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. 2025 Aug 8;12:48. doi: 10.1186/s40621-025-00590-0

Epidemiology of fatal and nonfatal community firearm violence in New York City, 2019–2023

Loren L Adams 1,✉, Vanya Jones 1, Daniel W Webster 1, Michael R Desjardins 1,2, Cassandra K Crifasi 1
PMCID: PMC12333219  PMID: 40781646

Abstract

Background

Endemic levels of community firearm violence in United States cities disproportionately burden certain sociodemographic groups. Nonfatal injuries are an understudied aspect of firearm violence. Police data in a large and heterogenous place like New York City (NYC) provide the unique opportunity to use a single data source to measure fatal and nonfatal community firearm violence.

Methods

The study analyzed epidemiologic trends in fatal and nonfatal community firearm violence using the publicly available New York Police Department (NYPD) Shooting Incident Dataset from 2019 to 2023. The analyses tabulated shooting victims by sociodemographic variables (age group, race and ethnicity, and sex) and case fatality rates, mapped shootings by census tract, and described social determinants of health (SDOH)—poverty, educational attainment, unemployment—in the census tracts where shootings occurred.

Results

From 2019 to 2023, the annual average number of shooting victims in NYC was 1,578. Shootings doubled from 2019 to 2020 though trended downward from 2021 to 2022 to 2023. Four out of five shootings were nonfatal. Males, people ages 18-24, and non-Hispanic Black people in NYC experienced the greatest proportion of shootings. Shootings coincided with census tracts also experiencing above median percentages of adverse SDOH (i.e., highest levels of unemployment, poverty, and low educational attainment).

Discussion

Nonfatal injuries are an important component of community firearm violence to monitor. The findings identify inequities in community firearm violence by sex, age group, and race and ethnicity in NYC. One contributor to racial inequities in violence is the impact of place-based SDOH in certain NYC neighborhoods.

Conclusions

This study leverages NYPD data to estimate the full magnitude of violence by tracking nonfatal in addition to fatal injuries. The study emphasizes the need for researchers to go beyond individual demography and better understand the place-based social determinants of firearm violence.

Keywords: Community firearm violence, Epidemiology, Social determinants of health, GIS, Mapping

Introduction

Firearm violence is a pressing public health concern in the United States (U.S.) [1]. As the COVID-19 pandemic exacerbated many social ills, firearm violence also worsened to record high levels in the U.S., unseen since 1994 [2–4]. Comparing 2023 to 2019, firearm homicides increased 24% [3–5]. Though recent data suggest firearm violence may be trending downward, it is not down to pre-pandemic levels [4, 6]. Even so, U.S. cities face endemic community firearm violence, defined here as the intentional, interpersonal use of a firearm that results in death or nonfatal injury [7–10]. Given the high prevalence of community firearm violence, improved surveillance is necessary to monitor inequities in who is affected by this important public health problem.

