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. Author manuscript; available in PMC: 2026 Sep 26.
Published in final edited form as: Soc Sci Med. 2025 Sep 26;386:118623. doi: 10.1016/j.socscimed.2025.118623

Social-spatial residence patterns and adolescent mental health: Investigating the influences of residential segregation, neighborhood social cohesion, and race on persistent, distressing psychotic-like experiences in the United States

Kirsten L Wade a, Michael Kramer b, Benson Ku c
PMCID: PMC12989066  NIHMSID: NIHMS2116996  PMID: 41072158

Abstract

Prior work suggests that living among people who share similar identities may be protective against psychosis, but the meaning of this association in the context of racialized residential segregation is not well understood. We investigated the effects of evenness and exposure residential segregation on persistent, distressing psychotic-like experiences (PLEs) in Black and White adolescents living in urban neighborhoods with high and low social cohesion in the United States. Data from the Adolescent Brain Cognitive Development Study were used (N=5,871), measuring evenness and exposure segregation in metropolitan statistical areas using the Dissimilarity Index (DI) and Exposure-Interaction Index (EII), respectively. Multi-level log binomial generalized estimating equations estimated the relative risk of PLEs by exposure to each domain of segregation. Evenness and exposure segregation were associated with risk of PLE (DI: aRR=1.11, 95% CI 1.06–1.17; EII: aRR=0.82, 95% CI 0.78–0.87). When both domains were present in the same model, the effect of evenness, but not exposure, was attenuated (DI: aRR=0.98, 95% CI 0.92–1.05; EII: aRR=0.81, 95% 0.75–0.88). These associations were not statistically significantly different for levels of race (p-values ranged 0.08–0.86) or neighborhood social cohesion (p-values ranged 0.16–0.56). Sensitivity analysis indicated the main effect was not altered by duration of exposure. Lower exposure to exposure domain segregation may be protective against risk of PLEs in young adolescents in urban areas. The association was similar in comparisons of Black-White racial groups and high-low neighborhood social cohesion groups.

Keywords: residential segregation, mental health, psychosis, social cohesion, ABCD study

INTRODUCTION

Psychotic spectrum disorders include a range of diagnoses from schizophrenia to depression and bipolar disorder with psychotic symptoms. The global prevalence of psychotic disorders is about 0.4%; prevalence estimates for the United States are similar, but data quality and generalizability have been questioned (Anglin, 2021, 2023; Desai et al., 2013; Wu et al., 2006). The burden of psychotic disorders includes direct medical costs; indirect costs due to cognitive and social impairments that can be life-long; and reduced life-span (Rössler et al., 2005). Psychotic disorders are estimated to cost $23 billion per year in the United States alone (Desai et al., 2013). Environmental and social risk factors – like urbanicity, early life trauma, air pollution, and migration – have been highlighted by epidemiological studies (K. Dean & Murray, 2005; Fusar-Poli et al., 2017), and overlapping genetic risk factors have been identified across psychotic disorders (Craddock et al., 2005; O’Donovan et al., 2009; Owen et al., 2007). However, neither genetics nor environmental influences alone is thought to be sufficient to cause psychotic disorders (Zwicker et al., 2018).

Clinical onset of psychotic disorders often occurs during young adulthood, but subclinical experiences of psychosis, as well as cognitive and behavioral signs, can be observed during childhood and adolescence (Yung & McGorry, 1996). Psychotic-like experiences (PLEs) are relatively common in children (median prevalence 7.5–17%) and are not guarantees of future psychiatric diagnosis (Kelleher & Cannon, 2011). The proneness-persistence-impairment model of psychotic disorders proposes that PLEs must be persistent in order to be clinically relevant, and subsequent work has characterized persistent PLEs that are perceived as distressing to be of additional relevance (Karcher et al., 2022; Kline et al., 2014; Murphy et al., 2018; Os et al., 2009; Wilson et al., 2020).

Urbanicity & social environments

The prevalence of psychosis is higher in urban areas, and debate remains regarding what aspects of city life contribute to these findings. A dose-response relationship was found between childhood exposure to urbanicity and risk of schizophrenia, such that longer duration of exposure to higher levels of urbanicity conferred significantly higher risk, independent of where one was born (Pedersen & Mortensen, 2001). In another study, adjustment for genetic risk factors only mildly attenuated the association between psychosis and urbanicity, suggesting observed disease patterns are not the result of a spatial selection factor (Krabbendam & van Os, 2005). A recent multi-level study using a population registry found that adjustment for thirteen social and material neighborhood factors, spanning domains of material and social deprivation, reduced the incident rate ratio for the effect between urbanicity and psychosis from 1.98 (95% confidence interval (95% CI): 1.77–2.22) to 1.30 (95% CI: 1.11–1.51) – indicating neighborhood characteristics may explain a large portion of the effect of urbanicity on the rate of psychosis (Pedersen et al., 2022).

