Exposure Assessment In Epidemiological Studies Research Articles

Development of an integrated model framework for multi-air-pollutant exposure assessments in high-density cities

Abstract. Exposure models for some criteria of air pollutants have been intensively developed in past research; multi-air-pollutant exposure models, especially for particulate chemical species, have been however overlooked in Asia. Lack of an integrated model framework to calculate multi-air-pollutant exposure has hindered the combined exposure assessment and the corresponding health assessment. This work applied the land-use regression (LUR) approach to develop an integrated model framework to estimate 2017 annual-average exposure of multiple air pollutants in a typical high-rise and high-density Asian city (Hong Kong, China) including four criteria of gaseous air pollutants (particulate matter with an aerodynamic diameter equal to or less than 10 µm (PM10) and 2.5 µm (PM2.5), nitrogen dioxide (NO2), and ozone (O3)), as well as four major PM10 chemical species. Our integrated multi-air-pollutant exposure model framework is capable of explaining 91 %–97 % of the variability of measured gaseous air pollutant concentration, with the leave-one-out cross-validation R2 values ranging from 0.73 to 0.93. Using the model framework, the spatial distribution of the concentration of various air pollutants at a spatial resolution of 500 m was generated. The LUR model-derived spatial distribution maps revealed weak-to-moderate spatial correlations between the PM10 chemical species and the criteria of air pollutants, which may help to distinguish their independent chronic health effects. In addition, further improvements in the development of air pollution exposure models are discussed. This study proposed an integrated model framework for estimating multi-air-pollutant exposure in high-density and high-rise urban areas, serving an important tool for multi-air-pollutant exposure assessment in epidemiological studies.

Size-resolved emission rates of episodic indoor sources and ultrafine particle dynamics

Indoor airborne ultrafine particles (UFPs) are mainly originated from occupant activities, such as candle burning and cooking. Elevated exposure to UFPs has been found to increase oxidative stress and cause DNA damage. UFPs originating from indoor sources undergo dynamic aerosol transformation mechanisms. This study investigates the dynamics of UFPs following episodic indoor releases of the six distinct emission sources: 1) candle, 2) gas stove, 3) clothes dryer, 4) tea & toast, 5) broiled fish, and 6) incense. Based on the analytical model of aerosol dynamic processes, this study reports size-resolved source emission rates along with relative contributions of coagulation, deposition, and ventilation to the particle size distribution dynamics. The study findings indicate a significant variation in the geometric mean diameter (GMD) and size-resolved number concentration over time for the sources that emit a substantial amount of UFPs smaller than 10 nm. As the emission progresses, the UFP number concentrations increase in a log-normal distribution, while the GMD shows a tendency to increase over time. The observed result suggests that coagulation can have a considerable impact on UFP number concentration and size, even during the indoor UFP emission. The estimated emission rates of the six indoor sources appear to follow a log-normal distribution while the emission rate ranges from 107 min−1 to 1012 min−1. The indoor UFP concentration and size distribution dynamics are substantially affected by the interplay of the three aerosol loss mechanisms that compete with each other, and this impact varies according to the source type and the indoor environmental conditions. Ultimately, using the aerosol transformation mechanisms examined in this study, researchers can refine exposure assessment for epidemiological studies on indoor ultrafine particles.

Bladder cancer risk in relation to occupations held in a nationwide case-control study in Iran.

Globally, bladder cancer has been identified as one of the most frequent occupational cancers, but our understanding of occupational bladder cancer risk in Iran is less advanced. This study aimed to assess the risk of bladder cancer in relation to occupation in Iran. We used the IROPICAN case-control study data including 717 incident cases and 3477 controls. We assessed the risk of bladder cancer in relation to ever working in major groups of the International Standard Classification of Occupations (ISCO-68) while controlling for cigarette smoking, opium consumption. Logistic regression models were used to estimate odds ratios (ORs) and 95% confidence intervals (CI). In men, decreased ORs for bladder cancer were observed in administrative and managerial workers (OR 0.4; CI: 0.2, 0.9), and clerks (OR 0.6; CI: 0.4, 0.9). Elevated ORs were observed in metal processors (OR 5.4; CI: 1.3, 23.4), and workers in occupations with likely exposure to aromatic amines (OR 2.2; CI: 1.2, 4.0). There was no evidence of interactions between working in aromatic amines-exposed occupations and tobacco smoking or opium use. Elevated risk of bladder cancer in men in metal processors and workers likely exposed to aromatic amines aligns with associations observed outside Iran. Other previously confirmed associations between high-risk occupations and bladder cancer were not observed, possibly due to small numbers or lack of details on exposure. Future epidemiological studies in Iran would benefit from the development of exposure assessment tools such as job exposure matrices, generally applicable for retrospective exposure assessment in epidemiological studies.