Existing epidemiological evidence on firearm violence highlights at least two important considerations. First, many data sources are limited in their ability to accurately measure the full scope of firearm violence. Particularly, nonfatal firearm injuries are captured poorly, despite studies indicating there are many more nonfatal than fatal firearm injuries [11–18]. For instance, the Centers for Disease Control and Prevention’s (CDC) National Violent Death Reporting System (NVDRS) includes only fatal firearm injuries. Even the available NVDRS data are about two years behind because NVDRS is a secondary surveillance system that relies on state-level data on deaths from a combination of sources including death certificates, medical examiner and coroner records, and law enforcement reports [19, 20]. The CDC Web-based Injury Statistics Query and Reporting System (WISQARS) only reports nonfatal injuries at the national level; however, the CDC does not currently recommend the WISQARS data that come from the National Electronic Injury Surveillance System All Injury Program to be used for national nonfatal firearm injury prevalence estimates because of small sample of hospitals and wide confidence intervals [18, 19, 21, 22]. Additionally, emergency department (ED) datasets, often used to measure nonfatal firearm violence, are flawed. Datasets based on ED visits alone rely on International Classification of Diseases coding practices that often lead to misclassification of assaults as unintentional injuries and can have reporting lags of one to three years [18, 19, 23]. Second, current evidence reveals that firearm violence disproportionately burdens specific groups. Community firearm violence tends to impact non-Hispanic Black and Hispanic/Latino communities and disproportionately involves teen boys and young adult men, where small networks of people can be caught in cycles of violence perpetration, victimization, and trauma [3, 4, 10, 24–34]. In 2023, non-Hispanic Black Americans were 14 times more likely to die by firearm homicide than non-Hispanic White Americans [5]. Young non-Hispanic Black boys and men are heavily impacted—while non-Hispanic Black males ages 15 to 34 years made up 4% of the U.S. population, they represented 37% of firearm homicides in 2023 [5]. Further, the broader community is affected by the cycle of firearm violence; even if community members (e.g., families, loved ones, and neighbors) are not directly involved in violence, they can be indirect victims because witnessing firearm violence and hearing gunshots leads to lower community safety and perception of safety, and harms physical and mental health (e.g., stress and trauma) [10, 35–38].

There is an opportunity to explore police data to monitor the epidemiology of firearm violence in NYC. Although an imperfect data source, past studies have relied on local police data on shootings to study interpersonal firearm violence [8, 29, 39–49]. These studies on large U.S. cities regarded police data as useful because they are comprehensive (i.e., they capture both fatal and nonfatal shootings), timely, routinely collected for public dissemination, and uniquely record geographic location and some circumstances of shootings [19]. In the absence of an integrated hospital and police database of firearm injuries to capture the full scope of firearm violence, the New York Police Department (NYPD) Shooting Incident Dataset can elucidate epidemiologic trends of both fatal and nonfatal injuries [50]. This study will leverage police data to tabulate firearm violence to identify subpopulations affected by violence. The problem of community firearm violence is complex and by describing who is involved and impacted, the intent is not to criminalize nor stigmatize people nor communities of color who are disproportionately harmed [26, 29, 51]. Instead, the community firearm violence risks are place-based, going beyond the individual level to the broader social context including the effects of structural racism that leads to concentrated disadvantage [40, 44, 52]. This study contextualizes why racial inequities exist by exploring how select social determinants of health (SDOH) influence community firearm violence.

Methods

Overview

This descriptive, cross-sectional study used the NYPD Shooting Incident Dataset from 2019 to 2023 to investigate inequities in community firearm violence by sociodemographic factors, social determinants of health (SDOH), and place (i.e., census tracts), in New York City (NYC).

Data sources

This paper utilized the NYPD Shooting Incident Dataset. These data are de-identified and publicly available on NYC’s Open Data portal [50]. Each row in the dataset represents a single person who was shot in NYC and contains details of the shooting (e.g., date, time, location, and fatal versus nonfatal) and victim demography (i.e., age group, race and ethnicity, and sex). Data for selected SDOH (employment, poverty, and educational attainment) at the census tract level (proxies for neighborhoods; between 1,200 and 8,000 people) and were derived from the U.S. Census Bureau’s American Community Survey (ACS), five-year estimates (2019–2023).

Measures

The primary outcome of community firearm violence was determined using the NYPD Shooting Incident Dataset to measure interpersonal fatal and nonfatal shootings (hereafter, called shootings). The NYPD dataset excludes shootings caused by firearm suicide and attempt and unintentional firearm injury and death. The NYPD classified the race and ethnicity of people who were shot. The available race and ethnicity categories are American Indian/Alaskan Native, Asian/Pacific Islander, Black, Black Hispanic, White, and White Hispanic. The NYPD categorizes age as < 18, 18–24, 25–44, 45–64, and 65+ and sex as female and male.

