Prevalence of exposure to someone else’s firearm violence, threats, and risky behavior among a national sample of young people in the United States

Abstract

This article provides prevalence rates for a wide range of types of exposure to someone else’s firearm violence, threats, and risky behavior among youth and young adults across several different demographic and structural characteristics. Data are from the Growing Up with Guns study of 5,311 participants recruited through the AmeriSpeak Panel. Data were collected from September 2023 to January 2024. Eligibility included U.S. residents who were ages 10–34 years old and proficient in either English or Spanish. A majority of participants (69.5%) reported exposure to someone else’s firearm violence, threats, or risky firearm behavior—either interpersonal or self-directed—in their lifetime. Rates of exposure were high across age categories, ranging from 48.0% of 10–17 year olds to 80.3% of 25–34 year olds. Odds of exposure also varied by race, sexual minority identity as well as deficits in social determinants of health (SDOH). Such wide-spread exposure introduces opportunities to prevent shootings before they occur and can inform the development of bystander interventions targeting those who are witnesses or otherwise know about another person’s firearm violence, threats or risky behaviors.

Full text

Frontiers in Public Health 01 frontiersin.org
Prevalence of exposure to
someone else’s firearm violence,
threats, and risky behavior among
a national sample of young
people in the UnitedStates
KimberlyJ.Mitchell
1*, VictoriaBanyard
2, Bruce G.Taylor
3,
Elizabeth A.Mumford
3, WeiweiLiu
3 and Heather A.Turner
1
1 Crimes Against Children Research Center, University of New Hampshire, Durham, NH, UnitedStates,
2 Department of Social Work, Rutgers University, New Brunswick, NJ, UnitedStates, 3 NORC at the
University of Chicago, Chicago, IL, UnitedStates
This article provides prevalence rates for a wide range of types of exposure to
someone else’s firearm violence, threats, and risky behavior among youth and
young adults across several dierent demographic and structural characteristics.
Data are from the Growing Up with Guns study of 5,311 participants recruited
through the AmeriSpeak Panel. Data were collected from September 2023 to
January 2024. Eligibility included U.S. residents who were ages 10–34 years old
and proficient in either English or Spanish. A majority of participants (69.5%)
reported exposure to someone else’s firearm violence, threats, or risky firearm
behavior—either interpersonal or self-directed—in their lifetime. Rates of exposure
were high across age categories, ranging from 48.0% of 10–17 year olds to 80.3%
of 25–34 year olds. Odds of exposure also varied by race, sexual minority identity
as well as deficits in social determinants of health (SDOH). Such wide-spread
exposure introduces opportunities to prevent shootings before they occur and
can inform the development of bystander interventions targeting those who are
witnesses or otherwise know about another person’s firearm violence, threats
or risky behaviors.
KEYWORDS
firearm violence, bystanders, mental health, intervention, prevention, gun violence,
suicide
1 Introduction
Dozens of leading professional organizations, including the American Bar Association and
the American Medical Association, have endorsed a public health approach to gun violence
prevention (1). Central to a public health approach is the early identication of situations that
may lead to gun violence and understanding how and when people hear about it. Bystanders
may play an important role—they are third parties who witness violence, who may know about
students carrying rearms to school, or who may know about someone’s plan to use a rearm
for violence as expressed on social media. Violence prevention initiatives increasingly involve
these third parties in prevention training, seeing them as potentially important gatekeepers or
facilitators in prevention eorts. ere is already evidence that bystander action can reduce
intimate partner violence, sexual assault, stalking, and bullying in young adults and adolescents
(2–11). And, most recently, there is increasing attention to bystander action as a possible youth
violence prevention strategy (12).
OPEN ACCESS
EDITED BY
Dabney Evans,
Emory University, UnitedStates
REVIEWED BY
Melissa Osborne,
Kennesaw State University, UnitedStates
Dan Romer,
University of Pennsylvania, UnitedStates
*CORRESPONDENCE
Kimberly J. Mitchell
kimberly.mitchell@unh.edu
RECEIVED 18 June 2024
ACCEPTED 14 March 2025
PUBLISHED 03 April 2025
CITATION
Mitchell KJ, Banyard V, Taylor BG,
Mumford EA, Liu W and Turner HA (2025)
Prevalence of exposure to someone else’s
firearm violence, threats, and risky behavior
among a national sample of young people in
the UnitedStates.
Front. Public Health 13:1451268.
doi: 10.3389/fpubh.2025.1451268
COPYRIGHT
© 2025 Mitchell, Banyard, Taylor, Mumford,
Liu and Turner. This is an open-access article
distributed under the terms of the Creative
Commons Attribution License (CC BY). The
use, distribution or reproduction in other
forums is permitted, provided the original
author(s) and the copyright owner(s) are
credited and that the original publication in
this journal is cited, in accordance with
accepted academic practice. No use,
distribution or reproduction is permitted
which does not comply with these terms.
TYPE Original Research
PUBLISHED 03 April 2025
DOI 10.3389/fpubh.2025.1451268

Mitchell et al. 10.3389/fpubh.2025.1451268
Frontiers in Public Health 02 frontiersin.org
We know very little about this approach as a possible
prevention tool for situations involving risk for gun violence.
However, given the success of bystander-focused prevention
approaches for reducing other risk behaviors and changing social
norms toward anti-violence (13), a logical next step is to explore
the roles that bystanders might play in relation to reducing risky
rearm use (14). To do that, we rst need to understand the
context and frequency of youth and young adults’ exposure to
someone else’s gun violence, threats and risky behaviors using
general population samples that are national in scope. We also
need to better understand how social locations, including aspects
of social identity and features of community spaces where young
people live, relate to their likelihood of gun violence exposure, as
that may help determine how relevant they may perceive such
prevention eorts.
1.1 People can beexposed to firearm
violence, threats, and risky behaviors in a
variety of ways
Researchers note that the eld of rearm violence research has
most oen focused on direct victims and perpetrators of rearm
violence and outcomes including fatalities and injuries (15). ese are
critical public health questions. However, a growing body of work has
begun to examine the negative collateral eects of exposure to rearm
violence beyond direct victims and perpetrators (15). is has at times
involved limited denitions of exposure, such as studies using census
tract rates of rearm fatalities (16). Indeed, recent studies are moving
beyond dening exposure as seeing, hearing or knowing someone who
has been shot to understanding exposure to risky rearm use and
exposure to threats of rearm use as well (17). Bancalari’s review
describes a broader variety of ways exposure has been operationalized.
Specically, seeing gunre is one main way exposure is assessed with
self-reports, with some studies also adding hearing gunre. A few
studies also include knowing someone who was the victim of rearm
violence, knowing someone who carries a gun, or general levels of
awareness of gun violence in one’s community. Bancalari and
colleagues summarized three important categories of exposure:
bystanders (who hear or witness shots or witness someone being
threatened with a gun), vicarious exposure (knowing someone who
was shot or knowing someone exhibiting risky gun carrying), and
community (awareness of gun violence in one’s neighborhood).
Lennon and colleagues separated indirect exposure (hearing gunshots
or knowing someone who was a victim) and direct (witnessing a
shooting, being threatened with a gun, and being a victim directly by
being shot or injured). While this measure included threats, it also
confounded victimization of oneself along with witnessing a shooting
as part of the same category. Beseler and colleagues (18) also included
exposure to someone using guns for self-directed violence. Quimby
and colleagues (19) expanded this topic to focus on exposure to guns
(rather than violence) to include a variety of dimensions of gun access
as a risk factor. Researchers call for the continued development of
broader measures of exposure to rearm violence (15), including
understudied dimensions like exposure to guns and someone else’s
self-directed gun violence, and exposure to threats of gun violence. e
current study sought to examine a broader set of rearm exposure
items in a national general population sample.
1.2 The role of social determinants of
health in understanding exposure to
firearm violence
Firearm exposures are also unequally distributed across locations,
with communities bearing the adversity burden of greater decits in
social determinants of health (SDOH) also experiencing greater
rearm violence exposure (16). Studies of rearm violence broadly,
and exposure specically, are also turning to community models of
risk and resilience (20). ese models note constructs beyond the
individual that may inuence exposure risk and suggest innovative
prevention and intervention strategies. is is consistent with calls to
study gun violence exposure within models of systematic inequality
(15). Measuring SDOH is one strategy for enhancing models of risk
and protective factors. SDOH are conditions in the places where
people live, learn, work, and play that aect a wide range of health
and quality-of life-risks and outcomes, including mental health (21).
ere is a growing awareness of the importance of including
assessments that will capture and integrate such conditions into
public health research. Such measurement permits analysis of the
causes and conditions of diering rates of public health problems and
helps overcome the limitations of assessing only demographic
markers (22). Specically, SDOHs help identify modiable factors
that can bethe object of policy and program interventions to prevent
gun violence. ere is also a growing knowledge base about the
importance of SDOHs across the social ecology in relation to mental
health that shows risk for distress related to low income,
unemployment, neighborhood problems, social identity group
membership (23, 24).
1.3 Current study
e current study provides prevalence rates for a wide range of
dierent situations involving exposure to rearm violence, threats of
rearm violence, and risky rearm behavior (e.g., inappropriate
carrying or possession) among youth and young adults across several
dierent demographics and SDOH characteristics. e current study
builds on previous work by analyzing sub-types of exposure—rearm
violence, threats, and risky behaviors—reecting contextual SDOH
measures using a nationally representative general population sample
covering a wide developmental age range.
2 Materials and methods
2.1 Participants
Data were collected from 5,311 youth and young adults who were
part of the AmeriSpeak panel for the nationally representative
Growing up With Guns Study, an oen-used panel for health research
(25–28). e survey was administered from September 2023 to
January 2024. From the panel households, individual residents who
were eligible to participate in the study were youth or young adults,
ages 10–34 years old, who can speak or read either English or Spanish.
Demographic details of the weighted sample are in Table1.

