Application of the Public Health Exposome Framework to Estimate Phenotypes of Resilience in a Model Ohio African-American Women's Cohort

Community-level exposomics: a population-centered approach to address public health concerns

Environmental factors affecting health and vulnerability far outweigh genetics in accounting for disparities in health status and longevity in US communities. The concept of the exposome, the totality of exposure from conception onwards, provides a paradigm for researchers to investigate the complex role of the environment on the health of individuals. We propose a complementary framework, community-level exposomics, for population-level exposome assessment. The goal is to bring the exposome paradigm to research and practice on the health of populations, defined by various axes including geographic, social, and occupational. This framework includes the integration of community-level measures of the built, natural and social environments, environmental pollution-derived from conventional and community science approaches, internal markers of exposure that can be measured at the population-level and early responses associated with health status that can be tracked using population-based monitoring. Primary challenges to the implementation of the proposed framework include needed advancements in population-level measurement, lack of existing models with the capability to produce interpretable and actionable evidence and the ethical considerations of labeling geographically-bound populations by exposomic profiles. To address these challenges, we propose a set of recommendations that begin with greater engagement with and empowerment of affected communities and targeted investment in community-based solutions. Applications to urban settings and disaster epidemiology are discussed as examples for implementation.

The public health exposome and pregnancy-related mortality in the United States: a high-dimensional computational analysis

BACKGROUND

Racial inequities in maternal mortality in the U.S. continue to be stark.

METHODS

The 2015-2018, 4-year total population, county-level, pregnancy-related mortality ratio (PRM; deaths per 100,000 live births; National Center for Health Statistics (NCHS), restricted use mortality file) was linked with the Public Health Exposome (PHE). Using data reduction techniques, 1591 variables were extracted from over 62,000 variables for use in this analysis, providing information on the relationships between PRM and the social, health and health care, natural, and built environments. Graph theoretical algorithms and Bayesian analysis were applied to PHE/PRM linked data to identify latent networks.

RESULTS

PHE variables most strongly correlated with total population PRM were years of potential life lost and overall life expectancy. Population-level indicators of PRM were overall poverty, smoking, lack of exercise, heat, and lack of adequate access to food.

CONCLUSIONS

In this high-dimensional analysis, overall life expectancy, poverty indicators, and health behaviors were found to be the strongest predictors of pregnancy-related mortality. This provides strong evidence that maternal death is part of a broader constellation of both similar and unique health behaviors, social determinants and environmental exposures as other causes of death.

A Framework for Interfacing and Partnering with Environmental Justice Communities as a Prelude to Human Health and Hazard Identification in the Vulnerable Census Tracts of Columbus, Ohio

Columbus, Ohio is one of the more prosperous, well-educated, and progressive cities in the United States. However, it ranks as the second worst life expectancy at birth, has a census tract wealth gap (27-year disparity), and one of the higher infant mortality rates in the country. These data suggest that there are likely several high-risk, vulnerable neighborhoods in Columbus with residents experiencing disparate and adverse outcomes. Illustrative of this fact are studies that have examined the social processes and mechanisms through which neighborhood contexts are at the forefront, including exposures to chemical stressors such as particulate matter (PM_2.5) as well as non-chemical stressors including violence, social determinants of health, zoning, and land use policies. It is documented that disparate and adverse outcomes are magnified in the vulnerable neighborhoods on the Near East Side as compared to Columbus city proper, Franklin County and/or the state of Ohio. As such, we developed a nuanced community engagement framework to identify potential environmental hazards associated with adverse pregnancy outcomes in those census tracts. The refined framework uses a blended version of traditional community-based participatory research (CBPR) models and is referred to as E⁶, Enhancing Environmental Endeavors via e-Equity, Education, and Empowerment.

Big Data to Knowledge Analytics Reveals the Zika Virus Epidemic as Only One of Multiple Factors Contributing to a Year-Over-Year 28-Fold Increase in Microcephaly Incidence

During the 2015-2016 Zika Virus (ZIKV) epidemic in Brazil, the geographical distributions of ZIKV infection and microcephaly outbreaks did not align. This raised doubts about the virus as the single cause of the microcephaly outbreak and led to research hypotheses of alternative explanatory factors, such as environmental variables and factors, agrochemical use, or immunizations. We investigated context and the intermediate and structural determinants of health inequalities, as well as social environment factors, to determine their interaction with ZIKV-positive- and ZIKV-negative-related microcephaly. The results revealed the identification of 382 associations among 382 nonredundant variables of Zika surveillance, including multiple determinants of environmental public health factors and variables obtained from 5565 municipalities in Brazil. This study compared those factors and variables directly associated with microcephaly incidence positive to ZIKV and those associated with microcephaly incidence negative to ZIKV, respectively, and mapped them in case and control subnetworks. The subnetworks of factors and variables associated with low birth weight and birthweight where birth incidence served as an additional control were also mapped. Non-significant differences in factors and variables were observed, as were weights of associations between microcephaly incidence, both positive and negative to ZIKV, which revealed diagnostic inaccuracies that translated to the underestimation of the scope of the ZIKV outbreak. A detailed analysis of the patterns of association does not support a finding that vaccinations contributed to microcephaly, but it does raise concerns about the use of agrochemicals as a potential factor in the observed neurotoxicity arising from the presence of heavy metals in the environment and microcephaly not associated with ZIKV. Summary: A comparative network inferential analysis of the patterns of variables and factors associated with Zika virus infections in Brazil during 2015-2016 coinciding with a microcephaly epidemic identified multiple contributing determinants. This study advances our understanding of the cumulative interactive effects of exposures to chemical and non-chemical stressors in the built, natural, physical, and social environments on adverse pregnancy and health outcomes in vulnerable populations.

