Online Searches for Gun-Related Harm.

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Learn more: PMC Disclaimer | PMC Copyright Notice JAMA Netw Open . 2026 Apr 15;9(4):e267715. doi: 10.1001/jamanetworkopen.2026.7715 Search in PMC Search in PubMed View in NLM Catalog Add to search Online Searches for Gun-Related Harm Kimberly J Mitchell Kimberly J Mitchell , PhD 1 Crimes Against Children Research Center, University of New Hampshire, Durham Find articles by Kimberly J Mitchell 1, ✉ , Patrick J Guziewicz Patrick J Guziewicz , MA 1 Crimes Against Children Research Center, University of New Hampshire, Durham 2 Department of Sociology, University of New Hampshire, Durham Find articles by Patrick J Guziewicz 1, 2 , Chandler C Carter Chandler C Carter , MA 3 NORC at the University of Chicago, Chicago, Illinois Find articles by Chandler C Carter 3 , Elizabeth A Mumford Elizabeth A Mumford , PhD 3 NORC at the University of Chicago, Chicago, Illinois Find articles by Elizabeth A Mumford 3 Author information Article notes Copyright and License information 1 Crimes Against Children Research Center, University of New Hampshire, Durham 2 Department of Sociology, University of New Hampshire, Durham 3 NORC at the University of Chicago, Chicago, Illinois Accepted for Publication: February 24, 2026. Published: April 15, 2026. doi: 10.1001/jamanetworkopen.2026.7715 Open Access: This is an open access article distributed under the terms of the CC-BY-NC-ND License , which does not permit alteration or commercial use, including those for text and data mining, AI training, and similar technologies. © 2026 Mitchell KJ et al. JAMA Network Open . ✉ Corresponding Author: Kimberly J. Mitchell, PhD, Crimes Against Children Research Center, University of New Hampshire, 10 W Edge Dr, Ste 106, Durham, NH 03824 ( [email protected] ). Author Contributions: Dr Mitchell and Mr Guziewicz had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. Concept and design: Mitchell, Guziewicz, Mumford. Acquisition, analysis, or interpretation of data: All authors. Drafting of the manuscript: Mitchell, Guziewicz, Carter. Critical review of the manuscript for important intellectual content: Guziewicz, Carter, Mumford. Statistical analysis: Mitchell, Guziewicz. Obtained funding: Mitchell, Mumford. Administrative, technical, or material support: Carter. Supervision: Mitchell. Conflict of Interest Disclosures: None reported. Funding/Support: This work was supported by grant 1 R01CE003434-01-00 from the Centers for Disease Control and Prevention (CDC) Department of Health and Human Services. Role of the Funder/Sponsor: The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication. Disclaimer: The views expressed do not necessarily reflect the policies of CDC. CDC staff 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. Data Sharing Statement: See Supplement 2 . ✉ Corresponding author. Received 2025 Dec 19; Accepted 2026 Feb 24; Collection date 2026 Apr. Copyright 2026 Mitchell KJ et al. JAMA Network Open . This is an open access article distributed under the terms of the CC-BY-NC-ND License, which does not permit alteration or commercial use, including those for text and data mining, AI training, and similar technologies. PMC Copyright notice PMCID: PMC13084454  PMID: 41984473 Key Points Question How many people have conducted online searches for gun-related harm and why? Findings In this nationally representative cross-sectional survey of 4039 youth and young adults, 8.7% reported ever seeking online information about gun-related harm, most often through specific webpages and social media applications. Access was more common among participants facing structural disadvantages and mental health distress. Meaning These findings suggest that public health strategies that combine digital interventions with structural reforms offer promising avenues to reduce gun-related harm among vulnerable populations. This cross-sectional study examines the characteristics and reasons for searching online for information about gun-related harm among youths and young adults. Abstract Importance Gun violence continues to be a significant public health concern. Access to information about gun-related harm has increased with the advent of social media and websites that host information on this topic. Objective To provide prevalence estimates of how many adolescents and young adults have conducted online searches for gun-related harm. Design, Setting, and Participants This cross-sectional study included participants recruited from a nationally representative sample from the US through the AmeriSpeak