Use of segregation indices, Townsend Index, and air toxics data to assess lifetime cancer risk disparities in metropolitan Charleston, South Carolina, USA

Poverty Status at Birth Predicts Epigenetic Changes at Age 15

Epigenetic studies have provided new opportunities to better understand the biological effects of poverty and racial/ethnic minority status. However, little is known about sex differences in these processes.

Methods

We used 15 years of follow up of 854 racially and ethnically diverse birth cohort who were followed from birth to age 15. Structural equation modeling (SEM) was used to examine the effects of race/ethnicity, maternal education, and family structure on poverty at birth, as well as the effects of poverty at birth on epigenetic changes at age 15. We also explored variations by sex.

Results

Our findings indicate that Black and Latino families had lower maternal education and married family structure which in turn predicted poverty at birth. Poverty at birth then was predictive of epigenetic changes 15 years later when the index child was 15. This suggested that poverty at birth partially mediates the effects of race/ethnicity, maternal education, and family structure on epigenetic changes of youth at age 15. There was an effect of poverty status at birth on DNA methylation of male but not female youth at age 15. Thus, poverty at birth may have a more salient effect on long term epigenetic changes of male than female youth.

Conclusions

Further studies are needed to understand the mechanisms underlying the observed sex differences in the effects of poverty as a mechanism that connects race/ethnicity, maternal education, and family structure to epigenetic changes later in life.

Race, Poverty Status at Birth, and DNA Methylation of Youth at Age 15

Epigenetic studies, which can reflect biological aging, have shown that measuring DNA methylation (DNAm) levels provides new insights into the biological effects of social environment and socioeconomic position (SEP). This study explores how race, family structure, and SEP (income to poverty ratio) at birth influence youth epigenetic aging at age 15. Data were obtained from the Future of Families and Child Wellbeing Study (FFCWS) cohort, with GrimAge used as a measure of DNAm levels and epigenetic aging. Our analysis included 854 racially and ethnically diverse participants followed from birth to age 15. Structural equation modeling (SEM) examined the relationships among race, SEP at birth, and epigenetic aging at age 15, controlling for sex, ethnicity, and family structure at birth. Findings indicate that race was associated with lower SEP at birth and faster epigenetic aging. Specifically, income to poverty ratio at birth partially mediated the effects of race on accelerated aging by age 15. The effect of income to poverty ratio at birth on DNAm was observed in male but not female youth at age 15. Thus, SEP partially mediated the effect of race on epigenetic aging in male but not female youth. These results suggest that income to poverty ratio at birth partially mediates the effects of race on biological aging into adolescence. These findings highlight the long-term biological impact of early-life poverty in explaining racial disparities in epigenetic aging and underscore the importance of addressing economic inequalities to mitigate these disparities. Policymakers should focus on poverty prevention in Black communities to prevent accelerated biological aging and associated health risks later in life. Interventions aimed at eliminating poverty and addressing racial inequities could have significant long-term benefits for public health. Future research should explore additional factors contributing to epigenetic aging and investigate potential interventions to slow down the aging process. Further studies are needed to understand the mechanisms underlying these associations and to identify effective strategies for mitigating the impact of SEP and racial disparities on biological aging.

BTEX profile and health risk at the largest bulk port in Latin America, Paranaguá Port

Port-related activities have a detrimental impact on the air quality both at the point of source and for considerable distances beyond. These activities include, but are not limited to, heavy cargo traffic, onboard, and at-berth emissions. Due to differences in construction, operation, location, and policies at ports, the site-specific air pollution cocktail could result in different human health risks. Thus, monitoring and evaluating such emissions are essential to predict the risk to the community. Environmental agencies often monitor key pollutants (PM_2.5, PM₁₀, NO₂, SO₂), but the volatile organic carbons (VOCs) most often are not, due to its analytical challenging. This study intends to fill that gap and evaluate the VOC emissions caused by activities related to the port of Paranaguá - one of the largest bulk ports in Latin America - by characterizing BTEX concentrations at the port and its surroundings. At seven different sites, passive samplers were used to measure the dispersion of BTEX concentrations throughout the port and around the city at weekly intervals from November 2018 to January 2019. The average and uncertainty of BTEX concentrations (µg m^-3) were 0.60 ± 0.43, 5.58 ± 3.80, 3.30 ± 2.41, 4.66 ± 3.67, and 2.82 ± 1.95 for benzene, toluene, ethylbenzene, m- and p-xylene, and o-xylene, respectively. Relationships between toluene and benzene and health risk analysis were used to establish the potential effects of BTEX emissions on the population of the city of Paranaguá. Ratio analysis (T/B, B/T, m,p X/Et, and m,p X/B) indicate that the BTEX levels are mainly from fresh emission sources and that photochemical ageing was at minimum. The cancer risk varied across the sampling trajectory, whereas ethylbenzene represented a moderate cancer risk development for the exposed population in some of the locations. This study provided the necessary baseline data to support policymakers on how to change the circumstances of those currently at risk, putting in place a sustainable operation.

Area deprivation index and segregation on the risk of HIV: a U.S. Veteran case-control study

Background

Preventing HIV infection remains a critically important tool in the continuing fight against HIV/AIDS. The primary aim is to evaluate the effect and interactions between a composite area-level social determinants of health measure and an area-level measure of residential segregation on the risk of HIV/AIDS in U.S. Veterans.

