Environmental Epidemiology最新文献

{"title":"Joint associations between neighborhood walkability, greenness, and particulate air pollution on cardiovascular mortality among adults with a history of stroke or acute myocardial infarction","authors":"Noelle S. Liao, S. K. Van Den Eeden, S. Sidney, K. Deosaransingh, J. Schwartz, S. Uong, S. Alexeeff","doi":"10.1097/EE9.0000000000000200","DOIUrl":"https://doi.org/10.1097/EE9.0000000000000200","url":null,"abstract":"Background: Fine particulate matter (PM2.5) is a known risk factor for cardiovascular disease (CVD). Neighborhood walkability and greenness may also be associated with CVD, but there is limited evidence on their joint or interacting effects with PM2.5. Methods: Cox proportional hazard models were used to estimate the risk of CVD mortality among adults with a history of acute myocardial infarction and/or stroke living in Northern California. We assessed the independent and joint effects of walkability, greenness (Normalized Differentiated Vegetation Index [NDVI]), and PM2.5 at residential addresses, controlling for age, sex, race/ethnicity, comorbidities, BMI, smoking, revascularization, medications, and socioeconomic status. Results: Greenness had a nonlinear association with CVD mortality (P = 0.038), with notably protective effects (HR = 0.87 [95% confidence interval {CI} = 0.78, 0.97]) at higher greenness levels (NDVI ≥ 0.3) and moderate attenuation after adjusting for PM2.5 (HR = 0.92 [95% CI = 0.82, 1.03]) per 0.1 increase in NDVI. Walkability had no independent effect on CVD mortality. PM2.5 had a strong independent effect in models adjusted for greenness and walkability (HR = 1.20 [95% CI = 1.08, 1.33)) per 10 μg/m3 increase in PM2.5. There was an interaction between walkability and PM2.5 (P = 0.037), where PM2.5 had slightly stronger associations in more walkable than less walkable neighborhoods (HR = 1.23 [95% CI = 1.06, 1.42] vs. 1.17 [95% CI = 1.04, 1.32]) per 10 μg/m3 increase in PM2.5. Greenness had no interaction with PM2.5 (P = 0.768) nor walkability (P = 0.385). Conclusions: High greenness may be protective of CVD mortality among adults with CVD history. PM2.5 associated CVD mortality risk varies slightly by level of neighborhood walkability, though these small differences may not be clinically meaningful.","PeriodicalId":11713,"journal":{"name":"Environmental Epidemiology","volume":" ","pages":""},"PeriodicalIF":3.6,"publicationDate":"2022-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46589821","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The P value plot does not provide evidence against air pollution hazards","authors":"D. Hicks","doi":"10.1097/EE9.0000000000000198","DOIUrl":"https://doi.org/10.1097/EE9.0000000000000198","url":null,"abstract":"Background: A number of papers by Young and collaborators have criticized epidemiological studies and meta-analyses of air pollution hazards using a graphical method that the authors call a P value plot, claiming to find zero effects, heterogeneity, and P hacking. However, the P value plot method has not been validated in a peer-reviewed publication. The aim of this study was to investigate the statistical and evidentiary properties of this method. Methods: A simulation was developed to create studies and meta-analyses with known real effects δ, integrating two quantifiable conceptions of evidence from the philosophy of science literature. The simulation and analysis is publicly available and automatically reproduced. Results: In this simulation, the plot did not provide evidence for heterogeneity or P hacking with respect to any condition. Under the right conditions, the plot can provide evidence of zero effects; but these conditions are not satisfied in any actual use by Young and collaborators. Conclusion: The P value plot does not provide evidence to support the skeptical claims about air pollution hazards made by Young and collaborators.","PeriodicalId":11713,"journal":{"name":"Environmental Epidemiology","volume":" ","pages":""},"PeriodicalIF":3.6,"publicationDate":"2022-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44122640","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Applying the exposome concept to working life health","authors":"A. Pronk, Miranda Loh, E. Kuijpers, M. Albin, Jenny Selander, L. Godderis, Manosij Ghosh, Roel C. H. Vermeulen, S. Peters, I. Mehlum, M. Turner, V. Schlünssen, M. Goldberg, M. Kogevinas, Barbara N Harding, S. Solovieva, T. Garani-Papadatos, M. V. van Tongeren, R. Stierum","doi":"10.1097/EE9.0000000000000185","DOIUrl":"https://doi.org/10.1097/EE9.0000000000000185","url":null,"abstract":"Exposures