Racialized economic segregation and firearm injuries: a network analysis of home and incident locations

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The objective of this study was to describe the association between residential racialized economic segregation and firearm injury location, conceptualized as spatial networks of shooting incidents within and between neighborhoods. This cross-sectional study linked hospital and death records to examine firearm assaults and homicides in or among residents living within the Boston metro area. Racialized economic segregation was measured using the ‘Index of Concentration at the Extremes’ for race and income at each residential and injury address. For the 802 individuals with gunshot wounds included for analysis, individuals living in more privileged neighborhoods were more likely to be injured: (1) farther from their homes (median [IQR] miles, 3.4[0.9–9.7] for lowest deprivation tertile [T1] vs 0.7[0.1–1.9] for highest deprivation tertile [T3], p = 0.0001), (2) outside their residential census tract (T1:82.8% vs T3:62.9%, p < 0.0001), and (3) in significantly higher deprivation neighborhoods (p = 0.0001). Spatial network analysis revealed that of the 338 census tracts represented in the data, deprived communities displayed higher centrality and connectivity in shooting networks, meaning that firearm violence was more concentrated within and across such neighborhoods. Individuals residing in all communities faced the highest likelihood of being shot in the most deprived neighborhoods, and individuals shot within the highest privilege neighborhoods were most likely to reside in the highest deprivation communities. Interventions prioritizing the most deprived neighborhoods are critical to preventing the spread of gun violence between populations. Figures Figure 1 Figure 2 Introduction Firearm injuries are a leading cause of morbidity and mortality in the US and a critical driver of health disparities. Compared to White Americans, Black Americans are 10 times more likely and Hispanic Americans are twice as likely to die by firearm homicide.[ 1 , 2 ] These extreme disparities are reflected in the spatial distribution of gun violence: in 2015, 26% of all firearm homicides in the US occurred in census tracts that contained only 1.5% of the population,[ 3 ] and these neighborhoods are disproportionately occupied by people of color.[ 4 – 8 ] Racialized economic segregation appears to be an important driver of this profound social and spatial concentration in firearm violence.[ 9 – 11 ] Racialized economic segregation is the systematic differentiation of spaces by both race and class. In U.S. cities, this phenomenon is most visible as the polarization of communities between predominantly Black, poor neighborhoods on one hand, and White, affluent neighborhoods on the other. Past work has demonstrated that most firearm violence occurs in the most impoverished, racially minoritized neighborhoods, and that only a small fraction occurs in the most economically privileged, predominantly White neighborhoods.[ 12 – 14 ] However, examining only where violence occurs may not fully capture the mechanisms through which racialized economic segregation shapes firearm violence. Neighborhood social conditions can influence violence either by (a) creating community-level conditions where disputes are more likely to turn lethal or (b) molding individual behavior through routine exposure to deprivation or privilege. Little work to date has examined the effects of home neighborhood exposure to racialized economic deprivation, but one study found that hospitalized violence survivors who returned home to more deprived neighborhoods were more likely to perpetrate future violence, compared to those who returned to less deprived neighborhoods.[ 15 ] Trauma systems planning and population health research often relies on patient home location as a proxy for incident location when recording the geographic frequency of trauma.[ 16 , 17 ] However, residential location provides an incomplete measure of daily activity space. Firearm injuries and homicides often occur within an individual’s own neighborhood and often at home,[ 18 , 19 ] but up to 75% of firearm injuries occur outside the victim’s residential census tract and up to 50% occur outside their zip code of residence.[ 17 , 20 , 21 ] Understanding the factors associated with where individuals are shot and where they live can improve our knowledge of local risk environments and inform violence intervention planning. In particular, exploring the connections between residential locations and injury locations may illuminate the outsized roles of certain risk environments for producing community firearm violence. The objective of this study was to describe the associations between residential neighborhood racialized economic segregation, firearm injury location, and residential location of victims based on spatial network analysis. Specifically, we constructed networks comprising neighborhoods as nodes and shooting incidents as ties between and within neighborhoods. Drawing on racialized economic segregation models, we hypothesized that neighborhoods with the highest levels of deprivation would pose the greatest risk for locations of firearm injuries, and these injuries would most likely occur among neighborhood residents. In contrast, individuals residing in privileged neighborhoods would be least likely to be hurt in their own neighborhoods and more likely to be injured far away from their homes. Methods The Boston University, Boston Medical Center (BMC) institutional review board approved this study with a waiver of Health Insurance Portability and Accountability Act of 1996 authorization for research and deemed this study exempt from federal regulations for the protection of human research participants. Informed consent was waived because it was deemed impracticable for retrospectively collected clinical data, given the large sample size and difficulty of relocating patients. Results were reported according to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline. Study Design and Population This cross-sectional study was performed using combined data from hospital and death records. A cohort of patients presenting to the Boston Medical Center (BMC) emergency department for a non-self-inflicted, nonaccidental gunshot wound between 2019 and 2022 was identified from the BMC Violence Intervention Advocacy Program (VIAP) clinical database, as previously described.[ 22 , 23 ] BMC, the region’s largest safety-net hospital, is a Level 1 trauma center that treats approximately 70% of the gunshot and stab wound victims in the city of Boston.[ 24 ] Clinical data was linked to firearm homicide data during the same time period, abstracted from the Massachusetts Department of Vital Statistics, based on international classification of disease (ICD) − 10 codes X93-X95. Duplicate records were linked by place of death (i.e., BMC), patient name, and dates of birth and death. Criteria for inclusion in this analysis required complete data for addresses of residence and injury for the entire study population. Individuals with either a residential or injury address within 30 miles from the geographic center of Boston were included to understand the spatial dynamics of gun violence within and between Boston and surrounding communities. Measures Age was assessed as a continuous variable and categorized into age groups. All patients were grouped with their affirmed gender for all analyses. We abstracted the self-identified race and ethnicity from the patient medical record or the death record. Race/ethnicity was classified into five categories: non-Hispanic White, non-Hispanic Black, Hispanic (any race), other race/ethnicity which includes all other races, and those missing race/ethnicity information (“unknown”). The site of injury (e.g., residence, street) and whether an individual was injured at their home was determined from the VIAP database or the death record. The primary exposure of interest was the relative social and economic context of patients’ residential neighborhood as measured by the Index of Concentration at the Extremes (ICE) for race and income by census tract. This measure captures racialized economic segregation, i.e. , the polarization of neighborhoods according to the extremes of social privilege (high-income, White race) and deprivation (low-income, Black race). The index has a range of -1 (most deprived) to 1 (most privileged).[ 9 , 11 ] Racialized economic segregation is the intentional product of historical and ongoing actions by governmental and private entities, including redlining, block busting, and gentrification, each of which has reinforced the spatial separation of racial groups and differential access to economic resources such as well-paying jobs and high-quality education.[ 25 – 30 ] Unlike other commonly-used measures ( e.g. , area deprivation index), the ICE measures not just deprivation but also privilege, so it more fully captures the consequences of segregation. The race-income ICE predicts community violence better than race or income alone[ 10 ], and may be the single best predictor of neighborhood-level violence rates.[ 12 , 15 ] Patients’ racialized economic segregation exposure was assigned based on the residential address they provided at intake in the emergency department or while inpatient in the hospital and corroborated, when possible, with VIAP records. Victims of firearm homicide had their residential address listed in the death record. Individuals experiencing homelessness were included if they were residing at a known shelter or temporary living situation. Residential and injury location addresses were geocoded using the Google Geocoding API. The race-income ICE measure relied on census tract data obtained from the 2018–2022 American Community Survey (ACS). ICE was calculated by subtracting the number of Black-headed households with incomes below the poverty line from the number of White-headed, affluent households (income greater than $ 100,000 year), then dividing this quantity by the total number of households.[ 9 ] A detailed explanation of the calculation of the ICE scale for income and race is described in Feldman, et al.[ 9 ] Census tracts are the smallest spatial units for which ICE can be calculated from census (i.e., ACS) data. However, census tracts are relatively arbitrary spatial units, so address-level ICE scores were assigned using inverse distance weighting from census tract centroids, with a power of 2 and a half-mile bandwidth. This spatial interpolation approach has been used in prior studies of urban violence.