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Kundu D, Kim S, Ward MH, Albert PS. A Comparison of Statistical Methods for Studying Interactions of Chemical Mixtures. STATISTICS IN BIOSCIENCES 2024; 16:503-519. [PMID: 39233714 PMCID: PMC11374107 DOI: 10.1007/s12561-023-09415-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2023] [Revised: 11/13/2023] [Accepted: 11/18/2023] [Indexed: 09/06/2024]
Abstract
Properly assessing the effects of environmental chemical exposures on disease risk remains a challenging problem in environmental epidemiology. Various analytic approaches have been proposed, but there are few papers that have compared the performance of different statistical methods on a single dataset. In this paper, we compare different regression-based approaches for estimating interactions between chemical mixture components using data from a case-control study on non-Hodgkin's lymphoma. An analytic challenge is the high percentage of exposures that are below the limit of detection (LOD). Using imputation for LOD, we compare different Bayesian shrinkage prior approaches including an approach that incorporates the hierarchical principle where interactions are only included when main effects exist. Further, we develop an approach where main and interactive effects are represented by a series of distinct latent functions. We also fit the Bayesian kernel machine regression to these data. All of these approaches show little evidence of an interaction among the chemical mixtures when measurements below the LOD were imputed. The imputation approach makes very strong assumptions about the relationship between exposure and disease risk for measurements below the LOD. As an alternative, we show the results of an analysis where we model the exposure relationship with two parameters per mixture component; one characterizing the effect of being below the LOD and the other being a linear effect above the LOD. In this later analysis, we identify numerous strong interactions that were not identified in the analyses with imputation. This case study demonstrated the importance of developing new approaches for mixtures when the proportions of exposure measurements below the LOD are high.
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Affiliation(s)
- Debamita Kundu
- Biostatistics Division, Public Health Sciences, School of Medicine, University of Virginia, Charlottesville, VA, USA
| | - Sungduk Kim
- Biostatistics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
| | - Mary H Ward
- Occupational and Environmental Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
| | - Paul S Albert
- Biostatistics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
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Amadou A, Giampiccolo C, Bibi Ngaleu F, Praud D, Coudon T, Grassot L, Faure E, Couvidat F, Frenoy P, Severi G, Romana Mancini F, Roy P, Fervers B. Multiple xenoestrogen air pollutants and breast cancer risk: Statistical approaches to investigate combined exposures effect. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2024; 351:124043. [PMID: 38679129 DOI: 10.1016/j.envpol.2024.124043] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Revised: 02/10/2024] [Accepted: 04/23/2024] [Indexed: 05/01/2024]
Abstract
Studies suggested that exposure to air pollutants, with endocrine disrupting (ED) properties, have a key role in breast cancer (BC) development. Although the population is exposed simultaneously to a mixture of multiple pollutants and ED pollutants may act via common biological mechanisms leading to synergic effects, epidemiological studies generally evaluate the effect of each pollutant separately. We aimed to assess the complex effect of exposure to a mixture of four xenoestrogen air pollutants (benzo-[a]-pyrene (BaP), cadmium, dioxin (2,3,7,8-Tétrachlorodibenzo-p-dioxin TCDD)), and polychlorinated biphenyl 153 (PCB153)) on the risk of BC, using three recent statistical methods, namely weighted quantile sum (WQS), quantile g-computation (QGC) and Bayesian kernel machine regression (BKMR). The study was conducted on 5222 cases and 5222 matched controls nested within the French prospective E3N cohort initiated in 1990. Annual average exposure estimates to the pollutants were assessed using a chemistry transport model, at the participants' residence address between 1990 and 2011. We found a positive association between the WQS index of the joint effect and the risk of overall BC (adjusted odds ratio (OR) = 1.10, 95% confidence intervals (CI): 1.03-1.19). Similar results were found for QGC (OR = 1.11, 95%CI: 1.03-1.19). Despite the association did not reach statistical significance in the BKMR model, we observed an increasing trend between the joint effect of the four pollutants and the risk of BC, when fixing other chemicals at their median concentrations. BaP, cadmium and PCB153 also showed positive trends in the multi-pollutant mixture, while dioxin showed a modest inverse trend. Despite we found a clear evidence of a positive association between the joint exposure to pollutants and BC risk only from WQS and QGC regression, we observed a similar suggestive trend using BKMR. This study makes a major contribution to the understanding of the joint effects of air pollution.
