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Dimitrov LV, Kaminski JW, Holbrook JR, Bitsko RH, Yeh M, Courtney JG, O'Masta B, Maher B, Cerles A, McGowan K, Rush M. A Systematic Review and Meta-analysis of Chemical Exposures and Attention-Deficit/Hyperactivity Disorder in Children. PREVENTION SCIENCE : THE OFFICIAL JOURNAL OF THE SOCIETY FOR PREVENTION RESEARCH 2024; 25:225-248. [PMID: 38108946 PMCID: PMC11132938 DOI: 10.1007/s11121-023-01601-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/17/2023] [Indexed: 12/19/2023]
Abstract
Exposure to certain chemicals prenatally and in childhood can impact development and may increase risk for attention-deficit/hyperactivity disorder (ADHD). Leveraging a larger set of literature searches conducted to synthesize results from longitudinal studies of potentially modifiable risk factors for childhood ADHD, we present meta-analytic results from 66 studies that examined the associations between early chemical exposures and later ADHD diagnosis or symptoms. Studies were eligible for inclusion if the chemical exposure occurred at least 6 months prior to measurement of ADHD diagnosis or symptomatology. Included papers were published between 1975 and 2019 on exposure to anesthetics (n = 5), cadmium (n = 3), hexachlorobenzene (n = 4), lead (n = 22), mercury (n = 12), organophosphates (n = 7), and polychlorinated biphenyls (n = 13). Analyses are presented for each chemical exposure by type of ADHD outcome reported (categorical vs. continuous), type of ADHD measurement (overall measures of ADHD, ADHD symptoms only, ADHD diagnosis only, inattention only, hyperactivity/impulsivity only), and timing of exposure (prenatal vs. childhood vs. cumulative), whenever at least 3 relevant effect sizes were available. Childhood lead exposure was positively associated with ADHD diagnosis and symptoms in all analyses except for the prenatal analyses (odds ratios (ORs) ranging from 1.60 to 2.62, correlation coefficients (CCs) ranging from 0.14 to 0.16). Other statistically significant associations were limited to organophosphates (CC = 0.11, 95% confidence interval (CI): 0.03-0.19 for continuous measures of ADHD outcomes overall), polychlorinated biphenyls (CC = 0.08, 95% CI: 0.02-0.14 for continuous measures of inattention as the outcome), and both prenatal and childhood mercury exposure (CC = 0.02, 95% CI: 0.00-0.04 for continuous measures of ADHD outcomes overall for either exposure window). Our findings provide further support for negative impacts of prenatal and/or childhood exposure to certain chemicals and raise the possibility that primary prevention and targeted screening could prevent or mitigate ADHD symptomatology. Furthermore, these findings support the need for regular review of regulations as our scientific understanding of the risks posed by these chemicals evolves.
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Affiliation(s)
- Lina V Dimitrov
- Division of Human Development and Disability, National Center On Birth Defects and Developmental Disabilities, Centers for Disease Control and Prevention, Atlanta, GA, USA.
- Oak Ridge Institute for Science and Education, Oak Ridge, TN, USA.
