Environmental Epidemiological Studies Research Articles

Spatial-temporal prediction of ambient nitrogen dioxide and ozone levels over Italy using a Random Forest model for population exposure assessment

We developed an integrated approach coupling a chemical transport model (CTM) with machine learning (ML) techniques to produce high spatial resolution NO2 and O3 daily concentration fields over Italy. Three years (2013–2015) simulations, at a spatial resolution of 5 km, performed by the Flexible Air quality Regional Model (FARM) were used as predictors, together with other spatial-temporal data, such as population, land-use, surface greenness and road networks, by a ML Random Forest (ML-RF) algorithm to produce daily concentrations at higher resolution (1 km) over the national territory. The evaluation of the adopted integrated approach was based on NO2 and O3 observations available from 530 and 293 monitoring stations across Italy, respectively. A good performance for NO2 and excellent results for O3 were obtained from the application of the CTM; as for NO2, the levels at urban traffic stations were not captured by the simulations due to the adopted horizontal resolution and related emissions uncertainties. Performance improvements were achieved with ML-RF predictions, reducing NO2 underestimation (near zero fractional bias results) and better capturing spatial contrasts. The results obtained in this work were used to support the national exposure assessment and environmental epidemiology studies planned in the BEEP (Big data in Environmental and occupational Epidemiology) project and confirm the potential of machine learning methods to adequately predict air pollutant levels at high spatial and temporal resolutions.

Retrospective Modeling of NO2 and PM10 Concentrations over the Lyon Metropolitan Area (France), 1990–2010—Performance Evaluation, Exposure Assessment and Correlation between Pollutants

Numerous cancers develop years after subjects have been exposed to chemical compounds. Thus, environmental epidemiological studies need to accurately reconstruct exposures over long periods. To estimate exposure to NO2 and PM10 concentrations, we modelled ground-level air concentrations, at very fine temporal (1 h) and spatial (10 m) resolutions, over a large European metropolitan area and at subject’s address of a French national cohort, for five different years (1990, 1995, 2000, 2005 and 2010). Model performances were assessed by comparing the annual modelled concentration against monitoring station measurements. As input data, we used background concentrations from a large-scale dispersion model. The relevance of our approach was assessed by comparing results in 2010, with a modelling using monitoring values as background data. The comparison with measurement data showed good performance of the model for the majority of the period, with a performance declined in 1990. Concentrations at the subject’s residence decreased by 45% for PM10 and 38% for NO2. The proportion of subjects exposed above the WHO recommendations declined from 100% to 50% for PM10 and from 79% to 16% for NO2. The results of this study would provide a reference for future models to assess chronic exposures to PM10 and NO2 on a larger scale.

Spatial Heterogeneity in Positional Errors: A Comparison of Two Residential Geocoding Efforts in the Agricultural Health Study.

Geocoding is a powerful tool for environmental exposure assessments that rely on spatial databases. Geocoding processes, locators, and reference datasets have improved over time; however, improvements have not been well-characterized. Enrollment addresses for the Agricultural Health Study, a cohort of pesticide applicators and their spouses in Iowa (IA) and North Carolina (NC), were geocoded in 2012–2016 and then again in 2019. We calculated distances between geocodes in the two periods. For a subset, we computed positional errors using “gold standard” rooftop coordinates (IA; N = 3566) or Global Positioning Systems (GPS) (IA and NC; N = 1258) and compared errors between periods. We used linear regression to model the change in positional error between time periods (improvement) by rural status and population density, and we used spatial relative risk functions to identify areas with significant improvement. Median improvement between time periods in IA was 41 m (interquartile range, IQR: −2 to 168) and 9 m (IQR: −80 to 133) based on rooftop coordinates and GPS, respectively. Median improvement in NC was 42 m (IQR: −1 to 109 m) based on GPS. Positional error was greater in rural and low-density areas compared to in towns and more densely populated areas. Areas of significant improvement in accuracy were identified and mapped across both states. Our findings underscore the importance of evaluating determinants and spatial distributions of errors in geocodes used in environmental epidemiology studies.

A systematic review on lagged associations in climate-health studies.