[ 15 , 31 ] We used data on residence and injury locations to create a spatial shooting network. Each census tract corresponds to a unique node in this network and connects to other nodes through directed ties with the residential census tract as the sender and injury census tract as the recipient. Ties are weighted to indicate the strength of the connection based on the number of shooting incidents between sending and receiving census tracts in the study population. Census tracts in which an individual was both residing and injured were included in the analysis as loops. Node attributes include the ICE racialized economic segregation score for the census tract and the distance of the tract centroid from the geographic center of Boston. The primary outcomes of this study are firearm injury location specifics—whether an individual was injured at their home address, within their own residential census tract, and the distance between locations of residence and injury—and neighborhood centrality within the spatial network of shooting incidents. We calculated the distance between locations of residence and injury as a straight-line measurement between geocoded addresses. Data Analysis We first compared baseline demographic and injury characteristics by residential racialized economic segregation tertile ( Supplementary Fig. 1 ). Variables with missing information (unknown) were included in the analysis, and data imputation was not used to replace missing values. Second, we used multivariable logistic regression models to estimate odds ratios (OR) and 95% confidence intervals (95% CI) for associations between residential racialized economic segregation and sustaining a firearm injury at home or a firearm injury within their residential census tract. Crude univariate estimates were derived for each covariate. All covariates were included in the full model. The final multivariable model was adjusted for age, race/ethnicity, and ICE residential racialized economic segregation. In all regression models, ICE values were assessed as a continuous variable. All ORs are reported as the change in risk per 0.1 unit change in ICE racialized economic segregation on a scale from 1 to -1 (i.e., the equivalent of increasing the proportion of neighborhood households with race-income deprivation by 10 percentage points, or a reduction in privileged households of the same magnitude). Next, we used Spearman's rank correlation to determine the relationship between residential racialized economic segregation and the distance between locations of residence and injury and used the Kruskal-Wallis equality-of-populations rank test to determine associations between residential racialized economic segregation and the change in ICE between locations of residence and injury. Analyses were conducted using Stata statistical software version 18 (StataCorp), and maps were constructed in ArcGIS Pro version 3.1 (Esri). We then performed a directed network analysis of all census tracts represented in the data as a neighborhood of residence or injury for the study population. Based on ICE values, census tracts were divided into five quintiles ranging from most privileged (1) to most deprived (5). Network analysis was conducted using the igraph package in R. Rates of shootings by census tract and rates of tract residents being shot were calculated by dividing the weighted sums of incoming and outgoing network ties by ACS census tract population estimates. Negative binomial regression models were used to estimate incident rate ratios (IRR) and 95% CIs for the population-weighted incidence of shootings in a census tract (i.e. the sum of weights of incoming ties per 100,00 tract residents) and the incidence of census tract residents being shot (i.e. the sum of weights of outgoing ties per 100,00 tract residents). Positive Moran’s I statistics were observed, indicating significant spatial clustering by census tract of shooting locations, residence locations of individuals shot, and ICE residential racialized economic segregation. For this reason, spatial autoregressive models were performed. Moran’s I of the Pearson residuals from the spatial autoregressive models was near zero but statistically significant (Moran’s I = 0.01, p < 0.0001), indicating that the overall spatial pattern appears close to random and residual spatial autocorrelation was minimal. Results Demographics and injury characteristics of the 802 individuals with firearm injuries are presented in Table 1 , separated into tertiles based on the level of residential racialized economic deprivation (Supplementary Fig. 1). After inverse distance weighting, address-level residential deprivation scores ranged from − 0.3 to 0.6 (median [IQR], − 0.09 [− 0.16 to 0.14]). Among all individuals with firearm injuries, the majority were male (89.9%) and Black (66.1%) or Hispanic (21.5%) with a median age of 28 years old (interquartile range [IQR]: 22–35 years). Most individuals were shot outside on the street (63.5%) or at a residence (23.2%). The median distance from the location of residence to the location of injury was 1.5 miles (IQR: 0.2-4 miles). Table 1 Baseline characteristics of individuals with firearm injuries, 2019–2022 Residential Index of Concentration at the Extremes (ICE) Total Tertile 1: Higher Privilege Tertile 2 Tertile 3: Higher Deprivation No. of individuals (row percentage) 802 268 (33.4) 267 (33.3) 267 (33.3) Residence ICE racialized poverty index, median (IQR) -0.09 (-0.16, 0.14) 0.19 (0.13, 0.30) -0.09 (-0.12, -0.02) -0.18 (-0.22, -0.16) Age, median (IQR) 28 (22–35) 28.5 (23-34.5) 27 (22–34) 28 (22–36) Age group, years < 20 107 (13.3) 24 (9.0) 36 (13.5) 47 (17.6) 20–29 353 (44.0) 120 (44.8) 129 (48.3) 104 (39.0) 30–39 220 (27.4) 86 (32.1) 68 (25.5) 66 (24.7) ≥ 40 122 (15.2) 38 (14.2) 34 (12.7) 50 (18.7) Gender Female 81 (10.1) 37 (13.8) 22 (8.2) 22 (8.2) Male 721 (89.9) 231 (86.2) 245 (91.8) 245 (91.8) Race/ethnicity Black 530 (66.1) 151 (56.3) 180 (67.4) 199 (74.5) Hispanic 172 (21.5) 58 (21.6) 59 (22.1) 55 (20.6) White 48 (6.0) 38 (14.2) 8 (3.0) 2 (0.8) Other 29 (3.6) 14 (5.2) 12 (4.5) 3 (1.1) Unknown 23 (2.9) 7 (2.6) 8 (3.0) 8 (3.0) Injury Specifics Injury ICE racialized poverty index, median (IQR) -0.12 (-0.18, 0.05) 0.03 (-0.15, 0.19) -0.11 (-0.17, 0.03) -0.17 (-0.21, -0.12) Injured at home Yes 99 (12.3) 33 (12.3) 29 (10.9) 37 (13.9) No 703 (87.7) 235 (87.7) 238 (89.1) 230 (86.1) Injured within residential census tract Yes 202 (25.2) 46 (17.2) 57 (21.4) 99 (37.1) No 600 (74.8) 222 (82.8) 210 (78.7) 168 (62.9) Distance from residence to injury location, median miles (IQR) 1.5 (0.2, 4.0) 3.4 (0.9, 9.7) 1.2 (0.3, 3.7) 0.7 (0.1, 1.9) Distance from residence to injury location excluding injuries occurring at home, median miles (IQR) 1.9 (0.6, 4.8) 4.5 (1.7, 11.4) 1.6 (0.5, 4.1) 0.9 (0.2, 2.0) Injury occurring > 10 miles from home location 103 (12.8) 66 (24.6) 31 (11.6) 6 (2.3) Site of injury Residence 186 (23.2) 61 (22.8) 60 (22.5) 65 (24.3) Street / Outside 509 (63.5) 176 (65.7) 165 (61.8) 168 (62.9) Place of business 39 (4.9) 13 (4.9) 13 (4.9) 13 (4.9) Unknown 68 (8.5) 18 (6.7) 29 (10.9) 21 (7.9) Year of Injury 2019 166 (20.7) 53 (19.8) 54 (20.2) 59 (22.1) 2020 261 (32.5) 78 (29.1) 82 (30.7) 101 (37.8) 2021 206 (25.7) 72 (26.9) 83 (31.1) 51 (19.1) 2022 169 (21.1) 65 (24.3) 48 (18.0) 56 (21.0) All values are frequencies and column percentages except Index of Concentration at the Extremes (ICE) deprivation index, age, and distances which are medians and interquartile ranges. “Other” race includes all other races. There were 99 individuals (12.3%) injured at their home address and 202 (25.2%) injured within their own neighborhood (i.e., census tract) (Table 2 and Supplementary Table 1 ). Multivariable logistic regression models estimated that for each 10% increase in residential neighborhood deprivation, there was no difference in the odds of individuals being shot at their homes (OR = 1.07; 95% CI = 0.95–1.21; p = 0.29), but 32% higher odds of being shot within their residential census tract (OR = 1.32; 95% CI = 1.19–1.47; p < 0.0001). We observed a moderate, positive correlation (r s = 0.35, p < 0.0001) between neighborhood privilege and distance between locations of residence and injury (Fig. 1 A), and only individuals residing in the most privileged areas were injured in neighborhoods with significantly higher levels of deprivation (Fig. 1 B). Table 2 Factors associated with firearm injury at home address and within residential census tract by level of neighborhood deprivation, 2019–2022 Injured at Home Address Injured Within Residential Census Tract n (%) Multivariable Model OR (95%CI) P [excluding unknown] n (%) Multivariable Model OR (95%CI) P [excluding unknown] 99 (12.3) 202 (25.2) ICE residential racialized economic segregation 1.07 (0.95–1.21) 0.29 1.32 (1.19–1.47) < 0.0001 Age group, years 0.002 0.0006 < 20 13 (12.2) 1.00 38 (35.5) 1.00 20–29 35 (9.9) 0.75 (0.38–1.48) 74 (21.0) 0.50 (0.30–0.81) 30–39 22 (10.0) 0.71 (0.34–1.49) 46 (20.9) 0.49 (0.29–0.83) ≥ 40 29 (23.8) 1.98 (0.96–4.11) 44 (36.1) 1.05 (0.60–1.84) Race/ethnicity 0.008 [0.006] 0.007 [0.007] Black 63 (11.9) 1.00 134 (25.3) 1.00 Hispanic 16 (9.3) 0.83 (0.46–1.49) 41 (23.8) 1.02 (0.67–1.54) White 14 (29.2) 3.54 (1.65–7.57) 17 (35.4) 3.45 (1.71–6.94) Other 5 (17.2) 1.76 (0.63–4.89) 7 (24.1) 1.34 (0.54–3.33) Unknown 1 (4.4) 0.34 (0.05–2.62) 3 (13.0) 0.44 (0.13–1.55) Logistic regression models were used to estimate odds ratios (OR) and 95% confidence intervals (95% CI). Multivariable models were adjusted for age, race/ethnicity, and ICE residential racialized economic segregation index. In all models, the Index of Concentration at the Extremes (ICE) is assessed as a continuous variable. All ORs are reported as the change in risk per 0.1 unit change in ICE racialized economic segregation on a scale from 1 to -1. Figure 1 C shows the locations of shooting incidents among our study population and displays census tract-level ICE racialized economic segregation scores for the city of Boston and surrounding areas. Each census tract containing a location of residence or injury in our study population was also included as a unique node in our directed network analysis. In total, 338 census tracts were represented in the network with 600 connections between census tracts (equal to the number of shootings) and 202 loops (i.e., individuals who resided and were injured within the same census tract). Of the network ties, 137 are multiple, meaning that the tie captures multiple shootings between the same two tracts (including within the same tract or loops). Most of these multiple ties are loops (N = 111) with three tracts, all in the most deprived ICE quintile, recording 9 or more shootings of its own residents. Multiplicity in cross-neighborhood shootings is relatively less layered with the maximum value being 3 shootings across the same two neighborhoods for three pairs of tracts. Eliminating these multiple ties yields a simplified network comprising 574 ties between tracts and 91 loops. This simplified shooting network is shown in Fig. 2 A with network characteristics detailed in Supplementary Table 2 and Supplementary Fig. 3 . In this diagram, 19 dyads are mutual (i.e., there is a reciprocal relationship between these nodes in the directed network) and 536 are asymmetric dyads (i.e., there is a one-way relationship between these nodes in the directed network). Figure 2 B shows the largest connected network component consisting of 253 nodes (75% of all census tracts) connected as a neighborhood of residence or injury in the shooting network. The size of nodes in this figure corresponds to nodal in-degree so that larger nodes are neighborhoods where more shootings occur. Node shade represents quintiles of neighborhood racialized economic segregation from most privileged (lightest color) to most deprived (darkest color). It is evident that the largest nodes are darker in color meaning that shootings occur most often in disadvantaged neighborhoods. The network is most densely connected around the large and dark nodes with fewer connections among light nodes showing high concentration of shooting events around the least privileged neighborhoods. This is confirmed by negative binomial regression models ( Supplementary Table 3 ), which estimate that for each 10% increase in residential deprivation, there was a 52% greater rate of shooting incidents in a neighborhood (IRR = 1.52; 95% CI = 1.47–1.58; p < 0.0001) and a 61% greater rate of neighborhood residents being shot (IRR = 1.61; 95% CI = 1.55–1.66; p < 0.0001). The disparate density of ties within quintiles can also be seen in Fig. 2 C, which shows shooting ties internal to each quintile. There is a drastic and profound increase in connectedness within the fifth (most-deprived) quintile. The table shows the number of ties from all quintiles to all others normalized by the size of the sending quintile. The highest numbers in each row and column are boldfaced. The boldfaced values in column 5 (most deprived neighborhoods) show that individuals residing in all quintiles face the highest likelihood of being shot in the most deprived neighborhoods. The boldfaced values in columns show that people who are shot in quintiles 1 and 2 (the most privileged quintiles) have the highest chance of living in quintile 5. Note also that the number of ties within quintile 5 is nearly ten times higher than the next highest value. Most shootings are concentrated within this quintile: it serves as both residence and location of shooting of the victim. Shootings internal to this quintile exceed shootings in any other cell of the table by a margin of 10 to nearly 140. Discussion This study found that neighborhoods of residence and injury among all shootings were highly connected, and individuals were most likely to be shot in highly deprived neighborhoods irrespective of their level of home neighborhood racialized