Racial Disparities in Triple Negative Breast Cancer: Toward a Causal Architecture Approach

Background: Triple negative breast cancer (TNBC) is an aggressive subtype of invasive breast cancer that disproportionately affects Black women and contributes to racial disparities in breast cancer mortality. Prior research has suggested that neighborhood effects may contribute to this disparity beyond individual risk factors.Methods: The sample included a cohort of 3,316 breast cancer cases diagnosed between 2012 and 2020 in New Castle County, Delaware, a geographic region of the US with elevated rates of TNBC. Multilevel methods and geospatial mapping evaluated whether the race, income, and race/income versions of the neighborhood Index of Concentration at the Extremes (ICE) metric could efficiently identify census tracts (CT) with higher odds of TNBC relative to other forms of invasive breast cancer. Odds ratios (OR) and 95% confidence intervals (CI) were reported; p-values <0.05 were significant. Additional analyses examined area-level differences in exposure to metabolic risk factors, including unhealthy alcohol use and obesity.Results: The ICE-Race, -Income-, and Race/Income metrics were each associated with greater census tract odds of TNBC on a bivariate basis. However, only ICE-Race was significantly associated with higher odds of TNBC after adjustment for patient-level age and race (most disadvantaged CT: OR= 2.09; 95%CI=1.40-3.13), providing support for neighborhood effects. Higher counts of alcohol and fast-food retailers, and correspondingly higher rates of unhealthy alcohol use and obesity, were observed in CTs that were classified into the most disadvantaged ICE-Race quintile and had the highest odds of TNBC. Conclusion: The use of ICE can facilitate the monitoring of cancer inequities and advance the study of racial disparities in breast cancer.

Equal Opportunities to Access the University in Chile? An Application with a Spatial Heckman Probit Model

This study contributes to the debate on accessibility of higher education in Chile, focusing on both socioeconomic and geospatial dimensions of access to university study. The central question we address in this paper is the following: Does geography (physical distance and neighborhood effects) play a significant role in determining accessibility of higher education in Chile? We use Heckman probit-type (Heckit) models to adjust for selection in the process of completing the trajectory towards higher education &ndash; that is, pre-selection, application to study at university, and ultimately admission (or denial) to a higher education institution. The results shows that the geospatial elements have a significant local effect on the student&rsquo;s application and access to Chilean universities.

Neighborhood Effects, the Life Course, and Educational Outcomes: Four Theoretical Models of Effect Heterogeneity

In this chapter, I review research analyzing heterogeneity in neighborhood effects on educational attainment. Using a life-course perspective on neighborhood effects, I describe four potential models of effect heterogeneity: cumulative advantage, cumulative disadvantage, advantage leveling, and compensatory advantage. Extant research most thoroughly explores effect heterogeneity by family socioeconomic background with evidence in support of multiple models. Research on secondary outcomes like achievement and dropout finds evidence of a cumulative disadvantage model, whereas research on bachelor’s degree completion finds evidence of an advantage leveling model. Still, scholarship on heterogeneity in neighborhood effects is in its nascency, and I conclude this chapter with several recommendations for future directions in research.

Equal Opportunities to Access the University in Chile? An Application with a Spatial Heckman Probit Model

This study contributes to the debate on accessibility of higher education in Chile, focusing on both socioeconomic and geospatial dimensions of access to university study. The central question we address in this paper is the following: Does geography (physical distance and neighborhood effects) play a significant role in determining accessibility of higher education in Chile? We use Heckman probit-type (Heckit) models to adjust for selection in the process of completing the trajectory towards higher education &ndash; that is, pre-selection, application to study at university, and ultimately admission (or denial) to a higher education institution. The results shows that the geospatial elements have a significant local effect on the student&rsquo;s application and access to Chilean universities.

CNN-Based Spectral Super-Resolution of Panchromatic Night-Time Light Imagery: City-Size-Associated Neighborhood Effects

Data on artificial night-time light (NTL), emitted from the areas, and captured by satellites, are available at a global scale in panchromatic format. In the meantime, data on spectral properties of NTL give more information for further analysis. Such data, however, are available locally or on a commercial basis only. In our recent work, we examined several machine learning techniques, such as linear regression, kernel regression, random forest, and elastic map models, to convert the panchromatic NTL images into colored ones. We compared red, green, and blue light levels for eight geographical areas all over the world with panchromatic light intensities and characteristics of built-up extent from spatially corresponding pixels and their nearest neighbors. In the meantime, information from more distant neighboring pixels might improve the predictive power of models. In the present study, we explore this neighborhood effect using convolutional neural networks (CNN). The main outcome of our analysis is that the neighborhood effect goes in line with the geographical extent of metropolitan areas under analysis: For smaller areas, optimal input image size is smaller than for bigger ones. At that, for relatively large cities, the optimal input image size tends to differ for different colors, being on average higher for red and lower for blue lights. Compared to other machine learning techniques, CNN models emerged comparable in terms of Pearson’s correlation but showed performed better in terms of WMSE, especially for testing datasets.

