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This article develops and assesses the concept of triple neighborhood disadvantage. We argue that a neighborhood’s well-being depends not only on its own socioeconomic conditions but also on the conditions of neighborhoods its residents visit and are visited by, connections that form through networks of everyday urban mobility. We construct measures of mobilitybased disadvantage using geocoded patterns of movement estimated from hundreds of millions of tweets sent by nearly 400,000 Twitter users over 18 months. Analyzing nearly 32,000 neighborhoods and 9,700 homicides in 37 of the largest U. S. cities, we show that neighborhood triple disadvantage independently predicts homicides, adjusting for traditional neighborhood correlates of violence, spatial proximity to disadvantage, prior homicides, and city fixed effects. Not only is triple disadvantage a stronger predictor than traditional measures, it accounts for a sizable portion of the association between residential neighborhood disadvantage and homicides. In turn, potential mechanisms such as neighborhood drug activity, interpersonal friction, and gun crime prevalence account for much of the association between triple disadvantage and homicides. These findings implicate structural mobility patterns as an important source of triple (dis) advantage for neighborhoods and have implications for a broad range of phenomena beyond crime, including community capacity, gentrification, transmission in a pandemic, and racial inequality.

This research analyzes how living in concentrated poverty during adolescence affects future college outcomes. Using Add Health data and propensity score methods to explore effect heterogeneity, I find that concentrated poverty has little direct impact on college matriculation. It does, however, strongly reduce the odds of graduating from college for adolescents least likely to reside in concentrated poverty. This indicates an advantage-leveling model in which individuals with prior advantages have the most to lose from neighborhood disadvantage during adolescence. I assess neighborhood socialization, school effects, and peer effects as potential explanations for the neighborhood effect. Supporting collective socialization theory, neighborhood economic opportunity and resource deprivation are key aspects of poverty-saturated neighborhoods that influence college graduation odds. Schools also play an important role in the relationship between neighborhoods and college outcomes. Main effects are likely to be causal as they are highly robust to unobserved confounding.

This study estimates the effect of neighborhood disadvantage on bachelor’s degree attainment with data from a long-term follow-up of the Project on Human Development in Chicago Neighborhoods. We focus on heterogeneous effects by race and class as well as individual and neighborhood mechanisms that might explain observed patterns, including parents’ educational expectations, collective efficacy, social relationships, and neighborhood violence. Using newly developed methods for estimating longitudinal treatment effects, we find that cumulative neighborhood disadvantage in adolescence is strongly associated with lower bachelor’s attainment among high-income blacks and Latinos. We find no effect for whites and at most a modest effect among low- and middle-income blacks/Latinos. A sensitivity analysis suggests that the estimated effect for high-income blacks/Latinos is plausibly causal. These results support an advantage-leveling model of neighborhood effects and add important nuance for research considering how and for whom neighborhoods influence life chances.

A longstanding tradition of research linking neighborhood disadvantage to higher rates of violence is based on the characteristics of where people reside. This Essay argues that we need to look beyond residential neighborhoods to consider flows of movement throughout the wider metropolis. Our basic premise is that a neighborhood's well-being depends not only on its own socioeconomic conditions but also on the conditions of neighborhoods that its residents visit and are visited by—connections that form through networks of everyday urban mobility. Based on the analysis of large-scale urban-mobility data, we find that while residents of both advantaged and disadvantaged neighborhoods in Chicago travel far and wide, their relative isolation by race and class persists. Among large U.S. cities, Chicago's level of racially segregated mobility is the second highest. Consistent with our major premise, we further show that mobility-based socioeconomic disadvantage predicts rates of violence in Chicago's neighborhoods beyond their residence-based disadvantage and other neighborhood characteristics, including during recent years that witnessed surges in violence and other broad social changes. Racial disparities in mobility-based disadvantage are pronounced—more so than residential neighborhood disadvantage. We discuss implications of these findings for theories of neighborhood effects on crime and criminal justice contact, collective efficacy, and racial inequality.

Do neighborhood conditions affect wealth accumulation? This study uses the National Longitudinal Survey of Youth 1979 cohort and a counterfactual estimation strategy to analyze the effect of prolonged exposure to neighborhood (dis)advantage from emerging adulthood through middle adulthood. Neighborhoods have sizable, plausibly causal effects on wealth, but these effects vary significantly by race/ethnicity and homeownership. White homeowners receive the largest payoff to reductions in neighborhood disadvantage. Black adults, regardless of homeownership, are doubly disadvantaged in the neighborhood–wealth relationship. They live in more-disadvantaged neighborhoods and receive little return to reductions in neighborhood disadvantage. Findings indicate that disparities in neighborhood (dis)advantage figure prominently in wealth inequality and the racial wealth gap.