at work have a major impact on noncommunicable diseases (NCDs). Current risk reduction policies and strategies are informed by existing scientific evidence, which is limited due to the challenges of studying the complex relationship between exposure at work and outside work and health. We define the working life exposome as all occupational and related nonoccupational exposures. The latter includes nonoccupational exposures that may be directly or indirectly influenced by or interact with the working life of the individual in their relation to health. The Exposome Project for Health and Occupational Research aims to advance knowledge on the complex working life exposures in relation to disease beyond the single high exposure–single health outcome paradigm, mapping and relating interrelated exposures to inherent biological pathways, key body functions, and health. This will be achieved by combining (1) large-scale harmonization and pooling of existing European cohorts systematically looking at multiple exposures and diseases, with (2) the collection of new high-resolution external and internal exposure data. Methods and tools to characterize the working life exposome will be developed and applied, including sensors, wearables, a harmonized job exposure matrix (EuroJEM), noninvasive biomonitoring, omics, data mining, and (bio)statistics. The toolbox of developed methods and knowledge will be made available to policy makers, occupational health practitioners, and scientists. Advanced knowledge on working life exposures in relation to NCDs will serve as a basis for evidence-based and cost-effective preventive policies and actions. The toolbox will also enable future scientists to further expand the working life exposome knowledge base.","PeriodicalId":11713,"journal":{"name":"Environmental Epidemiology","volume":" ","pages":""},"PeriodicalIF":3.6,"publicationDate":"2022-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47586093","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Prenatal exposure to insecticides and child cardiometabolic risk factors in the VHEMBE birth cohort","authors":"Joanne Kim, Seungmi Yang, E. Moodie, M. Obida, R. Bornman, B. Eskenazi, J. Chevrier","doi":"10.1097/EE9.0000000000000196","DOIUrl":"https://doi.org/10.1097/EE9.0000000000000196","url":null,"abstract":"Background: As part of malaria control programs, many countries spray dichlorodiphenyltrichloroethane (DDT) or pyrethroid insecticides inside dwellings in a practice called indoor residual spraying that results in high levels of exposure to local populations. Gestational exposure to these endocrine- and metabolism-disrupting chemicals may influence child cardiometabolic health. Methods: We measured the serum concentration of DDT and dichlorodiphenyldichloroethylene (DDE) and urinary concentration of pyrethroid metabolites (cis-DBCA, cis-DCCA, trans-DCCA, 3-PBA) in peripartum samples collected between August 2012 and December 2013 from 637 women participating in the Venda Health Examination of Mothers, Babies and their Environment (VHEMBE), a birth cohort study based in Limpopo, South Africa. We applied marginal structural models to estimate the relationship between biomarker concentrations and child-size (height and weight), adiposity (body mass index [BMI], body fat percentage, waist circumference) and blood pressure at 5 years of age. Results: Maternal concentrations of all four pyrethroid metabolites were associated with lower adiposity including reduced BMI z-scores, smaller waist circumferences, and decreased body fat percentages. Reductions in BMI z-score were observed only among children of mothers with sufficient energy intake during pregnancy (βcis-DCCA, trans-DCCA=−0.4, 95% confidence interval (CI) = −0.7,−0.1; pinteraction=0.03 and 0.04, respectively) but there was no evidence of effect modification for the other measures of adiposity. Maternal p,p’-DDT concentrations were associated with a reduction in body fat percentage (β = −0.4%, 95% CI = −0.8,−0.0). Conclusions: Gestational exposure to pyrethroids may reduce adiposity in children at 5 years of age.","PeriodicalId":11713,"journal":{"name":"Environmental Epidemiology","volume":" ","pages":""},"PeriodicalIF":3.6,"publicationDate":"2022-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45296251","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Erratum: Environmental risk factors for reduced kidney function due to undetermined cause in India: an environmental epidemiologic analysis: Erratum.","authors":"","doi":"10.1097/EE9.0000000000000197","DOIUrl":"https://doi.org/10.1097/EE9.0000000000000197","url":null,"abstract":"<p><p>[This corrects the article DOI: 10.1097/EE9.0000000000000170.].