Panel. Eligibility included individual residents who were youths or young adults aged 10 to 34 years old who could speak or read either English or Spanish. Wave 1 data were collected from September 2023 to January 2024, and wave 2 data were collected between September 2024 and February 2025. Data were analyzed from December 2025 to February 2026. Exposures Social determinants of health and individual experiences hypothesized to increase odd of the main outcome. Main Outcomes and Measures Searching online for information about “how to kill yourself with a gun”; “how to get a gun or make a gun”; “how to conceal a gun”; and “how to hurt someone else with a gun.” Weighted proportions and means with 95% CIs were used for descriptive analyses. Survey-weighted logistic regression models were used to examine demographic characteristics, social determinants of health, personal experiences (eg, experiences of gun violence, gun violence perpetration), and mental health factors associated with intentionally seeking gun-related harm information online for oneself. Results In this study, 8452 panelists were invited, 5311 completed the baseline survey, and 4039 completed the wave 2 survey (76.1% retention rate). The sample included 4039 participants (mean [SE] age, 23.0 [0.2] years; 1518 male [weighted percentage, 50.6%]). Among respondents, 361 had ever conducted an online search about gun-related harm (weighted percentage, 8.7%). Of these, 102 searched for information about how to kill oneself with a gun (weighted percentage, 2.5%), 175 about how to get a gun or make a gun (weighted percentage, 4.2%), 153 about how to conceal a gun (weighted percentage, 3.5%), and 70 about how to hurt other people with a gun (weighted percentage, 1.7%). Reasons given for searches varied. Overall, intentionally seeking out gun-related harm information online was less likely for females (aOR, 0.41; 95% CI, 0.29-0.57) and more likely for older participants (aOR, 1.03 per year; 95% CI, 1.01-1.05 per year), those living under poor home conditions (aOR, 1.20; 95% CI, 1.04-1.38), those who had been exposed to a greater variety of types of gun violence (aOR, 1.20 per exposure; 95% CI, 1.11-1.29 per exposure), and those who had ever had thoughts of suicide (aOR, 2.21; 95% CI, 1.47-3.33). Conclusions and Relevance In this cross-sectional study of US participants, intentional searches for gun-related harm content were reported by a notable minority of youth and young adults, especially those facing structural disadvantages and mental health distress. Public health strategies that combine digital interventions with structural reforms offer promising avenues to reduce gun-related harm among vulnerable populations. Introduction Gun violence encompasses a variety of acts, including both interpersonal and self-directed. Almost 47 000 people in the US died from a gun-related injury in 2023. 1 Of these, 58% were suicides and 38% were murders. Youth have been particularly impacted; gun deaths have been the leading cause of death among children aged 1 to 17 years since 2020 and have increased 106% between 2013 and 2022. 2 , 3 Gun deaths disproportionately affect Black youth, with rates 18 times higher than White youth. 4 Research suggests the etiology of gun violence is multifaceted and can include violent behavior, substance use, childhood victimization, socioeconomic disadvantage, community violence, and depression, 5 but like other health behaviors, the impact of technology cannot be discounted. 6 , 7 , 8 , 9 , 10 Access to information about guns and gun-related harm has increased with the advent of technology and websites that host information about this topic. Although websites aimed at preventing gun violence (eg, Sandy Hook Promise) 11 and suicide (eg, The Trevor Project) 12 exist, online information also promotes and encourages gun violence and risky behavior. Extant research on guns and technology has focused on gun advertisements on social media, 13 progun advocacy on the internet, 14 , 15 and gun-specific internet search patterns. 