistic exposure scenarios, MRI based human head and hand models, and two mobile phone models. The objectives are (i) to study the relevance of age when people are exposed to RF by comparing adult and child heads and (ii) to analyze and discuss the conservativeness of the SAM phantom for all age groups. Representative use conditions were simulated using detailed CAD models of two mobile phones operating between 900 MHz and 1950 MHz including configurations with the hand holding the phone, which were not considered in most previous studies. The peak spatial-average specific absorption rate (psSAR) in the head and the pinna tissues is assessed using anatomically accurate head and hand models. The first of the two mentioned studies involved nine head-, four hand- and two phone-models, the second study included six head-, four hand- and three simplified phone-models (over 400 configurations in total). In addition, both studies also evaluated the exposure using the SAM phantom. Results show no systematic differences between psSAR induced in the adult and child heads. The exposure level and its variation for different age groups may be different for particular phones, but no correlation between psSAR and model age was found. The psSAR from all exposure conditions was compared to the corresponding configurations using SAM, which was found to be conservative in the large majority of cases.

Large scale study on the variation of RF energy absorption in the head & brain regions of adults and children and evaluation of the SAM phantom conservativeness

NASA Astrophysics Data System (ADS)

Keshvari, J.; Kivento, M.; Christ, A.; Bit-Babik, G.

2016-04-01

This paper presents the results of two computational large scale studies using highly realistic exposure scenarios, MRI based human head and hand models, and two mobile phone models. The objectives are (i) to study the relevance of age when people are exposed to RF by comparing adult and child heads and (ii) to analyze and discuss the conservativeness of the SAM phantom for all age groups. Representative use conditions were simulated using detailed CAD models of two mobile phones operating between 900 MHz and 1950 MHz including configurations with the hand holding the phone, which were not considered in most previous studies. The peak spatial-average specific absorption rate (psSAR) in the head and the pinna tissues is assessed using anatomically accurate head and hand models. The first of the two mentioned studies involved nine head-, four hand- and two phone-models, the second study included six head-, four hand- and three simplified phone-models (over 400 configurations in total). In addition, both studies also evaluated the exposure using the SAM phantom. Results show no systematic differences between psSAR induced in the adult and child heads. The exposure level and its variation for different age groups may be different for particular phones, but no correlation between psSAR and model age was found. The psSAR from all exposure conditions was compared to the corresponding configurations using SAM, which was found to be conservative in the large majority of cases.

Modeling Particle Exposure in US Trucking Terminals

PubMed Central

Davis, ME; Smith, TJ; Laden, F; Hart, JE; Ryan, LM; Garshick, E

2007-01-01

Multi-tiered sampling approaches are common in environmental and occupational exposure assessment, where exposures for a given individual are often modeled based on simultaneous measurements taken at multiple indoor and outdoor sites. The monitoring data from such studies is hierarchical by design, imposing a complex covariance structure that must be accounted for in order to obtain unbiased estimates of exposure. Statistical methods such as structural equation modeling (SEM) represent a useful alternative to simple linear regression in these cases, providing simultaneous and unbiased predictions of each level of exposure based on a set of covariates specific to the exposure setting. We test the SEM approach using data from a large exposure assessment of diesel and combustion particles in the US trucking industry. The exposure assessment includes data from 36 different trucking terminals across the United States sampled between 2001 and 2005, measuring PM2.5 and its elemental carbon (EC), organic carbon (OC) components, by personal monitoring, and sampling at two indoor work locations and an outdoor “background” location. Using the SEM method, we predict: 1) personal exposures as a function of work related exposure and smoking status; 2) work related exposure as a function of terminal characteristics, indoor ventilation, job location, and background exposure conditions; and 3) background exposure conditions as a function of weather, nearby source pollution, and other regional differences across terminal sites. The primary advantage of SEMs in this setting is the ability to simultaneously predict exposures at each of the sampling locations, while accounting for the complex covariance structure among the measurements and descriptive variables. The statistically significant results and high R2 values observed from the trucking industry application supports the broader use of this approach in exposure assessment modeling. PMID:16856739

Optimizing cost-efficiency in mean exposure assessment - cost functions reconsidered

PubMed Central

2011-01-01

Background Reliable exposure data is a vital concern in medical epidemiology and intervention studies. The present study addresses the needs of the medical researcher to spend monetary resources devoted to exposure assessment with an optimal cost-efficiency, i.e. obtain the best possible statistical performance at a specified budget. A few previous studies have suggested mathematical optimization procedures based on very simple cost models; this study extends the methodology to cover even non-linear cost scenarios. Methods Statistical performance, i.e. efficiency, was assessed in terms of the precision of an exposure mean value, as determined in a hierarchical, nested measurement model with three stages. Total costs were assessed using a corresponding three-stage cost model, allowing costs at each stage to vary non-linearly with the number of measurements according to a power function. Using these models, procedures for identifying the optimally cost-efficient allocation of measurements under a constrained budget were developed, and applied on 225 scenarios combining different sizes of unit costs, cost function exponents, and exposure variance components. Results Explicit mathematical rules for identifying optimal allocation could be developed when cost functions were linear, while non-linear cost functions implied that parts of or the entire optimization procedure had to be carried out using numerical methods. For many of the 225 scenarios, the optimal strategy consisted in measuring on only one occasion from each of as many subjects as allowed by the budget. Significant deviations from this principle occurred if costs for recruiting subjects were large compared to costs for setting up measurement occasions, and, at the same time, the between-subjects to within-subject variance ratio was small. In these cases, non-linearities had a profound influence on the optimal allocation and on the eventual size of the exposure data set. Conclusions The analysis procedures developed in the present study can be used for informed design of exposure assessment strategies, provided that data are available on exposure variability and the costs of collecting and processing data. The present shortage of empirical evidence on costs and appropriate cost functions however impedes general conclusions on optimal exposure measurement strategies in different epidemiologic scenarios. PMID:21600023

Optimizing cost-efficiency in mean exposure assessment--cost functions reconsidered.

PubMed

Mathiassen, Svend Erik; Bolin, Kristian

2011-05-21

Reliable exposure data is a vital concern in medical epidemiology and intervention studies. The present study addresses the needs of the medical researcher to spend monetary resources devoted to exposure assessment with an optimal cost-efficiency, i.e. obtain the best possible statistical performance at a specified budget. A few previous studies have suggested mathematical optimization procedures based on very simple cost models; this study extends the methodology to cover even non-linear cost scenarios. Statistical performance, i.e. efficiency, was assessed in terms of the precision of an exposure mean value, as determined in a hierarchical, nested measurement model with three stages. Total costs were assessed using a corresponding three-stage cost model, allowing costs at each stage to vary non-linearly with the number of measurements according to a power function. Using these models, procedures for identifying the optimally cost-efficient allocation of measurements under a constrained budget were developed, and applied on 225 scenarios combining different sizes of unit costs, cost function exponents, and exposure variance components. Explicit mathematical rules for identifying optimal allocation could be developed when cost functions were linear, while non-linear cost functions implied that parts of or the entire optimization procedure had to be carried out using numerical methods.For many of the 225 scenarios, the optimal strategy consisted in measuring on only one occasion from each of as many subjects as allowed by the budget. Significant deviations from this principle occurred if costs for recruiting subjects were large compared to costs for setting up measurement occasions, and, at the same time, the between-subjects to within-subject variance ratio was small. In these cases, non-linearities had a profound influence on the optimal allocation and on the eventual size of the exposure data set. The analysis procedures developed in the present study can be used for informed design of exposure assessment strategies, provided that data are available on exposure variability and the costs of collecting and processing data. The present shortage of empirical evidence on costs and appropriate cost functions however impedes general conclusions on optimal exposure measurement strategies in different epidemiologic scenarios.

MANAGEMENT AND DISSEMINATION OF HUMAN EXPOSURE DATABASES AND OTHER DATABASES NEEDED FOR HUMAN EXPOSURE MODELING AND ANALYSIS

EPA Science Inventory

Researchers in the National Exposure Research Laboratory (NERL) have performed a number of large human exposure measurement studies during the past decade. It is the goal of the NERL to make the data available to other researchers for analysis in order to further the scientific ...

The Diesel Exhaust in Miners Study: I. Overview of the Exposure Assessment Process

PubMed Central

Stewart, Patricia A.; Coble, Joseph B.; Vermeulen, Roel; Schleiff, Patricia; Blair, Aaron; Lubin, Jay; Attfield, Michael; Silverman, Debra T.

2010-01-01

This report provides an overview of the exposure assessment process for an epidemiologic study that investigated mortality, with a special focus on lung cancer, associated with diesel exhaust (DE) exposure among miners. Details of several components are provided in four other reports. A major challenge for this study was the development of quantitative estimates of historical exposures to DE. There is no single standard method for assessing the totality of DE, so respirable elemental carbon (REC), a component of DE, was selected as the primary surrogate in this study. Air monitoring surveys at seven of the eight study mining facilities were conducted between 1998 and 2001 and provided reference personal REC exposure levels and measurements for other agents and DE components in the mining environment. (The eighth facility had closed permanently prior to the surveys.) Exposure estimates were developed for mining facility/department/job/year combinations. A hierarchical grouping strategy was developed for assigning exposure levels to underground jobs [based on job titles, on the amount of time spent in various areas of the underground mine, and on similar carbon monoxide (CO, another DE component) concentrations] and to surface jobs (based on the use of, or proximity to, diesel-powered equipment). Time trends in air concentrations for underground jobs were estimated from mining facility-specific prediction models using diesel equipment horsepower, total air flow rates exhausted from the underground mines, and, because there were no historical REC measurements, historical measurements of CO. Exposures to potentially confounding agents, i.e. respirable dust, silica, radon, asbestos, and non-diesel sources of polycyclic aromatic hydrocarbons, also were assessed. Accuracy and reliability of the estimated REC exposures levels were evaluated by comparison with several smaller datasets and by development of alternative time trend models. During 1998–2001, the average measured REC exposure level by facility ranged from 40 to 384 μg m−3 for the underground workers and from 2 to 6 μg m−3 for the surface workers. For one prevalent underground job, ‘miner operator’, the maximum annual REC exposure estimate by facility ranged up to 685% greater than the corresponding 1998–2001 value. A comparison of the historical CO estimates from the time trend models with 1976–1977 CO measurements not used in the modeling found an overall median relative difference of 29%. Other comparisons showed similar levels of agreement. The assessment process indicated large differences in REC exposure levels over time and across the underground operations. Method evaluations indicated that the final estimates were consistent with those from alternative time trend models and demonstrated moderate to high agreement with external data. PMID:20876233

The diesel exhaust in miners study: I. Overview of the exposure assessment process.

PubMed

Stewart, Patricia A; Coble, Joseph B; Vermeulen, Roel; Schleiff, Patricia; Blair, Aaron; Lubin, Jay; Attfield, Michael; Silverman, Debra T

2010-10-01

This report provides an overview of the exposure assessment process for an epidemiologic study that investigated mortality, with a special focus on lung cancer, associated with diesel exhaust (DE) exposure among miners. Details of several components are provided in four other reports. A major challenge for this study was the development of quantitative estimates of historical exposures to DE. There is no single standard method for assessing the totality of DE, so respirable elemental carbon (REC), a component of DE, was selected as the primary surrogate in this study. Air monitoring surveys at seven of the eight study mining facilities were conducted between 1998 and 2001 and provided reference personal REC exposure levels and measurements for other agents and DE components in the mining environment. (The eighth facility had closed permanently prior to the surveys.) Exposure estimates were developed for mining facility/department/job/year combinations. A hierarchical grouping strategy was developed for assigning exposure levels to underground jobs [based on job titles, on the amount of time spent in various areas of the underground mine, and on similar carbon monoxide (CO, another DE component) concentrations] and to surface jobs (based on the use of, or proximity to, diesel-powered equipment). Time trends in air concentrations for underground jobs were estimated from mining facility-specific prediction models using diesel equipment horsepower, total air flow rates exhausted from the underground mines, and, because there were no historical REC measurements, historical measurements of CO. Exposures to potentially confounding agents, i.e. respirable dust, silica, radon, asbestos, and non-diesel sources of polycyclic aromatic hydrocarbons, also were assessed. Accuracy and reliability of the estimated REC exposures levels were evaluated by comparison with several smaller datasets and by development of alternative time trend models. During 1998-2001, the average measured REC exposure level by facility ranged from 40 to 384 μg m⁻³ for the underground workers and from 2 to 6 μg m⁻³ for the surface workers. For one prevalent underground job, 'miner operator', the maximum annual REC exposure estimate by facility ranged up to 685% greater than the corresponding 1998-2001 value. A comparison of the historical CO estimates from the time trend models with 1976-1977 CO measurements not used in the modeling found an overall median relative difference of 29%. Other comparisons showed similar levels of agreement. The assessment process indicated large differences in REC exposure levels over time and across the underground operations. Method evaluations indicated that the final estimates were consistent with those from alternative time trend models and demonstrated moderate to high agreement with external data.

Neurobehavioral performance in adolescents is inversely associated with traffic exposure.

PubMed

Kicinski, Michal; Vermeir, Griet; Van Larebeke, Nicolas; Den Hond, Elly; Schoeters, Greet; Bruckers, Liesbeth; Sioen, Isabelle; Bijnens, Esmée; Roels, Harry A; Baeyens, Willy; Viaene, Mineke K; Nawrot, Tim S

2015-02-01

On the basis of animal research and epidemiological studies in children and elderly there is a growing concern that traffic exposure may affect the brain. The aim of our study was to investigate the association between traffic exposure and neurobehavioral performance in adolescents. We examined 606 adolescents. To model the exposure, we constructed a traffic exposure factor based on a biomarker of benzene (urinary trans,trans-muconic acid) and the amount of contact with traffic preceding the neurobehavioral examination (using distance-weighted traffic density and time spent in traffic). We used a Bayesian structural equation model to investigate the association between traffic exposure and three neurobehavioral domains: sustained attention, short-term memory, and manual motor speed. A one standard deviation increase in traffic exposure was associated with a 0.26 standard deviation decrease in sustained attention (95% credible interval: -0.02 to -0.51), adjusting for gender, age, smoking, passive smoking, level of education of the mother, socioeconomic status, time of the day, and day of the week. The associations between traffic exposure and the other neurobehavioral domains studied had the same direction but did not reach the level of statistical significance. The results remained consistent in the sensitivity analysis excluding smokers and passive smokers. The inverse association between sustained attention and traffic exposure was independent of the blood lead level. Our study in adolescents supports the recent findings in children and elderly suggesting that traffic exposure adversely affects the neurobehavioral function. Copyright © 2014 Elsevier Ltd. All rights reserved.

Analysis of real-time mixture cytotoxicity data following repeated exposure using BK/TD models.

PubMed

Teng, S; Tebby, C; Barcellini-Couget, S; De Sousa, G; Brochot, C; Rahmani, R; Pery, A R R

2016-08-15

Cosmetic products generally consist of multiple ingredients. Thus, cosmetic risk assessment has to deal with mixture toxicity on a long-term scale which means it has to be assessed in the context of repeated exposure. Given that animal testing has been banned for cosmetics risk assessment, in vitro assays allowing long-term repeated exposure and adapted for in vitro - in vivo extrapolation need to be developed. However, most in vitro tests only assess short-term effects and consider static endpoints which hinder extrapolation to realistic human exposure scenarios where concentration in target organs is varies over time. Thanks to impedance metrics, real-time cell viability monitoring for repeated exposure has become possible. We recently constructed biokinetic/toxicodynamic models (BK/TD) to analyze such data (Teng et al., 2015) for three hepatotoxic cosmetic ingredients: coumarin, isoeugenol and benzophenone-2. In the present study, we aim to apply these models to analyze the dynamics of mixture impedance data using the concepts of concentration addition and independent action. Metabolic interactions between the mixture components were investigated, characterized and implemented in the models, as they impacted the actual cellular exposure. Indeed, cellular metabolism following mixture exposure induced a quick disappearance of the compounds from the exposure system. We showed that isoeugenol substantially decreased the metabolism of benzophenone-2, reducing the disappearance of this compound and enhancing its in vitro toxicity. Apart from this metabolic interaction, no mixtures showed any interaction, and all binary mixtures were successfully modeled by at least one model based on exposure to the individual compounds. Copyright © 2016 Elsevier Inc. All rights reserved.

Development of land-use regression models for exposure assessment to ultrafine particles in Rome, Italy

NASA Astrophysics Data System (ADS)

Cattani, Giorgio; Gaeta, Alessandra; di Menno di Bucchianico, Alessandro; de Santis, Antonella; Gaddi, Raffaela; Cusano, Mariacarmela; Ancona, Carla; Badaloni, Chiara; Forastiere, Francesco; Gariazzo, Claudio; Sozzi, Roberto; Inglessis, Marco; Silibello, Camillo; Salvatori, Elisabetta; Manes, Fausto; Cesaroni, Giulia; The Viias Study Group

2017-05-01

The health effects of long-term exposure to ultrafine particles (UFPs) are poorly understood. Data on spatial contrasts in ambient ultrafine particles (UFPs) concentrations are needed with fine resolution. This study aimed to assess the spatial variability of total particle number concentrations (PNC, a proxy for UFPs) in the city of Rome, Italy, using land use regression (LUR) models, and the correspondent exposure of population here living. PNC were measured using condensation particle counters at the building facade of 28 homes throughout the city. Three 7-day monitoring periods were carried out during cold, warm and intermediate seasons. Geographic Information System predictor variables, with buffers of varying size, were evaluated to model spatial variations of PNC. A stepwise forward selection procedure was used to develop a "base" linear regression model according to the European Study of Cohorts for Air Pollution Effects project methodology. Other variables were then included in more enhanced models and their capability of improving model performance was evaluated. Four LUR models were developed. Local variation in UFPs in the study area can be largely explained by the ratio of traffic intensity and distance to the nearest major road. The best model (adjusted R2 = 0.71; root mean square error = ±1,572 particles/cm³, leave one out cross validated R2 = 0.68) was achieved by regressing building and street configuration variables against residual from the "base" model, which added 3% more to the total variance explained. Urban green and population density in a 5,000 m buffer around each home were also relevant predictors. The spatial contrast in ambient PNC across the large conurbation of Rome, was successfully assessed. The average exposure of subjects living in the study area was 16,006 particles/cm³ (SD 2165 particles/cm³, range: 11,075-28,632 particles/cm³). A total of 203,886 subjects (16%) lives in Rome within 50 m from a high traffic road and they experience the highest exposure levels (18,229 particles/cm³). The results will be used to estimate the long-term health effects of ultrafine particle exposure of participants in Rome.

REGRESSION MODELS OF RESIDENTIAL EXPOSURE TO CHLORPYRIFOS AND DIAZINON

EPA Science Inventory

This study examines the ability of regression models to predict residential exposures to chlorpyrifos and diazinon, based on the information from the NHEXAS-AZ database. The robust method was used to generate "fill-in" values for samples that are below the detection l...

Estimated long-term outdoor air pollution concentrations in a cohort study

NASA Astrophysics Data System (ADS)

Beelen, Rob; Hoek, Gerard; Fischer, Paul; Brandt, Piet A. van den; Brunekreef, Bert

Several recent studies associated long-term exposure to air pollution with increased mortality. An ongoing cohort study, the Netherlands Cohort Study on Diet and Cancer (NLCS), was used to study the association between long-term exposure to traffic-related air pollution and mortality. Following on a previous exposure assessment study in the NLCS, we improved the exposure assessment methods. Long-term exposure to nitrogen dioxide (NO 2), nitrogen oxide (NO), black smoke (BS), and sulphur dioxide (SO 2) was estimated. Exposure at each home address ( N=21 868) was considered as a function of a regional, an urban and a local component. The regional component was estimated using inverse distance weighed interpolation of measurement data from regional background sites in a national monitoring network. Regression models with urban concentrations as dependent variables, and number of inhabitants in different buffers and land use variables, derived with a Geographic Information System (GIS), as predictor variables were used to estimate the urban component. The local component was assessed using a GIS and a digital road network with linked traffic intensities. Traffic intensity on the nearest road and on the nearest major road, and the sum of traffic intensity in a buffer of 100 m around each home address were assessed. Further, a quantitative estimate of the local component was estimated. The regression models to estimate the urban component explained 67%, 46%, 49% and 35% of the variances of NO 2, NO, BS, and SO 2 concentrations, respectively. Overall regression models which incorporated the regional, urban and local component explained 84%, 44%, 59% and 56% of the variability in concentrations for NO 2, NO, BS and SO 2, respectively. We were able to develop an exposure assessment model using GIS methods and traffic intensities that explained a large part of the variations in outdoor air pollution concentrations.

Exposure to violence and parenting as mediators between poverty and psychological symptoms in urban African American adolescents.

PubMed

Grant, Kathryn E; McCormick, Anthony; Poindexter, LaShaunda; Simpkins, Tandra; Janda, Cassandra M; Thomas, Kina J; Campbell, Amanda; Carleton, Russell; Taylor, Jeremy

2005-08-01

The present study builds on past research that has found support for a conceptual model in which poverty is linked with adolescent psychological symptoms through economic stressors and impaired parenting. The present study examined this model in a sample of urban African American mothers and their adolescent children. In addition, an alternative hypothesis was examined: that exposure to community violence mediates the relation between poverty and psychological symptoms in urban youth. Limited support was found for a model in which poverty is linked with internalizing symptoms through exposure to community violence and with externalizing symptoms through economic stressors and inconsistent discipline. Interpretations, limitations, and directions for future research are outlined.

Analysis of an Environmental Exposure Health Questionnaire in a Metropolitan Minority Population Utilizing Logistic Regression and Support Vector Machines

PubMed Central

Chen, Chau-Kuang; Bruce, Michelle; Tyler, Lauren; Brown, Claudine; Garrett, Angelica; Goggins, Susan; Lewis-Polite, Brandy; Weriwoh, Mirabel L; Juarez, Paul D.; Hood, Darryl B.; Skelton, Tyler

2014-01-01

The goal of this study was to analyze a 54-item instrument for assessment of perception of exposure to environmental contaminants within the context of the built environment, or exposome. This exposome was defined in five domains to include 1) home and hobby, 2) school, 3) community, 4) occupation, and 5) exposure history. Interviews were conducted with child-bearing-age minority women at Metro Nashville General Hospital at Meharry Medical College. Data were analyzed utilizing DTReg software for Support Vector Machine (SVM) modeling followed by an SPSS package for a logistic regression model. The target (outcome) variable of interest was respondent's residence by ZIP code. The results demonstrate that the rank order of important variables with respect to SVM modeling versus traditional logistic regression models is almost identical. This is the first study documenting that SVM analysis has discriminate power for determination of higher-ordered spatial relationships on an environmental exposure history questionnaire. PMID:23395953

Analysis of an environmental exposure health questionnaire in a metropolitan minority population utilizing logistic regression and Support Vector Machines.

PubMed

Chen, Chau-Kuang; Bruce, Michelle; Tyler, Lauren; Brown, Claudine; Garrett, Angelica; Goggins, Susan; Lewis-Polite, Brandy; Weriwoh, Mirabel L; Juarez, Paul D; Hood, Darryl B; Skelton, Tyler

2013-02-01

The goal of this study was to analyze a 54-item instrument for assessment of perception of exposure to environmental contaminants within the context of the built environment, or exposome. This exposome was defined in five domains to include 1) home and hobby, 2) school, 3) community, 4) occupation, and 5) exposure history. Interviews were conducted with child-bearing-age minority women at Metro Nashville General Hospital at Meharry Medical College. Data were analyzed utilizing DTReg software for Support Vector Machine (SVM) modeling followed by an SPSS package for a logistic regression model. The target (outcome) variable of interest was respondent's residence by ZIP code. The results demonstrate that the rank order of important variables with respect to SVM modeling versus traditional logistic regression models is almost identical. This is the first study documenting that SVM analysis has discriminate power for determination of higher-ordered spatial relationships on an environmental exposure history questionnaire.

A simple method for assessing occupational exposure via the one-way random effects model.

PubMed

Krishnamoorthy, K; Mathew, Thomas; Peng, Jie

2016-11-01

A one-way random effects model is postulated for the log-transformed shift-long personal exposure measurements, where the random effect in the model represents an effect due to the worker. Simple closed-form confidence intervals are proposed for the relevant parameters of interest using the method of variance estimates recovery (MOVER). The performance of the confidence bounds is evaluated and compared with those based on the generalized confidence interval approach. Comparison studies indicate that the proposed MOVER confidence bounds are better than the generalized confidence bounds for the overall mean exposure and an upper percentile of the exposure distribution. The proposed methods are illustrated using a few examples involving industrial hygiene data.

Testing of the European Union exposure-response relationships and annoyance equivalents model for annoyance due to transportation noises: The need of revised exposure-response relationships and annoyance equivalents model.

PubMed

Gille, Laure-Anne; Marquis-Favre, Catherine; Morel, Julien

2016-09-01

An in situ survey was performed in 8 French cities in 2012 to study the annoyance due to combined transportation noises. As the European Commission recommends to use the exposure-response relationships suggested by Miedema and Oudshoorn [Environmental Health Perspective, 2001] to predict annoyance due to single transportation noise, these exposure-response relationships were tested using the annoyance due to each transportation noise measured during the French survey. These relationships only enabled a good prediction in terms of the percentages of people highly annoyed by road traffic noise. For the percentages of people annoyed and a little annoyed by road traffic noise, the quality of prediction is weak. For aircraft and railway noises, prediction of annoyance is not satisfactory either. As a consequence, the annoyance equivalents model of Miedema [The Journal of the Acoustical Society of America, 2004], based on these exposure-response relationships did not enable a good prediction of annoyance due to combined transportation noises. Local exposure-response relationships were derived, following the whole computation suggested by Miedema and Oudshoorn [Environmental Health Perspective, 2001]. They led to a better calculation of annoyance due to each transportation noise in the French cities. A new version of the annoyance equivalents model was proposed using these new exposure-response relationships. This model enabled a better prediction of the total annoyance due to the combined transportation noises. These results encourage therefore to improve the annoyance prediction for noise in isolation with local or revised exposure-response relationships, which will also contribute to improve annoyance modeling for combined noises. With this aim in mind, a methodology is proposed to consider noise sensitivity in exposure-response relationships and in the annoyance equivalents model. The results showed that taking into account such variable did not enable to enhance both exposure-response relationships and the annoyance equivalents model. Copyright © 2016 Elsevier Ltd. All rights reserved.

Chlorpyrifos PBPK/PD model for multiple routes of exposure.

PubMed

Poet, Torka S; Timchalk, Charles; Hotchkiss, Jon A; Bartels, Michael J

2014-10-01

1. Chlorpyrifos (CPF) is an important pesticide used to control crop insects. Human Exposures to CPF will occur primarily through oral exposure to residues on foods. A physiologically based pharmacokinetic/pharmacodynamic (PBPK/PD) model has been developed that describes the relationship between oral, dermal and inhalation doses of CPF and key events in the pathway for cholinergic effects. The model was built on a prior oral model that addressed age-related changes in metabolism and physiology. This multi-route model was developed in rats and humans to validate all scenarios in a parallelogram design. 2. Critical biological effects from CPF exposure require metabolic activation to CPF oxon, and small amounts of metabolism in tissues will potentially have a great effect on pharmacokinetics and pharmacodynamic outcomes. Metabolism (bioactivation and detoxification) was therefore added in diaphragm, brain, lung and skin compartments. Pharmacokinetic data are available for controlled human exposures via the oral and dermal routes and from oral and inhalation studies in rats. The validated model was then used to determine relative dermal versus inhalation uptake from human volunteers exposed to CPF in an indoor scenario.

Small car exposure data project. Phase 1 : methodology

DOT National Transportation Integrated Search

1985-10-01

The Small Car Exposure Data Project represents the first phase of an effort to build a data : base of exposure variables for crash-avoidance studies. Among these are: (1) vehicle make, : model, year, body style, wheel base, weight, and horsepower; (2...

ANIMAL MODELS OF CHRONIC PESTICIDE NEUROTOXICITY.

EPA Science Inventory

There is a wealth of literature on neurotoxicological outcomes of acute and short-term exposure to pesticides in laboratory animals, but there are relatively few studies of- long-term exposure. Many reports in the literature describing ;chronic' exposures to pesticides are, in fa...

Musculoskeletal disorder symptoms in correction officers: why do they increase rapidly with job tenure?

PubMed

Warren, Nicholas; Dussetschleger, Jeffrey; Punnett, Laura; Cherniack, Martin G

2015-03-01

In this study, we sought to explain the rapid musculoskeletal symptomatology increase in correction officers (COs). COs are exposed to levels of biomechanical and psychosocial stressors that have strong associations with musculoskeletal disorders (MSDs) in other occupations, possibly contributing to their rapid health deterioration. Baseline survey data from a longitudinal study of COs and manufacturing line workers were used to model musculoskeletal symptom prevalence and intensity in the upper (UE) and lower (LE) extremity. Outcomes were regressed on demographics and biomechanical and psychosocial exposures. COs reported significantly higher prevalence and intensity of LE symptoms compared to the industrial workers. In regression models, job tenure was a primary driver of CO musculoskeletal outcomes. In CO models, a single biomechanical exposure, head and arms in awkward positions, explained variance in both UE and LE prevalence (β of 0.338 and 0.357, respectively), and low decision latitude was associated with increased LE prevalence and intensity (β of 0.229 and 0.233, respectively). Manufacturing models were less explanatory. Examining demographic associations with exposure intensity, we found none to be significant in manufacturing, but in CO models, important psychosocial exposure levels increased with job tenure. Symptom prevalence and intensity increased more rapidly with job tenure in corrections, compared to manufacturing, and were related to both biomechanical and psychosocial exposures. Tenure-related increases in psychosocial exposure levels may help explain the CO symptom increase. Although exposure assessment improvements are proposed, findings suggest focusing on improving the psychosocial work environment to reduce MSD prevalence and intensity in corrections. © 2014, Human Factors and Ergonomics Society.

MODELING OF HUMAN EXPOSURE TO IN-VEHICLE PM2.5 FROM ENVIRONMENTAL TOBACCO SMOKE

PubMed Central

Cao, Ye; Frey, H. Christopher

2012-01-01

Environmental tobacco smoke (ETS) is estimated to be a significant contributor to in-vehicle human exposure to fine particulate matter of 2.5 µm or smaller (PM2.5). A critical assessment was conducted of a mass balance model for estimating PM2.5 concentration with smoking in a motor vehicle. Recommendations for the range of inputs to the mass-balance model are given based on literature review. Sensitivity analysis was used to determine which inputs should be prioritized for data collection. Air exchange rate (ACH) and the deposition rate have wider relative ranges of variation than other inputs, representing inter-individual variability in operations, and inter-vehicle variability in performance, respectively. Cigarette smoking and emission rates, and vehicle interior volume, are also key inputs. The in-vehicle ETS mass balance model was incorporated into the Stochastic Human Exposure and Dose Simulation for Particulate Matter (SHEDS-PM) model to quantify the potential magnitude and variability of in-vehicle exposures to ETS. The in-vehicle exposure also takes into account near-road incremental PM2.5 concentration from on-road emissions. Results of probabilistic study indicate that ETS is a key contributor to the in-vehicle average and high-end exposure. Factors that mitigate in-vehicle ambient PM2.5 exposure lead to higher in-vehicle ETS exposure, and vice versa. PMID:23060732

Construction of vapor chambers used to expose mice to alcohol during the equivalent of all three trimesters of human development.

PubMed

Morton, Russell A; Diaz, Marvin R; Topper, Lauren A; Valenzuela, C Fernando

2014-07-13

Exposure to alcohol during development can result in a constellation of morphological and behavioral abnormalities that are collectively known as Fetal Alcohol Spectrum Disorders (FASDs). At the most severe end of the spectrum is Fetal Alcohol Syndrome (FAS), characterized by growth retardation, craniofacial dysmorphology, and neurobehavioral deficits. Studies with animal models, including rodents, have elucidated many molecular and cellular mechanisms involved in the pathophysiology of FASDs. Ethanol administration to pregnant rodents has been used to model human exposure during the first and second trimesters of pregnancy. Third trimester ethanol consumption in humans has been modeled using neonatal rodents. However, few rodent studies have characterized the effect of ethanol exposure during the equivalent to all three trimesters of human pregnancy, a pattern of exposure that is common in pregnant women. Here, we show how to build vapor chambers from readily obtainable materials that can each accommodate up to six standard mouse cages. We describe a vapor chamber paradigm that can be used to model exposure to ethanol, with minimal handling, during all three trimesters. Our studies demonstrate that pregnant dams developed significant metabolic tolerance to ethanol. However, neonatal mice did not develop metabolic tolerance and the number of fetuses, fetus weight, placenta weight, number of pups/litter, number of dead pups/litter, and pup weight were not significantly affected by ethanol exposure. An important advantage of this paradigm is its applicability to studies with genetically-modified mice. Additionally, this paradigm minimizes handling of animals, a major confound in fetal alcohol research.

