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Apostolopoulos Y, Sönmez S, Thiese MS, Olufemi M, Gallos LK. A blueprint for a new commercial driving epidemiology: An emerging paradigm grounded in integrative exposome and network epistemologies. Am J Ind Med 2024; 67:515-531. [PMID: 38689533 DOI: 10.1002/ajim.23588] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2024] [Revised: 03/29/2024] [Accepted: 04/15/2024] [Indexed: 05/02/2024]
Abstract
Excess health and safety risks of commercial drivers are largely determined by, embedded in, or operate as complex, dynamic, and randomly determined systems with interacting parts. Yet, prevailing epidemiology is entrenched in narrow, deterministic, and static exposure-response frameworks along with ensuing inadequate data and limiting methods, thereby perpetuating an incomplete understanding of commercial drivers' health and safety risks. This paper is grounded in our ongoing research that conceptualizes health and safety challenges of working people as multilayered "wholes" of interacting work and nonwork factors, exemplified by complex-systems epistemologies. Building upon and expanding these assumptions, herein we: (a) discuss how insights from integrative exposome and network-science-based frameworks can enhance our understanding of commercial drivers' chronic disease and injury burden; (b) introduce the "working life exposome of commercial driving" (WLE-CD)-an array of multifactorial and interdependent work and nonwork exposures and associated biological responses that concurrently or sequentially impact commercial drivers' health and safety during and beyond their work tenure; (c) conceptualize commercial drivers' health and safety risks as multilayered networks centered on the WLE-CD and network relational patterns and topological properties-that is, arrangement, connections, and relationships among network components-that largely govern risk dynamics; and (d) elucidate how integrative exposome and network-science-based innovations can contribute to a more comprehensive understanding of commercial drivers' chronic disease and injury risk dynamics. Development, validation, and proliferation of this emerging discourse can move commercial driving epidemiology to the frontier of science with implications for policy, action, other working populations, and population health at large.
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Affiliation(s)
| | - Sevil Sönmez
- College of Business, University of Central Florida, Orlando, Florida, USA
| | - Matthew S Thiese
- Rocky Mountain Center for Occupational and Environmental Health, University of Utah, Salt Lake City, Utah, USA
| | - Mubo Olufemi
- Rocky Mountain Center for Occupational and Environmental Health, University of Utah, Salt Lake City, Utah, USA
| | - Lazaros K Gallos
- DIMACS, Center for Discrete Mathematics & Theoretical Computer Science, Rutgers University, Piscataway, New Jersey, USA
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Zhu G, Wen Y, Cao K, He S, Wang T. A review of common statistical methods for dealing with multiple pollutant mixtures and multiple exposures. Front Public Health 2024; 12:1377685. [PMID: 38784575 PMCID: PMC11113012 DOI: 10.3389/fpubh.2024.1377685] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2024] [Accepted: 04/15/2024] [Indexed: 05/25/2024] Open
Abstract
Traditional environmental epidemiology has consistently focused on studying the impact of single exposures on specific health outcomes, considering concurrent exposures as variables to be controlled. However, with the continuous changes in environment, humans are increasingly facing more complex exposures to multi-pollutant mixtures. In this context, accurately assessing the impact of multi-pollutant mixtures on health has become a central concern in current environmental research. Simultaneously, the continuous development and optimization of statistical methods offer robust support for handling large datasets, strengthening the capability to conduct in-depth research on the effects of multiple exposures on health. In order to examine complicated exposure mixtures, we introduce commonly used statistical methods and their developments, such as weighted quantile sum, bayesian kernel machine regression, toxic equivalency analysis, and others. Delineating their applications, advantages, weaknesses, and interpretability of results. It also provides guidance for researchers involved in studying multi-pollutant mixtures, aiding them in selecting appropriate statistical methods and utilizing R software for more accurate and comprehensive assessments of the impact of multi-pollutant mixtures on human health.