economic segregation. Compared to individuals living in the most deprived neighborhoods, those living in more privileged neighborhoods were just as likely to be shot at their homes, but more likely to be injured farther from their homes, outside their own neighborhoods, and in a neighborhood with significantly higher levels of racialized economic deprivation. Higher deprivation neighborhoods were associated with both increased numbers of shooting incidents and more residents being injured by firearms. These highly deprived neighborhoods were most central, more dense, and far more likely to be both the residence and site of shooting as compared to other tracts in the network depicting connections between home locations and shooting locations. These results are consistent with previous findings[ 8 , 18 , 19 , 21 ] that firearm injuries cluster in census tracts with high rates of poverty and are more likely to take place closer to home, and often within the home, compared to other types of traumatic injuries. However, this is the first analysis, to our knowledge, that examines how the consequences of racialized economic deprivation on firearm violence reach across a metropolitan area. We found that social conditions in the most-deprived subset of neighborhoods contributed to firearm violence in multiple ways. First, the residents of those neighborhoods were most likely to be shot in any context , whether high- or low-deprivation. Second, high deprivation neighborhoods were the contexts in which any Boston resident was most likely to be shot, regardless of their home neighborhood social conditions. Our finding that individuals living in less-deprived neighborhoods were injured farther from home and in neighborhoods of significantly higher deprivation and that shootings are overwhelmingly concentrated in the most deprived neighborhoods shows that contextual risk for firearm violence is predominantly clustered in a small number of high-risk neighborhoods, regardless of where the victim resides. In parallel, our network analysis also shows that residents of the most deprived neighborhoods face the highest risks of being shot within their own neighborhoods or other spaces facing similar levels of deprivation. Furthermore, residents of low-income neighborhoods are also most likely to be victims of shootings in relatively well-off environments, suggesting that the effects of neighborhood exposures are carried by residents wherever they go. These findings are important from the intervention standpoint because they suggest that social and economic investments in this subset of deprived neighborhoods might produce outsized benefits both in mitigating violence occurring in those neighborhoods as well as perhaps to their residents as they navigate other neighborhoods.[ 7 ] Many effective strategies for community violence intervention rely on trained frontline workers serving as credible messengers within a hospital or community setting.[ 23 , 32 , 33 ] These hospital-based violence intervention programs and neighborhood trauma and grief support services are critical for residents of communities most impacted by violence, but must also be available for victims livings in higher privilege neighborhoods. Comparing home and injury locations can also help guide individual-level interventions. Our study found that, similar to other investigations,[ 20 ] 75% of firearm injuries occurred outside an individual’s residential census tract and 13% occurred more than 10 miles from home. However, we observed high variability in these metrics by level of residential privilege; one quarter of individuals living in the highest privilege neighborhoods were injured more than 10 miles from home compared to only 2% of individuals living in the most deprived neighborhoods. Being injured close to home can create additional challenges for survivors once they discharge from hospital, including an erosion of safety and security, increased sense of risk for subsequent assaults, and heightened anxiety and symptoms of post-traumatic stress disorder.[ 34 ] These results also have implications for trauma systems planning and injury epidemiology research, both of which often rely on home location as a proxy for injury location.[ 16 , 17 ] Caution is warranted when interpreting geospatial injury data based on home locations, particularly when considering the true burden of gun violence in the most deprived communities. These data suggest a need for more accurate incident location data collection in trauma patients to ensure equity in trauma care and to develop appropriate interventions.[ 35 , 36 ] Mobility patterns could also partially explain our findings. Prior research has shown that home neighborhood social position is associated with daily mobility, i.e., residents of more deprived neighborhoods travel shorter distances in a typical day than residents of more privileged neighborhoods.[ 37 ] Thus, the risks of living in a neighborhood with high firearm violence risk may be amplified by the difficulty of spending time in safer neighborhoods. While reducing violence risk in highly deprived neighborhoods should be a central priority, another appropriate goal would be addressing the employment opportunity gaps, transportation options, and other factors that create mobility disparities. Previous investigations have used social network analysis methods to demonstrate how gun violence spreads through social networks from one individual to another in a process of social contagion.[ 38 – 40 ] Geospatial network analyses have reported limited spatial diffusion[ 41 ] of violence across neighborhoods over time, with gun violence persistently concentrated in communities of color with high rates of poverty, particularly in specific micro places or “hot spots”.[ 42 ] Violence across neighborhoods in a city is often the result of retaliatory incidents by rival gangs or groups of minority youth across turf boundaries.[ 43 ] In our network analysis, we found that neighborhoods were highly connected overall as locations of residence and injury, with the most deprived neighborhoods displaying the highest levels of centrality and connectivity within the network. The limited number of reciprocal relationships between neighborhoods (i.e., mutual dyads) in the shooting network as well as the concentration shootings in the most deprived quintile of neighborhoods shows that the majority of firearm injuries in our study population flowed in one direction from relatively advantaged communities of residence to injury in deprived neighborhoods or occurred among individuals residing and injured within the same deprived community. Overall, our results indicate that individuals in our study population followed three predominant patterns: (1) being shot in their own highly deprived neighborhoods, (2) traveling from more privileged neighborhoods to a highly deprived neighborhood where they were injured and, to a smaller degree, (3) residents of deprived neighborhoods facing greater risks of being shot in relatively well-off neighborhoods. Limitations There are limitations to consider when interpreting the results of this study. First, our population included individuals shot in a single US metro area, which limits the generalizability of our findings to populations in other US cities. Second, the nonfatal firearm injuries included in our study population presented to only a single Boston hospital, albeit the Boston hospital where the vast majority of gunshot wound patients present.[ 24 ] There were likely additional nonfatal injuries presenting to other medical centers in the city that were not included in our analysis and therefore certain neighborhoods may be underrepresented in our analysis as locations of injury or residence due to being outside the catchment area for the hospital where this study took place. Third, while only individuals with a known home and injury address listed in either medical records or death records were included for analysis, we cannot be certain that individuals actually resided at the listed addresses at the time of their firearm injury. Conclusion This cross-sectional study found that neighborhoods with high levels of racialized poverty were most implicated both as senders and receivers of violence in the spatial network of firearm assaults and homicides within the city of Boston, and individuals were more likely to be shot in these neighborhoods regardless of their home neighborhood. Individuals in our study population living in the most deprived neighborhoods were just as likely to be shot at their homes as those living in more privileged neighborhoods but were significantly more likely to be shot near their homes and within their own neighborhoods. Individuals from more privileged areas were shot farther from their homes and in census tracts with higher racialized poverty compared to their residential neighborhoods. The common denominator across high deprivation neighborhoods is that they have been disproportionately impacted by structural racism through official and unofficial policies and practices. These results suggest that investments in urban neighborhoods most impacted by gun violence can have outsized impacts on firearm injury prevention across a metro area. Declarations Financial Support Supported by the Department of Emergency Medicine, Boston Medical Center. Conflict of Interest Disclosure: The authors have no potential conflicts of interest to disclose. Acknowledgement Section : Dr Pino had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. References Kegler SR, Simon TR, Zwald ML, et al. Vital signs: changes in firearm homicide and suicide rates—United States, 2019–2020. Morb Mortal Wkly Rep. 2022;71(19):656. Centers for Disease Control and Prevention. Injury Prevention & Control: Data & Statistics (WISQ-ARSTM). Available at: http://www.cdc.gov/injury/wisqars/index.html . Accessed February 26, 2024. Aufrichtig A, Beckett L, Diehm J, Lartey J. Want to fix gun violence in America? 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J Epidemiol Community Health. 2015;69(12):1199–207. Krieger N, Feldman JM, Waterman PD, Chen JT, Coull BA, Hemenway D. 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. J Urban Health. 2017;94:244–58. Krieger N, Waterman PD, Spasojevic J, Li W, Maduro G, Van Wye G. Public health monitoring of privilege and deprivation with the index of concentration at the extremes. Am J Public Health. 2016;106(2):256–63. Jay J, Kondo MC, Lyons VH, Gause E, South EC. Neighborhood segregation, tree cover and firearm violence in 6 US cities, 2015–2020. Prev Med. 2022;165:107256. Schleimer JP, Buggs SA, McCort CD, et al. Neighborhood racial and economic segregation and disparities in violence during the COVID-19 pandemic. Am J Public Health. 2022;112(1):144–53. Uzzi M, Aune KT, Marineau L, et al. An intersectional analysis of historical and contemporary structural racism on non-fatal shootings in Baltimore, Maryland. Inj Prev. 2023;29(1):85–90. Pino EC, Jacoby SF, Dugan E, Jay J. Exposure to neighborhood racialized economic segregation and reinjury and violence perpetration among survivors of violent injuries. JAMA Netw open. 2023;6(4):e238404–238404. Myers SR, Branas CC, Kallan MJ, Wiebe DJ, Nance ML, Carr BG. The use of home location to proxy injury location and implications for regionalized trauma system planning. J Trauma Acute Care Surg. 2011;71(5):1428–34. Beiriger J, Silver D, Lu L, et al. The Geography of Injuries in Trauma Systems: Using Home as a Proxy for Incident Location. J Surg Res. 2023;2023/10/(01/):290:36–44. Haas B, Doumouras AG, Gomez D, et al. Close to home: an analysis of the relationship between location of residence and location of injury. J Trauma Acute Care Surg. 2015;78(4):860–5. Newgard CD, Sanchez BJ, Bulger EM, et al. A geospatial analysis of severe firearm injuries compared to other injury mechanisms: event characteristics, location, timing, and outcomes. Acad Emerg Med. 2016;23(5):554–65. Mills B, Hajat A, Rivara F, Nurius P, Matsueda R, Rowhani-Rahbar A. Firearm assault injuries by residence and injury occurrence location. Inj Prev. 2019;25(Suppl 1):i12–5. Hsia RY, Dai M, Wei R, Sabbagh S, Mann NC. Geographic Discordance Between Patient Residence and Incident Location in Emergency Medical Services Responses. Annals Emerg Med 2017/01/01/. 