Residential Racial Segregation and Youth Firearm Aggression: Neighborhood Disadvantage and Exposure to Violence as Mediators

Abstract

Youth interpersonal firearm violence disproportionately affects Black youth, with residential racial segregation as a key determinant. Racially segregated neighborhoods, which are economically isolated (e.g., neighborhood disadvantage), are linked to increased exposure to violence. This exposure, in turn, is a determinant of youth firearm aggression (i.e., using a gun on someone else). Mechanisms from residential racial segregation to firearm aggression, however, have not been evaluated. Therefore, we tested neighborhood disadvantage and exposure to violence as mediators in the association between residential racial segregation and youth firearm aggression. Participants were 338 Black youth who had used drugs in the past 6 months and sought care in an urban emergency department. Using serial mediation analysis, residential racial segregation was indirectly associated with youth firearm aggression via neighborhood disadvantage and then exposure to violence. While researchers have documented the association between structural racism and firearm violence injury and incidents, our study assessed multiple socioecological mechanisms simultaneously. Identifying the downstream socioecological consequences of residential segregation can guide the development of firearm aggression prevention programs addressing the consequences of racism.

Keywords

structural racism racial disparities socioecological mechanisms residential segregation exposure to violence firearm violence

While youth firearm violence presents a significant public health challenge in the United States, it disproportionately burdens Black youth, similar to other public health challenges (Centers for Disease Control and Prevention [CDC], 2021). For instance, in 2021, Black youth were 26.13 times more likely than White youth to prematurely die due to interpersonal firearm violence injury (CDC, 2021). Addressing this public health issue thus necessitates identifying the upstream factors contributing to youth firearm aggression. Researchers have suggested that structural racism, such as residential racial segregation, contributes to racial disparities in firearm injuries (both fatal and non-fatal; Jay et al., 2022; Knopov et al., 2019; Wong et al., 2020). However, two limitations are evident: (1) there is limited understanding of how residential racial segregation shapes the racial patterning of firearm violence injuries, and (2) few studies have specifically examined this phenomenon among youth, despite notable increases in firearm homicide rates during adolescence. To address these gaps, our study employs serial mediation analysis to explore how residential racial segregation may influence youth firearm aggression through neighborhood disadvantage and subsequent exposure to violence (see Figure 1).

Figure 1.

Conceptual model bridging residential racial segregation with youth interpersonal firearm violence.

Racial Disparities in Nonfatal and Fatal Firearm Assaults

Firearm homicide is a leading mechanism of death for all youth (ages 14–24), with homicides responsible for 61% of firearm deaths (CDC, 2021). Akin to other public health issues in the US, firearm homicide disproportionately affects Black youth and is the leading mechanism of death (Bottiani et al., 2021; CDC, 2021). Moreover, firearm homicides account for 87% of all firearm-related deaths for Black youth and 38.9% of firearm-related deaths for White youth (CDC, 2021). The overrepresentation of Black youth experiencing firearm injury and homicides has been consistently documented (Bottiani et al., 2021; Degli Esposti et al., 2022). Fewer researchers, however, have focused on identifying the root causes (e.g., structural racism) of the racial disparities underlying the disparities in these youth firearm violence outcomes (Jacoby et al., 2018; Krieger et al., 2017; Uzzi et al., 2023). This is a significant gap in firearm injury prevention research as race is a social construct that operates as a proxy for the enactment of racist ideology including racially discriminatory policies and practices (Smedley & Smedley, 2005). Thus, we have a critical need to assess how indices of structural racism (e.g., residential racial segregation) contributes to racial disparities in youth firearm violence, including firearm aggression (using a firearm on someone).

Residential Racial Segregation and Firearm Aggression

Residential racial segregation refers to the physical separation of racial or ethnic groups into different neighborhoods, a phenomenon that shapes the racial patterning of social, economic, and environmental resources across communities (Massey & Denton, 1993; Williams et al., 2019). Residential racial segregation, an indicator of structural racism, is a significant contributor to racial health disparities, including youth firearm violence injury (Jacoby et al., 2018; Jay et al., 2022; Poulson et al., 2021; Williams & Mohammed, 2013). Owing to the longstanding legacy of racist housing policies in the US (e.g., redlining; Benns et al., 2020; Jacoby et al., 2018) as well as the contemporary policies and practices (e.g., exclusionary zoning) that continue to reinforce the racially segregated structure of cities in the US (Krieger et al., 2017; Uzzi et al., 2023). Black and other racially minoritized groups are more likely to reside in neighborhoods that have concentrated poverty and limited societal resources (e.g., access to mental health care, high-quality schools), which Massey and Denton (1993) refer to as the “American Apartheid.” To this end, contemporary indicators of residential racial segregation have been consistently associated with increased rates of fatal and nonfatal firearm assault injuries (Krieger et al., 2017; Mehranbod et al., 2022; Schleimer et al., 2022; Wong et al., 2020). Despite these findings, fewer researchers examined the potential pathways through which residential racial segregation increases the likelihood of youth firearm aggression. To our knowledge, only a single study evaluated neighborhood-level socioeconomic indicators (i.e., poverty, low educational attainment) as mediators to the association between historical redlining and firearm shooting incidents (Poulson et al., 2021). Less is known about how residential racial segregation perpetuates racial disparities in firearm aggression among youth.

Mechanisms From Residential Segregation to Youth Firearm Aggression

Researchers theorized multiple mechanisms by which residential racial segregation may reinforce racial health disparities, including youth firearm aggression. Per the Framework for the Study of Racism and Health (Williams & Mohammed, 2013), residential racial segregation is a fundamental cause of racial health disparities. Researchers posited that residential segregation limits access to socioeconomic opportunities (e.g., stable labor market), societal resources and support services (e.g., access to health care, affordable housing), and increases the likelihood of encountering racism-related stressors (e.g., over-policing; Williams et al., 2019; Williams & Mohammed, 2013). Limited social and economic resources in neighborhoods (i.e., neighborhood disadvantage), in separate studies, have been associated with firearm violence injuries in racially minoritized communities (Kennedy et al., 1998; Poulson et al., 2021; Rowhani-Rahbar et al., 2022). In these areas, youths often encounter limited informal control (e.g., residents supervising youth), neighborhood physical disorder (e.g., abandoned lots and parks), and other socioecological risk factors that collectively increase exposure to violence (Brown & Perkins, 2002; Heinze et al., 2016; Taylor et al., 1981). More recently, Burrell and colleagues posited that structural racism such as residential racial segregation can contribute to community violence among young African American males living in an urban area (Burrell et al., 2021). These scholars postulated that economic disenfranchisement can invoke psychological consequences such as mistrust, compliance with violence norms (e.g., retaliatory attitudes), cultural disorientation (e.g., feeling disliked because of one’s race), and willingness to participate in an underground economy (e.g., illegal drug and/or firearm market), which, in turn, increase community stress/tension and community violence, including firearm assaults (Burrell et al., 2021). Finally, per social disorganization theory, by concentrating poverty and restricting access to quality education and employment, racially segregated neighborhoods can foster conditions that heighten stress and conflict among youth (e.g., limited informal control mechanisms). This, in turn, may increase the likelihood of firearm aggression as individuals navigate these challenging contexts, seeking respect and security in contexts marked by systemic neglect and marginalization (Shaw & McKay, 1942). Thus, when taken together, various downstream consequences of residential segregation may ultimately contribute to racial disparities in youth firearm aggression.