Comparative Assessment of Particulate Air Pollution Exposure from Municipal Solid Waste Incinerator Emissions

PubMed Central

Ashworth, Danielle C.; Fuller, Gary W.; Toledano, Mireille B.; Font, Anna; Elliott, Paul; Hansell, Anna L.; de Hoogh, Kees

2013-01-01

Background. Research to date on health effects associated with incineration has found limited evidence of health risks, but many previous studies have been constrained by poor exposure assessment. This paper provides a comparative assessment of atmospheric dispersion modelling and distance from source (a commonly used proxy for exposure) as exposure assessment methods for pollutants released from incinerators. Methods. Distance from source and the atmospheric dispersion model ADMS-Urban were used to characterise ambient exposures to particulates from two municipal solid waste incinerators (MSWIs) in the UK. Additionally an exploration of the sensitivity of the dispersion model simulations to input parameters was performed. Results. The model output indicated extremely low ground level concentrations of PM10, with maximum concentrations of <0.01 μg/m3. Proximity and modelled PM10 concentrations for both MSWIs at postcode level were highly correlated when using continuous measures (Spearman correlation coefficients ~ 0.7) but showed poor agreement for categorical measures (deciles or quintiles, Cohen's kappa coefficients ≤ 0.5). Conclusion. To provide the most appropriate estimate of ambient exposure from MSWIs, it is essential that incinerator characteristics, magnitude of emissions, and surrounding meteorological and topographical conditions are considered. Reducing exposure misclassification is particularly important in environmental epidemiology to aid detection of low-level risks. PMID:23935644

Air Pollution Exposure Model for Individuals (EMI) in Health Studies: Evaluation for Ambient PM2.5 in Central North Carolina

EPA Science Inventory

Air pollution health studies of fine particulate matter (diameter ≤2.5 μm, PM2.5) often use outdoor concentrations as exposure surrogates. Failure to account for variability of indoor infiltration of ambient PM2.5 and time indoors can induce exposure errors. We developed an...

Quantification of ETS exposure in hospitality workers who have never smoked

PubMed Central

2010-01-01

Background Environmental Tobacco Smoke (ETS) was classified as human carcinogen (K1) by the German Research Council in 1998. According to epidemiological studies, the relative risk especially for lung cancer might be twice as high in persons who have never smoked but who are in the highest exposure category, for example hospitality workers. In order to implement these results in the German regulations on occupational illnesses, a valid method is needed to retrospectively assess the cumulative ETS exposure in the hospitality environment. Methods A literature-based review was carried out to locate a method that can be used for the German hospitality sector. Studies assessing ETS exposure using biological markers (for example urinary cotinine, DNA adducts) or questionnaires were excluded. Biological markers are not considered relevant as they assess exposure only over the last hours, weeks or months. Self-reported exposure based on questionnaires also does not seem adequate for medico-legal purposes. Therefore, retrospective exposure assessment should be based on mathematical models to approximate past exposure. Results For this purpose a validated model developed by Repace and Lowrey was considered appropriate. It offers the possibility of retrospectively assessing exposure with existing parameters (such as environmental dimensions, average number of smokers, ventilation characteristics and duration of exposure). The relative risk of lung cancer can then be estimated based on the individual cumulative exposure of the worker. Conclusion In conclusion, having adapted it to the German hospitality sector, an existing mathematical model appears to be capable of approximating the cumulative exposure. However, the level of uncertainty of these approximations has to be taken into account, especially for diseases with a long latency period such as lung cancer. PMID:20704719

Quantification of ETS exposure in hospitality workers who have never smoked.

PubMed

Kolb, Stefanie; Brückner, Ulrike; Nowak, Dennis; Radon, Katja

2010-08-12

Environmental Tobacco Smoke (ETS) was classified as human carcinogen (K1) by the German Research Council in 1998. According to epidemiological studies, the relative risk especially for lung cancer might be twice as high in persons who have never smoked but who are in the highest exposure category, for example hospitality workers. In order to implement these results in the German regulations on occupational illnesses, a valid method is needed to retrospectively assess the cumulative ETS exposure in the hospitality environment. A literature-based review was carried out to locate a method that can be used for the German hospitality sector. Studies assessing ETS exposure using biological markers (for example urinary cotinine, DNA adducts) or questionnaires were excluded. Biological markers are not considered relevant as they assess exposure only over the last hours, weeks or months. Self-reported exposure based on questionnaires also does not seem adequate for medico-legal purposes. Therefore, retrospective exposure assessment should be based on mathematical models to approximate past exposure. For this purpose a validated model developed by Repace and Lowrey was considered appropriate. It offers the possibility of retrospectively assessing exposure with existing parameters (such as environmental dimensions, average number of smokers, ventilation characteristics and duration of exposure). The relative risk of lung cancer can then be estimated based on the individual cumulative exposure of the worker. In conclusion, having adapted it to the German hospitality sector, an existing mathematical model appears to be capable of approximating the cumulative exposure. However, the level of uncertainty of these approximations has to be taken into account, especially for diseases with a long latency period such as lung cancer.

Spatial variations in estimated chronic exposure to traffic-related air pollution in working populations: A simulation

PubMed Central

Setton, Eleanor M; Keller, C Peter; Cloutier-Fisher, Denise; Hystad, Perry W

2008-01-01

Background Chronic exposure to traffic-related air pollution is associated with a variety of health impacts in adults and recent studies show that exposure varies spatially, with some residents in a community more exposed than others. A spatial exposure simulation model (SESM) which incorporates six microenvironments (home indoor, work indoor, other indoor, outdoor, in-vehicle to work and in-vehicle other) is described and used to explore spatial variability in estimates of exposure to traffic-related nitrogen dioxide (not including indoor sources) for working people. The study models spatial variability in estimated exposure aggregated at the census tracts level for 382 census tracts in the Greater Vancouver Regional District of British Columbia, Canada. Summary statistics relating to the distributions of the estimated exposures are compared visually through mapping. Observed variations are explored through analyses of model inputs. Results Two sources of spatial variability in exposure to traffic-related nitrogen dioxide were identified. Median estimates of total exposure ranged from 8 μg/m3 to 35 μg/m3 of annual average hourly NO2 for workers in different census tracts in the study area. Exposure estimates are highest where ambient pollution levels are highest. This reflects the regional gradient of pollution in the study area and the relatively high percentage of time spent at home locations. However, for workers within the same census tract, variations were observed in the partial exposure estimates associated with time spent outside the residential census tract. Simulation modeling shows that some workers may have exposures 1.3 times higher than other workers residing in the same census tract because of time spent away from the residential census tract, and that time spent in work census tracts contributes most to the differences in exposure. Exposure estimates associated with the activity of commuting by vehicle to work were negligible, based on the relatively short amount of time spent in this microenvironment compared to other locations. We recognize that this may not be the case for pollutants other than NO2. These results represent the first time spatially disaggregated variations in exposure to traffic-related air pollution within a community have been estimated and reported. Conclusion The results suggest that while time spent in the home indoor microenvironment contributes most to between-census tract variation in estimates of annual average exposures to traffic-related NO2, time spent in the work indoor microenvironment contributes most to within-census tract variation, and time spent in transit by vehicle makes a negligible contribution. The SESM has potential as a policy evaluation tool, given input data that reflect changes in pollution levels or work flow patterns due to traffic demand management and land use development policy. PMID:18638398

Bayesian effect estimation accounting for adjustment uncertainty.

PubMed

Wang, Chi; Parmigiani, Giovanni; Dominici, Francesca

2012-09-01

Model-based estimation of the effect of an exposure on an outcome is generally sensitive to the choice of which confounding factors are included in the model. We propose a new approach, which we call Bayesian adjustment for confounding (BAC), to estimate the effect of an exposure of interest on the outcome, while accounting for the uncertainty in the choice of confounders. Our approach is based on specifying two models: (1) the outcome as a function of the exposure and the potential confounders (the outcome model); and (2) the exposure as a function of the potential confounders (the exposure model). We consider Bayesian variable selection on both models and link the two by introducing a dependence parameter, ω, denoting the prior odds of including a predictor in the outcome model, given that the same predictor is in the exposure model. In the absence of dependence (ω= 1), BAC reduces to traditional Bayesian model averaging (BMA). In simulation studies, we show that BAC, with ω > 1, estimates the exposure effect with smaller bias than traditional BMA, and improved coverage. We, then, compare BAC, a recent approach of Crainiceanu, Dominici, and Parmigiani (2008, Biometrika 95, 635-651), and traditional BMA in a time series data set of hospital admissions, air pollution levels, and weather variables in Nassau, NY for the period 1999-2005. Using each approach, we estimate the short-term effects of on emergency admissions for cardiovascular diseases, accounting for confounding. This application illustrates the potentially significant pitfalls of misusing variable selection methods in the context of adjustment uncertainty. © 2012, The International Biometric Society.

Improved heat transfer modeling of the eye for electromagnetic wave exposures.

PubMed

Hirata, Akimasa

2007-05-01

This study proposed an improved heat transfer model of the eye for exposure to electromagnetic (EM) waves. Particular attention was paid to the difference from the simplified heat transfer model commonly used in this field. From our computational results, the temperature elevation in the eye calculated with the simplified heat transfer model was largely influenced by the EM absorption outside the eyeball, but not when we used our improved model.

Bivariate Left-Censored Bayesian Model for Predicting Exposure: Preliminary Analysis of Worker Exposure during the Deepwater Horizon Oil Spill.

PubMed

Groth, Caroline; Banerjee, Sudipto; Ramachandran, Gurumurthy; Stenzel, Mark R; Sandler, Dale P; Blair, Aaron; Engel, Lawrence S; Kwok, Richard K; Stewart, Patricia A

2017-01-01

In April 2010, the Deepwater Horizon oil rig caught fire and exploded, releasing almost 5 million barrels of oil into the Gulf of Mexico over the ensuing 3 months. Thousands of oil spill workers participated in the spill response and clean-up efforts. The GuLF STUDY being conducted by the National Institute of Environmental Health Sciences is an epidemiological study to investigate potential adverse health effects among these oil spill clean-up workers. Many volatile chemicals were released from the oil into the air, including total hydrocarbons (THC), which is a composite of the volatile components of oil including benzene, toluene, ethylbenzene, xylene, and hexane (BTEXH). Our goal is to estimate exposure levels to these toxic chemicals for groups of oil spill workers in the study (hereafter called exposure groups, EGs) with likely comparable exposure distributions. A large number of air measurements were collected, but many EGs are characterized by datasets with a large percentage of censored measurements (below the analytic methods' limits of detection) and/or a limited number of measurements. We use THC for which there was less censoring to develop predictive linear models for specific BTEXH air exposures with higher degrees of censoring. We present a novel Bayesian hierarchical linear model that allows us to predict, for different EGs simultaneously, exposure levels of a second chemical while accounting for censoring in both THC and the chemical of interest. We illustrate the methodology by estimating exposure levels for several EGs on the Development Driller III, a rig vessel charged with drilling one of the relief wells. The model provided credible estimates in this example for geometric means, arithmetic means, variances, correlations, and regression coefficients for each group. This approach should be considered when estimating exposures in situations when multiple chemicals are correlated and have varying degrees of censoring. © The Author 2017. Published by Oxford University Press on behalf of the British Occupational Hygiene Society.

Spatiotemporal air pollution exposure assessment for a Canadian population-based lung cancer case-control study

PubMed Central

2012-01-01

Background Few epidemiological studies of air pollution have used residential histories to develop long-term retrospective exposure estimates for multiple ambient air pollutants and vehicle and industrial emissions. We present such an exposure assessment for a Canadian population-based lung cancer case-control study of 8353 individuals using self-reported residential histories from 1975 to 1994. We also examine the implications of disregarding and/or improperly accounting for residential mobility in long-term exposure assessments. Methods National spatial surfaces of ambient air pollution were compiled from recent satellite-based estimates (for PM2.5 and NO2) and a chemical transport model (for O3). The surfaces were adjusted with historical annual air pollution monitoring data, using either spatiotemporal interpolation or linear regression. Model evaluation was conducted using an independent ten percent subset of monitoring data per year. Proximity to major roads, incorporating a temporal weighting factor based on Canadian mobile-source emission estimates, was used to estimate exposure to vehicle emissions. A comprehensive inventory of geocoded industries was used to estimate proximity to major and minor industrial emissions. Results Calibration of the national PM2.5 surface using annual spatiotemporal interpolation predicted historical PM2.5 measurement data best (R2 = 0.51), while linear regression incorporating the national surfaces, a time-trend and population density best predicted historical concentrations of NO2 (R2 = 0.38) and O3 (R2 = 0.56). Applying the models to study participants residential histories between 1975 and 1994 resulted in mean PM2.5, NO2 and O3 exposures of 11.3 μg/m3 (SD = 2.6), 17.7 ppb (4.1), and 26.4 ppb (3.4) respectively. On average, individuals lived within 300 m of a highway for 2.9 years (15% of exposure-years) and within 3 km of a major industrial emitter for 6.4 years (32% of exposure-years). Approximately 50% of individuals were classified into a different PM2.5, NO2 and O3 exposure quintile when using study entry postal codes and spatial pollution surfaces, in comparison to exposures derived from residential histories and spatiotemporal air pollution models. Recall bias was also present for self-reported residential histories prior to 1975, with cases recalling older residences more often than controls. Conclusions We demonstrate a flexible exposure assessment approach for estimating historical air pollution concentrations over large geographical areas and time-periods. In addition, we highlight the importance of including residential histories in long-term exposure assessments. For submission to: Environmental Health PMID:22475580

Evaluating Exposure-Response Associations for Non-Hodgkin Lymphoma with Varying Methods of Assigning Cumulative Benzene Exposure in the Shanghai Women's Health Study.

PubMed

Friesen, Melissa C; Bassig, Bryan A; Vermeulen, Roel; Shu, Xiao-Ou; Purdue, Mark P; Stewart, Patricia A; Xiang, Yong-Bing; Chow, Wong-Ho; Ji, Bu-Tian; Yang, Gong; Linet, Martha S; Hu, Wei; Gao, Yu-Tang; Zheng, Wei; Rothman, Nathaniel; Lan, Qing

2017-01-01

To provide insight into the contributions of exposure measurements to job exposure matrices (JEMs), we examined the robustness of an association between occupational benzene exposure and non-Hodgkin lymphoma (NHL) to varying exposure assessment methods. NHL risk was examined in a prospective population-based cohort of 73087 women in Shanghai. A mixed-effects model that combined a benzene JEM with >60000 short-term, area benzene inspection measurements was used to derive two sets of measurement-based benzene estimates: 'job/industry-specific' estimates (our presumed best approach) were derived from the model's fixed effects (year, JEM intensity rating) and random effects (occupation, industry); 'calibrated JEM' estimates were derived using only the fixed effects. 'Uncalibrated JEM' (using the ordinal JEM ratings) and exposure duration estimates were also calculated. Cumulative exposure for each subject was calculated for each approach based on varying exposure definitions defined using the JEM's probability ratings. We examined the agreement between the cumulative metrics and evaluated changes in the benzene-NHL associations. For our primary exposure definition, the job/industry-specific estimates were moderately to highly correlated with all other approaches (Pearson correlation 0.61-0.89; Spearman correlation > 0.99). All these metrics resulted in statistically significant exposure-response associations for NHL, with negligible gain in model fit from using measurement-based estimates. Using more sensitive or specific exposure definitions resulted in elevated but non-significant associations. The robust associations observed here with varying benzene assessment methods provide support for a benzene-NHL association. While incorporating exposure measurements did not improve model fit, the measurements allowed us to derive quantitative exposure-response curves. Published by Oxford University Press on behalf of the British Occupational Hygiene Society 2017.

Assessing and reporting uncertainties in dietary exposure analysis - Part II: Application of the uncertainty template to a practical example of exposure assessment.

PubMed

Tennant, David; Bánáti, Diána; Kennedy, Marc; König, Jürgen; O'Mahony, Cian; Kettler, Susanne

2017-11-01

A previous publication described methods for assessing and reporting uncertainty in dietary exposure assessments. This follow-up publication uses a case study to develop proposals for representing and communicating uncertainty to risk managers. The food ingredient aspartame is used as the case study in a simple deterministic model (the EFSA FAIM template) and with more sophisticated probabilistic exposure assessment software (FACET). Parameter and model uncertainties are identified for each modelling approach and tabulated. The relative importance of each source of uncertainty is then evaluated using a semi-quantitative scale and the results expressed using two different forms of graphical summary. The value of this approach in expressing uncertainties in a manner that is relevant to the exposure assessment and useful to risk managers is then discussed. It was observed that the majority of uncertainties are often associated with data sources rather than the model itself. However, differences in modelling methods can have the greatest impact on uncertainties overall, particularly when the underlying data are the same. It was concluded that improved methods for communicating uncertainties for risk management is the research area where the greatest amount of effort is suggested to be placed in future. Copyright © 2017 The Authors. Published by Elsevier Ltd.. All rights reserved.

Integration of the predictions of two models with dose measurements in a case study of children exposed to the emissions of a lead smelter

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bonnard, R.; McKone, T.E.

2009-03-01

The predictions of two source-to-dose models are systematically evaluated with observed data collected in a village polluted by a currently operating secondary lead smelter. Both models were built up from several sub-models linked together and run using Monte-Carlo simulation, to calculate the distribution children's blood lead levels attributable to the emissions from the facility. The first model system is composed of the CalTOX model linked to a recoded version of the IEUBK model. This system provides the distribution of the media-specific lead concentrations (air, soil, fruit, vegetables and blood) in the whole area investigated. The second model consists of amore » statistical model to estimate the lead deposition on the ground, a modified version of the model HHRAP and the same recoded version of the IEUBK model. This system provides an estimate of the concentration of exposure of specific individuals living in the study area. The predictions of the first model system were improved in terms of accuracy and precision by performing a sensitivity analysis and using field data to correct the default value provided for the leaf wet density. However, in this case study, the first model system tends to overestimate the exposure due to exposed vegetables. The second model was tested for nine children with contrasting exposure conditions. It managed to capture the blood levels for eight of them. In the last case, the exposure of the child by pathways not considered in the model may explain the failure of the model. The interest of this integrated model is to provide outputs with lower variance than the first model system, but at the moment further tests are necessary to conclude about its accuracy.« less

Reliability of Current Biokinetic and Dosimetric Models for Radionuclides: A Pilot Study

DOE Office of Scientific and Technical Information (OSTI.GOV)

Leggett, Richard Wayne; Eckerman, Keith F; Meck, Robert A.

2008-10-01

This report describes the results of a pilot study of the reliability of the biokinetic and dosimetric models currently used by the U.S. Nuclear Regulatory Commission (NRC) as predictors of dose per unit internal or external exposure to radionuclides. The study examines the feasibility of critically evaluating the accuracy of these models for a comprehensive set of radionuclides of concern to the NRC. Each critical evaluation would include: identification of discrepancies between the models and current databases; characterization of uncertainties in model predictions of dose per unit intake or unit external exposure; characterization of variability in dose per unit intakemore » or unit external exposure; and evaluation of prospects for development of more accurate models. Uncertainty refers here to the level of knowledge of a central value for a population, and variability refers to quantitative differences between different members of a population. This pilot study provides a critical assessment of models for selected radionuclides representing different levels of knowledge of dose per unit exposure. The main conclusions of this study are as follows: (1) To optimize the use of available NRC resources, the full study should focus on radionuclides most frequently encountered in the workplace or environment. A list of 50 radionuclides is proposed. (2) The reliability of a dose coefficient for inhalation or ingestion of a radionuclide (i.e., an estimate of dose per unit intake) may depend strongly on the specific application. Multiple characterizations of the uncertainty in a dose coefficient for inhalation or ingestion of a radionuclide may be needed for different forms of the radionuclide and different levels of information of that form available to the dose analyst. (3) A meaningful characterization of variability in dose per unit intake of a radionuclide requires detailed information on the biokinetics of the radionuclide and hence is not feasible for many infrequently studied radionuclides. (4) The biokinetics of a radionuclide in the human body typically represents the greatest source of uncertainty or variability in dose per unit intake. (5) Characterization of uncertainty in dose per unit exposure is generally a more straightforward problem for external exposure than for intake of a radionuclide. (6) For many radionuclides the most important outcome of a large-scale critical evaluation of databases and biokinetic models for radionuclides is expected to be the improvement of current models. Many of the current models do not fully or accurately reflect available radiobiological or physiological information, either because the models are outdated or because they were based on selective or uncritical use of data or inadequate model structures. In such cases the models should be replaced with physiologically realistic models that incorporate a wider spectrum of information.« less

Calibration and validation of toxicokinetic-toxicodynamic models for three neonicotinoids and some aquatic macroinvertebrates.

PubMed

Focks, Andreas; Belgers, Dick; Boerwinkel, Marie-Claire; Buijse, Laura; Roessink, Ivo; Van den Brink, Paul J

2018-05-01

Exposure patterns in ecotoxicological experiments often do not match the exposure profiles for which a risk assessment needs to be performed. This limitation can be overcome by using toxicokinetic-toxicodynamic (TKTD) models for the prediction of effects under time-variable exposure. For the use of TKTD models in the environmental risk assessment of chemicals, it is required to calibrate and validate the model for specific compound-species combinations. In this study, the survival of macroinvertebrates after exposure to the neonicotinoid insecticide was modelled using TKTD models from the General Unified Threshold models of Survival (GUTS) framework. The models were calibrated on existing survival data from acute or chronic tests under static exposure regime. Validation experiments were performed for two sets of species-compound combinations: one set focussed on multiple species sensitivity to a single compound: imidacloprid, and the other set on the effects of multiple compounds for a single species, i.e., the three neonicotinoid compounds imidacloprid, thiacloprid and thiamethoxam, on the survival of the mayfly Cloeon dipterum. The calibrated models were used to predict survival over time, including uncertainty ranges, for the different time-variable exposure profiles used in the validation experiments. From the comparison between observed and predicted survival, it appeared that the accuracy of the model predictions was acceptable for four of five tested species in the multiple species data set. For compounds such as neonicotinoids, which are known to have the potential to show increased toxicity under prolonged exposure, the calibration and validation of TKTD models for survival needs to be performed ideally by considering calibration data from both acute and chronic tests.

How much, how long, what, and where: air pollution exposure assessment for epidemiologic studies of respiratory disease.

PubMed

Brauer, Michael

2010-05-01

Epidemiology has played an important role in the understanding of air pollution as a risk factor for respiratory disease and in the evidence base for air quality standards. With the widespread availability of genetic information and increasingly sophisticated measurements of molecular markers of adverse effects, there is a need for more specific and precise assessment of exposure to maximize the potential information to be derived from epidemiologic studies. Here advances in air pollution exposure assessment and their applications to studies of respiratory disease are reviewed, with a focus on recent studies of traffic-related air pollution and asthma. Although continuous measurements of personal exposures for all study subjects for a complete study period might be considered the desired "gold standard" for exposure, this is rarely, if ever, achieved due to feasibility constraints. Given this, exposure is typically estimated using models. Recent applications of geospatial (e.g., land use regression) models to studies of respiratory disease have made possible new study designs focused on spatial variability in exposure within urban areas and have provided new insights into the potential role of traffic-related air pollution (TRAP) as a risk factor for the development of childhood asthma. Substantial uncertainty remains, however, regarding what agent(s) within TRAP might be responsible for the observed associations. Future research will require increasing the specificity of exposure assessment to identify the potential roles of individual air pollution components, to elucidate potential mechanisms, and to facilitate studies of mixtures and gene-air pollution interactions.

Consolidated Human Activity Database (CHAD) for use in human exposure and health studies and predictive models

EPA Pesticide Factsheets

EPA scientists have compiled detailed data on human behavior from 22 separate exposure and time-use studies into CHAD. The database includes more than 54,000 individual study days of detailed human behavior.

Air Pollution Exposure Model for Individuals (EMI) in Health Studies

EPA Science Inventory

In health studies, traffic-related air pollution is associated with adverse respiratory effects. Due to cost and participant burden of personal measurements, health studies often estimate exposures using local ambient air monitors. Since outdoor levels do not necessarily reflect ...

Trichloroethylene and Cancer: Systematic and Quantitative Review of Epidemiologic Evidence for Identifying Hazards

PubMed Central

Scott, Cheryl Siegel; Jinot, Jennifer

2011-01-01

We conducted a meta-analysis focusing on studies with high potential for trichloroethylene (TCE) exposure to provide quantitative evaluations of the evidence for associations between TCE exposure and kidney, liver, and non-Hodgkin lymphoma (NHL) cancers. A systematic review documenting essential design features, exposure assessment approaches, statistical analyses, and potential sources of confounding and bias identified twenty-four cohort and case-control studies on TCE and the three cancers of interest with high potential for exposure, including five recently published case-control studies of kidney cancer or NHL. Fixed- and random-effects models were fitted to the data on overall exposure and on the highest exposure group. Sensitivity analyses examined the influence of individual studies and of alternative risk estimate selections. For overall TCE exposure and kidney cancer, the summary relative risk (RRm) estimate from the random effects model was 1.27 (95% CI: 1.13, 1.43), with a higher RRm for the highest exposure groups (1.58, 95% CI: 1.28, 1.96). The RRm estimates were not overly sensitive to alternative risk estimate selections or to removal of an individual study. There was no apparent heterogeneity or publication bias. For NHL, RRm estimates for overall exposure and for the highest exposure group, respectively, were 1.23 (95% CI: 1.07, 1.42) and 1.43 (95% CI: 1.13, 1.82) and, for liver cancer, 1.29 (95% CI: 1.07, 1.56) and 1.28 (95% CI: 0.93, 1.77). Our findings provide strong support for a causal association between TCE exposure and kidney cancer. The support is strong but less robust for NHL, where issues of study heterogeneity, potential publication bias, and weaker exposure-response results contribute uncertainty, and more limited for liver cancer, where only cohort studies with small numbers of cases were available. PMID:22163205

Effects of prenatal alcohol exposure on testosterone and pubertal development

PubMed Central

Carter, R.C.; Jacobson, J.L.; Dodge, N.C.; Granger, D.A.; Jacobson, S.W.

2014-01-01

Background Animal models have demonstrated fetal alcohol-related disruptions in neuroendocrine function in the hypothalamic-pituitary-gonadal (HPG) axis and downstream effects on pubertal development and sexual behavior in males and females, but little is known about these effects in humans. This study examined whether prenatal alcohol exposure is associated with alterations in testosterone during adolescence and whether it affects timing of pubertal development. Methods The sample consisted of 265 African American adolescents from the Detroit Longitudinal Cohort Study for whom testosterone and/or pubertal development data were available. Subjects were offspring of women recruited at their first prenatal clinic visit to over-represent moderate-to-heavy alcohol use, including a 5% random sample of low-level drinkers/abstainers. Mothers were interviewed at every prenatal visit about their alcohol consumption using a timeline follow-back approach and about their smoking and drug use and sociodemographic factors. At age 14 years, adolescents provided salivary samples, which were analyzed for testosterone (pg/mL), self-reported Tanner stages for pubertal development, and age at menarche (females). Results Prenatal alcohol exposure was related to elevated testosterone concentrations for males and females but not to changes in Tanner stages or age at menarche, after controlling for confounders. In regression models stratified by alcohol exposure, the expected relation between testosterone and pubic hair development was seen among males with light-to-no prenatal alcohol exposure but not among those with moderate-to-heavy prenatal alcohol exposure. This interaction between testosterone and prenatal alcohol exposure was confirmed in multivariable models including an alcohol exposure group X testosterone interaction term and potential confounders. Conclusions This study was the first to show a relation between prenatal alcohol exposure and increased testosterone during adolescence and evidence of decreased testosterone responsiveness in tissues related to pubertal development. Further studies examining androgen receptor expression and other hormonal and cellular factors affecting pubertal development may reveal important mechanisms underlying these teratogenic effects of alcohol exposure. PMID:24717169

Instrumental variables estimation of exposure effects on a time-to-event endpoint using structural cumulative survival models.

PubMed

Martinussen, Torben; Vansteelandt, Stijn; Tchetgen Tchetgen, Eric J; Zucker, David M

2017-12-01

The use of instrumental variables for estimating the effect of an exposure on an outcome is popular in econometrics, and increasingly so in epidemiology. This increasing popularity may be attributed to the natural occurrence of instrumental variables in observational studies that incorporate elements of randomization, either by design or by nature (e.g., random inheritance of genes). Instrumental variables estimation of exposure effects is well established for continuous outcomes and to some extent for binary outcomes. It is, however, largely lacking for time-to-event outcomes because of complications due to censoring and survivorship bias. In this article, we make a novel proposal under a class of structural cumulative survival models which parameterize time-varying effects of a point exposure directly on the scale of the survival function; these models are essentially equivalent with a semi-parametric variant of the instrumental variables additive hazards model. We propose a class of recursive instrumental variable estimators for these exposure effects, and derive their large sample properties along with inferential tools. We examine the performance of the proposed method in simulation studies and illustrate it in a Mendelian randomization study to evaluate the effect of diabetes on mortality using data from the Health and Retirement Study. We further use the proposed method to investigate potential benefit from breast cancer screening on subsequent breast cancer mortality based on the HIP-study. © 2017, The International Biometric Society.

Air-liquid interface exposure to aerosols of poorly soluble nanomaterials induces different biological activation levels compared to exposure to suspensions.

PubMed

Loret, Thomas; Peyret, Emmanuel; Dubreuil, Marielle; Aguerre-Chariol, Olivier; Bressot, Christophe; le Bihan, Olivier; Amodeo, Tanguy; Trouiller, Bénédicte; Braun, Anne; Egles, Christophe; Lacroix, Ghislaine

2016-11-03

Recently, much progress has been made to develop more physiologic in vitro models of the respiratory system and improve in vitro simulation of particle exposure through inhalation. Nevertheless, the field of nanotoxicology still suffers from a lack of relevant in vitro models and exposure methods to predict accurately the effects observed in vivo, especially after respiratory exposure. In this context, the aim of our study was to evaluate if exposing pulmonary cells at the air-liquid interface to aerosols of inhalable and poorly soluble nanomaterials generates different toxicity patterns and/or biological activation levels compared to classic submerged exposures to suspensions. Three nano-TiO 2 and one nano-CeO 2 were used. An exposure system was set up using VitroCell® devices to expose pulmonary cells at the air-liquid interface to aerosols. A549 alveolar cells in monocultures or in co-cultures with THP-1 macrophages were exposed to aerosols in inserts or to suspensions in inserts and in plates. Submerged exposures in inserts were performed, using similar culture conditions and exposure kinetics to the air-liquid interface, to provide accurate comparisons between the methods. Exposure in plates using classical culture and exposure conditions was performed to provide comparable results with classical submerged exposure studies. The biological activity of the cells (inflammation, cell viability, oxidative stress) was assessed at 24 h and comparisons of the nanomaterial toxicities between exposure methods were performed. Deposited doses of nanomaterials achieved using our aerosol exposure system were sufficient to observe adverse effects. Co-cultures were more sensitive than monocultures and biological responses were usually observed at lower doses at the air-liquid interface than in submerged conditions. Nevertheless, the general ranking of the nanomaterials according to their toxicity was similar across the different exposure methods used. We showed that exposure of cells at the air-liquid interface represents a valid and sensitive method to assess the toxicity of several poorly soluble nanomaterials. We underlined the importance of the cellular model used and offer the possibility to deal with low deposition doses by using more sensitive and physiologic cellular models. This brings perspectives towards the use of relevant in vitro methods of exposure to assess nanomaterial toxicity.

Modelling the seasonal variation of vitamin D due to sun exposure.

PubMed

Diffey, B L

2010-06-01

The current interest in vitamin D as a preventive agent in many chronic diseases has led to a reappraisal of adequate sun exposure. Yet just what constitutes adequacy remains to be clearly defined and validated. To do this requires an understanding of how behaviour outdoors during the year translates into seasonal changes in vitamin D status. To develop a model for estimating the changes in serum 25-hydroxyvitamin D [25(OH)D] levels as a consequence of sun exposure throughout the year. A novel mathematical model is described that incorporates the changes in serum 25(OH)D following a single, whole-body exposure to solar ultraviolet radiation with daily sun exposure in order to estimate the annual variation in serum 25(OH)D. The model yields results that agree closely with measured data from a large population-based study. Application of the model showed that current advice about 10-20 min of daily sun exposure during the summer months does little in the way of boosting overall 25(OH)D levels, while sufficient sun exposure that could achieve a worthwhile benefit would compromise skin health. There is little in the way of public health advice concerning the benefits of sun exposure that can be given as an effective means of maintaining adequate vitamin D levels throughout the year. Instead it would seem safer and more effective to fortify more foods with vitamin D and/or to consider the use of supplements during the winter months. Messages concerning sun exposure should remain focused on the detrimental effects of excessive sun exposure and should avoid giving specific advice on what might be 'optimal' sun exposure. © 2010 The Authors. Journal Compilation © 2010 British Association of Dermatologists.