Mitchell et al. 10.3389/fpubh.2025.1451268
Frontiers in Public Health 03 frontiersin.org
TABLE1 Sample demographic characteristics by report of any exposure to firearm violence, threats or risky behavior.
Characteristic All participants n (%) No firearm violence
exposure n (%)
Any firearm violence
exposure n (%)
p value
All participants N = 5,311 1,410 (30.5) 3,901 (69.5) –
Demographics
Age
10–17 1,189 (30.2) 551 (52.0) 638 (48.0) <0.001
18–24 853 (28.2) 168 (19.7) 685 (80.3)
25–34 3,269 (41.6) 691 (22.1) 2,578 (77.9)
Mean age
Sex assigned at birtha
Female 3,282 (49.6) 791 (28.1) 2,491 (71.9) <0.001
Male 1908 (47.9) 559 (31.9) 1,349 (68.1)
Intersex 17 (0.3) 3 (12.5) 14 (87.5)
Prefer not to answer 104 (2.3) 57 (55.1) 47 (44.9)
Gender identitya
Cisgender female 3,174 (47.4) 784 (29.3) 2,390 (70.7) <0.001
Cisgender male 1870 (47.1) 559 (32.5) 1,311 (67.5)
Gender minority 166 (3.6) 29 (14.9) 137 (85.1)
Missing 101 (1.9) 38 (38.3) 63 (61.7)
Sexual identity
Heterosexual 4,264 (81.7) 1,253 (33.4) 3,011 (66.6) <0.001
Sexual minority 1,047 (18.3) 157 (17.3) 890 (82.7)
Racea
White 2,803 (62.9) 812 (33.0) 1991 (67.0) <0.001
Black or African American 946 (14.2) 176 (21.2) 770 (78.8)
Asian 535 (7.1) 178 (34.5) 357 (65.5)
American Indian or Alaska
Native
63 (0.8) 9 (15.3) 54 (84.7)
Native Hawaiian 20 (0.4) 7 (34.0) 13 (66.0)
Other race 281 (4.1) 66 (25.7) 215 (74.3)
Two or more races 523 (7.5) 105 (23.4) 418 (76.6)
Prefer not to answer 140 (3.0) 57 (39.2) 83 (60.8)
Ethnicity
Not Hispanic or Latino 4,149 (77.6) 1,077 (29.6) 3,072 (70.4) 0.16
Hispanic of Latino 1,104 (22.4) 305 (32.7) 799 (67.3)
Structural and Social
Annual household incomea
Less than $30,000 1,273 (22.8) 305 (28.8) 968 (71.2) <0.001
$30,000 to under $60,000 1,373 (24.2) 331 (25.5) 1,042 (74.5)
$60,000 to under $100,000 1,260 (24.2) 338 (31.3) 922 (68.7)
$100,000 or more 1,365 (28.0) 416 (34.6) 949 (65.4)
Missing 40 (0.9) 20 (55.3) 20 (44.7)
Type of community
Urba n 2,260 (36.9) 537 (26.9) 1723 (73.1) 0.005
(Continued)

Mitchell et al. 10.3389/fpubh.2025.1451268
Frontiers in Public Health 04 frontiersin.org
2.2 Procedures
Randomly selected AmeriSpeak panelists were sent a
description of the study by email, and an invitation to complete
the survey. Incentives of $20 were provided for those participants
who completed the survey. With over 55,000U.S. residents, the
AmeriSpeak panel is designed to be representative of the
U.S. household population using probability-based sampling.
Details of recruitment into the AmeriSpeak panel are published
elsewhere (29). The survey completion rate among those sampled
for this study was 33.0%.
NORC at the University of Chicago’s Institutional Review Board
approved the project for data collection. e research team obtained
voluntary and informed consent from all participants either by the
participant consenting verbally for those completing a survey by phone
or by clicking a time-stamped box for those completing the online version
of the survey. Participants under the age of 18 provided assent aer
caregiver consent was obtained. Cognitive testing of the survey instrument
(n = 5 with some youth under 18 years old and some over age 18),
including questions about rearm exposures, helped ensure wording was
appropriate for the age range of participants.
2.3 Measures
Exposure to rearm violence, threats, or risky rearm behavior.
Wedeveloped a series of 10 questions for the current study that
queried exposures to someone else’s rearm violence, threats and
risky behaviors. Items were drawn from prior work about youth gun
violence exposure which included focus groups and cognitive
interviews with youth as young as age 10 (18). ese were designed
to expand upon previous measures that tend to focus mainly on
seeing or being present when someone was shot (30) or census tract
numbers of rearm homicides (16). Before these questions,
participants were told that wewere only asking about things they
may have seen or heard about in real life—not things they may have
seen on TV, in a movie, on the news or in a video game. Wegrouped
these questions together based on the type of situation into the
following ve categories. e questions asked, “Have youever…”
(yes/no):
Seen someone shooting a gun in a public place (like on the street,
or a parking lot, school, or store)?
Heard (but not seen) a gun being shot in a public place (like the
streets, parking lots or stores)?
Firearm threats
• Heard or seen anyone youknow talking or posting about hurting
someone else with a gun?
• Seen someone or heard anyone threaten to hurt someone else
with a gun?
Risky rearm access/possession
• Heard someone of any age talk about getting a gun or having a
gun when they aren’t supposed to?
• Known someone of any age who had a gun when or where they
were not supposed to?
TABLE1 (Continued)
Characteristic All participants n (%) No firearm violence
exposure n (%)
Any firearm violence
exposure n (%)
p value
Suburban 2,311 (48.8) 666 (33.1) 1,645 (66.9)
Rural 740 (14.3) 207 (30.7) 533 (69.3)
High neighborhood disorder
No 3,781 (73.0) 1,058 (32.5) 2,723 (67.5) <0.001
Ye s 1,530 (27.0) 352 (24.8) 1,178 (75.2)
Poor home conditions
No 4,107 (78.1) 1,223 (34.4) 2,884 (65.6) <0.001
Ye s 1,204 (21.9) 187 (16.3) 1,017 (83.7)
Not having enough money to pay bills
No 4,386 (82.5) 1,161 (30.1) 3,225 (69.9) 0.36
Ye s 925 (17.5) 249 (32.3) 676 (67.7)
Skip meals or eat less because did not have enough money for food
No 4,163 (79.3) 1,140 (31.6) 3,023 (68.4) 0.01
Ye s 1,148 (20.7) 270 (26.1) 878 (73.9)
Last saw dentist more than 2 years ago (or never)
No 4,209 (79.3) 1,208 (33.3) 3,001 (66.7) <0.001
Ye s 1,102 (20.7) 202 (19.3) 900 (80.7)
Non-victimization adversity: M
(SE)
2.12 (0.04) 1.02 (0.05) 2.61 (0.05) <0.001
Weighted percentages, unweighted N. Row percentages. aAlso signicantly dierent with missing values dropped.