16 , 17 , 18 We also know that such sites exist that promote, encourage, and instruct viewers how to die by suicide more generally, 19 , 20 , 21 yet, to our knowledge, no research exists about individuals seeking information online about gun-related harm. This study reports prevalence rates of online information-seeking about gun-related harm. We also examine the demographic, social determinants of health (SDOH), personal, and mental health factors of participants who have intentionally sought this information online and the characteristics of the people who visited these sites. Methods The research was conducted under the oversight of the NORC at the University of Chicago institutional review board. Informed consent was obtained from all participants aged 18 to 34 years and caregiver consent and youth assent for participants aged 10 to 17 years. This cross-sectional study is reported in accordance with the Strengthening the Reporting of Observational Studies in Epidemiology ( STROBE ) reporting guideline. Participants Data are from the Growing Up With Guns (GWG) study, which is a longitudinal nationally representative survey of 5311 youths, adolescents, and young adults aged 10 to 34 years old at baseline. 22 Participants were recruited from the AmeriSpeak panel. 23 , 24 , 25 , 26 Eligibility included being 10 to 34 years at baseline and being able to speak or read either English or Spanish. Selecting this age range enabled the analysis of a broad developmental span in exposure patterns. Data were collected from October 2023 to February 2024 for wave 1 and between September 2024 and February 2025 for wave 2. The analytic sample had a retention rate of 76.3% and included 4039 participants who completed wave 2. Overall, those who responded to both waves were slightly older and more highly educated, but the characteristics of the sample at wave 1 mirrored those of the sample at wave 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 more than 60 000 US residents, the AmeriSpeak panel was designed to be representative of the US household population using probability-based sampling. AmeriSpeak used probability-based sampling (area probability and address-based) from the NORC National Sample Frame and recruited households via mail, phone, and in-person follow-up. The household recruitment rate is 37%, and coverage is approximately 97% of the US household population. Analyses used survey weights calibrated to US Census benchmarks to account for selection probabilities and nonresponse (response propensity weighting). 27 Measures Searches for Online Information About Gun-Related Harm and Reasons for Accessing (Wave 2) The central measures used in our analyses were adapted from Mitchell et al 19 and were designed for the GWG survey. These items included 4 questions that inquired about whether the respondent had ever gone online to get information about 4 different gun-related topics: (1) “how to kill yourself with a gun”; (2) “how to get a gun or make a gun”; (3) “how to conceal a gun”; and (4) “how to hurt someone else with a gun.” Response options were yes or no. The respondent then had follow-up questions for each of their yes responses, which inquired about the reason or reasons they went online for that information. For each, participants were also asked where they got the information online: a specific webpage focused on this topic, a social media application, an online message board, an online chat group, and/or somewhere else. Social Determinants of Health (Wave 1) Neighborhood disorder was measured as the sum of 12 ordinal items that asked respondents to indicate how much of a problem they thought each of 12 indicators (eg, gangs, drugs, violent crime) of neighborhood disorder in their neighborhood. Responses to each indicator were on a 3-point scale from “no problem” to “a serious problem.” Poor home conditions were measured using a series of 8 dichotomous indicators. These indicators included (1) bugs everywhere; (2) mold; (3) lead paint or pipes; (4) not enough heat; (5) a nonworking oven or stove; (6) no or nonworking smoke detectors; (7) water leaks; and (8) no or frequent loss of electricity. Food insecurity was assessed with 1 item on a 5-point scale from “never” to “always” to determine how often they skipped meals or ate less because they or their family did not have enough money for food. Financial instability was assessed on a 5-point scale from “never” to “always” to determine how often in the past year their family did not have enough money to pay their bills. Personal Experience (Wave 1) The sum of 12 questions was used to assess lifetime experiences of violence from the Juvenile Victimization Questionnaire. 28 Types of experiences covered theft, physical assault, bullying, sexual abuse, and witnessing violence. Child maltreatment and violence was measured via 10 items capturing traumatic events (eg, parental imprisonment, serious illness) and chronic stressors (eg, family substance abuse, homelessness). 