Toxic Neighborhoods: The Effects of Concentrated Poverty and Environmental Lead Contamination on Early Childhood Development

Effect of Environmental and Socioeconomic Factors on Increased Early Childhood Blood Lead Levels: A Case Study in Chicago

This study analyzes the prevalence of elevated blood lead levels (BLLs) in children across Chicagoland zip codes from 2019 to 2021, linking them to socioeconomic, environmental, and racial factors. Wilcoxon tests and generalized additive model (GAM) regressions identified economic hardship, reflected in per capita income and unemployment rates, as a significant contributor to increased lead poisoning (LP) rates. Additionally, LP rates correlate with the average age of buildings, particularly post the 1978 lead paint ban, illustrating policy impacts on health outcomes. The study further explores the novel area of land surface temperature (LST) effects on LP, finding that higher nighttime LST, indicative of urban heat island effects, correlates with increased LP. This finding gains additional significance in the context of anthropogenic climate change. When these factors are combined with the ongoing expansion of urban territories, a significant risk exists of escalating LP rates on a global scale. Racial disparity analysis revealed that Black and Hispanic/Latino populations face higher LP rates, primarily due to unemployment and older housing. The study underscores the necessity for targeted public health strategies to address these disparities, emphasizing the need for interventions that cater to the unique challenges of these at-risk communities.

The Effects of the Flint water crisis on the educational outcomes of school-age children

In 2014, the municipal water source in Flint, Michigan was switched, causing lead from aging pipes to leach into the city's drinking water. While lead exposure in Flint children increased modestly on average, some children were exposed to high lead levels. Surveys of Flint residents show the water crisis was also associated with increased levels of stress, anxiety, and depression. We use Michigan's administrative education data and utilize synthetic control methods to examine the impact of the crisis on Flint's school-age children. We find decreases in math achievement and increases in special needs classification, even among children living in homes with copper (rather than lead) water service lines. Low socioeconomic status students and younger students experienced the largest effects on math achievement, and boys experienced the largest effects on special needs classification. Our results point toward the broad negative effects of the crisis on children and suggest that existing estimates may substantially underestimate the overall societal cost of the crisis.

Building towards an adolescent neural urbanome: Expanding environmental measures using linked external data (LED) in the ABCD study

Many recent studies have demonstrated that environmental contexts, both social and physical, have an important impact on child and adolescent neural and behavioral development. The adoption of geospatial methods, such as in the Adolescent Brain Cognitive Development (ABCD) Study, has facilitated the exploration of many environmental contexts surrounding participants' residential locations without creating additional burdens for research participants (i.e., youth and families) in neuroscience studies. However, as the number of linked databases increases, developing a framework that considers the various domains related to child and adolescent environments external to their home becomes crucial. Such a framework needs to identify structural contextual factors that may yield inequalities in children's built and natural environments; these differences may, in turn, result in downstream negative effects on children from historically minoritized groups. In this paper, we develop such a framework - which we describe as the "adolescent neural urbanome" - and use it to categorize newly geocoded information incorporated into the ABCD Study by the Linked External Data (LED) Environment & Policy Working Group. We also highlight important relationships between the linked measures and describe possible applications of the Adolescent Neural Urbanome. Finally, we provide a number of recommendations and considerations regarding the responsible use and communication of these data, highlighting the potential harm to historically minoritized groups through their misuse.

Multilayer network associations between the exposome and childhood brain development

Growing up in a high poverty neighborhood is associated with elevated risk for academic challenges and health problems. Here, we take a data-driven approach to exploring how measures of children's environments relate to the development of their brain structure and function in a community sample of children between the ages of 4 and 10 years. We constructed exposomes including measures of family socioeconomic status, children's exposure to adversity, and geocoded measures of neighborhood socioeconomic status, crime, and environmental toxins. We connected the exposome to two structural measures (cortical thickness and surface area, n = 170) and two functional measures (participation coefficient and clustering coefficient, n = 130). We found dense connections within exposome and brain layers and sparse connections between exposome and brain layers. Lower family income was associated with thinner visual cortex, consistent with the theory that accelerated development is detectable in early-developing regions. Greater neighborhood incidence of high blood lead levels was associated with greater segregation of the default mode network, consistent with evidence that toxins are deposited into the brain along the midline. Our study demonstrates the utility of multilayer network analysis to bridge environmental and neural explanatory levels to better understand the complexity of child development.

Recent Developments in Causal Inference and Machine Learning

This article reviews recent advances in causal inference relevant to sociology. We focus on a selective subset of contributions aligning with four broad topics: causal effect identification and estimation in general, causal effect heterogeneity, causal effect mediation, and temporal and spatial interference. We describe how machine learning, as an estimation strategy, can be effectively combined with causal inference, which has been traditionally concerned with identification. The incorporation of machine learning in causal inference enables researchers to better address potential biases in estimating causal effects and uncover heterogeneous causal effects. Uncovering sources of effect heterogeneity is key for generalizing to populations beyond those under study. While sociology has long emphasized the importance of causal mechanisms, historical and life-cycle variation, and social contexts involving network interactions, recent conceptual and computational advances facilitate more principled estimation of causal effects under these settings. We encourage sociologists to incorporate these insights into their empirical research.

Concentrated poverty, ambient air pollution, and child cognitive development

Why does growing up in a poor neighborhood impede cognitive development? Although a large volume of evidence indicates that neighborhood poverty negatively affects child outcomes, little is known about the mechanisms that might explain these effects. In this study, we outline and test a theoretical model of neighborhood effects on cognitive development that highlights the mediating role of early life exposure to neurotoxic air pollution. To evaluate this model, we analyze data from a national sample of American infants matched with information on their exposure to more than 50 different pollutants known or suspected to harm the central nervous system. Integrating methods of causal inference with supervised machine learning, we find that living in a high-poverty neighborhood increases exposure to many different air toxics during infancy, that it reduces cognitive abilities measured later at age 4 by about one-tenth of a standard deviation, and that about one-third of this effect can be attributed to disparities in air quality.