Lagged associations in climate-health studies have already been ubiquitously acknowledged in recent years. Despite extensive time-series models having proposed accounting for lags, few studies have addressed the question of maximum-lag specification, which could induce considerable deviations of effect estimates. We searched the PubMed and Scopus electronic databases for existing climate-health literature in the English language with a time-series or case-crossover study design published during 2000-2019 to summarize the statistical methodologies and reported lags of associations between climate variables and 14 common causes of morbidity and mortality. We also aggregated the results of the included studies by country and climate zone. The associations between infectious-disease outcomes and temperatures were found to be lagged for ∼1-2 weeks for influenza, 3-6 weeks for diarrhoea, 7-12 weeks for malaria and 6-16 weeks for dengue fever. Meanwhile, the associations between both cardiovascular and respiratory diseases and hot temperatures lasted for <5 days, whereas the associations between cardiovascular diseases and cold temperatures were observed to be 10-20 days. In addition, rainfall showed a 4- to 10-week lagged association with infectious diarrheal diseases, whereas the association could be further delayed to 8-12 weeks for vector-borne diseases. Our findings indicated some general patterns for possible lagged associations between some common health outcomes and climatic exposures, and suggested a necessity for a biologically plausible and reasonable definition of the effect lag in the modelling practices for future environmental epidemiological studies.

Novel insights into cytochrome P450 enzyme and solute carrier families in cadmium-induced liver injury of pigs

Cadmium (Cd) is a typical pollutant and carcinogen in environment. Exposure assessment of contaminants is an important component of occupational and environmental epidemiological studies. Early studies of Cd have focused on aquatic animals, chickens and rats. However, toxicological evaluation of Cd in pigs has not been reported. Therefore, twelve pigs were randomly divided into two groups (n = 6): the control group and the Cd group (Cd content: 15 ± 0.242 mg/kg feed) in this study, the experimental period was 30 d, and the toxic effects of Cd on the liver of weanling piglets were examined by antioxidant function, liver function, Cd content, histological examination and transcriptomics. The results showed that the changes of antioxidant function, liver function and Cd content were significant in the liver. Transcriptional profiling results showed that 399 differentially expressed genes (DEGs) were significantly up-regulated while 369 DEGs were remarkably down-regulated in Cd group, and which were concentrated in three ontologies: molecular function, cellular component and biological processes. Interestingly, significant changes in some genes of the cytochrome P450 enzyme (CYP450) and solute carrier (SLC) families have been observed and were consistent with qRT-PCR results. In conclusion, Cd could cause liver injury in weanling piglets and change the transcriptomic characteristics of liver. CYP450 and SLC families play an indispensable role in Cd-mediated hepatotoxicity. Importantly, changes in mRNA levels of CYP2B22, CYP7A1, CYP8B1, SLC26A8, SLC11A1, SLC27A2 and SLC22A7 induced by Cd have been reported for the first time. Our findings will provide a new insight for better assessing the mechanism of Cd toxicity to the liver.

Acute Effects of Personal Ozone Exposure on Biomarkers of Inflammation, Oxidative Stress, and Mitochondrial Oxidative Damage - Shanghai Municipality, China, May-October 2016.

What is already known on this topic?It remains inconclusive whether short-term ozone exposure can cause an inflammatory response and oxidative damage in the circulatory system, particularly at low concentrations.What is added by this report?This study made an accurate exposure assessment by conducting personal ozone monitoring, thus minimizing the exposure misclassification commonly found in previous environmental epidemiological studies. Our study found that even short-term exposure to low concentrations of ozone was associated with inflammation, lipid peroxidation, and mitochondrial oxidative damage.What are the implications for public health practice?Short-term exposure to low concentrations of ozone can still lead to subclinical cardiovascular effects, suggesting the current air quality standards for ozone need to be further tightened in China.

Methodological issues in descriptive environmental epidemiology. The example of study Sentieri.

Background:Descriptive epidemiology identifies associations between environmental exposures and health effects that require results from methodologically stronger studies before causation can be considered.Objective:To critically review the methodology and results of Sentieri, a descripitive study on residence in areas with one or more industrial source of pollution.Methods:We systematically reviewed the literature quoted by Sentieri for the selection of health effects of nine types of pollution sources of a-priori interest. We also reviewed and meta-analyzed the results of the first report of Sentieri, that analyzed mortality in 44 polluted sites (PS), and 17 causes of deaths during 1995-2002.Results:Among 159 study results quoted by Sentieri, 23.9 % were supportive of an association between residence near a pollution source and a health effect, 30.2 % were partially supportive, 10.7 % were not supportive, and 35.2 % were not relevant. Among 653 standardized mortality ratios for associations between PS-specific pollution sources and causes of death, 14.4% were significantly above 1.02, and 9.0% were significantly below 0.98. Among 48 meta-analysis, seven were significantly above 1.0, including five on exposure to asbestos.Conclusions:Sentieri exemplifies the limitations of descriptive environmental epidemiology studies, in which most hypotheses have limited prior support, most results do not show associations, data on potential confounders and other sources of bias are not available. Such studies tend to replicate well-known associations and occasionally can identify critical situations requiring more investigation, but cannot be used to infer causality either in general or in specific circumstances.

