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Changing built environment conditions to impact health mindsets and health equity may be a promising target for public health interventions. The present study was a cluster randomized controlled trial to test the impact of remediating vacant and abandoned properties on factors related to health mindset—including well-being, health interconnectedness, social capital markers, neighborhood disorder, and worry—as well as direct and indirect violence experiences and the moderating role of racial and income segregation on outcomes. A residential cohort of 405 participants from 194 randomly assigned geographic clusters was surveyed over five waves from 2019 to 2023. Compared to clusters with no treatment, participants in clusters where both vacant lots and abandoned homes were treated experienced significant increases in sense of community (83%, 95% CI = 71 to 96%, p  = 0.01). Among participants in randomization clusters where only vacant lots were treated, there were declines in perceived neighborhood disorder (− 55%, 95% CI = − 79 to − 5, p  = 0.06) and worry about community violence (− 56%, 95% CI = − 58 to − 12, p  = 0.06). There was also a moderating effect of racial and income spatial polarization, with the greatest changes in sense of community observed among more deprived areas with both homes and lots treated, and the largest changes in neighborhood worry and disorder were seen in more deprived areas with only lots treated. Remediation of vacant and abandoned properties may be one approach to change some but not all mindsets around health, and the effects may depend on the type of remediation as well as larger neighborhood conditions such as segregation.

Recent research has demonstrated that domains of social determinants of health (SDOH) (e.g. air pollution and social context) are associated with psychosis. However, SDOHs have often been studied in isolation. This study investigated distinct exposure profiles, estimated their associations with persistent distressing psychotic-like experiences (PLE), and evaluated whether involvement in physical activity partially explains this association. Analyses included 8,145 young adolescents from the Adolescent Brain and Cognitive Development Study. Data from the baseline and three follow-ups were included. Area-level geocoded variables spanning various domains of SDOH, including socioeconomic status, education, crime, built environment, social context, and crime, were clustered using a self-organizing map method to identify exposure profiles. Generalized linear mixed modeling tested the association between exposure profiles and persistent distressing PLE and physical activities (i.e. team and individual sports), adjusting for individual-level covariates including age, sex, race/ethnicity, highest level of parent education, family-relatedness, and study sites. Five exposure profiles were identified. Compared to the reference Profile 1 (suburban affluent areas), Profile 3 (rural areas with low walkability and high ozone), and Profile 4 (urban areas with high SES deprivation, high crime, and high pollution) were associated with greater persistent distressing PLE. Team sports mediated 6.14% of the association for Profile 3. This study found that neighborhoods characterized by rural areas with low walkability and urban areas with high socioeconomic deprivation, pollution concentrations, and crime were associated with persistent distressing PLE. Findings suggest that various social-environmental factors may differentially impact the development of psychosis.

Studies of neighborhood effects often attempt to identify causal effects of neighborhood characteristics on individual outcomes, such as income, education, employment, and health. However, selection looms large in this line of research, and it has been argued that estimates of neighborhood effects are biased because people nonrandomly select into neighborhoods based on their preferences, income, and the availability of alternative housing. We propose a two-step framework to disentangle selection processes in the relationship between neighborhood deprivation and earnings. We model neighborhood selection using a conditional logit model, from which we derive correction terms. Driven by the recognition that most households prefer certain types of neighborhoods rather than specific areas, we employ a principle components analysis to reduce these terms into eight correction components. We use these to adjust parameter estimates from a model of subsequent neighborhood effects on individual income for the unequal probability that a household chooses to live in a particular type of neighborhood. We apply this technique to administrative data from the Netherlands. After we adjust for the differential sorting of households into certain types of neighborhoods, the effect of neighborhood income on individual income diminishes but remains significant. These results further emphasize that researchers need to be attuned to the role of selection bias when assessing the role of neighborhood effects on individual outcomes. Perhaps more importantly, the persistent effect of neighborhood deprivation on subsequent earnings suggests that neighborhood effects reflect more than the shared characteristics of neighborhood residents: place of residence partially determines economic well-being.

We show that a common method of predicting individuals’ race in administrative records, Bayesian Improved Surname Geocoding (BISG), produces misclassification errors that are strongly correlated with demographic and socioeconomic factors. In addition to the high error rates for some racial subgroups, the misclassification rates are correlated with the political and economic characteristics of a voter’s neighborhood. Racial and ethnic minorities who live in wealthy, highly educated, and politically active areas are most likely to be misclassified as white by BISG. Inferences about the relationship between sociodemographic factors and political outcomes, like voting, are likely to be biased in models using BISG to infer race. We develop an improved method in which the BISG estimates are incorporated into a machine learning model that accounts for class imbalance and incorporates individual and neighborhood characteristics. Our model decreases the misclassification rates among non-white individuals, in some cases by as much as 50%.