The World Health Organization (WHO) defines social determinants of health as “the conditions in which people are born, grow, work, live and age, and the wider forces that shape the conditions of daily life” [53]. Guided by the WHO Social Determinants of Health and Health Inequity Framework, we focused on social determinants of health inequities in three domains—employment, income, and education—that reflect individual- and community-level socioeconomic position that stratifies people into different levels of advantage and thus shapes their access to resources and ultimately their health and well-being [10, 53–55]. The three ACS SDOH variables for NYC census tracts were unemployment, poverty, and low educational attainment, operationalized as follows [56]. The unemployment variable was defined as the percent of the census tract that is unemployed in the civilian labor force population ages ≥ 16. The poverty variable was the percentage of people of all ages with income below 150% of the federal poverty level in the census tract. The low educational attainment variable was the percent of adults ages ≥ 25 years without a high school diploma in the census tract. There are 2,327 census tracts in NYC according to the 2020 Decennial Census boundaries.

Statistical analysis

The analysis tabulated demography—age group, race and ethnicity, and sex—for shooting victims from 2019 to 2023. Because counts of shooting victims from the NYPD were meant to capture all fatal and nonfatal firearm injuries in New York City rather than a sample drawn from a population, inferential statistical tests were not performed on the count data [33]. Shootings were aggregated by year and fatal versus nonfatal for 2019–2023. We did a spatial join using the point-level shooting coordinates to aggregate them to the census tract where they occurred; all shootings in the NYPD Shooting Incident Dataset from 2019 to 2023 were assigned to a NYC census tract. The NYC census tracts were divided into quartiles based on lowest to highest percent for each SDOH variable (unemployment, poverty, low educational attainment). The number of shootings and relative percent of total shootings that happened in a census tract experiencing varying burdens of the selected SDOH was analyzed for each year, 2019–2023. We generated (1) a map of shootings using natural breaks and (2) three bivariate choropleth maps—based on median cut point for the SDOH and mean cut point for shootings—that explored the geographic relationship between shootings and SDOH variables. Bivariate mapping uses combined color schemes to display two variables on a single map, allowing for the simultaneous visualization of their spatial relationship and patterns. Analyses were conducted in R v4.2.3 and maps were created in ArcGIS Pro v3.5.0. This study was reviewed and determined to exempt from human subjects research by the Johns Hopkins Bloomberg School of Public Health Institutional Review Board.

Results

In 2023, 88.3% of shooting victims were males (Table 1). Adults ages 25–44 represented the highest percentage of total shootings victims (55.4%) in 2023, followed by ages 18–24 (23.8%) (Table 1). Non-Hispanic Black people represented the largest percentage (63.7%) of shooting victims, followed by Hispanic people (30.6%) (Table 1). Annually, from 2019 to 2023, the pattens of shooting victims by sex, age group, and race and ethnicity were relatively consistent whereby males, people ages 25–44 followed by ages 18–24, and non-Hispanic people represented the greatest proportion of shooting victims (Table 1).

Table 1.

Number and percent of people who were shot in NYC, by sex, age group, and race and ethnicity 2019–2023