Assessing risk of bias in human environmental epidemiology studies using three tools: different conclusions from different tools

BackgroundSystematic reviews are increasingly prevalent in environmental health due to their ability to synthesize evidence while reducing bias. Different systematic review methods have been developed by the US National Toxicology Program’s Office of Health Assessment and Translation (OHAT), the US Environmental Protection Agency’s (EPA) Integrated Risk Information System (IRIS), and by the US EPA under the Toxic Substances Control Act (TSCA), including the approach to assess risk of bias (ROB), one of the most vital steps which is used to evaluate internal validity of the studies. Our objective was to compare the performance of three tools (OHAT, IRIS, TSCA) in assessing ROB.MethodsWe selected a systematic review on polybrominated diphenyl ethers and intelligence quotient and/or attention deficit hyperactivity disorder because it had been endorsed by the National Academy of Sciences. Two reviewers followed verbatim instructions from the tools and independently applied each tool to assess ROB in 15 studies previously identified. We documented the time to apply each tool and the impact the ROB ratings for each tool had on the final rating of the quality of the overall body of evidence.ResultsThe time to complete the ROB assessments varied widely (mean = 20, 32, and 40 min per study for the OHAT, IRIS, and TSCA tools, respectively). All studies were rated overall “low” or “uninformative” using IRIS, due to “deficient” or “critically deficient” ratings in one or two domains. Similarly, all studies were rated “unacceptable” using the TSCA tool because of one “unacceptable” rating in a metric related to statistical power. Approximately half of the studies had “low” or “probably low ROB” ratings across all domains with the OHAT and Navigation Guide tools.ConclusionsTools that use overall ROB or study quality ratings, such as IRIS and TSCA, may reduce the available evidence to assess the harms of environmental exposures by erroneously excluding studies, which leads to inaccurate conclusions about the quality of the body of evidence. We recommend using ROB tools that circumvents these issues, such as OHAT and Navigation Guide.Systematic review registrationThis review has not been registered as it is not a systematic review.

The potential application of transcriptomics and methylomics in environmental epidemiological studies for chemical assessments

'Omics technologies can provide comprehensive information on molecular changes in cells and tissues in a cost-effective and efficient manner, and these technologies have the potential to improve chemical assessments through informing hazard identification and informing dose response. There are a growing number of environmental epidemiological studies that have examined chemical exposures and applied ‘omics analyses with the cohort’s biological samples. These environmental epidemiological studies can take the advantage of high-throughput ‘omics technology with classical epidemiology to examine associations with molecular responses. Therefore, this symposium presentation will primarily focus on the broad applications and potential of transcriptomic and methylomic data from environmental epidemiological studies to inform human mechanistic evidence for chemical assessments. This presentation will delve into a novel and complementary case study describing the integration of transcriptomic and methylomic data from environmental epidemiological studies examining chemical exposures. The altered expression of a single gene or methylated region may or may not be biologically relevant, yet a complementary examination of altered genes and regions mapping onto perturbed pathways can contribute to more informed biological plausibility. The presentation will discuss how patterns of DNA methylation and gene expression can complement each other in identifying molecular effects through differential enrichment of biological pathways. Analytical methods will also be discussed on the following: a) identifying differentially-expressed genes; b) using appropriate cellular deconvolution methods to derive differential methylation regions; c) performing gene-set enrichment analysis (GSEA) on the differential genes derived from transcriptomic and methylomic analyses; and d) benchmark dose modeling to characterize the dose ranges responsible for driving the enrichment of biologically-relevant gene expression changes.

