Seminar: Scalable Preprocessing Tools for Exposomic Data Analysis.

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.

The association between cardiovascular disease and breast and gynecologic cancers among black female patients.

INTRODUCTION: Little is known about whether a breast or gynecologic cancer diagnosis increases long-term cardiovascular disease (CVD) risk among Black females. The purpose of this study was to determine whether a breast or gynecologic cancer diagnosis is associated with CVD risk and identify determinants of subsequent CVD risk among Black females with an incident breast or gynecologic cancer diagnosis. METHODS: Using the Southern Community Cohort Study data from 2002-2016, this study was designed to analyze CVD incidence among Black females without cancer or CVD at enrollment. Cox proportional hazards regression models with or without covariates were used to explore the relationship between a breast or gynecologic cancer diagnosis and CVD risk among women without cancer as well as without CVD at enrollment (N=11,486). In addition, Cox proportional hazards regression models, excluding those who developed CVD before breast and gynecologic cancer diagnosis and those with other types of cancers, were used to assess determinants of CVD risk among breast and gynecologic cancer survivors. RESULTS: Of 11,486 Black females, 531 developed a breast or gynecological cancer (4.6%) over a median follow-up of 140 months (interquartile range: 123-159 months). Compared to women without cancer, women with a breast or gynecological cancers had greater than 20% higher risk of incident CVD during the follow-up period. Without adjusting for covariates, positive association between CVD risk and breast cancer was observed (hazard ratio (HR) = 1.24; 95% confidence interval (CI) = 1.11 - 1.39; p < 0.001); as well as between CVD risk and a gynecological cancer (HR = 1.23; 95% CI = 1.03 - 1.46; p = 0.021). Yet, after adjusting for covariates, CVD risk was only significantly associated with breast cancer (p = 0.001) but not gynecologic cancer. In cancer case-only analyses, CVD risk was significantly increasing with age (p < 0.05). CONCLUSIONS: Like study populations of predominantly White females, our results suggest that, adjusting for covariates, Black females possess a higher risk of CVD following a breast cancer diagnosis compared to women who did not develop breast cancer. Our results suggest a need for active CVD surveillance in the cancer survivorship phase.

Association of Psychosocial Factors on COVID-19 Testing among YWCA Service Recipients.

The purpose of this study was to examine how psychosocial factors affect receipt of COVID-19 testing among Black and Hispanic women. In this cross-sectional study of Black and Hispanic women who received services from the YWCAs in Atlanta, El Paso, Nashville, and Tucson between 2019 and 2021 (n = 662), we used Patient-Reported Outcomes Measurement Information Systems (PROMIS) item bank 1.0 short forms to examine the impact of psychosocial factors (i.e., depression, anxiety, social isolation, instrumental support, emotional support, and companionship) on COVID-19 testing. Multivariable logistic regression models were used to estimate odds ratios and 95% confidence intervals for receipt of a COVID-19 test associated with psychosocial factors while adjusting for confounders. There was little effect of moderate/severe depressions or anxiety on receipt of COVID-19 testing. Black (odds ratio [OR] 0.58, 95% confidence interval [CI] 0.26-1.29) and Hispanic (OR 0.61, 95% CI 0.38-0.96) women with high levels of emotional support were less likely to receive the COVID-19 test. While high levels of instrumental support was associated with less likely receipt of the COVID-19 test among Black women (OR 0.75, 95% CI 0.34-1.66), it was associated with more likely receipt among Hispanic women (OR 1.19, 95% CI 0.74-1.92). Our findings suggest that certain psychosocial factors influence one's decision to get a COVID-19 test which can be useful in encouraging preventive healthcare such as screening and vaccination.

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 (PM2.5) 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.

Recommendations for Increasing Physician Provision of Pre-Exposure Prophylaxis: Implications for Medical Student Training.