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Affiliation(s)
- Amina Amadou
- Department of Prevention Cancer Environment, Centre Léon Bérard, Lyon, France; Inserm U1296 Radiations: Défense, Santé, Environnement, Lyon, France.
| | - Camille Giampiccolo
- Department of Prevention Cancer Environment, Centre Léon Bérard, Lyon, France; Université Claude Bernard Lyon 1, Lyon, France; Service de Biostatistique-Bioinformatique, Pole Sante Publique, Hospices Civils de Lyon, Lyon, France; Laboratoire de Biometrie Et Biologie Evolutive, CNRS UMR 5558, Villeurbanne, France
| | - Fabiola Bibi Ngaleu
- Department of Prevention Cancer Environment, Centre Léon Bérard, Lyon, France; Inserm U1296 Radiations: Défense, Santé, Environnement, Lyon, France; Université Claude Bernard Lyon 1, Lyon, France
| | - Delphine Praud
- Department of Prevention Cancer Environment, Centre Léon Bérard, Lyon, France; Inserm U1296 Radiations: Défense, Santé, Environnement, Lyon, France
| | - Thomas Coudon
- Department of Prevention Cancer Environment, Centre Léon Bérard, Lyon, France; Inserm U1296 Radiations: Défense, Santé, Environnement, Lyon, France
| | - Lény Grassot
- Department of Prevention Cancer Environment, Centre Léon Bérard, Lyon, France; Inserm U1296 Radiations: Défense, Santé, Environnement, Lyon, France
| | - Elodie Faure
- Centre de Recherche en Epidémiologie et Santé des Populations (CESP, Inserm U1018), Facultés de Médecine, Université Paris-Saclay, UPS UVSQ, Gustave Roussy, Villejuif, France
| | - Florian Couvidat
- National Institute for industrial Environment and Risks (INERIS), Verneuil-en-Halatte, France
| | - Pauline Frenoy
- Centre de Recherche en Epidémiologie et Santé des Populations (CESP, Inserm U1018), Facultés de Médecine, Université Paris-Saclay, UPS UVSQ, Gustave Roussy, Villejuif, France
| | - Gianluca Severi
- Centre de Recherche en Epidémiologie et Santé des Populations (CESP, Inserm U1018), Facultés de Médecine, Université Paris-Saclay, UPS UVSQ, Gustave Roussy, Villejuif, France; Department of Statistics, Computer Science and Applications (DISIA), University of Florence, Italy
| | - Francesca Romana Mancini
- Centre de Recherche en Epidémiologie et Santé des Populations (CESP, Inserm U1018), Facultés de Médecine, Université Paris-Saclay, UPS UVSQ, Gustave Roussy, Villejuif, France.
| | - Pascal Roy
- Université Claude Bernard Lyon 1, Lyon, France; Service de Biostatistique-Bioinformatique, Pole Sante Publique, Hospices Civils de Lyon, Lyon, France; Laboratoire de Biometrie Et Biologie Evolutive, CNRS UMR 5558, Villeurbanne, France
| | - Béatrice Fervers
- Department of Prevention Cancer Environment, Centre Léon Bérard, Lyon, France; Inserm U1296 Radiations: Défense, Santé, Environnement, Lyon, France.