Statistical challenges in the analysis of blood-derived DNA methylation data from environmental epidemiology studies

Title: Statistical challenges in the analysis of blood-derived DNA methylation data from environmental epidemiology studiesAbstract: One of the foremost challenges in the analysis and interpretation of epigenomic data is the issue of cellular heterogeneity and its potential confounding effects on studies that aim to understand the relationship between DNA methylation and some phenotype or exposure. This is especially true of whole blood, which represents one of the most commonly used tissue types for environmental epigenomic studies due to its relatively easy accessibility, and because of a heightened interest in the immonomethylomic effects of environmental insults. Whole blood is a heterogenous and complex mixture of many different cell types each with distinct functions and roles. The cellular landscape of whole blood may be sensitive to certain environmental insults and such insults may impact patterns of DNA methylation in a cell-specific manner. Thus, in addition to the well-recognized potential for confounding, environmental exposures may elicit cell-specific effects, the detection of which represents a major challenge in the analysis whole-blood methylation signatures. The identification of cell-specific effects of methylation based on bulk-tissue methylation signatures is an ongoing area of research with important implications for environmental epidemiology studies of DNA methylation. This talk will orient the audience to the above challenges and will include a discussion of recent work that aims to address these challenges. Insight into the application of such methods for environmental epidemiology studies of DNA methylation will be given.

Evaluation of confounding by socioeconomic status (SES) on the association between PM2.5 and mortality in the U.S Medicare population

Background/Aim:Socioeconomic status (SES) is a complex construct that is influenced by many individual- and area-level factors encompassing material, human, and social capital. SES has been suggested to be an important confounder to consider in environmental epidemiological studies. However, many previous studies were limited in their ability to adequately capture the complexity of SES and likely have resulted in residual confounding. The objective of our study was to assess the potential for utilizing publicly available SES data to more accurately describe confounding by SES on the association between PM2.5 and mortality in the Medicare population in the U.S. Methods: Monthly mortality counts and PM2.5 concentrations in the contiguous U.S. for 2000-2012 were obtained at the ZIP code level. We also searched for publicly available SES data beyond what is typically used (such as income, education, and race). The base model was only adjusted for age and sex, while the multivariable models were adjusted for multiple SES indicators by controlling for individual SES variables as well as using Principal Component Analysis (PCA) approach. Results:29 publicly available SES data were collected from the Census Bureau and the Internal Revenue Service (IRS). Some examples of the SES indicators used in our study include income inequality (Gini index), non-salary wealth, % occupation in management/business/science, % moved from abroad, and % household with &gt; 1 person/room. We found that the increased risks of mortality associated with PM2.5 in our Medicare population were slightly confounded by SES with different methods of controlling for SES resulting in different magnitudes of confounding effect. Conclusion:These results warrant further investigation examining the best approach for using multiple SES data to accurately capture the confounding effect of SES. In addition, our study demonstrated the possibility of utilizing publicly available data to examine the complex confounding effect of SES in environmental epidemiological studies.

Inter-disciplinary assessment of greenspace health benefits applying the ecosystem services framework to urban heat reduction in Boston, MA

Background Vegetation is an indispensable component in cities for mitigation of negative health effects of urban exposures such as polluted air and urban heat islands. While there is epidemiology evidence showing urban vegetation exposure is associated with health benefits, knowledge on the underlying causal pathways is limited. We leveraged the ecosystem services framework (ESF) to bridge inter-disciplinary gaps between ecology and public health, and linked evapotranspiration produced by urban vegetation to heat-related exposure risks in Boston, MA.Methods ESF conceptualizes the benefits of nature as an outcome of supply and demand of services provided by ecosystems (ESS). We modeled hourly latent heat flux of tree canopy in Boston as the supply of heat reduction provided by ecosystems at 30 meter resolution. We mapped demand by calculating a risk score as a function of population, ambient temperature, and population vulnerability across multiple sociodemographic dimensions. Comparing the two products revealed to what extent the heat-reducing capacity of urban vegetation meets demand for this service by Boston residents.Results Our results generated fine scale metrics to quantify urban vegetation supply and demand in the context of heat exposure, and revealed spatial patterns of mismatch across Boston. Many areas where risk of heat-related health impacts was high received relatively low protection from extreme heat from surrounding trees, and these hotspots appeared mainly around large affordable housing complexes.Conclusions By applying an interdisciplinary ecosystem services framework, we generated a fine scale greenspace metric that reflects its direct impact on heat reduction. This novel metric can be used in environmental epidemiology studies across a range of heat related health outcomes, improving greenspace exposure assessment, and potentially advance analyses towards a causal pathway between urban vegetation and health.