Mitchell et al. 10.3389/fpubh.2025.1451268
Frontiers in Public Health 05 frontiersin.org
• Known about anyone bringing a gun to school or work? Do not
include situations where someone carried a gun to work because
that was part of their job, like police or security ocers.
• Been surprised or worried because someone youknew was
carrying a gun (not for their job)?
Someone else’s self-directed rearm violence
• Heard or seen anyone youknow talking or posting about hurting
themselves with a gun?
• Known anyone who has killed or tried to kill themselves
with a gun?
Variables were created to reflect any exposure within each of
these five aggregate categories as well as exposure to any of the
10 items.
Demographic characteristics. Demographic characteristics
measured include age, birth sex, gender identity, sexual identity, race,
and ethnicity. Details of categories within each are reported in
Table1.
Social and structural determinants of health measures are usually
drawn from secondary data sources (31) or involve screening tools
designed for in-person assessments in clinical settings (32). e
current measures were designed to build on these screening tools
using self-report to document decits social and structural
determinants of health across four domains:
Economic instability includes 2 items, adapted from the Youth Risk
Behavior Survey (32), for example: not having enough money to pay
bills in the past 12 months? Response options ranged from 1 (never)
to 5 (always).
Social context includes lifetime non-victimization adversity due to
non-violent traumatic events and chronic stressors and was measured
using 10 items (33) (for example, serious illnesses, accidents, family
homelessness) which were combined into a count variable for current
analyses (M = 2.12, SD = 2.15, α = 0.75).
Health care. Participants were asked, “When was the last time
yousaw a dentist for a check-up, exam, teeth cleaning, or other dental
work?” (34). Response options were: in the past 12 months, between
1 and 2years ago, more than 2 years ago, and Ihave never been to the
dentist. is was coded into a new variable reecting more than 2
years ago (or never) versus more recently.
Neighborhood and built environment.
Physical home environmental conditions included eight items
adapted from the American Academy of Family Physicians Social Needs
Screening Tool (35) covering problems where you currently live.
Participants are told to think about their permanent place of residence,
not a dorm room or other temporary housing and answer the following
questions: bugs everywhere, mold, lead paint or lead pipes, not enough
heat, the oven or stove does not work, there are no smoke detectors or
they do not work, water leaks, frequent loss or no electricity. Response
options for each were yes/no and summed to create a total count and
then dichotomized at 1 standard deviation above the mean or higher to
reect poor home conditions.
Physical neighborhood environmental conditions included 12
items adapted from Perkins and colleagues (36) to be more
meaningful to youth. Items measure residents’ perceptions of the
severity of dierent neighborhood conditions. Participants were
asked to rate each one as to whether it was: (0) no problem, (1) a
minor problem, or (2) a serious problem in their neighborhood (“by
neighborhood we mean the street you live on and a few streets
around it”): for example, gangs, grati, drugs, homelessness. Items
were summed to create a total neighborhood disorder score and then
dichotomized at one standard deviation above the mean or higher to
reect high neighborhood disorder.
2.4 Data analysis
We applied statistical weighting to adjust the data to US
census benchmarks to account for selection probabilities
(balanced by age, sex, race/ethnicity, education, and region) and
participant level non-response to the survey (using a response
propensity approach calculating the conditional probability that
a particular respondent completed the survey given observed
covariates) (37). Wederived the sampling weights using the final
panel weight used in all AmeriSpeak studies and the probability
of selection for the sampled panel members in our specific study
on firearm violence (non-response adjusted).
Missing data were minimal (3% or less) and conservatively
coded as “no exposure” for the main firearm violence questions.
This amounts to 51 cases with missing data on the main firearm
exposure measures. All analyses were also conducted with these
51 cases dropped and the results were the same both with these
cases included and coded as “0” as they were when dropped.
Missing data on four demographic characteristics are noted in
Table 1 and these participants were dropped in multivariate
analyses: sex at birth (n = 104), gender identity (n = 101),
household income (n= 40), and race (n = 140) for a total n of 271
dropped from multivariate analyses.
Demographic and SDOH characteristics were compared by any
exposure to rearm violence using two-way tabulations with tests of
independence for complex survey designs. en, prevalence rates and
95% condence intervals are reported for dierent types of rearm
violence exposure—both overall and for three age categories:
10–17 years olds, 18–24 year olds, and 25–34 year olds. Next, using
logistic regression analyses, we present adjusted odds ratios for
dierent demographic and SDOH characteristics for exposure to each
of the ve aggregate types of rearm exposure: (1) saw a shooting in a
public place, (2) interpersonal rearm violence threats, (3) heard (but
did not see) rearm shots in a public place, (4) knowledge of
inappropriate access/possession of rearms, and (5) knowledge of
someone’s self-directed rearm violence.
3 Results
Seven in 10 participants (69.5% of the sample) reported any
exposure to rearm violence, threats or risky behavior in their lifetime
using the 10-item denition as elded in this study (Table 1).
Signicant demographic dierences for any rearm violence exposure
were noted. Exposure was more common among older participants,
females or those intersex at birth, participants identifying as gender
minority, sexual minority, Black or African American, American
Indian or Alaska Native, or as two or more races. Dierent structural
and social factors were also signicantly related to rearm violence
exposure—namely, living in lower income households, in urban

Mitchell et al. 10.3389/fpubh.2025.1451268
Frontiers in Public Health 06 frontiersin.org
communities, in neighborhoods self-classied as having high disorder,
in a home with poor living conditions; having to skip meals or eat less
due to a lack of money for food, lack of dental care, and
non-victimization adversity.
3.1 Prevalence and types of lifetime firearm
violence exposures—overall and by age
As depicted in Figure1, lifetime exposure to rearm violence took
various forms which were grouped into ve main categories: (1) seeing
a shooting in a public place (12.5%), (2) hearing (but did not seeing)
rearm shots in a public place (45.4%), (3) exposure to interpersonal
rearm violence threats (24.7%), (4) risky access/possession of
rearms (46.7%), and (5) someone else’s self-directed rearm violence
(27.0%). Rates for specic types of exposures within each of these
categories are detailed in Table2.
Lifetime exposure to each type of firearm violence
significantly differed by age with the youngest participants (ages
10–17) being significantly less likely to report each experience
than older participants. Prevalence rates and 95% confidence
intervals by age are detailed in Table3. The two older age groups
(ages 18–24 and 25–34) were statistically similar to each other
across all types of firearm violence exposure except for hearing
but not seeing firearm shots, knowing someone who brought a
firearm to school or work, and hearing or seeing someone they
knew talking or posting about hurting themselves with a firearm
which were more common among the 18–24 age group compared
to the 25–34 group. The oldest age group was significantly more
likely than the 18–24 participants to know someone who killed
or tried to kill themselves with a firearm.
3.2 Adjusted odds of five dierent types of
firearm violence exposures by
demographic characteristics and SDOH
deficits
e odds of having ever seen someone shoot a rearm in a public
place were signicantly higher for older participants compared to
participants aged 10–17 years (see Table4). Black or African American
participants and American Indian or Alaska Native had higher odds
in comparison to White participants of seeing a gun being shot in a
public place. Participants living in poor home conditions, in
neighborhoods with high disorder, and having to skip meals or eat less
do a lack of money were more likely to have seen a shooting compared
with those who did not live under such conditions. For each additional
non-victimization adversity indicated, there was a 1.23 increase in
odds of having seen a shooting. Lifetime exposure to interpersonal
rearm violence threats was higher for sexual minority, Black or
African American, American Indian or Alaska Native, and those who
identied with two or more races compared to participants without
that racial identity, respectively. Living in an area with high
neighborhood structural disorder and experiencing non-victimization
adversity were associated with elevated odds of lifetime exposure to
rearm violence threats. Having heard (but not seen) rearm shots in
a public place was higher for Black or African American participants,
those living in poor home or neighborhood conditions, and those who
indicated experiences of non-victimization adversity.
Similar patterns were noted for elevated odds of lifetime exposure
to risky rearm carrying or possession, with the additional elevated
odds for respondents living in rural communities and living in higher
income households (Table5). Knowledge of someone else’s self-directed
violence with a rearm did not show the same racial identity
FIGURE1
Ways Young People are Exposed to Firearm Violence, Threats, and Risky Behavior.