| | - Jennifer W Kaminski
- Division of Human Development and Disability, National Center On Birth Defects and Developmental Disabilities, Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - Joseph R Holbrook
- Division of Human Development and Disability, National Center On Birth Defects and Developmental Disabilities, Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - Rebecca H Bitsko
- Division of Human Development and Disability, National Center On Birth Defects and Developmental Disabilities, Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - Michael Yeh
- Division of Environmental Health Science and Practice, National Center for Environmental Health, Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - Joseph G Courtney
- Division of Environmental Health Science and Practice, National Center for Environmental Health, Centers for Disease Control and Prevention, Atlanta, GA, USA
| | | | - Brion Maher
- Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
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Casey E, Li Z, Liang D, Ebelt S, Levey AI, Lah JJ, Wingo TS, Hüls A. Association between Fine Particulate Matter Exposure and Cerebrospinal Fluid Biomarkers of Alzheimer's Disease among a Cognitively Healthy Population-Based Cohort. ENVIRONMENTAL HEALTH PERSPECTIVES 2024; 132:47001. [PMID: 38567968 PMCID: PMC10989269 DOI: 10.1289/ehp13503] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Revised: 02/01/2024] [Accepted: 02/16/2024] [Indexed: 04/05/2024]
Abstract
BACKGROUND Epidemiological evidence suggests air pollution adversely affects cognition and increases the risk of Alzheimer's disease (AD), but little is known about the biological effects of fine particulate matter (PM 2.5 , particulate matter with aerodynamic diameter ≤ 2.5 μ m ) on early predictors of future disease risk. OBJECTIVES We investigated the association between 1-, 3-, and 5-y exposure to ambient and traffic-related PM 2.5 and cerebrospinal fluid (CSF) biomarkers of AD. METHODS We conducted a cross-sectional analysis using data from 1,113 cognitively healthy adults (45-75 y of age) from the Emory Healthy Brain Study in Georgia in the United States. CSF biomarker concentrations of A β 42 , tTau, and pTau, were collected at enrollment (2016-2020) and analyzed with the Roche Elecsys system. Annual ambient and traffic-related residential PM 2.5 concentrations were estimated at a 1 -km and 250 -m resolution, respectively, and computed for each participant's geocoded address, using three exposure time periods based on specimen collection date. Associations between PM 2.5 and CSF biomarker concentrations, considering continuous and dichotomous (dichotomized at clinical cutoffs) outcomes, were estimated with multiple linear/logistic regression, respectively, controlling for potential confounders (age, gender, race, ethnicity, body mass index, and neighborhood socioeconomic status). RESULTS Interquartile range (IQR; IQR = 0.845 ) increases in 1-y [β : - 0.101 ; 95% confidence interval (CI): - 0.18 , - 0.02 ] and 3-y (β : - 0.078 ; 95% CI: - 0.15 , - 0.00 ) ambient PM 2.5 exposures were negatively associated with A β 42 CSF concentrations. Associations between ambient PM 2.5 and A β 42 were similar for 5-y estimates (β : - 0.076 ; 95% CI: - 0.160 , 0.005). Dichotomized CSF variables revealed similar associations between ambient PM 2.5 and A β 42 . Associations with traffic-related PM 2.5 were similar but not significant. Associations between PM 2.5 exposures and tTau, pTau tTau / A β 42 , or pTau / A β 42 levels were mainly null. CONCLUSION In our study, consistent trends were found between 1-y PM 2.5 exposure and decreased CSF A β 42 , which suggests an accumulation of amyloid plaques in the brain and an increased risk of developing AD. https://doi.org/10.1289/EHP13503.
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Affiliation(s)
- Emma Casey
- Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, Georgia, USA
| | - Zhenjiang Li
- Gangarosa Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, Georgia, USA
| | - Donghai Liang
- Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, Georgia, USA
- Gangarosa Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, Georgia, USA
| | - Stefanie Ebelt
- Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, Georgia, USA
- Gangarosa Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, Georgia, USA
| | - Allan I. Levey
- Department of Neurology, School of Medicine, Emory University, Atlanta, Georgia, USA
| | - James J. Lah
- Department of Neurology, School of Medicine, Emory University, Atlanta, Georgia, USA
| | - Thomas S. Wingo
- Department of Neurology, School of Medicine, Emory University, Atlanta, Georgia, USA
- Department of Human Genetics, School of Medicine, Emory University, Atlanta, Georgia, USA
| | - Anke Hüls
- Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, Georgia, USA
- Gangarosa Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, Georgia, USA
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Boyle J, Ward MH, Cerhan JR, Rothman N, Wheeler DC. Modeling variation in mixture effects over space with a Bayesian spatially varying mixture model. Stat Med 2024; 43:1441-1457. [PMID: 38303638 PMCID: PMC10964969 DOI: 10.1002/sim.10022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Revised: 10/09/2023] [Accepted: 01/12/2024] [Indexed: 02/03/2024]
Abstract
Mixture analysis is an emerging statistical tool in epidemiological research that seeks to estimate the health effects associated with mixtures of several exposures. This approach acknowledges that individuals experience many simultaneous exposures and it can estimate the relative importance of components in the mixture. Health effects due to mixtures may vary over space driven by to political, demographic, environmental, or other differences. In such cases, estimating a global mixture effect without accounting for spatial variation would induce bias in effect estimates and potentially lower statistical power. To date, no methods have been developed to estimate spatially varying chemical mixture effects. We developed a Bayesian spatially varying mixture model that estimates spatially varying mixture effects and the importance weights of components in the mixture, while adjusting for covariates. We demonstrate the efficacy of the model through a simulation study that varies the number of mixtures (one and two) and spatial pattern (global, one-dimensional, radial) and magnitude of mixture effects, showing that the model is able to accurately reproduce the spatial pattern of mixture effects across a diverse set of scenarios. Finally, we apply our model to a multi-center case-control study of non-Hodgkin lymphoma (NHL) in Detroit, Iowa, Los Angeles, and Seattle. We identify significant spatially varying positive and inverse associations with NHL for two mixtures of pesticides in Iowa and do not find strong spatial effects at the other three centers. In conclusion, the Bayesian spatially varying mixture model represents a novel method for modeling spatial variation in mixture effects.