</p>","PeriodicalId":11713,"journal":{"name":"Environmental Epidemiology","volume":" ","pages":"e197"},"PeriodicalIF":3.6,"publicationDate":"2022-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8835539/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39927693","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Ecological studies of COVID-19 and air pollution: How useful are they?","authors":"Paul J Villeneuve,&nbsp;Mark S Goldberg","doi":"10.1097/EE9.0000000000000195","DOIUrl":"https://doi.org/10.1097/EE9.0000000000000195","url":null,"abstract":"<p><strong>Background: </strong>Results from ecological studies have suggested that air pollution increases the risk of developing and dying from COVID-19. Drawing causal inferences from the measures of association reported in ecological studies is fraught with challenges given biases arising from an outcome whose ascertainment is incomplete, varies by region, time, and across sociodemographic characteristics, and cannot account for clustering or within-area heterogeneity. Through a series of analyses, we illustrate the dangers of using ecological studies to assess whether ambient air pollution increases the risk of dying from, or transmitting, COVID-19.</p><p><strong>Methods: </strong>We performed an ecological analysis in the continental United States using county-level ambient concentrations of fine particulate matter (PM_2.5) between 2000 and 2016 and cumulative COVID-19 mortality counts through June 2020, December 2020, and April 2021. To show that spurious associations can be obtained in ecological data, we modeled the association between PM_2.5 and the prevalence of human immunodeficiency virus (HIV). We fitted negative binomial models, with a logarithmic offset for county-specific population, to these data. Natural cubic splines were used to describe the shape of the exposure-response curves.</p><p><strong>Results: </strong>Our analyses revealed that the shape of the exposure-response curve between PM_2.5 and COVID-19 changed substantially over time. Analyses of COVID-19 mortality through June 30, 2021, suggested a positive linear relationship. In contrast, an inverse pattern was observed using county-level concentrations of PM_2.5 and the prevalence of HIV.</p><p><strong>Conclusions: </strong>Our analyses indicated that ecological analyses are prone to showing spurious relationships between ambient air pollution and mortality from COVID-19 as well as the prevalence of HIV. We discuss the many potential biases inherent in any ecological-based analysis of air pollution and COVID-19.</p>","PeriodicalId":11713,"journal":{"name":"Environmental Epidemiology","volume":" ","pages":"e195"},"PeriodicalIF":3.6,"publicationDate":"2022-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/ac/52/ee9-6-e195.PMC8835551.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39927692","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The EXIMIOUS project-Mapping exposure-induced immune effects: connecting the exposome and the immunome.","authors":"Steven Ronsmans,&nbsp;Karin Sørig Hougaard,&nbsp;Tim S Nawrot,&nbsp;Michelle Plusquin,&nbsp;François Huaux,&nbsp;María Jesús Cruz,&nbsp;Horatiu Moldovan,&nbsp;Steven Verpaele,&nbsp;Murali Jayapala,&nbsp;Michael Tunney,&nbsp;Stéphanie Humblet-Baron,&nbsp;Hubert Dirven,&nbsp;Unni Cecilie Nygaard,&nbsp;Birgitte Lindeman,&nbsp;Nur Duale,&nbsp;Adrian Liston,&nbsp;Esben Meulengracht Flachs,&nbsp;Kenneth Kastaniegaard,&nbsp;Matthias Ketzel,&nbsp;Julia Goetz,&nbsp;Jeroen Vanoirbeek,&nbsp;Manosij Ghosh,&nbsp;Peter H M Hoet","doi":"10.1097/EE9.0000000000000193","DOIUrl":"https://doi.org/10.1097/EE9.0000000000000193","url":null,"abstract":"<p><p>Immune-mediated, noncommunicable diseases-such as autoimmune and inflammatory diseases-are chronic disorders, in which the interaction between environmental exposures and the immune system plays an important role. The prevalence and societal costs of these diseases are rising in the European Union. The EXIMIOUS consortium-gathering experts in immunology, toxicology, occupational health, clinical medicine, exposure science, epidemiology, bioinformatics, and sensor development-will study eleven European study populations, covering the entire lifespan, including prenatal life. Innovative ways of characterizing and quantifying the exposome will be combined with high-dimensional immunophenotyping and -profiling platforms to map the immune effects (immunome) induced by the exposome. We will use two main approaches that \"meet in the middle\"-one starting from the exposome, the other starting from health effects. Novel bioinformatics tools, based on systems immunology and machine learning, will be used to integrate and analyze these large datasets to identify immune fingerprints that reflect a person's lifetime exposome or that are early predictors of disease. This will allow researchers, policymakers, and clinicians to grasp the impact of the exposome on the immune system at the level of individuals and populations.