2017;69(1):44–e5143. Pino EC, Fontin F, Dugan E. Violence Intervention Advocacy Program and Community Interventions. In: Lee LK, Fleegler EW, editors. Pediatric Firearm Injuries and Fatalities: The Clinician’s Guide to Policies and Approaches to Firearm Harm Prevention. Cham: Springer International Publishing; 2021. pp. 157–77. Pino EC, Fontin F, James TL, Dugan E. Boston violence intervention advocacy program: challenges and opportunities for client engagement and goal achievement. Acad Emerg Med. 2021;28(3):281–91. Boston Medical Center. Injury Prevention Center Annual Report 2014–2015. Available at: https://www.bumc.bu.edu/emergencymedicine/files/2016/06/16114_IPC_AR_2016_web-FINAL.pdf . Accessed November 13, 2020. Sadler RC, Bilal U, Furr-Holden CD. Linking historical discriminatory housing patterns to the contemporary food environment in Baltimore. Spat spatio-temporal Epidemiol. 2021;36:100387. Jacoby SF, Dong B, Beard JH, Wiebe DJ, Morrison CN. The enduring impact of historical and structural racism on urban violence in Philadelphia. Soc Sci Med. 2018;199:87–95. Mehranbod CA, Gobaud AN, Jacoby SF, Uzzi M, Bushover BR, Morrison CN. Historical redlining and the epidemiology of present-day firearm violence in the United States: A multi-city analysis. Prev Med. 2022;165:107207. Williams DR, Collins C. Racial residential segregation: a fundamental cause of racial disparities in health. Public Health Rep. 2001;116(5):404. Aalbers MB. Do maps make geography? Part 1: Redlining, planned shrinkage, and the places of decline. ACME: Int J Crit Geographies. 2014;13(4):525–56. Nardone A, Chiang J, Corburn J. Historic redlining and urban health today in US cities. Environ Justice. 2020;13(4):109–19. Branas CC, Kondo MC, Murphy SM, South EC, Polsky D, MacDonald JM. Urban blight remediation as a cost-beneficial solution to firearm violence. Am J Public Health. 2016;106(12):2158–64. Purtle J, Dicker R, Cooper C, et al. Hospital-based violence intervention programs save lives and money. J Trauma Acute Care Surg. 2013;75(2):331–3. Jay J, Pino E, Georges M et al. Effects of a Hospital-Based Violence Intervention Program on Community Violence in Boston, Massachusetts. Ann Intern Med 2026. Hink AB, Atkins DL, Rowhani-Rahbar A. Not all survivors are the same: qualitative assessment of prior violence, risks, recovery and perceptions of firearms and violence among victims of firearm injury. J interpers Violence. 2022;37(15–16):NP14368–96. Conrick KM, Mills B, Mohamed K, et al. Improving data collection and abstraction to assess health equity in trauma care. J Med Syst. 2022;46(4):21. Butts JA, Roman CG, Bostwick L, Porter JR. Cure violence: a public health model to reduce gun violence. Annu Rev Public Health. 2015;36(1):39–53. Jay J, Bor J, Nsoesie EO, et al. Neighbourhood income and physical distancing during the COVID-19 pandemic in the United States. Nat Hum Behav. 2020;2020/12/01(12):1294–302. Green B, Horel T, Papachristos AV. Modeling contagion through social networks to explain and predict gunshot violence in Chicago, 2006 to 2014. JAMA Intern Med. 2017;177(3):326–33. Gill LM, Fox AM. Using social network analysis to examine gun violence. J Forensic Sci. 2022;67(6):2230–41. Papachristos AV, Braga AA, Hureau DM. Social networks and the risk of gunshot injury. J Urban Health. 2012;89:992–1003. Cohen J, Tita G. Diffusion in homicide: Exploring a general method for detecting spatial diffusion processes. J Quant Criminol. 1999;15:451–93. Braga AA, Papachristos AV, Hureau DM. The concentration and stability of gun violence at micro places in Boston, 1980–2008. J Quant Criminol. 2010;26:33–53. Papachristos AV. Murder by structure: Dominance relations and the social structure of gang homicide. Am J Sociol. 2009;115(1):74–128. Supplementary Files ResidenceIncidentICESupp20260213.docx Cite Share Download PDF Status: Under Review Version 1 posted Editor assigned by journal 18 Feb, 2026 First submitted to journal 13 Feb, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8874419","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":593591943,"identity":"31ad814e-abc0-4221-adeb-0634cbc3db53","order_by":0,"name":"Elizabeth Pino","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9klEQVRIiWNgGAWjYDACdsYGBiBiMACyDzAw2CQQ1sIM0SIB1ZJGjBYghmkBgsOEtfAzMzd/+LnDps6cgffg4YKK83m67QcYH1f8wq1FspmxwbD3TJqEZQNfwuEZZ24Xm51JYDY824dbi8FhxoYE3rbDEgYHeAwO87bdTtx2g4FNsrEHtxZ7oJaDf9v+w7ScI6zFgJmxsZm37QBMywGIloYfuLVIHGZsZpY9kyy54TDQLzxnkoF+SWw2bGzArYW/vf3xx7c77PgNjvce/sxTYZdndvzwwYcNf3BrQQBmHhgLFLltxGhh4EHmEGXLKBgFo2AUjBAAAFa1Vp2i1j0EAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0003-0626-5221","institution":"Boston Medical Center Department of Emergency Medicine","correspondingAuthor":true,"prefix":"","firstName":"Elizabeth","middleName":"","lastName":"Pino","suffix":""},{"id":593591944,"identity":"124a81fe-a572-41cb-99e2-979b28b30853","order_by":1,"name":"Neha Gondal","email":"","orcid":"","institution":"Boston University","correspondingAuthor":false,"prefix":"","firstName":"Neha","middleName":"","lastName":"Gondal","suffix":""},{"id":593591945,"identity":"47220b8b-c51b-457a-a207-330dc65660e5","order_by":2,"name":"Megan Georges","email":"","orcid":"","institution":"Boston Medical Center Department of Emergency Medicine","correspondingAuthor":false,"prefix":"","firstName":"Megan","middleName":"","lastName":"Georges","suffix":""},{"id":593591946,"identity":"a229efe4-e1b4-46bc-b247-93e6b029a01c","order_by":3,"name":"Jonathan Jay","email":"","orcid":"","institution":"Boston University School of Public Health","correspondingAuthor":false,"prefix":"","firstName":"Jonathan","middleName":"","lastName":"Jay","suffix":""}],"badges":[],"createdAt":"2026-02-13 17:36:20","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8874419/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8874419/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103981009,"identity":"10b1d599-733b-4a82-b56d-bcc6df7e99a5","added_by":"auto","created_at":"2026-03-05 09:28:27","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":488430,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure 1A: Distance between locations of residence and injury for individuals with firearm injuries, 2019-2022\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 1B: Index of Concentration at the Extremes (ICE) racialized economic segregation at locations of residence and injury for individuals with firearm injuries, 2019-2022\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 1C. Index of Concentration at the Extremes (ICE) for race and income by census tract and locations of shooting incidents\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8874419/v1/53b2ed43805f23b26a478ab4.png"},{"id":103981008,"identity":"22372e0d-c13d-42ba-9f6b-2bcd25f8e73b","added_by":"auto","created_at":"2026-03-05 09:28:26","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":883903,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure 2A-C. Directed network and features of the shooting network\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEach node in these diagrams represents a unique census tract (n = 338) corresponding to a location of residence or injury among our study population. Node color represents quintiles of neighborhood racialized economic segregation from most privileged (lightest color) to most deprived (darkest color). Arrow direction points from location of residence to location of firearm injury. Node size represents the strength (i.e., weighted degree) of the node within the network. Unconnected nodes represent isolates in the network in which an individual was residing and injured within the same census tract, and no other individuals in the study population were residing or injured within that tract. Figure 3A shows the complete network. Figure 3B shows connections between nodes in the largest component. Figure 3C shows networks internal to each quintile and a table of densities of ties between quintiles. The highest numbers in each row and column are boldfaced.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8874419/v1/16a8204c12acf488a6ad26d7.png"},{"id":103981014,"identity":"6424d302-3de5-466f-815a-56a8e7db2834","added_by":"auto","created_at":"2026-03-05 09:28:32","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2412167,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8874419/v1/57c98bc1-98f1-4798-976e-4d389d3496fd.pdf"},{"id":103981013,"identity":"dfb5855c-196e-4099-b9fd-5c95b5de600f","added_by":"auto","created_at":"2026-03-05 09:28:31","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":889739,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8874419/v1/68b76187-540b-47f1-a37d-2d4aa1eda907.pdf"},{"id":103981010,"identity":"a75c6bd8-cb3b-4ca9-a521-294e37039baf","added_by":"auto","created_at":"2026-03-05 09:28:27","extension":"docx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":2313265,"visible":true,"origin":"","legend":"","description":"","filename":"ResidenceIncidentICESupp20260213.docx","url":"https://assets-eu.researchsquare.com/files/rs-8874419/v1/27a71e2c3f48bda92f274e5e.docx"}],"financialInterests":"","formattedTitle":"Racialized economic segregation and firearm injuries: a network analysis of home and incident locations","fulltext":[{"header":"Introduction","content":"\u003cp\u003eFirearm injuries are a leading cause of morbidity and mortality in the US and a critical driver of health disparities. Compared to White Americans, Black Americans are 10 times more likely and Hispanic Americans are twice as likely to die by firearm homicide.[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e] These extreme disparities are reflected in the spatial distribution of gun violence: in 2015, 26% of all firearm homicides in the US occurred in census tracts that contained only 1.5% of the population,[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e] and these neighborhoods are disproportionately occupied by people of color.[\u003cspan additionalcitationids=\"CR5 CR6 CR7\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eRacialized economic segregation appears to be an important driver of this profound social and spatial concentration in firearm violence.[\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] Racialized economic segregation is the systematic differentiation of spaces by both race and class. In U.S. cities, this phenomenon is most visible as the polarization of communities between predominantly Black, poor neighborhoods on one hand, and White, affluent neighborhoods on the other. Past work has demonstrated that most firearm violence occurs in the most impoverished, racially minoritized neighborhoods, and that only a small fraction occurs in the most economically privileged, predominantly White neighborhoods.[\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eHowever, examining only where violence occurs may not fully capture the mechanisms through which racialized economic segregation shapes firearm violence. Neighborhood social conditions can influence violence either by (a) creating community-level conditions where disputes are more likely to turn lethal or (b) molding individual behavior through routine exposure to deprivation or privilege. Little work to date has examined the effects of home neighborhood exposure to racialized economic deprivation, but one study found that hospitalized violence survivors who returned home to more deprived neighborhoods were more likely to perpetrate future violence, compared to those who returned to less deprived neighborhoods.[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eTrauma systems planning and population health research often relies on patient home location as a proxy for incident location when recording the geographic frequency of trauma.[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] However, residential location provides an incomplete measure of daily activity space. Firearm injuries and homicides often occur within an individual\u0026rsquo;s own neighborhood and often at home,[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] but up to 75% of firearm injuries occur outside the victim\u0026rsquo;s residential census tract and up to 50% occur outside their zip code of residence.