The inclusion of social factors in assessing neighborhood effects on health raises questions about the relationship between the built and social environments in urban spaces. The spatiality paradigm is a theoretical approach to understanding the interrelationship between power, social processes, and physical space in cities (Hillier, 2008). It posits that social processes occur within, and play a critical role in shaping, physical spaces; thus, our understanding of social phenomena, such as politics, culture, and economics, can be informed by analyzing physical patterns in urban space (Lefebvre et al., 1991; Soja, 1996). This social-spatial thinking aligns well with Krieger’s eco-social model, which provides a framework for understanding how multi-level social and physical factors influence health through a process of embodiment (Krieger, 2012). In concert, a spatialist approach to the eco-social model might suggest a cyclical process through which broader social influences shape the spaces in which people live; those spaces influence the embodiment of their inhabitants; and the inhabitants in turn participate in the original overarching social processes – often in a manner proportionate to the level of power and privilege they enjoy. As such, all assessments of neighborhoods are, to some extent, assessments of the social environment, in that physical spaces reflect the social processes that conceived them and the social experiences unfolding within them.

The tie between social factors and the built environment becomes clear when the fragmentation of one leads to the disruption of the other. Several interdisciplinary works have documented the connection between mental health consequences and neighborhood social fragmentation: Fullilove’s description of PTSD in Black communities subjected to urban renewal (Fullilove, 2016); Narita, et al.’s examination of psychosis in those experiencing disruption due to gentrification (Narita et al., 2020); and Ku, et al.’s investigation into residential instability and psychosis risk (Ku et al., 2021). In the last, the authors compared residentially stable individuals living in areas of varying neighborhood stability; residentially unstable individuals moving between stable areas; and the overall effect of individual and residential instability among all participants (Ku et al., 2021). Notably, the largest effect was observed among individuals moving between stable areas, which the authors proposed may indicate the social stress experienced when detaching from one social network and integrating into another – the social penetrability of which may correlate with residential area instability.

Work examining psychotic disorders in Western Europe observed that incidence is higher in immigrant communities, especially among people who moved to areas that are most dissimilar to their place of origin (Selten et al., 2020). This association was also higher in Black compared to White immigrants; did not meaningfully change between first and second generations; and was only slightly attenuated by adjustment for socioeconomic status. The relationship between urbanicity and psychosis among immigrant communities may be related to intergenerational identity-based experiences in addition to the act of relocation itself.

Associations between residence patterns and psychosis have been explored at the group level in the ethnic density effect for psychosis, which suggests that “the risk of psychosis in minority group individuals is inversely related to neighborhood-level proportions of others belonging to the same group,” as described in a meta-analysis of studies conducted in seven White-majority countries across Europe, North America, and Australia (Baker et al., 2021). Several studies and systematic reviews present similar findings, suggesting that there is something about an individual living in an area where people have similar racial or ethnic backgrounds as their own that is protective against psychosis (Anglin et al., 2023; Baker et al., 2021; Pickett & Wilkinson, 2008; Schofield et al., 2011; Shankley & Laurence, 2022). Work analyzing other health outcomes has demonstrated a similar protective effect when individuals live in ethnic enclaves (Bell et al., 2006; Kramer et al., 2010; Williams & Collins, 2001). Given the role of social ties in physical space, it is plausible that neighborhood ethnic density acts as a proxy for a subtype of supportive social bonds experienced within one’s neighborhood.

However, many of these studies were focused in Western Europe and did not explicitly question if or how racialized processes of segregation might be interacting with the ethnic density effect. In her 2023 review, Anglin describes the medical and public health contexts specific to the United States that are inhibiting the replication of the European canon in the U.S., including a lack of comprehensive data from population registries, variable access to mental health care by race and ethnicity, and disproportionate misdiagnosis of minoritized populations (Anglin, 2023). The relative lack of studies focusing on the United States is important due to the unique and persistent influence of structural racism on health disparities present here.

Racism & residential segregation in the United States

The pervasiveness of structural racism in the United States by definition means that its effects on health outcomes are felt at a variety of different levels, through the interaction of institutions, systems of power, and cultural beliefs (L. T. Dean & Thorpe, 2022). Residential segregation is a form of institutionalized racism that consistently has been demonstrated to relate to negative health outcomes for minoritized communities – especially Black communities – in the United States (Kramer et al., 2010; Williams & Collins, 2001; Williams & Mohammed, 2013). Residential segregation is a sorting process that results in the physical separation of people in space in accordance with political, economic, and social systems rooted in racist beliefs (Kramer, 2018). In their 2009 paper, Kramer and Hogue emphasize that the clustered spatial distribution of people by race is not itself a negative phenomenon, but that it is the result of a broader process of structural racism that intentionally funnels minoritized groups into areas with restricted access to resources and opportunities (Kramer & Hogue, 2009). Residential segregation exists in different forms globally, but the United States is unique in the persistent, intergenerational isolation of and disinvestment in Black communities specifically.