Benefits and challenges of combining data from the CHEAR consortia

Background/Aim: The constraints of sample size in environmental epidemiology studies can limit the detection of meaningful associations. Additionally, study-specific biases may arise from differences in data collection methods, exposure and outcome definitions, and confounder selection. Pooling individual level data across studies may reduce implicit study bias. Methods: Using the Children’s Health Exposure Analysis Resource (CHEAR), we assessed the benefits and potential challenges of pooling data by combining study participants (206 children ages 4-16 years) from the School Inner-City Asthma Intervention Study and the Denver Asthma Panel Study who submitted urine samples to CHEAR laboratories for phthalate metabolite measurement. Composite Asthma Severity Index (CASI) score was used to assess the relationship of phthalates, singularly and as a mixture, on asthma severity in each cohort and combined. All models controlled for age, sex, race, income, body mass index percentile, and creatinine with a random effect for study when appropriate to account for intra-study correlation. Results: For individual log2 transformed phthalate analyses conducted for each study alone, no significant associations with CASI score were observed after applying a false discovery rate (FDR) p-value. However, when the studies were combined, four phthalate metabolites had significant associations with CASI after FDR adjustment. One challenge of pooling data was the need to harmonize covariates - for example, income definition: yearly income compared to last month’s income. An additional challenge was the range in exposure distributions across cohorts. We utilized CHEAR quality control samples to evaluate potential systematic lab differences versus study-specific differences. Conclusion: Although pooling data poses several challenges, we were able to overcome some of them and identified associations in the pooled data that were null in the individual cohorts. The increased sample size may be more suitable for studying potential effect modifiers, such as sex, that could not be found in one cohort alone.

A Hierarchical Model for Estimating Exposure-Response Curves from Multiple Studies of Household Air Pollution

Estimating flexible exposure-response curves is critical for quantifying the impacts of environmental exposures on health. In intervention trials targeting household air pollution, the limited range of concentrations and small sample sizes of individual studies are important factors that may be limiting their statistical power. Pooling data from multiple studies provides an opportunity to increase power for estimating the exposure-response relationship.We present hierarchical approaches to modeling exposure concentrations and combining data from multiple studies to estimate a common exposure-response curve. The models are designed to accommodate features relevant to many environmental epidemiology studies: exposure measurements and outcome ascertainments that are irregularly spaced across time, clustered by individual and groups of individuals, and impacted by seasonal trends. The exposure concentration model additionally incorporates shrinkage to reduce error from high-variance observations. The exposure-response curve model provides a flexible, semi-parametric estimate of the joint exposure-response relationship across multiple studies from different times and contexts.We demonstrate this modeling approach using data from three studies of cookstoves and acute lower respiratory infections in children in Nepal.

A Systematic Review on Lagged Association in Environmental Studies

The delayed association in environment-health studies has been acknowledged in recent years. However, the misspecification of lag dimension in time-series models, particularly distributed lag non-linear models (DLNM), would induce considerable deviation of effect estimate. This study reviewed the existing climate-health English literature with time-series and case-crossover design published during 2000-2019 to summarize the statistical methodologies used and the reported delays of association between meteorological variables and 14 common causes of morbidity and mortality. Generalized linear or additive model was the most widely employed method for regression analysis. Different types of lag design were adopted for infectious disease modeling, including cross-correlation analysis, single lag model, moving average lag model, unconstrained or polynomial distributed lag model, and DLNM, whereas studies on non-communicable diseases predominantly used DLNM to assess the delays of association. For infectious outcomes, the association of daily mean temperature was found to be lagged for one to two weeks for influenza, followed by two to five weeks for diarrhea, and eight to twelve weeks for dengue fever. Meanwhile, the association of both cardiovascular and respiratory diseases with hot temperatures lasted for less than five days, whereas the association of cardiovascular diseases with cold temperatures was observed for ten to twenty days. Additionally, rainfall, as a potential risk factor for infectious diseases, showed a four to eight weeks’ lagged association with diarrheal diseases, while the effect was further delayed to eight to twelve weeks for vector-borne diseases. This is the first systematic review that comprehensively provides epidemiological evidence on the delay of association of common meteorological parameters. Biologically plausible and reasonable definition of effect lag in the modeling process is warranted in further environmental epidemiological studies.