Integrating Environment and Aging Research: Opportunities for Synergy and Acceleration

Despite significant overlaps in mission, the fields of environmental health sciences and aging biology are just beginning to intersect. It is increasingly clear that genetics alone does not predict an individual’s neurological aging and sensitivity to disease. Accordingly, aging neuroscience is a growing area of mutual interest within environmental health sciences. The impetus for this review came from a workshop hosted by the National Academies of Sciences, Engineering, and Medicine in June of 2020, which focused on integrating the science of aging and environmental health research. It is critical to bridge disciplines with multidisciplinary collaborations across toxicology, comparative biology, epidemiology to understand the impacts of environmental toxicant exposures and age-related outcomes. This scoping review aims to highlight overlaps and gaps in existing knowledge and identify essential research initiatives. It begins with an overview of aging biology and biomarkers, followed by examples of synergy with environmental health sciences. New areas for synergistic research and policy development are also discussed. Technological advances including next-generation sequencing and other-omics tools now offer new opportunities, including exposomic research, to integrate aging biomarkers into environmental health assessments and bridge disciplinary gaps. This is necessary to advance a more complete mechanistic understanding of how life-time exposures to toxicants and other physical and social stressors alter biological aging. New cumulative risk frameworks in environmental health sciences acknowledge that exposures and other external stressors can accumulate across the life course and the advancement of new biomarkers of exposure and response grounded in aging biology can support increased understanding of population vulnerability. Identifying the role of environmental stressors, broadly defined, on aging biology and neuroscience can similarly advance opportunities for intervention and translational research. Several areas of growing research interest include expanding exposomics and use of multi-omics, the microbiome as a mediator of environmental stressors, toxicant mixtures and neurobiology, and the role of structural and historical marginalization and racism in shaping persistent disparities in population aging and outcomes. Integrated foundational and translational aging biology research in environmental health sciences is needed to improve policy, reduce disparities, and enhance the quality of life for older individuals.

Particulate Matter Exposure across Latino Ethnicities

The Hispanic/Latino health paradox is the well-known health advantage seen across the Hispanic/Latino racial category in the US. However, this racial category collapses several distinct ethnic groups with varying spatial distributions. Certain populations, such as Dominicans and Cubans, are concentrated in specific areas, compared to more dispersed groups such as Mexicans. Historical peculiarities have brought these populations into contact with specific types of environmental exposures. This paper takes a first step towards unraveling these diverse exposure profiles by estimating how exposure to particulate matter varies across demographic groups and narrows down which types of industries and chemicals are contributing the most to air toxins. Exposure to particulate matter is estimated for 72,271 census tracts in the continental US to evaluate how these exposures correlate with the proportion of the population classified within the four largest groups that make up the Hispanic population in the US: Mexican, Puerto Rican, Cuban, and Dominican. Using linear mixed models, with the state nested within US Environmental Protection Agency regulatory region, and controls for population density, we find that the Dominican population is significantly less exposed to PM_2.5 and PM₁₀ compared to non-Hispanic Whites. Moreover, those tracts with a higher proportion of Cuban residents are significantly less exposed to PM_2.5. However, those tracts with a higher proportion of foreign-born, Mexicans, and Puerto Ricans had significantly higher levels of exposure to all sizes of particulate matter. We discuss the need to consider the chemical components of these particles to better understand the risk of exposure to air pollution.

Spatiotemporal strategies to identify aggressive biology in precancerous breast biopsies

Over 90% of breast cancer is cured; yet there remain highly aggressive breast cancers that develop rapidly and are extremely difficult to treat, much less prevent. Breast cancers that rapidly develop between breast image screening are called “interval cancers.” The efforts of our team focus on identifying multiscale integrated strategies to identify biologically aggressive precancerous breast lesions. Our goal is to identify spatiotemporal changes that occur prior to development of interval breast cancers. To accomplish this requires integration of new technology. Our team has the ability to perform single cell in situ transcriptional profiling, noncontrast biological imaging, mathematical analysis, and nanoscale evaluation of receptor organization and signaling. These technological innovations allow us to start to identify multidimensional spatial and temporal relationships that drive the transition from biologically aggressive precancer to biologically aggressive interval breast cancer.

This article is categorized under:

Cancer > Computational Models

Cancer > Molecular and Cellular Physiology

Cancer > Genetics/Genomics/Epigenetics

The Health Opportunity Index: Understanding the Input to Disparate Health Outcomes in Vulnerable and High-Risk Census Tracts

The Health Opportunity Index (HOI) is a multivariate tool that can be more efficiently used to identify and understand the interplay of complex social determinants of health (SDH) at the census tract level that influences the ability to achieve optimal health. The derivation of the HOI utilizes the data-reduction technique of principal component analysis to determine the impact of SDH on optimal health at lower census geographies. In the midst of persistent health disparities and the present COVID-19 pandemic, we demonstrate the potential utility of using 13-input variables to derive a composite metric of health (HOI) score as a means to assist in the identification of the most vulnerable communities during the current pandemic. Using GIS mapping technology, health opportunity indices were layered by counties in Ohio to highlight differences by census tract. Collectively we demonstrate that our HOI framework, principal component analysis and convergence analysis methodology coalesce to provide results supporting the utility of this framework in the three largest counties in Ohio: Franklin (Columbus), Cuyahoga (Cleveland), and Hamilton (Cincinnati). The results in this study identified census tracts that were also synonymous with communities that were at risk for disparate COVID-19 related health outcomes. In this regard, convergence analyses facilitated identification of census tracts where different disparate health outcomes co-exist at the worst levels. Our results suggest that effective use of the HOI composite score and subcomponent scores to identify specific SDH can guide mitigation/intervention practices, thus creating the potential for better targeting of mitigation and intervention strategies for vulnerable communities, such as during the current pandemic.