Williams and Collins connect the historical de jure and present de facto process of racial residential segregation to health outcomes, describing segregation as a “fundamental cause” of racial health disparities (Williams & Collins, 2001). This process of residential segregation acts through a host of individual- and neighborhood-level pathways – such as socioeconomic status, housing stock, access to care and other resources, and social connection – to affect health (Kramer & Hogue, 2009; Williams & Collins, 2001). The role of neighborhoods in health often employs the language of a “geography of opportunity,” which is differentially created and experienced by people of different racial groups due to institutions like residential segregation (Kramer, 2018; Squires & Kubrin, 2005). This also highlights the importance of looking beyond one’s immediate neighborhood to interpret this opportunity, or the lack thereof, in the context of its availability elsewhere in the city. Thus, conceptualizing residential segregation in relation to health outcomes should incorporate the metropolitan-level context within which the sorting process is unfolding (Kramer & Hogue, 2009).

A variety of measures for residential segregation have been constructed, and many focus on two general domains through which to understand metropolitan-level patterns of racial segregation: evenness and exposure (Massey & Denton, 1988). In simple terms, exposure segregation addresses whether or not people of two races reside in mixed neighborhoods, while evenness addresses if clusters of those two groups are interspersed or separated throughout the city. Exposure is often referred to as the “experience” of segregation in that it measures a probability of interaction between two racial groups based on the proportions of those groups residing in a neighborhood (Massey & Denton, 1988). Evenness, on the other hand, indicates whether racial groups are on average “overrepresented in some areas and underrepresented in others” such that the gross grouping of racial groups are spatially distinct from one another (Massey & Denton, 1988). The practice of measuring segregation using different domains acknowledges that the process of segregation itself can be heterogeneous, potentially resulting in distinct observable physical forms, not all of which may be equally representative of disparities in the lived geography of opportunity and health.

It remains unclear how the potential protective effect of living in racially clustered neighborhoods (e.g. “ethnic density”) should be understood in the context of segregation-related health disparities in the United States. It could be that the stress of navigating landscapes and systems constructed by racism, and having limited access to resources, are embodied over the life course to contribute to increased risk of psychosis and related disorders. However, the observed protective aspect of living in clustered neighborhoods also suggests a possible protective role of local social support, perhaps through sharing social or identity bonds, or through the lower likelihood of encountering overt identity-based discrimination in day-to-day interactions. The present study aims, in a cohort of early adolescents, i) to estimate the association between exposure and evenness domains of residential segregation and risk of persistent, distressing PLEs; and ii) to estimate the magnitude of effect measure modification of the association by individual race and neighborhood social support.

METHODS

Participants

Data were acquired from the ABCD Study (Data Release 5.0, June 2023; DOI: 10.15154/8873-zj65), a large prospective cohort study that collects data on long-term brain development and health in adolescents in the United States (Haist & Jernigan, 2023). Participants ages 9–10 at baseline were sampled from 21 sites across the United States between 2016 and 2018. Data was collected from both participants and their caregivers. Exposure data were collected at baseline; outcome data are a composite value created from longitudinal, repeated measures spanning baseline through year three of follow-up. Only participants who self-identified at baseline as non-Hispanic Black or non-Hispanic White, who are natal sex male or female, and who resided in urban areas were included. Furthermore, participants were excluded if no data were available for the Dissimilarity Index, the Exposure-Interaction Index, or the Area Deprivation Index. The final analytic sample consisted of 5,871 adolescents. The Emory University Institutional Review Board determined that this study is exempt from review due to its secondary analysis of de-identified data.

Measures

Persistent Distressing Psychotic Like Experiences

Psychotic-like experiences were measured using the Prodromal Questionnaire-Brief Child Version (PQ-BC), which was adapted for use in young children to measure subclinical psychotic symptoms (Karcher et al., 2018; Loewy et al., 2011). The PQ-BC demonstrated internal reliability and consistency with other early indicators for risk of psychosis using baseline data from the ABCD cohort (Karcher et al., 2018, 2020). Participants can indicate the presence or absence of 21 different PLEs, as well as corresponding feelings of distress on a five-unit scale, where higher scores indicate greater perceived distress. Additional work has categorized PLEs based on how consistently they are experienced over follow-up (persistence) and how much distress they cause, aiming to identify subtypes with a higher likelihood of subsequent clinical relevance (Karcher et al., 2022). Persistence of distressing PLEs was defined as having experienced any PLE self-rated at least three out of five on a five-unit distress scale for at least two of the first four waves of data available (baseline to follow-up year 3) (Ku, et al., 2024a). Participants were dichotomized based on experience of persistent, distressing PLEs over baseline and the first three years of follow-up.