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Affiliation(s)
- Joseph Boyle
- Department of Biostatistics, Virginia Commonwealth University, Richmond, Virginia, USA
| | - Mary H. Ward
- Occupational and Environmental Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, Maryland, USA
| | - James R. Cerhan
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minnesota, USA
| | - Nat Rothman
- Occupational and Environmental Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, Maryland, USA
| | - David C. Wheeler
- Department of Biostatistics, Virginia Commonwealth University, Richmond, Virginia, USA
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Nguyen TQ, Roberts Lavigne LC, Brantner CL, Kirk GD, Mehta SH, Linton SL. Estimation of place-based vulnerability scores for HIV viral non-suppression: an application leveraging data from a cohort of people with histories of using drugs. BMC Med Res Methodol 2024; 24:21. [PMID: 38273277 PMCID: PMC10809603 DOI: 10.1186/s12874-023-02133-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2023] [Accepted: 12/20/2023] [Indexed: 01/27/2024] Open
Abstract
The relationships between place (e.g., neighborhood) and HIV are commonly investigated. As measurements of place are multivariate, most studies apply some dimension reduction, resulting in one variable (or a small number of variables), which is then used to characterize place. Typical dimension reduction methods seek to capture the most variance of the raw items, resulting in a type of summary variable we call "disadvantage score". We propose to add a different type of summary variable, the "vulnerability score," to the toolbox of the researchers doing place and HIV research. The vulnerability score measures how place, as known through the raw measurements, is predictive of an outcome. It captures variation in place characteristics that matters most for the particular outcome. We demonstrate the estimation and utility of place-based vulnerability scores for HIV viral non-suppression, using data with complicated clustering from a cohort of people with histories of injecting drugs.
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Affiliation(s)
- Trang Quynh Nguyen
- Department of Mental Health, Johns Hopkins Bloomberg School of Public Health (JHSPH), Baltimore, MD, USA.