Small area environmental health data: Challenges and solutions from a surveillance system perspective

Timely, locally-relevant information is important for conducting environmental epidemiology studies; however, public health data are often lagging, spatially unresolved, or unavailable. The Centers for Disease Control and Prevention’s Environmental Public Health Tracking Program (Tracking Program) is building a system of sub-county data to enhance the spatial resolution. This presentation covers the main challenges of increasing spatial resolution. The validity of epidemiologic research depends on positional accuracy of geocodes. Geocoding presents several challenges (e.g., handling special cases like PO Boxes and group quarters, match score thresholds, and how to categorize geocoding quality). The Tracking Program developed geocoding guidelines to transform address-level health data to census tract while addressing these issues. Many health outcomes cannot be displayed at census tract for reasons of confidentiality and stability due to too few cases and/or people in an area. To overcome this, the Tracking Program created standardized sub-county geographies (census tract aggregations) that can be compared over time, space, and dataset to allow data to be displayed at a finer scale while ensuring minimal suppression and instability. Multiple challenges were addressed, including selection of population thresholds and handling group quarters. There are multiple choices of denominator datasets available for public health surveillance (e.g., decennial census). The choice of denominator dataset may or may not have an impact on calculated rates and public health interpretation. To address this and answer the question of which denominator dataset to use, the Tracking Program created a framework to evaluate denominator datasets. The efforts from this project will help meet increasing demand for finer resolution data. These data can inform environmental health studies, where there is often local-level heterogeneity in the environment. Having small area environmental health data can allow for better understanding of local variation within counties, prioritize needs, and advance our understanding of environmental health processes and impacts.

Using simulation methods to assess the suitability of the case crossover design in environmental epidemiological studies of preterm birth

Background: Case crossover (CCO) studies are widely used in environmental epidemiology. CCO studies are case-only within-person comparisons, and therefore not confounded by time invariant characteristics. Proper inference, then, with CCO studies relies on appropriate selection of control time periods, and the time-stratified design serves as a robust method to account for long- and short-term-time trends. However, CCO studies assume stable baseline outcome risks. Since the baseline risk of birth increases secularly over gestation, the CCO should be evaluated for preterm birth. Our study utilizes simulations of extreme ambient temperature exposures to assess the appropriateness of CCO for preterm birth.Methods: We conducted simulations using 2018 data for New York State. Data were acquired from National Weather Service records for LaGuardia Airport (temperature) and the Centers for Disease Control’s epidemiologic database (birth data). Baseline birth rates per gestational age served as the basis for baseline risk (β0). Baseline risks were then combined with exposure data and simulated effects to create expected counts per day. Relative risks ranged from 0.9 to 1.25 per 10°F increase. We used bootstrapped Poisson random number generation to create 1000 datasets per simulated effect. Counts were disaggregated into individual records for CCO analyses, and estimated via conditional logistic regressions with 2-week and month stratified control period selection.Results: Preliminary results demonstrate upward bias in point estimates of all models. Bias was markedly smaller for 2-week stratified (ranging between 0.18-0.29%) compared to month-stratified (1.18-1.55%) models. Coverage of 95% confidence intervals was higher for 2-week stratified results; between 92.5% and 95.4% of all intervals included the simulated effect. Coverage ranged between 4.9% and 82.6% for month-stratified results.Conclusions: Future analyses will include pooled logistic regression of simulated cohorts and age-varying differences in baseline risk. Characterizing the performance of the CCO under various conditions can improve methodological rigor and innovation.

BKMR-CMA: A Novel R Command for Mediation Analysis in Environmental Mixture Studies

Background: Bayesian kernel machine regression (BKMR) is becoming a popular approach for studying the joint effect of environmental mixtures on health outcomes allowing for variable selection, which is particularly useful when continuous exposures display moderate correlations. An R package, bkmr, has been developed to implement this method. Recently, BKMR-causal mediation analysis (BKMR-CMA) has been proposed to estimate direct and indirect effects of mixtures through a hypothesized mediator. However, the current bkmr R package only allows the output of total effects of the mixtures. Method: We introduce two new commands within the bkmr package 1) BKMR-CMA: a command that allows the estimation of direct and indirect health effects of multiple environmental exposures through a single mediator; 2) BKMR-MI: a command that is used for valid estimation of environmental mixture effects and evaluation of uncertainty in the presence of missing data, which are imputed using multiple imputation techniques. The new commands also produce effective visualizations of the estimated causal effects and dose-response relationships. Results: The new computational approaches complement the existing bkmr package by allowing the study of direct and indirect effects and accounting for missing data in the estimation of the exposure effects. We illustrate the use of these novel commands in an environmental epidemiology study investigating the effects of metal mixtures on cardiovascular outcomes through a cardiovascular biomarker. Conclusion: Both BKMR-CMA and BKMR-MI are available on Github, and are expected to facilitate the conduct of reproducibility of mediation analysis in environmental epidemiology research.