Residential segregation

Validated indices were used to estimate metropolitan statistical area (MSA)-level exposure to residential segregation on two domains: evenness and exposure (Massey & Denton, 1988). The Dissimilarity Index (DI) measures the evenness dimension of residential segregation (Massey & Denton, 1988) and here represents the proportion of the Black population that would need to relocate to reach full integration in the broader community (Kramer & Hogue, 2009). A higher DI value indicates greater segregation in the evenness domain. The Exposure-Interaction Index (EII) measures the exposure domain of residential segregation, representing the likelihood of two different racial groups residing in the same area. Higher values of the EII indicate lower residential segregation in the exposure domain. DI and EII values at the MSA level can be suggestive of broader influences contributing to the “sorting processes” of residential segregation that are indicative of structural racism (Kramer et al., 2010; Kramer & Hogue, 2009). Both the DI and EII used here measured segregation between White and Black groups at the MSA level using five-year summary data from the 2014–2018 American Community Survey (US Census Bureau, 2019). Equations for both measures are in Supplemental Figure 1.

Neighborhood social cohesion

Perceived neighborhood social cohesion is a measure of social capital and was informed by the Social Cohesion subscale of the PhenX Neighborhood Collective Efficacy protocol (Earls et al., 1999; Hamilton et al., 2011). The subscale includes five items addressing if the neighborhood is perceived to be close-knit and if neighbors help each other, get along, share the same values, and can be trusted. The average of individual responses collected from parents of participants was computed, treating “don’t know” as neutral and refusal to answer as missing; only scores informed by at least three of the five items were included (Rios et al., 2012). Scores were dichotomized at the population median, with the reference group indicating those with up to and including the average perceived neighborhood social cohesion. Data were collected at the end of year two of follow-up.

Covariates

Baseline values for race, natal sex, family history of psychosis, Area Deprivation Index (ADI) (Kind & Buckingham, 2018), family income-to-needs ratio, and site ID were used as covariates. Race was self-reported by parents of participants; natal sex was included in the model to account for sex-based differences in age of onset of psychosis (Faraone et al., 1994). Natal sex and family history of psychosis were used as exposure-outcome confounders under the assumption that they are unbalanced across, but not casually related to, the exposure and outcome. Additional detail about covariates and descriptive information by site can be found in the Supplement.

Statistical Analysis

Missing data for individual characteristics was imputed using multiple imputation of chained estimating equations with fully conditional specification via the mice package in R; 25 imputations with 25 maximum iterations were run. Convergence was assessed using trace plots and by comparing observed and imputed density plots. Multicollinearity was evaluated by assessing variance inflation factors using a threshold of five. Multi-level model log binomial regression was estimated with generalized estimating equations (GEE) and baseline site ID as the clustering variable; all models used the geepack package in R. The quasi-likelihood under the independence model criterion (QIC) was used to understand general model fit. Univariate models estimated the association between each covariate and experience of persistent, distressing PLEs. Models estimating the association between each domain of residential segregation alone as well as both domains together were fit with all covariates. Race and neighborhood social cohesion were assessed for effect measure modification on the additive scale, using Wald tests for significance of the interaction term and stratified analyses, respectively, from multi-level binomial models with an identity link. The relationships between unadjusted risk of persistent, distressing PLE and the dissimilarity index, the interaction index, and social cohesion were visualized among White and Black groups using scatter plots of average scores at each study site.

Sensitivity Analysis

The decision to include all participants regardless of duration at their current address(es) operates under the assumption that any changes in residence demonstrated horizontal rather than vertical moves in terms of neighborhood characteristics. To evaluate this assumption, the sensitivity of the main effect to the duration of exposure was evaluated by restricting the sample using varying minimum thresholds of time lived at one’s primary residence. All multi-level models were fit first for the entire analytic sample and subsequently for samples restricted to those who had been living at their primary address for at least two and at least five years.

RESULTS

Demographic characteristics of the final, imputed analytic sample stratified by tertiles of each exposure (DI and EII) are shown in Tables 1A and 1B. General demographic characteristics in potential analytic samples by treatment of missingness are shown in Supplemental Table 1. The overall prevalence of persistent, distressing PLEs was 19.8% (n=1160/5871). As the value of DI increased (representing increased segregation), the proportion of adolescents reporting PLEs increased between the first and second tertile of DI values. A similar pattern was observed for EII: as residential segregation increased (EII decreased), the prevalence of relevant PLEs increased. The largest difference was observed between the 2nd and 3rd EII tertiles. Social cohesion score appeared to decrease, and the sample proportion Black to increase, as exposure to segregation increased for each segregation domain. Income-to-needs ratio appeared higher at higher levels of evenness segregation, but the same pattern was not as clear for exposure segregation.

Table 1a.