| | | | | | | | | | - Sabriya L Linton
- Department of Mental Health, Johns Hopkins Bloomberg School of Public Health (JHSPH), Baltimore, MD, USA
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Dalecká A, Bartošková A. Invited commentary on "Interactions between long-term ambient particle exposures and lifestyle on the prevalence of hypertension and diabetes: insight from a large community-based survey". J Epidemiol Community Health 2023; 77:419-420. [PMID: 37156605 DOI: 10.1136/jech-2023-220635] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/10/2023]
Affiliation(s)
- Andrea Dalecká
- RECETOX, Faculty of Science, Masaryk University, Brno, Czech Republic
| | - Anna Bartošková
- RECETOX, Faculty of Science, Masaryk University, Brno, Czech Republic
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Ilango SD, Leary CS, Ritchie E, Semmens EO, Park C, Fitzpatrick AL, Kaufman JD, Hajat A. An Examination of the Joint Effect of the Social Environment and Air Pollution on Dementia Among US Older Adults. Environ Epidemiol 2023; 7:e250. [PMID: 37304341 PMCID: PMC10256342 DOI: 10.1097/ee9.0000000000000250] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Accepted: 04/06/2023] [Indexed: 06/13/2023] Open
Abstract
Evidence suggests exposure to air pollution increases the risk of dementia. Cognitively stimulating activities and social interactions, made available through the social environment, may slow cognitive decline. We examined whether the social environment buffers the adverse effect of air pollution on dementia in a cohort of older adults. Methods This study draws from the Ginkgo Evaluation of Memory Study. Participants aged 75 years and older were enrolled between 2000 and 2002 and evaluated for dementia semi-annually through 2008. Long-term exposure to particulate matter and nitrogen dioxide was assigned from spatial and spatiotemporal models. Census tract-level measures of the social environment and individual measures of social activity were used as measures of the social environment. We generated Cox proportional hazard models with census tract as a random effect and adjusted for demographic and study visit characteristics. Relative excess risk due to interaction was estimated as a qualitative measure of additive interaction. Results This study included 2,564 individuals. We observed associations between increased risk of dementia and fine particulate matter (µg/m3), coarse particulate matter (µg/m3), and nitrogen dioxide (ppb); HRs per 5 unit increase were 1.55 (1.01, 2.18), 1.31 (1.07, 1.60), and 1.18 (1.02, 1.37), respectively. We found no evidence of additive interaction between air pollution and the neighborhood social environment. Conclusions We found no consistent evidence to suggest a synergistic effect between exposure to air pollution and measures of the social environment. Given the many qualities of the social environment that may reduce dementia pathology, further examination is encouraged.
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Affiliation(s)
- Sindana D Ilango
- Department of Epidemiology, University of Washington, Seattle, Washington, USA
| | - Cindy S Leary
- Center for Population Health Research, School of Public and Community Health Sciences, University of Montana, Missoula, Montana, USA
| | - Emily Ritchie
- Department of Epidemiology, University of Washington, Seattle, Washington, USA
| | - Erin O Semmens
- Center for Population Health Research, School of Public and Community Health Sciences, University of Montana, Missoula, Montana, USA
| | - Christina Park
- Department of Epidemiology, University of Washington, Seattle, Washington, USA
| | - Annette L Fitzpatrick
- Department of Epidemiology, University of Washington, Seattle, Washington, USA
- Department of Family Medicine and Global Health, University of Washington, Seattle, Washington, USA
| | - Joel D Kaufman
- Department of Epidemiology, University of Washington, Seattle, Washington, USA
- Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, Washington, USA
- Department of Medicine, University of Washington, Seattle, Washington, USA
| | - Anjum Hajat
- Department of Epidemiology, University of Washington, Seattle, Washington, USA
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Boyle J, Ward MH, Cerhan JR, Rothman N, Wheeler DC. Estimating mixture effects and cumulative spatial risk over time simultaneously using a Bayesian index low-rank kriging multiple membership model. Stat Med 2022; 41:5679-5697. [PMID: 36161724 PMCID: PMC9691549 DOI: 10.1002/sim.9587] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Revised: 08/29/2022] [Accepted: 09/11/2022] [Indexed: 01/11/2023]
Abstract
The exposome is an ideal in public health research that posits that individuals experience risk for adverse health outcomes from a wide variety of sources over their lifecourse. There have been increases in data collection in the various components of the exposome, but novel statistical methods are needed that capture multiple dimensions of risk at once. We introduce a Bayesian index low-rank kriging (LRK) multiple membership model (MMM) to simultaneously estimate the health effects of one or more groups of exposures, the relative importance of exposure components, and cumulative spatial risk over time using residential histories. The model employs an MMM to consider all residential locations for subjects weighted by duration and LRK to increase computational efficiency. We demonstrate the performance of the Bayesian index LRK-MMM through a simulation study, showing that the model accurately and consistently estimates the health effects of one or several group indices and has high power to identify a region of elevated spatial risk due to unmeasured environmental exposures. Finally, we apply our model to data from a multicenter case-control study of non-Hodgkin lymphoma (NHL), finding a significant positive association between one index of pesticides and risk for NHL in Iowa. Additionally, we find an area of significantly elevated spatial risk for NHL in Los Angeles. In conclusion, our Bayesian index LRK-MMM represents a step forward toward bringing the ideals of the exposome into practice for environmental risk analyzes.