Spatiotemporally resolved daily relative humidity predictions across Germany during 2000-2021: a Random Forest approach

BACKGROUND AND AIM The lack of high-resolution relative humidity (RH) datasets and the limited ability of available weather stations to fully capture the spatiotemporal RH variability might lead to errors in exposure assessment for epidemiological studies and biased health effects estimates. We aimed to predict German-wide 1×1 km daily mean RH during 2000-2021 by using a machine learning approach. METHODS We used data from multiple sources, including RH observations, modelled precipitation and wind speed as well as remote sensing elevation, vegetation and the visible red, green and blue light bands. Our main predictor for estimating RH was modelled daily mean air temperature that we previously estimated in 1×1 km across Germany through a multi-stage regression-based approach incorporating two linear mixed models. Additionally, we included date in our RH model, capturing the day-to-day variation of the response-explanatory variables relationship. All the aforementioned predictors were included in a Random Forest (RF) model, which was separately developed for each year. RESULTS Ten-fold cross validation showed that the RF model achieved high accuracy (R²=0.81) and low errors (Root Mean Square Error (RMSE)=5.26%). We also compared our output with an independent dataset from a dense monitoring network in Augsburg metropolitan area (R²=0.81, RMSE=5.42%). Our models displayed high RH overall (21y-average RH=78.8%) and high spatial variability within a year across the country, exceeding 12.1% on average. RH distribution followed spatial patterns including urbanization, mountains, rivers and coastlines. For instance, the Alps and the North Sea coast were areas with elevated RH, while extended urban cores or individual cities were less humid than the surrounding rural settings. CONCLUSIONS Our findings indicate the proposed RF model as suitable for countrywide RH modeling at high spatiotemporal resolution, providing a reliable dataset for subsequent epidemiological analyses and other research purposes. KEYWORDS: relative humidity, spatiotemporal modeling, random forest, exposure assessment

Spatiotemporal exposure modeling of environmental circadian misalignment

Background and Aim: Light exposure is the most powerful resetting signal for circadian rhythms. Environmental circadian misalignment (ECM) occurs from geographic variation in light exposure due to location within a time zone (delayed circadian phase from less morning and greater evening light exposure moving west in a time zone). The aim of this study was to develop and validate the first-ever nationwide spatiotemporal exposure model of ECM in the US. Methods: For all US census tracts, the ECM exposure model incorporated elevation (in meters) and the average difference (in minutes) in sunrise time and sunset time between the census tract center of population and the eastern-most time zone boundary. Higher ECM exposure values indicate higher circadian misalignment due to higher elevation (fewer obstructions/extended field of view) and larger differences in sunrise and sunset times. Spearman correlation coefficients and multivariable linear regression adjusting for age, sex, race, ethnicity, median household income, and population density were used to validate the ECM exposure model with census tract-level prevalence of obesity (2016-2019) (known outcome associated with circadian misalignment). Results : Among the 60,943 census tracts included in this study, obesity prevalence was 32.62%. ECM exposure was positively correlated with obesity prevalence, with the strongest correlation in the Eastern time zone (rs=0.44, p<0.01). The highest vs. lowest ECM quintile was associated with higher obesity prevalence in the Eastern (adjusted β=6.00, 95% CI 6.68, 7.11) and Central time zones (adjusted β=2.85, 95% CI 2.62, 3.09), which are the US time zones characterized by the largest east-to-west distance (i.e., opportunity for ECM). Conclusions: We developed and validated a US nationwide spatiotemporal ECM exposure model, which can be used for exposure assessment in epidemiologic studies to enable comprehensive characterization of geographic variation in light exposure potentially impacting circadian phase. Keywords: light exposure; circadian misalignment; exposure model

