Environmental Epidemiology Research Articles

Asking Why Is Necessary to Address Health Disparities: A Critical Approach for Solution-Oriented Environmental Epidemiological Research.

In environmental epidemiology, we use an array of tools from various, related disciplines to answer key questions about environmental exposures in relation to health outcomes. Typically, we ask questions related to what, who, where, when, and how. We value these questions because they contribute to novel scientific discovery and our understanding of disease etiology linked to environmental exposures. In addition, these questions help us better understand who might be at highest risk of exposure and subsequent risk of disease. Although necessary for the goals of environmental epidemiology, these questions are insufficient for addressing environmental health disparities. Specifically, these questions may be able to help us describe exposure-health outcome associations but are limited in their ability to move beyond identification to intervening on observed disparities to achieve environmental health equity. We sought to emphasize the need to value and routinely add the key question of "Why?" in environmental epidemiological studies. In asking this additional critical question, we can identify and incorporate the structural determinants and drivers of environmental exposure disparities and determine whether these factors are linked to existing and historically recalcitrant health disparities. Further, we can design effective studies that build on existing frameworks to address the fundamental causes of environmental health disparities. This commentary underscores the need to routinely incorporate "why" questions in the practice of environmental epidemiology. By asking and addressing "Why?" we can employ better, more solution-oriented study designs, improve data collection, and enhance our ability to collaborate with diverse study populations through trust-building and community-engaged research. Incorporating these approaches will move environmental epidemiology forward from mostly documenting to actively addressing environmental health disparities. https://doi.org/10.1289/EHP14513.

Environmental Public Health Tracking in England- support for Health Impact Assessments

Abstract Environmental epidemiology is a key function for the public health service with regards to investigating the public health effects of environmental hazards, monitoring the public health efforts to control the environmental risks, and providing the evidence that informs decisions making and preparedness. At the United Kingdom Health Security Agency (UKHSA), the Environmental Public Health Tracking (EPHT) Programme, initiated in 2010, has developed a number of data streams to help link environment and health data, to support Health Impact Assessment (HIA). The main aims of the EPHT programme in England are to: 1. Identify populations at risk from exposure to significant environmental and chemical hazards 2. Investigate relationships between hazard and disease 3. Inform public health policy making 4. Optimise intervention and prevention strategies 5. Generate hypotheses for further research. Such EPHT functions are based on three key components: a) by building a national EPHT program that integrates surveillance of environmental hazards, exposures and related health outcomes, to allow data linkage and assessment of environmental and health information; b) strengthening the epidemiology science, by conducting research and training of the workforce and c) developing strategies for response to immediate risks, incidents and preparing for future risks. Examples to support HIA include a) developing an air pollution exposure surveillance work programme, that has designed a pilot population vulnerability indicator; b) developing indicators for climate change impacts for public health, c) reporting regularly on the health impacts of public fluoridation water supply schemes (as mandated by Government) and d) exploring risk factors associated with elevated blood lead concentration in children, through laboratory reporting to surveillance systems. The talk will highlight some examples of practice and outline how the EPHT programme in England has supported HIA practices.

The INPHET- a global approach to Environmental Public Health Tracking

Abstract Environment and health monitoring and information systems are fundamental infrastructure for evidence provision for policy making. The International Network for Public Health and Environment Tracking (INPHET) was formed by a community of environmental public health practitioners to address common interests and activities that advance environmental public health surveillance (EPHS) and data systems, and their uses to inform public health actions. Its main goal is to address the vast inequalities across countries and communities in their ability to monitor, assess, inform and mitigate environmental and health issues. INPHET supports the relevant role of environmental epidemiology as the pillar science providing evidence for decision making and interventions aiming at reducing and preventing environmental related diseases and contributes to an overall vision for public health. As a “Community of Practice”, INPHET focuses on sharing experiences and expertise; addressing common challenges for implementing and enhancing the science of EPHS/tracking; improving understanding of the link between the environment and health; and providing the appropriate evidence that informs public policy. The network, initiated in conference meetings in 2012-13 and formally launched in Modena, Italy in 2014, has carried out several activities which aim at sharing expertise and learning from different models of environmental and health data integration and surveillance systems that directly inform policy. These include Twinning cooperation with Euro-Asian and Balkan countries and sharing of expertise with African Public Health Agencies. Some experiences are presented in this workshop and highlight the EPHS/EPHT model adopted by several national public health agencies. It promotes that such integration and uses of environmental and health data is key for EPHS, including their accessibility and usability for communities of public health researchers, practitioners and policy makers.