There is growing evidence that pre-exposure prophylaxis (PrEP) prevents HIV acquisition. However, in the United States, approximately only 4% of people who could benefit from PrEP are currently receiving it, and it is estimated only 1 in 5 physicians has ever prescribed PrEP. We conducted a scoping review to gain an understanding of physician-identified barriers to PrEP provision. Four overarching barriers presented in the literature: Purview Paradox, Patient Financial Constraints, Risk Compensation, and Concern for ART Resistance. Considering the physician-identified barriers, we make recommendations for how physicians and students may work to increase PrEP knowledge and competence along each stage of the PrEP cascade. We recommend adopting HIV risk assessment as a standard of care, improving physician ability to identify PrEP candidates, improving physician interest and ability in encouraging PrEP uptake, and increasing utilization of continuous care management to ensure retention and adherence to PrEP.

Structural and Social Determinants of Health Factors Associated with County-Level Variation in Non-Adherence to Antihypertensive Medication Treatment.

BACKGROUND: Non-adherence to antihypertensive medication treatment (AHM) is a complex health behavior with determinants that extend beyond the individual patient. The structural and social determinants of health (SDH) that predispose populations to ill health and unhealthy behaviors could be potential barriers to long-term adherence to AHM. However, the role of SDH in AHM non-adherence has been understudied. Therefore, we aimed to define and identify the SDH factors associated with non-adherence to AHM and to quantify the variation in county-level non-adherence to AHM explained by these factors. METHODS: Two cross-sectional datasets, the Centers for Disease Control and Prevention (CDC) Atlas of Heart Disease and Stroke (2014-2016 cycle) and the 2016 County Health Rankings (CHR), were linked to create an analytic dataset. Contextual SDH variables were extracted from the CDC-CHR linked dataset. County-level prevalence of AHM non-adherence, based on Medicare fee-for-service beneficiaries' claims data, was extracted from the CDC Atlas dataset. The CDC measured AHM non-adherence as the proportion of days covered (PDC) with AHM during a 365 day period for Medicare Part D beneficiaries and aggregated these measures at the county level. We applied confirmatory factor analysis (CFA) to identify the constructs of social determinants of AHM non-adherence. AHM non-adherence variation and its social determinants were measured with structural equation models. RESULTS: Among 3000 counties in the U.S., the weighted mean prevalence of AHM non-adherence (PDC < 80%) in 2015 was 25.0%, with a standard deviation (SD) of 18.8%. AHM non-adherence was directly associated with poverty/food insecurity (ß = 0.31, P-value < 0.001) and weak social supports (ß = 0.27, P-value < 0.001), but inversely with healthy built environment (ß = -0.10, P-value = 0.02). These three constructs explained one-third (R2 = 30.0%) of the variation in county-level AHM non-adherence. CONCLUSION: AHM non-adherence varies by geographical location, one-third of which is explained by contextual SDH factors including poverty/food insecurity, weak social supports and healthy built environments.

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.

The Effects of Social, Personal, and Behavioral Risk Factors and PM2.5 on Cardio-Metabolic Disparities in a Cohort of Community Health Center Patients.

(1) Background: Cardio-metabolic diseases (CMD), including cardiovascular disease, stroke, and diabetes, have numerous common individual and environmental risk factors. Yet, few studies to date have considered how these multiple risk factors together affect CMD disparities between Blacks and Whites. (2) Methods: We linked daily fine particulate matter (PM2.5) measures with survey responses of participants in the Southern Community Cohort Study (SCCS). Generalized linear mixed modeling (GLMM) was used to estimate the relationship between CMD risk and social-demographic characteristics, behavioral and personal risk factors, and exposure levels of PM2.5. (3) Results: The study resulted in four key findings: (1) PM2.5 concentration level was significantly associated with reported CMD, with risk rising by 2.6% for each µg/m3 increase in PM2.5; (2) race did not predict CMD risk when clinical, lifestyle, and environmental risk factors were accounted for; (3) a significant variation of CMD risk was found among participants across states; and (4) multiple personal, clinical, and social-demographic and environmental risk factors played a role in predicting CMD occurrence. (4) Conclusions: Disparities in CMD risk among low social status populations reflect the complex interactions of exposures and cumulative risks for CMD contributed by different personal and environmental factors from natural, built, and social environments.