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Affiliation(s)
- Joseph Boyle
- Department of BiostatisticsVirginia Commonwealth UniversityRichmondVirginiaUSA
| | - Mary H. Ward
- Occupational and Environmental Epidemiology Branch, Division of Cancer Epidemiology and GeneticsNational Cancer InstituteRockvilleMarylandUSA
| | - James R. Cerhan
- Department of Quantitative Health SciencesMayo ClinicRochesterMinnesotaUSA
| | - Nat Rothman
- Occupational and Environmental Epidemiology Branch, Division of Cancer Epidemiology and GeneticsNational Cancer InstituteRockvilleMarylandUSA
| | - David C. Wheeler
- Department of BiostatisticsVirginia Commonwealth UniversityRichmondVirginiaUSA
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Christensen GM, Li Z, Pearce J, Marcus M, Lah JJ, Waller LA, Ebelt S, Hüls A. The complex relationship of air pollution and neighborhood socioeconomic status and their association with cognitive decline. ENVIRONMENT INTERNATIONAL 2022; 167:107416. [PMID: 35868076 PMCID: PMC9382679 DOI: 10.1016/j.envint.2022.107416] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/15/2022] [Revised: 06/22/2022] [Accepted: 07/13/2022] [Indexed: 06/13/2023]
Abstract
BACKGROUND Air pollution and neighborhood socioeconomic status (nSES) have been shown to affect cognitive decline in older adults. In previous studies, nSES acts as both a confounder and an effect modifier between air pollution and cognitive decline. OBJECTIVES This study aims to examine the individual and joint effects of air pollution and nSES on cognitive decline on adults 50 years and older in Metro Atlanta, USA. METHODS Perceived memory and cognitive decline was assessed in 11,897 participants aged 50+ years from the Emory Healthy Aging Study (EHAS) using the cognitive function instrument (CFI). Three-year average air pollution concentrations for 12 pollutants and 16 nSES characteristics were matched to participants using census tracts. Individual exposure linear regression and LASSO models explore individual exposure effects. Environmental mixture modeling methods including, self-organizing maps (SOM), Bayesian kernel machine regression (BKMR), and quantile-based G-computation explore joint effects, and effect modification between air pollutants and nSES characteristics on cognitive decline. RESULTS Participants living in areas with higher air pollution concentrations and lower nSES experienced higher CFI scores (beta: 0.121; 95 % CI: 0.076, 0.167) compared to participants living in areas with low air pollution and high nSES. Additionally, the BKMR model showed a significant overall mixture effect on cognitive decline, suggesting synergy between air pollution and nSES. These joint effects explain protective effects observed in single-pollutant linear regression models, even after adjustment for confounding by nSES (e.g., an IQR increase in CO was associated with a 0.038-point lower (95 % CI: -0.06, -0.01) CFI score). DISCUSSION Observed protective effects of single air pollutants on cognitive decline can be explained by joint effects and effect modification of air pollutants and nSES. Researchers must consider nSES as an effect modifier if not a co-exposure to better understand the complex relationships between air pollution and nSES in urban settings.
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Affiliation(s)
- Grace M Christensen
- Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, GA, USA
| | - Zhenjiang Li
- Gangarosa Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA, USA
| | - John Pearce
- Department of Public Health Sciences, College of Medicine, Medical University of South Carolina, Charleston, SC, USA
| | - Michele Marcus
- Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, GA, USA; Gangarosa Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA, USA
| | - James J Lah
- Department of Neurology, School of Medicine, Emory University, Atlanta, GA, USA
| | - Lance A Waller
- Gangarosa Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA, USA; Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, GA, USA
| | - Stefanie Ebelt
- Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, GA, USA; Gangarosa Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA, USA
| | - Anke Hüls
- Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, GA, USA; Gangarosa Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA, USA.
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