Spatial variations in ambient ultrafine particle concentrations and the risk of incident prostate cancer: A case-control study.

PubMed

Weichenthal, Scott; Lavigne, Eric; Valois, Marie-France; Hatzopoulou, Marianne; Van Ryswyk, Keith; Shekarrizfard, Maryam; Villeneuve, Paul J; Goldberg, Mark S; Parent, Marie-Elise

2017-07-01

Diesel exhaust contains large numbers of ultrafine particles (UFPs, <0.1µm) and is a recognized human carcinogen. However, epidemiological studies have yet to evaluate the relationship between UFPs and cancer incidence. We conducted a case-control study of UFPs and incident prostate cancer in Montreal, Canada. Cases were identified from all main Francophone hospitals in the Montreal area between 2005 and 2009. Population controls were identified from provincial electoral lists of French Montreal residents and frequency-matched to cases using 5-year age groups. UFP exposures were estimated using a land use regression model. Exposures were assigned to residential locations at the time of diagnosis/recruitment as well as approximately 10-years earlier to consider potential latency between exposure and disease onset. Odds ratios (OR) and 95% confidence intervals (95% CI) were calculated per interquartile range (IQR) increase in UFPs (approximately 4000 particles/cm 3 ) using logistic regression models adjusting for individual-level and ecological covariates. Ambient UFP concentrations were associated with an increased risk of prostate cancer (OR=1.10, 95% CI: 1.01, 1.19) in fully adjusted models when exposures were assigned to residences 10-years prior to diagnosis. This risk estimate increased slightly (OR=1.17, 95% CI; 1.01, 1.35) when modeled as a non-linear natural spline function. A smaller increased risk (OR=1.04, 95% CI: 0.97, 1.11) was observed when exposures were assigned to residences at the time of diagnosis. Exposure to ambient UFPs may increase the risk of prostate cancer. Future studies are needed to replicate this finding as this is the first study to evaluate this relationship. Crown Copyright © 2017. Published by Elsevier Inc. All rights reserved.

Assessment of critical exposure and outcome windows in time-to-event analysis with application to air pollution and preterm birth study.

PubMed

Chang, Howard H; Warren, Joshua L; Darrow, Lnydsey A; Reich, Brian J; Waller, Lance A

2015-07-01

In reproductive epidemiology, there is a growing interest to examine associations between air pollution exposure during pregnancy and the risk of preterm birth (PTB). One important research objective is to identify critical periods of exposure and estimate the associated effects at different stages of pregnancy. However, population studies have reported inconsistent findings. This may be due to limitations from the standard analytic approach of treating PTB as a binary outcome without considering time-varying exposures together over the course of pregnancy. To address this research gap, we present a Bayesian hierarchical model for conducting a comprehensive examination of gestational air pollution exposure by estimating the joint effects of weekly exposures during different vulnerable periods. Our model also treats PTB as a time-to-event outcome to address the challenge of different exposure lengths among ongoing pregnancies. The proposed model is applied to a dataset of geocoded birth records in the Atlanta metropolitan area between 1999-2005 to examine the risk of PTB associated with gestational exposure to ambient fine particulate matter [Formula: see text]m in aerodynamic diameter (PM[Formula: see text]). We find positive associations between PM[Formula: see text] exposure during early and mid-pregnancy, and evidence that associations are stronger for PTBs occurring around week 30. © The Author 2015. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Modeling Population Exposure to Ultrafine Particles in a Major Italian Urban Area

PubMed Central

Spinazzè, Andrea; Cattaneo, Andrea; Peruzzo, Carlo; Cavallo, Domenico M.

2014-01-01

Average daily ultrafine particles (UFP) exposure of adult Milan subpopulations (defined on the basis of gender, and then for age, employment or educational status), in different exposure scenarios (typical working day in summer and winter) were simulated using a microenvironmental stochastic simulation model. The basic concept of this kind of model is that time-weighted average exposure is defined as the sum of partial microenvironmental exposures, which are determined by the product of UFP concentration and time spent in each microenvironment. In this work, environmental concentrations were derived from previous experimental studies that were based on microenvironmental measurements in the city of Milan by means of personal or individual monitoring, while time-activity patterns were derived from the EXPOLIS study. A significant difference was observed between the exposures experienced in winter (W: 28,415 pt/cm3) and summer (S: 19,558 pt/cm3). Furthermore, simulations showed a moderate difference between the total exposures experienced by women (S: 19,363 pt/cm3; W: 27,623 pt/cm3) and men (S: 18,806 pt/cm3; W: 27,897 pt/cm3). In addition, differences were found as a function of (I) age, (II) employment status and (III) educational level; accordingly, the highest total exposures resulted for (I) 55–59 years old people, (II) housewives and students and (III) people with higher educational level (more than 10 years of scholarity). Finally, significant differences were found between microenvironment-specific exposures. PMID:25321878

Modelling survival: exposure pattern, species sensitivity and uncertainty

PubMed Central

Ashauer, Roman; Albert, Carlo; Augustine, Starrlight; Cedergreen, Nina; Charles, Sandrine; Ducrot, Virginie; Focks, Andreas; Gabsi, Faten; Gergs, André; Goussen, Benoit; Jager, Tjalling; Kramer, Nynke I.; Nyman, Anna-Maija; Poulsen, Veronique; Reichenberger, Stefan; Schäfer, Ralf B.; Van den Brink, Paul J.; Veltman, Karin; Vogel, Sören; Zimmer, Elke I.; Preuss, Thomas G.

2016-01-01

The General Unified Threshold model for Survival (GUTS) integrates previously published toxicokinetic-toxicodynamic models and estimates survival with explicitly defined assumptions. Importantly, GUTS accounts for time-variable exposure to the stressor. We performed three studies to test the ability of GUTS to predict survival of aquatic organisms across different pesticide exposure patterns, time scales and species. Firstly, using synthetic data, we identified experimental data requirements which allow for the estimation of all parameters of the GUTS proper model. Secondly, we assessed how well GUTS, calibrated with short-term survival data of Gammarus pulex exposed to four pesticides, can forecast effects of longer-term pulsed exposures. Thirdly, we tested the ability of GUTS to estimate 14-day median effect concentrations of malathion for a range of species and use these estimates to build species sensitivity distributions for different exposure patterns. We find that GUTS adequately predicts survival across exposure patterns that vary over time. When toxicity is assessed for time-variable concentrations species may differ in their responses depending on the exposure profile. This can result in different species sensitivity rankings and safe levels. The interplay of exposure pattern and species sensitivity deserves systematic investigation in order to better understand how organisms respond to stress, including humans. PMID:27381500

Estimating Air-Manganese Exposures in Two Ohio Towns ...

EPA Pesticide Factsheets

Manganese (Mn), a nutrient required for normal metabolic function, is also a persistent air pollutant and a known neurotoxin at high concentrations. Elevated exposures can result in a number of motor and cognitive deficits. Quantifying chronic personal exposures in residential populations studied by environmental epidemiologists can be time-consuming and expensive. We developed an approach for quantifying chronic exposures for two towns (Marietta and East Liverpool, Ohio) with elevated air Mn concentrations (air-Mn) related to ambient emissions from industrial processes. This was accomplished through the use of measured and modeled data in the communities studied. A novel approach was developed because one of the facilities lacked emissions data for the purposes of modeling. A unit emission rate was assumed over the surface area of both source facilities, and offsite concentrations at receptor residences and air monitoring sites were estimated with the American Meteorological Society/Environmental Protection Agency Regulatory Model (AERMOD). Ratios of all modeled receptor points were created, and a long-running air monitor was identified as a reference location. All ratios were normalized to the reference location. Long-term averages at all residential receptor points were calculated using modeled ratios and data from the reference monitoring location. Modeled five-year average air-Mn exposures ranged from 0.03-1.61 µg/m3 in Marietta and 0.01-6.32 µg/m3 in E

Air pollution exposure modeling of individuals

EPA Science Inventory

Air pollution epidemiology studies of ambient fine particulate matter (PM2.5) often use outdoor concentrations as exposure surrogates. These surrogates can induce exposure error since they do not account for (1) time spent indoors with ambient PM2.5 levels attenuated from outdoor...

Is long-term exposure to traffic pollution associated with mortality? A small-area study in London.

PubMed

Halonen, Jaana I; Blangiardo, Marta; Toledano, Mireille B; Fecht, Daniela; Gulliver, John; Ghosh, Rebecca; Anderson, H Ross; Beevers, Sean D; Dajnak, David; Kelly, Frank J; Wilkinson, Paul; Tonne, Cathryn

2016-01-01

Long-term exposure to primary traffic pollutants may be harmful for health but few studies have investigated effects on mortality. We examined associations for six primary traffic pollutants with all-cause and cause-specific mortality in 2003-2010 at small-area level using linear and piecewise linear Poisson regression models. In linear models most pollutants showed negative or null association with all-cause, cardiovascular or respiratory mortality. In the piecewise models we observed positive associations in the lowest exposure range (e.g. relative risk (RR) for all-cause mortality 1.07 (95% credible interval (CI) = 1.00-1.15) per 0.15 μg/m(3) increase in exhaust related primary particulate matter ≤2.5 μm (PM2.5)) whereas associations in the highest exposure range were negative (corresponding RR 0.93, 95% CI: 0.91-0.96). Overall, there was only weak evidence of positive associations with mortality. That we found the strongest positive associations in the lowest exposure group may reflect residual confounding by unmeasured confounders that varies by exposure group. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.

A study on modeling nitrogen dioxide concentrations using land-use regression and conventionally used exposure assessment methods

NASA Astrophysics Data System (ADS)

Choi, Giehae; Bell, Michelle L.; Lee, Jong-Tae

2017-04-01

The land-use regression (LUR) approach to estimate the levels of ambient air pollutants is becoming popular due to its high validity in predicting small-area variations. However, only a few studies have been conducted in Asian countries, and much less research has been conducted on comparing the performances and applied estimates of different exposure assessments including LUR. The main objectives of the current study were to conduct nitrogen dioxide (NO2) exposure assessment with four methods including LUR in the Republic of Korea, to compare the model performances, and to estimate the empirical NO2 exposures of a cohort. The study population was defined as the year 2010 participants of a government-supported cohort established for bio-monitoring in Ulsan, Republic of Korea. The annual ambient NO2 exposures of the 969 study participants were estimated with LUR, nearest station, inverse distance weighting, and ordinary kriging. Modeling was based on the annual NO2 average, traffic-related data, land-use data, and altitude of the 13 regularly monitored stations. The final LUR model indicated that area of transportation, distance to residential area, and area of wetland were important predictors of NO2. The LUR model explained 85.8% of the variation observed in the 13 monitoring stations of the year 2009. The LUR model outperformed the others based on leave-one out cross-validation comparing the correlations and root-mean square error. All NO2 estimates ranged from 11.3-18.0 ppb, with that of LUR having the widest range. The NO2 exposure levels of the residents differed by demographics. However, the average was below the national annual guidelines of the Republic of Korea (30 ppb). The LUR models showed high performances in an industrial city in the Republic of Korea, despite the small sample size and limited data. Our findings suggest that the LUR method may be useful in similar settings in Asian countries where the target region is small and availability of data is low.

Large scale air pollution estimation method combining land use regression and chemical transport modeling in a geostatistical framework.

PubMed

Akita, Yasuyuki; Baldasano, Jose M; Beelen, Rob; Cirach, Marta; de Hoogh, Kees; Hoek, Gerard; Nieuwenhuijsen, Mark; Serre, Marc L; de Nazelle, Audrey

2014-04-15

In recognition that intraurban exposure gradients may be as large as between-city variations, recent air pollution epidemiologic studies have become increasingly interested in capturing within-city exposure gradients. In addition, because of the rapidly accumulating health data, recent studies also need to handle large study populations distributed over large geographic domains. Even though several modeling approaches have been introduced, a consistent modeling framework capturing within-city exposure variability and applicable to large geographic domains is still missing. To address these needs, we proposed a modeling framework based on the Bayesian Maximum Entropy method that integrates monitoring data and outputs from existing air quality models based on Land Use Regression (LUR) and Chemical Transport Models (CTM). The framework was applied to estimate the yearly average NO2 concentrations over the region of Catalunya in Spain. By jointly accounting for the global scale variability in the concentration from the output of CTM and the intraurban scale variability through LUR model output, the proposed framework outperformed more conventional approaches.

Subchronic Inhalation Exposure of Rats to Libby Amphibole and Amosite Asbestos: Effects at 1 and 3 Months Post Exposure**

EPA Science Inventory

Increased asbestosis, lung cancer, and mesothelioma rates are evident after exposures to Libby amphibole (LA). To support dosimetry model development and compare potency, a subchronic nose-only inhalation exposure study (6 hr/d, 5 d/wk, 13 wk) was conducted in male F344 rats. Rat...

Thinking Through Computational Exposure as an Evolving Paradign Shift for Exposure Science: Development and Application of Predictive Models from Big Data

EPA Science Inventory

Symposium Abstract: Exposure science has evolved from a time when the primary focus was on measurements of environmental and biological media and the development of enabling field and laboratory methods. The Total Exposure Assessment Method (TEAM) studies of the 1980s were class...

Psychopathy, traumatic exposure, and lifetime posttraumatic stress.

PubMed

Willemsen, Jochem; De Ganck, Julie; Verhaeghe, Paul

2012-06-01

This study examined two theoretical models on the interaction between psychopathy, traumatic exposure, and lifetime posttraumatic stress in a sample of 81 male detainees. In Model 1, the interpersonal and affective features of psychopathy were assumed to protect against posttraumatic stress. In Model 2, the lifestyle and antisocial traits of psychopathy were assumed to lead to a lifestyle that increases the risk of traumatic exposure and subsequent posttraumatic stress. The authors found significant negative bivariate associations between Psychopathy Checklist-Revised (PCL-R) total, Interpersonal and Affective facet scores, and posttraumatic stress. Model 1 was confirmed, as they found the interaction between the Affective facet and traumatic exposure had a significant negative effect on posttraumatic stress. Model 2 was rejected. The authors' findings confirm that the interpersonal and affective features of psychopathy are associated with an emotional deficit and that the affective features of psychopathy are crucial for understanding the relationship between psychopathy and anxiety.

A modeling investigation of the impact of street and building configurations on personal air pollutant exposure in isolated deep urban canyons.

PubMed

Ng, Wai-Yin; Chau, Chi-Kwan

2014-01-15

This study evaluated the effectiveness of different configurations for two building design elements, namely building permeability and setback, proposed for mitigating air pollutant exposure problems in isolated deep canyons by using an indirect exposure approach. The indirect approach predicted the exposures of three different population subgroups (i.e. pedestrians, shop vendors and residents) by multiplying the pollutant concentrations with the duration of exposure within a specific micro-environment. In this study, the pollutant concentrations for different configurations were predicted using a computational fluid dynamics model. The model was constructed based on the Reynolds-Averaged Navier-Stokes (RANS) equations with the standard k-ε turbulence model. Fifty-one canyon configurations with aspect ratios of 2, 4, 6 and different building permeability values (ratio of building spacing to the building façade length) or different types of building setback (recess of a high building from the road) were examined. The findings indicated that personal exposures of shop vendors were extremely high if they were present inside a canyon without any setback or separation between buildings and when the prevailing wind was perpendicular to the canyon axis. Building separation and building setbacks were effective in reducing personal air exposures in canyons with perpendicular wind, although their effectiveness varied with different configurations. Increasing the permeability value from 0 to 10% significantly lowered the personal exposures on the different population subgroups. Likewise, the personal exposures could also be reduced by the introduction of building setbacks despite their effects being strongly influenced by the aspect ratio of a canyon. Equivalent findings were observed if the reduction in the total development floor area (the total floor area permitted to be developed within a particular site area) was also considered. These findings were employed to formulate a hierarchy decision making model to guide the planning of deep canyons in high density urban cities. © 2013 Elsevier B.V. All rights reserved.

Comparative Exposure Assessment of ESBL-Producing Escherichia coli through Meat Consumption

PubMed Central

Pielaat, Annemarie; Smid, Joost H.; van Duijkeren, Engeline; Vennemann, Francy B. C.; Wijnands, Lucas M.; Chardon, Jurgen E.

2017-01-01

The presence of extended-spectrum β-lactamase (ESBL) and plasmidic AmpC (pAmpC) producing Escherichia coli (EEC) in food animals, especially broilers, has become a major public health concern. The aim of the present study was to quantify the EEC exposure of humans in The Netherlands through the consumption of meat from different food animals. Calculations were done with a simplified Quantitative Microbiological Risk Assessment (QMRA) model. The model took the effect of pre-retail processing, storage at the consumers home and preparation in the kitchen (cross-contamination and heating) on EEC numbers on/in the raw meat products into account. The contribution of beef products (78%) to the total EEC exposure of the Dutch population through the consumption of meat was much higher than for chicken (18%), pork (4.5%), veal (0.1%) and lamb (0%). After slaughter, chicken meat accounted for 97% of total EEC load on meat, but chicken meat experienced a relatively large effect of heating during food preparation. Exposure via consumption of filet americain (a minced beef product consumed raw) was predicted to be highest (61% of total EEC exposure), followed by chicken fillet (13%). It was estimated that only 18% of EEC exposure occurred via cross-contamination during preparation in the kitchen, which was the only route by which EEC survived for surface-contaminated products. Sensitivity analysis showed that model output is not sensitive for most parameters. However, EEC concentration on meat other than chicken meat was an important data gap. In conclusion, the model assessed that consumption of beef products led to a higher exposure to EEC than chicken products, although the prevalence of EEC on raw chicken meat was much higher than on beef. The (relative) risk of this exposure for public health is yet unknown given the lack of a modelling framework and of exposure studies for other potential transmission routes. PMID:28056081

An optimal sampling approach to modelling whole-body vibration exposure in all-terrain vehicle driving.

PubMed

Lü, Xiaoshu; Takala, Esa-Pekka; Toppila, Esko; Marjanen, Ykä; Kaila-Kangas, Leena; Lu, Tao

2017-08-01

Exposure to whole-body vibration (WBV) presents an occupational health risk and several safety standards obligate to measure WBV. The high cost of direct measurements in large epidemiological studies raises the question of the optimal sampling for estimating WBV exposures given by a large variation in exposure levels in real worksites. This paper presents a new approach to addressing this problem. A daily exposure to WBV was recorded for 9-24 days among 48 all-terrain vehicle drivers. Four data-sets based on root mean squared recordings were obtained from the measurement. The data were modelled using semi-variogram with spectrum analysis and the optimal sampling scheme was derived. The optimum sampling period was 140 min apart. The result was verified and validated in terms of its accuracy and statistical power. Recordings of two to three hours are probably needed to get a sufficiently unbiased daily WBV exposure estimate in real worksites. The developed model is general enough that is applicable to other cumulative exposures or biosignals. Practitioner Summary: Exposure to whole-body vibration (WBV) presents an occupational health risk and safety standards obligate to measure WBV. However, direct measurements can be expensive. This paper presents a new approach to addressing this problem. The developed model is general enough that is applicable to other cumulative exposures or biosignals.

Occupational exposures and non-Hodgkin's lymphoma: Canadian case-control study.

PubMed

Karunanayake, Chandima P; McDuffie, Helen H; Dosman, James A; Spinelli, John J; Pahwa, Punam

2008-08-07

The objective was to study the association between Non-Hodgkin's Lymphoma (NHL) and occupational exposures related to long held occupations among males in six provinces of Canada. A population based case-control study was conducted from 1991 to 1994. Males with newly diagnosed NHL (ICD-10) were stratified by province of residence and age group. A total of 513 incident cases and 1506 population based controls were included in the analysis. Conditional logistic regression was conducted to fit statistical models. Based on conditional logistic regression modeling, the following factors independently increased the risk of NHL: farmer and machinist as long held occupations; constant exposure to diesel exhaust fumes; constant exposure to ionizing radiation (radium); and personal history of another cancer. Men who had worked for 20 years or more as farmer and machinist were the most likely to develop NHL. An increased risk of developing NHL is associated with the following: long held occupations of faer and machinist; exposure to diesel fumes; and exposure to ionizing radiation (radium). The risk of NHL increased with the duration of employment as a farmer or machinist.

Application of the IEUBK model for linking Children's blood lead with environmental exposure in a mining site, south China.

PubMed

Zhang, Xin-Ying; Carpenter, David O; Song, Yong-Jin; Chen, Ping; Qin, Yaoming; Wei, Ni-Yu; Lin, Shan-Chun

2017-12-01

This study consisted of a site- and age-specific investigation linking children's blood lead level (BLL) to environmental exposures in a historic mining site in south China. A total of 151 children, aged 3-7 years, were included in this study. The geometric mean (GM) BLL was 8.22 μg/dl, indicating an elevated BLL. The Integrated Exposure Uptake Bio-Kinetic (IEUBK) model has proven useful at many sites for study of routes of exposure. Application of the IEUBK model to these children indicated that the GM difference between observed and predicted BLL levels was only 1.07 μg/dl. It was found that the key environmental exposure pathway was soil/dust intake, which contributed 86.3% to the total risk. Younger children had higher BLL than did older children. Therefore, of the various low risk-high benefit solutions, interventions for the children living near the site should be focused on the dust removal and soil remediation. Implementation of the China Eco-village Construction Plan and China New Rural Reconstruction Movement of the government may be a better solution. Copyright © 2017 Elsevier Ltd. All rights reserved.

Exposure to Concentrated Ambient PM2.5 Shortens Lifespan and Induces Inflammation-Associated Signaling and Oxidative Stress in Drosophila.

PubMed

Wang, Xiaoke; Chen, Minjie; Zhong, Mianhua; Hu, Ziying; Qiu, Lianglin; Rajagopalan, Sanjay; Fossett, Nancy G; Chen, Lung-Chi; Ying, Zhekang

2017-03-01

Exposure to ambient PM 2.5 is associated with human premature mortality. However, it has not yet been toxicologically replicated, likely due to the lack of suitable animal models. Drosophila is frequently used in longevity research due to many incomparable merits. The present study aims to validate Drosophila models for PM 2.5 toxicity study through characterizing their biological responses to exposure to concentrated ambient PM 2.5 (CAP). The survivorship curve demonstrated that exposure to CAP markedly reduced lifespan of Drosophila. This antilongevity effect of CAP exposure was observed in both male and female Drosophila, and by comparison, the male was more sensitive [50% survivals: 20 and 48 days, CAP- and filtered air (FA)-exposed males, respectively; 21 and 40 days, CAP- and FA-exposed females, respectively]. Similar to its putative pathogenesis in humans, CAP exposure-induced premature mortality in Drosophila was also coincided with activation of pro-inflammatory signaling pathways including Jak, Jnk, and Nf-κb and increased systemic oxidative stress. Furthermore, like in humans and mammals, exposure to CAP significantly increased whole-body and circulating glucose levels and increased mRNA expression of Ilp2 and Ilp5 , indicating that CAP exposure induces dysregulated insulin signaling in Drosophila. Similar to effects on humans exposure to CAP leads to premature mortality likely through induction of inflammation-associated signaling, oxidative stress, and metabolic abnormality in Drosophila, strongly supporting that it can be a useful model organism for PM 2.5 toxicity study. © The Author 2017. Published by Oxford University Press on behalf of the Society of Toxicology. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

A Comparison of the Different Animal Models of Fetal Alcohol Spectrum Disorders and Their Use in Studying Complex Behaviors

PubMed Central

Patten, Anna R.; Fontaine, Christine J.; Christie, Brian R.

2014-01-01

Prenatal ethanol exposure (PNEE) has been linked to widespread impairments in brain structure and function. There are a number of animal models that are used to study the structural and functional deficits caused by PNEE, including, but not limited to invertebrates, fish, rodents, and non-human primates. Animal models enable a researcher to control important variables such as the route of ethanol administration, as well as the timing, frequency and amount of ethanol exposure. Each animal model and system of exposure has its place, depending on the research question being undertaken. In this review, we will examine the different routes of ethanol administration and the various animal models of fetal alcohol spectrum disorders (FASD) that are commonly used in research, emphasizing their strengths and limitations. We will also present an up-to-date summary on the effects of prenatal/neonatal ethanol exposure on behavior across the lifespan, focusing on learning and memory, olfaction, social, executive, and motor functions. Special emphasis will be placed where the various animal models best represent deficits observed in the human condition and offer a viable test bed to examine potential therapeutics for human beings with FASD. PMID:25232537

Incorporating High-Throughput Exposure Predictions with ...

EPA Pesticide Factsheets

We previously integrated dosimetry and exposure with high-throughput screening (HTS) to enhance the utility of ToxCast™ HTS data by translating in vitro bioactivity concentrations to oral equivalent doses (OEDs) required to achieve these levels internally. These OEDs were compared against regulatory exposure estimates, providing an activity-to-exposure ratio (AER) useful for a risk-based ranking strategy. As ToxCast™ efforts expand (i.e., Phase II) beyond food-use pesticides towards a wider chemical domain that lacks exposure and toxicity information, prediction tools become increasingly important. In this study, in vitro hepatic clearance and plasma protein binding were measured to estimate OEDs for a subset of Phase II chemicals. OEDs were compared against high-throughput (HT) exposure predictions generated using probabilistic modeling and Bayesian approaches generated by the U.S. EPA ExpoCast™ program. This approach incorporated chemical-specific use and national production volume data with biomonitoring data to inform the exposure predictions. This HT exposure modeling approach provided predictions for all Phase II chemicals assessed in this study whereas estimates from regulatory sources were available for only 7% of chemicals. Of the 163 chemicals assessed in this study, three or 13 chemicals possessed AERs <1 or <100, respectively. Diverse bioactivities y across a range of assays and concentrations was also noted across the wider chemical space su

Impact of non-constant concentration exposure on lethality of inhaled hydrogen cyanide.

PubMed

Sweeney, Lisa M; Sommerville, Douglas R; Channel, Stephen R

2014-03-01

The ten Berge model, also known as the toxic load model, is an empirical approach in hazard assessment modeling for estimating the relationship between the inhalation toxicity of a chemical and the exposure duration. The toxic load (TL) is normally expressed as a function of vapor concentration (C) and duration (t), with TL equaling C(n) × t being a typical form. Hypothetically, any combination of concentration and time that yields the same "toxic load" will give a constant biological response. These formulas have been developed and tested using controlled, constant concentration animal studies, but the validity of applying these assumptions to time-varying concentration profiles has not been tested. Experiments were designed to test the validity of the model under conditions of non-constant acute exposure. Male Sprague-Dawley rats inhaled constant or pulsed concentrations of hydrogen cyanide (HCN) generated in a nose-only exposure system for 5, 15, or 30 min. The observed lethality of HCN for the 11 different C versus t profiles was used to evaluate the ability of the model to adequately describe the lethality of HCN under the conditions of non-constant inhalation exposure. The model was found to be applicable under the tested conditions, with the exception of the median lethality of very brief, high concentration, discontinuous exposures.

Simulation of urinary excretion of 1-hydroxypyrene in various scenarios of exposure to polycyclic aromatic hydrocarbons with a generic, cross-chemical predictive PBTK-model.

PubMed

Jongeneelen, Frans; ten Berge, Wil

2012-08-01

A physiologically based toxicokinetic (PBTK) model can predict blood and urine concentrations, given a certain exposure scenario of inhalation, dermal and/or oral exposure. The recently developed PBTK-model IndusChemFate is a unified model that mimics the uptake, distribution, metabolism and elimination of a chemical in a reference human of 70 kg. Prediction of the uptake by inhalation is governed by pulmonary exchange to blood. Oral uptake is simulated as a bolus dose that is taken up at a first-order rate. Dermal uptake is estimated by the use of a novel dermal physiologically based module that considers dermal deposition rate and duration of deposition. Moreover, evaporation during skin contact is fully accounted for and related to the volatility of the substance. Partitioning of the chemical and metabolite(s) over blood and tissues is estimated by a Quantitative Structure-Property Relationship (QSPR) algorithm. The aim of this study was to test the generic PBTK-model by comparing measured urinary levels of 1-hydroxypyrene in various inhalation and dermal exposure scenarios with the result of model simulations. In the last three decades, numerous biomonitoring studies of PAH-exposed humans were published that used the bioindicator 1-hydroxypyrene (1-OH-pyrene) in urine. Longitudinal studies that encompass both dosimetry and biomonitoring with repeated sampling in time were selected to test the accuracy of the PBTK-model by comparing the reported concentrations of 1-OHP in urine with the model-predicted values. Two controlled human volunteer studies and three field studies of workers exposed to polycyclic aromatic hydrocarbons (PAH) were included. The urinary pyrene-metabolite levels of a controlled human inhalation study, a transdermal uptake study of bitumen fume, efficacy of respirator use in electrode paste workers, cokery workers in shale oil industry and a longitudinal study of five coke liquefaction workers were compared to the PBTK-predicted values. The simulations showed that the model-predicted concentrations of urinary pyrene and metabolites over time, as well as peak-concentrations and total excreted amount in different exposure scenarios of inhalation and transdermal exposure were in all comparisons within an order of magnitude. The model predicts that only a very small fraction is excreted in urine as parent pyrene and as free 1-OH-pyrene. The predominant urinary metabolite is 1-OH-pyrene-glucuronide. Enterohepatic circulation of 1-OH-pyrene-glucuronide seems the reason of the delayed release from the body. It appeared that urinary excretion of pyrene and pyrene-metabolites in humans is predictable with the PBTK-model. The model outcomes have a satisfying accuracy for early testing, in so-called 1st tier simulations and in range finding. This newly developed generic PBTK-model IndusChemFate is a tool that can be used to do early explorations of the significance of uptake of pyrene in the human body following industrial or environmental exposure scenarios. And it can be used to optimize the sampling time and urine sampling frequency of a biomonitoring program.

ARSENIC IN DRINKING WATER: EPIDEMIOOOGIC STUDIES OF LOW EXPOSURE IN THE UNITED STATES

EPA Science Inventory

Because there is no animal model fully adequate to study the mechanisms of arsenic toxicity and carcinogenicity; human epidemiological studies incorporating sensitive biomarkers for assessing exposure, cancer, noncancer effects and susceptibility of arsenic are needed to evalua...

The challenges of exposure assessment in health studies of Gulf War veterans

PubMed Central

Glass, Deborah C; Sim, Malcolm R

2006-01-01

A variety of exposures have been investigated in Gulf War veterans' health studies. These have most commonly been by self-report in a postal questionnaire but modelling and bio-monitoring have also been employed. Exposure assessment is difficult to do well in studies of any workplace environment. It is made more difficult in Gulf War studies where there are a number and variety of possible exposures, no agreed metrics for individual exposures and few contemporary records associating the exposure with an individual. In some studies, the exposure assessment was carried out some years after the war and in the context of media interest. Several studies have examined different ways to test the accuracy of exposure reporting in Gulf War cohorts. There is some evidence from Gulf War studies that self-reported exposures were subject to recall bias but it is difficult to assess the extent. Occupational exposure-assessment methodology can provide insights into the exposure-assessment process and how to do it well. This is discussed in the context of the Gulf War studies. Alternative exposure-assessment methodologies are presented, although these may not be suitable for widespread use in veteran studies. Due to the poor quality of and accessibility of objective military exposure records, self-assessed exposure questionnaires are likely to remain the main instrument for assessing the exposure for a large number of veterans. If this is to be the case, then validation methods with more objective methods need to be included in future study designs. PMID:16687267

Waif goodbye! Average-size female models promote positive body image and appeal to consumers.

PubMed

Diedrichs, Phillippa C; Lee, Christina

2011-10-01

Despite consensus that exposure to media images of thin fashion models is associated with poor body image and disordered eating behaviours, few attempts have been made to enact change in the media. This study sought to investigate an effective alternative to current media imagery, by exploring the advertising effectiveness of average-size female fashion models, and their impact on the body image of both women and men. A sample of 171 women and 120 men were assigned to one of three advertisement conditions: no models, thin models and average-size models. Women and men rated average-size models as equally effective in advertisements as thin and no models. For women with average and high levels of internalisation of cultural beauty ideals, exposure to average-size female models was associated with a significantly more positive body image state in comparison to exposure to thin models and no models. For men reporting high levels of internalisation, exposure to average-size models was also associated with a more positive body image state in comparison to viewing thin models. These findings suggest that average-size female models can promote positive body image and appeal to consumers.

Social inequalities in residential exposure to road traffic noise: an environmental justice analysis based on the RECORD Cohort Study.