Mitchell et al. 10.3389/fpubh.2025.1451268
Frontiers in Public Health 07 frontiersin.org
dierences as the other types of exposure, i.e., this form of exposure
over respondents’ lifetimes was distributed similarly across individuals
of dierent racial identity. However, elevated odds of knowing
someone at risk of self-directed rearm violence were identied for
sexual minority participants and those living in rural communities.
Odds of lifetime exposure to someone else’s self-directed rearm
violence were also higher for those living in higher income households.
Hispanic participants were signicantly less likely than non-Hispanic
participants to have known someone who had or was thinking about
using a rearm for self-harm. Living in poor neighborhood conditions,
having to skip meals due to a lack of money, and non-victimization
adversity were also associated with increased lifetime odds of this type
of exposure.
4 Discussion
Findings from the Growing up With Guns Study oers one of the
rst assessments of the prevalence of lifetime exposure to a wide range
of dierent situations involving rearm violence, threats, and risky
behavior in a general population sample from the UnitedStates. Rates
of exposure were high across age categories, ranging from 48% of
10–17 year olds to 80.3% of 25–34 year olds. Importantly, the
measures of exposure were broad, allowing for the study of an array
of experiences including bystander, vicarious, and community roles,
as described by Bancalari and colleagues (15), and also including
exposure to someone else’s threat or use of guns for self-directed
violence. is is a signicant measurement contribution of the current
study as it builds on previous critiques and recommendations.
Findings from this population-based, nationally representative data
provide insights into future opportunities for the development of
primary and secondary prevention and intervention eorts that may
beapplicable to a broad spectrum of youth and young adults who
represent high-risk for rearm violence exposures across dierent
developmental age groups.
e current study revealed high rates (more than 4 in 10
participants) of having seen shooting in a public place, the closest
proximity to rearm violence exposure measured in the current study.
is is a scenario which could place people who are bystanders in
physical danger. Data also revealed high rates of hearing rearm shots
in public places, including almost three in 10 youth; a situation found
to have negative consequences especially for young children (38).
ese are the more well-researched forms of rearm violence
exposure. e current study, however, also found high rates of
exposure (one in four participants) to visual or written interpersonal
rearm violence threats and almost one in two participants had
knowledge of risky access to or possession of a rearm. ese
situations present potential opportunities to prevent shootings as
bystanders can call for help and potentially keep rearms from being
used. is is consistent with recent studies showing a dierence
between completed school shootings and those that were averted due
to reports by friends and acquaintances (39).
e current study also highlighted both high rates of exposure to
someone else’s rearm use in suicidal behaviors. One in ve
participants knew someone who had killed or tried to kill themselves
with a rearm and 9.9% had heard or seen someone they know talking
or posting on social media about hurting themselves with a rearm.
is is critical from a public health perspective as the likelihood of
death by suicide is increased when someone has access to a rearm
(40, 41). is data suggests another potential point of bystander
intervention when friends work to connect at-risk peers to crisis
hotlines and services. Understanding and building strengths among
dierent groups of young people to educate them on alternatives to
rearm use is an important investment for prevention.
TABLE2 Prevalence rates and 95% confidence intervals for dierent types of exposure to gun violence, threats, and risky behaviors.
Type of firearm violence exposure All participants n (%) SE 95% confidence interval
Saw someone shoot rearm in public place 771 (12.5) 0.6 11.3, 13.7
Heard (but did not see) rearm shots in public place 2,575 (45.4) 0.9 43.6, 47.2
Interpersonal rearm violence threats (any) 1,450 (24.7) 0.8 23.2, 26.3
Seen or heard someone threaten to hurt someone else with rearm 1,181 (19.7) 0.7 18.4, 21.2
Heard or seen anyone youknow talking/posting about hurting someone
else with rearm
705 (12.4) 0.6 11.2, 13.6
Risky access/possession of rearms (any) 2,670 (46.7) 0.9 44.9, 48.6
Heard someone talk about getting or having a rearm when they were
not supposed to
1,514 (25.7) 0.8 24.2, 27.3
Known someone who had rearm when or where were not supposed to 1,545 (25.9) 0.8 24.4, 27.5
Surprised or worried because someone youknow was carrying a rearm
(not part of job)
1,027 (17.1) 0.7 15.8, 18.5
Known someone who brought rearm to school or work (not part of
job)
1,212 (22.4) 0.8 20.9, 24.0
Someone else’s self-directed rearm violence (any) 1,540 (27.0) 0.8 25.5, 28.7
Heard or seen anyone youknow talking/posting about hurting
themselves with rearm
548 (9.9) 0.6 8.9, 11.1
Known someone who killed or tried to kill themselves with rearm 1,298 (21.9) 0.7 20.5, 23.4
Any of the above 3,901 (69.5) 0.9 67.8, 71.3

Mitchell et al. 10.3389/fpubh.2025.1451268
Frontiers in Public Health 08 frontiersin.org
TABLE3 Prevalence rates and 95% confidence intervals for dierent types of exposure to gun violence, threats, and risky behaviors by age category.
Type of gun violence exposure 10–17 year olds (n = 1,189) 18–24 year olds (n = 853) 25–34 year olds (n = 3,269)
n (%) SE 95% CI n (%) SE 95% CI n (%) SE 95% CI
Saw someone shoot a gun in a public place 106 (7.1) 0.9 5.5, 9.1 143 (15.4) 1.5 12.6, 18.6 522 (14.4) 0.7 13.0, 16.0
Heard (but did not see) gun shots in a public place 383 (27.1) 1.6 24.1, 30.3 476 (56.3) 2.1 52.1, 60.4 1716 (51.3) 1.1 49.1, 53.4
Interpersonal gun threats (any) 205 (14.1) 1.2 11.8, 16.6 270 (29.3) 1.9 25.7, 33.2 975 (29.3) 1.0 27.3, 31.3
Saw or heard someone threaten to hurt someone else
with a gun
147 (9.7) 1.0 7.9, 11.9 213 (23.1) 1.8 19.8, 26.7 821 (24.7) 0.9 22.9, 26.6
Heard or saw anyone youknow talking/posting about
hurting someone else with a gun
121 (8.7) 1.0 6.9, 10.9 141 (15.2) 1.5 12.5, 18.4 443 (13.1) 0.7 11.7, 14.6
Risky access/possession of guns (any) 396 (28.9) 1.6 25.8, 32.2 488 (55.5) 2.1 51.3, 59.7 1786 (53.7) 1.1 51.6, 55.8
Heard someone talk about getting or having a gun
when they were not supposed to
228 (15.3) 1.2 13.1, 17.9 275 (30.3) 1.9 26.7, 34.2 1,011 (30.2) 1.0 28.3, 32.2
Knew someone who had a gun a when or where were
not supposed to
180 (12.7) 1.2 10.5, 15.2 278 (30.4) 1.9 26.7, 34.3 1,087 (32.4) 1.0 30.4, 34.4
Knew someone who brought a gun to school or work
(not part of job)
174 (12.9) 1.2 10.6, 15.5 260 (29.9) 1.9 26.3, 33.8 778 (24.3) 0.9 22.5, 26.1
Surprised or worried because someone youknew was
carrying a gun (not part of job)
106 (8.5) 1.0 6.7, 10.8 187 (20.6) 1.7 17.5, 24.1 734 (21.0) 0.9 19.3, 22.7
Someone else’s self-directed gun violence (any) 164 (12.7) 1.2 10.5, 15.4 280 (32.8) 2.0 29.0, 36.9 1,096 (33.5) 1.0 31.5, 35.5
Heard or saw anyone youknew talking/posting about
hurting themselves with a gun
71 (5.3) 0.8 3.9, 7.2 122 (14.0) 1.4 11.4, 17.1 355 (10.5) 0.7 9.2, 11.9
Known someone who killed or tried to kill themselves
with a gun
120 (8.9) 1.0 7.1, 11.1 216 (24.9) 1.8 21.5, 28.7 962 (29.3) 1.0 27.4, 31.2
Any of the above 638 (48.0) 1.8 44.4, 51.6 685 (80.3) 1.7 76.7, 83.4 2,578 (77.9) 0.9 76.1, 79.7