2019
(n = 967)		 2020 
(n = 1,948)		 2021
(n = 2,011)		 2022
(n = 1,716)		 2023
(n = 1,250)
Variable n (%) n (%) n (%) n (%) n (%)
  Sex
   Female 102 (10.5) 201 (10.3) 199 (9.9) 212 (12.4) 145 (11.6)
   Male 865 (89.5) 1,747 (89.7) 1,812 (90.1) 1,504 (87.6) 1,104 (88.3)
   Unknown - - - - 1 (0.1)
 Age group
   0–17 67 (6.9) 133 (6.8) 156 (7.8) 158 (9.2) 115 (9.2)
   18–24 272 (28.1) 576 (29.6) 601 (29.9) 482 (28.1) 298 (23.8)
   25–44 520 (53.8) 1,069 (54.9) 1,083 (53.9) 895 (52.2) 692 (55.4)
   45–64 97 (10.0) 157 (8.1) 157 (7.8) 165 (9.6) 118 (9.4)
   65+ 7 (0.7) 12 (0.6) 13 (0.6) 14 (0.8) 24 (1.9)
   Unknown 4 (0.4) 1 (0.1) 1 (0.0) 2 (0.1) 3 (0.2)
  Race and ethnicity
   American Indian/Alaskan Native (NH) - - - 1 (0.1) 1 (0.1)
   Asian/Pacific Islander (NH) 20 (2.1) 38 (2.0) 27 (1.3) 50 (2.9) 36 (2.9)
   Black (NH) 676 (69.9) 1,380 (70.8) 1,412 (70.2) 1,158 (67.5) 796 (63.7)
   Hispanic 242 (25.0) 491 (25.2) 532 (26.5) 468 (27.3) 383 (30.6)
    Black Hispanic  76 (7.9)  157 (8.1)  240 (11.9)  161 (9.4)  149 (11.9)
    White Hispanic  166 (17.2)  334 (17.1)  292 (14.5)  307 (17.9)  234 (18.7)
   White (NH) 28 (2.9) 39 (2.0) 40 (2.0) 38 (2.2) 30 (2.4)
   Unknown 1 (0.1) - - 1 (0.1) 4 (0.3)
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Within variable percentages do not always total to 100% due to rounding

NH = Non-Hispanic

Data source: NYPD Shooting Incident Dataset [50]

Annual trends and case fatality

In 2023, there were a total of 1,250 shooting victims in NYC, 260 fatal and 990 nonfatal (Fig. 1). Comparing 2020 to 2019, shootings in NYC doubled (Fig. 1). The number of firearm shootings decreased about 27.2% from 2022 to 2023 and decreased 37.8% from 2021 to 2023 (Fig. 1). The mean annual case fatality rate (i.e., number of fatal shootings divided by total shootings) from 2019 to 2023 was 19.9% (data not shown). There were far more nonfatal than fatal shootings in NYC; annually, on average, over 80% of shootings were nonfatal from 2019 to 2023 (data not shown).

Fig. 1.

Fig. 1

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Annual number of fatal and nonfatal shootings in NYC, 2019–2023 Data source: NYPD Shooting Incident Dataset [50]

Geographic distribution

Table 2 shows the number of people shot in census tracts stratified by the quartiles of each social determinant of health. In 2023, 45% of people were shot in census tracts with the highest quartile of unemployment (Table 2). 56% of people were shot in census tracts with the highest quartile of poverty in 2023 (Table 2). 44% of people were shot in census tracts with the highest quartile of low educational attainment in 2023 (Table 2). These patterns were consistent each year, 2019–2023. On average, from 2019 to 2023, 44% of people were shot in census tracts with the highest quartile of unemployment, 52% in census tracts with the highest quartile of poverty, and 40% with the highest quartile of low educational attainment. Nearly one quarter of people were shot in census tracts with the highest quartile of all three SDOH (i.e., the census tract was in quartile four for all three SDOH) in 2023 (data not shown).

Table 2.

Number and percent of people shot in census tracts with the selected social determinants, by quartile, 2019–2023