Insights to estimate exposure to regulated and non-regulated disinfection by-products in drinking water

BackgroundKnowledge about human exposure and health effects associated with non-routinely monitored disinfection by-products (DBPs) in drinking water is sparse.ObjectiveTo provide insights to estimate exposure to regulated and non-regulated DBPs in drinking water.MethodsWe collected tap water from homes (N = 42), bottled water (N = 10), filtered tap water with domestic activated carbon jars (N = 6) and reverse osmosis (N = 5), and urine (N = 39) samples of participants from Barcelona, Spain. We analyzed 11 haloacetic acids (HAAs), 4 trihalomethanes (THMs), 4 haloacetonitriles (HANs), 2 haloketones, chlorate, chlorite, and trichloronitromethane in water and HAAs in urine samples. Personal information on water intake and socio-demographics was ascertained in the study population (N = 39) through questionnaires. Statistical models were developed based on THMs as explanatory variables using multivariate linear regression and machine learning techniques to predict non-regulated DBPs.ResultsChlorate, THMs, HAAs, and HANs were quantified in 98–100% tap water samples with median concentration of 214, 42, 18, and 3.2 μg/L, respectively. Multivariate linear regression models had similar or higher goodness of fit (R2) compared to machine learning models. Multivariate linear models for dichloro-, trichloro-, and bromodichloroacetic acid, dichloroacetonitrile, bromochloroacetonitrile, dibromoacetonitrile, trichloropropnanone, and chlorite showed good predictive ability (R2 = 0.8–0.9) as 80–90% of total variance could be explained by THM concentrations. Activated carbon filters reduced DBP concentrations to a variable extent (27–80%), and reverse osmosis reduced DBP concentrations ≥98%. Only chlorate was detected in bottled water samples (N = 3), with median = 13.0 µg/L. Creatinine-adjusted trichloroacetic acid was the most frequently detected HAA in urine samples (69.2%), and moderately correlated with estimated drinking water intake (r = 0.48).SignificanceFindings provide valuable insights for DBP exposure assessment in epidemiological studies. Validation of predictive models in a larger number of samples and replication in different settings is warranted.Impact statementOur study focused on assessing and describing the occurrence of several classes of DBPs in drinking water and developing exposure models of good predictive ability for non-regulated DBPs.

A Task-Specific Algorithm to Estimate Occupational (1→3)-β-D-glucan Exposure for Farmers in the Biomarkers of Exposure and Effect in Agriculture Study.

Farmers may be exposed to glucans (a cell component of molds) through a variety of tasks. The magnitude of exposure depends on each farmer's activities and their duration. We developed a task-specific algorithm to estimate glucan exposure that combines measurements of (1→3)-β-D-glucan with questionnaire responses from farmers in the Biomarkers of Exposure and Effect in Agriculture (BEEA) study. To develop the algorithm, we first derived task-based geometric means (GMs) of glucan exposure for farming tasks using inhalable personal air sampling data from a prior air monitoring study in a subset of 32 BEEA farmers. Next, these task-specific GMs were multiplied by subject-reported activity frequencies for three time windows (the past 30 days, past 7 days, and past 1 day) to obtain subject-, task-, and time window-specific glucan scores. These were summed together to obtain a total glucan score for each subject and time window. We examined the within- and between-task correlation in glucan scores for different time frames. Additionally, we assessed the algorithm for the 'past 1 day' time window using full-shift concentrations from the 32 farmers who participated in air monitoring the day prior to an interview using multilevel statistical models to compare the measured glucan concentration with algorithm glucan scores. We focused on the five highest exposed tasks: poultry confinement (300 ng/m3), swine confinement (300 ng/m3), clean grain bins (200 ng/m3), grind feed (100 ng/m3), and stored seed or grain (50 ng/m3); the remaining tasks were <50 ng/m3 and had similar concentrations to each other. Overall, 67% of the participants reported at least one of these tasks. The most prevalent task was stored seed or grain (64%). The highest median glucan scores were observed for poultry confinement and swine confinement; these tasks were reported by 2% and 8% of the participants, respectively. The correlation between scores for the same task but different time windows was high for swine confinement and poultry confinement, but low for clean grain bins. Task-specific scores had low correlation with other tasks. Prior day glucan concentration was associated with the total glucan 'past 1 day' score and with swine confinement and clean grain bin task scores. This study provides insight into the variability and key sources of glucan exposure in a US farming population. It also provides a framework for better glucan exposure assessment in epidemiologic studies and is a crucial starting point for evaluating health risks associated with glucans in future epidemiologic evaluations of this population.