Mitchell et al. 10.3389/fpubh.2025.1451268
Frontiers in Public Health 09 frontiersin.org
TABLE4 Adjusted odds of lifetime exposure to interpersonal firearm threats and shots/shootings by participant demographic and structural and social characteristics.
Saw someone shoot a firearm in public place Exposure to interpersonal firearm threats Heard (but did not see) firearm shots in
public place
Odds ratio (95% CI) p value Odds ratio (95% CI) p value Odds ratio (95% CI) p value
Demographic
Gender and sexual identity
Female (ref) 1.0 1.0 1.0
Male 1.13 (0.89, 1.43) 0.32 1.11 (0.92, 1.35) 0.28 1.05 (0.89, 1.24) 0.54
Gender minority 1.63 (0.86, 3.10) 0.13 1.54 (1.00, 2.39) 0.05 1.72 (1.09, 2.74) 0.02
Sexual minority 0.96 (0.73, 1.27) 0.77 1.29 (1.02, 1.61) 0.03 1.14 (0.91, 1.41) 0.25
Age
10–17 (ref) 1.0 1.0 1.0
18–24 1.79 (1.21, 2.64) 0.004 1.80 (1.34, 2.43) <0.001 2.84 (2.22, 3.65) <0.001
25–34 1.44 (1.04, 2.00) 0.03 1.66 (1.30, 2.12) <0.001 2.03 (1.67, 2.48) <0.001
Race/Ethnicity
White (ref) 1.0 1.0 1.0
Black or African American 2.75 (2.04, 3.70) <0.001 2.48 (1.94, 3.17) <0.001 1.91 (1.53, 2.40) <0.001
Asian 1.27 (0.77, 2.09) 0.35 1.15 (0.83, 1.58) 0.39 0.81 (0.61, 1.05) 0.12
American Indian or Alaska Native 2.88 (1.13, 7.32) 0.03 2.60 (1.10, 6.13) 0.03 1.83 (0.85, 3.94) 0.12
Native Hawaiian 0.23 (0.03, 1.84) 0.17 0.74 (0.16, 3.34) 0.69 1.26 0.69
Other race 1.00 (0.58, 1.70) 0.99 1.17 (0.74, 1.85) 0.51 1.25 (0.84, 1.85) 0.27
Two or more races 1.30 (0.86, 1.96) 0.21 1.78 (1.30, 2.44) <0.001 1.26 (0.93, 1.71) 0.13
Hispanic ethnicity 1.33 (0.97, 1.81) 0.07 0.87 (0.67, 1.14) 0.33 0.99 (0.79, 1.24) 0.90
Structural and Social
Type of community
Urban (ref) 1.0 1.0 1.0
Suburban 0.70 (0.54, 0.91) 0.006 0.89 (0.73, 1.09) 0.26 0.77 (0.65, 0.91) 0.003
Rural 0.85 (0.59, 1.23) 0.39 0.92 (0.68, 1.23) 0.57 0.63 (0.48, 0.83) 0.001
Household Income
Less than $30,000 (ref) 1.0 1.0 1.0
$30,000 to <$60,000 1.35 (1.00, 1.81) 0.05 1.37 (1.06, 1.76) 0.01 1.37 (1.09, 1.73) 0.008
(Continued)

Mitchell et al. 10.3389/fpubh.2025.1451268
Frontiers in Public Health 10 frontiersin.org
Exposure to rearm violence is not evenly distributed across
social identity groups. Previous research has found dierential risk for
rearm violence by demographic variables including age, race, and
gender (42–44). Research also indicates place-based risk factors
including urban and rural locations, and community variables such as
poverty and crime rates (42, 45, 46). e current study also
documented demographic group dierences. Specically, sexual
minority participants had greater odds of rearm violence exposure
overall. is was not only for use of rearms in incidents of self-
directed violence, which is consistent with the higher risk of self-
directed violence among sexual minority communities (47), but also
elevated exposure to interpersonal rearm violence threats. Similarly,
Black and African American and American Indian or Alaska Native
had elevated odds of exposure to interpersonal rearm violence,
threats or risky behavior. African American children have the highest
rates of rearm mortality overall (5.7 per 100,000) (48) and the
current study suggests this is true of exposure to rearm violence as
well. Findings are in line with national data on rearm violence
exposure among Black, as well as American Indian or Alaska Native
adults (49). Firearm violence is yet another stressor that contributes
to the burden of health disparities faced by these communities (50,
51), communities which oen already have higher rates of depression
and anxiety (52, 53). Healthcare systems, including emergency rooms,
are increasingly playing a role in identifying rearm violence risk and
may bean important point of intervention for these groups who have
also faced historic and systematic oppression and discrimination.
Findings also highlight the importance of taking a life course
perspective to better prevent and intervene in dierent types of rearms
exposures. A developmental or life course perspective highlights the
importance of considering how age-related life stages—and characteristics
of social interaction, situational contexts, and choice-making over time
(54)–may shape risk and protective factors for rearm violence. A life
course perspective focuses on connections between adolescence and two
critical developmental time periods surrounding it–both childhood and
young adulthood (55). Underlying this perspective is the idea that no
developmental stage can beunderstood in isolation from others. Indeed,
social factors in childhood inuence the processes of development and
are the beginning of socially determined pathways to health and behavior
in later life (56). Such a perspective can also serve as a tool for
understanding health disparities across dierent vulnerable populations
of youth (57). Not surprisingly, the adolescents in the current study were
less likely to report rearm violence exposures compared with the young
adults, given this was a lifetime rate and thus they had less opportunity for
exposure. At the same time, 48% of these adolescents reported at least one
type of exposure to rearm violence, threats, or risky behaviors.
A key finding of prior research is how cumulative exposures
to victimization and other adversities, like firearm exposures,
lead to problematic developmental outcomes. Key concepts that
have emerged is that of the “poly-victim,” youth who suffer a
growing disproportionate quantity of serious victimization and a
much greater array and intensity of negative effects (58–61) and
adverse childhood experiences (62, 63) with a linear relationship
between the accumulation of adversity types and the level of
adverse outcomes (64). Firearm factors may play into the
adversity accumulation cycle in various ways. Negative firearm
exposures, for example, may be particularly salient or
traumatizing contributions to the cycle. Firearm fascination,
acquisition and carrying may be a response among highly
TABLE4 (Continued)
Saw someone shoot a firearm in public place Exposure to interpersonal firearm threats Heard (but did not see) firearm shots in
public place
Odds ratio (95% CI) p value Odds ratio (95% CI) p value Odds ratio (95% CI) p value
$60,000 to under $100,000 1.04 (0.74, 1.45) 0.83 1.27 (0.96, 1.68) 0.09 1.07 (0.83, 1.38) 0.59
$100,000 or more 1.15 (0.79, 1.67) 0.46 1.27 (0.95, 1.71) 0.11 1.12 (0.87, 1.44) 0.37
High neighborhood disorder 1.73 (1.32, 2.27) <0.001 1.49 (1.20, 1.85) <0.001 1.48 (1.21, 1.81) <0.001
Poor home conditions 1.34 (1.01, 1.78) 0.04 1.16 (0.93, 1.45) 0.18 1.24 (1.01, 1.53) 0.04
Not having enough money to pay bills 0.87 (0.64, 1.18) 0.36 1.02 (0.79, 1.31) 0.86 0.87 (0.69, 1.11) 0.25
Skip meals or eat less because did not
have enough money for food
1.40 (1.03, 1.91) 0.03 1.21 (0.94, 1.55) 0.13 0.82 (0.64, 1.04) 0.10
Last saw dentist more than 2 years ago
(or never)
1.21 (0.93, 1.57) 0.16 1.07 (0.85, 1.34) 0.57 1.20 (0.96, 1.50) 0.11
Non-victimization adversity 1.23 (1.17, 1.29) <0.001 1.33 (1.27, 1.38) <0.001 1.30 (1.25, 1.35) <0.001
N = 271 participants dropped from the multivariate analysis due to missing data on sex at birth, gender, income and/or race. ***p ≤0.001, **p ≤ 0.01, *p ≤ 0.05. Ref, reference group.

