Exposure In Epidemiological Studies Research Articles

Occupational exposure to organic solvents and the risk of developing testicular germ cell tumors (TESTIS study): Effect of combined exposure assessment on risk estimation.

Etiological factors of testicular germ cell tumors (TGCT) remain largely unknown, but a causal role of occupational exposures to solvents has been suggested. Previous studies analyzing these exposures reported discordant results, potentially related to exposure assessment methods. The aim of this study was to investigate the role of occupational exposure to solvents on the risk of developing TGCT among young men. This study examined occupational exposures to solvents and TGCT risk based on the lifetime work histories of 454 cases and 670 controls, aged 18-45 years, of the French national TESTIS case-control study. Solvent exposure was estimated using: (i) exposure assignment by job-exposure matrix (JEM) and (ii) JEM combined with self-reported exposure data from specific questionnaires (SQ) and expert assessment (EA). Odds ratios (OR) and 95% confidence intervals (CI) were estimated using conditional logistic regression models. Both approaches (JEM and JEM+SQ+EA) showed a consistent association between TGCT and trichloroethylene exposure (exposed versus not exposed; JEM=OR 1.80 [95% confidence interval (CI) 1.12-2.90] and JEM+SQ+EA= OR 2.59 (95% CI 1.42-4.72). Both approaches also observed positive associations with ketone esters and fuels & petroleum-based solvents. The results suggest that some organic solvents might be involved in the pathogenesis of TGCT among occupationally exposed men. The combined use of JEM+SQ+EA seemed to limit misclassification by considering individual exposure variability and is, therefore, an appealing approach to assess occupational exposures in epidemiological studies.

Challenges in health risk assessment of multiple chemical exposures in epidemiological studies.

Challenges in health risk assessment of multiple chemical exposures in epidemiological studies.

Untangling the effects of multiple exposures with a common reference group in an epidemiologic study: A practical revisit

When assessing multiple exposures in epidemiologic studies, epidemiologists often use multivariable regression models with main effects only to control for confounding. This method can mask the true effects of individual exposures, potentially leading to wrong conclusions. We revisited a simple, practical, and often overlooked approach to untangle effects of the exposures of interest, in which the combinations of all levels of the exposures of interest are recoded into a single, multicategory variable. One category, usually the absence of all exposures of interest, is selected as the common reference group (CRG). All other categories representing individual and joint exposures are then compared to the CRG using indicator variables in a regression model or in a 2×2 contingency table analysis. Using real data examples, we showed that using the CRG analysis results in estimates of individual and joint effects that are mutually comparable and free of each other’s confounding effects, yielding a clear, accurate, intuitive, and simple summarization of epidemiologic study findings involving multiple exposures of interest.

Predicting Monthly Community-Level Radon Concentrations with Spatial Random Forest in the Northeastern and Midwestern United States.

In 1987, the United States Environmental Protection Agency recommended installing a mitigation system when the indoor concentration of radon, a well-known carcinogenic radioactive gas, is at or above 148 Bq/m3. In response, tens of millions of short-term radon measurements have been conducted in residential buildings over the past three decades either for disclosure or to initially evaluate the need for mitigation. These measurements, however, are currently underutilized to assess population radon exposure in epidemiological studies. Based on two relatively small radon surveys, Lawrence Berkeley National Laboratory developed a state-of-the-art national radon model. However, this model only provides coarse and invariant radon estimations, which limits the ability of epidemiological studies to accurately investigate the health effects of radon, particularly the effects of acute exposure. This study involved obtaining over 2.8 million historical short-term radon measurements from independent laboratories. With the use of these measurements, an innovative spatial random forest (SRF) model was developed based on geological, architectural, socioeconomical, and meteorological predictors. The model was used to estimate monthly community-level radon concentrations for ZIP Code Tabulation Areas (ZCTAs) in the northeastern and midwestern regions of the United States from 2001 to 2020. Via cross-validation, we found that our ZCTA-level predictions were highly correlated with observations. The prediction errors declined quickly as the number of radon measurements in a ZCTA increased. When ≥15 measurements existed, the mean absolute error was 24.6 Bq/m3, or 26.5% of the observed concentrations (R2 = 0.70). Our study demonstrates the potential of the large amount of historical short-term radon measurements that have been obtained to accurately estimate longitudinal ZCTA-level radon exposures at unprecedented levels of resolutions and accuracy.