[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eUnderstanding the factors associated with where individuals are shot and where they live can improve our knowledge of local risk environments and inform violence intervention planning. In particular, exploring the connections between residential locations and injury locations may illuminate the outsized roles of certain risk environments for producing community firearm violence. The objective of this study was to describe the associations between residential neighborhood racialized economic segregation, firearm injury location, and residential location of victims based on spatial network analysis. Specifically, we constructed networks comprising neighborhoods as nodes and shooting incidents as ties between and within neighborhoods. Drawing on racialized economic segregation models, we hypothesized that neighborhoods with the highest levels of deprivation would pose the greatest risk for locations of firearm injuries, and these injuries would most likely occur among neighborhood residents. In contrast, individuals residing in privileged neighborhoods would be least likely to be hurt in their own neighborhoods and more likely to be injured far away from their homes.\u003c/p\u003e "},{"header":"Methods","content":"\u003cp\u003e The Boston University, Boston Medical Center (BMC) institutional review board approved this study with a waiver of Health Insurance Portability and Accountability Act of 1996 authorization for research and deemed this study exempt from federal regulations for the protection of human research participants. Informed consent was waived because it was deemed impracticable for retrospectively collected clinical data, given the large sample size and difficulty of relocating patients. Results were reported according to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline.\u003c/p\u003e\n\u003ch3\u003eStudy Design and Population\u003c/h3\u003e\n\u003cp\u003eThis cross-sectional study was performed using combined data from hospital and death records. A cohort of patients presenting to the Boston Medical Center (BMC) emergency department for a non-self-inflicted, nonaccidental gunshot wound between 2019 and 2022 was identified from the BMC Violence Intervention Advocacy Program (VIAP) clinical database, as previously described.[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] BMC, the region\u0026rsquo;s largest safety-net hospital, is a Level 1 trauma center that treats approximately 70% of the gunshot and stab wound victims in the city of Boston.[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] Clinical data was linked to firearm homicide data during the same time period, abstracted from the Massachusetts Department of Vital Statistics, based on international classification of disease (ICD)\u0026thinsp;\u0026minus;\u0026thinsp;10 codes X93-X95. Duplicate records were linked by place of death (i.e., BMC), patient name, and dates of birth and death.\u003c/p\u003e \u003cp\u003eCriteria for inclusion in this analysis required complete data for addresses of residence and injury for the entire study population. Individuals with either a residential or injury address within 30 miles from the geographic center of Boston were included to understand the spatial dynamics of gun violence within and between Boston and surrounding communities.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eMeasures\u003c/h2\u003e \u003cp\u003eAge was assessed as a continuous variable and categorized into age groups. All patients were grouped with their affirmed gender for all analyses. We abstracted the self-identified race and ethnicity from the patient medical record or the death record. Race/ethnicity was classified into five categories: non-Hispanic White, non-Hispanic Black, Hispanic (any race), other race/ethnicity which includes all other races, and those missing race/ethnicity information (\u0026ldquo;unknown\u0026rdquo;). The site of injury (e.g., residence, street) and whether an individual was injured at their home was determined from the VIAP database or the death record.\u003c/p\u003e \u003cp\u003eThe primary exposure of interest was the relative social and economic context of patients\u0026rsquo; residential neighborhood as measured by the Index of Concentration at the Extremes (ICE) for race and income by census tract. This measure captures racialized economic segregation, \u003cem\u003ei.e.\u003c/em\u003e, the polarization of neighborhoods according to the extremes of social privilege (high-income, White race) and deprivation (low-income, Black race). The index has a range of -1 (most deprived) to 1 (most privileged).[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] Racialized economic segregation is the intentional product of historical and ongoing actions by governmental and private entities, including redlining, block busting, and gentrification, each of which has reinforced the spatial separation of racial groups and differential access to economic resources such as well-paying jobs and high-quality education.[\u003cspan additionalcitationids=\"CR26 CR27 CR28 CR29\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] Unlike other commonly-used measures (\u003cem\u003ee.g.\u003c/em\u003e, area deprivation index), the ICE measures not just deprivation but also privilege, so it more fully captures the consequences of segregation. The race-income ICE predicts community violence better than race or income alone[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], and may be the single best predictor of neighborhood-level violence rates.[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/p\u003e \u003cp\u003ePatients\u0026rsquo; racialized economic segregation exposure was assigned based on the residential address they provided at intake in the emergency department or while inpatient in the hospital and corroborated, when possible, with VIAP records. Victims of firearm homicide had their residential address listed in the death record. Individuals experiencing homelessness were included if they were residing at a known shelter or temporary living situation. Residential and injury location addresses were geocoded using the Google Geocoding API. The race-income ICE measure relied on census tract data obtained from the 2018\u0026ndash;2022 American Community Survey (ACS). ICE was calculated by subtracting the number of Black-headed households with incomes below the poverty line from the number of White-headed, affluent households (income greater than \u003cspan\u003e$\u003c/span\u003e100,000 year), then dividing this quantity by the total number of households.[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] A detailed explanation of the calculation of the ICE scale for income and race is described in Feldman, et al.[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] Census tracts are the smallest spatial units for which ICE can be calculated from census (i.e., ACS) data. However, census tracts are relatively arbitrary spatial units, so address-level ICE scores were assigned using inverse distance weighting from census tract centroids, with a power of 2 and a half-mile bandwidth. This spatial interpolation approach has been used in prior studies of urban violence.[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eWe used data on residence and injury locations to create a spatial shooting network. Each census tract corresponds to a unique node in this network and connects to other nodes through directed ties with the residential census tract as the sender and injury census tract as the recipient. Ties are weighted to indicate the strength of the connection based on the number of shooting incidents between sending and receiving census tracts in the study population. Census tracts in which an individual was both residing and injured were included in the analysis as loops. Node attributes include the ICE racialized economic segregation score for the census tract and the distance of the tract centroid from the geographic center of Boston.\u003c/p\u003e \u003cp\u003eThe primary outcomes of this study are firearm injury location specifics\u0026mdash;whether an individual was injured at their home address, within their own residential census tract, and the distance between locations of residence and injury\u0026mdash;and neighborhood centrality within the spatial network of shooting incidents. We calculated the distance between locations of residence and injury as a straight-line measurement between geocoded addresses.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eData Analysis\u003c/h2\u003e \u003cp\u003eWe first compared baseline demographic and injury characteristics by residential racialized economic segregation tertile (\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eSupplementary Fig.\u0026nbsp;1\u003c/span\u003e). Variables with missing information (unknown) were included in the analysis, and data imputation was not used to replace missing values.\u003c/p\u003e \u003cp\u003eSecond, we used multivariable logistic regression models to estimate odds ratios (OR) and 95% confidence intervals (95% CI) for associations between residential racialized economic segregation and sustaining a firearm injury at home or a firearm injury within their residential census tract. Crude univariate estimates were derived for each covariate. All covariates were included in the full model. The final multivariable model was adjusted for age, race/ethnicity, and ICE residential racialized economic segregation. In all regression models, ICE values were assessed as a continuous variable. All ORs are reported as the change in risk per 0.1 unit change in ICE racialized economic segregation on a scale from 1 to -1 (i.e., the equivalent of increasing the proportion of neighborhood households with race-income deprivation by 10 percentage points, or a reduction in privileged households of the same magnitude). Next, we used Spearman's rank correlation to determine the relationship between residential racialized economic segregation and the distance between locations of residence and injury and used the Kruskal-Wallis equality-of-populations rank test to determine associations between residential racialized economic segregation and the change in ICE between locations of residence and injury. Analyses were conducted using Stata statistical software version 18 (StataCorp), and maps were constructed in ArcGIS Pro version 3.1 (Esri).\u003c/p\u003e \u003cp\u003eWe then performed a directed network analysis of all census tracts represented in the data as a neighborhood of residence or injury for the study population. Based on ICE values, census tracts were divided into five quintiles ranging from most privileged (1) to most deprived (5). Network analysis was conducted using the \u003cem\u003eigraph\u003c/em\u003e package in R.\u003c/p\u003e \u003cp\u003eRates of shootings by census tract and rates of tract residents being shot were calculated by dividing the weighted sums of incoming and outgoing network ties by ACS census tract population estimates. Negative binomial regression models were used to estimate incident rate ratios (IRR) and 95% CIs for the population-weighted incidence of shootings in a census tract (i.e. the sum of weights of incoming ties per 100,00 tract residents) and the incidence of census tract residents being shot (i.e. the sum of weights of outgoing ties per 100,00 tract residents). Positive Moran\u0026rsquo;s I statistics were observed, indicating significant spatial clustering by census tract of shooting locations, residence locations of individuals shot, and ICE residential racialized economic segregation. For this reason, spatial autoregressive models were performed. Moran\u0026rsquo;s I of the Pearson residuals from the spatial autoregressive models was near zero but statistically significant (Moran\u0026rsquo;s I\u0026thinsp;=\u0026thinsp;0.01, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), indicating that the overall spatial pattern appears close to random and residual spatial autocorrelation was minimal.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eDemographics and injury characteristics of the 802 individuals with firearm injuries are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, separated into tertiles based on the level of residential racialized economic deprivation (Supplementary Fig.