Close physical proximity on the job-An exposure matrix.

The workplace is a setting for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)transmission via respiratory droplets and aerosols for those working in close proximity to others. Currently, limited methods exist for assessing SARS-CoV-2 exposure. Since occupation serves as a surrogate measure, job exposure matrices (JEMs) can be useful for population-based exposure assessment for a portion of exposure. We developed a JEM to assess physical proximity at work. Scores for questions related to frequency of face-to-face discussions, working closely with others, and working with a team were extracted from a US-based, comprehensive source of descriptive occupational information (Occupational Information Network [O*NET]). We described score distributions using univariate analyses, devised thresholds, and assigned exposure levels for 968 O*NET occupations. Three exposure measures were constructed using combinations of O*NET data, with expert judgment, and accounting for telework. National and California employment estimates were used to assess the workforce proportions by proximity level and demographic characteristics. We categorized 535 USCensus occupations (2010) into four-level ordinal exposure levels (not close to very close). Overall, an estimated 56% of the California workforce worked in very close proximity, which decreased to 46% when accounting for telework. The occupational groups working very close across all three measures were: healthcare support, healthcare practitioner, food preparation and serving, building and grounds cleaning and maintenance, and protective service occupations. Latinos and women were overrepresented within occupations working in very close physical proximity. JEMs can inform SARS-CoV-2 exposure assessment for epidemiologic studies, assist in resource allocation, and inform prevention strategies.

Variability of lead in urine and blood in healthy individuals

BackgroundLead is a non-essential toxic trace element. Lead in blood (BPb) is the most common biomarker of lead exposure but lead in urine (UPb) has also been used. There is, however, limited data on the variability of UPb in the general population and the association with BPb. ObjectivesOur aims were to assess variability of lead in repeated blood and urine samples. The diurnal variation of UPb was also examined as well as associations with BPb. MethodsWe established an openly available biobank including 60 healthy non-smoking individuals, 29 men and 31 women, 21–64 years of age (median 31 years), with repeated sampling of blood and urine. Timed urine samples were collected at six fixed time points in two 24 h periods, about one week apart, and adjusted for creatinine and specific gravity (SG). BPb and UPb were analyzed by inductively coupled plasma mass spectrometry. The within- and between-individual variabilities and intra-class correlation coefficients (ICCs; ratios of the between-individual to total observed variances) were calculated using mixed-effects models. ResultsThe ICCs for UPb samples were mostly above 0.5, when adjusted for creatinine or SG, and higher for overnight samples compared with daytime samples. The highest ICCs were obtained for BPb (ICC = 0.97) and for urine samples corrected for dilution by SG or creatinine. The ICC was 0.66 for overnight samples adjusted for creatinine. High correlations with BPb were found for 24 h UPb (rs = 0.77) and overnight samples, e.g. rs = 0.74 when adjusted for SG. There was diurnal variation of UPb with lowest excretion rate in overnight samples. There was also a significant association between the Pb excretion rate and urinary flow rate. ConclusionsIn addition to BPb, UPb adjusted for creatinine or SG seems to be a useful biomarker for exposure assessment in epidemiological studies.

무선 주파수 전자기장 인체영향 연구 현안

This study describes the basis of the International Commission on Non-Ionizing Radiation Protection guidelines and IEEE standards for protecting the human body from exposure to electromagnetic fields (EMF) in the radio frequency band. A brief summary of epidemiological studies that have been conducted on the health risks of long-term EMF exposure in the mobile communication frequency range are reviewed. EMF exposure assessment for epidemiological studies should incorporate mobile-device usage patterns, mobile communication uplink and downlin\k exposure analysis in real environments, age-specific human model development, and dosimetry. Trends and limitations on these topics are described.