Developmental Significance

It is especially important to examine mechanisms underlying the link between racism and firearm aggression during youth. With exception of a single study (Ousey & Augustine, 2001), research examining racism and firearm violence focused on rates of all firearm violence incidents (e.g., firearm homicide and non-fatal assault rates) across geographic levels. Rates of fatal and non-fatal firearm assaults, however, begin to surge and reach its peak during youth (ages 14–24; CDC, 2021). This is especially important for Black youth as firearm homicide is the leading cause of death for Black youth. Thus, elucidating mechanisms that contribute to youth firearm aggression in racially minoritized communities can elucidate the upstream, structural factors that lead to racial disparities in youth firearm violence injury. Preventing injury and fatality from firearm violence during youth may also have long-lasting effects that curbs racial disparities in firearm violence injury in later life stages. For instance, youth exposed to firearm violence are more likely to engage in future firearm aggression (Carter et al., 2015; Cunningham et al., 2015). Preventing youth firearm aggression may play a critical role in building a foundation for a safer society in the future.

Present Study

To address this gap, we will leverage serial mediation analysis to evaluate whether residential racial segregation influences firearm aggression via neighborhood disadvantage (mediator 1) and then exposure to violence (mediator 2; see Figure 1) among Black youth. Of note, firearm aggression is an indicator of firearm violence that entails using a gun on someone else. We hypothesize that residential racial segregation is positively associated with neighborhood disadvantage and that neighborhood disadvantage is associated with higher levels of exposure to violence, which, in turn, increases the risk for youth firearm aggression. We also hypothesize that the influence of residential racial segregation on youth firearm aggression will be mediated by neighborhood disadvantage and exposure to violence.

Method

Participants

The Flint Youth Injury (FYI) study consists of 570 drug-using youth (i.e., ages 14–24) from a prospective cohort study (Cunningham et al., 2015). Youth were recruited from an emergency department in Flint, Michigan, and those enrolled were followed at baseline and then in 6-month intervals for 24 months (i.e., 5 measurement periods). The objective of the FYI study was to assess the timing and pattern of health behaviors and outcomes among drug-using, urban youth who were admitted to the emergency department (ED) with and without an acute violent injury. Violent crime rates in Flint are comparable to other de-industrialized cities (e.g., Youngstown, OH; FBI Federal Bureau of Investigations, 2020). Since the objective of the study was to examine a pathway between residential racial segregation and youth firearm aggression among Black youth, our analytic sample consisted of only the Black youth (N = 349) from FYI. Table 1 provides detailed information on the demographic characteristics of our analytic sample. Participant home addresses were geo-coded to census tracts to assess residential racial segregation and neighborhood disadvantage. The vast majority of participants (98%) were from Genesee County (i.e., where Flint, Michigan is located) with the remaining 2% of participants from surrounding counties (e.g., Wayne, Oakland county).

Table 1.

Descriptive Statistics.

Predictors	M (SD) or N (%)
Male	203 (41.8%)
Age	19.91 (2.37)
Public assistance	185 (54.7%)
Violent injury	206 (59%)
Alcohol use	1.18 (1.19)
Tobacco use	3.34 (2.76)
Marijuana use	2.97 (1.22)
Internalizing symptoms	0.65 (0.72)
PTSD diagnosis	0.10 (0.30)
Residential segregation (2000)	0.39 (0.62)
Exposure to violence (wave 2)	0.78 (0.77)
Neighborhood disadvantage (2010)	0.38 (0.14)
Neighborhood disadvantage (2000)	0.15 (0.06)
Youth firearm violence	42 (12%)
Youth firearm violence (waves 3–5)	43 (12.35)

Note. M = mean; SD = standard deviation; N = sample size, % = proportion of the sample who self-reported into the category.

Study Context

At the onset of the study period (2010), 56.3% of Flint residents identified as Black, and 33.3% were living below the poverty threshold as defined by the US Census—a figure that starkly contrasts with Michigan’s overall poverty rate (13.4%) and Black population (14.1%). Additionally, Flint’s per capita homicide rate of 59.5 per 100,000 exceeded the state of Michigan’s 5.87 per 100,000 in 2010. These rates highlight the urgent need to investigate the upstream mechanisms that contribute to racial disparities in youth firearm violence.

Procedures

FYI was conducted in the region’s only Level-1 trauma center in Flint, Michigan (Cunningham et al., 2015). Participants included youth (ages 14–24) seeking ED treatment for an assault injury and reporting past 6-month drug use (AIG; N = 350). A proportionally sampled comparison group of youth presenting for other reasons who also reported past 6-month drug use were recruited and enrolled (CG; N = 250). Participants were recruited from 12/2009 to 9/2011, and trained RAs recruited participants every day (excluding holidays). On Tuesday and Wednesdays, participants were recruited 21 hours a day (except from 5 am to 2 am), whereas participants were recruited 24 hours a day on the other days. After obtaining the participants’ written consent (or assent from youth with parental consent if youth is younger than age 18), participants completed a screening survey. Participants were considered assault-injured if their injury was intentionally caused by another person. Drug use in the past 6 months was measured using the National Institute on Drug Abuse Alcohol, Smoking, and Substance Involvement Screening Test (i.e., NIDA-ASSIST). Exclusion criteria included ED presentation for sexual assault, suicidal ideation/attempt, child maltreatment, or a cognitive condition precluding consent (e.g., acute psychosis, alcohol intoxication). In addition, youth who arrived at the ED in active police custody or those not speaking English were excluded. Youth were included in the AIG cohort if they screened positive for an assault injury and past 6-month drug use. Youth in the CG cohort were recruited in parallel with the AIG cohort (to limit seasonal and temporal variation). Both cohorts were balanced across age groups (i.e., 14–17, 18–20, 21–24) and sex (male/female). Youth enrolled in the AIG and CG cohort completed a self-administered baseline survey in conjunction with an RA-administered structured interview. Follow-up assessments were conducted in-person at 6, 12, 18, and 24 months post baseline. Participants received $1 for completing the screening survey, $20 for the baseline, and $35, $40, $40, and $50 for the 6-, 12-, 18-, and 24-month follow-ups. The FYI Study was approved by the University of Michigan (HUM00026787) and Hurley Medical Center IRBs (IRBNet ID: 177665) and an NIH certificate of confidentiality (COC) was obtained.