Mitchell et al. 10.3389/fpubh.2025.1451268
Frontiers in Public Health 11 frontiersin.org
exposed children and youth, which may in turn aggravate the
cycle. It is critical to continue to build our knowledge of the range
of firearm exposures for youth and to collect data on the contexts
and subgroups of youth for whom these exposures are related to
greater risk of harm over time.
Social and structural determinants of health are conditions in the
places where people live, learn, work, and play that aect a wide range
of health and health indicators (21). ere is growing awareness of the
importance of including assessments of SDOH as they help overcome
the limitations of assessing only demographic markers (22). e
TABLE5 Adjusted odds of lifetime knowledge of inappropriate firearm carrying / possession and someone else’s self-directed violence by participant
demographic and structural and social characteristics.
Knowledge of inappropriate firearm carrying /
possession
Knowledge of self-directed firearm
violence
Odds ratio (95% CI) p value Odds ratio (95% CI) p value
Demographic
Gender and sexual identity
Female (ref) 1.0 1.0
Male 1.03 (0.87, 1.21) 0.75 0.95 (0.79, 1.15) 0.59
Gender minority 1.33 (0.79, 2.23) 0.28 0.77 (0.49, 1.21) 0.26
Sexual minority 1.24 (0.99, 1.55) 0.07 1.27 (1.02, 1.58) 0.03
Age
10–17 (ref) 1.0 1.0
18–24 2.44 (1.90, 3.14) <0.001 2.91 (2.15, 3.94) <0.001
25–34 2.07 (1.70, 2.53) <0.001 2.79 (2.15, 3.62) <0.001
Race/Ethnicity
White (ref) 1.0 1.0
Black or African American 1.73 (1.38, 2.18) <0.001 1.01 (0.78, 1.30) 0.95
Asian 0.73 (0.56, 0.96) 0.03 0.52 (0.36, 0.75) <0.001
American Indian or Alaska Native 2.04 (0.95, 4.37) 0.07 0.84 (0.29, 2.43) 0.75
Native Hawaiian 0.28 (0.06, 1.20) 0.09 0.32 (0.06, 1.56) 0.16
Other race 1.24 (0.83, 1.85) 0.29 0.84 (0.50, 1.41) 0.51
Two or more races 1.50 (1.12, 2.00) 0.006 1.04 (0.76, 1.43) 0.79
Hispanic ethnicity 0.96 (0.76, 1.21) 0.73 0.67 (0.51, 0.89) 0.005
Structural and Social
Type of community
Urban (ref) 1.0 1.0
Suburban 0.90 (0.76, 1.08) 0.26 0.92 (0.76, 1.12) 0.43
Rural 1.34 (1.03, 1.74) 0.03 1.86 (1.41, 2.44) <0.001
Household Income
Less than $30,000 (ref) 1.0 1.0
$30,000 to <$60,000 1.31 (1.03, 1.66) 0.03 1.29 (1.01, 1.66) 0.04
$60,000 to under $100,000 1.32 (1.03, 1.70) 0.03 1.63 (1.23, 2.15) 0.001
$100,000 or more 1.47 (1.13, 1.90) 0.004 1.73 (1.30, 2.29) <0.001
High neighborhood disorder 1.21 (0.99, 1.49) 0.07 1.49 (1.20, 1.85) <0.001
Poor home conditions 1.53 (1.24, 1.89) <0.001 1.04 (0.83, 1.30) 0.72
Not having enough money to pay bills 1.04 (0.82, 1.32) 0.77 0.88 (0.67, 1.15) 0.36
Skip meals or eat less because did not have
enough money for food
1.11 (0.87, 1.41) 0.39 1.29 (1.00, 1.66) 0.05
Last saw dentist more than 2 years ago (or
never)
1.04 (0.84, 1.29) 0.73 0.98 (0.78, 1.23) 0.87
Non-victimization adversity: M (SE) 1.33 (1.27, 1.39) <0.001 1.28 (1.22, 1.33) <0.001
N = 271 participants dropped from the multivariate analysis due to missing data on sex at birth, gender, income and/or race. Ref, reference group.

Mitchell et al. 10.3389/fpubh.2025.1451268
Frontiers in Public Health 12 frontiersin.org
current study found elevated odds of rearm violence exposure for
those living in poor home conditions, in neighborhoods with high
disorder, and experiencing economic instability, dental care
constraints, and non-victimization adversity. is is consistent with
community-level and community-focused studies of rearm violence
that seek to move beyond individual risk and protective factors (13).
Awareness of rearms and related violence, threats and risky behavior
may be a response to safety concerns in neighborhoods that are
systematically under-resourced by policies and systemic practices that
sustain chronic lack of investment in some communities. ese
ndings remind us that interventions need to beinclusive of settings,
not just people.
4.1 Limitations
Findings should beconsidered in the context of some limitations.
Our data are cross-sectional and weare thus unable to make causal
statements about our ndings. e lifetime measures of rearm
violence exposure across age groups are dierentiated by
opportunities of exposure based on time; wecannot discern if there
are actual cohort eects. Data are self-reported and thus susceptible
to under- or over-reporting of responses. e measures for structural
conditions in this study were based on current experiences and may
not reect the community in which the participant became aware of
the rearm violence situation.
5 Conclusion
e current ndings suggest some promise for innovative prevention
and intervention strategies that move beyond victims and perpetrators of
rearm violence to engage bystanders. e study underscores ways that
exposures to someone else’s rearm violence, threats, and risky behaviors
are associated with low resources associated with SDOH decits.
Prevention and intervention strategies need to change communities, and
especially access to resources, not just individual attitudes and behaviors.
A more thorough contextual understanding of rearm violence
exposures, including relationships among the people involved, dierent
ways people intervene, and barriers to intervention, are critical next steps
that can help guide the development of new bystander interventions
targeting rearm violence.
Data availability statement
e raw data supporting the conclusions of this article will
bemade available by the authors, without undue reservation.
Ethics statement
e studies involving humans were approved by NORC at the
University of Chicago’s Institutional Review Board. e studies were
conducted in accordance with the local legislation and institutional
requirements. Written informed consent for participation in this study
was provided by the participants’ legal guardians/next of kin.
Author contributions
KM: Conceptualization, Formal analysis, Funding acquisition,
Methodology, Writing – original dra. BT: Conceptualization,
Funding acquisition, Investigation, Methodology, Writing– review &
editing. EM: Conceptualization, Methodology, Writing– review &
editing. WL: Writing– review & editing. HT: Writing– review &
editing. VB: Conceptualization, Writing - original dra.
Funding
e author(s) declare that nancial support was received for
the research and/or publication of this article. is work was
supported by grant 1 R01CE003434–01-00 from Centers for
Disease Control and Prevention (CDC), Department of Health and
Human Services. e views expressed do not necessarily reect the
policies of CDC. CDC sta did not work with the study
investigators on the study research questions, measures and
research design and were not involved in the collection, analysis,
or interpretation of data, in the writing of this paper, or in the
decision to submit this article for publication.
Conflict of interest
e authors declare that the research was conducted in the
absence of any commercial or nancial relationships that could
beconstrued as a potential conict of interest.
Publisher’s note
All claims expressed in this article are solely those of the
authors and do not necessarily represent those of their affiliated
organizations, or those of the publisher, the editors and the
reviewers. Any product that may beevaluated in this article, or
claim that may bemade by its manufacturer, is not guaranteed or
endorsed by the publisher.
References
1. Bulger EM, Kuhls DA, Campbell BT, B onne S, Cunningham RM, Betz M, et al.
Proceedings from the medical summit on rearm injury prevention: a public health
approach to reduce death and disability in the US. J AmColl Surg. (2019) 229:415–30.
doi: 10.1016/j.jamcollsurg.2019.05.018
2. Banyard VL, Moynihan MM, Plante EG. Sexual violence prevention through
bystander education: an experimental evaluation. J Community Psychol. (2007)
35:463–81. doi: 10.1002/jcop.20159
3. Frye V. e informal social control of intimate partner violence against women:
exploring personal attitudes and perceived neighborhood social cohesion. J Community
Psychol. (2007) 35:1001–18. doi: 10.1002/jcop.20209
4. Warner BD. Neighborhood factors related to the likelihood of successful informal
social control eorts. J Crim Just. (2014) 42:421–30. doi: 10.1016/j.jcrimjus.2014.07.001
5. Banyard VL. Measurement and correlates of prosocial bystander behavior: the case
of interpersonal violence. Violence Vict. (2008) 23:83–97. doi: 10.1891/0886-6708.23.1.83