High-resolution Estimation of Spatio-temporal Variation in PM2.5 Concentrations in the Beijing-Tianjin-Hebei Region

This study developed a two-stage statistical model (linear mixed effect (LME) model+geographical weight regression (GWR) model) to determine the spatio-temporal variation of PM2.5 concentrations in the Beijing-Tianjin-Hebei (BTH) region with full-coverage, high resolution, and high accuracy. The model employs multi-angle implementation of atmospheric correction aerosol optical depth (MAIAC AOD) data, with a 1 km spatial resolution, as the main predictor and meteorological data/land-use data as the auxiliary predictors. To determine the characteristics of heavy PM2.5 pollution in the BTH region, unique predictors such as AOD2 were also introduced into the two-stage model. The two-stage model was used to estimate the daily PM2.5 concentrations with a 1 km resolution. After being cross-validated against ground observations, the R2 of PM2.5 was found to be 0.94, with a slope value of 0.95 and RMSPE value of 13.14 μg·m-3. Compared to previous studies such as LME, the two-stage model has much higher accuracy, suitable for estimating PM2.5 concentrations. The PM2.5 concentration in the BTH region ranged from 0 to 89.89 μg·m-3 in 2017, with a mean value of 44.96 μg·m-3. The spatio-temporal variability of PM2.5 over the BTH region was significant, exhibiting high values over the southwestern plain, moderate values over the northeastern plain, and low values over the mountainous plateau. In terms of seasonal variation, PM2.5 concentrations were high in winter, low in summer, and moderate in spring and autumn. The estimated PM2.5 concentrations, with high spatio-temporal resolution, are useful for exposure assessments in epidemiological studies and identifying the spatio-temporal variation of pollution sources at a fine spatial scale. The results show that the locations of vital pollution sources over the severely polluted south-central Hebei piedmont plain may have changed since the implementation of the Air Pollution Prevention and Control Action Plan. This study could provide a scientific basis for the prevention and control of air pollution in the BHT region.

Bayesian spatial modelling of terrestrial radiation in Switzerland

The geographic variation of terrestrial radiation can be exploited in epidemiological studies of the health effects of protracted low-dose exposure. Various methods have been applied to derive maps of this variation. We aimed to construct a map of terrestrial radiation for Switzerland. We used airborne γ-spectrometry measurements to model the ambient dose rates from terrestrial radiation through a Bayesian mixed-effects model and conducted inference using Integrated Nested Laplace Approximation (INLA). We predicted higher levels of ambient dose rates in the alpine regions and Ticino compared with the western and northern parts of Switzerland. We provide a map that can be used for exposure assessment in epidemiological studies and as a baseline map for assessing potential contamination.

Trace element concentrations in ambient air as a function of distance from road

ABSTRACT Traffic-related air pollution is associated with various adverse health effects. In the absence of more complicated exposure assessment techniques, many environmental health studies have used the natural logarithm of distance to road as a proxy for traffic-related exposures. However, research validating this proxy and further explaining the spatial patterns and elemental composition of traffic-related particulate matter air pollution remains limited. In this study, we collected air samples using a mobile particle concentrator that allowed for high sample loading from major roadways in the Greater Boston Area. We found that concentrations of Cl, Ti, V, Cr, Mn, Fe, Co, Cu, Zn, Sr, Zr, Sn, Ba, and Pb were significantly associated with the natural logarithm of distance to road in coarse particulate matter, and total fine particulate mass concentrations of Al, Ca, Ti, Cr, Mn, Fe, Cu, and Zn were significantly associated with natural logarithm of distance to road in fine particulate matter. Road type (A1 or A2 [primary roads or highways] versus A3 [secondary and connecting roads]) was not a significant predictor of any traffic-related elements in particulate matter air pollution. Our results help identify traffic-related elements in particulate matter air pollution and support the use of logarithm of distance to road as a proxy for traffic-related particulate matter air pollution exposure assessment in epidemiological studies.