Curricular Interventions in Medical Schools for Assessing Adverse Childhood Experience.

Scientific evidence is accumulating about the range of adverse health, mental health, and risky behavioral sequelae across the life continuum arising from exposure to Adverse Childhood Experiences (ACEs). Research findings show a clear relationship between the number of ACEs experienced by a person during childhood and the adverse health outcomes of adulthood. The purpose of this systematic review was to assess the extent to which medical schools are teaching medical students about ACEs. Published articles were identified through searches of several databases using a combination of major and minor MeSH terms. Out of a total of 715 publications screened, 13 studies were identified that focused on medical education efforts to address ACEs. Educational interventions were conducted in a variety of formats, including lectures, perspective-taking exercises, and small group discussions. Our systematic review found little evidence to suggest that medical schools are teaching students how to address ACEs among their patients.

Preparing Medical Students to Address the Needs of Vulnerable Patient Populations: Implicit Bias Training in US Medical Schools.

Little is known about how medical students are trained to identify and reduce their own biases toward vulnerable patient groups. A survey was conducted among US medical schools to determine whether their curricula addressed physician implicit biases toward three vulnerable patient groups: lesbian, gay, bisexual, transgender, and questioning (LGBTQ) individuals, persons experiencing homelessness, and migrant farmworkers. Of 141 US medical schools, 71 (50%) responded. Survey respondents indicated that implicit bias is not routinely addressed in medical education, and training specific to vulnerable populations is infrequent. Recommendations for incorporating implicit bias training in medical school curricula are discussed.

Opioid prescription rates and risk for substantiated child abuse and neglect: A Bayesian spatiotemporal analysis.

OBJECTIVES: To determine the association between opioid prescribing rates and substantiated abuse and neglect across Tennessee counties during an 11-year period. METHODS: We adopted a Bayesian spatiotemporal approach to determine the association between opioid prescribing and rates of substantiated child abuse and neglect over and above environmental and population-level covariates. Annual county-level data for Tennessee (2006-2016) included rates of substantiated child abuse and neglect, rates of drug and non-drug crime incidents, racial and Hispanic composition, per capita income, child poverty and teen birth rates, and vacant housing. RESULTS: Higher opioid prescribing rates were associated with greater risk for substantiated child abuse and neglect across Tennessee counties. Risk for substantiated child abuse and neglect was positively associated with vacant housing, child poverty, teen birth rates, and rates of both drug and non-drug criminal incidents - including stimulant arrests. Risk for substantiated child abuse and neglect was negatively associated with percentages of African Americans. CONCLUSIONS: Results underscore the importance of opioid prescribing and crime rates as independent determinants of spatial and temporal variation in risk for substantiated child abuse and neglect. Policies that regulate and reduce opioid prescribing have the potential to reduce risk for child abuse and neglect.

Training to reduce LGBTQ-related bias among medical, nursing, and dental students and providers: a systematic review.

BACKGROUND: Lesbian, gay, bisexual, transgender and questioning (LGBTQ) individuals experience higher rates of health disparities. These disparities may be driven, in part, by biases of medical providers encountered in health care settings. Little is known about how medical, nursing, or dental students are trained to identify and reduce the effects of their own biases toward LGBTQ individuals. Therefore, a systematic review was conducted to determine the effectiveness of programs to reduce health care student or provider bias towards these LGBTQ patients. METHODS: The authors performed searches of online databases (MEDLINE/PubMed, PsycINFO, Web of Science, Scopus, Ingenta, Science Direct, and Google Scholar) for original articles, published in English, between March 2005 and February 2017, describing intervention studies focused on reducing health care student or provider bias towards LGBTQ individuals. Data extracted included sample characteristics (i.e., medical, nursing, or dental students or providers), study design (i.e., pre-post intervention tests, qualitative), program format, program target (i.e., knowledge, comfort level, attitudes, implicit bias), and relevant outcomes. Study quality was assessed using a five-point scale. RESULTS: The search identified 639 abstracts addressing bias among medical, nursing, and dental students or providers; from these abstracts, 60 articles were identified as medical education programs to reduce bias; of these articles, 13 described programs to reduce bias towards LGBTQ patients. Bias-focused educational interventions were effective at increasing knowledge of LGBTQ health care issues. Experiential learning interventions were effective at increasing comfort levels working with LGBTQ patients. Intergroup contact was effective at promoting more tolerant attitudes toward LGBTQ patients. Despite promising support for bias education in increasing knowledge and comfort levels among medical, nursing, and dental students or providers towards LGBTQ persons, this systematic review did not identify any interventions that assessed changes in implicit bias among students or providers. CONCLUSIONS: Strategies for assessing and mitigating implicit bias towards LGBTQ patients are discussed and recommendations for medical, nursing, and dental school curricula are presented.