Mitchell et al. 10.3389/fpubh.2025.1451268
Frontiers in Public Health 13 frontiersin.org
6. Banyard VL, Moynihan MM, Cares AC, Warner R. How do weknow if it works?
Measuring outcomes in bystander-focused abuse prevention on campuses. Psychol
Violence. (2014) 4:101–15. doi: 10.1037/a0033470
7. Edwards KM, Rodenhizer-Stämpi KA, Eckstein RP. Bystander action in situations
of dating and sexual aggression: a mixed methodological study of high school youth. J
Youth Adolesc. (2015) 44:2321–36. doi: 10.1007/s10964-015-0307-z
8. Hamby S, Weber MC, Grych J, Banyard V. What dierence do bystanders make?
e association of bystander involvement with victim outcomes in a community sample.
Psychol Violence. (2016) 6:91–102. doi: 10.1037/a0039073
9. McCauley HL, Tancredi DJ, Silverman JG, Decker MR, Austin SB, McCormick MC,
et al. Gender-equitable attitudes, bystander behavior, and recent abuse perpetration
against heterosexual dating partners of male high school athletes. Am J Public Health.
(2013) 103:1882–7. doi: 10.2105/AJPH.2013.301443
10. Potter SJ. Using a multimedia social marketing campaign to increase active bystanders
on the college campus. J AmColl Heal. (2012) 60:282–95. doi: 10.1080/07448481.2011.599350
11. Espelage D, Green H, Polanin J. Willingness to intervene in bullying episodes
among middle school students: individual and peer-group inuences. J Early Adolesc.
(2012) 32:776–801. doi: 10.1177/0272431611423017
12. Kovalenko AG, Abraham C, Graham-Rowe E, Levine M, O’Dwyer S. What works
in violence prevention among young people?: a systematic review of reviews. Trau ma
Violence Abuse. (2022) 23:1388–404. doi: 10.1177/1524838020939130
13. Coker AL, Fisher BS, Bush HM, Swan SC, Williams CM, Clear ER, et al. Evaluation of
the Green dot bystander intervention to reduce interpersonal violence among college students
across three campuses. Violence Against Women. (2015) 21:1507–27. doi:
10.1177/1077801214545284
14. Staub E. Witnesses/bystanders: the tragic fruits of passivity, the power of
bystanders, and promoting active bystandership in children, adults, and groups. J Soc
Issues. (2019) 75:1262–93. doi: 10.1111/josi.12351
1 5. Bancalari P, Sommer M, Rajan S. Youth exposure to endemic community gun violence:
a systematic review. Adolesc Res Rev. (2022) 7:383–417. doi: 10.1007/s40894-022-00178-5
16. Martin R, Rajan S, Shareef F, Xie KC, Allen KA, Zimmerman M, et al. Racial
disparities in child exposure to rearm violence before and during COVID-19. Am J
Prev Me d. (2022) 63:204–12. doi: 10.1016/j.amepre.2022.02.007
17. Lennon T, Kemal S, Heernan ME, Bendelow A, Sheehan K, Davis MM, et al.
Childhood exposure to rearm violence in Chicago and its impact on mental health.
Acad Pediatr. (2023) 24:982–6. doi: 10.1016/j.acap.2023.12.001
18. Beseler C, Mitchell KJ, Jones LM, Turner HA, Hamby S, Wade R Jr. e youth rearm
risk and safety tool (youth-FiRST): psychometrics and validation of a gun attitudes and
violence exposure assessment tool. Violence Vict. (2020) 35:635–55. doi: 10.1891/VV-D-
19-00085
19. Quimby D, Dusing CR, Deane K, DiClemente CM, Morency MM, Miller KM,
et al. Gun exposure among black American youth residing in low-income urban
environments. J Black Psychol. (2018) 44:322–46. doi: 10.1177/0095798418773188
20. Wang EA, Riley C, Wood G, Greene A, Horton N, Williams M, et al. Building
community resilience to prevent and mitigate community impact of gun violence:
conceptual framework and intervention design. BMJ Open. (2020) 10:e040277. doi:
10.1136/bmjopen-2020-040277
21. Centers for Disease Control and Prevention. Social determinants of health at CDC.
Atlanta, GA: U.S. Department of Health & Human Services (2023).
22. Hamby S. On the use of race and ethnicity as variables in violence research. Psychol
Violence. (2015) 5:1–7. doi: 10.1037/a0038470
23. Alegría M, NeMoyer A, Falgàs Bagué I, Wang Y, Alvarez K. Social determinants
of mental health: where weare and where weneed to go. Curr Psychiatry Rep. (2018)
20:1–13. doi: 10.1007/s11920-018-0969-9
24. Silva M, Loureiro A, Cardoso G. Social determinants of mental health: a review of
the evidence. European J Psychiatry. (2016) 30:259–92.
25. McGinty EE, Presskreischer R, Han H, Barry CL. Psychological distress and
loneliness reported by US adults in 2018 and April 2020. JAMA. (2020) 324:93–4. doi:
10.1001/jama.2020.9740
26. Gollust SE, Fowler EF, Vogel RI, Rothman AJ, Yzer M, Nagler RH. Americans'
perceptions of health disparities over the rst year of the COVID-19 pandemic: results from
three nationally-representative surveys. Prev Med. (2022) 162:107135. doi:
10.1016/j.ypmed.2022.107135
27. Abdalla SM, Ettman CK, Cohen GH, Galea S. Mental health consequences of
COVID-19: a nationally representative cross-sectional study of pandemic-related
stressors and anxiety disorders in the USA. BMJ Open. (2021) 11:e044125. doi:
10.1136/bmjopen-2020-044125
28. Campos-Castillo C, Laestadius LI. Mental healthcare utilization, modalities, and
disruptions during spring 2021 of the COVID-19 pandemic among US adolescents. J
Adolesc Health. (2022) 71:512–5. doi: 10.1016/j.jadohealth.2022.06.012
29. Montgomery R, Dennis JM, Ganesh N. Response rate calculation methodology
for recruitment of a two-phase probability-based panel: e case of AmeriSpeak. (2016).
Chicago, IL: NORC at the University of Chicago.
30. Lanfear CC, Bucci R, Kirk DS, Sampson RJ. Inequalities in exposure to rearm
violence by race, sex, and birth cohort from childhood to age 40 years, 1995-2021. JAMA
Netw Open. (2023) 6:e2312465. doi: 10.1001/jamanetworkopen.2023.12465
31. Xiao Y, Mann JJ, Chow JC-C, Brown TT, Snowden LR, Yip PS-F, et al. Patterns of
social determinants of health and child mental health, cognition, and physical health.
JAMA Pediatr. (2023) 177:1294–305. doi: 10.1001/jamapediatrics.2023.4218
32. American Academy of Family Physicians. Social Needs Screening To ol. Leewood, KS.
(2018). Available online at: https://www.aafp.org/dam/AAFP/documents/patient_care/
everyone_project/hops19-physician-form-sdoh.pdf (Accessed March 19, 2025).
3 3. Turner HA, Butler MJ. Direct and indirect eects of childhood adversity on depressive
symptoms in young adults. J Youth Adolesc . (2003) 32:89–103. doi: 10.1023/A:1021853600645
34. C enters for Disease Control & Prevention. Youth risk behavior surveillance system
(YRBSS). Atlanta, GA: U.S. Department of Health and Human Services (2020).
35. American Academy of Family Physicians. Social needs screening tool. Leawood,
KS: American Academy of Family Physicians (2018). 2 p.
36. Perkins DD, Florin P, Rich RC, Wandersman A, Chavis DM. Participation and the
social and physical environment of residential blocks: crime and community context.
Am J Community Psychol. (1990) 18:83–115. doi: 10.1007/BF00922690
37. Bethlehem J, Cobben F, Schouten B. e use of response propensities In:
Handbook of nonresponse in household surveys. Editors: J. Bethlehem, F. Cobben, & B.
Schouten. John Wiley & Sons, Hoboken, NJ (2011). 327–52.
38. Mitchell KJ, Jones LM, Turner HA, Beseler CL, Hamby S, Wade R Jr. Understanding the
impact of seeing gun violence and hearing gunshots in public places: ndings from the youth
rearm risk and safety study. J Interpers Violence. (2021) 36:8835–51. doi:
10.1177/0886260519853393
39. Rocque M, Gerdes M, Fox JA, Duwe G, Clark M. Averting tragedy: an exploration
of thwarted mass public shootings relative to completed attacks. Crim Justice Rev. (2023)
48:277–99. doi: 10.1177/07340168221117107
40. Kivisto AJ, Kivisto KL, Gurnell E, Phalen P, Ray B. Adolescent suicide, household
rearm ownership, and the eects of child access prevention laws. J AmAcad Child
Adolesc Psychiatry. (2021) 60:1096–104. doi: 10.1016/j.jaac.2020.08.442
41. Swanson SA, Eyllon M, Sheu Y-H, Miller M. Firearm access and adolescent suicide
risk: toward a clearer understanding of eect size. Inj Prev. (2021) 27:264–70. doi:
10.1136/injuryprev-2019-043605
42. Kegler SR, Simon TR, Zwald ML, Chen MS, Mercy JA, Jones CM, et al. Vital signs:
changes in rearm homicide and suicide rates—UnitedStates, 2019–2020. Morb Mortal
Wkly Rep. (2022) 71:656. doi: 10.15585/mmwr.mm7119e1
43. Wintemute GJ. e epidemiology of rearm violence in the twenty-rst century
UnitedStates. Annu Rev Public Health. (2015) 36:5–19. doi: 10.1146/annurev-publhealth-
031914-122535
44. Lee LK, Fleegler EW, Goyal MK, Doh KF, Laraque-Arena D, Homan BD, et al.
Firearm-related injuries and deaths in children and youth. Pediatrics. (2022)
150:e2022060071. doi: 10.1542/peds.2022-060071
45. Barrett JT, Lee LK, Monuteaux MC, Farrell CA, Homann JA, Fleegler EW.
Association of county-level poverty and inequities with rearm-related mortality in US
youth. JAMA Pediatr. (2022) 176:e214822. doi: 10.1001/jamapediatrics.2021.4822
46. Karb RA, Subramanian S, Fleegler EW. County poverty concentration and
disparities in unintentional injury deaths: a fourteen-year analysis of 1.6 million US
fatalities. PLoS One. (2016) 11:e0153516. doi: 10.1371/journal.pone.0153516
47. Birkett M, Espelage DL, Koenig B. LGB and questioning students in schools: the
moderating eects of homophobic bullying and school climate on negative outcomes. J
Youth Adolesc. (2009) 38:989–1000. doi: 10.1007/s10964-008-9389-1
48. Centers for Disease Control and Prevention: National Center for Injury Prevention
and Control. Web-based injury statistics reporting system (WISQARS). (2020).
49. Anestis MD, Moceri-Brooks J, Ziminski D, Barnes RT, Semenza D. Firearm access
and gun violence exposure among American Indian or Alaska native and black adults.
JAMA Netw Open. (2024) 7:e240073. doi: 10.1001/jamanetworkopen.2024.0073
50. Mumford EA, Maitra P, Sheridan J, Rothman EF, Olsen E, Roberts E. Technology-
facilitated abuse of young adults in the UnitedStates: a latent class analysis. Cyberpsychol:
J Psychosoc Res Cyberspace. (2023) 17:Article 7. doi: 10.5817/CP2023-3-7
51. Harland KK, Peek-Asa C, Salas AF. Intimate partner violence and controlling
behaviors experienced by emergency department patients: dierences by sexual
orientation and gender identication. J Interpers Violence. (2021) 36:NP6125–43. doi:
10.1177/0886260518812070
52. Asnaani A, Richey JA, Dimaite R, Hinton DE, Hofmann SG. A cross-ethnic
comparison of lifetime prevalence rates of anxiety disorders. J Nerv Ment Dis. (2010)
198:551–5. doi: 10.1097/NMD.0b013e3181ea169f
53. Latzman RD, Naifeh JA, Watson D, Vaidya JG, Heiden LJ, Damon JD, et al. Racial
dierences in symptoms of anxiety and depression among three cohorts of students in
the southern UnitedStates. Psychiatry Interpers Biolog Processes. (2011) 74:332–48. doi:
10.1521/psyc.2011.74.4.332
54. Elder GH. e life course and human development In: W Damon and RM Lerner,
editors. Handbook of child psychology. NewYork: John Wiley & Sons, Inc. (1998). 939–91.
55. Johnson MK, Crosnoe R, Elder GH Jr. Insights on adolescence from a life course
perspective. J Res Adolesc. (2011) 21:273–80. doi: 10.1111/j.1532-7795.2010.00728.x
56. Wadsworth M. Health inequalities in the life course perspective. Soc Sci Med.
(1997) 44:859–69. doi: 10.1016/S0277-9536(96)00187-6
57. Braveman P, Barclay C. Health disparities beginning in childhood: a life-course
perspective. Pediatrics. (2009) 124:S163–75. doi: 10.1542/peds.2009-1100D