Methods

Using the individual-level patient data from the U.S. Department of Veterans Affairs, we constructed a case-control study of veterans living with HIV/AIDS (VLWH) and age-, sex assigned at birth- and index date-matched controls. We geocoded patient's residential address to ascertain their neighborhood and linked their information to two measures of neighborhood-level disadvantage: area deprivation index (ADI) and isolation index (ISOL). We used logistic regression to estimate the odds ratio (OR) and 95% confidence interval (CI) for comparing VLWH with matched controls. We performed analyses for the entire U.S. and separately for each U.S. Census division.

Findings

Overall, living in minority-segregated neighborhoods was associated with a higher risk of HIV (OR: 1.88 (95% CI: 1.79-1.97) while living in higher ADI neighborhoods was associated with a lower risk of HIV (OR: 0.88; 95% CI: 0.84-0.92). The association between living in a higher ADI neighborhood and HIV was inconsistent across divisions, while living in minority-segregated neighborhoods was consistently associated with increased risk across all divisions. In the interaction model, individuals from low ADI and high ISOL neighborhoods had a higher risk of HIV in three divisions: East South Central; West South Central, and Pacific.

Interpretation

Our results suggest that residential segregation may prevent people in disadvantaged neighborhoods from protecting themselves from HIV independent from access to health care. There is the need to advance knowledge about the neighborhood-level social-structural factors that influence HIV vulnerability toward developing interventions needed to achieve the goal of ending the HIV epidemic.

Funding

US National Cancer Institute.

Association of racial residential segregation with all-cause and cancer-specific mortality in the reasons for geographic and racial differences in stroke (REGARDS) cohort study

•Increased racial residential segregation increased the risk of all-cause mortality among White participants.•Higher interaction lowered the risk of all-cause mortality among White participants.•Higher isolation lowered the risk of cancer mortality among Black participants.

Unequal airborne exposure to toxic metals associated with race, ethnicity, and segregation in the USA

Persons of color have been exposed to a disproportionate burden of air pollution across the United States for decades. Yet, the inequality in exposure to known toxic elements of air pollution is unclear. Here, we find that populations living in racially segregated communities are exposed to a form of fine particulate matter with over three times higher mass proportions of known toxic and carcinogenic metals. While concentrations of total fine particulate matter are two times higher in racially segregated communities, concentrations of metals from anthropogenic sources are nearly ten times higher. Populations living in racially segregated communities have been disproportionately exposed to these environmental stressors throughout the past decade. We find evidence, however, that these disproportionate exposures may be abated though targeted regulatory action. For example, recent regulations on marine fuel oil not only reduced vanadium concentrations in coastal cities, but also sharply lessened differences in vanadium exposure by segregation.

Neighborhood disadvantage and lung cancer risk in a national cohort of never smoking Black women

BACKGROUND

Compared to women of other races who have never smoked, Black women have a higher risk of lung cancer. Whether neighborhood disadvantage, which Black women experience at higher rates than other women, is linked to never-smoking lung cancer risk remains unclear. This study investigates the association of neighborhood disadvantage and lung cancer risk in Black never-smoking women.

METHODS AND MATERIALS

This research utilized data from the Black Women's Health Study, a prospective cohort of 59,000 Black women recruited from across the US in 1995 and followed by biennial questionnaires. Associations of lung cancer incidence with neighborhood-level factors (including two composite variables derived from Census Bureau data: neighborhood socioeconomic status and neighborhood concentrated disadvantage), secondhand smoke exposure, and PM_2.5 were estimated using Fine-Gray subdistribution hazard models.

RESULTS

Among 37,650 never-smokers, 77 were diagnosed with lung cancer during follow-up from 1995 to 2018. The adjusted subdistribution hazard ratio (sHR) of lung cancer incidence with ten unit increase in neighborhood concentrated disadvantage index was 1.30 (95 % CI: 1.04, 1.63, p = 0.023). Exposure to secondhand smoke at work was associated with increased risk (sHR = 1.93, 95 % CI: 1.21, 3.10, p = 0.006), but exposure to secondhand smoke at home and PM_2.5 was not.

CONCLUSION

Worse neighborhood concentrated disadvantage was associated with increased lung cancer risk in Black women who never smoked. These findings suggest that non-tobacco-related factors in disadvantaged neighborhoods may be linked to lung cancer risk in Black women and that these factors must be understood and targeted to achieve health equity.

Liver Cancer Incidence and Area-Level Geographic Disparities in Pennsylvania-A Geo-Additive Approach

Many neighborhood socioeconomic index measures (nSES) that capture neighborhood deprivation exist but the impact of measure selection on liver cancer (LC) geographic disparities remains unclear. We introduce a Bayesian geoadditive modeling approach to identify clusters in Pennsylvania (PA) with higher than expected LC incidence rates, adjusted for individual-level factors (age, sex, race, diagnosis year) and compared them to models with 7 different nSES index measures to elucidate the impact of nSES and measure selection on LC geospatial variation. LC cases diagnosed from 2007–2014 were obtained from the PA Cancer Registry and linked to nSES measures from U.S. census at the Census Tract (CT) level. Relative Risks (RR) were estimated for each CT, adjusted for individual-level factors (baseline model). Each nSES measure was added to the baseline model and changes in model fit, geographic disparity and state-wide RR ranges were compared. All 7 nSES measures were strongly associated with high risk clusters. Tract-level RR ranges and geographic disparity from the baseline model were attenuated after adjustment for nSES measures. Depending on the nSES measure selected, up to 60% of the LC burden could be explained, suggesting methodologic evaluations of multiple nSES measures may be warranted in future studies to inform LC prevention efforts.