Measures

Youth firearm aggression

Firearm aggression relates to threatening or inflicting harm on someone using a firearm. Three items were aggregated to measure youth firearm aggression (i.e., “in the past 6 months, you pulled a gun on someone,” “you used a gun on someone,” and “you used a gun on him/her [partner]”) at waves 3 to 5 (i.e., 12- to 24-month follow up period). Participants who endorsed higher than “never” on any of the 3 firearm aggression items at waves 3, 4, or 5, received a score of 1 (i.e., firearm aggression during the 12- to 24-month follow-up period). A score of 0 indicates that the participant did not engage in firearm aggression during the measurement period. This variable, therefore, reflects firearm aggression at least once within 12 to 24 months after their baseline visit. Using this dichotomized firearm aggression variable, 12.03% of participants reported firearm a during the past 24 months. Lastly, baseline firearm aggression was also calculated at wave 1 and included as a covariate in the mediation analyses.

Residential racial segregation

We measured residential racial segregation using the Index of Concentrations at the Extremes (ICE_race), which quantifies the social polarization of non-Hispanic, White (White), and non-Hispanic, Black (Black) households within a census tract (Krieger et al., 2017). To compute ICE_race, we first assessed the difference between the proportion of Black (non-Hispanic) households and White (non-Hispanic) households within the census tract. This difference was then divided by the total number of Black and White households. ICE_race ranges from −1 to 1, with lower, negative scores indicating a greater concentration of Black households relative to White households. Alternatively, positive scores indicate a greater concentration of White households relative to Black households in the census tract. A score of 0 can occur for two reasons. First, and more plausibly, 0 could reflect that the census tract has no Black households and only White households. Second, 0 may reflect an equal concentration of Black and White households.

We leveraged American Community Survey (ACS) estimates for 2000 to compute ICE_race scores. Our data, however, only contained 2010 census-tract information and census tracts from 2010 cannot be directly compared to census tracts from 2000 since census tracts are updated at each decennial census. Thus, the 2000 ACS estimates used census tracts from the 2000 census. To match the census tracts from 2000 to 2010, we used the census bureau’s tract-level relationship file (Census Reference Files, 2023). Specifically, we matched 2010 tracts to 2000 tracts if over 95% of the population in the 2010 tract resided in the 2000 tract’s geography. Of the 176 unique tracts in the FYI data, 172 census tracts could be matched using these narrow criteria.

Neighborhood disadvantage

To evaluate neighborhood disadvantage (i.e., socioeconomic hardships and challenges in a neighborhood), we used the ACS 2010 census tract level measure of the (1) percent of households with an income below the federal poverty line, (2) percent of unemployed residents between age 16 to 65, (3) percent of residents age 21 or older who did not graduate high school, (4) percent of household receiving food stamps or the supplemental nutrition assistance program, and (5) percent of housing units that have more people than rooms (i.e., overcrowding). A unidimensional factor analytic model was estimated to develop a composite measure of neighborhood disadvantage (ω = 0.89). Lastly, we also estimated a similar factor analytic model for neighborhood disadvantage in 2000 to include as covariate in the mediation analysis.

Exposure to violence

Exposure to violence (i.e., the extent to which youth experienced or witnessed violent acts) was measured by aggregating the participants’ response across four measures at wave 2 (i.e., 6-month follow-up): (1) violent victimization with a weapon, (2) community violence, (3) partner aggression, and (4) non-partner aggression. Violent victimization with a weapon was a five-item measure from the National Longitudinal Study of Adolescent Health (ω = 0.83; Resnick et al., 1997). Participants responded on a Likert-type scale of 0 (never) to 6 (20+ times) and sample items include “someone shot you” and “Someone cut or stabbed you.” Community violence was measured using 5-items from the Survey of Exposure to Community Violence (ω = 0.81; Richters & Saltzman, 1990). Participants responded on a Likert-type scale of 0 (never) to 3 (many times), and sample items include “I have seen somebody get stabbed or shot” and “my house has been broken into.” Partner aggression was assessed with 12 items from the Conflict Tactics Scale (CTS-2) (ω = .92; Murray et al., 2021). Participants responded on a scale of 0 (never) to 6 (20+ times) and sample items include “he/she grabbed you” and “he/she choked you” (Murray et al., 2021). Lastly, non-partner aggression—that is, acts of violence that happened to the participant by a non-partner such as a friend, stranger, or neighbor—was assessed with 12 items from the CTS-2 (ω = .90; Murray et al., 2021). Participants responded on a scale of 0 (never) to 6 (20+ times) and sample items include “someone pushed or shoved you” and “someone slapped you” (Murray et al., 2021). We first standardized the participants’ average score across on each measure since the measures are on different scales. By doing so, it ensures that each scales contributes proportionately to the overall exposure to violence score. Next, we fit a unidimensional factor analytic model using the four indicators to develop a composite measure of exposure to violence. Lastly, baseline levels of the exposure to violence indicators were averaged at wave 1 and included as a covariate in the mediation analyses.

Covariates

All models controlled for sociodemographic factors including sex (female vs. male), age, public assistance status (no assistance vs. public assistance) at baseline (wave 1). We also controlled for youth who presented to the ED at baseline with or without a violent injury. Two mental health variables were assessed at baseline—post-traumatic stress disorder (PTSD) and symptoms of internalizing problems. Participants were administered the Mini International Neuropsychiatric Interview(Sheehan et al., 1998) by a research assistant to determine whether participants met the diagnostic criteria for PTSD. The Brief Symptom Inventory was administered to assess the participants’ internalizing symptoms in the past 6 months (i.e., depressive & anxiety symptoms; Derogatis & Melisaratos, 1983). The frequency of cigarette use, marijuana use, and alcohol use in the past 6 months was assessed at baseline. The frequency of marijuana and cigarette use were measured on Likert scale of 0 (never) to 6 (every day/almost daily), while 0 (never) to 4 (4 or more times a week) was used for alcohol use. Finally, baseline firearm aggression and exposure to violence were included as covariates.