PubMed

Havard, Sabrina; Reich, Brian J; Bean, Kathy; Chaix, Basile

2011-05-01

To explore social inequalities in residential exposure to road traffic noise in an urban area. Environmental injustice in road traffic noise exposure was investigated in Paris, France, using the RECORD Cohort Study (n = 2130) and modelled noise data. Associations were assessed by estimating noise exposure within the local area around participants' residence, considering various socioeconomic variables defined at both individual and neighbourhood level, and comparing different regression models attempting or not to control for spatial autocorrelation in noise levels. After individual-level adjustment, participants' noise exposure increased with neighbourhood educational level and dwelling value but also with proportion of non-French citizens, suggesting seemingly contradictory findings. However, when country of citizenship was defined according to its human development level, noise exposure in fact increased and decreased with the proportions of citizens from advantaged and disadvantaged countries, respectively. These findings were consistent with those reported for the other socioeconomic characteristics, suggesting higher road traffic noise exposure in advantaged neighbourhoods. Substantial collinearity between neighbourhood explanatory variables and spatial random effects caused identifiability problems that prevented successful control for spatial autocorrelation. Contrary to previous literature, this study shows that people living in advantaged neighbourhoods were more exposed to road traffic noise in their residential environment than their deprived counterparts. This case study demonstrates the need to systematically perform sensitivity analyses with multiple socioeconomic characteristics to avoid incorrect inferences about an environmental injustice situation and the complexity of effectively controlling for spatial autocorrelation when fixed and random components of the model are correlated.

Source characterization and exposure modeling of gas-phase polycyclic aromatic hydrocarbon (PAH) concentrations in Southern California

NASA Astrophysics Data System (ADS)

Masri, Shahir; Li, Lianfa; Dang, Andy; Chung, Judith H.; Chen, Jiu-Chiuan; Fan, Zhi-Hua (Tina); Wu, Jun

2018-03-01

Airborne exposures to polycyclic aromatic hydrocarbons (PAHs) are associated with adverse health outcomes. Because personal air measurements of PAHs are labor intensive and costly, spatial PAH exposure models are useful for epidemiological studies. However, few studies provide adequate spatial coverage to reflect intra-urban variability of ambient PAHs. In this study, we collected 39-40 weekly gas-phase PAH samples in southern California twice in summer and twice in winter, 2009, in order to characterize PAH source contributions and develop spatial models that can estimate gas-phase PAH concentrations at a high resolution. A spatial mixed regression model was constructed, including such variables as roadway, traffic, land-use, vegetation index, commercial cooking facilities, meteorology, and population density. Cross validation of the model resulted in an R2 of 0.66 for summer and 0.77 for winter. Results showed higher total PAH concentrations in winter. Pyrogenic sources, such as fossil fuels and diesel exhaust, were the most dominant contributors to total PAHs. PAH sources varied by season, with a higher fossil fuel and wood burning contribution in winter. Spatial autocorrelation accounted for a substantial amount of the variance in total PAH concentrations for both winter (56%) and summer (19%). In summer, other key variables explaining the variance included meteorological factors (9%), population density (15%), and roadway length (21%). In winter, the variance was also explained by traffic density (16%). In this study, source characterization confirmed the dominance of traffic and other fossil fuel sources to total measured gas-phase PAH concentrations while a spatial exposure model identified key predictors of PAH concentrations. Gas-phase PAH source characterization and exposure estimation is of high utility to epidemiologist and policy makers interested in understanding the health impacts of gas-phase PAHs and strategies to reduce emissions.

Source Characterization and Exposure Modeling of Gas-Phase Polycyclic Aromatic Hydrocarbon (PAH) Concentrations in Southern California.

PubMed

Masri, Shahir; Li, Lianfa; Dang, Andy; Chung, Judith H; Chen, Jiu-Chiuan; Fan, Zhi-Hua Tina; Wu, Jun

2018-03-01

Airborne exposures to polycyclic aromatic hydrocarbons (PAHs) are associated with adverse health outcomes. Because personal air measurements of PAHs are labor intensive and costly, spatial PAH exposure models are useful for epidemiological studies. However, few studies provide adequate spatial coverage to reflect intra-urban variability of ambient PAHs. In this study, we collected 39-40 weekly gas-phase PAH samples in southern California twice in summer and twice in winter, 2009, in order to characterize PAH source contributions and develop spatial models that can estimate gas-phase PAH concentrations at a high resolution. A spatial mixed regression model was constructed, including such variables as roadway, traffic, land-use, vegetation index, commercial cooking facilities, meteorology, and population density. Cross validation of the model resulted in an R 2 of 0.66 for summer and 0.77 for winter. Results showed higher total PAH concentrations in winter. Pyrogenic sources, such as fossil fuels and diesel exhaust, were the most dominant contributors to total PAHs. PAH sources varied by season, with a higher fossil fuel and wood burning contribution in winter. Spatial autocorrelation accounted for a substantial amount of the variance in total PAH concentrations for both winter (56%) and summer (19%). In summer, other key variables explaining the variance included meteorological factors (9%), population density (15%), and roadway length (21%). In winter, the variance was also explained by traffic density (16%). In this study, source characterization confirmed the dominance of traffic and other fossil fuel sources to total measured gas-phase PAH concentrations while a spatial exposure model identified key predictors of PAH concentrations. Gas-phase PAH source characterization and exposure estimation is of high utility to epidemiologist and policy makers interested in understanding the health impacts of gas-phase PAHs and strategies to reduce emissions.

Kernel Density Estimation as a Measure of Environmental Exposure Related to Insulin Resistance in Breast Cancer Survivors.

PubMed

Jankowska, Marta M; Natarajan, Loki; Godbole, Suneeta; Meseck, Kristin; Sears, Dorothy D; Patterson, Ruth E; Kerr, Jacqueline

2017-07-01

Background: Environmental factors may influence breast cancer; however, most studies have measured environmental exposure in neighborhoods around home residences (static exposure). We hypothesize that tracking environmental exposures over time and space (dynamic exposure) is key to assessing total exposure. This study compares breast cancer survivors' exposure to walkable and recreation-promoting environments using dynamic Global Positioning System (GPS) and static home-based measures of exposure in relation to insulin resistance. Methods: GPS data from 249 breast cancer survivors living in San Diego County were collected for one week along with fasting blood draw. Exposure to recreation spaces and walkability was measured for each woman's home address within an 800 m buffer (static), and using a kernel density weight of GPS tracks (dynamic). Participants' exposure estimates were related to insulin resistance (using the homeostatic model assessment of insulin resistance, HOMA-IR) controlled by age and body mass index (BMI) in linear regression models. Results: The dynamic measurement method resulted in greater variability in built environment exposure values than did the static method. Regression results showed no association between HOMA-IR and home-based, static measures of walkability and recreation area exposure. GPS-based dynamic measures of both walkability and recreation area were significantly associated with lower HOMA-IR ( P < 0.05). Conclusions: Dynamic exposure measurements may provide important evidence for community- and individual-level interventions that can address cancer risk inequities arising from environments wherein breast cancer survivors live and engage. Impact: This is the first study to compare associations of dynamic versus static built environment exposure measures with insulin outcomes in breast cancer survivors. Cancer Epidemiol Biomarkers Prev; 26(7); 1078-84. ©2017 AACR . ©2017 American Association for Cancer Research.

Impacts of Transit-Oriented Compact-Growth on Air Pollutant Concentrations and Exposures in the Tampa Region

DOT National Transportation Integrated Search

2018-03-31

Amy L. Stuart (ORCID # 0000-0003-1229-493) The objective of this study was to model the potential impacts of alternative transit-oriented urban design scenarios on community exposures to roadway air pollution. We used a modeling framework developed p...

PREDICTING PARTICULATE (PM-10) FREQUENCY DISTRIBUTIONS FOR URBAN POPULATIONS USING A RANDOM COMPONENT SUPERPOSITION MODEL (RCS) MODEL

EPA Science Inventory

Health risk evaluations usually require the frequency distribution of personal exposures of a given population. For particles, personal exposure field studies have been conducted in only a few urban areas, such as Riverside, CA; Philipsburg, NJ; and Toronto, Ontario. This paper...

Bayesian multinomial probit modeling of daily windows of susceptibility for maternal PM2.5 exposure and congenital heart defects

EPA Science Inventory

Past epidemiologic studies suggest maternal ambient air pollution exposure during critical periods of the pregnancy is associated with fetal development. We introduce a multinomial probit model that allows for the joint identification of susceptible daily periods during the pregn...

Use of novel inhalation kinetic studies to refine physiologically-based-pharmacokinetic models for ethanol in non-pregnant and pregnant rats

EPA Science Inventory

Ethanol (EtOH) exposure induces a variety of concentration-dependent neurological and developmental effects in the rat. Physiologically-based pharmacokinetic (PBPK) models have been used to predict the inhalation exposure concentrations necessary to produce blood EtOH concentrat...

An Overview of Exposure Assessment Models Used by the U.S. Environmental Protection Agency

EPA Science Inventory

Models are often used in addition to or in lieu of monitoring data to estimate environmental concentrations and exposures for use in risk assessments or epidemiological studies, and to support regulatory standards and voluntary programs (Jayjock et al., 2007; US EPA, 1989, 1992)....

Bayesian algorithm implementation in a real time exposure assessment model on benzene with calculation of associated cancer risks.

PubMed

Sarigiannis, Dimosthenis A; Karakitsios, Spyros P; Gotti, Alberto; Papaloukas, Costas L; Kassomenos, Pavlos A; Pilidis, Georgios A

2009-01-01

The objective of the current study was the development of a reliable modeling platform to calculate in real time the personal exposure and the associated health risk for filling station employees evaluating current environmental parameters (traffic, meteorological and amount of fuel traded) determined by the appropriate sensor network. A set of Artificial Neural Networks (ANNs) was developed to predict benzene exposure pattern for the filling station employees. Furthermore, a Physiology Based Pharmaco-Kinetic (PBPK) risk assessment model was developed in order to calculate the lifetime probability distribution of leukemia to the employees, fed by data obtained by the ANN model. Bayesian algorithm was involved in crucial points of both model sub compartments. The application was evaluated in two filling stations (one urban and one rural). Among several algorithms available for the development of the ANN exposure model, Bayesian regularization provided the best results and seemed to be a promising technique for prediction of the exposure pattern of that occupational population group. On assessing the estimated leukemia risk under the scope of providing a distribution curve based on the exposure levels and the different susceptibility of the population, the Bayesian algorithm was a prerequisite of the Monte Carlo approach, which is integrated in the PBPK-based risk model. In conclusion, the modeling system described herein is capable of exploiting the information collected by the environmental sensors in order to estimate in real time the personal exposure and the resulting health risk for employees of gasoline filling stations.

Bayesian Algorithm Implementation in a Real Time Exposure Assessment Model on Benzene with Calculation of Associated Cancer Risks

PubMed Central

Sarigiannis, Dimosthenis A.; Karakitsios, Spyros P.; Gotti, Alberto; Papaloukas, Costas L.; Kassomenos, Pavlos A.; Pilidis, Georgios A.

2009-01-01

The objective of the current study was the development of a reliable modeling platform to calculate in real time the personal exposure and the associated health risk for filling station employees evaluating current environmental parameters (traffic, meteorological and amount of fuel traded) determined by the appropriate sensor network. A set of Artificial Neural Networks (ANNs) was developed to predict benzene exposure pattern for the filling station employees. Furthermore, a Physiology Based Pharmaco-Kinetic (PBPK) risk assessment model was developed in order to calculate the lifetime probability distribution of leukemia to the employees, fed by data obtained by the ANN model. Bayesian algorithm was involved in crucial points of both model sub compartments. The application was evaluated in two filling stations (one urban and one rural). Among several algorithms available for the development of the ANN exposure model, Bayesian regularization provided the best results and seemed to be a promising technique for prediction of the exposure pattern of that occupational population group. On assessing the estimated leukemia risk under the scope of providing a distribution curve based on the exposure levels and the different susceptibility of the population, the Bayesian algorithm was a prerequisite of the Monte Carlo approach, which is integrated in the PBPK-based risk model. In conclusion, the modeling system described herein is capable of exploiting the information collected by the environmental sensors in order to estimate in real time the personal exposure and the resulting health risk for employees of gasoline filling stations. PMID:22399936

Trends in OSHA Compliance Monitoring Data 1979-2011: Statistical Modeling of Ancillary Information across 77 Chemicals.

PubMed

Sarazin, Philippe; Burstyn, Igor; Kincl, Laurel; Lavoué, Jérôme

2016-05-01

The Integrated Management Information System (IMIS) is the largest multi-industry source of exposure measurements available in North America. However, many have suspected that the criteria through which worksites are selected for inspection are related to exposure levels. We investigated associations between exposure levels and ancillary variables in IMIS in order to understand the predictors of high exposure within an enforcement context. We analyzed the association between nine variables (reason for inspection, establishment size, total amount of penalty, Occupational Safety and Health Administration (OSHA) plan, OSHA region, union status, inspection scope, year, and industry) and exposure levels in IMIS using multimodel inference for 77 agents. For each agent, we used two different types of models: (i) logistic models were used for the odds ratio (OR) of exposure being above the threshold limit value (TLV) and (ii) linear models were used for exposure concentrations restricted to detected results to estimate percent increase in exposure level, i.e. relative index of exposure (RIE). Meta-analytic methods were used to combine results for each variable across agents. A total of 511,047 exposure measurements were modeled for logistic models and 299,791 for linear models. Higher exposures were measured during follow-up inspections than planned inspections [meta-OR = 1.61, 95% confidence interval (CI): 1.44-1.81; meta-RIE = 1.06, 95% CI: 1.03-1.09]. Lower exposures were observed for measurements collected under state OSHA plans compared to measurements collected under federal OSHA (meta-OR = 0.82, 95% CI: 0.73-0.92; meta-RIE = 0.86, 95% CI: 0.81-0.91). A 'high' total historical amount of penalty relative to none was associated with higher exposures (meta-OR = 1.54, 95% CI: 1.40-1.71; meta-RIE = 1.18, 95% CI: 1.13-1.23). The relationships observed between exposure levels and ancillary variables across a vast majority of agents suggest that certain elements of OSHA's process of selecting worksites for inspection influence the exposure levels that OSHA inspectors encounter. Nonetheless, given the paucity of other sources of exposure data and the lack of a more demonstrably representative data source, our study considers the use of IMIS data for the estimation of exposures in the broader universe of worksites in the USA. © The Author 2016. Published by Oxford University Press on behalf of the British Occupational Hygiene Society.

Mortality and morbidity among people living close to incinerators: a cohort study based on dispersion modeling for exposure assessment

PubMed Central

2011-01-01

Background Several studies have been conducted on the possible health effects for people living close to incinerators and well-conducted reviews are available. Nevertheless, several uncertainties limit the overall interpretation of the findings. We evaluated the health effects of emissions from two incinerators in a pilot cohort study. Methods The study area was defined as the 3.5 km radius around two incinerators located near Forlì (Italy). People who were residents in 1/1/1990, or subsequently became residents up to 31/12/2003, were enrolled in a longitudinal study (31,347 individuals). All the addresses were geocoded. Follow-up continued until 31/12/2003 by linking the mortality register, cancer registry and hospital admissions databases. Atmospheric Dispersion Model System (ADMS) software was used for exposure assessment; modelled concentration maps of heavy metals (annual average) were considered the indicators of exposure to atmospheric pollution from the incinerators, while concentration maps of nitrogen dioxide (NO2) were considered for exposure to other pollution sources. Age and area-based socioeconomic status adjusted rate ratios and 95% Confidence Intervals were estimated with Poisson regression, using the lowest exposure category to heavy metals as reference. Results The mortality and morbidity experience of the whole cohort did not differ from the regional population. In the internal analysis, no association between pollution exposure from the incinerators and all-cause and cause-specific mortality outcomes was observed in men, with the exception of colon cancer. Exposure to the incinerators was associated with cancer mortality among women, in particular for all cancer sites (RR for the highest exposure level = 1.47, 95% CI: 1.09, 1.99), stomach, colon, liver and breast cancer. No clear trend was detected for cancer incidence. No association was found for hospitalizations related to major diseases. NO2 levels, as a proxy from other pollution sources (traffic in particular), did not exert an important confounding role. Conclusions No increased risk of mortality and morbidity was found in the entire area. The internal analysis of the cohort based on dispersion modeling found excesses of mortality for some cancer types in the highest exposure categories, especially in women. The interpretation of the findings is limited given the pilot nature of the study. PMID:21435200

Flexible Meta-Regression to Assess the Shape of the Benzene–Leukemia Exposure–Response Curve

PubMed Central

Vlaanderen, Jelle; Portengen, Lützen; Rothman, Nathaniel; Lan, Qing; Kromhout, Hans; Vermeulen, Roel

2010-01-01

Background Previous evaluations of the shape of the benzene–leukemia exposure–response curve (ERC) were based on a single set or on small sets of human occupational studies. Integrating evidence from all available studies that are of sufficient quality combined with flexible meta-regression models is likely to provide better insight into the functional relation between benzene exposure and risk of leukemia. Objectives We used natural splines in a flexible meta-regression method to assess the shape of the benzene–leukemia ERC. Methods We fitted meta-regression models to 30 aggregated risk estimates extracted from nine human observational studies and performed sensitivity analyses to assess the impact of a priori assessed study characteristics on the predicted ERC. Results The natural spline showed a supralinear shape at cumulative exposures less than 100 ppm-years, although this model fitted the data only marginally better than a linear model (p = 0.06). Stratification based on study design and jackknifing indicated that the cohort studies had a considerable impact on the shape of the ERC at high exposure levels (> 100 ppm-years) but that predicted risks for the low exposure range (< 50 ppm-years) were robust. Conclusions Although limited by the small number of studies and the large heterogeneity between studies, the inclusion of all studies of sufficient quality combined with a flexible meta-regression method provides the most comprehensive evaluation of the benzene–leukemia ERC to date. The natural spline based on all data indicates a significantly increased risk of leukemia [relative risk (RR) = 1.14; 95% confidence interval (CI), 1.04–1.26] at an exposure level as low as 10 ppm-years. PMID:20064779

Space radiation and cardiovascular disease risk

PubMed Central

Boerma, Marjan; Nelson, Gregory A; Sridharan, Vijayalakshmi; Mao, Xiao-Wen; Koturbash, Igor; Hauer-Jensen, Martin

2015-01-01

Future long-distance space missions will be associated with significant exposures to ionizing radiation, and the health risks of these radiation exposures during manned missions need to be assessed. Recent Earth-based epidemiological studies in survivors of atomic bombs and after occupational and medical low dose radiation exposures have indicated that the cardiovascular system may be more sensitive to ionizing radiation than was previously thought. This has raised the concern of a cardiovascular disease risk from exposure to space radiation during long-distance space travel. Ground-based studies with animal and cell culture models play an important role in estimating health risks from space radiation exposure. Charged particle space radiation has dense ionization characteristics and may induce unique biological responses, appropriate simulation of the space radiation environment and careful consideration of the choice of the experimental model are critical. Recent studies have addressed cardiovascular effects of space radiation using such models and provided first results that aid in estimating cardiovascular disease risk, and several other studies are ongoing. Moreover, astronauts could potentially be administered pharmacological countermeasures against adverse effects of space radiation, and research is focused on the development of such compounds. Because the cardiovascular response to space radiation has not yet been clearly defined, the identification of potential pharmacological countermeasures against cardiovascular effects is still in its infancy. PMID:26730293

Traffic-related air pollution increased the risk of Parkinson's disease in Taiwan: A nationwide study.

PubMed

Lee, Pei-Chen; Liu, Li-Ling; Sun, Yu; Chen, Yu-An; Liu, Chih-Ching; Li, Chung-Yi; Yu, Hwa-Lung; Ritz, Beate

2016-11-01

Ambient air pollution has been associated with many health conditions, but little is known about its effects on neurodegenerative diseases, such as Parkinson's disease (PD). In this study, we investigated the influence of ambient air pollution on PD in a nationwide population-based case-control study in Taiwan. We identified 11,117 incident PD patients between 2007 and 2009 from the Taiwanese National Health Insurance Research Database and selected 44,468 age- and gender-matched population controls from the longitudinal health insurance database. The average ambient pollutant exposure concentrations from 1998 through the onset of PD were estimated using quantile-based Bayesian Maximum Entropy models. Basing from logistic regression models, we estimated the odds ratios (ORs) and 95% confidence intervals (CIs) of ambient pollutant exposures and PD risk. We observed positive associations between NO x , CO exposures, and PD. In multi-pollutant models, for NO x and CO above the 75th percentile exposure compared with the lowest percentile, the ORs of PD were 1.37 (95% CI=1.23-1.52) and 1.17 (95% CI=1.07-1.27), respectively. This study suggests that ambient air pollution exposure, especially from traffic-related pollutants such as NO x and CO, increases PD risk in the Taiwanese population. Copyright © 2016 Elsevier Ltd. All rights reserved.

Space radiation and cardiovascular disease risk.

PubMed

Boerma, Marjan; Nelson, Gregory A; Sridharan, Vijayalakshmi; Mao, Xiao-Wen; Koturbash, Igor; Hauer-Jensen, Martin

2015-12-26

Future long-distance space missions will be associated with significant exposures to ionizing radiation, and the health risks of these radiation exposures during manned missions need to be assessed. Recent Earth-based epidemiological studies in survivors of atomic bombs and after occupational and medical low dose radiation exposures have indicated that the cardiovascular system may be more sensitive to ionizing radiation than was previously thought. This has raised the concern of a cardiovascular disease risk from exposure to space radiation during long-distance space travel. Ground-based studies with animal and cell culture models play an important role in estimating health risks from space radiation exposure. Charged particle space radiation has dense ionization characteristics and may induce unique biological responses, appropriate simulation of the space radiation environment and careful consideration of the choice of the experimental model are critical. Recent studies have addressed cardiovascular effects of space radiation using such models and provided first results that aid in estimating cardiovascular disease risk, and several other studies are ongoing. Moreover, astronauts could potentially be administered pharmacological countermeasures against adverse effects of space radiation, and research is focused on the development of such compounds. Because the cardiovascular response to space radiation has not yet been clearly defined, the identification of potential pharmacological countermeasures against cardiovascular effects is still in its infancy.

An emission-weighted proximity model for air pollution exposure assessment.

PubMed

Zou, Bin; Wilson, J Gaines; Zhan, F Benjamin; Zeng, Yongnian

2009-08-15

Among the most common spatial models for estimating personal exposure are Traditional Proximity Models (TPMs). Though TPMs are straightforward to configure and interpret, they are prone to extensive errors in exposure estimates and do not provide prospective estimates. To resolve these inherent problems with TPMs, we introduce here a novel Emission Weighted Proximity Model (EWPM) to improve the TPM, which takes into consideration the emissions from all sources potentially influencing the receptors. EWPM performance was evaluated by comparing the normalized exposure risk values of sulfur dioxide (SO(2)) calculated by EWPM with those calculated by TPM and monitored observations over a one-year period in two large Texas counties. In order to investigate whether the limitations of TPM in potential exposure risk prediction without recorded incidence can be overcome, we also introduce a hybrid framework, a 'Geo-statistical EWPM'. Geo-statistical EWPM is a synthesis of Ordinary Kriging Geo-statistical interpolation and EWPM. The prediction results are presented as two potential exposure risk prediction maps. The performance of these two exposure maps in predicting individual SO(2) exposure risk was validated with 10 virtual cases in prospective exposure scenarios. Risk values for EWPM were clearly more agreeable with the observed concentrations than those from TPM. Over the entire study area, the mean SO(2) exposure risk from EWPM was higher relative to TPM (1.00 vs. 0.91). The mean bias of the exposure risk values of 10 virtual cases between EWPM and 'Geo-statistical EWPM' are much smaller than those between TPM and 'Geo-statistical TPM' (5.12 vs. 24.63). EWPM appears to more accurately portray individual exposure relative to TPM. The 'Geo-statistical EWPM' effectively augments the role of the standard proximity model and makes it possible to predict individual risk in future exposure scenarios resulting in adverse health effects from environmental pollution.

Incorporating Measurement Error from Modeled Air Pollution Exposures into Epidemiological Analyses.

PubMed

Samoli, Evangelia; Butland, Barbara K

2017-12-01

Outdoor air pollution exposures used in epidemiological studies are commonly predicted from spatiotemporal models incorporating limited measurements, temporal factors, geographic information system variables, and/or satellite data. Measurement error in these exposure estimates leads to imprecise estimation of health effects and their standard errors. We reviewed methods for measurement error correction that have been applied in epidemiological studies that use model-derived air pollution data. We identified seven cohort studies and one panel study that have employed measurement error correction methods. These methods included regression calibration, risk set regression calibration, regression calibration with instrumental variables, the simulation extrapolation approach (SIMEX), and methods under the non-parametric or parameter bootstrap. Corrections resulted in small increases in the absolute magnitude of the health effect estimate and its standard error under most scenarios. Limited application of measurement error correction methods in air pollution studies may be attributed to the absence of exposure validation data and the methodological complexity of the proposed methods. Future epidemiological studies should consider in their design phase the requirements for the measurement error correction method to be later applied, while methodological advances are needed under the multi-pollutants setting.

Exposure to Radiofrequency Electromagnetic Fields and Sleep Quality: A Prospective Cohort Study

PubMed Central

Mohler, Evelyn; Frei, Patrizia; Fröhlich, Jürg; Braun-Fahrländer, Charlotte; Röösli, Martin

2012-01-01

Background There is persistent public concern about sleep disturbances due to radiofrequency electromagnetic field (RF-EMF) exposure. The aim of this prospective cohort study was to investigate whether sleep quality is affected by mobile phone use or by other RF-EMF sources in the everyday environment. Methods We conducted a prospective cohort study with 955 study participants aged between 30 and 60 years. Sleep quality and daytime sleepiness was assessed by means of standardized questionnaires in May 2008 (baseline) and May 2009 (follow-up). We also asked about mobile and cordless phone use and asked study participants for consent to obtain their mobile phone connection data from the mobile phone operators. Exposure to environmental RF-EMF was computed for each study participant using a previously developed and validated prediction model. In a nested sample of 119 study participants, RF-EMF exposure was measured in the bedroom and data on sleep behavior was collected by means of actigraphy during two weeks. Data were analyzed using multivariable regression models adjusted for relevant confounders. Results In the longitudinal analyses neither operator-recorded nor self-reported mobile phone use was associated with sleep disturbances or daytime sleepiness. Also, exposure to environmental RF-EMF did not affect self-reported sleep quality. The results from the longitudinal analyses were confirmed in the nested sleep study with objectively recorded exposure and measured sleep behavior data. Conclusions We did not find evidence for adverse effects on sleep quality from RF-EMF exposure in our everyday environment. PMID:22624036

SUBCHRONIC INHALATION EXPOSURE OF RATS TO LIBBY AMPHIBOLE AND AMOSITE ASBESTOS

EPA Science Inventory

Exposure to Libby amphibole (LA) is associated with significant increases in asbestosis, lung cancer, and mesothelioma. To support biological potency assessment and dosimetry model development, a subchronic nose-only inhalation exposure study (6 hr/d, 5 d/wk, 13 wk) was conducted...

RECONSTRUCTING POPULATION EXPOSURES FROM DOSE BIOMARKERS: INHALATION OF TRICHLOROETHYLENE (TCE) AS A CASE STUDY

EPA Science Inventory

Physiologically based pharmacokinetic (PBPK) modeling is a well-established toxicological tool designed to relate exposure to a target tissue dose. The emergence of federal and state programs for environmental health tracking and the availability of exposure monitoring through bi...

Swimmer illness associated with marine water exposure and water quality indicators: impact of widely used assumptions

EPA Science Inventory

Studies of health risks associated with recreational water exposure require investigators to make choices about water quality indicator averaging techniques, exposure definitions, follow-up periods, and model specifications; but, investigators seldom describe the impact of these ...

DIETARY EXPOSURES OF YOUNG CHILDREN, PART 1: MODEL DEVELOPMENT AND STUDY DESIGN

EPA Science Inventory

Young children contact surfaces (hands, floors, etc.) that may be contaminated with pesticides. Thus, dietary exposures of young children are difficult to measure, but are needed to support the aggregate exposure assessments. Evaluation of dietary field protocols and a total die...

A simulation study to quantify the impacts of exposure ...

EPA Pesticide Factsheets

BackgroundExposure measurement error in copollutant epidemiologic models has the potential to introduce bias in relative risk (RR) estimates. A simulation study was conducted using empirical data to quantify the impact of correlated measurement errors in time-series analyses of air pollution and health.MethodsZIP-code level estimates of exposure for six pollutants (CO, NOx, EC, PM2.5, SO4, O3) from 1999 to 2002 in the Atlanta metropolitan area were used to calculate spatial, population (i.e. ambient versus personal), and total exposure measurement error.Empirically determined covariance of pollutant concentration pairs and the associated measurement errors were used to simulate true exposure (exposure without error) from observed exposure. Daily emergency department visits for respiratory diseases were simulated using a Poisson time-series model with a main pollutant RR = 1.05 per interquartile range, and a null association for the copollutant (RR = 1). Monte Carlo experiments were used to evaluate the impacts of correlated exposure errors of different copollutant pairs.ResultsSubstantial attenuation of RRs due to exposure error was evident in nearly all copollutant pairs studied, ranging from 10 to 40% attenuation for spatial error, 3–85% for population error, and 31–85% for total error. When CO, NOx or EC is the main pollutant, we demonstrated the possibility of false positives, specifically identifying significant, positive associations for copoll

Violence Exposure in Home and Community: Influence on Posttraumatic Stress Symptoms in Army Recruits

ERIC Educational Resources Information Center

Chapin, Mark G.

2004-01-01

This study assessed the levels and types of violence exposure, levels of posttraumatic stress symptoms, and the relationship among exposure to violence, posttraumatic stress symptoms, and early discharge in U.S. Army recruits at Basic Combat Training (BCT). The study applied a modified ABCX model of family stress adaptation developed by McCubbin,…

Statistical modeling of crystalline silica exposure by trade in the construction industry using a database compiled from the literature.

PubMed

Sauvé, Jean-François; Beaudry, Charles; Bégin, Denis; Dion, Chantal; Gérin, Michel; Lavoué, Jérôme

2012-09-01

A quantitative determinants-of-exposure analysis of respirable crystalline silica (RCS) levels in the construction industry was performed using a database compiled from an extensive literature review. Statistical models were developed to predict work-shift exposure levels by trade. Monte Carlo simulation was used to recreate exposures derived from summarized measurements which were combined with single measurements for analysis. Modeling was performed using Tobit models within a multimodel inference framework, with year, sampling duration, type of environment, project purpose, project type, sampling strategy and use of exposure controls as potential predictors. 1346 RCS measurements were included in the analysis, of which 318 were non-detects and 228 were simulated from summary statistics. The model containing all the variables explained 22% of total variability. Apart from trade, sampling duration, year and strategy were the most influential predictors of RCS levels. The use of exposure controls was associated with an average decrease of 19% in exposure levels compared to none, and increased concentrations were found for industrial, demolition and renovation projects. Predicted geometric means for year 1999 were the highest for drilling rig operators (0.238 mg m(-3)) and tunnel construction workers (0.224 mg m(-3)), while the estimated exceedance fraction of the ACGIH TLV by trade ranged from 47% to 91%. The predicted geometric means in this study indicated important overexposure compared to the TLV. However, the low proportion of variability explained by the models suggests that the construction trade is only a moderate predictor of work-shift exposure levels. The impact of the different tasks performed during a work shift should also be assessed to provide better management and control of RCS exposure levels on construction sites.

Using the Integrative Model to Explain How Exposure to Sexual Media Content Influences Adolescent Sexual Behavior

PubMed Central

Bleakley, Amy; Hennessy, Michael; Fishbein, Martin; Jordan, Amy

2017-01-01

Published research demonstrates an association between exposure to media sexual content and a variety of sex-related outcomes for adolescents. What is not known is the mechanism through which sexual content produces this “media effect” on adolescent beliefs, attitudes, and behavior. Using the Integrative Model of Behavioral Prediction, this paper uses data from a longitudinal study of adolescents ages 16–18 (n=460) to determine how exposure to sexual media content influences sexual behavior. Path analysis and structural equation modeling demonstrated that intention to engage in sexual intercourse is determined by a combination of attitudes, normative pressure, and self efficacy but that exposure to sexual media content only affects normative pressure beliefs. By applying the Integrative Model, we are able to identify which beliefs are influenced by exposure to media sex and improve the ability of health educators, researchers, and others to design effective messages for health communication campaigns and messages pertaining to adolescents’ engaging in sexual intercourse. PMID:21606378

Ethanol toxicokinetics resulting from inhalation exposure in human volunteers and toxicokinetic modeling.