A longstanding finding is that neighborhood racial segregation is linked to violence. In this paper, we look beyond neighborhoods of residence to consider the everyday mobility of urbanites in their daily rounds. Analyzing estimates of neighborhood mobility from largescale social media data in the 50 largest American cities, we find that residential segregation by race is not only associated with higher violence but also lower equitability of travel across neighborhoods and a lower concentration of visits to common hubs. Further, the interaction of equitable and concentrated mobility is significantly associated with rates of violence, controlling for both racial and income segregation, education, city size, and density. There is little evidence, however, that patterns of everyday mobility mediate the influence of residential racial segregation. Both dimensions of the structural connectedness of cities—one rooted in place of residence, and the other encompassing interneighborhood exposure based on travel throughout the metropolis—are implicated in violence.

Background Amidst the COVID-19 pandemic, there has been growing concern about the declining mental health and healthy behaviors compared to pre-pandemic levels. Despite this, there is a lack of longitudinal studies that have examined the relationship between health behaviors and mental health during the pandemic. In response, the statewide COVIDsmart longitudinal study was launched. The study’s main objective is to better understand the effects of the pandemic on mental health. Findings may provide a foundation for the identification of public health strategies to mitigate future negative impacts of the pandemic. Methods Following online recruitment in spring of 2021, adults, ages 18 to 87, filled out social, mental, economic, occupational, and physical health questionnaires on the digital COVIDsmart platform at baseline and through six monthly follow-ups. Changes in the participant’s four health behaviors (e.g., tobacco and alcohol consumption, physical activity, and social media use), along with sex, age, loneliness score, and reported social and economic (SE) hardships, were analyzed for within-between group associations with depression and anxiety scores using Mixed Models Repeated Measures. Results In this study, of the 669 individuals who reported, the within-between group analysis indicated that younger adults (F = 23.81, p  < 0.0001), loneliness (F = 234.60, p  < 0.0001), SE hardships (F = 31.25, p  < 0.0001), increased tobacco use (F = 3.05, p  = 0.036), decreased physical activity (F = 6.88, p  = 0.0002), and both positive and negative changes in social media use (F = 7.22, p  = 0.0001) were significantly associated with worse depression scores. Additionally, females (F = 6.01, p  = 0.015), younger adults (F = 32.30, p  < 0.0001), loneliness (F = 154.59, p  < 0.0001), SE hardships (F = 22.13, p  < 0.0001), increased tobacco use (F = 4.87, p  = 0.004), and both positive and negative changes in social media use (F = 3.51, p  = 0.016) were significantly associated with worse anxiety scores. However, no significant changes were observed in the within-between group measurements of depression and anxiety scores over time ( p  > 0.05). Physical activity was not associated with anxiety nor was alcohol consumption with both depression and anxiety ( p  > 0.05). Conclusions This study demonstrates the longitudinal changes in behaviors within the context of the COVID-19 pandemic. These findings may facilitate the design of preventative population-based health approaches during the COVID-19 pandemic or future pandemics.

IntroductionCurrent pharmacological treatment options for painful diabetic neuropathy (PDN) often fail to provide adequate pain relief. However, in the recent SENZA-PDN study, high-frequency 10 kHz spinal cord stimulation (SCS) demonstrated significant long-term improvements in lower limb pain and health-related quality of life (HRQoL) in a PDN population. Furthermore, more than half of 10 kHz SCS recipients showed improved sensory function based on non-blinded clinical assessments in post hoc analysis. We report the design of the PDN-Sensory study, which aims to evaluate changes in pain and neurological function with 10 kHz SCS in the treatment of PDN. The study will include objective measures of neurological function, including the modified Toronto Clinical Neuropathy Score (mTCNS) and intraepidermal nerve fibre density (IENFD).Methods and analysisThis multicentre, prospective, randomised controlled trial will compare conventional medical management (CMM) with 10 kHz SCS+CMM in individuals with diabetes and chronic, intractable lower limb pain due to PDN. Participants will be randomised 1:1 to CMM alone or 10 kHz SCS+CMM, with optional crossover at 6 months. The primary outcome is the proportion of participants at 6 months achieving ≥50% pain relief from baseline. The key secondary endpoint is the proportion of participants at 6 months with a reduction in mTCNS of ≥3 points from baseline (excluding changes in foot pain). Additional endpoints at 6 and 12 months include changes from baseline in mTCNS, IENFD, 7-day averaged pain score, pain-related interference, HRQoL, sleep, psychological outcomes, functional status and metabolic parameters.Ethics and disseminationThe study protocol received central approval from the Western Institutional Review Board (IRB #20230954). Local IRB approval will be required before initiation of the study at each participating clinical site. The study complies with Good Clinical Practice guidelines (ISO 14155), the Declaration of Helsinki, and all applicable national, federal and local regulatory requirements. Dissemination plans include presentations at national and international conferences and publication in a peer-reviewed journal with open access.Trial registration numberNCT05777317.