Analytic approach

As a precursor to evaluating our conceptual model (see Figure 1), we first fit unidimensional factor analytic models for neighborhood disadvantage and exposure to violence.(Wang & Wang, 2019) Unidimensional models were specified given the limited number of indicators per latent construct (e.g., 4 indicators for exposure to violence, 5 indicators for neighborhood disadvantage). Models were determined to fit the data well if the: (1) chi-square test of absolute fit is not statistically significant; (2) root mean square error of approximation (RMSEA) ≤ .06; and, (3) confirmatory factor index (CFI) ≥ .90 (Kline, 2015). Of note, we adjusted standard errors to account for non-independence across observations (i.e., participants nested in census tracts). (Abadie et al., 2023) After fitting measurement models, we estimated a structural equation model (SEM) with serial mediation analysis to evaluate direct and indirect effects of residential racial segregation on youth firearm aggression via neighborhood disadvantage and then exposure to violence (see Figure 1). Indirect effects were estimated using the products of coefficients method (MacKinnon et al., 2007). Bias-corrected bootstrapping was implemented to estimate the sampling distribution of the model coefficients. Thus, we report 95% bias-corrected confidence intervals (95% BCI) to determine the statistical significance of direct and indirect effects. Lagrange Multiplier (LM) tests were used to identify sources of model misfit and implement theory-driven model modifications to improve fit (Kline, 2015). Given the binary nature of our dependent variable (i.e., youth firearm aggression), weighted least square mean and variance adjustment estimator was used. As in the measurement models, cluster adjusted standard errors were used (Abadie et al., 2023). We selected these analytic to evaluate indirect effects from residential racial segregation to youth firearm aggression, with the exposure, mediators, and outcome assessed at either the neighborhood- or respondent-level. Statistical tests were conducted using Mplus (v 8.9; Muthén & Muthén, 2017).

Results

Descriptive statistics are presented in Table 1. The measurement model for neighborhood disadvantage fit the data well (i.e., χ² (4) = 5.52, p = 0.24; RMSEA = .03, CFI = .99). Standardized factor loadings ranged from 0.51 (percent unemployment) to 1.01 (percent poverty). The measurement model for exposure to violence also fit the data well (i.e., χ² (2) = 2.59, p = 0.27; RMSEA = .03, CFI = .99), with standardized factor loadings ranging from .30 (community violence) to .89 (non-partner aggression). Thus, the measurement models for neighborhood disadvantage and exposure to violence fit the data well (see Table 2).

Table 2.

Measurement Models for Mediators.

	Λ	SE	p
Neighborhood disadvantage (2010)
% receives food stamps/SNAP	0.89	0.04	<.001
% unemployed (age ≥16)	0.51	0.08	<.001
% Did not graduate HS (age ≥25)	0.44	0.14	<.001
% Overcrowding at home	0.63	0.06	<.001
% Household income below poverty level	1.01	0.04	<.001
Exposure to violence (Wave 2)
Community violence	0.30	0.05	<.001
Non-partner aggression	0.89	0.06	<.001
Partner aggression	0.67	0.08	<.001
Violence weapon (knife/firearm) victimization	0.43	0.08	<.001

Note. Model was estimated using weighted least square robust mean and variance adjustment estimator. Λ = factor loading; SE = standard error; p = p-value.

Next, we estimated structural models to evaluate whether residential racial segregation is directly and indirectly associated with youth firearm violence, with neighborhood disadvantage and exposure to violence as mediators (see Table 3). The structural model did not fit the data well (χ² (227) = 227.56, p = .01; RMSEA = .04, CFI = .82). Per the model modification index, estimating residual correlations between neighborhood disadvantage (from 2000) and public assistance, partner and non-partner aggression, and violence victimization and non-partner aggression improved model fit (χ² (233) = 259.15, p = 0.12; RMSEA = .02, CFI = .90). Post-hoc modifications are consistent with research, which has indicated that residents of disadvantaged neighborhoods are more likely to participate in public assistance programs (e.g., food stamps; Cohen, 2019), and that exposure to violence is not a singular event as individuals may be exposed to more than one type of violent incident (Carter et al., 2015; CDC, 2021). That is, violently victimized youth are more likely to be revictimized in that same way, as well as in new and distinct ways (Radtke et al., 2024). While the association between residential racial segregation and youth firearm aggression was not significant (b = −0.04, 95% BCI [−0.22, 0.12]), residential racial segregation was linked with neighborhood disadvantage (b = −0.26; 95% BCI [−0.53, −0.04]), suggesting that a greater concentration of Black households (relative to White households) were associated with higher levels of neighborhood disadvantage. In addition, neighborhood disadvantage was positively associated with exposure to violence (b = 0.21; 95% BCI [0.01, 0.34]), and exposure to violence was positively associated with youth firearm aggression (b = 0.46; 95% BCI [0.16, 0.80]). Lastly, residential racial segregation and youth firearm aggression were indirectly associated through neighborhood disadvantage and then exposure to violence (b = −0.03; 95% BCI [−0.09, −0.01]). Other indirect pathways (i.e., exposure to violence or neighborhood disadvantage as independent mediators) were not statistically significant. Lastly, neighborhood disadvantage in 2000 was positively associated with neighborhood disadvantage in 2010 (b = 1.08; 95% BCI [0.93, 1.29]), exposure to violence at baseline (wave 1) was associated with exposure to violence at wave 2 (b = 0.20; 95% BCI [0.02, 0.37]), and alcohol use at baseline was associated with exposure to violence (b = 0.26; 95% BCI [0.04, 0.45]). Other control variables were not significant in the structural model.

Table 3.

Structural Equation Model With Serial Mediation Analysis.

	β	95% bias-corrected bootstrapped confidence interval
Youth firearm violence (Waves 3 to 5)
Residential segregation (2000)	−0.04	[−0.22, 0.12]
Exposure to violence (wave 2)	0.46	[0.16, 0.80]
Neighborhood disadvantage (2010)	0.003	[−0.26, 0.20]
Baseline youth firearm violence (wave 1)	0.11	[−0.04, 0.28
Male	0.12	[−0.05, 0.31]
Public assistance	0.01	[−0.17, 0.22]
Age	−0.05	[−0.26, 0.22]
Violent injury	−0.03	[−0.27, 0.14]
Alcohol use	−0.13	[−0.41, 0.07]
Marijuana use	−0.03	[−0.24, 0.17]
Tobacco use	0.14	[−0.03, 0.29]
Internalizing symptoms	−0.09	[−0.39, 0.13]
PTSD	0.05	[−0.18, 0.22]
Exposure to violence (wave 1)	0.08	[−0.15, 0.28]
Exposure to violence (wave 2)
Residential segregation (2000)	−0.04	[−0.22, 0.12]
Neighborhood disadvantage (2010)	0.21	[0.01, 0.34]
Baseline youth firearm violence (wave 1)	0.07	[−0.15, 0.30]
Exposure to violence (wave 1)	0.20	[0.02, 0.37
Male	−0.03	[−0.17, 0.11]
Public assistance	0.02	[−0.14, 0.24]
Age	−0.15	[−0.32, 0.03]
Violent injury	−0.01	[−0.14, 0.14]
Alcohol use	0.26	[0.04, 0.45]
Marijuana use	0.14	[−0.03, 0.33]
Tobacco use	0.11	[−0.04, 0.27]
Internalizing symptoms	0.06	[−0.13, 0.27]
PTSD	0.14	[−0.09, 0.39]
Neighborhood disadvantage (2010)
Residential segregation (2000)	−0.26	[−0.53, −0.04]
Neighborhood disadvantage (2000)	1.08	[0.93, 1.29]
Indirect Effects
Residential segregation (2000) → Neighborhood Disadvantage (2010) → Youth firearm violence (waves 2-4)	−0.001	[−0.06, 0.06]
Residential Segregation (2000) → Exposure to Violence (wave 2) → Youth firearm violence (waves 3-5)	−0.02	[−0.15, 0.06]
Residential Segregation (2000) → Neighborhood Disadvantage (2010) → Exposure to Violence (wave 2) → Youth firearm violence (waves 3-5)	−0.03	[−0.09, −0.01]