2019
(n = 967)		 2020
(n = 1,948)		 2021
(n = 2,011)		 2022
(n = 1,716)		 2023
(n = 1,250)
Variable n (%) n (%) n (%) n (%) n (%)
   Unemployment1
      Quartile 1 (lowest unemployment) 133 (13.8) 248 (12.7) 207 (10.3) 253 (14.7) 151 (12.1)
      Quartile 2 171 (17.7) 339 (17.4) 373 (18.5) 272 (15.9) 175 (14.0)
      Quartile 3 260 (26.9) 451 (23.2) 529 (26.3) 440 (25.6) 351 (28.1)
      Quartile 4 (highest unemployment) 396 (41.0) 892 (45.8) 867 (43.1) 734 (42.8) 568 (45.4)
      No data 7 (0.7) 18 (0.9) 35 (1.7) 17 (0.9) 5 (0.4)
   Poverty2
      Quartile 1 (lowest poverty) 88 (9.1) 121 (6.2) 154 (7.7) 157 (9.1) 92 (7.4)
      Quartile 2 124 (12.8) 278 (14.3) 307 (15.3) 225 (13.1) 171 (13.7)
      Quartile 3 252 (26.1) 501 (25.7) 508 (25.3) 419 (24.4) 288 (23.0)
      Quartile 4 (highest poverty) 496 (51.3) 1,032 (53.0) 1,008 (50.1) 898 (52.3) 694 (55.5)
      No data 7 (0.7) 16 (0.8) 34 (1.7) 17 (0.9) 5 (0.4)
   Low educational attainment3
      Quartile 1 (highest educational attainment) 80 (8.3) 167 (8.6) 182 (9.1) 177 (10.3) 93 (7.4)
      Quartile 2 243 (25.1) 401 (20.6) 391 (19.4) 336 (19.6) 243 (19.4)
      Quartile 3 292 (30.2) 580 (29.8) 568 (28.2) 489 (28.5) 361 (28.9)
      Quartile 4 (lowest educational attainment) 345 (35.7) 785 (40.3) 838 (41.7) 699 (40.7) 548 (43.8)
      No data 7 (0.7) 15 (0.8) 32 (1.6) 15 (0.9) 5 (0.4)
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1Unemployment quartile (Q) ranges: Q1 0– ≤4.1%; Q2 > 4.1– ≤6.8%; Q3 > 6.8– ≤7.90%; Q4 > 7.9–100%

2Poverty quartile ranges: Q1 0– ≤13.3%; Q2 > 13.3–22.1%; Q3 > 22.1– ≤34.2%; Q4 > 34.2–100%

3Low educational attainment quartile ranges: Q1 0– ≤8.4%; Q2 > 8.4–14.7%; Q3 > 14.7– ≤23.7%; Q4 > 23.7–70.2%

Within variable percentages do not always total to 100% due to rounding

Data sources: Shootings - NYPD Shooting Incident Dataset [50]. Social determinants of health - U.S. Census Bureau American Community Survey, 5-year estimates (2019-2023) [57]

There were census tracts in each borough with higher violence represented by the darkest purple color on the map (Fig. 2).

Fig. 2.

Fig. 2

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Number of fatal and nonfatal shooting victims in NYC, by census tract, 2019-2023. This figure illustrates the combined number of fatal and nonfatal shooting victims in NYC by census tract. The cut points in the legend are determined by jenks (natural breaks) classification scheme. Data source: NYPD Shooting Incident Dataset [50]

Bivariate choropleth maps in Figs. 3, 4 and 5 show the geographic relationship between firearm violence and each social determinant. Hereafter, “low” shootings refer to below mean number of shootings per census tract while “high” shootings refer to above mean shootings. In the bivariate legend (Figs. 3, 4 and 5), the light gray indicates where there were both low shootings and low percentage of the social determinant in the given census tract, while the purple indicates high shootings and high percentage of the social determinant. In all three maps (Figs. 3, 4 and 5), most census tracts were shaded light gray (low shootings and low percentage of the SDOH in the given census tract) or shaded purple (high number of shootings and high percentage of the SDOH), suggesting there is a geographic relationship between shootings and each social determinant. The percentage of census tracts with concordant levels of shootings and social determinant (i.e., low/low (gray census tracts) and high/high (purple census tracts) was as follows for each map: (1) shootings and unemployment—62.6%, (2) shootings and poverty—67.2%, and (3) shootings and low educational attainment—62.1%.

Fig. 3.