\u0026nbsp;1). After inverse distance weighting, address-level residential deprivation scores ranged from \u0026minus;\u0026thinsp;0.3 to 0.6 (median [IQR], \u0026minus;\u0026thinsp;0.09 [\u0026minus;\u0026thinsp;0.16 to 0.14]). Among all individuals with firearm injuries, the majority were male (89.9%) and Black (66.1%) or Hispanic (21.5%) with a median age of 28 years old (interquartile range [IQR]: 22\u0026ndash;35 years). Most individuals were shot outside on the street (63.5%) or at a residence (23.2%). The median distance from the location of residence to the location of injury was 1.5 miles (IQR: 0.2-4 miles).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline characteristics of individuals with firearm injuries, 2019\u0026ndash;2022\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e \u003cp\u003eResidential Index of Concentration at the Extremes (ICE)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTertile 1: Higher Privilege\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTertile 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTertile 3: Higher Deprivation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo. of individuals (row percentage)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e802\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e268 (33.4)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e267 (33.3)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e267 (33.3)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResidence ICE racialized poverty index, median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.09 (-0.16, 0.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.19 (0.13, 0.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.09 (-0.12, -0.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.18 (-0.22, -0.16)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28 (22\u0026ndash;35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e28.5 (23-34.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e27 (22\u0026ndash;34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e28 (22\u0026ndash;36)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge group, years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e107 (13.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e24 (9.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e36 (13.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e47 (17.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20\u0026ndash;29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e353 (44.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e120 (44.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e129 (48.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e104 (39.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30\u0026ndash;39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e220 (27.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e86 (32.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e68 (25.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e66 (24.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e122 (15.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e38 (14.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e34 (12.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e50 (18.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e81 (10.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e37 (13.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22 (8.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e22 (8.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e721 (89.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e231 (86.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e245 (91.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e245 (91.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRace/ethnicity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlack\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e530 (66.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e151 (56.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e180 (67.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e199 (74.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHispanic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e172 (21.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e58 (21.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e59 (22.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e55 (20.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48 (6.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e38 (14.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8 (3.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2 (0.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29 (3.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14 (5.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12 (4.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3 (1.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eUnknown\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e23 (2.9)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e7 (2.6)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e8 (3.0)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e8 (3.0)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eInjury Specifics\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInjury ICE racialized poverty index, median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.12 (-0.18, 0.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.03 (-0.15, 0.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.11 (-0.17, 0.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.17 (-0.21, -0.12)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInjured at home\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e99 (12.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e33 (12.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e29 (10.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e37 (13.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e703 (87.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e235 (87.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e238 (89.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e230 (86.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInjured within residential census tract\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e202 (25.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e46 (17.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e57 (21.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e99 (37.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e600 (74.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e222 (82.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e210 (78.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e168 (62.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDistance from residence to injury location, median miles (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.5 (0.2, 4.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.4 (0.9, 9.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.2 (0.3, 3.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.7 (0.1, 1.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDistance from residence to injury location excluding injuries occurring at home, median miles (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.9 (0.6, 4.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.5 (1.7, 11.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.6 (0.5, 4.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.9 (0.2, 2.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInjury occurring\u0026thinsp;\u0026gt;\u0026thinsp;10 miles from home location\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e103 (12.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e66 (24.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e31 (11.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6 (2.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSite of injury\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResidence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e186 (23.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e61 (22.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e60 (22.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e65 (24.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStreet / Outside\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e509 (63.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e176 (65.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e165 (61.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e168 (62.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlace of business\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39 (4.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13 (4.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13 (4.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13 (4.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eUnknown\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e68 (8.5)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e18 (6.7)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e29 (10.9)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e21 (7.9)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear of Injury\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e166 (20.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e53 (19.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e54 (20.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e59 (22.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e261 (32.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e78 (29.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e82 (30.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e101 (37.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e206 (25.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e72 (26.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e83 (31.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e51 (19.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e169 (21.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e65 (24.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e48 (18.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e56 (21.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eAll values are frequencies and column percentages except Index of Concentration at the Extremes (ICE) deprivation index, age, and distances which are medians and interquartile ranges. \u0026ldquo;Other\u0026rdquo; race includes all other races.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThere were 99 individuals (12.3%) injured at their home address and 202 (25.2%) injured within their own neighborhood (i.e., census tract) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eSupplementary Table\u0026nbsp;1\u003c/span\u003e). Multivariable logistic regression models estimated that for each 10% increase in residential neighborhood deprivation, there was no difference in the odds of individuals being shot at their homes (OR\u0026thinsp;=\u0026thinsp;1.07; 95% CI\u0026thinsp;=\u0026thinsp;0.95\u0026ndash;1.21; p\u0026thinsp;=\u0026thinsp;0.29), but 32% higher odds of being shot within their residential census tract (OR\u0026thinsp;=\u0026thinsp;1.32; 95% CI\u0026thinsp;=\u0026thinsp;1.19\u0026ndash;1.47; p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). We observed a moderate, positive correlation (r\u003csub\u003es\u003c/sub\u003e = 0.35, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) between neighborhood privilege and distance between locations of residence and injury (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e1\u003c/span\u003eA), and only individuals residing in the most privileged areas were injured in neighborhoods with significantly higher levels of deprivation (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e1\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFactors associated with firearm injury at home address and within residential census tract by level of neighborhood deprivation, 2019\u0026ndash;2022\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eInjured at Home Address\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003eInjured Within Residential Census Tract\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMultivariable Model\u003c/p\u003e \u003cp\u003eOR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e [excluding unknown]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMultivariable Model\u003c/p\u003e \u003cp\u003eOR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e [excluding unknown]\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e99 (12.3)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e202 (25.2)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eICE residential racialized economic segregation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.07 (0.95\u0026ndash;1.