Note. β = standardized coefficient.

Discussion

Our study evaluated a pathway from residential racial segregation to youth firearm aggression, mediated by neighborhood disadvantage and exposure to violence. Investigating such pathways is crucial for identifying multiple intervention points to mitigate youth firearm aggression. Specifically, residential racial segregation—as operationalized by ICE_race – was indirectly associated with future youth firearm aggression by increasing neighborhood disadvantage and exposure to violence. As a result of the enduring effects of historical redlining and other segregation practices, neighborhoods with a higher concentration of Black households relative to White households are often associated with neighborhood disadvantage (Poulson et al., 2021). Residential racial segregation leads to neighborhood disadvantage by restricting access to societal resources and opportunities for upward socioeconomic mobility, thereby perpetuating concentrated poverty (e.g., higher unemployment and poverty rate; Williams et al., 2019; Williams & Mohammed, 2013). Our findings also reveal that higher neighborhood disadvantage is associated with higher levels of exposure to violence (Chang et al., 2016; Vogel & South, 2016; Vogel & Van Ham, 2018). In line with social disorganization theory (Markowitz et al., 2001; Sampson et al., 1997; Sampson & Groves, 1989; Shaw & McKay, 1942), neighborhoods characterized by concentrated poverty often face a scarcity of resources for both formal (e.g., community policing) and informal control mechanisms (e.g., caring adult neighbors). This scarcity, in turn, may cultivate a perception among residents that there are few repercussions for engaging in violence which may cultivate norms and values that normalize violence. Lastly, our finding that exposure to violence was associated with youth firearm aggression is consistent with past research (Lee et al., 2020, 2022). Retaliatory attitudes (Copeland-Linder et al., 2007; Lee et al., 2022) and diminished future expectations (Lee et al., 2020) have operated as potential pathways. For example, exposure to violence was found to elevate the risk of retaliatory attitudes and, in separate study, to lower future expectations. These effects, in turn, have been associated with increased firearm aggression and carriage, respectively (Lee et al., 2020, 2022).

The indirect influence of residential racial segregation on youth firearm aggression signals that neighborhood racial composition, on its own, is not enough to understand the influence of structural racism on youth firearm aggression. Racially segregated neighborhoods contend with socioeconomic disinvestment and isolation, and typically have limited societal resources (Williams et al., 2019; Williams & Mohammed, 2013). To this end, firearm violence prevention strategies must include policies and practices that address the downstream consequences of residential racial segregation. For instance, improving access to high-quality educational opportunities and vocational trainings (Nation et al., 2021; Schnippel et al., 2023) and revitalizing neighborhoods (e.g., vacant lot greening; Gong et al., 2023; Marçal & Maguire-Jack, 2021) are potential approaches for addressing neighborhood disadvantage and reducing youth exposure to violence. Furthermore, prevention programs that situate youth in prosocial activities within a supportive, intergenerational network of positive adult mentors and peers may help reduce youth exposure to violence (Jones et al., 2021; Lee et al., 2022; Tolan et al., 2014). This is important, as exposure to violence predicts youth firearm aggression.(Carter et al., 2015; Goldstick et al., 2017).

Of note, contrary to other studies, residential racial segregation was not directly associated with youth firearm aggression or injury (Jay et al., 2022; Krieger et al., 2017). To date, most researchers have evaluated the association between residential segregation and firearm violence fatality and injury for adults and youth using place-based, administrative data (e.g., arrest records; Krieger et al., 2017). One might surmise that analyzing data at different levels of aggregation (e.g., administrative- and respondent-level data) can yield distinct results. For instance, place-based measures of firearm injury may be primarily influenced by other place-based, contextual factors such as residential racial segregation or neighborhood disadvantage (Jay et al., 2022; Poulson et al., 2021; Uzzi et al., 2023). Alternatively, the influence of place-based factors such as residential racial segregation on youth self-report of their firearm aggression may be mediated by respondent-level individual characteristics (e.g., exposure to violence). Our study provides a first look at how residential segregation may operate to influence a self-report measure of youth firearm aggression.

Lastly, neither neighborhood disadvantage or exposure to violence independently mediated the longitudinal association between residential racial segregation and youth firearm aggression. The ecological consequences of residential racial segregation such as neighborhood disadvantage may increase the likelihood of youth firearm aggression through its effects on prior exposure to violence. Our study highlights the importance of examining the interplay between ecological (e.g., neighborhood disadvantage) and social (e.g., exposure to violence) factors to comprehensively understand how residential racial segregation plays a role in youth firearm aggression. Identifying these mechanisms can inform youth firearm violence prevention programs that address the multi-level consequences of residential racial segregation to foster safer communities. For instance, interventions that address poverty (e.g., community economic development initiatives, affordable housing programs; Woo & Joh, 2015) and place-based violence interventions (e.g., vacant land stabilization), in tandem, may be more efficacious for disrupting the underlying mechanisms from institutional racism to youth firearm aggression (Hohl et al., 2019).

While our study contributes to our understanding of how residential racial segregation shapes firearm aggression among youth, several limitations require attention. First, our study focused on adolescents living in one city with elevated violence and crime rates. Additionally, our participants reported drug use within the prior 6 months and sought medical care at an urban emergency department. It is, therefore, important to acknowledge that our findings may not generalize to non-clinical or non-substance-using youth residing in different geographic locations, such as rural areas. Nevertheless, our sample offers insights into the influence of residential racial segregation on youth firearm aggression, specifically through the pathways of neighborhood disadvantage and exposure to violence. Our results, however, may be particularly relevant for youth in contexts characterized by high rates of violence which is often observed in economically challenged small cities across the U.S. Second, it is also important to consider that our study was based on data collected over 10 years ago, so it may be time specific. Yet, researchers using more recent data have observed that residential racial segregation, neighborhood disadvantage, and exposure to violence remain significant risk factors for firearm violence (Poulson et al., 2021; Uzzi et al., 2023). Our study is important because we begin to assess the underlying mechanisms of how residential racial segregation may be associated with youth firearm aggression. Lastly, while we assess longitudinal pathways from residential racial segregation, future research that employs quasi-experimental designs (e.g., propensity score matching) to support the ability to draw causal inferences would be useful. Nonetheless, our study was the first we know about that has examined the relationships between historical policies of racism and firearm aggression using a longitudinal design.