Demographic characteristics for analytic sample by tertiles of Dissimilarity Index

1st tertile (0.312–0.508)
n=2075 		2nd tertile (0.509–0.632)
n=2033 		3rd tertile (0.633–0.788)
n=1763
Persistent, distressing PLE, n (%) 289 (13.9) 460 (22.6) 411 (23.3)
Individual
 Age (years), mean (SD) 9.87 (0.72) 9.96 (0.70) 9.95 (0.70)
 Sex (Female), n (%) 968 (46.7) 959 (47.2) 845 (47.9)
 Family history of psychosis, n (%) 53 (2.6) 39 (1.9) 52 (2.9)
 Race (Black), n (%) 234 (11.3) 572 (28.1) 660 (37.4)
 Social cohesion score, mean (SD) 3.91 (0.67) 3.71 (0.71) 3.69 (0.72)
 Income-to-needs ratio, mean (SD)
Household income, n (%)		 4.22 (2.73) 4.32 (3.09) 3.84 (3.02)
  <$5,000 52 (2.5) 88 (4.3) 92 (5.2)
  $5,000–11,999 45 (2.2) 85 (4.2) 87 (4.9)
  $12,000–15,999 26 (1.3) 39 (1.9) 50 (2.8)
  $16,000–24,999 46 (2.2) 79 (3.9) 84 (4.8)
  $25,000–34,999 83 (4.0) 108 (5.3) 111 (6.3)
  $35,000–49,999 139 (6.7) 130 (6.4) 169 (9.6)
  $50,000–74,999 304 (14.7) 247 (12.1) 258 (14.6)
  $75,000–99,999 369 (17.8) 293 (14.4) 227 (12.9)
  $100,000–199,999 760 (36.6) 647 (31.8) 472 (26.8)
  $200,000–299,999 251 (12.1) 317 (15.6) 213 (12.1)
Residential
 Exposure-Interaction Index, mean (SD) 0.61 (0.15) 0.39 (0.12) 0.32 (0.12)
 Area Deprivation Index, mean (SD) 92.0 (17.8) 95.0 (20.6) 99.2 (23.7)
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Table 1b.

Demographic characteristics for analytic sample by tertiles of Exposure-Interaction Index

1st tertile (0.165–0.337)
n=1979 		2nd tertile (0.338–0.530)
n=1854 		3rd tertile (0.531–0.895)
n=2088
Persistent, distressing PLE, n (%) 482 (25.0) 436 (23.5) 242 (11.6)
Individual
 Age (years), mean (SD) 9.96 (0.69) 9.87 (0.71) 9.95 (0.73)
 Sex (Female), n (%) 946 (49.0) 867 (46.8) 959 (45.9)
 Family history of psychosis, n (%) 49 (2.5) 43 (2.3) 52 (2.5)
 Race (Black), n (%) 683 (35.4) 500 (27.0) 283 (13.6)
 Social cohesion score, mean (SD) 3.70 (0.71) 3.70 (0.70) 3.91 (0.68)
 Income-to-needs ratio, mean (SD) 4.30 (3.18) 3.98 (2.98) 4.13 (2.68)
 Household income, n (%)
  <$5,000 90 (4.7) 96 (5.2) 46 (2.2)
  $5,000–11,999 84 (4.4) 75 (4.0) 58 (2.8)
  $12,000–15,999 40 (2.1) 39 (2.1) 36 (1.7)
  $16,000–24,999 74 (3.8) 80 (4.3) 55 (2.6)
  $25,000–34,999 97 (5.0) 121 (6.5) 84 (4.0)
  $35,000–49,999 163 (8.4) 132 (7.1) 143 (6.8)
  $50,000–74,999 246 (12.8) 250 (13.5) 313 (15.0)
  $75,000–99,999 267 (13.8) 294 (15.9) 328 (15.7)
  $100,000–199,999 552 (28.6) 554 (29.9) 773 (37.0)
  $200,000–299,999 316 (16.4) 213 (11.5) 252 (12.1)
Residential
 Dissimilarity Index, mean (SD) 0.68 (0.07) 0.53 (0.08) 0.51 (0.08)
 Area Deprivation Index, mean (SD) 90.3 (28.2) 98.1 (17.6) 97.2 (13.5)
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Controlling for all covariates and site, a one standard deviation increase in DI – indicating higher evenness racialized segregation – was associated with 1.11 times (aRR=1.11, 95% CI 1.06–1.17; p<0.001) risk of persistent, distressing PLEs over three years of follow-up. Similarly, controlling for covariates, a one standard deviation increase in EII – indicating lower exposure domain segregation – was associated with 0.82 times (aRR=0.82, 95% CI 0.78–0.87; p<0.001) risk of persistent, distressing PLEs over the same follow-up. However, when both evenness and exposure segregation were considered in the same model, evenness segregation was attenuated in magnitude; 95% confidence intervals included the null (aRR=0.98, 95% CI 0.92–1.05; p=0.60); and the estimated effect of exposure segregation was not meaningfully altered (aRR=0.81, 95% CI 0.75–0.88; p<0.001). Comparison of the QICs between models indicated improvement in general model fit when exposure segregation was included as a covariate, regardless of the presence or absence of evenness segregation. All multi-level model results are in Table 2.

Table 2.