Independent and joint contributions of economic, social and physical environmental characteristics to mortality in the Detroit Metropolitan Area: A study of cumulative effects and pathways

OBJECTIVE

Previous studies have demonstrated associations between race-based residential segregation, neighborhood socioeconomic and physical environmental characteristics, and mortality. Relatively few studies have examined independent and joint effects of these multiple neighborhood characteristics and mortality, including potential mediating pathways. In this study we examine the extent to which associations between race-based residential segregation and all-cause mortality may be explained by multiple socioeconomic indicators and exposure to air pollutants.

METHODS

Drawing on data from multiple sources, we assessed bivariate associations between race-based residential segregation (operationalized as percent non-Hispanic Black), education (percent with graduate equivalency degree), poverty (percent below poverty), income inequality (GINI coefficient) and air pollution (ambient PM2.5) and age adjusted all-cause, all race mortality (henceforth all cause mortality) at the census tract level in the Detroit Metropolitan Area. We used inequality curves to assess the (in)equitable distribution of economic and environmental characteristics by census tract racial composition. Finally, we used generalized estimating equations (GEE) to examine independent and joint associations among percent NHB, education, income inequality, and air pollution to all-cause mortality, and test for mediating effects.

RESULTS

Bivariate associations between racial composition, education, poverty, income inequality, PM2.5 and all-cause mortality were statistically significant. Census tracts with higher concentrations of NHB residents had significantly lower educational attainment, higher poverty, and greater exposure to PM2.5. In multivariate models, education, income inequality and PM2.5 fully attenuated associations between racial composition and all-cause mortality.

CONCLUSIONS

Results are consistent with the hypothesis that race-based residential segregation is associated with heightened all-cause mortality, and that those effects are mediated by education, income inequality, and exposure to air pollution at the census tract level. Public health and cross-sector interventions to eliminate race-based residential segregation or to eliminate the maldistribution of educational and economic resources, and environmental exposures, across census tracts could substantially reduce regional inequities in all-cause mortality.

Towards precision public health: Geospatial analytics and sensitivity/specificity assessments to inform liver cancer prevention

Objectives

Liver cancer (LC) continues to rise, partially due to limited resources for prevention. To test the precision public health (PPH) hypothesis that fewer areas in need of LC prevention could be identified by combining existing surveillance data, we compared the sensitivity/specificity of standard recommendations to target geographic areas using U.S. Census demographic data only (percent (%) Hispanic, Black, and those born 1950–1959) to an alternative approach that couples additional geospatial data, including neighborhood socioeconomic status (nSES), with LC disease statistics.

Methods

Pennsylvania Cancer Registry data from 2007-2014 were linked to 2010 U.S. Census data at the Census tract (CT) level. CTs in the top 80th percentile for 3 standard demographic variables, %Hispanic, %Black, %born 1950–1959, were identified. Spatial scan statistics (SatScan) identified CTs with significantly elevated incident LC rates (p-value<0.05), adjusting for age, gender, diagnosis year. Sensitivity, specificity, and positive predictive value (PPV) of a CT being located in an elevated risk cluster and/or testing positive/negative for at least one standard variable were calculated. nSES variables (deprivation, stability, segregation) significantly associated with LC in regression models (p < 0.05) were systematically evaluated for improvements in sensitivity/specificity.

Results

9,460 LC cases were diagnosed across 3,217 CTs. 1,596 CTs were positive for at least one of 3 standard variables. 5 significant elevated risk clusters (CTs = 402) were identified. 324 CTs were positive for a high risk cluster AND standard variable (sensitivity = 92%; specificity = 37%; PPV = 17.4%). Incorporation of 3 new nSES variables with one standard variable (%Black) further improved sensitivity (93%), specificity (62.9%), and PPV (26.3%).

Conclusions

We introduce a quantitative assessment of PPH by applying established sensitivity/specificity assessments to geospatial data. Coupling existing disease cluster and nSES data can more precisely identify intervention targets with a liver cancer burden than standard demographic variables. Thus, this approach may inform prioritization of limited resources for liver cancer prevention.

•

Precision Public Health calls for linking surveillance data to identify fewer neighborhoods for intervention.

•

Sensitivity/specificity methods can measure the utility of Precision Public Health by identifying optimal data combinations.

•

Select combinations of linked Census and liver cancer registry data reduced neighborhood targets more than Census data alone.

•

Precision Public Health improves the prioritization of liver cancer prevention efforts.

Understanding Racial Disparities in Exposure to Traffic-Related Air Pollution: Considering the Spatiotemporal Dynamics of Population Distribution

This study investigates the effect of spatiotemporal distributions of racial groups on disparities in exposure to traffic-related air pollution by considering people's daily movement patterns. Due to human mobility, a residential neighborhood does not fully represent the true geographic context in which people experience racial segregation and unequal exposure to air pollution. Using travel-activity survey data containing individuals' activity locations and time spent at each location, this study measures segregation levels that an individual might experience during the daytime and nighttime, estimates personal exposure by integrating hourly pollution maps and the survey data, and examines the association between daytime/nighttime segregation and exposure levels. The proximity of each activity location to major roads is also evaluated to further examine the unequal exposure. The results reveal that people are more integrated for work in high-traffic areas, which contributes to similarly high levels of exposure for all racial groups during the daytime. However, white people benefit from living in suburbs/exurbs away from busy roads. The finding suggests that policies for building an extensive and equitable public transit system should be implemented together with the policies for residential mixes among racial groups to reduce everyone's exposure to traffic-related air pollution and achieve environmental justice.