29 Responses to these items were summed to create a count variable representing the number of different types of adversity experienced. Depression and anxiety were measured using the Mental Health Inventory (MHI-5), 30 a 5-item screening inventory of symptoms consistent with these clinical conditions. Thoughts of suicide (yes or no) was assessed by asking participants, “Have you ever seriously thought about killing yourself?” To assess exposure to gun violence, threats, or risky gun access, we developed a series of 10 questions for the current study that queried lifetime exposures to someone else’s gun violence, threats, and risky behaviors. 31 Questions included hearing gun shots, seeing a shooting, interpersonal gun threats and violence, and knowing someone who considered or attempted suicide with a gun (eTable in Supplement 1 ). Using a multimethod approach, all analyses converge on a robust unidimensional structure with excellent stability and no redundant items. 31 Experience of gun violence was defined as endorsing any of 4 lifetime experiences: being threatened with a gun, robbed with a gun (personally or in the home), or shot at with real bullets. Gun violence perpetration was defined as ever threatening someone with a gun, using a gun to rob someone, or shooting at someone with real bullets. Statistical Analysis Missing data were minimal (3% or less) and were conservatively coded as no exposure for the primary gun violence exposure variables. All analyses incorporated sampling weights calibrated to 2023 US Census benchmarks, adjusting for selection probabilities and nonresponse using response propensity weighting to balance key demographic characteristics. All reported estimates reflect weighted data and account for the complex survey design using Taylor-series linearized variance estimation. Descriptive analyses included weighted proportions and means with 95% CIs. Group differences by exposure were evaluated using design-based Wald tests derived from survey-adjusted linear regression models for continuous variables and Rao-Scott adjusted χ 2 tests for categorical variables. We then estimated a series of survey-weighted logistic regression models to examine demographic characteristics, social determinants of health (SDOH), personal experiences (eg, experiences of gun violence, gun violence perpetration), and mental health factors associated with intentionally seeking gun-related harm information online for oneself: (1) any gun-related harm information, (2) how to kill oneself with a gun, (3) how to hurt others with a gun, (4) how to get or make a gun, and (5) how to conceal a gun. Adjusted odds ratios (aORs) and 95% CIs are reported. Finally, we estimated additional survey-weighted logistic regression models examining associations between the same covariates and online information-seeking about (1) gun self-harm, (2) harming others with a gun, (3) acquiring or making a gun, and (4) concealing a gun. All statistical tests were 2-sided with statistical significance defined as α = .05. Analyses were conducted using StataNow/SE version 19.5 (StataCorp). Data were analyzed from December 2025 to February 2026. Results Sample Characteristics A total of 4039 participants were included (mean [SE] age, 23.0 [0.15]); 1518 identified as male (weighted percentage, 50.6%), 2462 as female (weighted percentage, 48.0%), 128 as a gender minority (weighted percentage, 3.7%), and 797 as a sexual minority (weighted percentage, 18.6%). Additionally, 901 were Black participants (weighted percentage, 17.1%), 821 were Hispanic or Latino participants (weighted percentage, 22.2%), and 2477 were White participants (weighted percentage, 68.8%). Demographic characteristics of the analytic sample are provided in Table 1 . Table 1. Demographic, Personal, and SDOH Characteristics Between Participants Who Did and Did Not Conduct Online Searches for Gun-Related Harm a . Characteristic Participants, No. (weighted %) P value All participants (N = 4039) Gun-related harm No online (n = 3678) Online (n = 361) Self-reported demographic characteristics Age, y 10-17 815 (27.1) 769 (28.3) 46 (15.3) <.001 18-24 653 (29.1) 583 (28.5) 70 (35.5) 25-34 2571 (43.8) 2326 (43.3) 245 (49.3) Mean (SE) age, y 23.0 (0.2) 22.8 (0.2) 24.6 (0.4) <.001 Birth sex Male 1518 (50.6) 1324 (49.3) 194 (64.1) <.001 Female 2462 (48.0) 2305 (49.4) 157 (32.7) Missing 59 (1.4) 49 (1.2) 10 (3.2) Gender identity Cisgender 3911 (96.3) 3582 (96.8) 329 (91.5) <.001 Gender minority identity 128 (3.7) 96 (3.2) 32 (8.5) Sexual identity Heterosexual 3242 (81.4) 2985 (82.5) 257 (70.7) <.001 Sexual minority identity 797 (18.6) 693 (17.5) 104 (29.3) Race a American Indian or Alaska Native 156 (3.1) 140 (2.9) 16 (5.0) .13 Asian 525 (9.9) 478 (10.1) 47 (8.8) .54 Black