Maternal hair segments reveal metal(loid) levels over the course of pregnancy: a preliminary study in Southern China.

Characterization of metal(loid) variation during pregnancy and identification of the affecting factors are important for assessing pregnancy exposures in epidemiological studies. In this study, maternal hair was collected in three segments (each 3 cm) from pregnant women in Guangzhou, China. Ten metal(loid)s, including six essential trace metal(loid)s and four toxic trace metal(loid)s, were analyzed to investigate the levels of various metal(loid)s during pregnancy and the factors that influence them. Strong pairwise correlations were observed between manganese (Mn), cobalt (Co), and vanadium (V), between selenium (Se), arsenic (As), and antimony (Sb), and between cadmium (Cd) and lead (Pb). All metal(loid)s except for Se, Mn, and Co showed strong correlations among the three hair segments, and most of the metal(loid)s had good reproducibility, with intraclass correlation coefficients (ICCs) ranging from 0.510 to 0.931, except for As (ICC = 0.334), Mn (ICC = 0.231), and Co (ICC = 0.235). Zn levels decreased, while Sb increased, in maternal hair during pregnancy. Maternal sociodemographic characteristics and dietary intake affected metal(loid) levels in maternal hair. These results provide foundational data for using maternal hair segmental analysis to evaluate exposure variation to metal(loid)s during pregnancy and the potential factors associated with them.

The Use of Urine Biomarker-Creatinine Ratio as the Exposure in Epidemiological Studies Alters the Exposure-Outcome Relationships

The Use of Urine Biomarker-Creatinine Ratio as the Exposure in Epidemiological Studies Alters the Exposure-Outcome Relationships

Impact of occupational pesticide exposure assessment method on risk estimates for prostate cancer, non-Hodgkin’s lymphoma and Parkinson’s disease: results of three meta-analyses

Assessment of occupational pesticide exposure in epidemiological studies of chronic diseases is challenging. Biomonitoring of current pesticide levels might not correlate with past exposure relevant to disease aetiology, and indirect...

Modeling spatial variation of gaseous air pollutants and particulate matters in a Metropolitan area using mobile monitoring data

Geo-statistical models have been applied to assess fine-scale air pollution exposures in epidemiological studies. Many of the models were developed for criteria air pollutants rather than others that have not been regulated (e.g., ultrafine particles, black carbon, and benzene) which may also be harmful to human health. We aim to develop spatial models for regulated and non-regulated air pollutants using 6 algorithms and compare their prediction performances. A mobile platform with fast-response monitors was used to measure gaseous air pollutants (nitrogen dioxides, carbon monoxide, sulfur dioxides, ozone, benzene, toluene, methanol) and particulate matters (black carbon, surface area, count- and volume-concentrations of ultrafine particles) in Beijing, China for 30 days from July to October 2008. Mobile monitoring data for model building were spatially aggregated into 130 road segments of approximately 600-m interval on the sampling routes after temporal adjustment of background concentrations. The best models for the air pollutants were dominated by traffic variables, which explained more than 60% of the spatial variations (range: 0.61 for methanol to 0.88 for ozone) based on the highest cross-validation R2 and the lowest root mean square error among different algorithms. Amongst the 6 algorithms, the spatial models using partial least squares regression (PLS, a dimension reduction algorithm) and random forest (RF, a machine learning algorithm) algorithms outperformed the models with other algorithms. Exposure predictions from the best models varied substantially with distinct spatial patterns between the air pollutants. Predictions with multiple modeling algorithms were moderately correlated with each other for the same pollutant at the fine-scale grids across the city. Exposure models, especially based on PLS and RF algorithms, captured the spatial variation of short-term average concentrations, had adequate predictive validity, and could be applied to assess toxic air pollutant exposures in human health studies.