Despite these limitations, our findings suggest that residential racial segregation is associated with downstream consequences (i.e., neighborhood disadvantage, exposure to violence) which, in turn, increase the likelihood of youth firearm aggression. Our results suggest several approaches to prevent youth firearm aggression. First, our results underscore the importance of implementing place-based policies aimed at mitigating residential racial segregation practices (Steil & Lens, 2023). Examples of such policies include inclusionary zoning and the low-income housing tax credit, which have the potential to foster safer communities by addressing firearm aggression (Steil & Lens, 2023). Second, implementing place-based policies and programs that prioritize increasing socioeconomic opportunities within low-income communities of color may disrupt the link between residential racial segregation and youth firearm aggression (Rowhani-Rahbar et al., 2022). Lastly, programs that reduce youth exposure to violence, such as those that facilitate positive adult and peer interactions (e.g., organized activities; Lee et al., 2022), present valuable opportunities for preventing youth firearm aggression within the context of residential segregation and its consequences.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The corresponding author (Daniel B. Lee) received funding from the Eunice Kennedy Shriver National Institute of Child Health and Human Development for this research (NICHD; 1R03HD112613-01).

ORCID iD

Daniel B Lee

Data Availability Statement

Data sharing not applicable to this article as no datasets were generated or analyzed during the current study.

Author Biographies

Daniel B. Lee is a Research Assistant Professor at the University of Michigan’s Institute for Firearm Injury Prevention.

Philip Stallworth is a Statistician at the University of Michigan’s Institute for Firearm Injury Prevention.

Rebecca M. Cunningham is a Professor at the University of Michigan’s School of Public Health (Department of Health Behavior and Health Education), Institute for Firearm Injury Prevention, Michigan Medicine (Department of Emergency Medicine), and Hurley Medical Center (Department of Emergency Medicine).

Maureen A. Walton is a Professor at the University of Michigan’s Institute for Firearm Injury Prevention and Michigan Medicine’s Department of Psychiatry.

Enrique W. Neblett is a professor at the University of Michigan’s School of Public Health (Department of Health Behavior and Health Education).

Patrick M. Carter is a Professor at the University of Michigan’s School of Public Health (Department of Health Behavior and Health Education), Co-Director of the Institute for Firearm Injury Prevention, and Michigan Medicine (Department of Emergency Medicine).

References

Abadie

Athey

Imbens

G. W.

Wooldridge

J. M.

(2023). When should you adjust standard errors for clustering?*. The Quarterly Journal of Economics, 138(1), 1–35. https://doi.org/10.1093/qje/qjac038

Benns

Ruther

Nash

Bozeman

Harbrecht

Miller

(2020). The impact of historical racism on modern gun violence: Redlining in the city of Louisville, KY. Injury, 51(10), 2192–2198. https://doi.org/10.1016/j.injury.2020.06.042

Bottiani

J. H.

Camacho

D. A.

Lindstrom Johnson

Bradshaw

C. P.

(2021). Annual research review: Youth firearm violence disparities in the United States and implications for prevention. Journal of Child Psychology and Psychiatry, 62(5), 563–579. https://doi.org/10.1111/jcpp.13392

Brown

B. B.

Perkins

D. D.

(2002). Neighborhood revitalization and disorder: An intervention evaluation: Final project report. National Criminal Justice Reference Service.

Burrell

White

A. M.

Frerichs

Funchess

Cerulli

DiGiovanni

Lich

K. H.

(2021). Depicting “the system”: How structural racism and disenfranchisement in the United States can cause dynamics in community violence among males in urban black communities. Social Science & Medicine, 272, 113469. https://doi.org/10.1016/j.socscimed.2020.113469

Carter

P. M.

Walton

M. A.

Roehler

D. R.

Goldstick

Zimmerman

M. A.

Blow

F. C.

Cunningham

R. M.

(2015). Firearm violence among high-risk emergency department youth after an assault injury. Pediatrics, 135(5), 805–815. https://doi.org/10.1542/peds.2014-3572

Census Reference Files. (2023, February 10). United States Census Bureau. https://www.census.gov/geographies/reference-files/time-series/geo/relationship-files.2010.html#list-tab-1709067297

Centers for Disease Control and Prevention. (2021). Web-based injury statistics query and reporting system (WISQARS): Fatal and non-fatal injury data. Centers for Disease Control and Prevention, National Center for Injury Prevention and Control. https://www.cdc.gov/injury/wisqars

Chang

L.-Y.

Wang

M.-Y.

Tsai

P.-S.

(2016). Neighborhood disadvantage and physical aggression in children and adolescents: A systematic review and meta-analysis of multilevel studies. Aggressive Behavior, 42(5), 441–454. https://doi.org/10.1002/ab.21641

10.

Cohen

(2019). SNAP at the community scale: How neighborhood characteristics affect participation and food access. American Journal of Public Health, 109(12), 1646–1651. https://doi.org/10.2105/AJPH.2019.305363

11.

Copeland-Linder

Jones

V. C.

Haynie

D. L.

Simons-Morton

B. G.

Wright

J. L.

Cheng

T. L.

(2007). Factors associated with retaliatory attitudes among African American adolescents who have been assaulted. Journal of Pediatric Psychology, 32(7), 760–770. https://doi.org/10.1093/jpepsy/jsm007

12.

Cunningham

R. M.

Carter

P. M.

Ranney

Zimmerman

M. A.

Blow

F. C.

Booth

B. M.

Goldstick

Walton

M. A.

(2015). Violent reinjury and mortality among youth seeking emergency department care for assault-related injury: A 2-year prospective cohort study. JAMA Pediatrics, 169(1), 63–70. https://doi.org/10.1001/jamapediatrics.2014.1900

13.

Degli Esposti

Hsieh

H.-F.

Goldstick

J. E.

(2022). Firearm injuries are a critical driver of health disparities in the United States. JAMA Network Open, 5(11), e2244240. https://doi.org/10.1001/jamanetworkopen.2022.44240

14.

Derogatis

L. R.

Melisaratos

(1983). The Brief Symptom Inventory: An introductory report. Psychological Medicine, 13(3), 595–605.