</p>","PeriodicalId":11713,"journal":{"name":"Environmental Epidemiology","volume":" ","pages":"e193"},"PeriodicalIF":3.6,"publicationDate":"2022-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/99/cb/ee9-6-e193.PMC8835560.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39927691","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Chronic fine particulate matter exposure, habitual exercise, and dyslipidemia: A longitudinal cohort study.","authors":"Yi Qian Zeng,&nbsp;Ly-Yun Chang,&nbsp;Cui Guo,&nbsp;Changqing Lin,&nbsp;Yacong Bo,&nbsp;Martin C S Wong,&nbsp;Tony Tam,&nbsp;Alexis K H Lau,&nbsp;Xiang Qian Lao","doi":"10.1097/EE9.0000000000000190","DOIUrl":"https://doi.org/10.1097/EE9.0000000000000190","url":null,"abstract":"<p><strong>Background: </strong>Physical activity may increase the intake of air pollutants due to a higher ventilation rate, which may exacerbate the adverse health effects. This study investigated the combined effects of habitual exercise and long-term exposure to fine particulate matter (PM_2.5) on the incidence of dyslipidemia in a large longitudinal cohort in Taiwan.</p><p><strong>Methods: </strong>A total of 121,948 adults (≥18 years) who received at least two medical examinations from 2001 to 2016 were recruited, yielding 407,821 medical examination records. A satellite-based spatiotemporal model was used to estimate the 2-year average PM_2.5 concentration (i.e., the year of and the year before the medical examination) at each participant's address. Information on habitual exercise within 1 month before the medical examination was collected using a standard self-administered questionnaire. A Cox regression model with time-dependent covariates was used to investigate the combined effects.</p><p><strong>Results: </strong>Compared with inactivity, moderate and high levels of exercise were associated with a lower incidence of dyslipidemia, with hazard ratios (HRs) (95% confidence intervals [CIs]) of 0.91 (0.88, 0.94) and 0.73 (0.71, 0.75), respectively. Participants with a moderate (22.37-25.96 μg/m³) or high (>25.96 μg/m³) level of PM_2.5 exposure had a higher incidence of dyslipidemia than those with a low level of PM_2.5 exposure (≤22.37 μg/m³), with HRs (95% CIs) of 1.36 (1.32, 1.40), and 1.90 (1.81, 1.99), respectively. We observed a statistically significant, but minor, interaction effect of PM_2.5 exposure and exercise on the development of dyslipidemia, with an overall hazard ratios (95% CI) of 1.08 (1.05, 1.10), indicating that an incremental increase in the level of exercise was associated with an 8% increase in the risk of dyslipidemia associated with every 10 μg/m³ increase in PM_2.5 exposure. However, the negative association between habitual exercise and dyslipidemia remained, regardless of the level of PM_2.5 exposure, suggesting that the benefits of increased habitual exercise outweighed the adverse effects of the increase in PM_2.5 intake during exercise.</p><p><strong>Conclusions: </strong>Increased levels of exercise and reduced levels of PM_2.5 exposures were associated with a lower incidence of dyslipidemia. Although an increase in habitual exercise slightly increased the risk of dyslipidemia associated with PM_2.5 exposure, the benefits of the increased habitual exercise outweighed the risks. Our findings suggest that habitual exercise is an effective approach for dyslipidemia prevention, even for people residing in relatively polluted areas.</p>","PeriodicalId":11713,"journal":{"name":"Environmental Epidemiology","volume":" ","pages":"e190"},"PeriodicalIF":3.6,"publicationDate":"2022-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/a4/99/ee9-6-e190.PMC8835602.