Cancer risk from air toxics in relation to neighborhood isolation and sociodemographic characteristics: A spatial analysis of the St. Louis metropolitan area, USA

BACKGROUND

A growing body of research has examined relationships between neighborhood characteristics and exposure to air toxics in the United States. However, a limited number of studies have addressed neighborhood isolation, a measure of spatial segregation. We investigated the spatial distribution of carcinogenic air toxics in the St. Louis metropolitan area and tested the hypothesis that neighborhood isolation and sociodemographic characteristics are associated with exposure to carcinogenic air toxics.

METHODS

We obtained lifetime air toxics cancer risk data from the United States Environmental Protection Agency's National Air Toxic Assessment and sociodemographic data from the American Community Survey. We used geographic information systems to identify statistically significant clusters of census tracts with elevated all-site cancer risk due to air toxics in the St. Louis metropolitan area. Relative Risks (RR) were estimated for the association between neighborhood characteristics and air toxic hot spots. Using a local spatial isolation index to evaluate residential segregation, we also evaluated the association between neighborhood racial and economic isolation and air toxic hot spots.

RESULTS

Approximately 14% (85 of the 615) of census tracts had elevated cancer risk due to air toxics (p < 0.01). These air toxic hot spots were independently associated with neighborhoods with high levels of poverty and unemployment and low levels of education. Census tracts with the highest levels of both racial isolation of Blacks and economic isolation of poverty were more likely to be located in air toxic hotspots than those with low combined racial and economic isolation (RR = 5.34; 95% CI = 3.10-9.22).

CONCLUSIONS

These findings provide strong evidence of unequal distribution of carcinogenic air toxics in the St. Louis metropolitan area. Study results may be used to inform public health efforts to eliminate sociodemographic inequalities in exposure to air pollutants.

Health and Environmental Justice Implications of Retiring Two Coal-Fired Power Plants in the Southern Front Range Region of Colorado

Despite improvements in air quality over the past 50 years, ambient air pollution remains an important public health issue in the United States. In particular, emissions from coal-fired power plants still have a substantial impact on both nearby and regional populations. Of particular concern is the potential for this impact to fall disproportionately on low-income communities and communities of color. We conducted a quantitative health impact assessment to estimate the health benefits of the proposed decommissioning of two coal-fired electricity generating stations in the Southern Front Range region of Colorado. We estimated changes in exposures to fine particulate matter and ozone using the Community Multiscale Air Quality model and predicted avoided health impacts and related economic values. We also quantitatively assessed the distribution of these benefits by population-level socioeconomic status. Across the study area, decommissioning the power plants would result in 2 (95% CI: 1-3) avoided premature deaths each year due to reduced PM2.5 exposures and greater reductions in hospitalizations and other morbidities. Health benefits resulting from the modeled shutdowns were greatest in areas with lower educational attainment and other economic indicators. Our results suggest that decommissioning these power plants and replacing them with zero-emissions sources could have broad public health benefits for residents of Colorado, with larger benefits for those that are socially disadvantaged. Our results also suggested that researchers and decision makers need to consider the unique demographics of their study areas to ensure that important opportunities to reduce health disparities associated with point-source pollution.

Associations with Corneal Hysteresis in a Population Cohort: Results from 96 010 UK Biobank Participants

PURPOSE

To describe the distribution of corneal hysteresis (CH) in a large cohort and explore its associated factors and possible clinical applications.

DESIGN

Cross-sectional study within the UK Biobank, a large cohort study in the United Kingdom.

PARTICIPANTS

We analyzed CH data from 93 345 eligible participants in the UK Biobank cohort, aged 40 to 69 years.

METHODS

All analyses were performed using left eye data. Linear regression models were used to evaluate associations between CH and demographic, lifestyle, ocular, and systemic variables. Piecewise logistic regression models were used to explore the relationship between self-reported glaucoma and CH.

MAIN OUTCOME MEASURES

Corneal hysteresis (mmHg).

RESULTS

The mean CH was 10.6 mmHg (10.4 mmHg in male and 10.8 mmHg in female participants). After adjusting for covariables, CH was significantly negatively associated with male sex, age, black ethnicity, self-reported glaucoma, diastolic blood pressure, and height. Corneal hysteresis was significantly positively associated with smoking, hyperopia, diabetes, systemic lupus erythematosus (SLE), greater deprivation (Townsend index), and Goldmann-correlated intraocular pressure (IOPg). Self-reported glaucoma and CH were significantly associated when CH was less than 10.1 mmHg (odds ratio, 0.86; 95% confidence interval, 0.79-0.94 per mmHg CH increase) after adjusting for covariables. When CH exceeded 10.1 mmHg, there was no significant association between CH and self-reported glaucoma.

CONCLUSIONS

In our analyses, CH was significantly associated with factors including age, sex, and ethnicity, which should be taken into account when interpreting CH values. In our cohort, lower CH was significantly associated with a higher prevalence of self-reported glaucoma when CH was less than 10.1 mmHg. Corneal hysteresis may serve as a biomarker aiding glaucoma case detection.

Hybrid Resiliency-Stressor Conceptual Framework for Informing Decision Support Tools and Addressing Environmental Injustice and Health Inequities

While structural factors may drive health inequities, certain health-promoting attributes of one's "place" known as salutogens may further moderate the cumulative impacts of exposures to socio-environmental stressors that behave as pathogens. Understanding the synergistic relationship between socio-environmental stressors and resilience factors is a critical component in reducing health inequities; however, the catalyst for this concept relies on community-engaged research approaches to ultimately strengthen resiliency and promote health. Furthermore, this concept has not been fully integrated into environmental justice and cumulative risk assessment screening tools designed to identify geospatial variability in environmental factors that may be associated with health inequities. As a result, we propose a hybrid resiliency-stressor conceptual framework to inform the development of environmental justice and cumulative risk assessment screening tools that can detect environmental inequities and opportunities for resilience in vulnerable populations. We explore the relationship between actual exposures to socio-environmental stressors, perceptions of stressors, and one's physiological and psychological stress response to environmental stimuli, which collectively may perpetuate health inequities by increasing allostatic load and initiating disease onset. This comprehensive framework expands the scope of existing screening tools to inform action-based solutions that rely on community-engaged research efforts to increase resiliency and promote positive health outcomes.