Exposure Misclassification: Temporal and Spatial Variability in Estimates of Individual Species and Mixtures of Disinfection Byproducts

Disinfection of drinking water is a public health advance, but the potential risk of unintended consequences of disinfection byproduct (DBP) formation remains unclear. Over 600 DBPs have been identified to date, although most have not been analytically quantified in disinfected drinking water. Some DBP exposures occur through multiple routes which can complicate exposure assessment in epidemiological studies especially based on aggregate data derived from routinely-collected monitoring data. Therefore it is important to quantify the spatiotemporal variability in occurrence to minimize measurement error and exposure misclassification. In the absence of biomonitoring data, concentration-based exposure scores based on routinely-collected monitoring data can be spatial water system averages or location-specific estimates. However, intra-water system variation in DBP concentrations results from formation and degradation processes occurring over space and residence time. Temporal variability can also occur from diurnal variation to longer-term trends such as seasonality noted for trihalomethanes (THMs) and haloacetic acids (HAAs). Temporally averaged data may estimate lifetime exposure for cancer outcomes or trimester-specific windows for developmental effects, but they ill fully account for the potential impact of peak exposures. Although the DBP surrogacy issue is beyond the scope here, data availability limitations may preclude examination and delineation of potential risk for individual DBPs or DBP mixtures especially when the measurement error structures can vary due to use of aggregate exposure estimates. With few DBPs regulated or routinely measured, this review will characterize strengths and limitations of existing approaches based on HAAs and THMs and will offer evidence from simulations and sensitivity analyses and offer recommendations to help address the aforementioned challenges. Disclaimer: The views expressed in this abstract are those of the author and do not necessarily reflect the views or policies of the U.S. EPA. Keywords: Water, water quality, disinfection byproducts, environmental epidemiology

Considering Exposure Assessment in Epidemiological Studies of Chronic Health in Military Populations.

Exposure assessment is an important factor in all epidemiological research seeking to identify, evaluate, and control health risks. In the military and veteran context, population health research to explore exposure-response links is complicated by the wide variety of environments and hazards encountered during active service, long latency periods, and a lack of information on exposures in potentially vulnerable subgroups. This paper summarizes some key considerations for exposure assessment in long-term health studies of military populations, including the identification of hazards related to military service, characterization of potentially exposed groups, exposure data collection, and assignment of exposures to estimate health risks. Opportunities and future directions for exposure assessment in this field are also discussed.

Determinants of personal exposure to fine particulate matter in the retired adults – Results of a panel study in two megacities, China

This study aimed to investigate the relationship between outdoor, indoor, and personal PM2.5 exposure in the retired adults and explore the effects of potential determinants in two Chinese megacities. A longitudinal panel study was conducted in Nanjing (NJ) and Beijing (BJ), China, and thirty-three retired non-smoking adults aged 43–86 years were recruited in each city. Repeated measurements of outdoor-indoor-personal PM2.5 concentrations were measured for five consecutive 24-h periods during both heating and non-heating seasons using real-time and gravimetric methods. Time-activity and household characteristics were recorded. Mixed-effects models were applied to analyze the determinants of personal PM2.5 exposure. In total, 558 complete sets of collocated 24-h outdoor-indoor-personal PM2.5 concentrations were collected. The median 24-h personal PM2.5 exposure concentrations ranged from 43 to 79 μg/m3 across cities and seasons, which were significantly greater than their corresponding indoor levels (ranging from 36 to 68 μg/m3, p < 0.001), but significantly lower than outdoor levels (ranging from 43 to 95 μg/m3, p < 0.001). Indoor and outdoor PM2.5 concentrations were the strongest determinants of personal exposures in both cities and seasons, with RM2 ranging from 0.814 to 0.915 for indoor and from 0.698 to 0.844 for outdoor PM2.5 concentrations, respectively. The personal-outdoor regression slopes varied widely among seasons, with a pronounced effect in BJ (NHS: 0.618 ± 0.042; HS: 0.834 ± 0.023). Ventilation status, indoor PM2.5 sources, personal characteristics, and meteorological factors, were also found to influence personal exposure levels. The city and season-specific models developed here are able to account for 89%–93% of the variance in personal PM2.5 exposure. A LOOCV analysis showed an R2 (RMSE) of 0.80–0.90 (0.21–0.36), while a 10-fold CV analysis demonstrated a R2 (RMSE) of 0.83–0.90 (0.20–0.35). By incorporating potentially significant determinants of personal exposure, this modeling approach can improve the accuracy of personal PM2.5 exposure assessment in epidemiologic studies.