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.32 (1.19\u0026ndash;1.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge group, years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.0006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13 (12.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e38 (35.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20\u0026ndash;29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e35 (9.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.75 (0.38\u0026ndash;1.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e74 (21.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.50 (0.30\u0026ndash;0.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30\u0026ndash;39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22 (10.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.71 (0.34\u0026ndash;1.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e46 (20.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.49 (0.29\u0026ndash;0.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e29 (23.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.98 (0.96\u0026ndash;4.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e44 (36.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.05 (0.60\u0026ndash;1.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRace/ethnicity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.008 [0.006]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.007 [0.007]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlack\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e63 (11.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e134 (25.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHispanic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16 (9.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.83 (0.46\u0026ndash;1.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e41 (23.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.02 (0.67\u0026ndash;1.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14 (29.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.54 (1.65\u0026ndash;7.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e17 (35.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3.45 (1.71\u0026ndash;6.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5 (17.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.76 (0.63\u0026ndash;4.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7 (24.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.34 (0.54\u0026ndash;3.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1 (4.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.34 (0.05\u0026ndash;2.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3 (13.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.44 (0.13\u0026ndash;1.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eLogistic regression models were used to estimate odds ratios (OR) and 95% confidence intervals (95% CI). Multivariable models were adjusted for age, race/ethnicity, and ICE residential racialized economic segregation index. In all models, the Index of Concentration at the Extremes (ICE) is assessed as a continuous variable. All ORs are reported as the change in risk per 0.1 unit change in ICE racialized economic segregation on a scale from 1 to -1.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e1\u003c/span\u003eC shows the locations of shooting incidents among our study population and displays census tract-level ICE racialized economic segregation scores for the city of Boston and surrounding areas. Each census tract containing a location of residence or injury in our study population was also included as a unique node in our directed network analysis. In total, 338 census tracts were represented in the network with 600 connections between census tracts (equal to the number of shootings) and 202 loops (i.e., individuals who resided and were injured within the same census tract). Of the network ties, 137 are multiple, meaning that the tie captures multiple shootings between the same two tracts (including within the same tract or loops). Most of these multiple ties are loops (N\u0026thinsp;=\u0026thinsp;111) with three tracts, all in the most deprived ICE quintile, recording 9 or more shootings of its own residents. Multiplicity in cross-neighborhood shootings is relatively less layered with the maximum value being 3 shootings across the same two neighborhoods for three pairs of tracts.\u003c/p\u003e \u003cp\u003eEliminating these multiple ties yields a simplified network comprising 574 ties between tracts and 91 loops. This simplified shooting network is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e2\u003c/span\u003eA with network characteristics detailed in \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eSupplementary Table\u0026nbsp;2\u003c/span\u003e and \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eSupplementary Fig.\u0026nbsp;3\u003c/span\u003e. In this diagram, 19 dyads are mutual (i.e., there is a reciprocal relationship between these nodes in the directed network) and 536 are asymmetric dyads (i.e., there is a one-way relationship between these nodes in the directed network). Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e2\u003c/span\u003eB shows the largest connected network component consisting of 253 nodes (75% of all census tracts) connected as a neighborhood of residence or injury in the shooting network. The size of nodes in this figure corresponds to nodal in-degree so that larger nodes are neighborhoods where more shootings occur. Node shade represents quintiles of neighborhood racialized economic segregation from most privileged (lightest color) to most deprived (darkest color). It is evident that the largest nodes are darker in color meaning that shootings occur most often in disadvantaged neighborhoods. The network is most densely connected around the large and dark nodes with fewer connections among light nodes showing high concentration of shooting events around the least privileged neighborhoods. This is confirmed by negative binomial regression models (\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eSupplementary Table\u0026nbsp;3\u003c/span\u003e), which estimate that for each 10% increase in residential deprivation, there was a 52% greater rate of shooting incidents in a neighborhood (IRR\u0026thinsp;=\u0026thinsp;1.52; 95% CI\u0026thinsp;=\u0026thinsp;1.47\u0026ndash;1.58; p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) and a 61% greater rate of neighborhood residents being shot (IRR\u0026thinsp;=\u0026thinsp;1.61; 95% CI\u0026thinsp;=\u0026thinsp;1.55\u0026ndash;1.66; p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001).\u003c/p\u003e \u003cp\u003eThe disparate density of ties within quintiles can also be seen in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e2\u003c/span\u003eC, which shows shooting ties internal to each quintile. There is a drastic and profound increase in connectedness within the fifth (most-deprived) quintile. The table shows the number of ties from all quintiles to all others normalized by the size of the sending quintile. The highest numbers in each row and column are boldfaced. The boldfaced values in column 5 (most deprived neighborhoods) show that individuals residing in all quintiles face the highest likelihood of being shot in the most deprived neighborhoods. The boldfaced values in columns show that people who are shot in quintiles 1 and 2 (the most privileged quintiles) have the highest chance of living in quintile 5. Note also that the number of ties within quintile 5 is nearly ten times higher than the next highest value. Most shootings are concentrated within this quintile: it serves as both residence and location of shooting of the victim. Shootings internal to this quintile exceed shootings in any other cell of the table by a margin of 10 to nearly 140.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study found that neighborhoods of residence and injury among all shootings were highly connected, and individuals were most likely to be shot in highly deprived neighborhoods irrespective of their level of home neighborhood racialized economic segregation. Compared to individuals living in the most deprived neighborhoods, those living in more privileged neighborhoods were just as likely to be shot at their homes, but more likely to be injured farther from their homes, outside their own neighborhoods, and in a neighborhood with significantly higher levels of racialized economic deprivation. Higher deprivation neighborhoods were associated with both increased numbers of shooting incidents and more residents being injured by firearms. These highly deprived neighborhoods were most central, more dense, and far more likely to be both the residence and site of shooting as compared to other tracts in the network depicting connections between home locations and shooting locations.\u003c/p\u003e \u003cp\u003eThese results are consistent with previous findings[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] that firearm injuries cluster in census tracts with high rates of poverty and are more likely to take place closer to home, and often within the home, compared to other types of traumatic injuries. However, this is the first analysis, to our knowledge, that examines how the consequences of racialized economic deprivation on firearm violence reach across a metropolitan area. We found that social conditions in the most-deprived subset of neighborhoods contributed to firearm violence in multiple ways. First, the residents of those neighborhoods were most likely to be shot \u003cem\u003ein any context\u003c/em\u003e, whether high- or low-deprivation. Second, high deprivation neighborhoods were the contexts in which any Boston resident was most likely to be shot, regardless of their home neighborhood social conditions.\u003c/p\u003e \u003cp\u003eOur finding that individuals living in less-deprived neighborhoods were injured farther from home and in neighborhoods of significantly higher deprivation and that shootings are overwhelmingly concentrated in the most deprived neighborhoods shows that contextual risk for firearm violence is predominantly clustered in a small number of high-risk neighborhoods, regardless of where the victim resides. In parallel, our network analysis also shows that residents of the most deprived neighborhoods face the highest risks of being shot within their own neighborhoods or other spaces facing similar levels of deprivation. Furthermore, residents of low-income neighborhoods are also most likely to be victims of shootings in relatively well-off environments, suggesting that the effects of neighborhood exposures are carried by residents wherever they go. These findings are important from the intervention standpoint because they suggest that social and economic investments in this subset of deprived neighborhoods might produce outsized benefits both in mitigating violence occurring in those neighborhoods as well as perhaps to their residents as they navigate other neighborhoods.[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] Many effective strategies for community violence intervention rely on trained frontline workers serving as credible messengers within a hospital or community setting.[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e] These hospital-based violence intervention programs and neighborhood trauma and grief support services are critical for residents of communities most impacted by violence, but must also be available for victims livings in higher privilege neighborhoods.