Multilevel modeling results for evenness and exposure domains of residential segregation

Univariate models
 Evenness Segregation
 Exposure Segregation
 Evenness + Exposure Segregation
RR 95% CI QIC aRR 95% CI QIC aRR 95% CI QIC aRR 95% CI QIC
Intercept - - - - 0.15 5573 0.15 5543 0.15 5545
Dissimilarity Index
(scaled) 		1.21 1.15 1.27 5787 1.11 1.06 1.17 - - - 0.98 0.92 1.05
Interaction index
(scaled) 		0.75 0.72 0.80 5731 - - - 0.82 0.78 0.87 0.81 0.75 0.88
Race (Black) 2.26 2.04 2.50 5611 2.00 1.79 2.22 1.91 1.72 2.13 1.92 1.72 2.13
Neighborhood social cohesion 0.66 0.59 0.74 5783 0.81 0.72 0.90 0.82 0.73 0.92 0.82 0.73 0.92
Natal sex (Female) 1.25 1.13 1.39 5822 1.20 1.08 1.33 1.19 1.07 1.31 1.19 1.07 1.31
Family history of psychosis 1.46 1.12 1.90 5834 1.16 0.90 1.50 1.17 0.90 1.52 1.17 0.90 1.52
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There was no strong evidence of additive scale statistical interaction or effect measure modification for race or for perceived neighborhood social cohesion (Figure 1; Supplemental Table 2). The aRD with DI as the exposure was more similar between racial groups (Black: aRD=0.02, 95% CI: −0.01, 0.05; White: aRD=0.02, 95% CI: 0.01, 0.03; p=0.79) than with the EII alone as exposure, which itself only slightly differed between groups and neared but did not reach statistical significance (Black: aRD=−0.06, 95% CI: −0.10, −0.03; White: aRD=−0.03, 95% CI: −0.04, −0.02; p=0.08). With both DI and EII in the model, the same pattern held. A similar pattern for both exposures was observed when examining aRDs stratified by level of neighborhood social cohesion, but the slight difference between race strata with EII as exposure was even smaller for cohesion strata.

Figure 1. Adjusted risk differences for PLE stratified by potential moderators: Race and neighborhood social cohesion.

Figure 1.

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Model 1: Dissimilarity index (scaled) with confounders

Model 2: Exposure-interaction index (scaled) with confounders

Model 3: Dissimilarity index (scaled) and Exposure-interaction index (scaled) with confounders DI: Dissimilarity Index; EII: Exposure-Interaction Index

Note: corresponding tables available in Supplemental Table 2.

In the fully adjusted log binomial models (Table 2), Black participants were found to have higher average risk of persistent, distressing PLEs than White participants, and Black-White disparities were mildly attenuated when exposure segregation was included compared to evenness segregation alone; results for exposure segregation alone and exposure segregation in combination with evenness segregation were very similar. Higher neighborhood social cohesion was associated with a lower risk of persistent, distressing PLEs compared to lower cohesion regardless of type of residential segregation in the model, controlling for covariates.

Examining the unadjusted, race-specific average risk of persistent, distressing PLEs by site and domain of residential segregation suggested a slight variation in the relationship between risk and segregation that was no longer detectable after accounting for sex and family history of psychosis (Figure 2). This graph of unadjusted average PLE risk compared to unadjusted average neighborhood social cohesion by site clearly shows racial disparities in both level of perceived cohesion and risk, but again the relationship between cohesion and risk was not meaningfully different after adjustment.

Figure 2. Racial disparities in unadjusted PLE risk by domain of segregation and by social cohesion.

Figure 2.

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Individual Exposure-Interaction Index vs. Dissimilarity Index scores (upper left). Average risk of persistent, distressing PLEs vs. average neighborhood characteristic scores by race for each site with at least 10 Black participants in the analytic sample: risk vs. average neighborhood social cohesion score (upper right), vs. average Dissimilarity Index score (lower left), and vs. average Exposure-Interaction Index score (lower right)

Sensitivity Analyses

Restriction of the analytic sample by duration of time lived at the primary address did not meaningfully change the effect estimates or the conclusion of statistical significance for the DI, the EII, or the combination of the two (Supplemental Table 3). Effect estimates were similar regardless of duration at primary address for neighborhood social cohesion and race, as well. Confidence intervals were wider for the two samples restricted by duration, as expected for the decreased sample size.

DISCUSSION

Higher racialized residential segregation in both the evenness and exposure domains is associated with a greater risk of persistent, distressing PLEs in a cohort of early adolescents, after accounting for family history of psychosis and natal sex. However, the effect of evenness segregation was approximately null and was no longer significant after including exposure segregation in the same model. There was a large disparity in risk of persistent, distressing PLEs by race, with Black individuals having about twice the average risk as White individuals, and the proportion of Black participants was greater at higher levels of exposure to segregation. However, the magnitude of the association between segregation and PLE did not vary by race or between areas with high versus low neighborhood social cohesion.