The COVID-19 pandemic has affected people's lives beyond severe and long-term physical health symptoms. Social distancing and quarantine have led to adverse mental health outcomes. COVID-19-induced economic setbacks have also likely exacerbated the psychological distress affecting broader aspects of physical and mental well-being. Remote digital health studies can provide information about the pandemic's socioeconomic, mental, and physical impact. COVIDsmart was a collaborative effort to deploy a complex digital health research study to understand the impact of the pandemic on diverse populations. We describe how digital tools were used to capture the effects of the pandemic on the overall well-being of diverse communities across large geographical areas within the state of Virginia. The aim is to describe the digital recruitment strategies and data collection tools applied in the COVIDsmart study and share the preliminary study results. COVIDsmart conducted digital recruitment, e-Consent, and survey collection through a Health Insurance Portability and Accountability Act-compliant digital health platform. This is an alternative to the traditional in-person recruitment and onboarding method used for studies. Participants in Virginia were actively recruited over 3 months using widespread digital marketing strategies. Six months of data were collected remotely on participant demographics, COVID-19 clinical parameters, health perceptions, mental and physical health, resilience, vaccination status, education or work functioning, social or family functioning, and economic impact. Data were collected using validated questionnaires or surveys, completed in a cyclical fashion and reviewed by an expert panel. To retain a high level of engagement throughout the study, participants were incentivized to stay enrolled and complete more surveys to further their chances of receiving a monthly gift card and one of multiple grand prizes. Virtual recruitment demonstrated relatively high rates of interest in Virginia (N=3737), and 782 (21.1%) consented to participate in the study. The most successful recruitment technique was the effective use of newsletters or emails (n=326, 41.7%). The primary reason for contributing as a study participant was advancing research (n=625, 79.9%), followed by the need to give back to their community (n=507, 64.8%). Incentives were only reported as a reason among 21% (n=164) of the consented participants. Overall, the primary reason for contributing as a study participant was attributed to altruism at 88.6% (n=693). The COVID-19 pandemic has accelerated the need for digital transformation in research. COVIDsmart is a statewide prospective cohort to study the impact of COVID-19 on Virginians' social, physical, and mental health. The study design, project management, and collaborative efforts led to the development of effective digital recruitment, enrollment, and data collection strategies to evaluate the pandemic's effects on a large, diverse population. These findings may inform effective recruitment techniques across diverse communities and participants' interest in remote digital health studies.

Labor migration offers an important mechanism to reallocate workers when there are regional differences in employment conditions. Whereas conventional wisdom suggests migration rates should increase during recessions as workers move out of areas that are hit hardest, initial evidence suggested that overall migration rates declined during the Great Recession, despite large regional differences in unemployment and growth rates. In this paper we use data from the American Community Survey to analyze internal migration trends before and during the economic downturn. First, we find only a modest decline in the odds of adults leaving distressed labor market areas during the Great Recession, which may result in part from challenges related to the housing price crash. Second, we estimate conditional logit models of destination choice for individuals who migrate across labor market areas; we find a substantial effect of economic factors such as labor demand, unemployment, and housing values. We also estimate latent class conditional logit models that test whether there is heterogeneity in preferences for destination characteristics among migrants. Over all, the latent class models suggest that roughly equal percentages of migrants were motivated by economic factors before and during the Great Recession. We conclude that fears of dramatic declines in labor migration seem to be unsubstantiated.