PubMed

Dumas-Campagna, Josée; Tardif, Robert; Charest-Tardif, Ginette; Haddad, Sami

2014-02-01

Uncertainty exists regarding the validity of a previously developed physiologically-based pharmacokinetic model (PBPK) for inhaled ethanol in humans to predict the blood levels of ethanol (BLE) at low level exposures (<1000 ppm). Thus, the objective of this study is to document the BLE resulting from low levels exposures in order to refine/validate this PBPK model. Human volunteers were exposed to ethanol vapors during 4 h at 5 different concentrations (125-1000 ppm), at rest, in an inhalation chamber. Blood and exhaled air were sampled. Also, the impact of light exercise (50 W) on the BLE was investigated. There is a linear relationship between the ethanol concentrations in inhaled air and (i) BLE (women: r²= 0.98/men: r²= 0.99), as well as (ii) ethanol concentrations in the exhaled air at end of exposure period (men: r²= 0.99/women: r²= 0.99). Furthermore, the exercise resulted in a net and significant increase of BLE (2-3 fold). Overall, the original model predictions overestimated the BLE for all low exposures performed in this study. To properly simulate the toxicokinetic data, the model was refined by adding a description of an extra-hepatic biotransformation of high affinity and low capacity in the richly perfused tissues compartment. This is based on the observation that total clearance observed at low exposure levels was much greater than liver blood flow. The results of this study will facilitate the refinement of the risk assessment associated with chronic inhalation of low levels of ethanol in the general population and especially among workers.

Neonatal Ethanol Exposure Causes Behavioral Deficits in Young Mice.

PubMed

Xu, Wenhua; Hawkey, Andrew B; Li, Hui; Dai, Lu; Brim, Howard H; Frank, Jacqueline A; Luo, Jia; Barron, Susan; Chen, Gang

2018-04-01

Fetal ethanol (EtOH) exposure can damage the developing central nervous system and lead to cognitive and behavioral deficits, known as fetal alcohol spectrum disorders (FASD). EtOH exposure to mouse pups during early neonatal development was used as a model of EtOH exposure that overlaps the human third-trimester "brain growth spurt"-a model that has been widely used to study FASD in rats. C57BL/6 male and female mice were exposed to EtOH (4 g/kg/d) on postnatal days (PD) 4 to 10 by oral intubation. Intubated and nontreated controls were also included. Behavioral testing of the offspring, including open field, elevated plus maze, and Morris water maze, was performed on PD 20 to 45. EtOH exposure during PD 4 to 10 resulted in hyperactivity and deficits in learning and memory in young mice with no apparent sex differences. Based on these data, this neonatal intubation mouse model may be useful for future mechanistic and genetic studies of FASD and for screening of novel therapeutic agents. Copyright © 2018 by the Research Society on Alcoholism.

Residential energy use emissions dominate health impacts from exposure to ambient particulate matter in India.

PubMed

Conibear, Luke; Butt, Edward W; Knote, Christoph; Arnold, Stephen R; Spracklen, Dominick V

2018-02-12

Exposure to ambient fine particulate matter (PM 2.5 ) is a leading contributor to diseases in India. Previous studies analysing emission source attributions were restricted by coarse model resolution and limited PM 2.5 observations. We use a regional model informed by new observations to make the first high-resolution study of the sector-specific disease burden from ambient PM 2.5 exposure in India. Observed annual mean PM 2.5 concentrations exceed 100 μg m -3 and are well simulated by the model. We calculate that the emissions from residential energy use dominate (52%) population-weighted annual mean PM 2.5 concentrations, and are attributed to 511,000 (95UI: 340,000-697,000) premature mortalities annually. However, removing residential energy use emissions would avert only 256,000 (95UI: 162,000-340,000), due to the non-linear exposure-response relationship causing health effects to saturate at high PM 2.5 concentrations. Consequently, large reductions in emissions will be required to reduce the health burden from ambient PM 2.5 exposure in India.

Health effects of long-term mercury exposure among chloralkali plant workers.

PubMed

Frumkin, H; Letz, R; Williams, P L; Gerr, F; Pierce, M; Sanders, A; Elon, L; Manning, C C; Woods, J S; Hertzberg, V S; Mueller, P; Taylor, B B

2001-01-01

Inorganic mercury is toxic to the nervous system, kidneys, and reproductive system. We studied the health effects of mercury exposure among former employees of a chloralkali plant that operated from 1955 to 1994 in Georgia. Former plant workers and unexposed workers from nearby employers were studied. Exposure was assessed with a job-exposure matrix based on historical measurements and personnel records. Health outcomes were assessed with interviews, physical examinations, neurological and neurobehavioral testing, renal function testing, and urinary porphyrin measurements. Exposure-disease associations were assessed with multivariate modeling. Exposed workers reported more symptoms, and tended toward more physical examination abnormalities, than unexposed workers. Exposed workers performed worse than unexposed subjects on some quantitative tests of vibration sense, motor speed and coordination, and tremor, and on one test of cognitive function. Few findings remained significant when exposure was modeled as a continuous variable. Neither renal function nor porphyrin excretion was associated with mercury exposure. Mercury-exposed chloralkali plant workers reported more symptoms than unexposed controls, but no strong associations were demonstrated with neurological or renal function or with porphyrin excretion. Copyright 2001 Wiley-Liss, Inc.

THE ENVIRONMENT AND SUSCEPTIBILITY TO SCHIZOPHRENIA

PubMed Central

Brown, Alan S.

2010-01-01

In the present article the putative role of environmental factors in schizophrenia is reviewed and synthesized. Accumulating evidence from recent studies suggests that environmental exposures may play a more significant role in the etiopathogenesis of this disorder than previously thought. This expanding knowledge base is largely a consequence of refinements in the methodology of epidemiologic studies, including birth cohort investigations, and in preclinical research that has been inspired by the evolving literature on animal models of environmental exposures. The bulk of evidence supports a contribution of environmental factors acting during fetal and perinatal life; these include infections, nutritional deficiencies, paternal age, fetal/neonatal hypoxic insults, maternal stress and other exposures. A considerable amount of data supports cannabis use in adolescence, migration, unfavorable neighborhood environments, and possibly infections at different points in the lifespan as risk factors for schizophrenia. Animal models have yielded evidence suggesting that these exposures cause brain and behavioral phenotypes that are analogous to findings observed in patients with schizophrenia. It is suggested that future studies attempt to replicate these findings, identify new risk factors, explore the gestational specificity of environmental insults, elaborate developmental trajectories, and examine relationships between environmental exposures and structural and functional brain anomalies in schizophrenia patients. Future research on gene-environment interactions and epigenetic effects of environmental exposures should shed further light on genes and exposures that may not be identified in the absence of these integrated approaches. Moreover, translational studies should further facilitate the discovery of neurodevelopmental mechanisms that increase susceptibility to schizophrenia. The study of environmental factors in schizophrenia may have important implications for the prevention of this disorder, and offers the potential to complement, and refine, existing efforts on explanatory neurodevelopmental models. PMID:955757

Prenatal mercury exposure and infant birth weight in the Norwegian Mother and Child Cohort Study.

PubMed

Vejrup, Kristine; Brantsæter, Anne Lise; Knutsen, Helle K; Magnus, Per; Alexander, Jan; Kvalem, Helen E; Meltzer, Helle M; Haugen, Margaretha

2014-09-01

To examine the association between calculated maternal dietary exposure to Hg in pregnancy and infant birth weight in the Norwegian Mother and Child Cohort Study (MoBa). Exposure was calculated with use of a constructed database of Hg in food items and reported dietary intake during pregnancy. Multivariable regression models were used to explore the association between maternal Hg exposure and infant birth weight, and to model associations with small-for-gestational-age offspring. The study is based on data from MoBa. The study sample consisted of 62 941 women who answered a validated FFQ which covered the habitual diet during the first five months of pregnancy. Median exposure to Hg was 0·15 μg/kg body weight per week and the contribution from seafood intake was 88 % of total Hg exposure. Women in the highest quintile compared with the lowest quintile of Hg exposure delivered offspring with 34 g lower birth weight (95 % CI -46 g, -22 g) and had an increased risk of giving birth to small-for-gestational-age offspring, adjusted OR = 1·19 (95 % CI 1·08, 1·30). Although seafood intake was positively associated with increased birth weight, stratified analyses showed negative associations between Hg exposure and birth weight within strata of seafood intake. Although seafood intake in pregnancy is positively associated with birth weight, Hg exposure is negatively associated with birth weight. Seafood consumption during pregnancy should not be avoided, but clarification is needed to identify at what level of Hg exposure this risk might exceed the benefits of seafood.

Biomarkers of environmental benzene exposure.

PubMed Central

Weisel, C; Yu, R; Roy, A; Georgopoulos, P

1996-01-01

Environmental exposures to benzene result in increases in body burden that are reflected in various biomarkers of exposure, including benzene in exhaled breath, benzene in blood and urinary trans-trans-muconic acid and S-phenylmercapturic acid. A review of the literature indicates that these biomarkers can be used to distinguish populations with different levels of exposure (such as smokers from nonsmokers and occupationally exposed from environmentally exposed populations) and to determine differences in metabolism. Biomarkers in humans have shown that the percentage of benzene metabolized by the ring-opening pathway is greater at environmental exposures than that at higher occupational exposures, a trend similar to that found in animal studies. This suggests that the dose-response curve is nonlinear; that potential different metabolic mechanisms exist at high and low doses; and that the validity of a linear extrapolation of adverse effects measured at high doses to a population exposed to lower, environmental levels of benzene is uncertain. Time-series measurements of the biomarker, exhaled breath, were used to evaluate a physiologically based pharmacokinetic (PBPK) model. Biases were identified between the PBPK model predictions and experimental data that were adequately described using an empirical compartmental model. It is suggested that a mapping of the PBPK model to a compartmental model can be done to optimize the parameters in the PBPK model to provide a future framework for developing a population physiologically based pharmacokinetic model. PMID:9118884

Crystalline silica exposure and lung cancer mortality in diatomaceous earth industry workers: a quantitative risk assessment.

PubMed

Rice, F L; Park, R; Stayner, L; Smith, R; Gilbert, S; Checkoway, H

2001-01-01

To use various exposure-response models to estimate the risk of mortality from lung cancer due to occupational exposure to respirable crystalline silica dust. Data from a cohort mortality study of 2342 white male California diatomaceous earth mining and processing workers exposed to crystalline silica dust (mainly cristobalite) were reanalyzed with Poisson regression and Cox's proportional hazards models. Internal and external adjustments were used to control for potential confounding from the effects of time since first observation, calendar time, age, and Hispanic ethnicity. Cubic smoothing spline models were used to assess the fit of the models. Exposures were lagged by 10 years. Evaluations of the fit of the models were performed by comparing their deviances. Lifetime risks of lung cancer were estimated up to age 85 with an actuarial approach that accounted for competing causes of death. Exposure to respirable crystalline silica dust was a significant predictor (p<0.05) in nearly all of the models evaluated and the linear relative rate model with a 10 year exposure lag seemed to give the best fit in the Poisson regression analysis. For those who died of lung cancer the linear relative rate model predicted rate ratios for mortality from lung cancer of about 1.6 for the mean cumulative exposure to respirable silica compared with no exposure. The excess lifetime risk (to age 85) of mortality from lung cancer for white men exposed for 45 years and with a 10 year lag period at the current Occupational Safety and Health Administration (OSHA) standard of about 0.05 mg/m(3) for respirable cristobalite dust is 19/1000 (95% confidence interval (95% CI) 5/1000 to 46/1000). There was a significant risk of mortality from lung cancer that increased with cumulative exposure to respirable crystalline silica dust. The predicted number of deaths from lung cancer suggests that current occupational health standards may not be adequately protecting workers from the risk of lung cancer.

Detrimental effect of electromagnetic pulse exposure on permeability of in vitro blood-brain-barrier model.

PubMed

Zhou, Jia Xing; Ding, Gui Rong; Zhang, Jie; Zhou, Yong Chun; Zhang, Yan Jun; Guo, Guo Zhen

2013-02-01

To study the effect of electromagnetic pulse (EMP) exposure on permeability of in vitro blood-brain-barrier (BBB) model. An in vitro BBB model, established by co-culturing brain microvascular endothelial cells (BMVEC) and astroglial cells (AC) isolated from rat brain, was exposed to EMP at 100 kV/m and 400 kV/m, respectively. Permeability of the model was assayed by measuring the transendothelial electrical resistance (TEER) and the horseradish peroxidase (HRP) transmission at different time points. Levels of BBB tight junction-related proteins were measured at 0, 1, 2, 4, 8, 12, 16, 20, 24 h after EMP exposure by Western blotting. The TEER level was lower in BBB model group than in control group at 12 h after EMP, exposure which returned to its normal level at 24 h. The 24 h recovery process was triphasic and biphasic respectively after EMP exposure at 100 kV/m and 400 kV/m. Following exposure to 400 kV/m EMP, the HRP permeability increased at 1-12 h and returned to its normal level at 24 h. Western blotting showed that the claudin-5 and ZO-1 protein levels were changed after EMP exposure. EMP exposure at 100 kV/m and 400 kV/m can increase the permeability of in vitro BBB model and BBB tight junction-related proteins such as ZO-1 and claudin-5 may change EMP-induced BBB permeability. Copyright © 2013 The Editorial Board of Biomedical and Environmental Sciences. Published by China CDC. All rights reserved.

Air pollution and nonmalignant respiratory mortality in 16 cohorts within the ESCAPE project.

PubMed

Dimakopoulou, Konstantina; Samoli, Evangelia; Beelen, Rob; Stafoggia, Massimo; Andersen, Zorana Jovanovic; Hoffmann, Barbara; Fischer, Paul; Nieuwenhuijsen, Mark; Vineis, Paolo; Xun, Wei; Hoek, Gerard; Raaschou-Nielsen, Ole; Oudin, Anna; Forsberg, Bertil; Modig, Lars; Jousilahti, Pekka; Lanki, Timo; Turunen, Anu; Oftedal, Bente; Nafstad, Per; Schwarze, Per E; Penell, Johanna; Fratiglioni, Laura; Andersson, Niklas; Pedersen, Nancy; Korek, Michal; De Faire, Ulf; Eriksen, Kirsten Thorup; Tjønneland, Anne; Becker, Thomas; Wang, Meng; Bueno-de-Mesquita, Bas; Tsai, Ming-Yi; Eeftens, Marloes; Peeters, Petra H; Meliefste, Kees; Marcon, Alessandro; Krämer, Ursula; Kuhlbusch, Thomas A J; Vossoughi, Mohammad; Key, Timothy; de Hoogh, Kees; Hampel, Regina; Peters, Annette; Heinrich, Joachim; Weinmayr, Gudrun; Concin, Hans; Nagel, Gabriele; Ineichen, Alex; Jacquemin, Bénédicte; Stempfelet, Morgane; Vilier, Alice; Ricceri, Fulvio; Sacerdote, Carlotta; Pedeli, Xanthi; Katsoulis, Michalis; Trichopoulou, Antonia; Brunekreef, Bert; Katsouyanni, Klea

2014-03-15

Prospective cohort studies have shown that chronic exposure to particulate matter and traffic-related air pollution is associated with reduced survival. However, the effects on nonmalignant respiratory mortality are less studied, and the data reported are less consistent. We have investigated the relationship of long-term exposure to air pollution and nonmalignant respiratory mortality in 16 cohorts with individual level data within the multicenter European Study of Cohorts for Air Pollution Effects (ESCAPE). Data from 16 ongoing cohort studies from Europe were used. The total number of subjects was 307,553. There were 1,559 respiratory deaths during follow-up. Air pollution exposure was estimated by land use regression models at the baseline residential addresses of study participants and traffic-proximity variables were derived from geographical databases following a standardized procedure within the ESCAPE study. Cohort-specific hazard ratios obtained by Cox proportional hazard models from standardized individual cohort analyses were combined using metaanalyses. We found no significant associations between air pollution exposure and nonmalignant respiratory mortality. Most hazard ratios were slightly below unity, with the exception of the traffic-proximity indicators. In this study of 16 cohorts, there was no association between air pollution exposure and nonmalignant respiratory mortality.

Animal models of maternal high fat diet exposure and effects on metabolism in offspring: a meta-regression analysis.

PubMed

Ribaroff, G A; Wastnedge, E; Drake, A J; Sharpe, R M; Chambers, T J G

2017-06-01

Animal models of maternal high fat diet (HFD) demonstrate perturbed offspring metabolism although the effects differ markedly between models. We assessed studies investigating metabolic parameters in the offspring of HFD fed mothers to identify factors explaining these inter-study differences. A total of 171 papers were identified, which provided data from 6047 offspring. Data were extracted regarding body weight, adiposity, glucose homeostasis and lipidaemia. Information regarding the macronutrient content of diet, species, time point of exposure and gestational weight gain were collected and utilized in meta-regression models to explore predictive factors. Publication bias was assessed using Egger's regression test. Maternal HFD exposure did not affect offspring birthweight but increased weaning weight, final bodyweight, adiposity, triglyceridaemia, cholesterolaemia and insulinaemia in both female and male offspring. Hyperglycaemia was found in female offspring only. Meta-regression analysis identified lactational HFD exposure as a key moderator. The fat content of the diet did not correlate with any outcomes. There was evidence of significant publication bias for all outcomes except birthweight. Maternal HFD exposure was associated with perturbed metabolism in offspring but between studies was not accounted for by dietary constituents, species, strain or maternal gestational weight gain. Specific weaknesses in experimental design predispose many of the results to bias. © 2017 The Authors. Obesity Reviews published by John Wiley & Sons Ltd on behalf of World Obesity Federation.

Developmental fluoride neurotoxicity: a systematic review and meta-analysis.

PubMed

Choi, Anna L; Sun, Guifan; Zhang, Ying; Grandjean, Philippe

2012-10-01

Although fluoride may cause neurotoxicity in animal models and acute fluoride poisoning causes neurotoxicity in adults, very little is known of its effects on children's neurodevelopment. We performed a systematic review and meta-analysis of published studies to investigate the effects of increased fluoride exposure and delayed neurobehavioral development. We searched the MEDLINE, EMBASE, Water Resources Abstracts, and TOXNET databases through 2011 for eligible studies. We also searched the China National Knowledge Infrastructure (CNKI) database, because many studies on fluoride neurotoxicity have been published in Chinese journals only. In total, we identified 27 eligible epidemiological studies with high and reference exposures, end points of IQ scores, or related cognitive function measures with means and variances for the two exposure groups. Using random-effects models, we estimated the standardized mean difference between exposed and reference groups across all studies. We conducted sensitivity analyses restricted to studies using the same outcome assessment and having drinking-water fluoride as the only exposure. We performed the Cochran test for heterogeneity between studies, Begg's funnel plot, and Egger test to assess publication bias, and conducted meta-regressions to explore sources of variation in mean differences among the studies. The standardized weighted mean difference in IQ score between exposed and reference populations was -0.45 (95% confidence interval: -0.56, -0.35) using a random-effects model. Thus, children in high-fluoride areas had significantly lower IQ scores than those who lived in low-fluoride areas. Subgroup and sensitivity analyses also indicated inverse associations, although the substantial heterogeneity did not appear to decrease. The results support the possibility of an adverse effect of high fluoride exposure on children's neurodevelopment. Future research should include detailed individual-level information on prenatal exposure, neurobehavioral performance, and covariates for adjustment.

Affecting Factors of Secondhand Smoke Exposure in Korea: Focused on Different Exposure Locations.

PubMed

Sun, Li Yuan; Cheong, Hae Kwan; Lee, Eun Whan; Kang, Kyeong Jin; Park, Jae Hyun

2016-09-01

Exposure to secondhand smoke (SHS) not only can cause serious illness, but is also an economic and social burden. Contextual and individual factors of non-smoker exposure to SHS depend on location. However, studies focusing on this subject are lacking. In this study, we described and compared the factors related to SHS exposure according to location in Korea. Regarding individual factors related to SHS exposure, a common individual variable model and location-specific variable model was used to evaluate SHS exposure at home/work/public locations based on sex. In common individual variables, such as age, and smoking status showed different relationships with SHS exposure in different locations. Among home-related variables, housing type and family with a single father and unmarried children showed the strongest positive relationships with SHS exposure in both males and females. In the workplace, service and sales workers, blue-collar workers, and manual laborers showed the strongest positive association with SHS exposure in males and females. For multilevel analysis in public places, only SHS exposure in females was positively related with cancer screening rate. Exposure to SHS in public places showed a positive relationship with drinking rate and single-parent family in males and females. The problem of SHS embodies social policies and interactions between individuals and social contextual factors. Policy makers should consider the contextual factors of specific locations and regional and individual context, along with differences between males and females, to develop effective strategies for reducing SHS exposure.

Doubly Robust Additive Hazards Models to Estimate Effects of a Continuous Exposure on Survival.

PubMed

Wang, Yan; Lee, Mihye; Liu, Pengfei; Shi, Liuhua; Yu, Zhi; Abu Awad, Yara; Zanobetti, Antonella; Schwartz, Joel D

2017-11-01

The effect of an exposure on survival can be biased when the regression model is misspecified. Hazard difference is easier to use in risk assessment than hazard ratio and has a clearer interpretation in the assessment of effect modifications. We proposed two doubly robust additive hazards models to estimate the causal hazard difference of a continuous exposure on survival. The first model is an inverse probability-weighted additive hazards regression. The second model is an extension of the doubly robust estimator for binary exposures by categorizing the continuous exposure. We compared these with the marginal structural model and outcome regression with correct and incorrect model specifications using simulations. We applied doubly robust additive hazard models to the estimation of hazard difference of long-term exposure to PM2.5 (particulate matter with an aerodynamic diameter less than or equal to 2.5 microns) on survival using a large cohort of 13 million older adults residing in seven states of the Southeastern United States. We showed that the proposed approaches are doubly robust. We found that each 1 μg m increase in annual PM2.5 exposure was associated with a causal hazard difference in mortality of 8.0 × 10 (95% confidence interval 7.4 × 10, 8.7 × 10), which was modified by age, medical history, socioeconomic status, and urbanicity. The overall hazard difference translates to approximately 5.5 (5.1, 6.0) thousand deaths per year in the study population. The proposed approaches improve the robustness of the additive hazards model and produce a novel additive causal estimate of PM2.5 on survival and several additive effect modifications, including social inequality.

Exploring lifetime occupational exposure and SLE flare: a patient-focussed pilot study

PubMed Central

Squance, Marline L; Guest, Maya; Reeves, Glenn; Attia, John; Bridgman, Howard

2014-01-01

Introduction Environmental effectors, such as ultraviolet radiation exposure, infection and stress, have been established as having a role in exacerbating lupus symptoms. However, unpredictable patterns of flare events still remain a mystery. Occupational effectors have also been suggested as having a contributing role; however, they are not widely researched. In this paper we report a pilot study designed to generate focus areas for future research regarding occupational exposures and systemic lupus erythematosus (SLE). Methods The study explored potential links between exposures and the occurrence of patient-reported flare events in 80 Australian women with SLE (American College of Rheumatology (ACR) criteria classified). Specifically, the study assessed the hypothesis that occupational exposure is associated with significant changes in the likelihood of lupus flares. Lifetime employment history was analysed with the Finnish Job Exposure Matrix (FINJEM), 40 different semiquantified exposure class estimates for a wide number of occupations based on probability of exposure (p≥5%=exposed) were analysed with the construction of negative binomial regression models to test relationships between occupational agents and flare days. A backward stepwise elimination was used to generate a parsimonious model. Results Significant associations were noted for exposure classes of manual handling burden, (p=0.02, incidence rate ratio (IRR) 1.01), Iron (p=0.00, IRR 1.37), wood dust (p=0.00, IRR 3.34) and asbestos (p=0.03, IRR 2.48). Conclusion Exposure assessment results indicated that occupations, such as nursing, with a high manual handling burden, posed increased risk to patients with SLE, however, the greatest risk was associated with wood dust and iron exposure with teachers and specialist labourers. PMID:25379190

Occupational Exposures and Subclinical Interstitial Lung Disease. The MESA (Multi-Ethnic Study of Atherosclerosis) Air and Lung Studies.

PubMed

Sack, Coralynn S; Doney, Brent C; Podolanczuk, Anna J; Hooper, Laura G; Seixas, Noah S; Hoffman, Eric A; Kawut, Steven M; Vedal, Sverre; Raghu, Ganesh; Barr, R Graham; Lederer, David J; Kaufman, Joel D

2017-10-15

The impact of a broad range of occupational exposures on subclinical interstitial lung disease (ILD) has not been studied. To determine whether occupational exposures to vapors, gas, dust, and fumes (VGDF) are associated with high-attenuation areas (HAA) and interstitial lung abnormalities (ILA), which are quantitative and qualitative computed tomography (CT)-based measurements of subclinical ILD, respectively. We performed analyses of participants enrolled in MESA (Multi-Ethnic Study of Atherosclerosis), a population-based cohort aged 45-84 years at recruitment. HAA was measured at baseline and on serial cardiac CT scans in 5,702 participants. ILA was ascertained in a subset of 2,312 participants who underwent full-lung CT scanning at 10-year follow-up. Occupational exposures were assessed by self-reported VGDF exposure and by job-exposure matrix (JEM). Linear mixed models and logistic regression were used to determine whether occupational exposures were associated with log-transformed HAA and ILA. Models were adjusted for age, sex, race/ethnicity, education, employment status, tobacco use, and scanner technology. Each JEM score increment in VGDF exposure was associated with 2.64% greater HAA (95% confidence interval [CI], 1.23-4.19%). Self-reported vapors/gas exposure was associated with an increased odds of ILA among those currently employed (1.76-fold; 95% CI, 1.09-2.84) and those less than 65 years old (1.97-fold; 95% CI, 1.16-3.35). There was no consistent evidence that occupational exposures were associated with progression of HAA over the follow-up period. JEM-assigned and self-reported exposures to VGDF were associated with measurements of subclinical ILD in community-dwelling adults.

BEHAVIORAL ASSESSMENTS OF LONG EVANS RATS FOLLOWING A 13 WEEK SUBCHRONIC TOLUENE EXPOSURE.

EPA Science Inventory

Whereas the acute effects of volatile organic compounds (VOCs) are relatively well understood, there is some controversy regarding the potential for persistent effects following long-term exposure. The current study sought to develop an animal model of subchronic exposure to VOCs...

Gene expression profiles in the cerebellum and hippocampus following exposure to a neurotoxicant, Aroclor 1254: Developmental effects.

EPA Science Inventory

The developmental consequences of exposure to the polychlorinated biphenyls (PCBs) have been widely studied, making PCBs a unique model to understand issues related to environmental mixture of persistent chemicals. PCB exposure in humans adversely affects neurocognitive developm...

GPS-based Microenvironment Tracker (MicroTrac) Model to ...

EPA Pesticide Factsheets

A critical aspect of air pollution exposure assessment is the estimation of the time spent by individuals in various microenvironments (ME). Accounting for the time spent in different ME with different pollutant concentrations can reduce exposure misclassifications, while failure to do so can add uncertainty and bias to risk estimates. In this study, a classification model, called MicroTrac, was developed to estimate time of day and duration spent in eight ME (indoors and outdoors at home, work, school; inside vehicles; other locations) from global positioning system (GPS) data and geocoded building boundaries. Based on a panel study, MicroTrac estimates were compared to 24 h diary data from 7 participants on workdays and 2 participants on nonworkdays, with corresponding GPS data and building boundaries of home, school, and work. MicroTrac correctly classified the ME for 99.5% of the daily time spent by the participants. The capability of MicroTrac could help to reduce the time-location uncertainty in air pollution exposure models and exposure metrics for individuals in health studies. The National Exposure Research Laboratory’s (NERL’s) Human Exposure and Atmospheric Sciences Division (HEASD) conducts research in support of EPA’s mission to protect human health and the environment. HEASD’s research program supports Goal 1 (Clean Air) and Goal 4 (Healthy People) of EPA’s strategic plan. More specifically, our division conducts research to characterize

Calculated values of atomic oxygen fluences and solar exposure on selected surfaces of LDEF

NASA Technical Reports Server (NTRS)

Gillis, J. R.; Pippin, H. G.; Bourassa, R. J.; Gruenbaum, P. E.

1995-01-01

Atomic oxygen (AO) fluences and solar exposure have been modeled for selected hardware from the Long Duration Exposure Facility (LDEF). The atomic oxygen exposure was modeled using the microenvironment modeling code SHADOWV2. The solar exposure was modeled using the microenvironment modeling code SOLSHAD version 1.0.

A physiologically based pharmacokinetic model for developmental exposure to BDE-47 in rats

DOE Office of Scientific and Technical Information (OSTI.GOV)

Emond, Claude, E-mail: claude.emond@umontreal.c; BioSimulation Consulting Inc., Newark, DE 19711; Raymer, James H.

2010-02-01

Polybrominated diphenyl ethers (PBDEs) are used commercially as additive flame retardants and have been shown to transfer into environmental compartments, where they have the potential to bioaccumulate in wildlife and humans. Of the 209 possible PBDEs, 2,2',4,4'-tetrabromodiphenyl ether (BDE-47) is usually the dominant congener found in human blood and milk samples. BDE-47 has been shown to have endocrine activity and produce developmental, reproductive, and neurotoxic effects. The objective of this study was to develop a physiologically based pharmacokinetic (PBPK) model for BDE-47 in male and female (pregnant and non-pregnant) adult rats to facilitate investigations of developmental exposure. This model consistsmore » of eight compartments: liver, brain, adipose tissue, kidney, placenta, fetus, blood, and the rest of the body. Concentrations of BDE-47 from the literature and from maternal-fetal pharmacokinetic studies conducted at RTI International were used to parameterize and evaluate the model. The results showed that the model simulated BDE-47 tissue concentrations in adult male, maternal, and fetal compartments within the standard deviations of the experimental data. The model's ability to estimate BDE-47 concentrations in the fetus after maternal exposure will be useful to design in utero exposure/effect studies. This PBPK model is the first one designed for any PBDE pharmaco/toxicokinetic description. The next steps will be to expand this model to simulate BDE-47 pharmacokinetics and distributions across species (mice), and then extrapolate it to humans. After mouse and human model development, additional PBDE congeners will be incorporated into the model and simulated as a mixture.« less

Childhood incident asthma and traffic-related air pollution at home and school.

PubMed

McConnell, Rob; Islam, Talat; Shankardass, Ketan; Jerrett, Michael; Lurmann, Fred; Gilliland, Frank; Gauderman, Jim; Avol, Ed; Künzli, Nino; Yao, Ling; Peters, John; Berhane, Kiros

2010-07-01

Traffic-related air pollution has been associated with adverse cardiorespiratory effects, including increased asthma prevalence. However, there has been little study of effects of traffic exposure at school on new-onset asthma. We evaluated the relationship of new-onset asthma with traffic-related pollution near homes and schools. Parent-reported physician diagnosis of new-onset asthma (n = 120) was identified during 3 years of follow-up of a cohort of 2,497 kindergarten and first-grade children who were asthma- and wheezing-free at study entry into the Southern California Children's Health Study. We assessed traffic-related pollution exposure based on a line source dispersion model of traffic volume, distance from home and school, and local meteorology. Regional ambient ozone, nitrogen dioxide (NO(2)), and particulate matter were measured continuously at one central site monitor in each of 13 study communities. Hazard ratios (HRs) for new-onset asthma were scaled to the range of ambient central site pollutants and to the residential interquartile range for each traffic exposure metric. Asthma risk increased with modeled traffic-related pollution exposure from roadways near homes [HR 1.51; 95% confidence interval (CI), 1.25-1.82] and near schools (HR 1.45; 95% CI, 1.06-1.98). Ambient NO(2) measured at a central site in each community was also associated with increased risk (HR 2.18; 95% CI, 1.18-4.01). In models with both NO(2) and modeled traffic exposures, there were independent associations of asthma with traffic-related pollution at school and home, whereas the estimate for NO(2) was attenuated (HR 1.37; 95% CI, 0.69-2.71). Traffic-related pollution exposure at school and homes may both contribute to the development of asthma.

Radon-induced lung cancer deaths may be overestimated due to failure to account for confounding by exposure to diesel engine exhaust in BEIR VI miner studies.

PubMed

Cao, Xiaodong; MacNaughton, Piers; Laurent, Jose Cedeno; Allen, Joseph G

2017-01-01

EPA reported that radon is the second leading cause of lung cancer in the United States, killing 21,100 people per year. EPA relies on the BEIR VI models, based on an evaluation of radon exposure and lung cancer risk in studies of miners. But these models did not account for co-exposure to diesel exhaust, a known human carcinogen recently classified by IARC. It is probable then that a portion of the lung cancer deaths in the miner cohorts are originally attributable to the exposure to diesel rather than radon. To re-evaluate EPA's radon attributable lung cancer estimates accounting for diesel exposure information in the miner cohorts. We used estimates of historical diesel concentrations, combined with diesel exposure-response functions, to estimate the risks of lung cancer attributable to diesel engine exhaust (DEE) exposure in the miner studies. We re-calculated the fatal lung cancer risk attributable to radon after accounting for risk from diesel and re-estimated the number of U.S. deaths associated with radon in the U.S. using EPA's methodology. Considering the probable confounding with DEE exposure and using the same estimate of baseline mortality from 1989-91 that the EPA currently uses in their calculations, we estimate that radon-induced lung cancer deaths per year are 15,600 (95% CI: 14,300, 17,000)- 19,300 (95% CI: 18,800, 20,000) in the U.S. population, a reduction of 9%-26%. The death estimates would be 12,900-15,900 using 2014 baseline vital statistics. We recommend further research on re-evaluating the health effects of exposure to radon that accounts for new information on diesel exhaust carcinogenicity in BEIR VI models, up-to-date vital statistics and new epidemiological evidence from residential studies.