The overall findings remained unchanged when the sample was restricted by the duration of time lived at one’s primary residence, indicating robustness of the findings to duration of exposure. This result is interesting in the context of the previously observed dose-response relationship with childhood urbanicity and risk of schizophrenia; together, these findings may suggest a residual underlying aspect of urban life contributing to the dose-response element previously observed (Pedersen & Mortensen, 2001). Alternatively, the analytic sample may not be fully representative of experiences of racialized residential segregation, or the dose-response relationship may not hold for PLEs in the same way as observed for clinical diagnosis of schizophrenia.

The absence of effect measure modification between residential segregation and hypothesized moderators of race and social cohesion was surprising and may indicate that the segregation indices used are capturing some undescribed factor contributing to the risk of PLEs that is distinct from, but overlapping with, the racialized consequences of segregation itself. We expected that the relationship between residential segregation and PLE risk would be different by race such that a change in exposure to segregation among Black participants would have a larger impact than among White participants. However, we observed that the association of segregation with PLE was similar across both racial groups. Nonetheless, the higher prevalence of both outcome and exposure among Black participants may suggest that exposure to segregation is contributing to the observed racial disparity in risk of persistent, distressing PLEs (Ward et al., 2019). In contrast, we expected that residential segregation would have a smaller influence on PLE risk among those with higher perceived neighborhood social cohesion, such that high cohesion dampened the harmful influence of exposure to segregation. We observed a protective effect of cohesion, but level of cohesion did not meaningfully impact the relationship between either domain of residential segregation and risk of PLE.

Other studies have found similarly mixed results when trying to parse the health effects of neighborhood characteristics and elements of social support (Abada et al., 2007; Dawson et al., 2019; Ku, et al., 2024b; Pickett & Wilkinson, 2008; Rios et al., 2012; Schulz et al., 2006). Some have found that the putative protective effect of ethnic density might only be relevant at moderate levels of residential segregation, suggesting that the access to or restriction from resources and opportunities at the extremes of segregation may overshadow the supportive role of social bonds through co-ethnic neighborhood density (Shankley & Laurence, 2022). That segregation may facilitate protective social bonds as well as harmful restriction from resources was proposed as a contributing influence in a study observing worse physical and better mental health outcomes among Black compared to White adults in Indiana (N. C. Smith, 2024). Another study investigating the influence of evenness and exposure segregation on psychological distress in White and Black adults reported similar domain-specific effects as those found here, but no effect was observed among the White population (Phuong et al., 2019). Our study focused on Black-White segregation and health outcomes due to the specific context of racism in the United States, but the role of residential segregation and social support likely varies depending on one’s combination of race, place, and social context. Other studies looking at mental health among Latino populations in the United States have observed that higher residential segregation is associated with restricted access to mental health resources (Dinwiddie et al., 2013) but also with lower self-reported mental distress in adults (Nobles et al., 2017).

Furthermore, social cohesion has been suggested to be both a moderator and a mediator of the effect of neighborhood quality on mental health, while others have found it is only protective for general health outcomes and not specifically meaningful for mental health (Abada et al., 2007; Dawson et al., 2019; Johns et al., 2012; Rios et al., 2012; Schulz et al., 2006). Other studies have investigated a potential fluctuation in the role of social cohesion for mental health at different points over the life course (R. J. Smith et al., 2022); this concept may be particularly relevant for the present study considering the participants are early adolescents but the perceived level of neighborhood social cohesion was provided by parents of participants. It could be that the parent’s perception of cohesion is distinct from that experienced by the participants.

The present study has several limitations. Multilevel estimation with GEE provides robust population-level estimates, but unlike random effects models, does not permit calculation of intra-cluster correlation, which is the proportion of overall variation among participants attributable to differences at the study site level. There may also be unresolved selection bias due to restrictions on the availability of residential characteristics and a lack of consistency of exposure to residential segregation, given metropolitan statistical areas are large administrative areal units. Our sensitivity analysis provided some resolution to consistency concerns, but it did not account for participants living across multiple addresses or moving during follow-up. Additionally, residential characteristics like ADI and urbanicity are based on census tract membership, which may not reflect the lived experiences of residents due to reliance on arbitrary geographic boundaries. Furthermore, because geographic information is protected, the ability to understand area-level characteristics in context is limited. This may be particularly important if there are regional variations in the physical form of residential segregation that were not captured in the present study and that limit the interpretation of segregation domains across study sites.

Additionally, the overall prevalence of persistent, distressing PLEs was high compared to the expected median prevalence of all PLEs reported in the literature, indicating potential limitations in the selection of the cohort or the analytic sample. This could also be related to loss to follow-up, a limitation inherent in the use of cohort studies. Participants in the ABCD cohort were still relatively young throughout the data collection waves, and incorporating additional waves into the outcome measurement may enhance our understanding of the observed relationships. There may also be misclassification due to operationalization of social cohesion if missing responses were not random, if construct validity was reduced by our requiring only three of the five items, or if the responses of participants’ parents are not representative of the experience of participants themselves. Given the developmental significance of adolescence in psychosis, future efforts should aim to use a more direct measure of adolescent participants’ social bonds and experiences.