A Model-to-Monitor Evaluation of 2011 National-Scale Air Toxics Assessment (NATA)

Environmental research has widely utilized the ambient concentrations of hazardous air pollutants (HAPs) modeled by the National-Scale Air Toxics Assessment (NATA) program; however, limited studies have evaluated the model's performance. This study aims to evaluate the model-to-monitor agreement of the 2011 NATA data with the monitoring data reported to the U.S. Environmental Protection Agency's (EPA) Air Quality System (AQS). Concentrations of 27 representative HAPs measured at 274 sites in the U.S. in 2011 were merged with NATA data by census tract. The comparison consisted of two steps for each HAP: first, the model-monitor difference at each site was compared with the limit of quantitation (LOQ); second, the modeled annual average was compared to the 95% confidence interval of the monitored annual average. Nationally, NATA could predict national medians of most HAPs well; however, it was unable to capture high concentrations. At individual sites, a large portion of model-monitor differences was below the LOQs, indicating they were unquantifiable. Model-to-monitor agreement displayed inconsistent patterns in terms of chemical groups or EPA regions and was strongly impacted by the comparison methods. The substantial non-agreements of NATA predictions with monitoring data require caution in environmental epidemiology and justice studies that are based on NATA data.

Neighbourhood deprivation and lung cancer risk: a nested case-control study in the USA

OBJECTIVES

To examine the association between neighbourhood deprivation and lung cancer risk.

DESIGN

Nested case-control study.

SETTING

Southern Community Cohort Study of persons residing in 12 states in the southeastern USA.

PARTICIPANTS

1334 cases of lung cancer and 5315 controls.

PRIMARY OUTCOME MEASURE

Risk of lung cancer.

RESULTS

After adjustment for smoking status and other confounders, and additional adjustment for individual-level measures of socioeconomic status (SES), there was no monotonic increase in risk with worsening deprivation score overall or within sex and race groups. There was an increase among current and shorter term former smokers (p=0.04) but not among never and longer term former smokers. There was evidence of statistically significant interaction by sex among whites, but not blacks, in which the effect of worsening deprivation on lung cancer existed in males but not in females.

CONCLUSIONS

Area-level measures of SES were associated with lung cancer risk in current and shorter term former smokers only in this population.

Ethnic density and cancer: A review of the evidence

Accumulating data suggest that factors in the social environment may be associated with cancer-related outcomes. Ethnic density, defined as the proportion of racial/ethnic minority individuals residing in a given geographic area, is 1 of the most frequently studied social environment factors, but studies on ethnic density and cancer have yielded inconsistent findings. Thus, the objective of the current review was to summarize the extant data on ethnic density and cancer-related outcomes (cancer risk, stage at diagnosis, and mortality) with the aim of identifying pathways by which ethnic density may contribute to outcomes across populations. In general, the findings indicated an association between ethnic density and increased risk for cancers of infectious origin (eg, liver, cervical) but lower risk for breast and colorectal cancers, particularly among Hispanic and Asian Americans. Hispanic ethnic density was associated with greater odds of late-stage cancer diagnosis, whereas black ethnic density was associated with greater mortality. In addition, this review highlights several methodological and conceptual issues surrounding the measurement of ethnic neighborhoods and their available resources. Clarifying the role of neighborhood ethnic density is critical to developing a greater understanding of the health risks and benefits accompanying these environments and how they may affect racial and ethnic disparities in cancer-related outcomes. Cancer 2018;124:1877-903. © 2018 American Cancer Society.

Cancer Incidence and Multilevel Measures of Residential Economic and Racial Segregation for Cancer Registries

BACKGROUND

The handful of studies (<30) on cancer and residential segregation have focused on racial segregation, primarily at the city/town level. We tested a priori hypotheses about choice of measure and level by extending use of the Index of Concentration at the Extremes (ICE) to quantify both economic and racial residential segregation, singly and combined, and conducted analyses for the total population and stratified by race/ethnicity.

METHODS

Outcomes comprised Massachusetts incidence rates (2010-2014) for invasive breast, cervical, and lung cancer, analyzed in relation to census tract and city/town ICE measures for income, race/ethnicity, race/ethnicity + income, and the federal poverty line. Multilevel Poisson regression modeled observed counts of incident cases.

RESULTS

Both choice of metric and level mattered. As illustrated by cervical cancer, in models including both the census tract and city/town levels, the rate ratio for the worst to best quintile for the total population was greatest at the census tract level for the ICE for racialized economic segregation (3.0, 95% confidence interval [CI] = 2.1 to 4.3) and least for the poverty measure (1.9, 95% CI = 1.4 to 2.6), with null associations at the city/town level. In analogous models with both levels for lung cancer, however, for the non-Hispanic black and Hispanic populations, the rate ratios for, respectively, the ICE and poverty measures, were larger (and excluded 1) at the city/town compared with the census tract level.

CONCLUSIONS

Our study suggests that the ICE for racialized economic segregation, at multiple levels, can be used to improve monitoring and analysis of cancer inequities.