Developing air pollution concentration fields for health studies using multiple methods: Cross-comparison and evaluation

Past air pollution epidemiological studies have used a wide range of methods to develop concentration fields for health analyses. The fields developed differ considerably among these methods. The reasons for these differences and comparisons of their strengths, as well as the limitations for estimating exposures, remains under-investigated. Here, we applied nine methods to develop fields of eight pollutants (carbon monoxide (CO), nitrogen dioxide (NO2), sulfur dioxide (SO2), ozone (O3), fine particulate matter (PM2.5), and three speciated PM2.5 constituents including elemental carbon (EC), organic carbon (OC), and sulfate (SO4)) for the metropolitan Atlanta region for five years. The nine methods are Central Monitor (CM), Site Average (SA), Inverse Distance Weighting (IDW), Kriging (KRIG), Land Use Regression (LUR), satellite Aerosol Optical Depth (AOD), CMAQ model, CMAQ with kriging adjustment (CMAQ-KRIG), and CMAQ based data fusion (CMAQ-DF). Additionally, we applied an increasingly popular method, Random Forest (RF), and compared its results for NO2 and PM2.5 with other methods. For statistical evaluation, we focused our discussion on the temporal coefficient of determination, although other metrics are also calculated. Raw output from the CMAQ model contains modeling biases and errors, which are partially mitigated by fusing observational data in the CMAQ-KRIG and CMAQ-DF methods. Based on analyses that simulated model responses to more limited input data, the RF model is more robust and outperforms LUR for PM2.5. These results suggest RF may have potential in air pollution health studies, especially when limited measurement data are available. The RF method has several important weaknesses, including a relatively poor performance for NO2, diagnostic challenges, and computational intensiveness. The results of this study will help to improve our understanding of the strengths and weaknesses of different methods for estimating air pollutant exposures in epidemiological studies.

Concurrent urinary organophosphate metabolites and acetylcholinesterase activity in Ecuadorian adolescents

BackgroundOrganophosphates are insecticides that inhibit the enzymatic activity of acetylcholinesterase (AChE). Because of this, AChE is considered a physiological marker of organophosphate exposure in agricultural settings. However, limited research exists on the associations between urinary organophosphate metabolites and AChE activity in children. MethodsThis study included 526 participants from 2 exams (April and July–October 2016) of ages 12–17 years living in agricultural communities in Ecuador. AChE activity was measured at both examinations, and organophosphate metabolites, including para-nitrophenol (PNP), 3,5,6-trichloro-2-pyridinol (TCPy), and malathion dicarboxylic acid (MDA) were measured in urine collected in July–October. We used generalized estimating equation generalized linear model (GEEGLM), adjusting for hemoglobin, creatinine, and other demographic and anthropometric covariates, to estimate associations of urinary metabolite concentrations with AChE activity (July–October) and AChE% change between April and July–October. ResultsThe mean (SD) of AChE and AChE% change (April vs July–October) were 3.67 U/mL (0.54) and −2.5% (15.4%), respectively. AChE activity was inversely associated with PNP concentration, whereas AChE% change was inversely associated with PNP and MDA. There was evidence of a threshold: difference was only significant above the 80th percentile of PNP concentration (AChE difference per SD increase of metabolite = −0.12 U/mL [95%CI: 0.20, −0.04]). Likewise, associations with AChE% change were significant only above the 80th percentile of TCPy (AChE % change per SD increase of metabolite = −1.38% [95%CI: 2.43%, −0.32%]) and PNP -2.47% [95%CI: 4.45%, −0.50%]). PNP concentration at ≥80th percentile was associated with elevated ORs for low AChE activity of 2.9 (95% CI: 1.5, 5.7) and for AChE inhibition of ≤ -10% of 3.7 (95% CI: 1.4, 9.8). ConclusionsUrinary organophosphate metabolites, including PNP, TCPy and MDA, particularly at concentrations above the 80th percentile, were associated with lower AChE activity among adolescents. These findings bring attention to the value of using multiple constructs of pesticide exposure in epidemiologic studies.