or African American 901 (17.1) 787 (16.5) 114 (23.4) .005 White 2477 (68.8) 2293 (69.3) 184 (63.6) .08 Other b 290 (6.1) 267 (6.3) 23 (4.3) .17 Hispanic or Latino ethnicity 821 (22.2) 742 (22.1) 79 (23.5) .64 SDOH Annual household income <$30 000 974 (22.9) 864 (22.5) 110 (26.5) .55 $30 000 to <$60 000 1066 (25.1) 975 (25.0) 91 (25.4) $60 000 to <$100 000 928 (23.1) 844 (23.1) 84 (22.9) ≥$100 000 or more 1046 (28.2) 973 (28.5) 73 (24.8) Missing 25 (0.8) 22 (0.8) 3 (0.4) Lives in urban area 1698 (36.3) 1534 (36.0) 164 (40.2) .23 Lives in rural area 572 (14.9) 528 (15.2) 44 (12.1) .20 Neighborhood disorder, mean (SE) 1.47 (.01) 1.46 (.01) 1.63 (.04) <.001 Poor home conditions, mean (SE) 0.40 (.02) 0.36 (.02) 0.78 (.09) <.001 Financial instability, mean (SE) 1.89 (.02) 1.87 (.03) 2.11 (.08) .004 Food insecurity, mean (SE) 1.53 (.02) 1.49 (.02) 1.92 (.08) <.001 Personal experience Adversity, mean (SE) 2.19 (.05) 2.12 (.05) 2.88 (.16) <.001 Child maltreatment and violence, mean (SE) 2.83 (.06) 2.70 (.06) 4.18 (.21) <.001 Any gun violence perpetration 144 (3.3) 104 (2.8) 40 (8.5) <.001 Any gun violence experiences 458 (9.5) 383 (8.9) 75 (15.8) <.001 No. gun violence exposures, mean (SE) 2.17 (.05) 2.04 (.05) 3.54 (.18) <.001 Mental health Depression or anxiety, mean (SE) 2.75 (.02) 2.72 (.02) 3.11 (.06) <.001 Thoughts of suicide 908 (20.1) 758 (18.0) 150 (42.1) <.001 Open in a new tab Abbreviation: SDOH, social determinants of health. a Multiple responses possible. Participants were able to identify with more than 1 race, so each race is a separate construct. b The other race category was primarily used to provide more details on response to questions about specific races (often referring to ethnicity), for example, Hispanic, Mexican, Puerto Rican, Portuguese, Spanish, and Salvadorean. Prevalence of Searches for Online Information About Gun-Related Harm Of the 4039 participants in this study, 361 (weighted percentage, 8.7%) had ever searched for gun-related harm information online, 102 (weighted percentage, 2.5%) had searched for information about how to kill oneself with a gun, 175 (weighted percentage, 4.2%) for information about how to get a gun or make a gun, 153 (weighted percentage, 3.5%) for information about how to conceal a gun, and 70 (weighted percentage, 1.7%) for information about how to hurt other people with a gun. Searching for gun-related harm information was more common among older participants, males, gender minority, sexual minority, and Black participants at the bivariate level ( Table 1 ). Purposeful online searching was also more common among participants reporting SDOH deficits and personal characteristics, including poor mental health and direct and indirect gun violence exposures at the bivariate level. Reasons and Where People Found Gun-Related Harm Information Online Of the 361 participants who searched for any gun-related harm information online, 316 reported intentionally seeking this information for themselves (weighted percentage, 87.2%). Of the 102 participants who searched for information about how to kill themselves with a gun, 76 reported intentionally seeking this information themselves (weighted percentage, 78.0%). Of the 175 participants who sought information about how to get or make a gun, 157 reported intentionally seeking this information for themselves (weighted percentage, 88.3%). Of the 153 participants who sought information about how to conceal a gun, 136 reported intentionally seeking this information for themselves (weighted percentage, 88.9%). Of the 70 participants who sought information about how to hurt others with a gun, 46 reported intentionally seeking this information for themselves (weighted percentage, 74.1%) ( Table 2 ).The reasons participants gave for their searches included being curious and wanting more information about the issue (224 participants; weighted percentage, 58.7%), wanting information and not wanting anyone else to know (96; weighted, percentage, 28.8%), not knowing anyone offline who could answer their specific questions (62 participants; weighted percentage, 16.9%), being embarrassed to ask someone or admitting they did not know about the topic (49; weighted percentage, 14.5%), and someone suggesting they go there (32; weighted percentage, 8.3%). Despite the precursor question about intentionally searching for information, 47 participants reported that they saw relevant information online by accident (weighted percentage, 12.8%), and 26 participants sought information because they were worried about a friend or family member (weighted percentage, 6.9%). Table 