15.

Federal Bureau of Investigations. (2020). FBI Supplementary homicide report, 2016–2020. https://cde.ucr.cjis.gov/LATEST/webapp/#/pages/home

16.

Goldstick

J. E.

Carter

P. M.

Walton

M. A.

Dahlberg

L. L.

Sumner

S. A.

Zimmerman

M. A.

Cunningham

R. M.

(2017). Development of the SaFETy Score: A clinical screening tool for predicting future Firearm violence risk. Annals of Internal Medicine, 166(10), 707–714. https://doi.org/10.7326/M16-1927

17.

Gong

C. H.

Bushman

Hohl

B. C.

Kondo

M. C.

Carter

P. M.

Cunningham

R. M.

Rupp

L. A.

Grodzinski

Branas

C. C.

Vagi

K. J.

Zimmerman

M. A.

(2023). Community engagement, greening, and violent crime: A test of the greening hypothesis and Busy Streets. American Journal of Community Psychology, 71(1–2), 198–210. https://doi.org/10.1002/ajcp.12622

18.

Heinze

J. E.

Reischl

T. M.

Bai

Roche

J. S.

Morrel-Samuels

Cunningham

R. M.

Zimmerman

M. A.

(2016). A comprehensive prevention approach to reducing assault offenses and assault injuries among youth. Prevention Science, 17(2), 167–176. https://doi.org/10.1007/s11121-015-0616-1

19.

Hohl

B. C.

Kondo

M. C.

Kajeepeta

MacDonald

J. M.

Theall

K. P.

Zimmerman

M. A.

Branas

C. C.

(2019). Creating safe and healthy neighborhoods with place-based violence interventions. Health Affairs, 38(10), 1687–1694. https://doi.org/10.1377/hlthaff.2019.00707

20.

Jacoby

S. F.

Dong

Beard

J. H.

Wiebe

D. J.

Morrison

C. N.

(2018). The enduring impact of historical and structural racism on urban violence in Philadelphia. Social Science & Medicine, 199, 87–95. https://doi.org/10.1016/j.socscimed.2017.05.038

21.

Jay

Kondo

M. C.

Lyons

V. H.

Gause

South

E. C.

(2022). Neighborhood segregation, tree cover and firearm violence in 6 U.S. cities, 2015–2020. Preventive Medicine, 165, 107256. https://doi.org/10.1016/j.ypmed.2022.107256

22.

Jones

Becote-Jackson

Parnham

Lewis

Ryan

L. M.

(2021). Violence prevention through mentoring for youth with emergency department treated peer assault injuries. The Journal of Pediatrics: X, 6, 100064. https://doi.org/10.1016/j.ympdx.2021.100064

23.

Kennedy

B. P.

Kawachi

Prothrow-Stith

Lochner

Gupta

(1998). Social capital, income inequality, and firearm violent crime. Social Science & Medicine, 47(1), 7–17. https://doi.org/10.1016/S0277-9536(98)00097-5

24.

Kline

R. B.

(2015). Principles and practice of structural equation modeling (4th ed.). Guilford Publications.

25.

Knopov

Rothman

E. F.

Cronin

S. W.

Franklin

Cansever

Potter

Mesic

Sharma

Xuan

Siegel

Hemenway

(2019). The role of racial residential segregation in Black-White disparities in Firearm homicide at the state level in the United States, 1991–2015. Journal of the National Medical Association, 111(1), 62–75. https://doi.org/10.1016/j.jnma.2018.06.002

26.

Krieger

Feldman

J. M.

Waterman

P. D.

Chen

J. T.

Coull

B. A.

Hemenway

(2017). Local residential segregation matters: Stronger association of census tract compared to conventional city-level measures with fatal and non-fatal assaults (total and Firearm related), using the index of concentration at the extremes (ICE) for racial, economic, and racialized economic segregation, Massachusetts (US), 1995–2010. Journal of Urban Health: Bulletin of the New York Academy of Medicine, 94(2), 244–258. https://doi.org/10.1007/s11524-016-0116-z

27.

Lee

D. B.

Hsieh

H.-F.

Stoddard

S. A.

Heinze

J. E.

Carter

P. M.

Goldstick

J. E.

Cunningham

M. C.

Cunningham

R. M.

Zimmerman

M. A.

(2020). Longitudinal pathway from violence exposure to firearm carriage among adolescents: The role of future expectation. Journal of Adolescence, 81, 101–113. https://doi.org/10.1016/j.adolescence.2020.03.009

28.

Lee

D. B.

Schmidt

C. J.

Heinze

J. E.

Carter

P. M.

Cunningham

R. M.

Walton

M. A.

Zimmerman

M. A.

(2022). Retaliatory attitudes as mediator of exposure to violence and firearm aggression among youth: The protective role of organized activity involvement. Developmental Psychology, 58(5), 990–1002. https://doi.org/10.1037/dev0001339

29.

MacKinnon

D. P.

Fairchild

A. J.

Fritz

M. S.

(2007). Mediation analysis. Annual Review of Psychology, 58, 593. https://doi.org/10.1146/annurev.psych.58.110405.085542

30.

Marçal

K. E.

Maguire-Jack

(2021). Housing insecurity and adolescent well-being: Relationships with child welfare and criminal justice involvement. Child Abuse & Neglect, 115, 105009. https://doi.org/10.1016/j.chiabu.2021.105009

31.

Markowitz

F. E.

Bellair

P. E.

Liska

A. E.

Liu

(2001). Extending social disorganization theory: Modeling the relationships between cohesion, disorder, and fear*. Criminology, 39(2), 293–319. https://doi.org/10.1111/j.1745-9125.2001.tb00924.x

32.

Massey

D. S.

Denton

N. A.

(1993). American apartheid: Segregation and the making of the underclass. Harvard University Press.

33.

Mehranbod

C. A.

Gobaud

A. N.

Jacoby

S. F.

Uzzi

Bushover

B. R.

Morrison

C. N.

(2022). Historical redlining and the epidemiology of present-day firearm violence in the United States: A multi-city analysis. Preventive Medicine, 165, 107207. https://doi.org/10.1016/j.ypmed.2022.107207

34.

Murray

A. L.

Eisner

Ribeaud

Kaiser

McKenzie

Murray

(2021). Validation of a brief self-report measure of adolescent bullying perpetration and victimization. Assessment, 28(1), 128–140. https://doi.org/10.1177/1073191119858406

35.

Muthén

L. K.

Muthén

B. O.

(2017). Mplus user’s guide (8th ed.).

36.

Nation

Chapman

D. A.

Edmonds

Cosey-Gay

F. N.

Jackson

Marshall

K. J.

Gorman-Smith

Sullivan

Trudeau

A.-R. T.