High spatial and temporal resolution estimates of air pollutants from the TEMPO satellite: Methodological opportunities and challenges for environmental epidemiology studies

The NASA Tropospheric Emissions: Monitoring of Pollution (TEMPO) mission is scheduled to launch its satellite in 2022. This satellite is specifically designed to provide some of the finest spatial (2.1 km*4.7 km) and temporal (hourly) resolution estimates of gaseous and particulate air pollutants across North America, including ozone (O3), nitrogen dioxide (NO2), sulfur dioxide (SO2), and formaldehyde (H2CO). Given its geostationary orbit, it will have repeated daytime measurements of each pollutant. These data will provide new opportunities and challenges for exposure science and environmental epidemiology. Within-day measurements allows for epidemiologic analyses of how within-day variability (and peak exposures) of air pollutants effects human health. Simultaneous measurement of multiple pollutants allows for mixtures modeling rather than traditional single pollutant approaches. Finally, increased resolution of measurements can improve understanding of environmental justice and both exposure and health disparities. Potential challenges include identification and use of health datasets matching the fine-scale resolution of exposure, and the management of large exposure and health datasets. Furthermore, careful exposure science will be required to translate satellite estimates (which are integrated over an atmospheric column) into ground-level estimates most relevant to human exposure. Our session will discuss the TEMPO satellite, the epidemiological basis for studying peak exposures rather than daily averages, and the methodological opportunities and challenges that these efforts represent.

Risk Assessment for People Exposed to PM2.5 and Constituents at Different Vertical Heights in an Urban Area of Taiwan

Environmental epidemiological studies have consistently reported associations between ambient particulate matter (PM) concentrations and everyday mortality/morbidity. Many urban dwellers in Asia live in high-rise apartment buildings; thus, the pollutant concentrations of their immediate outdoor environments are affected by the vertical distribution of pollutants in the atmosphere. The vertical distributions of pollutants provide unique information about their sources and dynamic transport in urban areas, as well as their relationship to people’s exposure at ground level, while the vertical distributions of pollutants have rarely been considered in exposure assessment. In the current study, PM concentrations (with aerodynamic diameters less than 1.0 μm (PM1), 2.5 μm (PM2.5), and 10 μm (PM10)), nanoparticles, black carbon (BC), and particle-bound polycyclic aromatic hydrocarbons (p-PAHs) were measured at different residential heights—6 m, 16 m, and 27 m—at Feng Chia University near a popular night market in Western Taiwan. PM2.5 data were further adopted for health risk estimations. In winter, the magnitude of PM1, PM2.5, and PM10 concentrations were 16 m &gt; 6 m &gt; 27 m; nanoparticle concentrations were 6 m &gt; 27 m &gt; 16 m; and BC and p-PAHs concentrations were 27 m &gt; 16 m &gt; 6 m. In summer, PM1, PM2.5, and PM10 concentrations ranged from 6 m &gt; 16 m &gt; 27 m; nanoparticle concentrations were 6 m &gt; 16 m; and BC and p-PAHs concentrations were from 27 m &gt; 16 m. PM and constituents concentrations during winter were significantly higher in the nighttime than those in daytime, and levels of PM1, PM2.5, and PM10 increased rapidly on 6 m and 16 m heights (but did not increase at 27 m) after 5 pm, whereas these trends became less significant in summer. Health risk analysis for PM2.5 concentrations showed a decrease in lung cancer mortality rate and an extended lifespan for residents living at 27 m. Overall, the current study investigated the vertical profile of particulate matters and analyzed health impacts of PM2.5 at different residential heights in urban area of Taiwan. As the distributions of PM and the constituents varied at different residential heights, exposure and risk assessment of particle concentrations with multiple sizes and various components at broader vertical heights should be further investigated.