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

Abstract

We report integration of the United States Environmental Protection Agency's (USEPA) United States Environmental Justice Screen (EJSCREEN) database with our Public Health Exposome dataset to interrogate 9232 census blocks to model the complexity of relationships among environmental and socio-demographic variables toward estimating adverse pregnancy outcomes [low birth weight (LBW) and pre-term birth (PTB)] in all Ohio counties. Using a hill-climbing algorithm in R software, we derived a Bayesian network that mapped all controlled associations among all variables available by applying a mapping algorithm. The results revealed 17 environmental and socio-demographic variables that were represented by nodes containing 69 links accounting for a network with 32.85% density and average degree of 9.2 showing the most connected nodes in the center of the model. The model predicts that the socio-economic variables low income, minority, and under age five populations are correlated and associated with the environmental variables; particulate matter (PM 2.5 ) level in air, proximity to risk management facilities, and proximity to direct discharges in water are linked to PTB and LBW in 88 Ohio counties. The methodology used to derive significant associations of chemical and non-chemical stressors linked to PTB and LBW from indices of geo-coded environmental neighborhood deprivation serves as a proxy for design of an African-American women's cohort to be recruited in Ohio counties from federally qualified community health centers within the 9232 census blocks. The results have implications for the development of severity scores for endo-phenotypes of resilience based on associations and linkages for different chemical and non-chemical stressors that have been shown to moderate cardio-metabolic disease within a population health context.

Particulate Matter Exposure across Latino Ethnicities

“…Concepts such as the social determinants of health [ 49 ], and the exposome [ 50 ] require new thinking and operationalization of riskscapes. Scholars working in this area have derived algorithms to parse out relationships between socio-demographic variables and highlight those which are the most meaningful [ 51 ]. Such work needs to be done for air toxins.…”

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

“…In combination with the Public Health Exposome, the question as to how chemical stressors such as components of ambient pollution negatively impact vulnerable populations to result in premature death, including ischemic cardiovascular disease, stroke, respiratory infections, chronic obstructive pulmonary disease, and lung cancer might be more appropriately addressed [ 28 , 29 , 30 , 31 , 32 ]. Using the Public Health Exposome [ 33 , 34 , 35 ] and HOI analytics to integrate public health with exposure science and population-based epidemiological designs will likely provide novel and innovative theories and interventions to address disparate health outcomes. A recent study used data from the Global Burden of Disease 2016 study to address the question: “How much does exposure to chemical stressor (as PM 2.5 air pollution) shorten human life expectancy around the world?” The study used a prior dataset that evaluated chemical stressor levels in 2000 and 2007 from 585 US counties and effects on life expectancy.…”

A Scoping Review on the Characteristics of Human Exposome Studies

“…Five studies were case-control (6%) [94,[96][97][98][99] and three case-control studies nested in cohorts (4%) [100][101][102]. In addition to studies that were based on primary data collection and analysis, some studies utilized secondary data, such as, county-aggregated level data (four studies (5%)) [103][104][105][106], two studies used the National Health and Nutrition Examination Survey (NHANES) database (2%) [107,108] and another one used data collected from numerous occupational disease consultation centers in France (1%) [109]. Also, one study was a retrospective case-series (1%) using the dentine matrix [110].…”

“…48,50,63,67,70,95,97,100,103,104,106,[118][119][120][121][122][123]. Environmental factors were examined in association with gene expression profiling in four studies (5%)[65,71,78,108].…”