County-level socioeconomic and crime risk factors for substantiated child abuse and neglect.

Rates of substantiated child abuse and neglect vary significantly across counties. Despite strong cross-sectional support for links between social-contextual characteristics and abuse and neglect, few longitudinal studies have tested relations between these risk factors and substantiated rates of abuse/neglect. The goal of this study was to identify county-level socioeconomic and crime factors associated with substantiated abuse/neglect rates over 13 years (2004-2016). Annual county-level data for Tennessee, obtained from the KIDS COUNT Data Center, included rates of substantiated child abuse and neglect, children's race and ethnicity, births to unmarried women, teen birth rate, children in families receiving Supplemental Nutrition Assistance Program (SNAP) benefits, and children in families receiving Temporary Assistance for Needy Families. Annual county-level crime report data, obtained from the Tennessee Incident Based Reporting System, included sexual offenses, non-sexual assaults, stalking incidents, thefts, property damage, and drug-related offenses. Bayesian spatio-temporal models indicated that substantiated child abuse and neglect rates were independently and positively associated with teen birth rates, percentages of births to unmarried mothers, drug-related offenses, and percentages of children receiving SNAP benefits. In contrast, substantiated child abuse and neglect rates were negatively associated with percentages of African-American youth. The findings highlighted distinct demographic, socioeconomic, and crime factors associated with substantiated child abuse and neglect rates and have the potential to enhance identification of high-risk counties that could benefit from targeted abuse and neglect prevention efforts.

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

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 (PM2.5) 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.

Connecting Child Maltreatment Risk With Crime and Neighborhood Disadvantage Across Time and Place: A Bayesian Spatiotemporal Analysis.

Child maltreatment is a major public health problem. Although maltreatment rates vary over time and are influenced by neighborhood characteristics, the unique effects of crime and disadvantage on risk are not well understood. This study utilized a Bayesian spatiotemporal approach to examine risk factors for substantiated child abuse and neglect over a 9-year period across zip codes in Davidson County, TN. Risk of child sexual and physical abuse decreased from 2008 to 2016. In contrast, risk of child neglect increased from 2011 to 2014, followed by a rapid decrease in risk. Whereas higher percentages of families living in poverty were associated with higher risk of all maltreatment subtypes, higher unemployment rates were uniquely associated with risk of child neglect. Crime rates were positively associated with risk of child physical and sexual abuse but not neglect. Results have implications for tailoring prevention strategies according to geographic area and maltreatment subtype.

A novel approach to analyzing lung cancer mortality disparities: Using the exposome and a graph-theoretical toolchain.