Air pollution’s numerical, spatial, and temporal heterogeneous impacts on childhood hand, foot and mouth disease: a multi-model county-level study from China

BackgroundWhile stationary links between childhood hand, foot and mouth disease (HFMD) and air pollution are known, a comprehensive study on their heterogeneous relationships (nonstationarity), jointly considering numerical, temporal and spatial dimensions, has not been reported.MethodsMonthly HFMD incidence and air pollution data were collected at the county level from Sichuan-Chongqing, China (2009–2011), alongside meteorological and social environmental covariates. Key influential factors were identified using random forest (RF) under the stationary assumption. Factors’ numerically, temporally, and spatially heterogeneous relationships with HFMD were assessed using generalized additive model (GAM) and geographically and temporally weighted regression (GTWR).ResultsOur findings highlighted the relatively higher stationary contributions of fine particulate matter (PM2.5) and ozone (O3) to HFMD incidence across Sichuan-Chongqing counties. We further uncovered heterogeneous impacts of PM2.5 and O3 from three nonstationary perspectives. Numerically, PM2.5 showed an inverse ‘V’-shaped relationship with HFMD incidence, while O3 exhibited a complex pattern, with increased HFMD incidence at low PM2.5 and moderate O3 concentrations. Temporally, PM2.5’s impact peaked in autumn and was weakest in spring, whereas O3’s effect was strongest in summer. Spatially, hotspot mapping revealed high-risk clusters for PM2.5 impact across all seasons, with notable geographical variations, and for O3 in spring, summer, and autumn, concentrated in specific regions of Sichuan-Chongqing.ConclusionsThis study underscores the nuanced and three-perspective heterogeneous influences of air pollution on HFMD in small areas, emphasizing the need for differentiated, localized, and time-sensitive prevention and control strategies to enhance the precision of dynamic early warnings and predictive models for HFMD and other infectious diseases, particularly in the fields of environmental and spatial epidemiology.

A scalable two-stage Bayesian approach accounting for exposure measurement error in environmental epidemiology.

Accounting for exposure measurement errors has been recognized as a crucial problem in environmental epidemiology for over two decades. Bayesian hierarchical models offer a coherent probabilistic framework for evaluating associations between environmental exposures and health effects, which take into account exposure measurement errors introduced by uncertainty in the estimated exposure as well as spatial misalignment between the exposure and health outcome data. While two-stage Bayesian analyses are often regarded as a good alternative to fully Bayesian analyses when joint estimation is not feasible, there has been minimal research on how to properly propagate uncertainty from the first-stage exposure model to the second-stage health model, especially in the case of a large number of participant locations along with spatially correlated exposures. We propose a scalable two-stage Bayesian approach, called a sparse multivariate normal (sparse MVN) prior approach, based on the Vecchia approximation for assessing associations between exposure and health outcomes in environmental epidemiology. We compare its performance with existing approaches through simulation. Our sparse MVN prior approach shows comparable performance with the fully Bayesian approach, which is a gold standard but is impossible to implement in some cases. We investigate the association between source-specific exposures and pollutant (nitrogen dioxide [NO2])-specific exposures and birth weight of full-term infants born in 2012 in Harris County, Texas, using several approaches, including the newly developed method.

North India Cancer Risk: A Detailed Review with Focus on Jammu and Kashmir Demographics.