\u003c/p\u003e \u003cp\u003eComparing home and injury locations can also help guide individual-level interventions. Our study found that, similar to other investigations,[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] 75% of firearm injuries occurred outside an individual\u0026rsquo;s residential census tract and 13% occurred more than 10 miles from home. However, we observed high variability in these metrics by level of residential privilege; one quarter of individuals living in the highest privilege neighborhoods were injured more than 10 miles from home compared to only 2% of individuals living in the most deprived neighborhoods. Being injured close to home can create additional challenges for survivors once they discharge from hospital, including an erosion of safety and security, increased sense of risk for subsequent assaults, and heightened anxiety and symptoms of post-traumatic stress disorder.[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] These results also have implications for trauma systems planning and injury epidemiology research, both of which often rely on home location as a proxy for injury location.[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] Caution is warranted when interpreting geospatial injury data based on home locations, particularly when considering the true burden of gun violence in the most deprived communities. These data suggest a need for more accurate incident location data collection in trauma patients to ensure equity in trauma care and to develop appropriate interventions.[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eMobility patterns could also partially explain our findings. Prior research has shown that home neighborhood social position is associated with daily mobility, i.e., residents of more deprived neighborhoods travel shorter distances in a typical day than residents of more privileged neighborhoods.[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e] Thus, the risks of living in a neighborhood with high firearm violence risk may be amplified by the difficulty of spending time in safer neighborhoods. While reducing violence risk in highly deprived neighborhoods should be a central priority, another appropriate goal would be addressing the employment opportunity gaps, transportation options, and other factors that create mobility disparities.\u003c/p\u003e \u003cp\u003ePrevious investigations have used social network analysis methods to demonstrate how gun violence spreads through social networks from one individual to another in a process of social contagion.[\u003cspan additionalcitationids=\"CR39\" citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e] Geospatial network analyses have reported limited spatial diffusion[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e] of violence across neighborhoods over time, with gun violence persistently concentrated in communities of color with high rates of poverty, particularly in specific micro places or \u0026ldquo;hot spots\u0026rdquo;.[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e] Violence across neighborhoods in a city is often the result of retaliatory incidents by rival gangs or groups of minority youth across turf boundaries.[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e] In our network analysis, we found that neighborhoods were highly connected overall as locations of residence and injury, with the most deprived neighborhoods displaying the highest levels of centrality and connectivity within the network. The limited number of reciprocal relationships between neighborhoods (i.e., mutual dyads) in the shooting network as well as the concentration shootings in the most deprived quintile of neighborhoods shows that the majority of firearm injuries in our study population flowed in one direction from relatively advantaged communities of residence to injury in deprived neighborhoods or occurred among individuals residing and injured within the same deprived community. Overall, our results indicate that individuals in our study population followed three predominant patterns: (1) being shot in their own highly deprived neighborhoods, (2) traveling from more privileged neighborhoods to a highly deprived neighborhood where they were injured and, to a smaller degree, (3) residents of deprived neighborhoods facing greater risks of being shot in relatively well-off neighborhoods.\u003c/p\u003e \u003cp\u003eLimitations\u003c/p\u003e \u003cp\u003eThere are limitations to consider when interpreting the results of this study. First, our population included individuals shot in a single US metro area, which limits the generalizability of our findings to populations in other US cities. Second, the nonfatal firearm injuries included in our study population presented to only a single Boston hospital, albeit the Boston hospital where the vast majority of gunshot wound patients present.[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] There were likely additional nonfatal injuries presenting to other medical centers in the city that were not included in our analysis and therefore certain neighborhoods may be underrepresented in our analysis as locations of injury or residence due to being outside the catchment area for the hospital where this study took place. Third, while only individuals with a known home and injury address listed in either medical records or death records were included for analysis, we cannot be certain that individuals actually resided at the listed addresses at the time of their firearm injury.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis cross-sectional study found that neighborhoods with high levels of racialized poverty were most implicated both as senders and receivers of violence in the spatial network of firearm assaults and homicides within the city of Boston, and individuals were more likely to be shot in these neighborhoods regardless of their home neighborhood. Individuals in our study population living in the most deprived neighborhoods were just as likely to be shot at their homes as those living in more privileged neighborhoods but were significantly more likely to be shot near their homes and within their own neighborhoods. Individuals from more privileged areas were shot farther from their homes and in census tracts with higher racialized poverty compared to their residential neighborhoods. The common denominator across high deprivation neighborhoods is that they have been disproportionately impacted by structural racism through official and unofficial policies and practices. These results suggest that investments in urban neighborhoods most impacted by gun violence can have outsized impacts on firearm injury prevention across a metro area.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eFinancial Support\u003c/h2\u003e \u003cp\u003eSupported by the Department of Emergency Medicine, Boston Medical Center.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eConflict of Interest Disclosure:\u003c/h2\u003e \u003cp\u003eThe authors have no potential conflicts of interest to disclose.\u003c/p\u003e \u003c/p\u003e \u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e \u003cp\u003e \u003cb\u003eSection\u003c/b\u003e: Dr Pino had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eKegler SR, Simon TR, Zwald ML, et al. 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Nat Hum Behav. 2020;2020/12/01(12):1294\u0026ndash;302.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGreen B, Horel T, Papachristos AV. Modeling contagion through social networks to explain and predict gunshot violence in Chicago, 2006 to 2014. JAMA Intern Med. 2017;177(3):326\u0026ndash;33.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGill LM, Fox AM. Using social network analysis to examine gun violence. J Forensic Sci. 2022;67(6):2230\u0026ndash;41.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePapachristos AV, Braga AA, Hureau DM. Social networks and the risk of gunshot injury. J Urban Health. 2012;89:992\u0026ndash;1003.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCohen J, Tita G. Diffusion in homicide: Exploring a general method for detecting spatial diffusion processes. J Quant Criminol. 1999;15:451\u0026ndash;93.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBraga AA, Papachristos AV, Hureau DM. The concentration and stability of gun violence at micro places in Boston, 1980\u0026ndash;2008. J Quant Criminol. 2010;26:33\u0026ndash;53.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePapachristos AV. Murder by structure: Dominance relations and the social structure of gang homicide. Am J Sociol. 2009;115(1):74\u0026ndash;128.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-urban-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jurh","sideBox":"Learn more about [Journal of Urban Health](https://www.springer.com/journal/11524)","snPcode":"11524","submissionUrl":"https://www.editorialmanager.com/jurh","title":"Journal of Urban Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-8874419/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8874419/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eUnderstanding the factors associated with the relationship between where individuals are shot and where they reside can improve our knowledge of local risk environments and inform violence intervention planning. The objective of this study was to describe the association between residential racialized economic segregation and firearm injury location, conceptualized as spatial networks of shooting incidents within and between neighborhoods. This cross-sectional study linked hospital and death records to examine firearm assaults and homicides in or among residents living within the Boston metro area. Racialized economic segregation was measured using the \u0026lsquo;Index of Concentration at the Extremes\u0026rsquo; for race and income at each residential and injury address. For the 802 individuals with gunshot wounds included for analysis, individuals living in more privileged neighborhoods were more likely to be injured: (1) farther from their homes (median [IQR] miles, 3.4[0.9\u0026ndash;9.7] for lowest deprivation tertile [T1] vs 0.7[0.1\u0026ndash;1.9] for highest deprivation tertile [T3], p\u0026thinsp;=\u0026thinsp;0.0001), (2) outside their residential census tract (T1:82.8% vs T3:62.9%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), and (3) in significantly higher deprivation neighborhoods (p\u0026thinsp;=\u0026thinsp;0.0001). Spatial network analysis revealed that of the 338 census tracts represented in the data, deprived communities displayed higher centrality and connectivity in shooting networks, meaning that firearm violence was more concentrated within and across such neighborhoods. Individuals residing in all communities faced the highest likelihood of being shot in the most deprived neighborhoods, and individuals shot within the highest privilege neighborhoods were most likely to reside in the highest deprivation communities. Interventions prioritizing the most deprived neighborhoods are critical to preventing the spread of gun violence between populations.\u003c/p\u003e","manuscriptTitle":"Racialized economic segregation and firearm injuries: a network analysis of home and incident locations","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-05 09:28:20","doi":"10.21203/rs.3.rs-8874419/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorAssigned","content":"","date":"2026-02-19T00:53:58+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Urban Health","date":"2026-02-13T12:36:13+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-urban-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jurh","sideBox":"Learn more about [Journal of Urban Health](https://www.springer.com/journal/11524)","snPcode":"11524","submissionUrl":"https://www.editorialmanager.com/jurh","title":"Journal of Urban Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"ff31162f-53ca-45ab-afdd-0cf1a6353f22","owner":[],"postedDate":"March 5th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-03-05T09:28:21+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-05 09:28:20","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8874419","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8874419","identity":"rs-8874419","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}