Radon-induced lung cancer deaths may be overestimated due to failure to account for confounding by exposure to diesel engine exhaust in BEIR VI miner studies

PubMed Central

MacNaughton, Piers; Laurent, Jose Cedeno; Allen, Joseph G.

2017-01-01

Background EPA reported that radon is the second leading cause of lung cancer in the United States, killing 21,100 people per year. EPA relies on the BEIR VI models, based on an evaluation of radon exposure and lung cancer risk in studies of miners. But these models did not account for co-exposure to diesel exhaust, a known human carcinogen recently classified by IARC. It is probable then that a portion of the lung cancer deaths in the miner cohorts are originally attributable to the exposure to diesel rather than radon. Objective To re-evaluate EPA’s radon attributable lung cancer estimates accounting for diesel exposure information in the miner cohorts. Methods We used estimates of historical diesel concentrations, combined with diesel exposure-response functions, to estimate the risks of lung cancer attributable to diesel engine exhaust (DEE) exposure in the miner studies. We re-calculated the fatal lung cancer risk attributable to radon after accounting for risk from diesel and re-estimated the number of U.S. deaths associated with radon in the U.S. using EPA’s methodology. Results Considering the probable confounding with DEE exposure and using the same estimate of baseline mortality from 1989–91 that the EPA currently uses in their calculations, we estimate that radon-induced lung cancer deaths per year are 15,600 (95% CI: 14,300, 17,000)– 19,300 (95% CI: 18,800, 20,000) in the U.S. population, a reduction of 9%–26%. The death estimates would be 12,900–15,900 using 2014 baseline vital statistics. Conclusions We recommend further research on re-evaluating the health effects of exposure to radon that accounts for new information on diesel exhaust carcinogenicity in BEIR VI models, up-to-date vital statistics and new epidemiological evidence from residential studies. PMID:28886109

Accommodating the ecological fallacy in disease mapping in the absence of individual exposures.

PubMed

Wang, Feifei; Wang, Jian; Gelfand, Alan; Li, Fan

2017-12-30

In health exposure modeling, in particular, disease mapping, the ecological fallacy arises because the relationship between aggregated disease incidence on areal units and average exposure on those units differs from the relationship between the event of individual incidence and the associated individual exposure. This article presents a novel modeling approach to address the ecological fallacy in the least informative data setting. We assume the known population at risk with an observed incidence for a collection of areal units and, separately, environmental exposure recorded during the period of incidence at a collection of monitoring stations. We do not assume any partial individual level information or random allocation of individuals to observed exposures. We specify a conceptual incidence surface over the study region as a function of an exposure surface resulting in a stochastic integral of the block average disease incidence. The true block level incidence is an unavailable Monte Carlo integration for this stochastic integral. We propose an alternative manageable Monte Carlo integration for the integral. Modeling in this setting is immediately hierarchical, and we fit our model within a Bayesian framework. To alleviate the resulting computational burden, we offer 2 strategies for efficient model fitting: one is through modularization, the other is through sparse or dimension-reduced Gaussian processes. We illustrate the performance of our model with simulations based on a heat-related mortality dataset in Ohio and then analyze associated real data. Copyright © 2017 John Wiley & Sons, Ltd.

SYN-JEM: A Quantitative Job-Exposure Matrix for Five Lung Carcinogens.

PubMed

Peters, Susan; Vermeulen, Roel; Portengen, Lützen; Olsson, Ann; Kendzia, Benjamin; Vincent, Raymond; Savary, Barbara; Lavoué, Jérôme; Cavallo, Domenico; Cattaneo, Andrea; Mirabelli, Dario; Plato, Nils; Fevotte, Joelle; Pesch, Beate; Brüning, Thomas; Straif, Kurt; Kromhout, Hans

2016-08-01

The use of measurement data in occupational exposure assessment allows more quantitative analyses of possible exposure-response relations. We describe a quantitative exposure assessment approach for five lung carcinogens (i.e. asbestos, chromium-VI, nickel, polycyclic aromatic hydrocarbons (by its proxy benzo(a)pyrene (BaP)) and respirable crystalline silica). A quantitative job-exposure matrix (JEM) was developed based on statistical modeling of large quantities of personal measurements. Empirical linear models were developed using personal occupational exposure measurements (n = 102306) from Europe and Canada, as well as auxiliary information like job (industry), year of sampling, region, an a priori exposure rating of each job (none, low, and high exposed), sampling and analytical methods, and sampling duration. The model outcomes were used to create a JEM with a quantitative estimate of the level of exposure by job, year, and region. Decreasing time trends were observed for all agents between the 1970s and 2009, ranging from -1.2% per year for personal BaP and nickel exposures to -10.7% for asbestos (in the time period before an asbestos ban was implemented). Regional differences in exposure concentrations (adjusted for measured jobs, years of measurement, and sampling method and duration) varied by agent, ranging from a factor 3.3 for chromium-VI up to a factor 10.5 for asbestos. We estimated time-, job-, and region-specific exposure levels for four (asbestos, chromium-VI, nickel, and RCS) out of five considered lung carcinogens. Through statistical modeling of large amounts of personal occupational exposure measurement data we were able to derive a quantitative JEM to be used in community-based studies. © The Author 2016. Published by Oxford University Press on behalf of the British Occupational Hygiene Society.

Local- and regional-scale air pollution modelling (PM10) and exposure assessment for pregnancy trimesters, infancy, and childhood to age 15 years: Avon Longitudinal Study of Parents And Children (ALSPAC).

PubMed

Gulliver, John; Elliott, Paul; Henderson, John; Hansell, Anna L; Vienneau, Danielle; Cai, Yutong; McCrea, Adrienne; Garwood, Kevin; Boyd, Andy; Neal, Lucy; Agnew, Paul; Fecht, Daniela; Briggs, David; de Hoogh, Kees

2018-04-01

We established air pollution modelling to study particle (PM 10 ) exposures during pregnancy and infancy (1990-1993) through childhood and adolescence up to age ~15 years (1991-2008) for the Avon Longitudinal Study of Parents And Children (ALSPAC) birth cohort. For pregnancy trimesters and infancy (birth to 6 months; 7 to 12 months) we used local (ADMS-Urban) and regional/long-range (NAME-III) air pollution models, with a model constant for local, non-anthropogenic sources. For longer exposure periods (annually and the average of birth to age ~8 and to age ~15 years to coincide with relevant follow-up clinics) we assessed spatial contrasts in local sources of PM 10 with a yearly-varying concentration for all background sources. We modelled PM 10 (μg/m 3 ) for 36,986 address locations over 19 years and then accounted for changes in address in calculating exposures for different periods: trimesters/infancy (n = 11,929); each year of life to age ~15 (n = 10,383). Intra-subject exposure contrasts were largest between pregnancy trimesters (5 th to 95 th centile: 24.4-37.3 μg/m 3 ) and mostly related to temporal variability in regional/long-range PM 10 . PM 10 exposures fell on average by 11.6 μg/m 3 from first year of life (mean concentration = 31.2 μg/m 3 ) to age ~15 (mean = 19.6 μg/m 3 ), and 5.4 μg/m 3 between follow-up clinics (age ~8 to age ~15). Spatial contrasts in 8-year average PM 10 exposures (5 th to 95 th centile) were relatively low: 25.4-30.0 μg/m 3 to age ~8 years and 20.7-23.9 μg/m 3 from age ~8 to age ~15 years. The contribution of local sources to total PM 10 was 18.5%-19.5% during pregnancy and infancy, and 14.4%-17.0% for periods leading up to follow-up clinics. Main roads within the study area contributed on average ~3.0% to total PM 10 exposures in all periods; 9.5% of address locations were within 50 m of a main road. Exposure estimates will be used in a number of planned epidemiological studies. Copyright © 2018 The Authors. Published by Elsevier Ltd.. All rights reserved.

Toxicological Assessment of Inhaled Nanoparticles: Role of in Vivo, ex Vivo, in Vitro, and in Silico Studies

PubMed Central

Fröhlich, Eleonore; Salar-Behzadi, Sharareh

2014-01-01

The alveolar epithelium of the lung is by far the most permeable epithelial barrier of the human body. The risk for adverse effects by inhaled nanoparticles (NPs) depends on their hazard (negative action on cells and organism) and on exposure (concentration in the inhaled air and pattern of deposition in the lung). With the development of advanced in vitro models, not only in vivo, but also cellular studies can be used for toxicological testing. Advanced in vitro studies use combinations of cells cultured in the air-liquid interface. These cultures are useful for particle uptake and mechanistic studies. Whole-body, nose-only, and lung-only exposures of animals could help to determine retention of NPs in the body. Both approaches also have their limitations; cellular studies cannot mimic the entire organism and data obtained by inhalation exposure of rodents have limitations due to differences in the respiratory system from that of humans. Simulation programs for lung deposition in humans could help to determine the relevance of the biological findings. Combination of biological data generated in different biological models and in silico modeling appears suitable for a realistic estimation of potential risks by inhalation exposure to NPs. PMID:24646916

Operational evaluation of the RLINE dispersion model for studies of traffic-related air pollutants

NASA Astrophysics Data System (ADS)

Milando, Chad W.; Batterman, Stuart A.

2018-06-01

Exposure to traffic-related air pollutants (TRAP) remains a key public health issue, and improved exposure measures are needed to support health impact and epidemiologic studies and inform regulatory responses. The recently developed Research LINE source model (RLINE), a Gaussian line source dispersion model, has been used in several epidemiologic studies of TRAP exposure, but evaluations of RLINE's performance in such applications have been limited. This study provides an operational evaluation of RLINE in which predictions of NOx, CO and PM2.5 are compared to observations at air quality monitoring stations located near high traffic roads in Detroit, MI. For CO and NOx, model performance was best at sites close to major roads, during downwind conditions, during weekdays, and during certain seasons. For PM2.5, the ability to discern local and particularly the traffic-related portion was limited, a result of high background levels, the sparseness of the monitoring network, and large uncertainties for certain processes (e.g., formation of secondary aerosols) and non-mobile sources (e.g., area, fugitive). Overall, RLINE's performance in near-road environments suggests its usefulness for estimating spatially- and temporally-resolved exposures. The study highlights considerations relevant to health impact and epidemiologic applications, including the importance of selecting appropriate pollutants, using appropriate monitoring approaches, considering prevailing wind directions during study design, and accounting for uncertainty.

Geographic Model and Biomarker-Derived Measures of Pesticide Exposure and Parkinson’s Disease

PubMed Central

RITZ, BEATE; COSTELLO, SADIE

2013-01-01

For more than two decades, reports have suggested that pesticides and herbicides may be an etiologic factor in idiopathic Parkinson’s disease (PD). To date, no clear associations with any specific pesticide have been demonstrated from epidemiological studies perhaps, in part, because methods of reliably estimating exposures are lacking. We tested the validity of a Geographic Information Systems (GIS)-based exposure assessment model that estimates potential environmental exposures at residences from pesticide applications to agricultural crops based on California Pesticide Use Reports (PUR). Using lipid-adjusted dichlorodiphenyldichloroethylene (DDE) serum levels as the “gold standard” for pesticide exposure, we conducted a validation study in a sample taken from an ongoing, population-based case–control study of PD in Central California. Residential, occupational, and other risk factor data were collected for 22 cases and 24 controls from Kern county, California. Environmental GIS–PUR-based organochlorine (OC) estimates were derived for each subject and compared to lipid-adjusted DDE serum levels. Relying on a linear regression model, we predicted log-transformed lipid-adjusted DDE serum levels. GIS–PUR-derived OC measure, body mass index, age, gender, mixing and loading pesticides by hand, and using pesticides in the home, together explained 47% of the DDE serum level variance (adjusted r2 = 0.47). The specificity of using our environmental GIS–PUR-derived OC measures to identify those with high-serum DDE levels was reasonably good (87%). Our environmental GIS–PUR-based approach appears to provide a valid model for assessing residential exposures to agricultural pesticides. PMID:17119217

Rethinking the role of worry in generalized anxiety disorder: evidence supporting a model of emotional contrast avoidance.

PubMed

Llera, Sandra J; Newman, Michelle G

2014-05-01

The Contrast Avoidance model (Newman & Llera, 2011) proposes that individuals with generalized anxiety disorder (GAD) are hypersensitive to sharp upward shifts in negative emotion that typically accompany negative events, and use worry to maintain sustained intrapersonal negativity in an attempt to avoid these shifts. Although research shows that worry increases negative emotionality and mutes further emotional reactivity to a stressor when compared to the worry period (e.g., Llera & Newman, 2010), no study has tracked changes in negative emotionality from baseline to worry inductions followed by a range of emotional exposures. Further, no study has yet assessed participants' subjective appraisals of prior worry on helping to cope with such exposures. The present study tested the main tenets of the Contrast Avoidance model by randomly assigning participants with GAD (n=48) and nonanxious controls (n=47) to experience worry, relaxation, and neutral inductions prior to sequential exposure to fearful, sad, and humorous film clips. Both physiological (nonspecific skin conductance responses [NS-SCRs]) and self-reported emotional changes were observed. Results indicated that worry boosted negative emotionality from baseline, which was sustained across negative exposures, whereas low negative emotionality during relaxation and neutral inductions allowed for sharp increases in response to exposures. Furthermore, GAD participants found worry to be more helpful than other conditions in coping with exposures, whereas control participants reported the opposite pattern. Results provide preliminary support for the Contrast Avoidance model. This suggests that treatment should focus on underlying avoidance patterns before attempting to reduce worry behavior. Copyright © 2014. Published by Elsevier Ltd.

Application of the Calculating Formula for the Mean Neutron Exposure in CEPM-s and CEPM-r/s Stars %Kstars: AGB and post-AGB, nuclear reactions, nucleosynthesis, abundances, methods: analytical

NASA Astrophysics Data System (ADS)

Zhang, F. H.; Zhang, L.; Cui, W. Y.; Zhang, B.

2017-09-01

Recent studies have shown that, for the current s-process nucleosynthesis model for the low-mass asymptotic giant branch (AGB) stars with (13C) pocket radiative burning during the interpulse period, the neutron exposure distribution in the nucleosynthesis region can be regarded as an exponential function, and the relation between the mean neutron exposure (τ0) and the model parameters is τ0 = - Δ τ/ln [q/(1 - r + q)]), in which (Δ τ) is the exposure value of each neutron irradiation, (r) is the overlap factor, and (q) is the mass ratio of the (13C) shell to the He intershell. Using the published data resulted from fitting the observed abundances of neutron-capture elements in 20 CEMP (Carbon-Enhanced Metal-Poor)-s and CEMP-s/r stars with the parametric AGB stellar s-process model, the reliability of the derived formula is tested, and further more the application of the formula in the s-process nucleosynthesis study is explored preliminarily. Our results show that, under the radiative s-process nucleosynthesis mechanism, the formula is suitable for CEMP stars experiencing recurrent neutron exposures. Combined with the parametric AGB nucleosynthesis model, the formula could be regarded as an effective tool to screen the CEMP stars with a single neutron exposure or a special type. Considering the uncertainty of the (13C) pocket, the role of this formula in understanding the physical conditions necessary to reproduce the observed s-process abundances in CEMP stars needs further study.

Toward refined estimates of ambient PM2.5 exposure: Evaluation of a physical outdoor-to-indoor transport model

NASA Astrophysics Data System (ADS)

Hodas, Natasha; Meng, Qingyu; Lunden, Melissa M.; Turpin, Barbara J.

2014-02-01

Because people spend the majority of their time indoors, the variable efficiency with which ambient PM2.5 penetrates and persists indoors is a source of error in epidemiologic studies that use PM2.5 concentrations measured at central-site monitors as surrogates for ambient PM2.5 exposure. To reduce this error, practical methods to model indoor concentrations of ambient PM2.5 are needed. Toward this goal, we evaluated and refined an outdoor-to-indoor transport model using measured indoor and outdoor PM2.5 species concentrations and air exchange rates from the Relationships of Indoor, Outdoor, and Personal Air Study. Herein, we present model evaluation results, discuss what data are most critical to prediction of residential exposures at the individual-subject and populations levels, and make recommendations for the application of the model in epidemiologic studies. This paper demonstrates that not accounting for certain human activities (air conditioning and heating use, opening windows) leads to bias in predicted residential PM2.5 exposures at the individual-subject level, but not the population level. The analyses presented also provide quantitative evidence that shifts in the gas-particle partitioning of ambient organics with outdoor-to-indoor transport contribute significantly to variability in indoor ambient organic carbon concentrations and suggest that methods to account for these shifts will further improve the accuracy of outdoor-to-indoor transport models.

Using Bayesian Models to Assess the Effects of Under-reporting of Cannabis Use on the Association with Birth Defects, National Birth Defects Prevention Study, 1997–2005

PubMed Central

van Gelder, Marleen M. H. J.; Rogier, A.; Donders, T.; Devine, Owen; Roeleveld, Nel; Reefhuis, Jennita

2015-01-01

Background Studies on associations between periconceptional cannabis exposure and birth defects have mainly relied on self-reported exposure. Therefore, the results may be biased due to underreporting of the exposure. The aim of this study was to quantify the potential effects of this form of exposure misclassification. Methods Using multivariable logistic regression, we re-analyzed associations between periconceptional cannabis use and 20 specific birth defects using data from the National Birth Defects Prevention Study from 1997–2005 for 13 859 case infants and 6556 control infants. For seven birth defects, we implemented four Bayesian models based on various assumptions concerning the sensitivity of self-reported cannabis use to estimate odds ratios (ORs), adjusted for confounding and underreporting of the exposure. We used information on sensitivity of self-reported cannabis use from the literature for prior assumptions. Results The results unadjusted for underreporting of the exposure showed an association between cannabis use and anencephaly (posterior OR 1.9 [95% credible interval (CRI) 1.1, 3.2]) which persisted after adjustment for potential exposure misclassification. Initially, no statistically significant associations were observed between cannabis use and the other birth defect categories studied. Although adjustment for underreporting did not notably change these effect estimates, cannabis use was associated with esophageal atresia (posterior OR 1.7 [95% CRI 1.0, 2.9]), diaphragmatic hernia (posterior OR 1.8 [95% CRI 1.1, 3.0]) and gastroschisis (posterior OR 1.7 [95% CRI 1.2, 2.3]) after correction for exposure misclassification. Conclusions Underreporting of the exposure may have obscured some cannabis-birth defect associations in previous studies. However, the resulting bias is likely to be limited. PMID:25155701

Using bayesian models to assess the effects of under-reporting of cannabis use on the association with birth defects, national birth defects prevention study, 1997-2005.

PubMed

van Gelder, Marleen M H J; Donders, A Rogier T; Devine, Owen; Roeleveld, Nel; Reefhuis, Jennita

2014-09-01

Studies on associations between periconceptional cannabis exposure and birth defects have mainly relied on self-reported exposure. Therefore, the results may be biased due to under-reporting of the exposure. The aim of this study was to quantify the potential effects of this form of exposure misclassification. Using multivariable logistic regression, we re-analysed associations between periconceptional cannabis use and 20 specific birth defects using data from the National Birth Defects Prevention Study from 1997-2005 for 13 859 case infants and 6556 control infants. For seven birth defects, we implemented four Bayesian models based on various assumptions concerning the sensitivity of self-reported cannabis use to estimate odds ratios (ORs), adjusted for confounding and under-reporting of the exposure. We used information on sensitivity of self-reported cannabis use from the literature for prior assumptions. The results unadjusted for under-reporting of the exposure showed an association between cannabis use and anencephaly (posterior OR 1.9 [95% credible interval (CRI) 1.1, 3.2]) which persisted after adjustment for potential exposure misclassification. Initially, no statistically significant associations were observed between cannabis use and the other birth defect categories studied. Although adjustment for under-reporting did not notably change these effect estimates, cannabis use was associated with esophageal atresia (posterior OR 1.7 [95% CRI 1.0, 2.9]), diaphragmatic hernia (posterior OR 1.8 [95% CRI 1.1, 3.0]), and gastroschisis (posterior OR 1.7 [95% CRI 1.2, 2.3]) after correction for exposure misclassification. Under-reporting of the exposure may have obscured some cannabis-birth defect associations in previous studies. However, the resulting bias is likely to be limited. © 2014 John Wiley & Sons Ltd.

Human Exposure Modeling - Databases to Support Exposure Modeling

EPA Pesticide Factsheets

Human exposure modeling relates pollutant concentrations in the larger environmental media to pollutant concentrations in the immediate exposure media. The models described here are available on other EPA websites.

The Toxicological Evaluation of Realistic Emissions of Source Aerosols Study: Statistical Methods

PubMed Central

Coull, Brent A.; Wellenius, Gregory A.; Gonzalez-Flecha, Beatriz; Diaz, Edgar; Koutrakis, Petros; Godleski, John J.

2013-01-01

The Toxicological Evaluation of Realistic Emissions of Source Aerosols (TERESA) study involved withdrawal, aging, and atmospheric transformation of emissions of three coal-fired power plants. Toxicological evaluations were carried out in rats exposed to different emission scenarios with extensive exposure characterization. Data generated had multiple levels of resolution: exposure, scenario and constituent chemical composition. Here, we outline a multilayered approach to analyze the associations between exposure and health effects beginning with standard ANOVA models that treat exposure as a categorical variable. The model assessed differences in exposure effects across scenarios (by plant). To assess unadjusted associations between pollutant concentrations and health, univariate analyses were conducted using the difference between the response means under exposed and control conditions and a single constituent concentration as the predictor. Then, a novel multivariate analysis of exposure composition and health was used based on random forests, a recent extension of classification and regression trees that were applied to the outcome differences. For each exposure constituent, this approach yielded a nonparametric measure of the importance of that constituent in predicting differences in response on a given day, controlling for the other measured constituent concentrations in the model. Finally, an R2 analysis compared the relative importance of exposure scenario, plant, and constituent concentrations on each outcome. Peak expiratory flow is used to demonstrate how the multiple levels of the analysis complement each other to assess constituents most strongly associated with health effects. PMID:21913820

The toxicological evaluation of realistic emissions of source aerosols study: statistical methods.

PubMed

Coull, Brent A; Wellenius, Gregory A; Gonzalez-Flecha, Beatriz; Diaz, Edgar; Koutrakis, Petros; Godleski, John J

2011-08-01

The Toxicological Evaluation of Realistic Emissions of Source Aerosols (TERESA) study involved withdrawal, aging, and atmospheric transformation of emissions of three coal-fired power plants. Toxicological evaluations were carried out in rats exposed to different emission scenarios with extensive exposure characterization. Data generated had multiple levels of resolution: exposure, scenario, and constituent chemical composition. Here, we outline a multilayered approach to analyze the associations between exposure and health effects beginning with standard ANOVA models that treat exposure as a categorical variable. The model assessed differences in exposure effects across scenarios (by plant). To assess unadjusted associations between pollutant concentrations and health, univariate analyses were conducted using the difference between the response means under exposed and control conditions and a single constituent concentration as the predictor. Then, a novel multivariate analysis of exposure composition and health was used based on Random Forests(™), a recent extension of classification and regression trees that were applied to the outcome differences. For each exposure constituent, this approach yielded a nonparametric measure of the importance of that constituent in predicting differences in response on a given day, controlling for the other measured constituent concentrations in the model. Finally, an R(2) analysis compared the relative importance of exposure scenario, plant, and constituent concentrations on each outcome. Peak expiratory flow (PEF) is used to demonstrate how the multiple levels of the analysis complement each other to assess constituents most strongly associated with health effects.

Chronic toxicity of selected polycyclic aromatic hydrocarbons to algae and crustaceans using passive dosing.

PubMed

Bragin, Gail E; Parkerton, Thomas F; Redman, Aaron D; Letinksi, Daniel J; Butler, Josh D; Paumen, Miriam Leon; Sutherland, Cary A; Knarr, Tricia M; Comber, Mike; den Haan, Klaas

2016-12-01

Because of the large number of possible aromatic hydrocarbon structures, predictive toxicity models are needed to support substance hazard and risk assessments. Calibration and evaluation of such models requires toxicity data with well-defined exposures. The present study has applied a passive dosing method to generate reliable chronic effects data for 8 polycyclic aromatic hydrocarbons (PAHs) on the green algae Pseudokirchneriella subcapitata and the crustacean Ceriodaphnia dubia. The observed toxicity of these substances on algal growth rate and neonate production were then compared with available literature toxicity data for these species, as well as target lipid model and chemical activity-based model predictions. The use of passive dosing provided well-controlled exposures that yielded more consistent data sets than attained by past literature studies. Results from the present study, which were designed to exclude the complicating influence of ultraviolet light, were found to be well described by both target lipid model and chemical activity effect models. The present study also found that the lack of chronic effects for high molecular weight PAHs was consistent with the limited chemical activity that could be achieved for these compounds in the aqueous test media. Findings from this analysis highlight that variability in past literature toxicity data for PAHs may be complicated by both poorly controlled exposures and photochemical processes that can modulate both exposure and toxicity. Environ Toxicol Chem 2016;35:2948-2957. © 2016 SETAC. © 2016 SETAC.

Impacts of Lateral Boundary Conditions on US Ozone ...

EPA Pesticide Factsheets

Chemical boundary conditions are a key input to regional-scale photochemical models. In this study, we perform annual simulations over North America with chemical boundary conditions prepared from two global models (GEOS-CHEM and Hemispheric CMAQ). Results indicate that the impacts of different boundary conditions on ozone can be significant throughout the year. The National Exposure Research Laboratory (NERL) Computational Exposure Division (CED) develops and evaluates data, decision-support tools, and models to be applied to media-specific or receptor-specific problem areas. CED uses modeling-based approaches to characterize exposures, evaluate fate and transport, and support environmental diagnostics/forensics with input from multiple data sources. It also develops media- and receptor-specific models, process models, and decision support tools for use both within and outside of EPA.

Estimating associations of mobile phone use and brain tumours taking into account laterality: a comparison and theoretical evaluation of applied methods.

PubMed

Frederiksen, Kirsten; Deltour, Isabelle; Schüz, Joachim

2012-12-10

Estimating exposure-outcome associations using laterality information on exposure and on outcome is an issue, when estimating associations of mobile phone use and brain tumour risk. The exposure is localized; therefore, a potential risk is expected to exist primarily on the side of the head, where the phone is usually held (ipsilateral exposure), and to a lesser extent at the opposite side of the head (contralateral exposure). Several measures of the associations with ipsilateral and contralateral exposure, dealing with different sampling designs, have been presented in the literature. This paper presents a general framework for the analysis of such studies using a likelihood-based approach in a competing risks model setting. The approach clarifies the implicit assumptions required for the validity of the presented estimators, particularly that in some approaches the risk with contralateral exposure is assumed to be zero. The performance of the estimators is illustrated in a simulation study showing for instance that while in some scenarios there is a loss of statistical power, others - in case of a positive ipsilateral exposure-outcome association - would result in a negatively biased estimate of the contralateral exposure parameter, irrespective of any additional recall bias. In conclusion, our theoretical evaluations and results from the simulation study emphasize the importance of setting up a formal model, which furthermore allows for estimation in more complicated and perhaps more realistic exposure settings, such as taking into account exposure to both sides of the head. Copyright © 2012 John Wiley & Sons, Ltd.

Effects of stress on alcohol drinking: a review of animal studies

PubMed Central

Lopez, Marcelo F.; Doremus-Fitzwater, Tamara L.

2011-01-01

Rationale While stress is often proposed to play a significant role in influencing alcohol consumption, the relationship between stress and alcohol is complex and poorly understood. Over several decades, stress effects on alcohol drinking have been studied using a variety of animal models and experimental procedures, yet this large body of literature has generally produced equivocal results. Objectives This paper reviews results from animal studies in which alcohol consumption is evaluated under conditions of acute/sub-chronic stress exposure or models of chronic stress exposure. Evidence also is presented indicating that chronic intermittent alcohol exposure serves as a stressor that consequently influences drinking. Results The effects of various acute/sub-chronic stress procedures on alcohol consumption have generally been mixed, but most study outcomes suggest either no effect or decreased alcohol consumption. In contrast, most studies indicate that chronic stress, especially when administered early in development, results in elevated drinking later in adulthood. Chronic alcohol exposure constitutes a potent stressor itself, and models of chronic intermittent alcohol exposure reliably produce escalation of voluntary alcohol consumption. Conclusions A complex and dynamic interplay among a wide array of genetic, biological, and environmental factors govern stress responses, regulation of alcohol drinking, and the circumstances in which stress modulates alcohol consumption. Suggestions for future directions and new approaches are presented that may aid in developing more sensitive and valid animal models that not only better mimic the clinical situation, but also provide greater understanding of mechanisms that underlie the complexity of stress effects on alcohol drinking. PMID:21850445

Duration of Exposure and the Dose-Response Model of PTSD

ERIC Educational Resources Information Center

Kaysen, Debra; Rosen, Gerald; Bowman, Marilyn; Resick, Patricia A.

2010-01-01

A dose-response model underlies posttraumatic stress disorder (PTSD) and posits a relationship between event magnitude and clinical outcome. The present study examines whether one index of event magnitude--duration of exposure--contributes to risk of PTSD among female victims of sexual assault. Findings support a small but significant contribution…

Quantifying Children's Aggregate (Dietary and Residential) Exposure and Dose to Permethin: Application and Evaluation of EPA's Probabilistic SHED-Multimedia Model

EPA Science Inventory

Reliable, evaluated human exposure and dose models are important for understanding the health risks from chemicals. A case study focusing on permethrin was conducted because of this insecticide’s widespread use and potential health effects. SHEDS-Multimedia was applied to estimat...

Comparison of stationary and personal air sampling with an air dispersion model for children’s ambient exposure to manganese

EPA Science Inventory

Manganese (Mn) is ubiquitous in the environment and essential for normal growth and development, yet excessive exposure can lead to impairments in neurological function. This study modeled ambient Mn concentrations as an alternative to stationary and personal air sampling to asse...

Exposure to Psychological Aggression at Work and Job Performance: The Mediating Role of Job Attitudes and Personal Health

PubMed Central

Schat, Aaron; Frone, Michael R.

2011-01-01

Despite the growing literature on workplace aggression and the importance of employee performance at work, few studies have examined the relation between workplace aggression and job performance. The purpose of this study was to investigate the relations between psychological aggression at work and two forms of job performance (task performance and contextual performance) and potential mediators of these relations. Based on Conservation of Resources theory and prior research, a model was developed and tested in which overall job attitudes (i.e., job satisfaction and organizational commitment) and overall personal health (i.e., physical and psychological health) fully mediate the relations between exposure to psychological aggression at work and both task performance and contextual performance. Data were obtained from a national probability sample of US workers (N = 2376) and the model was tested using structural equation modelling. The results supported the hypothesized model, demonstrating that exposure to psychological aggression at work negatively predicted both task performance and contextual performance, and that these relations were explained by decrements in job attitudes and health associated with exposure to psychological aggression at work. PMID:21643471

Exposure to Psychological Aggression at Work and Job Performance: The Mediating Role of Job Attitudes and Personal Health.

PubMed

Schat, Aaron; Frone, Michael R

2011-01-01

Despite the growing literature on workplace aggression and the importance of employee performance at work, few studies have examined the relation between workplace aggression and job performance. The purpose of this study was to investigate the relations between psychological aggression at work and two forms of job performance (task performance and contextual performance) and potential mediators of these relations. Based on Conservation of Resources theory and prior research, a model was developed and tested in which overall job attitudes (i.e., job satisfaction and organizational commitment) and overall personal health (i.e., physical and psychological health) fully mediate the relations between exposure to psychological aggression at work and both task performance and contextual performance. Data were obtained from a national probability sample of US workers (N = 2376) and the model was tested using structural equation modelling. The results supported the hypothesized model, demonstrating that exposure to psychological aggression at work negatively predicted both task performance and contextual performance, and that these relations were explained by decrements in job attitudes and health associated with exposure to psychological aggression at work.

Evaluation of potential toxicity from co-exposure to three CNS depressants (toluene, ethylbenzene, and xylene) under resting and working conditions using PBPK modeling.