Fig. 3

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Relationship between number of shootings and percentage unemployed in NYC, by census tract, 2019-2023. Bivariate choropleth map. Data sources: (1) Shootings: NYPD Shooting Incident Dataset, 2019-2023 [50], (2) Unemployment status (Unemployed population ages ≥ 16 years in the civilian labor force): U.S. Census Bureau American Community Survey, 5-year estimates (2019-2023) [57]. Mean shootings = 4; median unemployment = 6.8%. Census tracts with missing data (n=89) are white on the map

Fig. 4.

Fig. 4

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Relationship between number of shootings and percentage living below poverty level in NYC, by census tract, 2019-2023. Bivariate choropleth map. Data sources: (1) Shootings: NYPD Shooting Incident Dataset, 2019-2023 [50], (2) Poverty (people of all ages living below 150% the federal poverty level: U.S. Census Bureau American Community Survey, 5-year estimates (2019-2023) [57]. Mean shootings = 4; median poverty = 22.1%. Census tracts with missing data (n=88) are white on the map

Fig. 5.

Fig. 5

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Relationship between number of shootings and percentage without diploma in NYC, by census tract, 2019-2023. Bivariate choropleth map. Data sources: (1) Shootings: NYPD Shooting Incident Dataset, 2019-2023 [50], (2) Low educational attainment (No high school diploma among adults ages ≥ 25 years): U.S. Census Bureau American Community Survey, 5-year estimates (2019-2023) [57]. Mean shootings = 4; median low educational attainment = 14.7%. Census tracts with missing data (n=84) are white on the map

Discussion

In NYC between 2019 and 2023, there were nearly 1,600 shootings each year. More than three-quarters of these incidents were nonfatal shootings, highlighting the importance of examining both fatal and nonfatal incidents to best understand the burden of community firearm violence. Our study found a geographic relationship between shootings and select SDOH. Generally, census tracts that had a high number of shootings also had a high percentage of unemployment, poverty, and low educational attainment (only one bivariate spatial relationship was observed at a time). This underscores the importance of studying community firearm violence more granularly than citywide, in addition to investigating social underpinning of violence.

In the U.S., there is no single administrative data source that provides a comprehensive account of community firearm violence [8, 14, 19, 23, 42, 47, 49, 58, 59]. There are two primary types of data sources on community firearm violence—police and hospital data. The main drawback of hospital data is that they are coded primarily for medical record and billing purposes—not public health surveillance—and shooting intent (i.e., unintentional, undetermined, self-harm, assault, and legal intervention) is often misclassified and inaccurate [19, 23]. Police data are much timelier, which aids with the near real-time study of community firearm violence.

Three studies highlight reasons for potential missingness in the two types of data sources. In a Philadelphia study that compared hospital trauma registry data and police data, researchers found that over a ten-year period, the police data contained twice the number of firearm assaults than the trauma registry [42]. Trauma registry data are expected to be incomplete because they exclude more minor firearm injuries treated at non-trauma center hospitals; whereas police data might capture minor firearm injuries not treated in a medical setting and firearm deaths pronounced at the scene of the shooting. In nearly all states, healthcare providers must report gunshot wounds to police which is another reason the Philadelphia police data might have captured more assaults [42, 60, 61]. A study in Louisville that integrated and overlapped hospital and police data found that the police database was significantly more successful in capturing 93% of fatal firearm injuries compared to the hospital data that captured about 60% of fatal firearm injuries. The law enforcement versus hospital database missed 20% versus 6% of nonfatal injuries, respectively. One key caveat is that reporting of firearm injuries by healthcare providers to police is not required in Kentucky, so this might partially explain why the police data missed more nonfatal injuries and the findings in another setting might be different than the Louisville study. An Indianapolis study that also linked police and clinical records for nonfatal firearm assaults similarly found that both types of data are imperfect alone and likely would be best integrated [14]. However, unlike the Louisville study, they found police data might have a more complete record of nonfatal firearm assaults; clinical data alone captured less than half. They also importantly note that clinical records often lack the location of the injury, which is one of the main reasons this study used NYPD data for fatal and nonfatal injuries in NYC [14, 62].