Predictors of urinary and blood Metal(loid) concentrations among pregnant women in Northern Puerto Rico

Given the potential adverse health effects related to toxic trace metal exposure and insufficient or excessive levels of essential trace metals in pregnant women and their fetuses, the present study characterizes biomarkers of metal and metalloid exposure at repeated time points during pregnancy among women in Puerto Rico. We recruited 1040 pregnant women from prenatal clinics and collected urine, blood, and questionnaire data on demographics, product use, food consumption, and water usage at up to three visits. All samples were analyzed for 16 metal(loid)s: arsenic (As), barium (Ba), beryllium (Be), cadmium (Cd), cobalt (Co), chromium (Cr), cesium (Cs), copper (Cu), mercury (Hg), manganese (Mn), nickel (Ni), lead (Pb), titanium (Ti), uranium (U), vanadium (V), and zinc (Zn). Urine samples were additionally analyzed for molybdenum (Mo), platinum (Pt), antimony (Sb), tin (Sn), and tungsten (W). Mean concentrations of most metal(loid)s were higher among participants compared to the general US female population. We found weak to moderate correlations for inter-matrix comparisons, and moderate to strong correlations between several metal(loid)s measured within each biological matrix. Blood concentrations of Cu, Zn, Mn, Hg, and Pb were shown to reflect reliable biomarkers of exposure. For other metals, repeated samples are recommended for exposure assessment in epidemiology studies. Predictors of metal(loid) biomarkers included fish and rice consumption (urinary As), fish and canned food (blood Hg), drinking public water (blood Pb), smoking (blood Cd), and iron/folic acid supplement use (urinary Cs, Mo, and Sb). Characterization of metal(loid) biomarker variation over time and between matrices, and identification of important exposure sources, may inform future epidemiology studies and exposure reduction strategies.

A review of exposure assessment methods for epidemiological studies of health effects related to industrially contaminated sites.

this paper is based upon work from COST Action ICSHNet. Health risks related to living close to industrially contaminated sites (ICSs) are a public concern. Toxicology-based risk assessment of single contaminants is the main approach to assess health risks, but epidemiological studies which investigate the relationships between exposure and health directly in the affected population have contributed important evidence. Limitations in exposure assessment have substantially contributed to uncertainty about associations found in epidemiological studies. to examine exposure assessment methods that have been used in epidemiological studies on ICSs and to provide recommendations for improved exposure assessment in epidemiological studies by comparing exposure assessment methods in epidemiological studies and risk assessments. after defining the multi-media framework of exposure related to ICSs, we discussed selected multi-media models applied in Europe. We provided an overview of exposure assessment in 54 epidemiological studies from a systematic review of hazardous waste sites; a systematic review of 41 epidemiological studies on incinerators and 52 additional studies on ICSs and health identified for this review. we identified 10 multi-media models used in Europe primarily for risk assessment. Recent models incorporated estimation of internal biomarker levels. Predictions of the models differ particularly for the routes 'indoor air inhalation' and 'vegetable consumption'. Virtually all of the 54 hazardous waste studies used proximity indicators of exposure, based on municipality or zip code of residence (28 studies) or distance to a contaminated site (25 studies). One study used human biomonitoring. In virtually all epidemiological studies, actual land use was ignored. In the 52 additional studies on contaminated sites, proximity indicators were applied in 39 studies, air pollution dispersion modelling in 6 studies, and human biomonitoring in 9 studies. Exposure assessment in epidemiological studies on incinerators included indicators (presence of source in municipality and distance to the incinerator) and air dispersion modelling. Environmental multi-media modelling methods were not applied in any of the three groups of studies. recommendations for refined exposure assessment in epidemiological studies included the use of more sophisticated exposure metrics instead of simple proximity indicators where feasible, as distance from a source results in misclassification of exposure as it ignores key determinants of environmental fate and transport, source characteristics, land use, and human consumption behaviour. More validation studies using personal exposure or human biomonitoring are needed to assess misclassification of exposure. Exposure assessment should take more advantage of the detailed multi-media exposure assessment procedures developed for risk assessment. The use of indicators can be substantially improved by linking definition of zones of exposure to existing knowledge of extent of dispersion. Studies should incorporate more often land use and individual behaviour.

A Scalable Database of Organ Doses for Common Diagnostic Fluoroscopy Procedures of Children: Procedures of Historical Practice for Use in Radiation Epidemiology Studies.