Multi-Contextual Segregation and Environmental Justice Research: Toward Fine-Scale Spatiotemporal Approaches

Many environmental justice studies have sought to examine the effect of residential segregation on unequal exposure to environmental factors among different social groups, but little is known about how segregation in non-residential contexts affects such disparity. Based on a review of the relevant literature, this paper discusses the limitations of traditional residence-based approaches in examining the association between socioeconomic or racial/ethnic segregation and unequal environmental exposure in environmental justice research. It emphasizes that future research needs to go beyond residential segregation by considering the full spectrum of segregation experienced by people in various geographic and temporal contexts of everyday life. Along with this comprehensive understanding of segregation, the paper also highlights the importance of assessing environmental exposure at a high spatiotemporal resolution in environmental justice research. The successful integration of a comprehensive concept of segregation, high-resolution data and fine-grained spatiotemporal approaches to assessing segregation and environmental exposure would provide more nuanced and robust findings on the associations between segregation and disparities in environmental exposure and their health impacts. Moreover, it would also contribute to significantly expanding the scope of environmental justice research.

Race/Ethnicity, Socioeconomic Status, Residential Segregation, and Spatial Variation in Noise Exposure in the Contiguous United States

BACKGROUND

Prior research has reported disparities in environmental exposures in the United States, but, to our knowledge, no nationwide studies have assessed inequality in noise pollution.

OBJECTIVES

We aimed to a) assess racial/ethnic and socioeconomic inequalities in noise pollution in the contiguous United States; and b) consider the modifying role of metropolitan level racial residential segregation.

METHODS

We used a geospatial sound model to estimate census block group–level median (L₅₀) nighttime and daytime noise exposure and 90th percentile (L₁₀) daytime noise exposure. Block group variables from the 2006–2010 American Community Survey (ACS) included race/ethnicity, education, income, poverty, unemployment, homeownership, and linguistic isolation. We estimated associations using polynomial terms in spatial error models adjusted for total population and population density. We also evaluated the relationship between race/ethnicity and noise, stratified by levels of metropolitan area racial residential segregation, classified using a multigroup dissimilarity index.

RESULTS

Generally, estimated nighttime and daytime noise levels were higher for census block groups with higher proportions of nonwhite and lower-socioeconomic status (SES) residents. For example, estimated nighttime noise levels in urban block groups with 75% vs. 0% black residents were 46.3 A-weighted decibels (dBA) [interquartile range (IQR): 44.3–47.8 dBA] and 42.3 dBA (IQR: 40.4–45.5 dBA), respectively. In urban block groups with 50% vs. 0% of residents living below poverty, estimated nighttime noise levels were 46.9 dBA (IQR: 44.7–48.5 dBA) and 44.0 dBA (IQR: 42.2–45.5 dBA), respectively. Block groups with the highest metropolitan area segregation had the highest estimated noise exposures, regardless of racial composition. Results were generally consistent between urban and suburban/rural census block groups, and for daytime and nighttime noise and robust to different spatial weight and neighbor definitions.

CONCLUSIONS

We found evidence of racial/ethnic and socioeconomic differences in model-based estimates of noise exposure throughout the United States. Additional research is needed to determine if differences in noise exposure may contribute to health disparities in the United States. https://doi.org/10.1289/EHP898

Baseline Air Quality Assessment of Goods Movement Activities before the Port of Charleston Expansion: A Community-University Collaborative

As the demand for goods continues to increase, a collective network of transportation systems is required to facilitate goods movement activities. This study examines air quality near the Port of Charleston before its expansion and briefly describes the establishment and structure of a community-university partnership used to monitor existing pollution. Particulate matter (PM) concentrations (PM_2.5 and PM₁₀) were measured using the Thermo Fisher Scientific Partisol 2000i-D Dichotomous Air Sampler, Thermo Scientific Dichotomous Sequential Air Sampler Partisol-Plus 2025-D, and Rupprecht & Patashnick TEOM Series 1400 Sampler at neighborhood (Union Heights, Rosemont, and Accabee) and reference (FAA_2.5 and Jenkins Street) sites. Descriptive statistics were performed and an ANOVA (analysis of variance) was calculated to find the difference in overall mean 24-hour PM average concentrations in communities impacted by environmental injustice. PM_2.5 (15.2 μg/m³) and PM₁₀ (27.2 μg/m³) maximum concentrations were highest in neighborhoods such as Union Heights neighborhoods due to more goods movement activities. Nevertheless, there was no statistically significant difference in mean concentrations of PM_2.5 and PM₁₀ across neighborhood sites. In contrast, mean PM₁₀ neighborhood concentrations were significantly lower than mean PM₁₀ reference concentrations for Union Heights (p = 0.00), Accabee (p ≤ 0.0001), and Rosemont (p = 0.01). Although PM concentrations were lower than current National Ambient Air Quality Standards, this study demonstrated how community-university partners can work collectively to document baseline PM concentrations that will be used to examine changes in air quality after the port expansion brings additional goods movement activities to the area.

Residential Segregation and Racial Cancer Disparities: A Systematic Review

BACKGROUND

This paper provides the first review of empirical studies of segregation and black-white cancer disparities.

METHODS

We searched all years of PubMed (through May 2016) using these terms: racial segregation, residential segregation, neighborhood racial composition (first terms) and (second terms) cancer incidence, mortality, survival, stage at diagnosis, screening. The 17 (of 668) articles that measured both segregation and a cancer outcome were retained.