PubMed

Dennison, James E; Bigelow, Philip L; Mumtaz, Moiz M; Andersen, Melvin E; Dobrev, Ivan D; Yang, Raymond S H

2005-03-01

Under OSHA and American Conference of Governmental Industrial Hygienists (ACGIH) guidelines, the mixture formula (unity calculation) provides a method for evaluating exposures to mixtures of chemicals that cause similar toxicities. According to the formula, if exposures are reduced in proportion to the number of chemicals and their respective exposure limits, the overall exposure is acceptable. This approach assumes that responses are additive, which is not the case when pharmacokinetic interactions occur. To determine the validity of the additivity assumption, we performed unity calculations for a variety of exposures to toluene, ethylbenzene, and/or xylene using the concentration of each chemical in blood in the calculation instead of the inhaled concentration. The blood concentrations were predicted using a validated physiologically based pharmacokinetic (PBPK) model to allow exploration of a variety of exposure scenarios. In addition, the Occupational Safety and Health Administration and ACGIH occupational exposure limits were largely based on studies of humans or animals that were resting during exposure. The PBPK model was also used to determine the increased concentration of chemicals in the blood when employees were exercising or performing manual work. At rest, a modest overexposure occurs due to pharmacokinetic interactions when exposure is equal to levels where a unity calculation is 1.0 based on threshold limit values (TLVs). Under work load, however, internal exposure was 87%higher than provided by the TLVs. When exposures were controlled by a unity calculation based on permissible exposure limits (PELs), internal exposure was 2.9 and 4.6 times the exposures at the TLVs at rest and workload, respectively. If exposure was equal to PELs outright, internal exposure was 12.5 and 16 times the exposure at the TLVs at rest and workload, respectively. These analyses indicate the importance of (1) selecting appropriate exposure limits, (2) performing unity calculations, and (3) considering the effect of work load on internal doses, and they illustrate the utility of PBPK modeling in occupational health risk assessment.

POPULATION EXPOSURES TO PARTICULATE MATTER: A COMPARISON OF EXPOSURE MODEL PREDICTIONS AND MEASUREMENT DATA

EPA Science Inventory

The US EPA National Exposure Research Laboratory (NERL) is currently developing an integrated human exposure source-to-dose modeling system (HES2D). This modeling system will incorporate models that use a probabilistic approach to predict population exposures to environmental ...

Exposure assessment of mobile phone base station radiation in an outdoor environment using sequential surrogate modeling.

PubMed

Aerts, Sam; Deschrijver, Dirk; Joseph, Wout; Verloock, Leen; Goeminne, Francis; Martens, Luc; Dhaene, Tom

2013-05-01

Human exposure to background radiofrequency electromagnetic fields (RF-EMF) has been increasing with the introduction of new technologies. There is a definite need for the quantification of RF-EMF exposure but a robust exposure assessment is not yet possible, mainly due to the lack of a fast and efficient measurement procedure. In this article, a new procedure is proposed for accurately mapping the exposure to base station radiation in an outdoor environment based on surrogate modeling and sequential design, an entirely new approach in the domain of dosimetry for human RF exposure. We tested our procedure in an urban area of about 0.04 km(2) for Global System for Mobile Communications (GSM) technology at 900 MHz (GSM900) using a personal exposimeter. Fifty measurement locations were sufficient to obtain a coarse street exposure map, locating regions of high and low exposure; 70 measurement locations were sufficient to characterize the electric field distribution in the area and build an accurate predictive interpolation model. Hence, accurate GSM900 downlink outdoor exposure maps (for use in, e.g., governmental risk communication and epidemiological studies) are developed by combining the proven efficiency of sequential design with the speed of exposimeter measurements and their ease of handling. Copyright © 2013 Wiley Periodicals, Inc.

Combined Effects of Prenatal Exposures to Environmental Chemicals on Birth Weight.

PubMed

Govarts, Eva; Remy, Sylvie; Bruckers, Liesbeth; Den Hond, Elly; Sioen, Isabelle; Nelen, Vera; Baeyens, Willy; Nawrot, Tim S; Loots, Ilse; Van Larebeke, Nick; Schoeters, Greet

2016-05-12

Prenatal chemical exposure has been frequently associated with reduced fetal growth by single pollutant regression models although inconsistent results have been obtained. Our study estimated the effects of exposure to single pollutants and mixtures on birth weight in 248 mother-child pairs. Arsenic, copper, lead, manganese and thallium were measured in cord blood, cadmium in maternal blood, methylmercury in maternal hair, and five organochlorines, two perfluorinated compounds and diethylhexyl phthalate metabolites in cord plasma. Daily exposure to particulate matter was modeled and averaged over the duration of gestation. In single pollutant models, arsenic was significantly associated with reduced birth weight. The effect estimate increased when including cadmium, and mono-(2-ethyl-5-carboxypentyl) phthalate (MECPP) co-exposure. Combining exposures by principal component analysis generated an exposure factor loaded by cadmium and arsenic that was associated with reduced birth weight. MECPP induced gender specific effects. In girls, the effect estimate was doubled with co-exposure of thallium, PFOS, lead, cadmium, manganese, and mercury, while in boys, the mixture of MECPP with cadmium showed the strongest association with birth weight. In conclusion, birth weight was consistently inversely associated with exposure to pollutant mixtures. Chemicals not showing significant associations at single pollutant level contributed to stronger effects when analyzed as mixtures.

Spatio-temporal modeling of chronic PM 10 exposure for the Nurses' Health Study

NASA Astrophysics Data System (ADS)

Yanosky, Jeff D.; Paciorek, Christopher J.; Schwartz, Joel; Laden, Francine; Puett, Robin; Suh, Helen H.

2008-06-01

Chronic epidemiological studies of airborne particulate matter (PM) have typically characterized the chronic PM exposures of their study populations using city- or county-wide ambient concentrations, which limit the studies to areas where nearby monitoring data are available and which ignore within-city spatial gradients in ambient PM concentrations. To provide more spatially refined and precise chronic exposure measures, we used a Geographic Information System (GIS)-based spatial smoothing model to predict monthly outdoor PM10 concentrations in the northeastern and midwestern United States. This model included monthly smooth spatial terms and smooth regression terms of GIS-derived and meteorological predictors. Using cross-validation and other pre-specified selection criteria, terms for distance to road by road class, urban land use, block group and county population density, point- and area-source PM10 emissions, elevation, wind speed, and precipitation were found to be important determinants of PM10 concentrations and were included in the final model. Final model performance was strong (cross-validation R2=0.62), with little bias (-0.4 μg m-3) and high precision (6.4 μg m-3). The final model (with monthly spatial terms) performed better than a model with seasonal spatial terms (cross-validation R2=0.54). The addition of GIS-derived and meteorological predictors improved predictive performance over spatial smoothing (cross-validation R2=0.51) or inverse distance weighted interpolation (cross-validation R2=0.29) methods alone and increased the spatial resolution of predictions. The model performed well in both rural and urban areas, across seasons, and across the entire time period. The strong model performance demonstrates its suitability as a means to estimate individual-specific chronic PM10 exposures for large populations.

PARAMETER EVALUATION AND MODEL VALIDATION OF OZONE EXPOSURE ASSESSMENT USING HARVARD SOUTHERN CALIFORNIA CHRONIC OZONE EXPOSURE STUDY DATA

EPA Science Inventory

To examine factors influencing long-term ozone exposures by children living in urban communities, we analyzed longitudinal data on personal, indoor, and outdoor ozone concentrations as well as related housing and other questionnaire information collected in the one-year-long Harv...

LONG TERM RESPONSE OF RATS TO SINGLE INTRATRACHEAL EXPOSURE OF LIBBY AMPHIBOLE (LA) OR AMOSITE

EPA Science Inventory

In former mine workers of Libby, Montana, exposure to amphibole-contaminated vermiculite has been associated with increased incidences of asbestosis and mesothelioma. In this study, we investigated long term effects of Libby amphibole (LA) exposure in a rat model. Rat respirable ...

NEUROBEHAVIORAL EFFECTS OF CHRONIC DIETARY AND REPEATED HIGH-LEVEL SPIKE EXPOSURE TO CHLORPYRIFOS IN RATS.

EPA Science Inventory

This study aimed to model long-term subtoxic human exposure to an organophosphorus pesticide, chlorpyrifos, and to examine the influence of that exposure on the response to intermittent high-dose acute challenges. Adult rats were maintained on a chlorpyrifos-containing diet to p...

Violence Exposure and Psychopathology in Urban Youth: The Mediating Role of Posttraumatic Stress

ERIC Educational Resources Information Center

Ruchkin, Vladislav; Henrich, Christopher C.; Jones, Stephanie M.; Vermeiren, Robert; Schwab-Stone, Mary

2007-01-01

Understanding the mechanisms underlying the development of violence exposure sequelae is essential to providing effective treatments for traumatized youth. This longitudinal study examined the mediating role of posttraumatic stress in the relationship between violence exposure and psychopathology, and compared the mediated models by gender. Urban…

Development and Evaluation of a New Air Exchange Rate Algorithm for the Stochastic Human Exposure and Dose Simulation Model (ISES Presentation)

EPA Science Inventory

Previous exposure assessment panel studies have observed considerable seasonal, between-home and between-city variability in residential pollutant infiltration. This is likely a result of differences in home ventilation, or air exchange rates (AER). The Stochastic Human Exposure ...

Using a physiologically based pharmacokinetic model to link urinary biomarker concentrations to dietary exposure of perchlorate

EPA Science Inventory

Exposure to perchlorate is widespread in the United States and many studies have attempted to character the perchlorate exposure by estimating the average daily intakes of perchlorate. These approaches provided population-based estimates, but did not provide individual-level exp...

National-scale exposure prediction for long-term concentrations of particulate matter and nitrogen dioxide in South Korea.

PubMed

Kim, Sun-Young; Song, Insang

2017-07-01

The limited spatial coverage of the air pollution data available from regulatory air quality monitoring networks hampers national-scale epidemiological studies of air pollution. The present study aimed to develop a national-scale exposure prediction model for estimating annual average concentrations of PM 10 and NO 2 at residences in South Korea using regulatory monitoring data for 2010. Using hourly measurements of PM 10 and NO 2 at 277 regulatory monitoring sites, we calculated the annual average concentrations at each site. We also computed 322 geographic variables in order to represent plausible local and regional pollution sources. Using these data, we developed universal kriging models, including three summary predictors estimated by partial least squares (PLS). The model performance was evaluated with fivefold cross-validation. In sensitivity analyses, we compared our approach with two alternative approaches, which added regional interactions and replaced the PLS predictors with up to ten selected variables. Finally, we predicted the annual average concentrations of PM 10 and NO 2 at 83,463 centroids of residential census output areas in South Korea to investigate the population exposure to these pollutants and to compare the exposure levels between monitored and unmonitored areas. The means of the annual average concentrations of PM 10 and NO 2 for 2010, across regulatory monitoring sites in South Korea, were 51.63 μg/m3 (SD = 8.58) and 25.64 ppb (11.05), respectively. The universal kriging exposure prediction models yielded cross-validated R 2 s of 0.45 and 0.82 for PM 10 and NO 2 , respectively. Compared to our model, the two alternative approaches gave consistent or worse performances. Population exposure levels in unmonitored areas were lower than in monitored areas. This is the first study that focused on developing a national-scale point wise exposure prediction approach in South Korea, which will allow national exposure assessments and epidemiological research to answer policy-related questions and to draw comparisons among different countries. Copyright © 2017 Elsevier Ltd. All rights reserved.

The margin of internal exposure (MOIE) concept for dermal risk assessment based on oral toxicity data - A case study with caffeine.

PubMed

Bessems, Jos G M; Paini, Alicia; Gajewska, Monika; Worth, Andrew

2017-12-01

Route-to-route extrapolation is a common part of human risk assessment. Data from oral animal toxicity studies are commonly used to assess the safety of various but specific human dermal exposure scenarios. Using theoretical examples of various user scenarios, it was concluded that delineation of a generally applicable human dermal limit value is not a practicable approach, due to the wide variety of possible human exposure scenarios, including its consequences for internal exposure. This paper uses physiologically based kinetic (PBK) modelling approaches to predict animal as well as human internal exposure dose metrics and for the first time, introduces the concept of Margin of Internal Exposure (MOIE) based on these internal dose metrics. Caffeine was chosen to illustrate this approach. It is a substance that is often found in cosmetics and for which oral repeated dose toxicity data were available. A rat PBK model was constructed in order to convert the oral NOAEL to rat internal exposure dose metrics, i.e. the area under the curve (AUC) and the maximum concentration (C max ), both in plasma. A human oral PBK model was constructed and calibrated using human volunteer data and adapted to accommodate dermal absorption following human dermal exposure. Use of the MOIE approach based on internal dose metrics predictions provides excellent opportunities to investigate the consequences of variations in human dermal exposure scenarios. It can accommodate within-day variation in plasma concentrations and is scientifically more robust than assuming just an exposure in mg/kg bw/day. Copyright © 2017 The Authors. Published by Elsevier B.V. All rights reserved.

Collinearity and Causal Diagrams: A Lesson on the Importance of Model Specification.

PubMed

Schisterman, Enrique F; Perkins, Neil J; Mumford, Sunni L; Ahrens, Katherine A; Mitchell, Emily M

2017-01-01

Correlated data are ubiquitous in epidemiologic research, particularly in nutritional and environmental epidemiology where mixtures of factors are often studied. Our objectives are to demonstrate how highly correlated data arise in epidemiologic research and provide guidance, using a directed acyclic graph approach, on how to proceed analytically when faced with highly correlated data. We identified three fundamental structural scenarios in which high correlation between a given variable and the exposure can arise: intermediates, confounders, and colliders. For each of these scenarios, we evaluated the consequences of increasing correlation between the given variable and the exposure on the bias and variance for the total effect of the exposure on the outcome using unadjusted and adjusted models. We derived closed-form solutions for continuous outcomes using linear regression and empirically present our findings for binary outcomes using logistic regression. For models properly specified, total effect estimates remained unbiased even when there was almost perfect correlation between the exposure and a given intermediate, confounder, or collider. In general, as the correlation increased, the variance of the parameter estimate for the exposure in the adjusted models increased, while in the unadjusted models, the variance increased to a lesser extent or decreased. Our findings highlight the importance of considering the causal framework under study when specifying regression models. Strategies that do not take into consideration the causal structure may lead to biased effect estimation for the original question of interest, even under high correlation.

Through the smoke: Use of in vivo and in vitro cigarette smoking models to elucidate its effect on female fertility

DOE Office of Scientific and Technical Information (OSTI.GOV)

Camlin, Nicole J.; McLaughlin, Eileen A., E-mail: eileen.mclaughlin@newcastle.edu.au; Holt, Janet E.

A finite number of oocytes are established within the mammalian ovary prior to birth to form a precious ovarian reserve. Damage to this limited pool of gametes by environmental factors such as cigarette smoke and its constituents therefore represents a significant risk to a woman's reproductive capacity. Although evidence from human studies to date implicates a detrimental effect of cigarette smoking on female fertility, these retrospective studies are limited and present conflicting results. In an effort to more clearly understand the effect of cigarette smoke, and its chemical constituents, on female fertility, a variety of in vivo and in vitromore » animal models have been developed. This article represents a systematic review of the literature regarding four of experimental model types: 1) direct exposure of ovarian cells and follicles to smoking constituents’ in vitro, 2) direct exposure of whole ovarian tissue with smoking constituents in vitro, 3) whole body exposure of animals to smoking constituents and 4) whole body exposure of animals to cigarette smoke. We summarise key findings and highlight the strengths and weaknesses of each model system, and link these to the molecular mechanisms identified in smoke-induced fertility changes. - Highlights: • In vivo exposure to individual cigarette smoke chemicals alters female fertility. • The use of in vitro models in determining molecular mechanisms • Whole cigarette smoke inhalation animal models negatively affect ovarian function.« less

Occupational exposure and risk of chronic obstructive pulmonary disease: a systematic review and meta-analysis.

PubMed

Alif, Sheikh M; Dharmage, Shyamali C; Bowatte, Gayan; Karahalios, Amalia; Benke, Geza; Dennekamp, Martine; Mehta, Amar J; Miedinger, David; Künzli, Nino; Probst-Hensch, Nicole; Matheson, Melanie C

2016-08-01

Due to contradictory literature we have performed a systematic review and meta-analyse of population-based studies that have used Job Exposure Matrices to assess occupational exposure and risk of Chronic Obstructive Pulmonary Disease (COPD). Two researchers independently searched databases for published articles using predefined inclusion criteria. Study quality was assessed, and results pooled for COPD and chronic bronchitis for exposure to biological dust, mineral dust, and gases/fumes using a fixed and random effect model. Five studies met predetermined inclusion criteria. The meta-analysis showed low exposure to mineral dust, and high exposure to gases/fumes were associated with an increased risk of COPD. We also found significantly increased the risk of chronic bronchitis for low and high exposure to biological dust and mineral dust. Expert commentary: The relationship between occupational exposure assessed by the JEM and the risk of COPD and chronic bronchitis shows significant association with occupational exposure. However, the heterogeneity of the meta-analyses suggests more wide population-based studies with older age groups and longitudinal phenotype assessment of COPD to clarify the role of occupational exposure to COPD risk.

Environmental exposure modeling and monitoring of human pharmaceutical concentrations in the environment

USGS Publications Warehouse

Versteeg, D.J.; Alder, A. C.; Cunningham, V. L.; Kolpin, D.W.; Murray-Smith, R.; Ternes, T.

2005-01-01

Human pharmaceuticals are receiving increased attention as environmental contaminants. This is due to their biological activity and the number of monitoring programs focusing on analysis of these compounds in various environmental media and compartments. Risk assessments are needed to understand the implications of reported concentrations; a fundamental part of the risk assessment is an assessment of environmental exposures. The purpose of this chapter is to provide guidance on the use of predictive tools (e.g., models) and monitoring data in exposure assessments for pharmaceuticals in the environment. Methods to predict environmental concentrations from equations based on first principles are presented. These equations form the basis of existing GIS (geographic information systems)-based systems for understanding the spatial distribution of pharmaceuticals in the environment. The pharmaceutical assessment and transport (PhATE), georeferenced regional exposure assessment tool for European rivers (GREAT-ER), and geographical information system (GIS)-ROUT models are reviewed and recommendations are provided concerning the design and execution of monitoring studies. Model predictions and monitoring data are compared to evaluate the relative utility of each approach in environmental exposure assessments. In summary, both models and monitoring data can be used to define representative exposure concentrations of pharmaceuticals in the environment in support of environmental risk assessments.

On the equivalence of case-crossover and time series methods in environmental epidemiology.

PubMed

Lu, Yun; Zeger, Scott L

2007-04-01

The case-crossover design was introduced in epidemiology 15 years ago as a method for studying the effects of a risk factor on a health event using only cases. The idea is to compare a case's exposure immediately prior to or during the case-defining event with that same person's exposure at otherwise similar "reference" times. An alternative approach to the analysis of daily exposure and case-only data is time series analysis. Here, log-linear regression models express the expected total number of events on each day as a function of the exposure level and potential confounding variables. In time series analyses of air pollution, smooth functions of time and weather are the main confounders. Time series and case-crossover methods are often viewed as competing methods. In this paper, we show that case-crossover using conditional logistic regression is a special case of time series analysis when there is a common exposure such as in air pollution studies. This equivalence provides computational convenience for case-crossover analyses and a better understanding of time series models. Time series log-linear regression accounts for overdispersion of the Poisson variance, while case-crossover analyses typically do not. This equivalence also permits model checking for case-crossover data using standard log-linear model diagnostics.

Comparison of Ordinal and Nominal Classification Trees to Predict Ordinal Expert-Based Occupational Exposure Estimates in a Case–Control Study

PubMed Central

Wheeler, David C.; Archer, Kellie J.; Burstyn, Igor; Yu, Kai; Stewart, Patricia A.; Colt, Joanne S.; Baris, Dalsu; Karagas, Margaret R.; Schwenn, Molly; Johnson, Alison; Armenti, Karla; Silverman, Debra T.; Friesen, Melissa C.

2015-01-01

Objectives: To evaluate occupational exposures in case–control studies, exposure assessors typically review each job individually to assign exposure estimates. This process lacks transparency and does not provide a mechanism for recreating the decision rules in other studies. In our previous work, nominal (unordered categorical) classification trees (CTs) generally successfully predicted expert-assessed ordinal exposure estimates (i.e. none, low, medium, high) derived from occupational questionnaire responses, but room for improvement remained. Our objective was to determine if using recently developed ordinal CTs would improve the performance of nominal trees in predicting ordinal occupational diesel exhaust exposure estimates in a case–control study. Methods: We used one nominal and four ordinal CT methods to predict expert-assessed probability, intensity, and frequency estimates of occupational diesel exhaust exposure (each categorized as none, low, medium, or high) derived from questionnaire responses for the 14983 jobs in the New England Bladder Cancer Study. To replicate the common use of a single tree, we applied each method to a single sample of 70% of the jobs, using 15% to test and 15% to validate each method. To characterize variability in performance, we conducted a resampling analysis that repeated the sample draws 100 times. We evaluated agreement between the tree predictions and expert estimates using Somers’ d, which measures differences in terms of ordinal association between predicted and observed scores and can be interpreted similarly to a correlation coefficient. Results: From the resampling analysis, compared with the nominal tree, an ordinal CT method that used a quadratic misclassification function and controlled tree size based on total misclassification cost had a slightly better predictive performance that was statistically significant for the frequency metric (Somers’ d: nominal tree = 0.61; ordinal tree = 0.63) and similar performance for the probability (nominal = 0.65; ordinal = 0.66) and intensity (nominal = 0.65; ordinal = 0.65) metrics. The best ordinal CT predicted fewer cases of large disagreement with the expert assessments (i.e. no exposure predicted for a job with high exposure and vice versa) compared with the nominal tree across all of the exposure metrics. For example, the percent of jobs with expert-assigned high intensity of exposure that the model predicted as no exposure was 29% for the nominal tree and 22% for the best ordinal tree. Conclusions: The overall agreements were similar across CT models; however, the use of ordinal models reduced the magnitude of the discrepancy when disagreements occurred. As the best performing model can vary by situation, researchers should consider evaluating multiple CT methods to maximize the predictive performance within their data. PMID:25433003

Appraisal of levels and patterns of occupational exposure to 1,3-butadiene.

PubMed

Scarselli, Alberto; Corfiati, Marisa; Di Marzi, Davide; Iavicoli, Sergio

2017-09-01

Objectives 1,3-butadiene is classified as carcinogenic to human by inhalation and the association with leukemia has been observed in several epidemiological studies. The aim of this study was to evaluate data about occupational exposure levels to 1,3-butadiene in the Italian working force. Methods Airborne concentrations of 1,3-butadiene were extracted from the Italian database on occupational exposure to carcinogens in the period 1996-2015. Descriptive statistics were calculated for exposure-related variables. An analysis through linear mixed model was performed to determine factors influencing the exposure level. The probability of exceeding the exposure limit was predicted using a mixed-effects logistic model. Concurrent exposures with other occupational carcinogens were investigated using the two-step cluster analysis. Results The total number of exposure measurements selected was 23 885, with an overall arithmetic mean of 0.12 mg/m3. The economic sector with the highest number of measurements was manufacturing of chemicals (18 744). The most predictive variables of the exposure level resulted to be the occupational group and its interaction with the measurement year. The highest likelihood of exceeding the exposure limit was found in the manufacture of coke and refined petroleum products. Concurrent exposures were frequently detected, mainly with benzene, acrylonitrile and ethylene dichloride, and three main clusters were identified. Conclusions Exposure to 1,3-butadiene occurs in a wide variety of activity sectors and occupational groups. The use of several statistical analysis methods applied to occupational exposure databases can help to identify exposure situations at high risk for workers' health and better target preventive interventions and research projects.

Semivolatile Organic Compounds in Homes: Strategies for Efficient and Systematic Exposure Measurement Based on Empirical and Theoretical Factors

PubMed Central

2014-01-01

Residential exposure can dominate total exposure for commercial chemicals of health concern; however, despite the importance of consumer exposures, methods for estimating household exposures remain limited. We collected house dust and indoor air samples in 49 California homes and analyzed for 76 semivolatile organic compounds (SVOCs)—phthalates, polybrominated diphenyl ethers (PBDEs), polychlorinated biphenyls (PCBs), polycyclic aromatic hydrocarbons (PAHs), and pesticides. Sixty chemicals were detected in either dust or air and here we report 58 SVOCs detected in dust for the first time. In dust, phthalates (bis(2-ethylhexyl) phthalate, benzyl butyl phthalate, di-n-butyl phthalate) and flame retardants (PBDE 99, PBDE 47) were detected at the highest concentrations relative to other chemicals at the 95th percentile, while phthalates were highest at the median. Because SVOCs are found in both gas and condensed phases and redistribute from their original source over time, partitioning models can clarify their fate indoors. We use empirical data to validate air-dust partitioning models and use these results, combined with experience in SVOC exposure assessment, to recommend residential exposure measurement strategies. We can predict dust concentrations reasonably well from measured air concentrations (R2 = 0.80). Partitioning models and knowledge of chemical Koa elucidate exposure pathways and suggest priorities for chemical regulation. These findings also inform study design by allowing researchers to select sampling approaches optimized for their chemicals of interest and study goals. While surface wipes are commonly used in epidemiology studies because of ease of implementation, passive air sampling may be more standardized between homes and also relatively simple to deploy. Validation of passive air sampling methods for SVOCs is a priority. PMID:25488487

JP-8 jet fuel exposure potentiates tumor development in two experimental model systems.

PubMed

Harris, D T; Sakiestewa, D; Titone, D; He, X; Hyde, J; Witten, M

2007-11-01

The US Air Force has implemented the widespread use of JP-8 jet fuel in its operations, although a thorough understanding of its potential effects upon exposed personnel is unclear. Previous work has reported that JP-8 exposure is immunosuppressive. Exposure of mice to JP-8 for 1 h/day resulted in immediate secretion of two immunosuppressive agents; namely, interleukin-10 (IL-10) and prostaglandin E2 (PGE2). Thus, it was of interest to determine if jet fuel exposure might promote tumor growth and metastasis. The syngeneic B16 tumor model was used for these studies. Animals were injected intravenously with tumor cells, and lung colonies were enumerated. Animals were also examined for metastatic spread of the tumor. Mice were either exposed to 1000 mg/m3 JP-8 (1 h/ day) for 7 days before tumor injection or were exposed to JP-8 at the time of tumor injection. All animals were killed 17 days after tumor injection. In the present study, JP8 exposure potentiated the growth and metastases of B16 tumors in an animal model. Exposure of mice to JP-8 for 1 h/day before tumor induction resulted in an approximately 8.7-fold increase in tumors, whereas those mice exposed to JP8 at the time of tumor induction had a 5.6-fold increase in tumor numbers. Thus, low concentration JP-8 jet fuel exposures have significant immune suppressive effects on the immune system that can result in increased tumor formation and metastases. We have now extended the observations to an experimental subcutaneous tumor model. JP8 exposure at the time of tumor induction in this model did not affect the growth of the tumor. However, JP8-exposed, tumor-bearing animals died at an accelerated rate as compared with air-exposed, tumor-bearing mice.

Using exposure windows to explore an elusive biomarker: blood manganese.

PubMed

Baker, Marissa G; Stover, Bert; Simpson, Christopher D; Sheppard, Lianne; Seixas, Noah S

2016-05-01

We sought to understand the time course between exposure to manganese (Mn) and uptake into the blood, to allow a more meaningful interpretation of exposure biomarker data, and to determine the utility of blood as a biomarker of Mn exposure. Welder trainees were monitored over the course of a five-quarter training program. Each quarter, trainees gave eight blood samples and had personal air monitoring four times. A mixed model was fit to obtain estimates of airborne exposure by welding type (fixed effect), adjusted for subject (random effect). Considering weekends and days absent as zero exposure, estimated exposures were summed over various exposure windows and related to measured blood manganese (MnB) using a mixed model. A relationship consistent with zero was found between MnB and modeled 1 or 7 days of exposure. After 30 days of preceding exposure, a 1 mg-days/m(3) increase in air Mn is associated with a 0.57 ng/mL increase in MnB (95% CI -0.04, 1.19). Considering a 90-day exposure window and a cumulative exposure window, a 1 mg-days/m(3) increase in air Mn is associated with a 0.26 (95% CI 0.005, 0.51) and 0.09 (95% CI 0.006, 0.17) ng/mL increase in MnB, respectively. From this analysis, MnB may begin to act as a biomarker of Mn exposure over longer time periods, or at higher levels of exposure. This novel study design allowed investigation of how MnB relates to different time windows of exposure, representing the most robust Mn exposure assessment in the biomarker literature.

Experimental and numerical study on particle distribution in a two-zone chamber

NASA Astrophysics Data System (ADS)

Lai, Alvin C. K.; Wang, K.; Chen, F. Z.

Better understanding of aerosol dynamics is an important step for improving personal exposure assessments in indoor environments. Although the limitation of the assumptions in a well-mixed model is well known, there has been very little research reported in the published literature on the discrepancy of exposure assessments between numerical models which take account of gravitational effects and the well-mixed model. A new Eulerian-type drift-flux model has been developed to simulate particle dispersion and personal exposure in a two-zone geometry, which accounts for the drift velocity resulting from gravitational settling and diffusion. To validate the numerical model, a small-scale chamber was fabricated. The airflow characteristics and particle concentrations were measured by a phase Doppler Anemometer. Both simulated airflow and concentration profiles agree well with the experimental results. A strong inhomogeneous concentration was observed experimentally for 10 μm aerosols. The computational model was further applied to study a simple hypothetical, yet more realistic scenario. The aim was to explore different levels of exposure predicted by the new model and the well-mixed model. Aerosols are initially uniformly distributed in one zone and subsequently transported and dispersed to an adjacent zone through an opening. Owing to the significant difference in the rates of transport and dispersion between aerosols and gases, inferred from the results, the well-mixed model tends to overpredict the concentration in the source zone, and under-predict the concentration in the exposed zone. The results are very useful to illustrate that the well-mixed assumption must be applied cautiously for exposure assessments as such an ideal condition may not be applied for coarse particles.

Negative control exposure studies in the presence of measurement error: implications for attempted effect estimate calibration

PubMed Central

Sanderson, Eleanor; Macdonald-Wallis, Corrie; Davey Smith, George

2018-01-01

Abstract Background Negative control exposure studies are increasingly being used in epidemiological studies to strengthen causal inference regarding an exposure-outcome association when unobserved confounding is thought to be present. Negative control exposure studies contrast the magnitude of association of the negative control, which has no causal effect on the outcome but is associated with the unmeasured confounders in the same way as the exposure, with the magnitude of the association of the exposure with the outcome. A markedly larger effect of the exposure on the outcome than the negative control on the outcome strengthens inference that the exposure has a causal effect on the outcome. Methods We investigate the effect of measurement error in the exposure and negative control variables on the results obtained from a negative control exposure study. We do this in models with continuous and binary exposure and negative control variables using analysis of the bias of the estimated coefficients and Monte Carlo simulations. Results Our results show that measurement error in either the exposure or negative control variables can bias the estimated results from the negative control exposure study. Conclusions Measurement error is common in the variables used in epidemiological studies; these results show that negative control exposure studies cannot be used to precisely determine the size of the effect of the exposure variable, or adequately adjust for unobserved confounding; however, they can be used as part of a body of evidence to aid inference as to whether a causal effect of the exposure on the outcome is present. PMID:29088358

Negative control exposure studies in the presence of measurement error: implications for attempted effect estimate calibration.

PubMed

Sanderson, Eleanor; Macdonald-Wallis, Corrie; Davey Smith, George

2018-04-01

Negative control exposure studies are increasingly being used in epidemiological studies to strengthen causal inference regarding an exposure-outcome association when unobserved confounding is thought to be present. Negative control exposure studies contrast the magnitude of association of the negative control, which has no causal effect on the outcome but is associated with the unmeasured confounders in the same way as the exposure, with the magnitude of the association of the exposure with the outcome. A markedly larger effect of the exposure on the outcome than the negative control on the outcome strengthens inference that the exposure has a causal effect on the outcome. We investigate the effect of measurement error in the exposure and negative control variables on the results obtained from a negative control exposure study. We do this in models with continuous and binary exposure and negative control variables using analysis of the bias of the estimated coefficients and Monte Carlo simulations. Our results show that measurement error in either the exposure or negative control variables can bias the estimated results from the negative control exposure study. Measurement error is common in the variables used in epidemiological studies; these results show that negative control exposure studies cannot be used to precisely determine the size of the effect of the exposure variable, or adequately adjust for unobserved confounding; however, they can be used as part of a body of evidence to aid inference as to whether a causal effect of the exposure on the outcome is present.

Lung cancer risk due to residential radon exposures: estimation and prevention.