2. Descriptive Details About Where the Participant Got the Gun-Related Harm Information Online and Why (n = 4039). Descriptive detail Participant, No. (weighted %) Any search Information sought How to kill self with a gun How to get or make a gun How to conceal a gun How to hurt others with a gun Total 361 (8.7) 102 (2.5) 175 (4.2) 153 (3.5) 70 (1.7) Reasons for accessing information Any intentional for self 316 (87.2) 76 (78.0) 157 (88.3) 136 (88.9) 46 (74.1) I wanted information and did not want anyone to know 96 (28.8) 55 (58.0) 27 (16.0) 23 (14.5) 11 (20.1) I was curious and wanted to learn more about the issue 224 (58.7) 25 (23.6) 122 (63.8) 95 (63.7) 20 (26.6) I was embarrassed to ask someone or admit I did not know 49 (14.5) 13 (10.2) 13 (7.9) 20 (14.3) 12 (22.8) I do not know anyone offline who could answer my specific questions 62 (16.9) 9 (9.3) 33 (20.0) 21 (12.3) 6 (9.6) Someone I know suggested I go there 32 (8.3) 5 (6.3) 12 (4.9) 12 (8.1) 7 (13.9) I saw it by accident 47 (12.8) 19 (16.3) 12 (10.6) 10 (5.9) 15 (13.6) I was worried about a friend or family member 26 (6.9) 7 (5.5) 5 (3.4) 7 (6.2) 9 (11.3) Where accessed the information A specific webpage that focused on this 231 (61.5) 41 (34.5) 126 (68.0) 99 (67.6) 16 (22.9) Social media app 97 (26.8) 20 (16.3) 28 (21.1) 41 (23.5) 28 (37.0) Online message board 85 (22.7) 31 (31.5) 30 (14.0) 22 (12.7) 13 (17.8) Online chat group 41 (11.0) 11 (8.8) 13 (8.7) 13 (8.9) 12 (13.2) Open in a new tab Specific webpages were the most common source of gun-related harm (231 participants; weighted percentage, 61.5%); followed by social media (97 participants; weighted percentage, 26.8%), online message boards (85 participants; weighted percentage, 22.7%), and chat groups (41 participants; weighted percentage, 11.0%). Information about obtaining or making a gun and harming others was most often found on specific webpages (126 participants; weighted percentage, 68.0%). Searches about killing oneself with a gun were most commonly linked to specific webpages (41 participants; weighted percentage, 34.5%), whereas content about harming others was most often encountered on social media (28 participants; weighted percentage, 37.0%). Factors Associated With Intentional Seeking of Gun-Related Information Online In adjusted models ( Table 3 ), intentionally visiting any gun-related harm website was less likely among female participants (aOR, 0.41; 95% CI, 0.29-0.57) and more likely with older age (aOR, 1.03 per year; 95% CI, 1.01-1.05 per year), poor home conditions (aOR, 1.20; 95% CI, 1.04-1.38), greater cumulative gun violence exposures (aOR, 1.20 per exposure; 95% CI, 1.11-1.29 per exposure), and past-year suicidal thoughts (aOR, 2.21; 95% CI, 1.47-3.33). Race, sexual and gender minority status, depression, and most other social and personal adversity measures were not significantly associated. Table 3. Adjusted Odds of Characteristics and Experiences for Intentionally Accessing Gun-Related Harm Content Online (n = 4039) a . Characteristic Any search aOR (95% CI) P value Demographics Sex Female 0.41 (0.29-0.57) <.001 Male 1 [Reference] NA Sexual identity Heterosexual 1 [Reference] NA Sex minority 1.15 (0.71-1.89) .57 Gender identity Cisgender 1 [Reference] NA Gender minority 1.08 (0.56-2.11) .81 Race Black or African American 1.23 (0.80-1.90) .33 White 0.90 (0.61-1.32) .60 Age b 1.03 (1.01-1.05) .02 SDOH Neighborhood disorder 0.98 (0.69-1.39) .92 Poor home conditions 1.20 (1.04-1.38) .01 Financial instability 0.91 (0.79-1.05) .19 Food insecurity 1.17 (0.97-1.41) .10 Personal experience Child maltreatment and violence 1.05 (0.97-1.13) .27 Adversity 0.95 (0.87-1.04) .25 Any gun violence perpetration 1.62 (0.84-3.14) .15 Any gun violence experience 0.66 (0.39-1.14) .13 Count of gun violence exposures 1.20 (1.11-1.29) <.001 Mental health Depression 1.01 (0.85-1.21) .86 Thoughts of suicide 2.21 (1.47-3.33) <.001 F (17, 4022) c 10.62 <.001 Open in a new tab Abbreviations: aOR, adjusted odds ratio; NA, not applicable; SDOH, social determinants of health. a Survey-weighted logistic regression models were estimated using Stata’s svy framework, incorporating sampling weights and accounting for the complex survey design (eg, clustering and stratification) with Taylor-series linearized standard errors. Results are presented as aORs. Taylor-series linearization is a design-based method that approximates nonlinear estimators to produce variance estimates that account for survey weights, clustering, and stratification. b Age was included as a continuous variable. A positive aOR means that older participants were more