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39926771","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Estimating long-term average household air pollution concentrations from repeated short-term measurements in the presence of seasonal trends and crossover.","authors":"Joshua P Keller,&nbsp;Maggie L Clark","doi":"10.1097/EE9.0000000000000188","DOIUrl":"https://doi.org/10.1097/EE9.0000000000000188","url":null,"abstract":"<p><p>Estimating long-term exposure to household air pollution is essential for quantifying health effects of chronic exposure and the benefits of intervention strategies. However, typically only a small number of short-term measurements are made. We compare different statistical models for combining these short-term measurements into predictions of a long-term average, with emphasis on the impact of temporal trends in concentrations and crossover in study design. We demonstrate that a linear mixed model that includes time adjustment provides the best predictions of long-term average, which have lower error than using household averages or mixed models without time, for a variety of different study designs and underlying temporal trends. In a case study of a cookstove intervention study in Honduras, we further demonstrate how, in the presence of strong seasonal variation, long-term average predictions from the mixed model approach based on only two or three measurements can have less error than predictions based on an average of up to six measurements. These results have important implications for the efficiency of designs and analyses in studies assessing the chronic health impacts of long-term exposure to household air pollution.</p>","PeriodicalId":11713,"journal":{"name":"Environmental Epidemiology","volume":" ","pages":"e188"},"PeriodicalIF":3.6,"publicationDate":"2021-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/7d/cb/ee9-6-e188.PMC8835562.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39926769","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Early environmental quality and life-course mental health effects: The Equal-Life project.","authors":"Irene van Kamp,&nbsp;Kerstin Persson Waye,&nbsp;Katja Kanninen,&nbsp;John Gulliver,&nbsp;Alessandro Bozzon,&nbsp;Achilleas Psyllidis,&nbsp;Hendriek Boshuizen,&nbsp;Jenny Selander,&nbsp;Peter van den Hazel,&nbsp;Marco Brambilla,&nbsp;Maria Foraster,&nbsp;Jordi Julvez,&nbsp;Maria Klatte,&nbsp;Sonja Jeram,&nbsp;Peter Lercher,&nbsp;Dick Botteldooren,&nbsp;Gordana Ristovska,&nbsp;Jaakko Kaprio,&nbsp;Dirk Schreckenberg,&nbsp;Maarten Hornikx,&nbsp;Janina Fels,&nbsp;Miriam Weber,&nbsp;Ella Braat-Eggen,&nbsp;Julia Hartmann,&nbsp;Charlotte Clark,&nbsp;Tanja Vrijkotte,&nbsp;Lex Brown,&nbsp;Gabriele Bolte","doi":"10.1097/EE9.0000000000000183","DOIUrl":"https://doi.org/10.1097/EE9.0000000000000183","url":null,"abstract":"<p><strong>Background: </strong>There is increasing evidence that a complex interplay of factors within environments in which children grows up, contributes to children's suboptimal mental health and cognitive development. The concept of the life-course exposome helps to study the impact of the physical and social environment, including social inequities, on cognitive development and mental health over time.</p><p><strong>Methods: </strong>Equal-Life develops and tests combined exposures and their effects on children's mental health and cognitive development. Data from eight birth-cohorts and three school studies (N = 240.000) linked to exposure data, will provide insights and policy guidance into aspects of physical and social exposures hitherto untapped, at different scale levels and timeframes, while accounting for social inequities. Reasoning from the outcome point of view, relevant stakeholders participate in the formulation and validation of research questions, and in the formulation of environmental hazards. Exposure assessment combines GIS-based environmental indicators with omics approaches and new data sources, forming the early-life exposome. Statistical tools integrate data at different spatial and temporal granularity and combine exploratory machine learning models with hypothesis-driven causal modeling.</p><p><strong>Conclusions: </strong>Equal-Life contributes to the development and utilization of the exposome concept by (1) integrating the internal, physical and social exposomes, (2) studying a distinct set of life-course effects on a child's development and mental health (3) characterizing the child's environment at different developmental stages and in different activity spaces, (4) looking at supportive environments for child development, rather than merely pollutants, and (5) combining physical, social indicators with novel effect markers and using new data sources describing child activity patterns and environments.</p>","PeriodicalId":11713,"journal":{"name":"Environmental Epidemiology","volume":" ","pages":"e183"},"PeriodicalIF":3.6,"publicationDate":"2021-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/91/af/ee9-6-e183.PMC8835570.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39624143","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}