RESULTS

Segregation contributed significantly to cancer and to racial cancer disparities in 70% of analyses, even after controlling for socioeconomic status and health insurance. Residing in segregated African-American areas was associated with higher odds of later-stage diagnosis of breast and lung cancers, higher mortality rates and lower survival rates from breast and lung cancers, and higher cumulative cancer risks associated with exposure to ambient air toxics. There were no studies of many types of cancer (e.g., cervical). Studies differed in their measure of segregation, and 40% used an invalid measure. Possible mediators of the segregation effect usually were not tested.

CONCLUSIONS

Empirical analysis of segregation and racial cancer disparities is a recent area of research. The literature is limited to 17 studies that focused primarily on breast cancer. Studies differed in their measure of segregation, yet segregation nonetheless contributed to cancer and to racial cancer disparities in 70% of analyses. This suggests the need for further research that uses valid measures of segregation, examines a variety of types of cancers, and explores the variables that may mediate the segregation effect.

Social inequalities and cancer: can the European deprivation index predict patients' difficulties in health care access? a pilot study

Context

The European Deprivation Index (EDI), is a new ecological estimate for Socio-Economic Status (SES). This study postulates that Time-To-Treatment could be used as a cancer quality-of -care surrogate in order to identify the association between cancer patient's SES and quality of care in a French comprehensive cancer center.

Methods

retrospective mono-centered cohort study. All consecutive incoming adult patients diagnosed for breast cancer(BC), prostate cancer(PC), colorectal cancer (CRC), lung cancer(LC) or sarcoma(S) were included between January 2013 and December 2013. The association of EDI and Time-To-Diagnosis(TTD), as well as Time-To-Treatment(TTT) was analyzed using a cox regression, and a strata analysis per tumor site was performed.

Results

969 patients were included. Primitive tumor site was 505 BC(52%), 169 PC(17%), 145 LC(15%), 116 CRC(12%), and 34 S(4%). Median TTD was 1.41 months (Q1-Q3 0.5 to 3.5 months). Median TTT was 0.9 months (0.4 - 1.4). In a multivariate analysis, we identified the tumor site as a predictive factor to influence TTD, shorter for BC (0.75months, [0.30- 1.9]) than PC (4.69 months [1.6-29.7]), HR 0.27 95%CI= [0.22-0.34], p < 0.001. TTT was also shorter for BC (0.75months [0.4-1.1]) than PC (2.02 [0.9-3.2]), HR 0.32 95%CI= [0.27-0.39], p < 0.001. EDI quintiles were not found associated with either TTT or TTD.

Conclusions

Deprivation estimated by the EDI does not appear to be related to an extension of the Time-to-Diagnosis or Time-to-Treatment in our real-life population. Further research should be done to identify other frailty-sensitive factors that could be responsible for delays in care.

Metrics for monitoring cancer inequities: residential segregation, the Index of Concentration at the Extremes (ICE), and breast cancer estrogen receptor status (USA, 1992-2012)

PURPOSE

To address the paucity of evidence on residential segregation and cancer, we explored their relationship using a new metric: the Index of Concentration at the Extremes (ICE). We focused on breast cancer estrogen receptor (ER) status, a biomarker associated with survival and, etiologically, with social and economic privilege.

METHODS

We obtained data from the 13 registry group of US Surveillance, Epidemiology, and End Results (SEER) program for 1992-2012 on all women aged 25-84 who were diagnosed with primary invasive breast cancer (n = 516,382). We appended to each case's record her annual county median household income quintile and the quintile for her annual county value for ICE measures for income (≤20th vs. ≥80th household income quintile), race/ethnicity (black vs. white), and income plus race/ethnicity (low-income black vs. high-income white). The odds of being ER+ versus ER- were estimated in relation to the county-level income and ICE measures, adjusting for relevant covariates.

RESULTS

Women in the most privileged versus deprived county quintile for household income and for all three ICE measures had a 1.1- to 1.3-fold increased odds (95 % confidence intervals excluding 1) of having an ER+ tumor. These results were robust to adjustment for age at diagnosis, cancer registry, tumor characteristics (tumor stage, size, histology, grade), and race/ethnicity.

CONCLUSION

A focus on segregation offers news possibilities for understanding how inequitable group relations contribute to cancer inequities. The utility of employing the ICE for monitoring cancer inequities should be investigated in relation to other cancer outcomes.

Racial isolation and exposure to airborne particulate matter and ozone in understudied US populations: Environmental justice applications of downscaled numerical model output

BACKGROUND

Researchers and policymakers are increasingly focused on combined exposures to social and environmental stressors, especially given how often these stressors tend to co-locate. Such exposures are equally relevant in urban and rural areas and may accrue disproportionately to particular communities or specific subpopulations.

OBJECTIVES

To estimate relationships between racial isolation (RI), a measure of the extent to which minority racial/ethnic group members are exposed to only one another, and long-term particulate matter with an aerodynamic diameter of <2.5μ (PM2.5) and ozone (O3) levels in urban and nonurban areas of the eastern two-thirds of the US.

METHODS

Long-term (5year average) census tract-level PM2.5 and O3 concentrations were calculated using output from a downscaler model (2002-2006). The downscaler uses a linear regression with additive and multiplicative bias coefficients to relate ambient monitoring data with gridded output from the Community Multi-scale Air Quality (CMAQ) model. A local, spatial measure of RI was calculated at the tract level, and tracts were classified by urbanicity, RI, and geographic region. We examined differences in estimated pollutant exposures by RI, urbanicity, and demographic subgroup (e.g., race/ethnicity, education, socioeconomic status, age), and used linear models to estimate associations between RI and air pollution levels in urban, suburban, and rural tracts.