likely than younger to do this. c Model significance was assessed using a design-based Wald F test, which accounts for the survey design. In content-specific models ( Table 4 ), female participants had lower odds of searching how to get or make a gun (aOR, 0.30; 95% CI, 0.18-0.50) and how to conceal a gun (aOR, 0.51; 95% CI, 0.32-0.80). Suicidal thoughts were strongly associated with searching how to kill oneself with a gun (aOR, 5.31; 95% CI, 2.06-13.71) and were also associated with searching how to get or make a gun (aOR, 2.40; 95% CI, 1.40-4.13), while cumulative gun violence exposure was associated with searches about killing oneself (aOR, 1.23; 95% CI, 1.08-1.40; P = .001), getting or making a gun (aOR, 1.22; 95% CI, 1.09-1.36; P < .001), and concealing a gun (aOR, 1.20; 95% CI, 1.07-1.33; P = .001). Poor home conditions were associated with searches about how to kill oneself with a gun (aOR, 1.44; 1.17-1.77; P < .001) and get or make a gun (aOR, 1.32; 95% CI, 1.10-1.58; P = .002), food insecurity was associated with searches about how to hurt others, and Black race (aOR, 2.51; 95% CI, 1.62-3.91; P < .001) and older age (aOR, 1.07; 95% CI, 1.03-1.10; P < .001) were associated with searches about how to conceal a gun. Table 4. Adjusted Odds of Characteristics and Experiences for Intentionally Accessing Gun-Related Harm Content Online (n = 4039) a . Characteristic Model 1: how to kill self with a gun Model 2: how to hurt others with a gun Model 3: how to get or make a gun Model 4: how to conceal a gun aOR (95% CI) P value aOR (95% CI) P value aOR (95% CI) P value aOR (95% CI) P value Demographics Sex Female 0.46 (0.20-1.06) .07 0.52 (0.25-1.09) .09 0.30 (0.18-0.50) <.001 0.51 (0.32-0.80) .004 Male 1 [Reference] NA 1 [Reference] NA 1 [Reference] NA 1 [Reference] NA Sexual identity Heterosexual 1 [Reference] NA 1 [Reference] NA 1 [Reference] NA 1 [Reference] NA Sex minority 1.57 (0.50-4.91) .43 0.92 (0.33-2.53) .87 1.44 (0.83-2.48) .19 0.45 (0.23-0.87) .02 Gender identity Cisgender 1 [Reference] NA 1 [Reference] NA 1 [Reference] NA 1 [Reference] NA Gender minority 0.98 (0.28-3.41) .97 1.10 (0.20-4.96) 1.00 0.99 (0.42-2.35) .98 0.45 (0.13-1.63) .23 Race Black or African American 1.01 (0.45-2.23) .98 1.62 (0.67-3.89) .28 0.93 (0.47-1.85) .84 2.51 (1.62-3.91) <.001 White 0.65 (0.31-1.37) .26 0.29 (0.10-0.88) .03 0.73 (0.41-1.31) .29 1.72 (1.13-2.61) .01 Age b 1.02 (0.98-1.07) .28 0.96 (0.91-1.02) .19 1.01 (0.98-1.04) .51 1.07 (1.03-1.10) <.001 SDOH Neighborhood disorder 0.29 (0.13-0.66) .003 1.65 (0.83-3.27) .15 1.05 (0.65-1.67) .85 1.22 (0.80-1.87) .35 Poor home conditions 1.44 (1.17-1.77) .001 1.11 (0.88-1.40) .38 1.32 (1.10-1.58) .002 0.99 (0.81-1.20) .89 Financial instability 0.83 (0.66-1.05) .11 1.19 (0.90-1.57) .22 0.81 (0.67-1.00) .05 1.01 (0.83-1.22) .94 Food insecurity 1.10 (0.79-1.53) .55 1.89 (1.32-2.72) .001 1.09 (0.84-1.41) .54 0.99 (0.79-1.24) .91 Personal experience Experience of violence 0.98 (0.84-1.14) .80 1.12 (0.94-1.34) .20 1.12 (0.99-1.26) .08 0.99 (0.90-1.09) .81 Adversity 0.95 (0.81-1.12) .57 0.89 (0.73-1.08) .24 0.95 (0.85-1.07) .41 1.01 (0.90-1.14) .82 Any gun violence perpetration 1.42 (0.55-3.61) .47 2.56 (1.02-6.45) .05 1.27 (0.47-3.41) .64 1.18 (0.52-2.67) .69 Any gun violence experience 0.68 (0.27-1.67) .39 0.75 (0.21-2.61) .65 0.67 (0.30-1.51) .34 0.77 (0.40-1.48) .44 Count of gun violence exposures 1.23 (1.08-1.40) .001 0.96 (0.78-1.19) .71 1.22 (1.09-1.36) <.001 1.20 (1.07-1.33) .001 Mental health Depression 1.49 (1.10-2.01) .01 0.93 (0.66-1.30) .66 0.89 (0.72-1.11) .31 0.99 (0.80-1.24) .95 Thoughts of suicide 5.31 (2.06-13.71) .001 2.02 (0.75-5.43) .16 2.40 (1.40-4.13) .002 1.43 (0.75-2.74) .28 F (17, 4022) c 10.28 <.001 8.40 <.001 10.21 <.001 5.93 <.001 Open in a new tab Abbreviations: aOR, adjusted odds ratio; NA, not applicable; SDOH, social determinants of health. a Survey-weighted logistic regression models were estimated using Stata’s svy framework, incorporating sampling weights and accounting for the complex survey design (eg, clustering and stratification) with Taylor-series linearized standard errors. Results are presented as aORs. Taylor-series linearization is a design-based method that approximates nonlinear estimators to produce variance estimates that account for survey weights, clustering, and stratification. b Age was included as a continuous variable. A positive aOR means that older participants were more likely than younger to do this. c Model significance was assessed using a design-based Wald F test, which accounts for the survey design. Discussion Findings from the nationally representative