Cancer is a global medical challenge, and research is at its peak to understand the unique mechanisms of cancer cells. The expanding field of epidemiology, including molecular and environmental studies, helps us better understand the distribution of molecular changes and environmental risk factors in the population. In the present review, we aimed to find out the different genes and environmental factors that are associated with different cancers in the Jammu & Kashmir (J&K) region of the North Indian population. A Systematic approach of literature survey was used to curate research data based on genetic and environmental epidemiology specifying the J&K region. Of 640 articles found initially and screening of 490 records, 97 studies were included for the final review. It was observed that numerous genes that are strongly linked to various cancer types have been discovered as a result of the rising genotyping trend, which has grown in the demography exponentially over the last few decades. The majority of these genes are related to cell cycle regulation, cell growth signaling, and apoptosis regulation. Additionally, high promoter hypermethylation of various genes which were found to be attributed to the presence of distinct dietary patterns. The most important environmental risk attributes were salt tea consumption and dried pickles. In conclusion, the J&K population possesses many common polymorphisms in various genes with a small effect size that makes individuals more prone to different forms of cancers interacting with different environmental factors. What we can't do is, change the gene sequence or molecular changes which are the main changes for determining the susceptibility of any altered condition but what we can do is lower/ limit the exposure to the environmental factors which is a key element playing with the susceptibility's threshold. Therefore, limiting exposure to environmental factors could be a major step in lowering the risk of disease.

Exploring the pathways of urban green space exposure on respiratory health: An empirical study in Nanjing, China

Pathways of association between green space (GS) exposure and respiratory health continue stimulating scholars' research interest. However, the mechanisms underlying the multidimensional association between GS exposure and respiratory health remain to be clarified. Therefore, this study aimed to construct a research framework for exploring the mechanisms affecting the hypothesized pathways (physical activity, air pollution) based on the dynamic quantification of multidimensional exposure indicators (spatial and visual exposure). This study used urban green spaces in Nanjing, China, as sample sites for a pilot experiment. The parallel mediating effect of GS exposure on respiratory health through physical activity and air pollution was explored by partial least squares (PLS) modeling, and the effect of differences in GS exposure levels on this parallel mediating model was explored. The results showed that a dynamic monitoring system of multiple indicators is more conducive to characterizing the actual GS exposure of the population. Promoting physical activity and reducing air quality were both mediating pathways affecting GS exposure and respiratory health, with physical activity being the most critical mediating variable, accounting for 43.06% and 51.02% of the total indirect effects, respectively. And variables such as age, BMI, and gender do not affect the mechanism. The respiratory health-promoting benefits of performing short-term exposure in urban green spaces had a lagged effect of at least 4hours. The results of this study will help environmental epidemiologists to elucidate the pathways and mechanisms by which the green space system affects respiratory health and provide evidence for local governments to implement efficient and respiratory-healthy green space planning and design.

Unbiased temperature-related mortality estimates using weekly and monthly health data: a new method for environmental epidemiology and climate impact studies

Exposure to environmental factors has a high burden on human health, with millions of premature annual deaths associated with the short-term health effects of ambient temperatures and air pollution. However, direct estimations of exposure-related mortality from real data are still not available in most parts of the world, especially in low-resource settings, due to the unavailability of daily health records to calibrate epidemiological models. In this study, we have filled the crucial gap in available direct estimations by developing a method to make valid inference for the relationship between exposure and response data that uses only exposure and temporally aggregated response data. We provided the mathematical derivation of the method, and compared the results by using simulations applied to daily temperature and daily, weekly, and monthly mortality data. The method was then applied to the newly created database of the EARLY-ADAPT project. The daily and weekly models produced similar and unbiased estimates of the temperature-related relative risks and attributable mortality, with only slightly more imprecision in the weekly model. Even the estimates of the monthly model were unbiased when using enough data, although at the expense of a substantial increase in variability. The real data analysis showed that the similarity between the regional values of two aggregation models increased with the number of years and regions of the dataset, and decreased with the difference in their degree of temporal aggregation. Our method opens the door to conducting epidemiological studies in low-resource settings, where access to daily health data is not possible. Moreover, it allows accurate estimation of the short-term health effects of environmental exposures in near-real time, when daily health data are still not available, such as in the estimation of the mortality burden of recent record-breaking heat episodes. Overall, our method represents an important new approach to how the public health community can use data to create new evidence for research, translation and policy making. European Research Council (ERC).