OBJECTIVES: The aim is to identify exposures associated with lung cancer mortality and mortality disparities by race and gender using an exposome database coupled to a graph theoretical toolchain. METHODS: Graph theoretical algorithms were employed to extract paracliques from correlation graphs using associations between 2162 environmental exposures and lung cancer mortality rates in 2067 counties, with clique doubling applied to compute an absolute threshold of significance. Factor analysis and multiple linear regressions then were used to analyze differences in exposures associated with lung cancer mortality and mortality disparities by race and gender. RESULTS: While cigarette consumption was highly correlated with rates of lung cancer mortality for both white men and women, previously unidentified novel exposures were more closely associated with lung cancer mortality and mortality disparities for blacks, particularly black women. CONCLUSIONS: Exposures beyond smoking moderate lung cancer mortality and mortality disparities by race and gender. POLICY IMPLICATIONS: An exposome approach and database coupled with scalable combinatorial analytics provides a powerful new approach for analyzing relationships between multiple environmental exposures, pathways and health outcomes. An assessment of multiple exposures is needed to appropriately translate research findings into environmental public health practice and policy.

Application of Citizen Science Risk Communication Tools in a Vulnerable Urban Community.

A public participatory geographical information systems (PPGIS) demographic, environmental, socioeconomic, health status portal was developed for the Stambaugh-Elwood (SE) community in Columbus, OH. We hypothesized that soil at SE residences would have metal concentrations above natural background levels. Three aims were developed that allowed testing of this hypothesis. Aim 1 focused on establishing partnerships between academia, state agencies and communities to assist in the development of a community voice. Aim 2 was to design and conduct soil sampling for residents of the SE community. Aim 3 was to utilize our interactive, customized portal as a risk communication tool by allowing residents to educate themselves as to the potential risks from industrial sources in close proximity to their community. Multiple comparisons of means were used to determine differences in soil element concentration by sampling location at p < 0.05. The results demonstrated that eight metals (As, Cd, Cu, Pb, Mo, Se, Tl, Zn) occurred at statistically-significantly greater levels than natural background levels, but most were below risk-based residential soil screening levels. Results were conveyed to residents via an educational, risk-communication informational card. This study demonstrates that community-led coalitions in collaboration with academic teams and state agencies can effectively address environmental concerns.

The public health exposome: a population-based, exposure science approach to health disparities research.

The lack of progress in reducing health disparities suggests that new approaches are needed if we are to achieve meaningful, equitable, and lasting reductions. Current scientific paradigms do not adequately capture the complexity of the relationships between environment, personal health and population level disparities. The public health exposome is presented as a universal exposure tracking framework for integrating complex relationships between exogenous and endogenous exposures across the lifespan from conception to death. It uses a social-ecological framework that builds on the exposome paradigm for conceptualizing how exogenous exposures "get under the skin". The public health exposome approach has led our team to develop a taxonomy and bioinformatics infrastructure to integrate health outcomes data with thousands of sources of exogenous exposure, organized in four broad domains: natural, built, social, and policy environments. With the input of a transdisciplinary team, we have borrowed and applied the methods, tools and terms from various disciplines to measure the effects of environmental exposures on personal and population health outcomes and disparities, many of which may not manifest until many years later. As is customary with a paradigm shift, this approach has far reaching implications for research methods and design, analytics, community engagement strategies, and research training.

The use of missing birth record data as a marker for adverse reproductive outcomes: a geocoded analysis of birth record data.

Adverse reproductive outcomes (AROs) disproportionately affect black American infants and significantly contribute to the U.S. infant mortality rate. Without accurate understanding of AROs, there remains little hope of ameliorating infant mortality rates or eliminating infant health disparities. However, despite the importance of monitoring infant mortality rates and health disparities, birth record data quality is not assured. Racial disparities in the reporting of birth record data have been documented, and missing birth record data for AROs appears to be disproportionate. Due to the extent of missing birth record data, innovative strategies have been developed to evaluate relationships between maternal socioeconomic status (SES) and community-based ARO rates. Because addresses convey aggregate information about income level, education and occupation, ZIP codes, census tracts and census block-groups have been applied to geocoding efforts. The goals of this study are to: 1) analyze the extent of missing birth record data for New Jersey areas with high rates of an ARO (preterm birth), 2) evaluate associations between the extent of missing birth record data and other AROs, and 3) consider how geocoding strategies could be applied to provide a basis for understanding maternal SES risk factors and ARO resource allocation for at-risk communities.