Neighborhoods Matter: Assessing the Evidence for Place Effects

How does one's place of residence affect individual behavior and long-run outcomes? Understanding neighborhood and place effects has been a leading question for social scientists during the past half-century. Recent empirical studies using experimental and quasi-experimental research designs have generated new insights on the importance of residential neighborhoods in childhood and adulthood. This paper summarizes the recent neighborhood effects literature and interprets the findings. Childhood neighborhoods affect long-run economic and educational outcomes in a manner consistent with exposure models of neighborhood effects. For adults, neighborhood environments matter for their health and well-being but have more ambiguous impacts on labor market outcomes. We discuss the evidence on the mechanisms behind the observed patterns and conclude by highlighting directions for future research.

Duration-Weighted Exposure to Neighborhood Disadvantage and Racial-Ethnic Differences in Adolescent Sexual Behavior

Racial-ethnic disparities in adolescent sexual risk behavior are associated with health disparities during adulthood and are therefore important to understand. Some scholars argue that neighborhood disadvantage induces disparities, yet prior research is mixed. We extend neighborhood-effects research by addressing long-term exposure to neighborhood disadvantage and estimation bias resulting from inclusion of time-varying covariates. Drawing from the Fragile Families and Child Well-Being Study, we compare a point-in-time proximal measure of neighborhood disadvantage with a duration-weighted measure using marginal structural models with inverse probability of treatment weights. Findings indicate that multiracial, non-Hispanic black, and Hispanic youth exhibit significantly higher sexual risk and duration-weighted exposure to neighborhood disadvantage than non-Hispanic white adolescents. Duration-weighted exposure is a better predictor of sexual initiation and number of partners by age 15 than a point-in-time proximal measure of neighborhood disadvantage and accounts for a substantial portion of the race-ethnic differences in sexual risk.

The Problems with Neighbors: An Examination of the Influence of Neighborhood Context Using Large-Scale Administrative Data

Where earlier conceptions of problem neighbors saw them as contributing to neighborhood level forms of disorder, neighbor problems, in contrast, occur in the everyday domestic setting of residential life and challenge conceptual boundaries between public/private and civility/incivility. As a result, there is a need to better understand the phenomenon of problems between neighbors beyond conceptions of public disorder and to understand the processes that influence how and why neighbor problems arise. In this study, we examine neighbor problems as manifest in reported complaints to a local municipality in Australia to understand how neighborhood features affect the likelihood of neighbors experiencing problems with each other. We propose five hypotheses to examine the social-interactive, environmental, and geographical mechanisms of neighborhood effects and test these hypotheses through logistic regression models on the way certain neighborhood features relate to the prevalence of neighbor problems. The findings reveal the sources of neighbor problems that typically reside in a combination of the social-interactive dynamics of the neighborhood itself—including the composition of the resident population—and the environmental features of the neighborhood in terms of the condition, density and use of dwellings, but not in the location of the neighborhood relative to larger-scale political and economic forces of the city. The paper concludes with a discussion of the significance of these findings for research, policy, and practice.

Peer Effects and Neighborhood Effects in Family Migration or Residence Decisions under the Background of Smoke-free Environment

Under the background of smoke-free environment, based on the survey data of CMDS 2013 and 2018, this study explored the differences in family migration and residence decisions of migrants living in different neighborhoods, and analyzed the influence of family migration scale on residence decision by using the hierarchical linear model (HLM). The results show that: Peer effects exist in same neighborhoods, while neighborhood effects exist in different neighborhoods, but the effect sizes and directions of various neighborhood factors are different; Family income level is still an important factor affecting the family migration and residence decisions, and the family migration scale has a positive effect on residence decision; There are significant inter-generational differences in family migration and residence decisions, but the conditions for the differences are different; Good neighborhood smoke-free environments and abundant neighborhood activities are helpful to strengthen the family migration and residence decision of migrant workers.