Improving the integration of epidemiological data into human health risk assessment: What risk assessors told us they want

One of the practical contributions of epidemiology studies is to inform risk assessment and management to protect public health. However, there is a perception among some that environmental and occupational epidemiology is falling short of satisfying the needs of risk assessors. The specific reasons for this are not clearly understood. To help identify the points of dissatisfaction and possible areas for mutual learning, we conducted a survey of risk assessors, seeking their opinions of epidemiology research. We present a few quantitative measures and a thematic analysis of responses to open-ended questions. Survey results suggest that some risk assessors (with some adamant exceptions) believe that epidemiology has great potential to contribute to risk assessment but can be deficient in many ways. For example, respondents identified the lack of full disclosure of methods, deficiencies in exposure assessment, the absence of comprehensive uncertainty analyses, and the failure to investigate or explore thresholds of effects as some of the common shortcomings. These could be straightforward to address. Respondents also brought up a wide collection of more complicated and subtle concerns that could lead to further improvement of useful results. We identify areas where mutually-educating interdisciplinary dialogue seems particularly promising. Epidemiology research is expensive, and risk management decisions even more so; therefore, it is desirable for the risk assessment and epidemiologic communities to work toward making epidemiologic research more useful for informing decisions.

Human health risk assessment of heavy metals in residential soil-Houston, Texas.

Human health risk assessment increasingly recognizes the need to integrate participatory-based research, geospatial analysis, and environmental epidemiology, particularly to address contamination concerns in underserved and disadvantaged communities. Here, we demonstrate the combined application of such methods within the Greater Fifth Ward neighborhood in Northeast Houston. In particular, in tandem with community members, we collected soil samples from 193 residential sites using a complete canvassing method from July to November 2021 to characterize contaminant concentrations, focusing on heavy metals and metalloids As, Ba, Cd, Cr, Pb, Se, Ag, and Hg measured using Inductively Coupled Plasma-Mass Spectrometry. Individual heavy metals as well as cumulative cancer and non-cancer risks were calculated for children and adults using the USEPA Regional Soil Screening Levels and benchmarks for specific land uses, such as crop growing. Soils from most sites had low or typical background levels expected in urban areas, but samples from several locations had significantly elevated lead levels (>1200 mg/kg) that warrant additional examination. Geospatial analysis suggested clustering of heavy metal contaminants within one geographic area of the neighborhood. This study highlights how participatory research in underserved environmental justice communities can help characterize current conditions as well as establish priorities for future investigation.

The unique Legionella longbeachae capsule favors intracellular replication and immune evasion.

Legionella longbeachae and Legionella pneumophila are the most common causative agents of Legionnaires' disease. While the clinical manifestations caused by both species are similar, species-specific differences exist in environmental niches, disease epidemiology, and genomic content. One such difference is the presence of a genomic locus predicted to encode a capsule. Here, we show that L. longbeachae indeed expresses a capsule in post-exponential growth phase as evidenced by electron microscopy analyses, and that capsule expression is abrogated when deleting a capsule transporter gene. Capsule purification and its analysis via HLPC revealed the presence of a highly anionic polysaccharide that is absent in the capsule mutant. The capsule is important for replication and virulence in vivo in a mouse model of infection and in the natural host Acanthamoeba castellanii. It has anti-phagocytic function when encountering innate immune cells such as human macrophages and it is involved in the low cytokine responses in mice and in human monocyte derived macrophages, thus dampening the innate immune response. Thus, the here characterized L. longbeachae capsule is a novel virulence factor, unique among the known Legionella species, which may aid L. longbeachae to survive in its specific niches and which partly confers L. longbeachae its unique infection characteristics.

Feasibility Study on the Use of NO2 and PM2.5 Sensors for Exposure Assessment and Indoor Source Apportionment at Fixed Locations.