Assessment of health effects from low-dose radiation exposures in patients undergoing diagnostic imaging is an active area of research. High-quality dosimetry information pertaining to these medical exposures is generally not readily available to clinicians or epidemiologists studying radiation-related health risks. The purpose of this study was to provide methods for organ dose estimation in pediatric patients undergoing four common diagnostic fluoroscopy procedures: the upper gastrointestinal (UGI) series, the lower gastrointestinal (LGI) series, the voiding cystourethrogram (VCUG) and the modified barium swallow (MBS). Abstracted X-ray film data and physician interviews were combined to generate procedure outlines detailing X-ray beam projections, imaged anatomy, length of X-ray exposure, and presence and amount of contrast within imaged anatomy. Monte Carlo radiation transport simulations were completed for each of the four diagnostic fluoroscopy procedures across the 162-member (87 males and 75 females) University of Florida/National Cancer Institute pediatric phantom library, which covers variations in both subject height and weight. Absorbed doses to 28 organs, including the active marrow and bone endosteum, were assigned for all 162 phantoms by procedure. Additionally, we provide dose coefficients (DCs) in a series of supplementary tables. The DCs give organ doses normalized to procedure-specific dose metrics, including: air kerma-area product (µGy/mGy · cm2), air kerma at the reference point (µGy/µGy), number of spot films (SF) (µGy/number of SFs) and total fluoroscopy time (µGy/s). Organs accumulating the highest absorbed doses per procedure were as follows: kidneys between 0.9-25.4 mGy, 1.1-16.6 mGy and 1.1-9.7 mGy for the UGI, LGI and VCUG procedures, respectively, and salivary glands between 0.2-3.7 mGy for the MBS procedure. Average values of detriment-weighted dose, a phantom-specific surrogate for the effective dose based on ICRP Publication 103 tissue-weighting factors, were 0.98 mSv, 1.16 mSv, 0.83 mSv and 0.15 mSv for the UGI, LGI, VCUG and MBS procedures, respectively. Scalable database of organ dose coefficients by patient sex, height and weight, and by procedure exposure time, reference point air kerma, kerma-area product or number of spot films, allows clinicians and researchers to compute organ absorbed doses based on their institution-specific and patient-specific dose metrics. In addition to informing on patient dosimetry, this work has the potential to facilitate exposure assessments in epidemiological studies designed to investigate radiation-related risks.

Developing a predictive model for indoor black carbon for the Denver, CO metropolitan area

PDS 65: Exposure assessment: implications for epidemiology, Exhibition Hall (PDS), Ground floor, August 27, 2019, 1:30 PM - 3:00 PM Background: Though individuals spend ~90% of their time indoors, ambient air pollution data are frequently used in exposure assessment for epidemiologic studies. We sought to understand the relation between indoor and outdoor concentrations of black carbon (BC) and additional housing characteristics associated with indoor BC concentrations among cohort study participants in Denver, CO. Methods: Households from the Healthy Start cohort were selected for monitoring based on diversity in housing type and built environmental characteristics. For one week in spring and summer 2018, households were provided two low-cost air samplers, one for outdoor and one for indoor measurement. Participants completed questionnaires addressing housing specifics such as building type, flooring, and use of heating and cooling systems. BC was measured using transmissometry and weekly mean concentrations were log transformed prior to model fitting. A linear model was fit using all available predictors obtained from surveys as well as outdoor BC concentrations. Model selection was performed using stepwise backwards elimination; model fit was evaluated using AIC. Results: A total of 25 households participated in the study: 11 filters had BC measurements below LOD, and data were available for 39 filters. The average (SD) indoor and outdoor concentrations were 1.1 (0.8) and 1.2 (0.6) μg/m3, respectively. Outdoor BC concentrations were significant predictors of log(indoor BC) in a single-predictor model (β = 0.49, p = 0.02, R2=0.12). The final model included outdoor BC, single family home, tile flooring, and use of electric heater and had an adjusted R2 of 0.5. Conclusion: Outdoor BC and housing characteristics were able to account for ~50% of the variability in indoor BC concentrations measured in Denver, CO homes. In the absence of personal monitoring, household characteristics and time-activity patterns may be used in calibrating ambient air pollution concentrations for personal exposure estimation.