RESULTS

High RI tracts (≥80th percentile) had higher average PM2.5 levels in each category of urbanicity compared to low RI tracts (<20th percentile), with the exception of the rural West. Patterns in O3 levels by urbanicity and RI differed by region. Linear models indicated that PM2.5 concentrations were significantly and positively associated with RI. The largest association between PM2.5 and RI was observed in the rural Midwest, where a one quintile increase in RI was associated with a 0.90μg/m(3) (95% confidence interval: 0.83, 0.99μg/m(3)) increase in PM2.5 concentration. Associations between O3 and RI in the Northeast, Midwest and West were positive and highest in suburban and rural tracts, even after controlling for potential confounders such as percentage in poverty.

CONCLUSION

RI is associated with higher 5year estimated PM2.5 concentrations in urban, suburban, and rural census tracts, adding to evidence that segregation is broadly associated with disparate air pollution exposures. Disproportionate burdens to adverse exposures such as air pollution may be a pathway to racial/ethnic disparities in health.

Exploring perceptions of cancer risk, neighborhood environmental risks, and health behaviors of blacks

Cancer risk perceptions and cancer worry are shaped by race/ethnicity, and social, economic, and environmental factors, which in turn shape health decision-making. A paucity of studies has explored risk perceptions and worry in metropolitan areas with disparate environmental conditions and cancer outcomes. This study examined perceptions of cancer risk, neighborhood environmental health risks, and risk-reducing health behaviors among Blacks. A 59-item survey was administered to respondents in Metropolitan Charleston, South Carolina from March to September 2013. A convenience sample of males and females was recruited at local venues and community events. Descriptive statistics, bivariate analyses (Chi square tests), and logistic regression models were estimated using SAS 9.3 software. Respondents (N = 405) were 100% Black, 81% female (n = 323), and ranged from 18 to 87 years of age (M = 49.55, SD = 15.27). Most respondents reported lower perceptions of cancer risk (37%) and equated their cancer beliefs to direct or indirect (i.e. personal or family) experiences. Low perceived cancer risk (absolute risk) was significantly associated (p < .05) with non-alcohol consumption, having a colon cancer screening test, being female, and being age 25-44 or 45-64. Cancer worry was significantly associated (p < .05) with being a current smoker, having a "fair" diet, non-alcohol consumption, and having any colon cancer screening test. Perceived cancer risk is an important indicator of health behaviors among Blacks. Direct or indirect experiences with cancer and/or the environment and awareness of family history of cancer may explain cancer risk perceptions.

Metrics in Urban Health: Current Developments and Future Prospects

The research community has shown increasing interest in developing and using metrics to determine the relationships between urban living and health. In particular, we have seen a recent exponential increase in efforts aiming to investigate and apply metrics for urban health, especially the health impacts of the social and built environments as well as air pollution. A greater recognition of the need to investigate the impacts and trends of health inequities is also evident through more recent literature. Data availability and accuracy have improved through new affordable technologies for mapping, geographic information systems (GIS), and remote sensing. However, less research has been conducted in low- and middle-income countries where quality data are not always available, and capacity for analyzing available data may be limited. For this increased interest in research and development of metrics to be meaningful, the best available evidence must be accessible to decision makers to improve health impacts through urban policies.

Assessment of sociodemographic and geographic disparities in cancer risk from air toxics in South Carolina

Populations of color and low-income communities are often disproportionately burdened by exposures to various environmental contaminants, including air pollution. Some air pollutants have carcinogenic properties that are particularly problematic in South Carolina (SC), a state that consistently has high rates of cancer mortality for all sites. The purpose of this study was to assess cancer risk disparities in SC by linking risk estimates from the U.S. Environmental Protection Agency's 2005 National Air Toxics Assessment (NATA) with sociodemographic data from the 2000 US Census Bureau. Specifically, NATA risk data for varying risk categories were linked by tract ID and analyzed with sociodemographic variables from the 2000 census using R. The average change in cancer risk from all sources by sociodemographic variable was quantified using multiple linear regression models. Spatial methods were further employed using ArcGIS 10 to assess the distribution of all source risk and percent non-white at each census tract level. The relative risk (RR) estimates of the proportion of high cancer risk tracts (defined as the top 10% of cancer risk in SC) and their respective 95% confidence intervals (CIs) were calculated between the first and latter three quartiles defined by sociodemographic factors, while the variance in the percentage of high cancer risk between quartile groups was tested using Pearson's chi-square. The average total cancer risk for SC was 26.8 people/million (ppl/million). The risk from on-road sources was approximately 5.8 ppl/million, higher than the risk from major, area, and non-road sources (1.8, 2.6, and 1.3 ppl/million), respectively. Based on our findings, addressing on-road sources may decrease the disproportionate cancer risk burden among low-income populations and communities of color in SC.

Advancing research on racial-ethnic health disparities: improving measurement equivalence in studies with diverse samples

To conduct meaningful, epidemiologic research on racial-ethnic health disparities, racial-ethnic samples must be rendered equivalent on other social status and contextual variables via statistical controls of those extraneous factors. The racial-ethnic groups must also be equally familiar with and have similar responses to the methods and measures used to collect health data, must have equal opportunity to participate in the research, and must be equally representative of their respective populations. In the absence of such measurement equivalence, studies of racial-ethnic health disparities are confounded by a plethora of unmeasured, uncontrolled correlates of race-ethnicity. Those correlates render the samples, methods, and measures incomparable across racial-ethnic groups, and diminish the ability to attribute health differences discovered to race-ethnicity vs. to its correlates. This paper reviews the non-equivalent yet normative samples, methodologies and measures used in epidemiologic studies of racial-ethnic health disparities, and provides concrete suggestions for improving sample, method, and scalar measurement equivalence.