Seasonal Variation of Radon Concentrations in Russian Residential High-Rise Buildings

Assessment of the annual radon concentration is often required in indoor radon surveys of territories and individual dwellings for comparison with reference levels, studying factors affecting radon accumulation in dwellings, assessment of exposure in epidemiological studies, etc. The indoor radon surveys were carried out in multistorey buildings in eight Russian cities using solid state nuclear track detectors with an exposure period of three months. For these surveys, the estimation of annual indoor radon concentration was required to compare radon levels in buildings of high- and low-energy-efficiency classes located in different cities. To develop approaches to seasonal normalization in high-rise buildings, long-term one-hour radon concentration series obtained applying radon-monitors in 20 flats were analyzed. The dependency of indoor radon concentration on the indoor–outdoor temperature difference was studied taking into account the known natural, technogenic and anthropogenic factors affecting radon levels. The developed model of seasonal variations in multistorey buildings includes winter, summer, and demi-season periods, which differ both in ventilation intensity and dependency of radon concentration on the temperature difference. The developed model allows to estimate annual radon concentration taking into account the actual distribution of outdoor temperatures during the exposure of the track detectors.

Time-series analysis of daily ambient temperature and emergency department visits in five US cities with a comparison of exposure metrics derived from 1-km meteorology products

BackgroundAmbient temperature observations from single monitoring stations (usually located at the major international airport serving a city) are routinely used to estimate heat exposures in epidemiologic studies. This method of exposure assessment does not account for potential spatial variability in ambient temperature. In environmental health research, there is increasing interest in utilizing spatially-resolved exposure estimates to minimize exposure measurement error.MethodsWe conducted time-series analyses to investigate short-term associations between daily temperature metrics and emergency department (ED) visits for well-established heat-related morbidities in five US cities that represent different climatic regions: Atlanta, Los Angeles, Phoenix, Salt Lake City, and San Francisco. In addition to airport monitoring stations, we derived several exposure estimates for each city using a national meteorology data product (Daymet) available at 1 km spatial resolution.ResultsAcross cities, we found positive associations between same-day temperature (maximum or minimum) and ED visits for heat-sensitive outcomes, including acute renal injury and fluid and electrolyte imbalance. We also found that exposure assessment methods accounting for spatial variability in temperature and at-risk population size often resulted in stronger relative risk estimates compared to the use of observations at airports. This pattern was most apparent when examining daily minimum temperature and in cities where the major airport is located further away from the urban center.ConclusionEpidemiologic studies based on single monitoring stations may underestimate the effect of temperature on morbidity when the station is less representative of the exposure of the at-risk population.

Essential concepts for interpreting the dose-response of low-level arsenic exposure in epidemiological studies

Scientifically robust selections of epidemiological studies and assessments of the dose-response of inorganic arsenic in the low-dose range must consider key issues specific to arsenic in order to reduce risk of bias. The abundance of toxicological, mechanistic, and epidemiological evidence on arsenic enables a nuanced assessment of risk of bias in epidemiological studies of low-level arsenic, as opposed to a generic evaluation based only on standard principles. Important concepts in this context include 1) arsenic metabolism and mode of action for toxicity and carcinogenicity; 2) effects of confounding factors such as diet, health status including nutritional deficiencies, use of tobacco and other substances, and body composition; 3) strengths and limitations of various metrics for assessing relevant exposures consistent with the mode of action; and 4) the potential for bias in the positive direction for the observed dose-response relationship as exposure increases in the low-dose range. As an example, evaluation of a recent dose-response modeling using eight epidemiological studies of inorganic arsenic and bladder cancer demonstrated that the pooled risk estimate was markedly affected by the single study that was ranked as having a high risk of bias, based on the above factors. The other seven studies were also affected by these factors to varying, albeit lesser, degrees that can influence the apparent dose-response in the low-dose range (i.e., drinking water concentration of 65 µg/L or dose of approximately ≤1 µg/kg-day). These issues are relevant considerations for assessing health risks of oral exposures to inorganic arsenic in the U.S. population, and setting evidence-based regulatory limits to protect human health.