(2021). Social and Structural determinants of health and youth violence: Shifting the paradigm of youth violence prevention. American Journal of Public Health, 111(S1), S28–S31. https://doi.org/10.2105/AJPH.2021.306234

37.

Ousey

G. C.

Augustine

M. C.

(2001). Young guns: Examining alternative explanations of juvenile Firearm homicide rates*. Criminology, 39(4), 933–968. https://doi.org/10.1111/j.1745-9125.2001.tb00945.x

38.

Poulson

Neufeld

M. Y.

Dechert

Allee

Kenzik

K. M.

(2021). Historic redlining, structural racism, and firearm violence: A structural equation modeling approach. The Lancet Regional Health - Americas, 3, 100052. https://doi.org/10.1016/j.lana.2021.100052

39.

Radtke

S. R.

Wretman

C. J.

Fraga Rizo

Franchino-Olsen

Williams

D. Y.

Chen

W.-T.

Macy

R. J.

(2024). A systematic review of conceptualizations and operationalizations of youth polyvictimization. Trauma, Violence, & Abuse. Advance online publication. https://doi.org/10.1177/15248380231224026

40.

Resnick

M. D.

Bearman

P. S.

Blum

R. Wm.

Bauman

K. E.

Harris

K. M.

Jones

Tabor

Beuhring

Sieving

R. E.

Shew

Ireland

Bearinger

L. H.

Udry

J. R.

(1997). Protecting adolescents from harm: Findings from the national longitudinal study on adolescent health. JAMA, 278(10), 823–832. https://doi.org/10.1001/jama.1997.03550100049038

41.

Richters

J. E.

Saltzman

(1990). Survey of children’s exposure to community violence (National Institute of Mental Health).

42.

Rowhani-Rahbar

Schleimer

J. P.

Moe

C. A.

Rivara

F. P.

Hill

H. D.

(2022). Income support policies and firearm violence prevention: A scoping review. Preventive Medicine, 165, 107133. https://doi.org/10.1016/j.ypmed.2022.107133

43.

Sampson

R. J.

Groves

W. B.

(1989). Community structure and crime: Testing social-disorganization theory. American Journal of Sociology, 94(4), 774–802. https://doi.org/10.1086/229068

44.

Sampson

R. J.

Raudenbush

S. W.

Earls

(1997). Neighborhoods and violent crime: A multilevel study of collective efficacy. Science, 277(5328), 918–924. https://doi.org/10.1126/science.277.5328.918

45.

Schleimer

J. P.

Buggs

S. A.

McCort

C. D.

Pear

V. A.

Biasi

A. D.

Tomsich

Shev

A. B.

Laqueur

H. S.

Wintemute

G. J.

(2022). Neighborhood racial and economic segregation and disparities in violence during the COVID-19 pandemic. American Journal of Public Health, 112(1), 144–153. https://doi.org/10.2105/AJPH.2021.306540

46.

Schnippel

Larson

Jay

Szkola

O’Toole

M. J.

(2023). Costs of summer youth employment to prevent violence: An analysis and implementer’s tool. Journal of Urban Health, 100(4), 676–685. https://doi.org/10.1007/s11524-023-00753-8

47.

Shaw

C. R.

McKay

H. D.

(1942). Juvenile delinquency and urban areas (pp. xxxii, 451). University of Chicago Press.

48.

Sheehan

D. V.

Lecrubier

Sheehan

K. H.

Amorim

Janavs

Weiller

Hergueta

Baker

Dunbar

G. C.

(1998). The Mini-International Neuropsychiatric Interview (M.I.N.I.): The development and validation of a structured diagnostic psychiatric interview for DSM-IV and ICD-10. The Journal of Clinical Psychiatry, 59(Suppl. 20), 11980.

49.

Smedley

B. D.

(2005). Race as biology is fiction, racism as a social problem is real: Anthropological and historical perspectives on the social construction of race. American Psychologist, 60, 16–26. https://doi.org/10.1037/0003-066X.60.1.16

50.

Steil

Lens

(2023, April 27). Public policies to address residential segregation and improve health (Health Policy Brief). Health Affairs. https://www.healthaffairs.org/do/10.1377/hpb20230321.466701/full/

51.

Taylor

R. B.

Gottfredson

S. D.

Brower

S. N.

(1981). Informal Control in the Urban Residential Environment (p. 348). National Institute of Justice, US Department of Justice. https://www.ncjrs.gov/App/Publications/abstract.aspx?ID=79204

52.

Tolan

P. H.

Henry

D. B.

Schoeny

M. S.

Lovegrove

Nichols

(2014). Mentoring programs to affect delinquency and associated outcomes of youth at risk: A comprehensive meta-analytic review. Journal of Experimental Criminology, 10(2), 179–206. https://doi.org/10.1007/s11292-013-9181-4

53.

Uzzi

Aune

K. T.

Marineau

Jones

F. K.

Dean

L. T.

Jackson

J. W.

Latkin

C. A.

(2023). An intersectional analysis of historical and contemporary structural racism on non-fatal shootings in Baltimore, Maryland. Injury Prevention, 29(1), 85–90. https://doi.org/10.1136/ip-2022-044700

54.

Vogel

South

S. J.

(2016). Spatial dimensions of the effect of neighborhood disadvantage on delinquency*. Criminology, 54(3), 434–458. https://doi.org/10.1111/1745-9125.12110

55.

Vogel

Van Ham

(2018). Unpacking the relationships between impulsivity, neighborhood disadvantage, and adolescent violence: An application of a neighborhood-based group decomposition. Journal of Youth and Adolescence, 47(4), 859–871. https://doi.org/10.1007/s10964-017-0695-3

56.

Wang

(2019). Structural equation modeling: Applications using Mplus. John Wiley & Sons.

57.

Williams

D. R.

Lawrence

J. A.

Davis

B. A.

(2019). Racism and Health: Evidence and Needed Research. Annual Review of Public Health, 40, 105–125. https://doi.org/10.1146/annurev-publhealth-040218-043750

58.

Williams

D. R.

Mohammed

S. A.

(2013). Racism and health I: Pathways and scientific evidence. American Behavioral Scientist, 57(8), 1152–1173. https://doi.org/10.1177/0002764213487340

59.

Wong

Bernstein

Jay

Siegel

(2020). Differences in racial disparities in firearm homicide across cities: The role of racial residential segregation and gaps in structural disadvantage. Journal of the National Medical Association, 112(5), 518–530. https://doi.org/10.1016/j.jnma.2020.05.014

60.

Woo

Joh

(2015). Beyond anecdotal evidence: Do subsidized housing developments increase neighborhood crime? Applied Geography, 64, 87–96. https://doi.org/10.1016/j.apgeog.2015.09.004