This article tests the hypothesis that low-income residents disproportionately move out of neighbourhoods in close proximity to new rail transit stations. This transit-induced gentrification scenario posits that the development of rail transit will place an upward pressure on land and housing values and that higher-income residents will outbid low-income residents for this new amenity. The most transit-dependent population may therefore be displaced from the most accessible locations, forming a paradox in the investment in new transit systems. We test this hypothesis using the Panel Study on Income Dynamics (PSID) dataset to trace the out-migration of residents across the United States from census tracts within five years of the opening of a new station, between 1970 and 2014. We find that low-income individuals are more likely to move, regardless of their neighbourhood. However, we do not find significant evidence that low-income individuals are more likely to move out of transit neighbourhoods, after controlling for both individual and other neighbourhood characteristics. The odds of moving out of a transit neighbourhood for low-income residents is statistically insignificant. In other words, they do not have a heightened probability of leaving new transit neighbourhoods compared with other residents. Our results are robust across decades, when examining renters alone, for different time spans and for varying definitions of transit neighbourhoods. We further find that those living in transit neighbourhoods are not more likely to live in a crowded dwelling. Our results therefore suggest that, on average, across the nation, low-income residents do not disproportionately exit new transit neighbourhoods. 本文检验了这样一个假设：低收入居民会不成比例地搬离新的轨道交通站附近的街区。这种“公交导致的绅士化”理论假定：轨道交通的开发将对土地和住房价格产生上行压力，而高收入居民将与低收入居民争夺这些新的便利设施并将后者排挤出去。因此，大多数依赖公交的人口可能会被从最接近公交的地点排挤出去，这对新公交系统的投资而言便形成了悖论。我们使用家庭收入动态追踪调查（PSID）数据集追踪1970年至2014年期间美国相关人口普查区在新轨道交通站开放后五年内居民的外迁，从而检验这一假设。我们发现，无论处于何种街区，低 收入个人都更有可能搬迁。然而，在剔除个人和其他街区特征之后，我们没有发现显著证据表明低收入人群更有可能搬离公交街区。低收入居民搬离公交街区的几率不具有统计显著性。换句话说，与其他居民相比，他们搬离新的公交街区的可能性并不会更高。如果只考虑租房者，那么，在这几十年的时间跨度内，我们的研究结果在不同时段之间、并且在不同定义的公交街区之间保持了相当的稳定性。我们进一步发现，居住在公交街区的人生活在拥挤的住宅中的几率并不会更高。因此，我们的结果表明，在全国范围内，平均而言低收入居民不会不成比例地搬离新的公交街区。

This analysis examines the geography of subjective wellbeing within a single country via a novel dataset consisting of more than 26,000 respondents embedded in 3100 postal code areas in Finland. We include a detailed indicator on the level of urbanity of the respondent’s location derived from a 250 × 250 m GIS grid, contextual measures of the postal code area´s socioeconomic status as well as proximity to the nearest urban locality and capital city. This analytical framework model makes it possible to examine both individual and contextual determinants for perceived quality of life (QoL). In addition, we include individual-level measures on mental health (Mental Health Inventory MHI-5) and satisfaction with housing and neighbourhood characteristics. The results show that when controlling for socioeconomic factors living in an inner urban area or a neighbourhood (postal code area) with a high unemployment rate are associated with lower QoL and. Also, the share of population with a tertiary education in a postal code area has a positive effect for individual QoL. However, the effects of inner urban living and unemployment rate become insignificant when including mental health indicators and perceived loneliness. In sum, the results confirm and add more detail to earlier findings on lower QoL in urban context and connect living in inner urban area to mental health indicators. As such, the analysis provides further evidence for the positive QoL effects of more rural living while having an access to health and other services.

Background Loneliness is a public health epidemic in the United States (US), with older adults being vulnerable to experiencing loneliness. Predictors of loneliness are less understood among racial/ethnic groups of US older adults, and few studies have included perceived institutional discrimination (PID), stressful life events (SLE), and perceived neighborhood characteristics (PNC) as antecedent stressors of loneliness in diverse older adult samples. Our study assessed the relationship between these stressors and loneliness among specific racial/ethnic groups of older adults. Methods We used the Health and Retirement Study data ( n  = 9,904) to examine whether PID, SLE, and PNC were associated with loneliness. Loneliness was measured using the 11-item UCLA Loneliness Scale. PID included unfairly not hired for a job, unfairly prevented from moving into a neighborhood, and unfairly treated by the police. SLE included moving to a worse neighborhood/residence, being robbed or burglarized, and unemployed/looking for a job. PNC were measured as discohesion and disorder. Lagged multivariate linear regression models regressed loneliness (2014/2016 HRS waves) on PID, SLE and PNC (2010/2012 HRS waves) measured as cumulative totals and individual items. Models were stratified by Black (BOAs), Hispanic/Latinx (HOAs), and White (WOAs) older adults. Results Cumulative totals of PID, SLE, and neighborhood discohesion were associated with loneliness among BOAs while only discohesion was associated with loneliness among HOAs. Cumulative totals for PID, SLE, and PNC were associated with loneliness among WOAs. Individual stressors predicting loneliness for BOAs were moving to a worse residence and being robbed/burglarized. For HOAs, being prevented from moving to a neighborhood was associated with greater loneliness while being robbed/burglarized was associated with less loneliness. Individual stressors predicting greater loneliness for WOAs were being unfairly not hired for a job, receiving unfair treatment during police encounters, and moving to a worse residence. Conclusions Our study finds racial/ethnic variation in psychosocial stressors predicting loneliness four years later. Nevertheless, neighborhood discohesion was the most salient stressor and was associated with greater loneliness across all racial/ethnic groups. Future research and interventions should consider the differing stress appraisal processes across groups and to support the development of resources and policies to ameliorate loneliness among diverse older adults.