Furthering a partnership: Air quality modeling and improving public health

ABSTRACT Air pollution is one of the top five risk factors for population health globally. In recent years, advances in air pollution data and modeling have occurred simultaneously with advances in data and methods available for health studies. To realize the potential of such advances, the air quality modeling and public health communities should continue to strengthen their engagements and build effective interdisciplinary teams. These partnerships recognize the tight coupling between air quality and health data and methods and the value of expertise from multiple fields to ensure that this information is applied appropriately with a deep understanding of its capabilities and limitations. Building effective multidisciplinary teams takes a sustained commitment to engage with partners with different expertise to establish working partnerships and collaborations to better address public exposures to air pollution. Effective partnerships enable better targeting of research resources to answer important questions and provide essential information to protect public health. Implications: Air quality models are an effective tool that can be used to estimate air pollution exposure in epidemiologic studies and risk assessments. Working together in collaborative multidisciplinary teams will lead to greater advancements in understanding of air pollution impacts and in useful information informing actions to improve public health.

What Uses of Connected Mobile Microsensors of Air Pollution in the General and Vulnerable Populations?

This Research article summarizes the potential uses of mobile microsensors by the public and some vulnerable populations. Firstly, use of such sensors could help promote individual behaviors aimed at limiting exposures and the generation of air pollutants. However, access to the best information, if taken alone, does not necessarily translate into a change in individual behavior. Knowledge can promote changes in behavior when there are measures available that can be implemented and if the benefits of the actions can be verified in the short run and if they are combined with coaching by scientific or medical experts. Secondly, the use of mobile sensors could promote collective behavior and influence public policies that promote air quality. Nowadays, information intended for the public is only provided through media outlets during periods of elevated air pollution and consists of restrictive advice that are not specifically relevant for at-risk groups. Mobile microsensor users could establish forums and share information regarding air quality. Thirdly, the use of mobile microsensors is likely to provide more accurate estimations of exposure in epidemiological studies and increase the density of the air quality monitoring network.

A practical framework for predicting residential indoor PM2.5 concentration using land-use regression and machine learning methods

People typically spend most of their time indoors. It is of importance to establish prediction models to estimate PM2.5 concentration in indoor environments (e.g., residential households) to allow accurate assessments of exposure in epidemiological studies. This study aimed to develop models to predict PM2.5 concentration in residential households. PM2.5 concentration and related parameters (e.g., basic information about the households and ventilation settings) were collected in 116 households during the winter and summer seasons in Hong Kong. Outdoor PM2.5 concentration at households was estimated using a land-use regression model. The random forest machine learning algorithm was then applied to develop indoor PM2.5 prediction models. The results show that the random forest model achieved a promising predictive accuracy, with R2 and cross-validation R2 values of 0.93 and 0.65, respectively. Outdoor PM2.5 concentration was the most important predictor variable, followed in descending order by the household marked number, outdoor temperature, outdoor relative humidity, average household area and air conditioning. The external validation result using an independent dataset confirmed the potential application of the random forest model, with an R2 value of 0.47. Overall, this study shows the value of a combined land-use regression and machine learning approach in establishing indoor PM2.5 prediction models that provide a relatively accurate assessment of exposure for use in epidemiological studies.

Environmental and occupational pesticide exposure and human sperm parameters: An updated review

Background: In recent decades, global sperm counts have declined, and this has coincided with a proliferation of chemicals with endocrine-disrupting properties to which humans are frequently exposed. Previous reviews of studies published between 1991 through 2013 have reported several associations between environmental and occupational pesticide exposure and reduced human sperm quality, specifically adverse relationships between exposure and sperm concentration. Objectives: This review critically evaluated the current body of evidence associating sperm quality with contemporary pesticide exposure in epidemiological studies. Methods: We conducted a systematic literature review using the Navigation Guide systematic review framework. PubMed, Scopus, and Web of Science databases were searched for all English-language articles published since September 2012. Only original observational studies that assessed human sperm quality parameters and individual-level pesticide exposure data were included. Discussion: Eleven studies assessing environmental or occupational pesticide exposure and sperm parameters were included. Nine (82%) of these studies found at least one significant association with reduced sperm quality, specifically sperm concentration, motility, morphology, and DNA integrity. The quality of evidence was determined to be “moderate” following Navigation Guide protocol, but consistent negative effect estimates and alignment with the results of previous reviews led to the determination that there is sufficient evidence that pesticide exposure adversely affects sperm quality. Conclusion: Taken together with the results of previous reviews, which found significant negative associations between pesticide exposure and sperm quality in 13 of 20 studies published between 1991 and 2008, and in 14 of 17 studies published between 2008 and 2012, this review provides strong evidence associating pesticide exposure with reduced sperm quality. Actions should be taken to reduce global pesticide exposure.