Recent advances in sensor technology for air pollution monitoring open new possibilities in the field of environmental epidemiology. The low spatial resolution of fixed outdoor measurement stations and modelling uncertainties currently limit the understanding of personal exposure. In this context, air quality sensor systems (AQSSs) offer significant potential to enhance personal exposure assessment. A pilot study was conducted to investigate the feasibility of the NO2 sensor model B43F and the particulate matter (PM) sensor model OPC-R1, both from Alphasense (UK), for use in epidemiological studies. Seven patients with chronic obstructive pulmonary disease (COPD) or asthma had built-for-purpose sensor systems placed inside and outside of their homes at fixed locations for one month. Participants documented their indoor activities, presence in the house, window status, and symptom severity and performed a peak expiratory flow test. The potential inhaled doses of PM2.5 and NO2 were calculated using different data sources such as outdoor data from air quality monitoring stations, indoor data from AQSSs, and generic inhalation rates (IR) or activity-specific IR. Moreover, the relation between indoor and outdoor air quality obtained with AQSSs, an indoor source apportionment study, and an evaluation of the suitability of the AQSS data for studying the relationship between air quality and health were investigated. The results highlight the value of the sensor data and the importance of monitoring indoor air quality and activity patterns to avoid exposure misclassification. The use of AQSSs at fixed locations shows promise for larger-scale and/or long-term epidemiological studies.

Pre-natal and early life lead exposure and childhood inhibitory control: an item response theory approach to improve measurement precision of inhibitory control

BackgroundNeurodevelopmental performance tasks are often separately analyzed, even when they tap into a similar construct. This may yield mixed findings for associations of an exposure-neurobehavioral outcome. We develop an item response theory (IRT) approach to integrate multiple task variables together to improve measurement precision of the underlying construct. We apply this approach to create an integrative measure of childhood inhibitory control, and study impacts of pre/post-natal lead exposure.MethodsUsing data from a prospective cohort based in Mexico (N = 533), we created an inhibitory control scale that integrates accuracy and reaction time information from four inhibitory control tasks (Go/NoGo Letter, Go/NoGo Neutral, Go/NoGo Happy, Delis-Kaplan Executive Function System (D-KEFS) Color-Word Interference Test, Condition 3). Using a generalized partial credit item response theory model, we estimated an inhibitory control index for each participant. We then assessed adjusted associations between umbilical cord blood and 4-year lead and childhood inhibitory control. We developed a resampling approach to incorporate error estimates from the inhibitory control variable to confirm the consistency of the lead-inhibitory control associations. We modeled time-varying associations of lead with each inhibitory control measure separately.ResultsParticipants had a median age of 9 years; 51.4% were males. Umbilical cord blood [-0.06 (95% CI: -0.11, -0.01)] and 4-year lead [-0.07 (95% CI: -0.12, -0.02)] were associated with inhibitory control index at 8–10 years. A resampling approach confirmed that 4-year lead was consistently associated with childhood inhibitory control index. Umbilical cord blood and 4-year lead were each associated with 3 out of 8 measures in separate models.ConclusionThis is the first application of IRT in environmental epidemiology to create a latent variable for inhibitory control that integrates accuracy and reaction time information from multiple, related tasks. This framework can be applied to other correlated neurobehavioral assessments or other phenotype data.

Qualitative and mixedmethods: informing and enhancing exposure science.

Addressing complex environmental health challenges necessitates the integration of multiple research methodologies to fully understand the social, economic, and health impacts of exposure to environmental hazards. Qualitative and mixed methods (QMM) are vital in uncovering the sociocultural dynamics that influence people's interactions with their environment and subsequent health-related outcomes. QMM has the potential to reveal insights that quantitative methods might overlook. However, QMM approaches have been underutilized in exposure science, with less than 1% of the studies published in the Journal of Exposure Science and Environmental Epidemiology (JESEE) from 2003 to 2023 employing these methods. JESEE studies that utilized QMM have enhanced exposure assessment, explored risk perceptions, and evaluated the impact of interventions, particularly among historically marginalized populations. QMM approaches have addressed gaps in traditional exposure assessment by allowing researchers to capture nuanced perspectives often missed by quantitative analyses, especially in understanding the lived experiences of affected communities. Exposure scientists are encouraged to adopt QMM to advance more comprehensive and inclusive approaches to studying and mitigating environmental risks. Fostering interdisciplinary collaborations that integrate the social sciences can enhance the development of robust, context-sensitive solutions to environmental health challenges.

