Racial and Economic Segregation Over the Life Course and Incident Hypertensive Disorders of Pregnancy Among Black Women in California

Black women in the United States have the highest incidence of hypertensive disorders of pregnancy (HDP) and are disproportionately burdened by its adverse sequalae, compared with women of all racial and ethnic groups. Segregation, a key driver of structural racism for Black families, can provide information critical to understanding these disparities. We examined the association between racial and economic segregation at 2 points and incident HDP using intergenerationally linked birth records of 45,204 Black California-born primiparous mothers (born 1982-1997) and their infants (born 1997-2011), with HDP ascertained from hospital discharge records. Women's early childhood and adulthood neighborhoods were categorized as deprived, mixed, or privileged based on the Index of Concentration at the Extremes (a measure of concentrated racial and economic segregation), yielding 9 life-course trajectories. Women living in deprived neighborhoods at both time points experienced the highest odds of HDP (from mixed effect logistic regression, unadjusted odds ratio = 1.26, 95% confidence interval: 1.13, 1.40) compared with women living in privileged neighborhoods at both time points. All trajectories involving residence in a deprived neighborhood in early childhood or adulthood were associated with increased odds of HDP, whereas mixed-privileged and privileged-mixed trajectories were not. Future studies should assess the causal nature of these associations.

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.

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 (β = 0.31, P-value < 0.001) and weak social supports (β = 0.27, P-value < 0.001), but inversely with healthy built environment (β = −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 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.

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 (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.

Two Mechanisms: The Role of Social Capital and Industrial Pollution Exposure in Explaining Racial Disparities in Self-Rated Health

This study provides an empirical test of two mechanisms (social capital and exposure to air pollution) that are theorized to mediate the effect of neighborhood on health and contribute to racial disparities in health outcomes. To this end, we utilize the Social Capital Benchmark Study, a national survey of individuals nested within communities in the United States, to estimate how multiple dimensions of social capital and exposure to air pollution, explain racial disparities in self-rated health. Our main findings show that when controlling for individual-confounders, and nesting within communities, our indicator of cognitive bridging, generalized trust, decreases the gap in self-rated health between African Americans and Whites by 84%, and the gap between Hispanics and Whites by 54%. Our other indicator of cognitive social capital, cognitive linking as represented by engagement in politics, decreases the gap in health between Hispanics and Whites by 32%, but has little impact on African Americans. We also assessed whether the gap in health was explained by respondents' estimated exposure to toxicity-weighted air pollutants from large industrial facilities over the previous year. Our results show that accounting for exposure to these toxins has no effect on the racial gap in self-rated health in these data. This paper contributes to the neighborhood effects literature by examining the impact that estimated annual industrial air pollution, and multiple measures of social capital, have on explaining the racial gap in health in a sample of individuals nested within communities across the United States.

Abstract 990: Fundamental causes of colorectal cancer outcomes

Introduction Colorectal Cancer is a major cause of mortality with 16.2 people out of 10,000 dying in 2009. Treatments for colorectal cancer exist, with screening done by physicians at clinics and hospitals around the US. Social inequalities in Colorectal Cancer mortality are well studied. However, three theories have arisen that may help to explain why these inequalities have arisen. Fundamental Cause Theory posits that these inequalities arise due to unequal access to resources while this may work in part with differential access to healthcare, and finally differential Diffusion of Knowledge is posited to speed and slow uptake of new medical innovations.

Method Using administrative and census data from 2005, mortality rates per county in 3139 counties were stratified by socio-economic status (SES), volume of acute care hospitals (ACHs), volume of primary care physicians (PCPs), and groups of states considered slow to fast diffusion. We controlled for race and gender.

Preliminary Results There were 4,683 ACHs and 278,961 PCPs in the analyses. White male averaged 6.42 deaths per 10,000 people, White female at 4.27, Black male at 4.42, Black female at 3.24, Other male at 2.14, and Other female at 2.93. For hospital volume, the average mortality for counties with zero acute hospitals was 7.32 deaths per 10,000, one hospital was 6.20, two hospitals was 6.44, three to fifty hospitals was 5.62, and fifty-five to eighty-nine hospitals was 4.55. For counties with PCPs, areas with zero to four PCPs had an average mortality of 7.48 deaths per 10,000 people, five to thirteen PCPs was 6.06, fourteen to fifty PCPs was 6.17, and fifty-one to eight thousand eight hundred sixty three PCPs was 5.93. The average mortality for PCPs per 100,000 between 0-50 was 7.16 per 10,000, 51-100 was 6.10, 101-149 was 5.78, and more than 150 was 5.08. Counties with high SES and few hospitals had an average mortality of 6.64 per 10,000, where high SES and high volume of hospitals had 5.62, low SES and low volume had 6.19, and low SES and high volume had 7.34. States with slow diffusion had a mortality of 6.33 per 10,000, medium-slow had 6.13, medium-fast had 6.61, and fast had 5.86.

Conclusion In this study, we show varying support for each of the three major theories. Fundamental cause theory suggests that SES was correlated with lower mortality rates, but SES played the greatest role in counties with large numbers of hospitals and primary care physicians. Access to healthcare clearly mattered, with more hospitals and primary care physicians correlating to lower colorectal mortality rates. Finally, being in an area typified as quick diffusing was related to lower mortality. This study thus suggests that fundamental cause theory works in part through access to health care. There are long term implications for policy makers looking to reduce social inequalities in colorectal cancer mortality.

Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 101st Annual Meeting of the American Association for Cancer Research; 2010 Apr 17-21; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2010;70(8 Suppl):Abstract nr 990.