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Affiliation(s)
- Guiming Zhu
- Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, China
- Key Laboratory of Coal Environmental Pathogenicity and Prevention (Shanxi Medical University), Ministry of Education, Taiyuan, China
| | - Yanchao Wen
- Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, China
- Key Laboratory of Coal Environmental Pathogenicity and Prevention (Shanxi Medical University), Ministry of Education, Taiyuan, China
| | - Kexin Cao
- Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, China
- Key Laboratory of Coal Environmental Pathogenicity and Prevention (Shanxi Medical University), Ministry of Education, Taiyuan, China
| | - Simin He
- Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, China
- Key Laboratory of Coal Environmental Pathogenicity and Prevention (Shanxi Medical University), Ministry of Education, Taiyuan, China
| | - Tong Wang
- Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, China
- Key Laboratory of Coal Environmental Pathogenicity and Prevention (Shanxi Medical University), Ministry of Education, Taiyuan, China
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Grady SK, Dojcsak L, Harville EW, Wallace ME, Vilda D, Donneyong MM, Hood DB, Valdez RB, Ramesh A, Im W, Matthews-Juarez P, Juarez PD, Langston MA. Seminar: Scalable Preprocessing Tools for Exposomic Data Analysis. ENVIRONMENTAL HEALTH PERSPECTIVES 2023; 131:124201. [PMID: 38109119 PMCID: PMC10727037 DOI: 10.1289/ehp12901] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Revised: 11/22/2023] [Accepted: 11/28/2023] [Indexed: 12/19/2023]
Abstract
BACKGROUND The exposome serves as a popular framework in which to study exposures from chemical and nonchemical stressors across the life course and the differing roles that these exposures can play in human health. As a result, data relevant to the exposome have been used as a resource in the quest to untangle complicated health trajectories and help connect the dots from exposures to adverse outcome pathways. OBJECTIVES The primary aim of this methods seminar is to clarify and review preprocessing techniques critical for accurate and effective external exposomic data analysis. Scalability is emphasized through an application of highly innovative combinatorial techniques coupled with more traditional statistical strategies. The Public Health Exposome is used as an archetypical model. The novelty and innovation of this seminar's focus stem from its methodical, comprehensive treatment of preprocessing and its demonstration of the positive effects preprocessing can have on downstream analytics. DISCUSSION State-of-the-art technologies are described for data harmonization and to mitigate noise, which can stymie downstream interpretation, and to select key exposomic features, without which analytics may lose focus. A main task is the reduction of multicollinearity, a particularly formidable problem that frequently arises from repeated measurements of similar events taken at various times and from multiple sources. Empirical results highlight the effectiveness of a carefully planned preprocessing workflow as demonstrated in the context of more highly concentrated variable lists, improved correlational distributions, and enhanced downstream analytics for latent relationship discovery. The nascent field of exposome science can be characterized by the need to analyze and interpret a complex confluence of highly inhomogeneous spatial and temporal data, which may present formidable challenges to even the most powerful analytical tools. A systematic approach to preprocessing can therefore provide an essential first step in the application of modern computer and data science methods. https://doi.org/10.1289/EHP12901.
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Affiliation(s)
- Stephen K. Grady
- Graduate School of Genome Science and Technology, University of Tennessee, Knoxville, Tennessee, USA
| | - Levente Dojcsak
- Department of Electrical Engineering and Computer Science, University of Tennessee, Knoxville, Tennessee, USA
| | - Emily W. Harville
- Department Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, Louisiana, USA
| | - Maeve E. Wallace
- Department of Social, Behavioral, and Population Sciences, Tulane University School of Public Health and Tropical Medicine, New Orleans, Louisiana, USA
| | - Dovile Vilda
- Department of Social, Behavioral, and Population Sciences, Tulane University School of Public Health and Tropical Medicine, New Orleans, Louisiana, USA
| | | | - Darryl B. Hood
- Division of Environmental Health Sciences, College of Public Health, Ohio State University, Columbus, Ohio, USA
| | - R. Burciaga Valdez
- Department of Economics, University of New Mexico, Albuquerque, New Mexico, USA
| | - Aramandla Ramesh
- Department of Biochemistry, Cancer Biology, Neuroscience & Pharmacology, Meharry Medical College, Nashville, Tennessee, USA
| | - Wansoo Im
- Department of Family and Community Medicine, Meharry Medical College, Nashville, Tennessee, USA
| | | | - Paul D. Juarez
- Department of Family and Community Medicine, Meharry Medical College, Nashville, Tennessee, USA
- Institute on Health Disparities, Equity, and the Exposome, Meharry Medical College, Nashville, Tennessee, USA
| | - Michael A. Langston
- Department of Electrical Engineering and Computer Science, University of Tennessee, Knoxville, Tennessee, USA
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Makris KC, Heibati B, Narui SZ. Chrono-modulated effects of external stressors on oxidative stress and damage in humans: A scoping review on night shift work. ENVIRONMENT INTERNATIONAL 2023; 178:108048. [PMID: 37463540 DOI: 10.1016/j.envint.2023.108048] [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: 02/14/2023] [Revised: 06/13/2023] [Accepted: 06/15/2023] [Indexed: 07/20/2023]
Abstract
BACKGROUND Oxidative stress and tissue damage (OSD) play a pivotal role as an early-stage process in chronic disease pathogenesis. However, there has been little research to better understand the temporal (χρόνος[chronos]) dimensions of OSD process associated with environmental (non-genetic, including behaviors/lifestyle) and/or occupational stressors, like night shift work. OSD processes have recently attracted attention in relation to time-resolved external stressor trajectories in personalized medicine (prevention) initiatives, as they seem to interact with circadian clock systems towards the improved delineation of the early stages of (chronic) disease process. OBJECTIVES This work critically reviewed human studies targeting the temporal dynamics of OSD and circadian clock system's activity in response to environmental/occupational stressors; the case of night shift work was examined. METHODS Being a key stressor influencing OSD processes and circadian rhythm, night shift work was evaluated as part of a scoping review of research in OSD, including inflammatory and metabolic processes to determine the extent of OSD research undertaken in human populations, methodologies, tools and biomarkers used and the extent that the temporal dimensions of exposure and biological effect(s) were accounted for. Online databases were searched for papers published from 2000 onwards, resulting in the selection of 53 original publications. RESULTS AND DISCUSSION The majority of studies (n = 41) took place in occupational settings, while the rest were conducted in the general population or patient groups. Most occupational studies targeted outcomes of oxidative stress/damage (n = 19), followed by the combination of OSD with inflammatory response (n = 10), and studies focused on metabolic outcomes (n = 12). Only a minor fraction of the studies measured biomarkers related to circadian rhythm, such as, melatonin, its metabolite, or cortisol. Night shift work was associated with select biomarkers of OSD and inflammation, albeit with mixed results. Although much progress in delineating the biological mechanisms of OSD process has been made, an equally thorough investigation on the temporal trajectory of OSD processes as triggered by environmental/occupational stressors in human studies has yet to fully evolve.