Due to challenges with community firearm violence data sources, estimating the epidemiology of both fatal and nonfatal firearm injuries can be challenging in many studies. There tends to be a focus on fatal firearm injuries despite studies, including the present study, finding many more nonfatal than fatal injuries [11–19]. One nationwide study combined fatal firearm injury data from the CDC with nonfatal injury data from the Healthcare Cost and Utilization Project - Nationwide Emergency Department Sample [13]. They found over two times more nonfatal than fatal shootings [13]. This study found that in NYC, in 2023, there were four times more nonfatal than fatal shootings. The directionality of these findings is the same—more nonfatal shootings but the current findings might be more complete since the NYPD has a dedicated database of shootings, including nonfatal shootings [16]. The Louisville study created a combined dataset of overlapping and non-overlapping firearm injuries from hospital (inpatient and ED) and police data. Their combined dataset estimated 20.4% fatality among the interpersonal firearm injuries. This is consistent with the current study that also found on average, 19.3% of interpersonal shootings were fatal.

While it is important to look at the demography of people injured by firearms, this study also highlights the link between community firearm violence and social determinants of health in NYC census tracts. The differences in the burden of firearm violence by race and ethnicity are inequitable because they represent injustice in the social, political, and economic environments to which Black people are exposed. Existing literature emphasizes the association of a myriad of place-based social and structural determinants of firearm violence in Black communities including but not limited to racial residential segregation, concentrated neighborhood disadvantage,​ and blighted built environment [26, 44, 45, 52, 63–71]. Future work should further explore other SDOH, especially by capturing how multiple facets of structural racism produce violence over safety [10, 40, 72–75].

Limitations

The results of our study should be interpreted considering several limitations. First, the NYPD Shooting Incident Dataset is imperfect. While it provides a recent and comprehensive account of fatal and nonfatal interpersonal shootings, it lacks motive and victim-perpetrator relationship data. Stricter community firearm violence definitions include shootings causing harm to an acquaintance or stranger but exclude shootings of an intimate partner or family member [9]. This dataset cannot measure community firearm violence exclusive of gender-based and domestic violence. The NYPD also excludes shooting intent categories like unintentional and self-harm shootings, which aligns to the community firearm violence definition. Officer-involved shootings are excluded. All these shooting intents are important, especially to compare to burden and inequities of community firearm violence but were beyond the scope of this paper. The NYPD might not know if a person ultimately dies from a shooting injury that was initially nonfatal. To that point, we were unable to explore severity of injury nor experience of long-term, firearm-related physical injuries. There are shootings unknown to police and missing in the dataset. The demographic and spatial patterns and NYC could somewhat differ if the NYPD dataset had the universe of all community firearm violence; there might be variability in these patterns by other data sources (e.g., hospital data in NYC) [49]. In the NYPD Shooting Incident Dataset, police classify race and ethnicity. This is a potential threat to internal validity, in a study interested in racial inequities. This further motivated us to go beyond using race alone and look at place-based social determinants that help explain observed racial inequities in community firearm violence. Next, it is unclear whether and how the NYPD data collection and reporting methodology has changed over time. Another limitation is modifiable areal unit problem; simply aggregating to different spatial units can modify the findings because the units are somewhat arbitrary [76, 77]. However, it is well established that community firearm violence happens in hyperlocal areas of cities [3, 8, 29, 33, 52, 78]. This justifies analyses at the census tract level, though census tract is not the only level of geography appropriate to understand community firearm violence and its social and structural antecedents. Future work can explore other and more granular geographies [79]. Finally, because of the availability of a police dataset for fatal and nonfatal firearm injuries in NYC, this study focused on one city and might not be generalizable to other metropolitan areas.