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Hoover JH, Coker ES, Erdei E, Luo L, Begay D, MacKenzie D, Lewis J. Preterm Birth and Metal Mixture Exposure among Pregnant Women from the Navajo Birth Cohort Study. ENVIRONMENTAL HEALTH PERSPECTIVES 2023; 131:127014. [PMID: 38109118 PMCID: PMC10727039 DOI: 10.1289/ehp10361] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/21/2021] [Revised: 10/17/2023] [Accepted: 10/19/2023] [Indexed: 12/19/2023]
Abstract
BACKGROUND Preterm birth (PTB), defined as birth before 37 wk gestation, is associated with hypertension, diabetes, inadequate prenatal care, unemployment or poverty, and metal exposure. Indigenous individuals are more likely to have maternal risk factors associated with PTB compared with other populations in the United States; however, the role of environmental metals on PTB among pregnant Indigenous women remains uncertain. Previous research identified associations between PTB and individual metals, but there is limited investigation on metal mixtures and this birth outcome. OBJECTIVES We used a mixtures analysis framework to investigate the association between metal mixtures and PTB among pregnant Indigenous women from the Navajo Birth Cohort Study (NBCS). METHODS Maternal urine and blood samples were collected at the time of study enrollment and analyzed for metals by inductively coupled plasma dynamic reaction cell mass spectrometry. Bayesian Profile Regression was used to identify subgroups (clusters) of individuals with similar patterns of coexposure and to model association with PTB. RESULTS Results indicated six subgroups of maternal participants with distinct exposure profiles, including one group with low exposure to all metals and one group with total arsenic, cadmium, lead, and uranium concentrations exceeding representative concentrations calculated from the National Health and Nutrition Examination Survey (NHANES). Compared with the reference group (i.e., the lowest exposure subgroup), the subgroup with the highest overall exposure had a relative risk of PTB of 2.9 times (95% credible interval: 1.1, 6.1). Exposures in this subgroup were also higher overall than NHANES median values for women 14-45 years of age. DISCUSSION Given the wide range of exposures and elevated PTB risk for the most exposed subgroups in a relatively small study, follow-up investigation is recommended to evaluate associations between metal mixture profiles and other birth outcomes and to test hypothesized mechanisms of action for PTB and oxidative stress caused by environmental metals. https://doi.org/10.1289/EHP10361.
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Affiliation(s)
- Joseph H. Hoover
- Community Environmental Health Program, College of Pharmacy, Department of Pharmaceutical Sciences, University of New Mexico Health Sciences Center, Albuquerque, New Mexico, USA
- Department of Environmental Science, College of Agriculture, Life and Environmental Sciences, University of Arizona, Tucson, Arizona, USA
| | - Eric S. Coker
- British Columbia Centre for Disease Control, Vancouver, British Columbia, Canada
| | - Esther Erdei
- Community Environmental Health Program, College of Pharmacy, Department of Pharmaceutical Sciences, University of New Mexico Health Sciences Center, Albuquerque, New Mexico, USA
| | - Li Luo
- Department of Internal Medicine and Comprehensive Cancer Center, University of New Mexico, Albuquerque, New Mexico, USA
| | - David Begay
- Community Environmental Health Program, College of Pharmacy, Department of Pharmaceutical Sciences, University of New Mexico Health Sciences Center, Albuquerque, New Mexico, USA
| | - Debra MacKenzie
- Community Environmental Health Program, College of Pharmacy, Department of Pharmaceutical Sciences, University of New Mexico Health Sciences Center, Albuquerque, New Mexico, USA
| | - NBCS Study Team
- Community Environmental Health Program, College of Pharmacy, Department of Pharmaceutical Sciences, University of New Mexico Health Sciences Center, Albuquerque, New Mexico, USA
| | - Johnnye Lewis
- Community Environmental Health Program, College of Pharmacy, Department of Pharmaceutical Sciences, University of New Mexico Health Sciences Center, Albuquerque, New Mexico, USA
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Kim S, Beane Freeman LE, Albert PS. A latent functional approach for modeling the effects of multidimensional exposures on disease risk. Stat Med 2023; 42:4776-4793. [PMID: 37635131 DOI: 10.1002/sim.9888] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Revised: 07/28/2023] [Accepted: 08/14/2023] [Indexed: 08/29/2023]
Abstract
Understanding the relationships between exposure and disease incidence is an important problem in environmental epidemiology. Typically, a large number of these exposures are measured, and it is found either that a few exposures transmit risk or that each exposure transmits a small amount of risk, but, taken together, these may pose a substantial disease risk. Further, these exposure effects can be nonlinear. We develop a latent functional approach, which assumes that the individual effect of each exposure can be characterized as one of a series of unobserved functions, where the number of latent functions is less than or equal to the number of exposures. We propose Bayesian methodology to fit models with a large number of exposures and show that existing Bayesian group LASSO approaches are a special case of the proposed model. An efficient Markov chain Monte Carlo sampling algorithm is developed for carrying out Bayesian inference. The deviance information criterion is used to choose an appropriate number of nonlinear latent functions. We demonstrate the good properties of the approach using simulation studies. Further, we show that complex exposure relationships can be represented with only a few latent functional curves. The proposed methodology is illustrated with an analysis of the effect of cumulative pesticide exposure on cancer risk in a large cohort of farmers.