The assessment of neighborhood-level factors and risk of mental health outcomes often includes control for some element of family history of mental health, but it is not clear what aspect of family history is reasonable to consider as a confounder of a structural exposure on individual health. Psychotic disorders like schizophrenia are generally accepted to have a strong genetic risk factor that interacts with environmental influences to yield actualized risk. However, control of general family history may mask the role of some structural exposures if intergenerational social and economic mobility were horizontal rather than vertical. In other words, if we believe one’s neighborhood is a meaningful influence on their health, then the neighborhood of their immediate predecessors may also have been a meaningful contributor to family history-related risk. As such, controlling for family history should precisely measure genetic risk rather than the general occurrence of psychosis.

Incorporating more information about local social safety net resources, regional patterns of segregation, and other nuances of lived experiences in urban spaces may add context to the role of social supports in mental health outcomes, such as psychosis. Further qualitative work may be necessary to understand the range of observed results of neighborhood social cohesion on mental health outcomes, and this could inform the development of more specific measurement tools for the types of social support relevant for mental health. Future work may benefit from distinguishing between generational persistence and transience in certain segregation contexts, as well as investigating the role of acculturation in health.

The prevalence of psychotic disorders, while relatively low among adults, has a profound impact on the lives of affected individuals and their support networks. Evidence suggests that psychotic disorders are not evenly distributed, with minoritized groups disproportionately affected in the United States (Oh et al., 2014; Schwartz & Blankenship, 2014). This disparity – in concert with the broader systematic barriers contributing to it – emphasizes the need to adopt a health equity approach in addressing mental health outcomes like psychosis (Anglin, 2023). Critical to this approach is a deeper understanding of how neighborhood context, influenced by decades of racist policies and social processes, affects mental health. Economic and housing policies in the United States have actively isolated or fragmented minoritized people – especially Black communities (Rothstein, 2017). Efforts to mitigate the deleterious effects of this isolation must not fragment potentially protective social bonds. The results of the present study contribute to our understanding of how the state of the built environment relates to mental health, so future policies and programs designed to alter the built environment can be designed with the well-being of the social environment in mind, as well.

CONCLUSIONS

Overall, the observed results suggest that exposure to residential segregation is associated with a higher risk of persistent, distressing PLEs in young adolescents. In all situations investigated, higher neighborhood social support was found to be protective. There is a large observed racial disparity in risk of persistent, distressing PLEs, but the general relationship between risk and residential segregation may not be different by race – potentially indicating another underlying factor being captured by the segregation indices that is not sufficiently explained by the present study. Further research is needed to understand the specific pathways through which segregation and neighborhood factors contribute to the risk of potentially clinically relevant psychotic-like experiences.

Supplementary Material

1

Highlights:

  • Residential segregation exposure was associated with psychotic-like experiences

  • This association was not meaningfully changed by duration of exposure

  • The relationship between segregation and psychosis was similar in Black/White youth

  • There is a health disparity in psychotic-like experiences between Black/White youth

  • The segregation-psychosis relationship was similar for high/low social cohesion scores

Acknowledgements

This work was supported by the National Institute of Mental Health (NIMH K23-MH129684; Dr. Ku). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health, the National Institute of Mental Health, or Emory University.

Data used in the preparation of this article were obtained from the Adolescent Brain Cognitive Development SM (ABCD) Study (https://abcdstudy.org), held in the NIMH Data Archive (NDA). The ABCD Study® is supported by the National Institutes of Health and additional federal partners under award numbers U01DA041048, U01DA050989, U01DA051016, U01DA041022, U01DA051018, U01DA051037, U01DA050987, U01DA041174, U01DA041106, U01DA041117, U01DA041028, U01DA041134, U01DA050988, U01DA051039, U01DA041156, U01DA041025, U01DA041120, U01DA051038, U01DA041148, U01DA041093, U01DA041089, U24DA041123, U24DA041147. A full list of supporters is available at https://abcdstudy.org/federal-partners.html. ABCD consortium investigators designed and implemented the study and/or provided data but did not participate in the analysis or writing of this report. This manuscript reflects the views of the authors and may not reflect the opinions or views of the NIH or ABCD consortium investigators. The ABCD data repository grows and changes over time.

Funding:

This work was supported by the National Institute of Mental Health (NIMH K23-MH129684; Dr. Ku).

Footnotes

Ethics Statement

The Emory University Institutional Review Board determined that this study is exempt from review due to its secondary analysis of de-identified data.

Declaration of Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Data availability

ABCD Study data used in this study are available with approved NDA Data Use Certification at the NIMH’s ABCD Data Repository (https://nda.nih.gov/abcd).

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

1
NIHMS2116996-supplement-1.docx (56.8KB, docx)

Data Availability Statement

ABCD Study data used in this study are available with approved NDA Data Use Certification at the NIMH’s ABCD Data Repository (https://nda.nih.gov/abcd).

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