Electroencephalography(EEG) in environmental epidemiology: Methods for studying neuro outcomes, exemplified by pesticide exposure research

Electroencephalography(EEG) in environmental epidemiology: Methods for studying neuro outcomes, exemplified by pesticide exposure research

Maximising research in environmental epidemiology using large cohorts such as UK Biobank – opportunities and pitfalls

Maximising research in environmental epidemiology using large cohorts such as UK Biobank – opportunities and pitfalls

EpiSEEM: A versatile StreetView imagery deep learning segmentation tool customized for environment epidemiology

EpiSEEM: A versatile StreetView imagery deep learning segmentation tool customized for environment epidemiology

Interactions of the Indoor Environment for Environmental Epidemiology

Interactions of the Indoor Environment for Environmental Epidemiology

Assessing external exposome by implementing an Environmental Data Management System using Open Data

Due to the increasing importance of exposome in environmental epidemiology, feasibility and usefulness of an Environmental Data Management System (EDMS) using Open Data was evaluated. The EDMS includes data from 10 European cities (Celje (Slovenia), Łódź (Poland), Manchester (UK), Palermo (Italy), Paris (France), Porto (Portugal), Regensburg (Germany), Reus (Spain), Rijeka (Croatia), Thessaloniki (Greece)) about external non-specific and specific exposome factors at the city or country level (2017–2020). Findings showed that the highest values of life expectancy were in Reus females (86 years) and Palermo males (81 years). UK had the highest obesity rate (28%), Croatia the highest prescribed drug consumption (62%), Greece and Portugal the highest smoking rates (37%, 42%) and daily alcohol consumption (21%), respectively. The most polluted cities were Thessaloniki for PM10 (38 µg/m3), Łódź for PM2.5 (25 µg/m3), Porto for NO2 (62 µg/m3) and Rijeka for O3 (92 µg/m3). Thessaloniki had the highest grey space (98%) and Łódź the highest cumulative amount of pollen (39,041 p/m3). The highest daily noise levels ≥ 55 dB was in Reus (81% to traffic) and Regensburg (21% to railway). In drinking water, arsenic had the highest value in Thessaloniki (6.4 µg/L), boron in Celje (24 mg/L) and lead in Paris (46.7 µg/L). Portugal and Greece showed the highest pesticide residues in food (7%). In conclusion, utilizing open-access databases enables the translation of research findings into actionable strategies for public health interventions.

Detecting univariate, bivariate, and overall effects of drug mixtures using Bayesian kernel machine regression

ABSTRACT Background: Innovative analytic approaches to drug studies are needed to understand better the co-use of opioids with non-opioids among people using illicit drugs. One approach is the Bayesian kernel machine regression (BKMR), widely applied in environmental epidemiology to study exposure mixtures but has received far less attention in substance use research. Objective: To describe the utility of the BKMR approach to study the effects of drug substance mixtures on health outcomes. Methods: We simulated data for 200 individuals. Using the Vale and Maurelli method, we simulated multivariate non-normal drug exposure data: xylazine (mean = 300 ng/mL, SD = 100 ng/mL), fentanyl (mean = 200 ng/mL, SD = 71 ng/mL), benzodiazepine (mean = 300 ng/mL, SD = 55 ng/mL), and nitazene (mean = 200 ng/mL, SD = 141 ng/mL) concentrations. We performed 10,000 MCMC sampling iterations with three Markov chains. Model diagnostics included trace plots, r-hat values, and effective sample sizes. We also provided visual relationships of the univariate and bivariate exposure-response and the overall mixture effect. Results: Higher levels of fentanyl and nitazene concentrations were associated with higher levels of the simulated health outcome, controlling for age. Trace plots, r-hat values, and effective sample size statistics demonstrated BKMR stability across multiple Markov chains. Conclusions: Our understanding of drug mixtures tends to be limited to studies of single-drug models. BKMR offers an innovative way to discern which substances pose a greater health risk than other substances and can be applied to assess univariate, bivariate, and cumulative drug effects on health outcomes.