Assessment of multiple air pollutant exposures using a mobile monitoring approach: a comparison of spatial modeling with multiple regression algorithms

Background/Aim: Geo-statistical models have been widely applied to assess fine-scale air pollution exposures in epidemiological studies, most of which were developed for criteria pollutants rather than other toxic air pollutants that may also be harmful for human health. Furthermore, the predictive ability of different algorithms in air pollution models have rarely been compared. We aim to develop short-term spatial models for 11 air exposure metrics and compare their performances using 6 modeling algorithms. Methods: We used a mobile laboratory with fast-response monitors to measure multiple gaseous pollutants (nitrogen dioxide, carbon monoxide, sulfur dioxide, ozone, benzene, toluene, methanol) and PM species (black carbon, surface area, and count- and volume-concentrations of ultrafine particles) in the Beijing metropolitan area. Data were collected from 130 repeated short-term monitoring locations for up to 31 days between July and September during the 2008 summer Olympics period and were calibrated with a central fixed site. We developed 6 spatial models for each pollutant using linear regression, dimension reduction, non-linear regression (NLR) and machine learning (ML) algorithms and included extensive geographic variables. Best models were selected using ten-fold cross-validation (CV). Results: The best models based on the largest CV R2 explained more than 60% of the variation for all the exposure metrics (range: 0.61 for methanol to 0.88 for ozone). Among the algorithms, random forest (RF, a ML algorithm) and the generalized additive model (GAM, a NLR algorithm) outperformed the other approaches for most of the pollutants (6 pollutants for RF and 3 for GAM). Incorporating a kriging model for model residuals improved the CV R2 by 3%~39%. Conclusions: Exposure models, especially based on ML and NLR algorithms, captured spatial variability of short-term average concentrations, had adequate predictive validity, and could be successfully applied to existing human health studies carried out during the Beijing Olympics period.

Effects of cooking methods on total isothiocyanate yield from cruciferous vegetables.

Cruciferous vegetables are primary sources of dietary isothiocyanates (ITCs), a group of phytochemicals showing promising cancer‐chemopreventive activities in multiple cancer models. However, no study has thoroughly examined how cooking affects the yields of ITCs from cruciferous vegetables. In this study, a high‐performance liquid chromatography (HPLC)‐based cyclocondensation assay was performed to examine the ITC yields from four major cruciferous vegetables (broccoli, cabbage, cauliflower, and kale) under six cooking conditions (stir‐frying, steaming, microwaving, boiling, stewing, and chip‐baking for kale only) and measured the level of ITCs under the raw condition for a comprehensive list of cruciferous vegetables and ITC‐containing condiments. A wide range of ITC yields was found across vegetables and condiments. Cooking significantly altered the ITC yields, showing an averagely four‐fold increase by lightly cooking (stir‐frying, steaming, and microwaving) and a 58% decrease by heavily cooking (boiling, stewing, and chip‐baking). These findings will provide the evidence‐based cooking guidance on cruciferous vegetable consumption and help better estimate dietary ITC exposure in epidemiologic studies.

Biases Arising from the Use of Ambient Measurements to Represent Personal Exposure in Evaluating Inflammatory Responses to Fine Particulate Matter: Evidence from a Panel Study in Beijing, China

Ambient particulate matter (PM) is often used as a proxy of personal exposure in epidemiological studies of PM-induced health effects, yet whether this proxy biases the estimates of health effects ...