Neighbourhood socioeconomic disadvantage has a profound impact on individuals’ earnings and life satisfaction. Since definitions of the neighbourhood and research designs vary greatly across studies, it is difficult to ascertain which neighbourhoods and outcomes matter the most. By conducting parallel analyses of the impact of neighbourhood deprivation on life satisfaction and earnings at multiple scales, we provide a direct empirical test of which scale matters the most and whether the effects vary between outcomes. Our identification strategy combines rich longitudinal information on individual characteristics, family background and initial job conditions for England and Wales with econometric estimators that address residential sorting bias, and we compare results for individuals living in choice-restricted social housing with results for those living in self-selected privately rented housing. We find that the effect of neighbourhood deprivation on life satisfaction and wages is negative for both outcomes and largely explained by strong residential sorting on both individual and neighbourhood characteristics rather than a genuine causal effect. We also find that the results overall do not vary by neighbourhood scale. 街区社会经济贫困对个人收入和生活满意度有着深远的影响。由于街区定义和研究设计在不同的研究中差异很大，很难确定哪种街区和结果最重要。通过在多个尺度上对街区贫困对生活满意度和收入的影响进行平行分析，我们提供了一个直接的经验测试，以确定哪个尺度最重要，以及影响在不同结果之间是否存在差异。我们的识别策略将英格兰和威尔士个人特征、家庭背景和初始工作条件方面的丰富纵向信息与解决住宅分类偏差的计量经济学估计值相结合，并将居住在选择受限的社会福利住房中的个人的结果与居住在自主选择的私人租赁住房中的个人的结果进行比较。我们发现，街区贫困对生活满意度和工资的影响对两种结果都是负面的，这在很大程度上可以解释为对个人和街区特征的强烈的居住分类，而不是真正的因果效应。我们还发现，整体结果不会因街区尺度而异。

Cancer presents a disproportionate burden, particularly among individuals from low socioeconomic status neighborhoods. Disparities in outcomes persist, influenced by limited access to healthcare services, cultural barriers, and neighborhood socioeconomic status. This nationwide study aimed to investigate the associations between neighborhood socioeconomic status and mortality/readmission among hospitalized Medicare-eligible patients with cancer. We conducted a retrospective cohort study of patients with cancer who were hospitalized between 2020 and 2022, using United States (U.S.) Medicare claims data. We used logistic regression models to explore the association between neighborhood socioeconomic status, measured via the corrected Duke Area Deprivation Index, and 1) 30-day mortality and 2) hospital readmission rates. Odds ratios were calculated to assess for associations in a stepwise manner after adjusting for sociodemographic characteristics, comorbidities, and regional/hospital characteristics. The study included 266,269 admissions. Patients from neighborhoods with a higher area deprivation index (i.e., lower socioeconomic status) exhibited higher mortality rates (adjusted odds ratio 1.06 [95 % confidence interval 1.01, 1.12]) compared to patients from lower area deprivation index neighborhoods. There were no overall differences in readmission rates for patients from high area deprivation index neighborhoods. High area deprivation index neighborhoods were associated with less teaching hospitals (30.2 % vs 39.9 %), more public hospitals (16.4 % vs 11.2 %), and less primary care providers (mean 66 vs 93.2) when compared to low area deprivation index neighborhoods. The study revealed significant associations between neighborhood socioeconomic status and mortality in patients with cancer in the U.S. Understanding the interplay between neighborhood socioeconomic status and oncologic outcomes is crucial for developing targeted interventions to provide equitable oncology care. •Lower neighborhood socioeconomic status (SES) is linked to higher cancer mortality.•No significant association was found between neighborhood SES and readmission.•Both individual-level risks and regional deprivation affect cancer mortality rates.

The increase in physical inactivity prevalence in the USA has been associated with neighborhood characteristics. While several studies have found an association between neighborhood and health, the relative importance of each component related to physical inactivity or how this value varies geographically (i.e., across different neighborhoods) remains unexplored. This study ranks the contribution of seven socioecological neighborhood factors to physical inactivity prevalence in Chicago, Illinois, using machine learning models at the census tract level, and evaluates their predictive capabilities. First, we use geographical random forest (GRF), a recently proposed nonlinear machine learning regression method that assesses each predictive factor's spatial variation and contribution to physical inactivity prevalence. Then, we compare the predictive performance of GRF to geographically weighted artificial neural networks, another recently proposed spatial machine learning algorithm. Our results suggest that poverty is the most important determinant in the Chicago tracts, while on the other hand, green space is the least important determinant in the rise of physical inactivity prevalence. As a result, interventions can be designed and implemented based on specific local circumstances rather than broad concepts that apply to Chicago and other large cities. The online version contains supplementary material available at 10.1007/s10109-023-00415-y.