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Affiliation(s)
- Sungduk Kim
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Maryland, USA
| | - Laura E Beane Freeman
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Maryland, USA
| | - Paul S Albert
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Maryland, USA
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Martins SS, Bruzelius E, Stingone JA, Wheeler-Martin K, Akbarnejad H, Mauro CM, Marziali ME, Samples H, Crystal S, S. Davis C, Rudolph KE, Keyes KM, Hasin DS, Cerdá M. Prescription Opioid Laws and Opioid Dispensing in US Counties: Identifying Salient Law Provisions With Machine Learning. Epidemiology 2021; 32:868-876. [PMID: 34310445 PMCID: PMC8556655 DOI: 10.1097/ede.0000000000001404] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
BACKGROUND Hundreds of laws aimed at reducing inappropriate prescription opioid dispensing have been implemented in the United States, yet heterogeneity in provisions and their simultaneous implementation have complicated evaluation of impacts. We apply a hypothesis-generating, multistage, machine-learning approach to identify salient law provisions and combinations associated with dispensing rates to test in future research. METHODS Using 162 prescription opioid law provisions capturing prescription drug monitoring program (PDMP) access, reporting and administration features, pain management clinic provisions, and prescription opioid limits, we used regularization approaches and random forest models to identify laws most predictive of county-level and high-dose dispensing. We stratified analyses by overdose epidemic phases-the prescription opioid phase (2006-2009), heroin phase (2010-2012), and fentanyl phase (2013-2016)-to further explore pattern shifts over time. RESULTS PDMP patient data access provisions most consistently predicted high-dispensing and high-dose dispensing counties. Pain management clinic-related provisions did not generally predict dispensing measures in the prescription opioid phase but became more discriminant of high dispensing and high-dose dispensing counties over time, especially in the fentanyl period. Predictive performance across models was poor, suggesting prescription opioid laws alone do not strongly predict dispensing. CONCLUSIONS Our systematic analysis of 162 law provisions identified patient data access and several pain management clinic provisions as predictive of county prescription opioid dispensing patterns. Future research employing other types of study designs is needed to test these provisions' causal relationships with inappropriate dispensing and to examine potential interactions between PDMP access and pain management clinic provisions. See video abstract at, http://links.lww.com/EDE/B861.
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Affiliation(s)
| | | | | | | | | | | | | | | | - Stephen Crystal
- Rutgers University, Center for Health Services Research, Institute for Health, and School of Social Work
| | | | | | | | - Deborah S. Hasin
- Columbia University Department of Epidemiology
- Columbia University Department of Psychiatry
| | - Magdalena Cerdá
- NYU Grossman School of Medicine Department of Population Health
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Tanner E, Lee A, Colicino E. Environmental mixtures and children's health: identifying appropriate statistical approaches. Curr Opin Pediatr 2020; 32:315-320. [PMID: 31934891 PMCID: PMC7895326 DOI: 10.1097/mop.0000000000000877] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
PURPOSE OF REVIEW Biomonitoring studies have shown that children are constantly exposed to complex patterns of chemical and nonchemical exposures. Here, we briefly summarize the rationale for studying multiple exposures, also called mixture, in relation to child health and key statistical approaches that can be used. We discuss advantages over traditional methods, limitations and appropriateness of the context. RECENT FINDINGS New approaches allow pediatric researchers to answer increasingly complex questions related to environmental mixtures. We present methods to identify the most relevant exposures among a high-multitude of variables, via shrinkage and variable selection techniques, and identify the overall mixture effect, via Weighted Quantile Sum and Bayesian Kernel Machine regressions. We then describe novel extensions that handle high-dimensional exposure data and allow identification of critical exposure windows. SUMMARY Recent advances in statistics and machine learning enable researchers to identify important mixture components, estimate joint mixture effects and pinpoint critical windows of exposure. Despite many advantages over single chemical approaches, measurement error and biases may be amplified in mixtures research, requiring careful study planning and design. Future research requires increased collaboration between epidemiologists, statisticians and data scientists, and further integration with causal inference methods.
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Affiliation(s)
- Eva Tanner
- Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Alison Lee
- Department of Pediatrics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Elena Colicino
- Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA
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