Implications

Health departments have traditionally relied on hospital data for nonfatal shootings and other sources like vital statistics for fatal shootings [80, 81]. This study demonstrates that police data can be used to monitor community firearm violence in NYC and potentially in other U.S cities. Community firearm violence is a public health problem, and efforts are being made to improve public health data infrastructure for community firearm violence [19, 23, 82, 83]. A possible outcome of this study is health departments, police departments, and other violence prevention partners can foster new data sharing relationships and collaborate on public health approaches to community firearm violence. This could include matching or combining police and public health/hospital data to create a more comprehensive and higher quality database of shooting victims to prioritize community firearm violence research and intervention efforts [13, 14, 46–49]. The goal is not to rely on law enforcement-only approaches to violence nor ignore historic and present-day harmful effects of policing and mass incarceration; instead, the goal is to promote multi-sector approaches to violence prevention and public safety. Future research should recognize that police data are potentially underutilized and study the utility of police data compared to non-police data sources, in NYC and other cities [8, 14, 23, 42]. Finally, future work can explore other spatial patterns in community firearm violence to inform place-based interventions (e.g., elucidate how case fatality rates differ spatially, patterns within census tracts or at smaller geographies, and spatial cluster analysis) [79].

Conclusion

The findings identify stark inequities in community firearm violence by race and ethnicity, age, and sex in NYC. Importantly, they also shift our attention to social and structural drivers of community firearm violence. Overall, violence interventions need to tackle root causes of violence and injustices in social environments where violence persists. We need to address the impacts of social determinants of health, at every level of society—structural, community, interpersonal, and individual. This research highlights the need for violence interventions beyond individual behavior change. Public health approaches to prevent community firearm violence at the population-level must advance health equity, dismantle structural racism, and direct resources to communities most harmed. These interventions must be developed in collaboration with the most impacted communities and tailored to their unique needs and lived experiences to promote safety, health, and well-being.

Acknowledgements

Not applicable.

Abbreviations

NYC

New York City

SDOH

Social determinants of health

CDC

Centers for Disease Control and Prevention

NVDRS

National Violent Death Reporting System

ED

Emergency department

ACS

American Community Survey

WISQARS

Web–based Injury Statistics Query and Reporting System

NYPD

New York Police Department

WHO

World Health Organization

Author contributions

LLA, CKC, VJ, DWW conceptualized the study and methodology. LLA retrieved and processed the data. LLA analyzed the data and wrote the original draft. MRD and LLA created the maps. LLA, CKC, VJ, DWW, MRD reviewed and edited multiple drafts.

Funding

This research was supported, in part, by a grant from the Robert Wood Johnson Foundation.

Data availability

The New York Police Department Shooting Incident dataset analyzed in the current study are available at https://data.cityofnewyork.us/Public-Safety/NYPD-Shooting-Incident-Data-Historic-/833y-fsy8/about_data The 2019-2023 U.S. Census Bureau American Community Survey 5-Year estimates analyzed during the current study are available at https://www.census.gov/data/developers/data-sets/acs-5year.html.

Declarations

Ethics approval and consent to participate

This study was reviewed and determined to be not human subjects research by the Johns Hopkins Bloomberg School of Public Health Institutional Review Board. The approval form is available upon request. All methods were performed in accordance with the ethical standards as laid down in the Declaration of Helsinki and its later amendments or comparable ethical standards.

Consent for publication

Not applicable, although see ethical approval and consent to participate above.

Competing interests

The authors declare no competing interests.

Footnotes

Publisher’s note

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References

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Associated Data

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

Data Availability Statement

The New York Police Department Shooting Incident dataset analyzed in the current study are available at https://data.cityofnewyork.us/Public-Safety/NYPD-Shooting-Incident-Data-Historic-/833y-fsy8/about_data The 2019-2023 U.S. Census Bureau American Community Survey 5-Year estimates analyzed during the current study are available at https://www.census.gov/data/developers/data-sets/acs-5year.html.

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