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Affiliation(s)
- Konstantinos C Makris
- Cyprus International Institute for Environmental and Public Health, Cyprus University of Technology, Limassol, Cyprus.
| | - Behzad Heibati
- Cyprus International Institute for Environmental and Public Health, Cyprus University of Technology, Limassol, Cyprus; Department of Research, Cancer Registry Norway, Oslo, Norway
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Zhang Y, Wang X, Yang X, Hu Q, Chawla K, Hang B, Mao JH, Snijders AM, Chang H, Xia Y. Chemical mixture exposure patterns and obesity among U.S. adults in NHANES 2005-2012. ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY 2022; 248:114309. [PMID: 36427371 PMCID: PMC10012331 DOI: 10.1016/j.ecoenv.2022.114309] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/01/2022] [Revised: 11/14/2022] [Accepted: 11/15/2022] [Indexed: 05/11/2023]
Abstract
BACKGROUND The effect of chemical exposure on obesity has raised great concerns. Real-world chemical exposure always imposes mixture impacts, however their exposure patterns and the corresponding associations with obesity have not been fully evaluated. OBJECTIVES To discover obesity-related mixed chemical exposure patterns in the general U.S. METHODS Sparse Decompositional Regression (SDR), a model adapted from sparse representation learning technique, was developed to identify exposure patterns of chemical mixtures with exclusion (non-targeted model) and inclusion (targeted model) of health outcomes. We assessed the relationships between the identified chemical mixture patterns and obesity-related indexes. We also conducted a comprehensive evaluation of this SDR model by comparing to the existing models, including generalized linear regression model (GLM), principal component analysis (PCA), and Bayesian kernel machine regression (BKMR). RESULTS Eight core exposure patterns were identified using the non-targeted SDR model. Patterns of high levels of MEP, high levels of naphthalene metabolites (ΣOH-Nap), and a pattern of high exposure levels of MCOP, MCNP, and MCPP were positively associated with obesity. Patterns of high levels of BP3, and a pattern of higher mixed levels of MPB, PPB, and MEP were found to have negative associations. Associations were strengthened using the targeted SDR model. In the single chemical analysis by GLM, BP3, MBP, PPB, MCOP, and MCNP showed significant associations with obesity or body indexes. The SDR model exceeded the performance of PCA in pattern identification. Both SDR and BKMR identified a positive contribution of ΣOH-Nap and MCOP, as well as a negative contribution of BP3 and PPB to obesity. CONCLUSION Our study identified five core exposure patterns of chemical mixtures significantly associated with obesity using the newly developed SDR model. The SDR model could open a new avenue for assessing health effects of environmental mixture contaminants.
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Affiliation(s)
- Yuqing Zhang
- Department of Obstetrics and Gynecology, Women's Hospital of Nanjing Medical University,Nanjing Maternity and Child Health Care Hospital, Nanjing 210004, China
| | - Xu Wang
- Department of endocrinology, Children's Hospital of Nanjing Medical University, Nanjing 210008, China
| | - Xu Yang
- State Key Laboratory of Reproductive Medicine, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China; Key Laboratory of Modern Toxicology of Ministry of Education, School of Public Health, Nanjing Medical University, Nanjing 211166, China
| | - Qi Hu
- State Key Laboratory of Reproductive Medicine, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China; Key Laboratory of Modern Toxicology of Ministry of Education, School of Public Health, Nanjing Medical University, Nanjing 211166, China
| | - Kuldeep Chawla
- Scientific Computing Group, Information Technology Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA; Berkeley Biomedical Data Science Center, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Bo Hang
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Jian-Hua Mao
- Berkeley Biomedical Data Science Center, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA; Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Antoine M Snijders
- Berkeley Biomedical Data Science Center, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA; Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Hang Chang
- Berkeley Biomedical Data Science Center, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA; Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA.
| | - Yankai Xia
- State Key Laboratory of Reproductive Medicine, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China; Key Laboratory of Modern Toxicology of Ministry of Education, School of Public Health, Nanjing Medical University, Nanjing 211166, China.
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