Mitchell et al. 10.3389/fpubh.2025.1451268
Frontiers in Public Health 14 frontiersin.org
58. Finkelhor D, Ormrod RK, Turner HA. Poly-victimization: a neglected
component in child victimization. Child Abuse Negl. (2007) 31:7–26. doi:
10.1016/j.chiabu.2006.06.008
59. Finkelhor D, Ormrod RK, Turner HA. Polyvictimization and trauma in a national
longitudinal cohort. Dev Psychopathol. (2007) 19:149–66. doi: 10.1017/S0954579407070083
60. Turner HA, Finkelhor D, Ormrod R. Poly-victimization in a national sample of
children and youth. Am J Prev Med. (2010) 38:323–30. doi: 10.1016/j.amepre.2009.11.012
61. Finkelhor D, Turner HA, Hamby SL, Ormrod R. Polyvictimization: Children's
exposure to multiple types of violence, crime, and abuse: US Department of Justice,
Oce of Juvenile Justice and Delinquency Prevention, Washington, DC; (2011).
62. Felitti V, Anda R, Nordenberg D, Williamson D, Spitz A, Edwards V, et al.
Relationship of childhood abuse and household dysfunction to many of the leading
causes of death in adults: the adverse childhood experiences (ACE) study. Am J Prev
Med. (1998) 14:245–58. doi: 10.1016/S0749-3797(98)00017-8
63 . Dube SR, Felitti VJ, Dong M, Giles WH, Anda RF. e impact of adverse childhood
experiences on health problems: evidence from four birth cohorts dating back to 1900.
Prev Me d. (2003) 37:268–77. doi: 10.1016/S0091-7435(03)00123-3
64. Appleyard K, Egeland B, Van Dulmen MHM, Sroufe LA. When more is not better:
the role of cumulative risk in child behavior outcomes. J Child Psychol Psychiatry. (2005)
46:235–45. doi: 10.1111/j.1469-7610.2004.00351.x