PubMed

Truta, L A; Hofmann, W; Cosma, C

2014-07-01

Epidemiological studies proved that cumulative exposure to radon is the second leading cause of lung cancer, the world's most common cancer. The objectives of the present study are (i) to analyse lung cancer risk for chronic, low radon exposures based on the transformation frequency-tissue response (TF-TR) model formulated in terms of alpha particle hits in cell nuclei; (ii) to assess the percentage of attributable lung cancers in six areas of Transylvania where the radon concentration was measured and (iii) to point out the most efficient remediation measures tested on a pilot house in Stei, Romania. Simulations performed with the TF-TR model exhibit a linear dose-effect relationship for chronic, residential radon exposures. The fraction of lung cancer cases attributed to radon ranged from 9 to 28% for the investigated areas. Model predictions may represent a useful tool to complement epidemiological studies on lung cancer risk and to establish reasonable radiation protection regulations for human safety. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Sunlight exposure and cardiovascular risk factors in the REGARDS study: a cross-sectional split-sample analysis

PubMed Central

2014-01-01

Background Previous research has suggested that vitamin D and sunlight are related to cardiovascular outcomes, but associations between sunlight and risk factors have not been investigated. We examined whether increased sunlight exposure was related to improved cardiovascular risk factor status. Methods Residential histories merged with satellite, ground monitor, and model reanalysis data were used to determine previous-year sunlight radiation exposure for 17,773 black and white participants aged 45+ from the US. Exploratory and confirmatory analyses were performed by randomly dividing the sample into halves. Logistic regression models were used to examine relationships with cardiovascular risk factors. Results The lowest, compared to the highest quartile of insolation exposure was associated with lower high-density lipoprotein levels in adjusted exploratory (−2.7 mg/dL [95% confidence interval: −4.2, −1.2]) and confirmatory (−1.5 mg/dL [95% confidence interval: −3.0, −0.1]) models. The lowest, compared to the highest quartile of insolation exposure was associated with higher systolic blood pressure levels in unadjusted exploratory and confirmatory, as well as the adjusted exploratory model (2.3 mmHg [95% confidence interval: 0.8, 3.8]), but not the adjusted confirmatory model (1.6 mg/dL [95% confidence interval: −0.5, 3.7]). Conclusions The results of this study suggest that lower long-term sunlight exposure has an association with lower high-density lipoprotein levels. However, all associations were weak, thus it is not known if insolation may affect cardiovascular outcomes through these risk factors. PMID:24946776

Zebrafish as a model to study the role of DNA methylation in environmental toxicology.

PubMed

Kamstra, Jorke H; Aleström, Peter; Kooter, Jan M; Legler, Juliette

2015-11-01

Environmental epigenetics is a rapidly growing field which studies the effects of environmental factors such as nutrition, stress, and exposure to compounds on epigenetic gene regulation. Recent studies have shown that exposure to toxicants in vertebrates is associated with changes in DNA methylation, a major epigenetic mechanism affecting gene transcription. Zebra fish, a well-known model in toxicology and developmental biology, are emerging as a model species in environmental epigenetics despite their evolutionary distance to rodents and humans. In this review, recent insights in DNA methylation during zebra fish development are discussed and compared to mammalian models in order to evaluate zebra fish as a model to study the role of DNA methylation in environmental toxicology. Differences exist in DNA methylation reprogramming during early development, whereas in later developmental stages, tissue distribution of both 5-methylcytosine and 5-hydroxymethylcytosine seems more conserved between species, as well as basic DNA (de)methylation mechanisms. All DNA methyl transferases identified so far in mammals are present in zebra fish, as well as a number of major demethylation pathways. However, zebra fish appear to lack some methylation pathways present in mammals, such as parental imprinting. Several studies report effects on DNA methylation in zebra fish following exposure to environmental contaminants, such as arsenic, benzo[a]pyrene, and tris(1,3-dichloro-2-propyl)phosphate. Though more research is needed to examine heritable effects of contaminant exposure on DNA methylation, recent data suggests the usefulness of the zebra fish as a model in environmental epigenetics.

Innate Immunity and the Inter-exposure Interval Determine the Dynamics of Secondary Influenza Virus Infection and Explain Observed Viral Hierarchies.

PubMed

Cao, Pengxing; Yan, Ada W C; Heffernan, Jane M; Petrie, Stephen; Moss, Robert G; Carolan, Louise A; Guarnaccia, Teagan A; Kelso, Anne; Barr, Ian G; McVernon, Jodie; Laurie, Karen L; McCaw, James M

2015-08-01

Influenza is an infectious disease that primarily attacks the respiratory system. Innate immunity provides both a very early defense to influenza virus invasion and an effective control of viral growth. Previous modelling studies of virus-innate immune response interactions have focused on infection with a single virus and, while improving our understanding of viral and immune dynamics, have been unable to effectively evaluate the relative feasibility of different hypothesised mechanisms of antiviral immunity. In recent experiments, we have applied consecutive exposures to different virus strains in a ferret model, and demonstrated that viruses differed in their ability to induce a state of temporary immunity or viral interference capable of modifying the infection kinetics of the subsequent exposure. These results imply that virus-induced early immune responses may be responsible for the observed viral hierarchy. Here we introduce and analyse a family of within-host models of re-infection viral kinetics which allow for different viruses to stimulate the innate immune response to different degrees. The proposed models differ in their hypothesised mechanisms of action of the non-specific innate immune response. We compare these alternative models in terms of their abilities to reproduce the re-exposure data. Our results show that 1) a model with viral control mediated solely by a virus-resistant state, as commonly considered in the literature, is not able to reproduce the observed viral hierarchy; 2) the synchronised and desynchronised behaviour of consecutive virus infections is highly dependent upon the interval between primary virus and challenge virus exposures and is consistent with virus-dependent stimulation of the innate immune response. Our study provides the first mechanistic explanation for the recently observed influenza viral hierarchies and demonstrates the importance of understanding the host response to multi-strain viral infections. Re-exposure experiments provide a new paradigm in which to study the immune response to influenza and its role in viral control.

Innate Immunity and the Inter-exposure Interval Determine the Dynamics of Secondary Influenza Virus Infection and Explain Observed Viral Hierarchies

PubMed Central

Cao, Pengxing; Yan, Ada W. C.; Heffernan, Jane M.; Petrie, Stephen; Moss, Robert G.; Carolan, Louise A.; Guarnaccia, Teagan A.; Kelso, Anne; Barr, Ian G.; McVernon, Jodie; Laurie, Karen L.; McCaw, James M.

2015-01-01

Influenza is an infectious disease that primarily attacks the respiratory system. Innate immunity provides both a very early defense to influenza virus invasion and an effective control of viral growth. Previous modelling studies of virus–innate immune response interactions have focused on infection with a single virus and, while improving our understanding of viral and immune dynamics, have been unable to effectively evaluate the relative feasibility of different hypothesised mechanisms of antiviral immunity. In recent experiments, we have applied consecutive exposures to different virus strains in a ferret model, and demonstrated that viruses differed in their ability to induce a state of temporary immunity or viral interference capable of modifying the infection kinetics of the subsequent exposure. These results imply that virus-induced early immune responses may be responsible for the observed viral hierarchy. Here we introduce and analyse a family of within-host models of re-infection viral kinetics which allow for different viruses to stimulate the innate immune response to different degrees. The proposed models differ in their hypothesised mechanisms of action of the non-specific innate immune response. We compare these alternative models in terms of their abilities to reproduce the re-exposure data. Our results show that 1) a model with viral control mediated solely by a virus-resistant state, as commonly considered in the literature, is not able to reproduce the observed viral hierarchy; 2) the synchronised and desynchronised behaviour of consecutive virus infections is highly dependent upon the interval between primary virus and challenge virus exposures and is consistent with virus-dependent stimulation of the innate immune response. Our study provides the first mechanistic explanation for the recently observed influenza viral hierarchies and demonstrates the importance of understanding the host response to multi-strain viral infections. Re-exposure experiments provide a new paradigm in which to study the immune response to influenza and its role in viral control. PMID:26284917

Examining a Dual-Process Model of Desensitization and Hypersensitization to Community Violence in African American Male Adolescents.

PubMed

Gaylord-Harden, Noni K; Bai, Grace J; Simic, Dusan

2017-10-01

The purpose of the current study was to examine a dual-process model of reactivity to community violence exposure in African American male adolescents from urban communities. The model focused on desensitization and hypersensitization effects as well as desensitization and hypersensitization as predictors of aggressive behavior. Participants were 133 African American male high school students, mean age = 15.17 years, SD = 0.96. Participants completed measures of exposure to community violence, depressive symptoms, hyperarousal symptoms, aggressive beliefs, and aggressive behaviors at two time points. Community violence exposure predicted changes in aggression, β = .25, p = .004, and physiological arousal, β = .22, p = .010, over time, but not aggressive beliefs. The curvilinear association between community violence exposure and changes in depression over time was not significant, β = .42, p = .083, but there was a significant linear association between the exposure to community violence (ECV) and changes in levels of depression over time, β = .21, p = .014. Results indicated a significant mediation effect for hyperarousal on the association between community violence exposure and aggressive behavior, B = 0.20, 95% CI = [0.04, 0.54]. Results showed support for physiological hypersensitization, with hypersensitization increasing the risk for aggressive behavior. Copyright © 2017 International Society for Traumatic Stress Studies.

Body dissatisfaction: can a short media literacy message reduce negative media exposure effects amongst adolescent girls?

PubMed

Halliwell, Emma; Easun, Alice; Harcourt, Diana

2011-05-01

This experimental study examined whether a brief video intervention identifying the artificial nature of media images could protect adolescent girls from negative media exposure effects and body dissatisfaction. A 2 (intervention condition)×2 (exposure condition) between-groups design was used. Participants were 127 British girls aged between 10 and 13 recruited from two secondary schools. Girls were assigned to one of four experimental conditions. An intervention video was shown to half of the girls immediately before they viewed ultra-thin models or control images. The video was developed by Dove's Self-Esteem Fund and has the benefits of being professionally produced and freely available through the Internet. In the absence of the intervention video, viewing thin idealized models was associated with lower state body satisfaction and lower state body esteem than exposure to control images. However, viewing the video intervention immediately before exposure prevented this negative exposure effect. The results suggest that, in the short term, this widely available video prevents girls from making damaging social comparisons with media models. Although this study only examined short-term effects, the findings add to the growing evidence that media literacy interventions may be useful tools in protecting young girls from body dissatisfaction. ©2010 The British Psychological Society.

Parameter and model uncertainty in a life-table model for fine particles (PM2.5): a statistical modeling study

PubMed Central

Tainio, Marko; Tuomisto, Jouni T; Hänninen, Otto; Ruuskanen, Juhani; Jantunen, Matti J; Pekkanen, Juha

2007-01-01

Background The estimation of health impacts involves often uncertain input variables and assumptions which have to be incorporated into the model structure. These uncertainties may have significant effects on the results obtained with model, and, thus, on decision making. Fine particles (PM2.5) are believed to cause major health impacts, and, consequently, uncertainties in their health impact assessment have clear relevance to policy-making. We studied the effects of various uncertain input variables by building a life-table model for fine particles. Methods Life-expectancy of the Helsinki metropolitan area population and the change in life-expectancy due to fine particle exposures were predicted using a life-table model. A number of parameter and model uncertainties were estimated. Sensitivity analysis for input variables was performed by calculating rank-order correlations between input and output variables. The studied model uncertainties were (i) plausibility of mortality outcomes and (ii) lag, and parameter uncertainties (iii) exposure-response coefficients for different mortality outcomes, and (iv) exposure estimates for different age groups. The monetary value of the years-of-life-lost and the relative importance of the uncertainties related to monetary valuation were predicted to compare the relative importance of the monetary valuation on the health effect uncertainties. Results The magnitude of the health effects costs depended mostly on discount rate, exposure-response coefficient, and plausibility of the cardiopulmonary mortality. Other mortality outcomes (lung cancer, other non-accidental and infant mortality) and lag had only minor impact on the output. The results highlight the importance of the uncertainties associated with cardiopulmonary mortality in the fine particle impact assessment when compared with other uncertainties. Conclusion When estimating life-expectancy, the estimates used for cardiopulmonary exposure-response coefficient, discount rate, and plausibility require careful assessment, while complicated lag estimates can be omitted without this having any major effect on the results. PMID:17714598

Parameter and model uncertainty in a life-table model for fine particles (PM2.5): a statistical modeling study.

PubMed

Tainio, Marko; Tuomisto, Jouni T; Hänninen, Otto; Ruuskanen, Juhani; Jantunen, Matti J; Pekkanen, Juha

2007-08-23

The estimation of health impacts involves often uncertain input variables and assumptions which have to be incorporated into the model structure. These uncertainties may have significant effects on the results obtained with model, and, thus, on decision making. Fine particles (PM2.5) are believed to cause major health impacts, and, consequently, uncertainties in their health impact assessment have clear relevance to policy-making. We studied the effects of various uncertain input variables by building a life-table model for fine particles. Life-expectancy of the Helsinki metropolitan area population and the change in life-expectancy due to fine particle exposures were predicted using a life-table model. A number of parameter and model uncertainties were estimated. Sensitivity analysis for input variables was performed by calculating rank-order correlations between input and output variables. The studied model uncertainties were (i) plausibility of mortality outcomes and (ii) lag, and parameter uncertainties (iii) exposure-response coefficients for different mortality outcomes, and (iv) exposure estimates for different age groups. The monetary value of the years-of-life-lost and the relative importance of the uncertainties related to monetary valuation were predicted to compare the relative importance of the monetary valuation on the health effect uncertainties. The magnitude of the health effects costs depended mostly on discount rate, exposure-response coefficient, and plausibility of the cardiopulmonary mortality. Other mortality outcomes (lung cancer, other non-accidental and infant mortality) and lag had only minor impact on the output. The results highlight the importance of the uncertainties associated with cardiopulmonary mortality in the fine particle impact assessment when compared with other uncertainties. When estimating life-expectancy, the estimates used for cardiopulmonary exposure-response coefficient, discount rate, and plausibility require careful assessment, while complicated lag estimates can be omitted without this having any major effect on the results.

Joint nonparametric correction estimator for excess relative risk regression in survival analysis with exposure measurement error

PubMed Central

Wang, Ching-Yun; Cullings, Harry; Song, Xiao; Kopecky, Kenneth J.

2017-01-01

SUMMARY Observational epidemiological studies often confront the problem of estimating exposure-disease relationships when the exposure is not measured exactly. In the paper, we investigate exposure measurement error in excess relative risk regression, which is a widely used model in radiation exposure effect research. In the study cohort, a surrogate variable is available for the true unobserved exposure variable. The surrogate variable satisfies a generalized version of the classical additive measurement error model, but it may or may not have repeated measurements. In addition, an instrumental variable is available for individuals in a subset of the whole cohort. We develop a nonparametric correction (NPC) estimator using data from the subcohort, and further propose a joint nonparametric correction (JNPC) estimator using all observed data to adjust for exposure measurement error. An optimal linear combination estimator of JNPC and NPC is further developed. The proposed estimators are nonparametric, which are consistent without imposing a covariate or error distribution, and are robust to heteroscedastic errors. Finite sample performance is examined via a simulation study. We apply the developed methods to data from the Radiation Effects Research Foundation, in which chromosome aberration is used to adjust for the effects of radiation dose measurement error on the estimation of radiation dose responses. PMID:29354018

Multiscale Spatial Modeling of Human Exposure from Local Sources to Global Intake.

PubMed

Wannaz, Cedric; Fantke, Peter; Jolliet, Olivier

2018-01-16

Exposure studies, used in human health risk and impact assessments of chemicals, are largely performed locally or regionally. It is usually not known how global impacts resulting from exposure to point source emissions compare to local impacts. To address this problem, we introduce Pangea, an innovative multiscale, spatial multimedia fate and exposure assessment model. We study local to global population exposure associated with emissions from 126 point sources matching locations of waste-to-energy plants across France. Results for three chemicals with distinct physicochemical properties are expressed as the evolution of the population intake fraction through inhalation and ingestion as a function of the distance from sources. For substances with atmospheric half-lives longer than a week, less than 20% of the global population intake through inhalation (median of 126 emission scenarios) can occur within a 100 km radius from the source. This suggests that, by neglecting distant low-level exposure, local assessments might only account for fractions of global cumulative intakes. We also study ∼10 000 emission locations covering France more densely to determine per chemical and exposure route which locations minimize global intakes. Maps of global intake fractions associated with each emission location show clear patterns associated with population and agriculture production densities.

Vessel Noise Affects Beaked Whale Behavior: Results of a Dedicated Acoustic Response Study

DTIC Science & Technology

2012-08-01

the analysis. Gaussian Models Shapiro-Wilk test (Normality) Breusch - Pagan test (Heteroscedasticity) Durbin-Watson test (Independence) Foraging duration...noise) may disrupt behavior. An experiment involving the exposure of target whale groups to intense vessel-generated noise tested how these exposures...exposure of target whale groups to intense vessel-generated noise tested how these exposures influenced the foraging behavior of Blainville?s beaked

Analysis of Longitudinal Studies With Repeated Outcome Measures: Adjusting for Time-Dependent Confounding Using Conventional Methods.

PubMed

Keogh, Ruth H; Daniel, Rhian M; VanderWeele, Tyler J; Vansteelandt, Stijn

2018-05-01

Estimation of causal effects of time-varying exposures using longitudinal data is a common problem in epidemiology. When there are time-varying confounders, which may include past outcomes, affected by prior exposure, standard regression methods can lead to bias. Methods such as inverse probability weighted estimation of marginal structural models have been developed to address this problem. However, in this paper we show how standard regression methods can be used, even in the presence of time-dependent confounding, to estimate the total effect of an exposure on a subsequent outcome by controlling appropriately for prior exposures, outcomes, and time-varying covariates. We refer to the resulting estimation approach as sequential conditional mean models (SCMMs), which can be fitted using generalized estimating equations. We outline this approach and describe how including propensity score adjustment is advantageous. We compare the causal effects being estimated using SCMMs and marginal structural models, and we compare the two approaches using simulations. SCMMs enable more precise inferences, with greater robustness against model misspecification via propensity score adjustment, and easily accommodate continuous exposures and interactions. A new test for direct effects of past exposures on a subsequent outcome is described.

Neurodevelopmental Low-dose Bisphenol A Exposure Leads to Early Life-stage Hyperactivity and Learning Deficits in Adult Zebrafish

PubMed Central

Saili, Katerine S.; Corvi, Margaret M.; Weber, Daniel N.; Patel, Ami U.; Das, Siba R.; Przybyla, Jennifer; Anderson, Kim A.; Tanguay, Robert L.

2011-01-01

Developmental bisphenol A (BPA) exposure has been implicated in adverse behavior and learning deficits. The mode of action underlying these effects is unclear. The zebrafish model was employed to investigate the neurobehavioral effects of developmental bisphenol A (BPA) exposure. The objectives of this study were to identify whether low-dose, developmental BPA exposure affects larval zebrafish locomotor behavior and whether learning deficits occur in adults exposed during development. Two control compounds, 17β-estradiol (an estrogen receptor ligand) and GSK4716 (a synthetic estrogen related receptor gamma ligand), were included. Larval toxicity assays were used to determine appropriate BPA, 17β-estradiol, and GSK4716 concentrations for behavior testing. BPA tissue uptake was analyzed using HPLC and lower doses were extrapolated using a linear regression analysis. Larval behavior tests were conducted using a ViewPoint Zebrabox. Adult learning tests were conducted using a custom-built T-maze. BPA exposure to ≤30 μM was nonteratogenic in zebrafish. Neurodevelopmental BPA exposure to 0.01, 0.1, or 1 μM led to larval hyperactivity or learning deficits in adult zebrafish. Exposure to 0.1 μM 17β-estradiol or GSK4716 also led to larval hyperactivity. This study demonstrates the efficacy of using the larval zebrafish model for studying the neurobehavioral effects of low-dose developmental BPA exposure. PMID:22108044

Diesel motor exhaust and lung cancer mortality: reanalysis of a cohort study in potash miners.

PubMed

Möhner, Matthias; Kersten, Norbert; Gellissen, Johannes

2013-02-01

The aim of the reanalysis is to reassess lung cancer risk associated with occupational exposure to diesel motor exhaust in potash miners, while controlling for potential confounders such as smoking and previous occupational history. Our investigation is based on a cohort study of nearly 6,000 German potash miners, who were followed up from 1970 to 2001. The reanalysis also takes into account the employment periods before potash mining, in particular uranium mining. Different approaches (nested case-control study and Cox model) were used to adjust for confounding. The exposure estimates were recalculated, lagging the exposure by 5 years. Exposure groups were defined by tertiles of cumulative respirable elemental carbon (REC) exposure estimates and occupational categories, where exposure was estimated originally by representative measurements of total carbon for different occupations. The highest REC concentration was measured for production workers, about twice as much as for other occupations. The reanalysis revealed that while about 4 % of all study subjects had worked earlier in uranium mines, 10.3 % of later lung cancer cases did so. Although their absolute number was small, the corresponding relative risk estimator was significantly elevated. Our analysis did not show any notable association between cumulative REC exposure and lung cancer risk. Introducing cumulative REC exposure as a continuous variable into the conditional logistic regression model yielded an odds ratio of OR = 1.04 [0.70-1.53]95 % adjusted for smoking and previous employment. The study results give no evidence for an association between REC exposure and lung cancer risk. Only for very high cumulative dose, corresponding to at least 20 years of exposure in the production area, some weak hints for a possible risk increase could be detected. The study underlines the importance of assessing the entire occupational history in occupational studies, especially if the supposed dose-response-relationship is weak.

Polynomial Chaos decomposition applied to stochastic dosimetry: study of the influence of the magnetic field orientation on the pregnant woman exposure at 50 Hz.

PubMed

Liorni, I; Parazzini, M; Fiocchi, S; Guadagnin, V; Ravazzani, P

2014-01-01

Polynomial Chaos (PC) is a decomposition method used to build a meta-model, which approximates the unknown response of a model. In this paper the PC method is applied to the stochastic dosimetry to assess the variability of human exposure due to the change of the orientation of the B-field vector respect to the human body. In detail, the analysis of the pregnant woman exposure at 7 months of gestational age is carried out, to build-up a statistical meta-model of the induced electric field for each fetal tissue and in the fetal whole-body by means of the PC expansion as a function of the B-field orientation, considering a uniform exposure at 50 Hz.

Considering common sources of exposure in association studies - Urinary benzophenone-3 and DEHP metabolites are associated with altered thyroid hormone balance in the NHANES 2007-2008.

PubMed

Kim, Sujin; Kim, Sunmi; Won, Sungho; Choi, Kyungho

2017-10-01

Epidemiological studies have shown that thyroid hormone balances can be disrupted by chemical exposure. However, many association studies have often failed to consider multiple chemicals with possible common sources of exposure, rendering their conclusions less reliable. In the 2007-2008 National Health and Nutrition Examination Survey (NHANES) from the U.S.A., urinary levels of environmental phenols, parabens, and phthalate metabolites as well as serum thyroid hormones were measured in a general U.S. population (≥12years old, n=1829). Employing these data, first, the chemicals or their metabolites associated with thyroid hormone measures were identified. Then, the chemicals/metabolites with possible common exposure sources were included in the analytical model to test the sensitivities of their association with thyroid hormone levels. Benzophenone-3 (BP-3), bisphenol A (BPA), and a metabolite of di(2-ethylhexyl) phthalate (DEHP) were identified as significant determinants of decreased serum thyroid hormones. However, significant positive correlations were detected (p-value<0.05, r=0.23 to 0.45) between these chemicals/metabolites, which suggests that they might share similar exposure sources. In the subsequent sensitivity analysis, which included the chemicals/metabolite with potentially similar exposure sources in the model, we found that urinary BP-3 and DEHP exposure were associated with decreased thyroid hormones among the general population but BPA exposure was not. In association studies, the presence of possible common exposure sources should be considered to circumvent possible false-positive conclusions. Copyright © 2017 Elsevier Ltd. All rights reserved.

Associations of Mortality with Long-Term Exposures to Fine and Ultrafine Particles, Species and Sources: Results from the California Teachers Study Cohort

PubMed Central

Hu, Jianlin; Goldberg, Debbie; Reynolds, Peggy; Hertz, Andrew; Bernstein, Leslie; Kleeman, Michael J.

2015-01-01

Background Although several cohort studies report associations between chronic exposure to fine particles (PM2.5) and mortality, few have studied the effects of chronic exposure to ultrafine (UF) particles. In addition, few studies have estimated the effects of the constituents of either PM2.5 or UF particles. Methods We used a statewide cohort of > 100,000 women from the California Teachers Study who were followed from 2001 through 2007. Exposure data at the residential level were provided by a chemical transport model that computed pollutant concentrations from > 900 sources in California. Besides particle mass, monthly concentrations of 11 species and 8 sources or primary particles were generated at 4-km grids. We used a Cox proportional hazards model to estimate the association between the pollutants and all-cause, cardiovascular, ischemic heart disease (IHD), and respiratory mortality. Results We observed statistically significant (p < 0.05) associations of IHD with PM2.5 mass, nitrate, elemental carbon (EC), copper (Cu), and secondary organics and the sources gas- and diesel-fueled vehicles, meat cooking, and high-sulfur fuel combustion. The hazard ratio estimate of 1.19 (95% CI: 1.08, 1.31) for IHD in association with a 10-μg/m3 increase in PM2.5 is consistent with findings from the American Cancer Society cohort. We also observed significant positive associations between IHD and several UF components including EC, Cu, metals, and mobile sources. Conclusions Using an emissions-based model with a 4-km spatial scale, we observed significant positive associations between IHD mortality and both fine and ultrafine particle species and sources. Our results suggest that the exposure model effectively measured local exposures and facilitated the examination of the relative toxicity of particle species. Citation Ostro B, Hu J, Goldberg D, Reynolds P, Hertz A, Bernstein L, Kleeman MJ. 2015. Associations of mortality with long-term exposures to fine and ultrafine particles, species and sources: results from the California Teachers Study cohort. Environ Health Perspect 123:549–556; http://dx.doi.org/10.1289/ehp.1408565 PMID:25633926

Bayesian Hierarchical Modeling of Cardiac Response to Particulate Matter Exposure

EPA Science Inventory

Studies have linked increased levels of particulate air pollution to decreased autonomic control, as measured by heart rate variability (HRV), particularly in populations such as the elderly. In this study, we use data obtained from the 1998 USEPA epidemiology-exposure longitudin...

Demographic and geographic differences in exposure to secondhand smoke in Missouri workplaces, 2007-2008.

PubMed

Harris, Jenine K; Geremakis, Caroline; Moreland-Russell, Sarah; Carothers, Bobbi J; Kariuki, Barbara; Shelton, Sarah C; Kuhlenbeck, Matthew

2011-11-01

African Americans, Hispanics, service and blue-collar workers, and residents of rural areas are among those facing higher rates of workplace secondhand smoke exposure in states without smokefree workplace laws. Consequently, these groups also experience more negative health effects resulting from secondhand smoke exposure. The objective of this study was to examine disparities in workplace secondhand smoke exposure in a state without a comprehensive statewide smokefree workplace law and to use this information in considering a statewide law. We developed a logistic multilevel model by using data from a 2007-2008 county-level study to account for individual and county-level differences in workplace secondhand smoke exposure. We included sex, age, race, annual income, education level, smoking status, and rural or urban residence as predictors of workplace secondhand smoke exposure. Factors significantly associated with increased exposure to workplace secondhand smoke were male sex, lower education levels, lower income, living in a small rural or isolated area, and current smoking. For example, although the overall rate of workplace exposure in Missouri is 11.5%, our model predicts that among young white men with low incomes and limited education living in small rural areas, 40% of nonsmokers and 56% of smokers may be exposed to secondhand smoke at work. Significant disparities exist in workplace secondhand smoke exposure across Missouri. A statewide smokefree workplace law would protect all citizens from workplace secondhand smoke exposure.

Physiologically-Based Toxicokinetic Modeling of Zearalenone and Its Metabolites: Application to the Jersey Girl Study

PubMed Central

Mukherjee, Dwaipayan; Royce, Steven G.; Alexander, Jocelyn A.; Buckley, Brian; Isukapalli, Sastry S.; Bandera, Elisa V.; Zarbl, Helmut; Georgopoulos, Panos G.

2014-01-01

Zearalenone (ZEA), a fungal mycotoxin, and its metabolite zeranol (ZAL) are known estrogen agonists in mammals, and are found as contaminants in food. Zeranol, which is more potent than ZEA and comparable in potency to estradiol, is also added as a growth additive in beef in the US and Canada. This article presents the development and application of a Physiologically-Based Toxicokinetic (PBTK) model for ZEA and ZAL and their primary metabolites, zearalenol, zearalanone, and their conjugated glucuronides, for rats and for human subjects. The PBTK modeling study explicitly simulates critical metabolic pathways in the gastrointestinal and hepatic systems. Metabolic events such as dehydrogenation and glucuronidation of the chemicals, which have direct effects on the accumulation and elimination of the toxic compounds, have been quantified. The PBTK model considers urinary and fecal excretion and biliary recirculation and compares the predicted biomarkers of blood, urinary and fecal concentrations with published in vivo measurements in rats and human subjects. Additionally, the toxicokinetic model has been coupled with a novel probabilistic dietary exposure model and applied to the Jersey Girl Study (JGS), which involved measurement of mycoestrogens as urinary biomarkers, in a cohort of young girls in New Jersey, USA. A probabilistic exposure characterization for the study population has been conducted and the predicted urinary concentrations have been compared to measurements considering inter-individual physiological and dietary variability. The in vivo measurements from the JGS fall within the high and low predicted distributions of biomarker values corresponding to dietary exposure estimates calculated by the probabilistic modeling system. The work described here is the first of its kind to present a comprehensive framework developing estimates of potential exposures to mycotoxins and linking them with biologically relevant doses and biomarker measurements, including a systematic characterization of uncertainties in exposure and dose estimation for a vulnerable population. PMID:25474635

Modelling the effects of pulse exposure of several PSII inhibitors on two algae.

PubMed

Copin, Pierre-Jean; Chèvre, Nathalie

2015-10-01

Subsequent to crop application and during precipitation events, herbicides can reach surface waters in pulses of high concentrations. These pulses can exceed the Annual Average Environmental Quality Standards (AA-EQS), defined in the EU Water Framework Directive, which aims to protect the aquatic environment. A model was developed in a previous study to evaluate the effects of pulse exposure for the herbicide isoproturon on the alga Scenedesmus vacuolatus. In this study, the model was extended to other substances acting as photosystem II inhibitors and to other algae. The measured and predicted effects were equivalent when pulse exposure of atrazine and diuron were tested on S. vacuolatus. The results were consistent for isoproturon on the alga Pseudokirchneriella subcapitata. The model is thus suitable for the effect prediction of phenylureas and triazines and for the algae used: S. vacuolatus and P. subcapitata. The toxicity classification obtained from the dose-response curves (diuron>atrazine>isoproturon) was conserved for the pulse exposure scenarios modelled for S. vacuolatus. Toxicity was identical for isoproturon on the two algae when the dose-response curves were compared and also for the pulse exposure scenarios. Modelling the effects of any pulse scenario of photosystem II inhibitors on algae is therefore feasible and only requires the determination of the dose-response curves of the substance and growth rate of unexposed algae. It is crucial to detect the longest pulses when measurements of herbicide concentrations are performed in streams because the model showed that they principally affect the cell density inhibition of algae. Copyright © 2015 Elsevier Ltd. All rights reserved.

High‐resolution trench photomosaics from image‐based modeling: Workflow and error analysis

USGS Publications Warehouse

Reitman, Nadine G.; Bennett, Scott E. K.; Gold, Ryan D.; Briggs, Richard; Duross, Christopher

2015-01-01

Photomosaics are commonly used to construct maps of paleoseismic trench exposures, but the conventional process of manually using image‐editing software is time consuming and produces undesirable artifacts and distortions. Herein, we document and evaluate the application of image‐based modeling (IBM) for creating photomosaics and 3D models of paleoseismic trench exposures, illustrated with a case‐study trench across the Wasatch fault in Alpine, Utah. Our results include a structure‐from‐motion workflow for the semiautomated creation of seamless, high‐resolution photomosaics designed for rapid implementation in a field setting. Compared with conventional manual methods, the IBM photomosaic method provides a more accurate, continuous, and detailed record of paleoseismic trench exposures in approximately half the processing time and 15%–20% of the user input time. Our error analysis quantifies the effect of the number and spatial distribution of control points on model accuracy. For this case study, an ∼87  m2 exposure of a benched trench photographed at viewing distances of 1.5–7 m yields a model with <2  cm root mean square error (rmse) with as few as six control points. Rmse decreases as more control points are implemented, but the gains in accuracy are minimal beyond 12 control points. Spreading control points throughout the target area helps to minimize error. We propose that 3D digital models and corresponding photomosaics should be standard practice in paleoseismic exposure archiving. The error analysis serves as a guide for future investigations that seek balance between speed and accuracy during photomosaic and 3D model construction.