Growing Up with Guns Study offer the first comprehensive look at online searches for gun-related harm among US youths and young adults, including the motivations that drive these behaviors. Although only a minority (7.6%) of respondents conducted an online search about gun-related harm (3.5% to 4.2%), gun self-harm (2.5%), or other-directed gun harm (1.7%), these behaviors may serve as important indicators for future harmful behaviors. Indeed, population-level studies of internet search data suggest that self-harm–related internet use is associated with higher self-harm risk. 32 , 33 , 34 , 35 Thus, our findings point to opportunities to implement responsible interventions on search engine pages to direct gun violence information-seekers to crisis resources or mental health resources. Our findings also underscore the role of SDOH in shaping online information-seeking about gun-related harm, which was more common among participants with poor home conditions, food insecurity, exposure to gun violence, and mental health distress. Elevated self-harm–gun-related searches among those with structural disadvantage likely reflect clustering of risk factors that compound distress. 36 Differences in access, concealment, and interpersonal harm searches may similarly stem from SDOH-related exposures that heighten perceived threat and limit trusted offline resources. If gun-related harm information-seeking signals elevated risk, digital interventions should be paired with upstream strategies to improve living conditions, food and housing security, and access to community-based mental health support. Living in a more disordered neighborhood was associated with a lower likelihood of searching for gun self-harm information. This finding is notable because prior research often links neighborhood disorder and structural disadvantage to higher exposure to violence and elevated suicide risk. 37 One might therefore expect greater engagement with firearm-related content in these contexts. Instead, our findings suggest that structural disadvantage may operate differently in the domain of digital information seeking. Similarly, financial instability was associated with a lower likelihood of gun acquisition searches. One possibility is that individuals facing economic hardship perceive a gun purchase as a lower priority than meeting basic needs. Additionally, while a broad array of health and wellness concerns afflict communities that have limited financial and infrastructure resources, it is important to recognize the potential social strengths of a community that may thwart harmful outcomes. 38 This study also examined motivations for gun-related harm information-seeking. Curiosity was the most common reason, particularly for acquisition and concealment searches, suggesting exploratory rather than action-oriented intent, although exposure may still normalize gun use. Approximately 30% of respondents reported wanting privacy, especially for self-harm information searches, highlighting the role of anonymity in seeking sensitive information. Others cited lack of offline sources or embarrassment, consistent with patterns observed in self-harm and mental health contexts. 39 Less common motives included referrals and concern for others; for these individuals, bystander-style digital interventions may help equip people to support peers safely and effectively. 40 , 41 Overall, the most common sources of gun-related harm content were specific webpages and social media applications. However, the prevalence of content sources varied by topic. People seeking highly specific information (eg, how to make or conceal a gun) may prefer dedicated websites, which offer more credible information and greater detail. Online message boards for self-harm content may provide a sense of privacy and anonymity as well as peer support. These spaces may allow for interactive discussions and emotional validation, exactly the type of support that could be offered through online mental health services. 42 Through algorithm-driven recommendations, social media platforms may expose users to violent content without actively searching for it. 43 Aggressive or violent content may circulate in social networks where norms around guns and violence are reinforced. This reality suggests that our estimates of intentional searches for online information that may lead to gun violence or suicide underestimates the scope of both the problem of rampant online information and the opportunity to leverage search algorithms, social media, chat groups, and website linkages for the benefit of individual wellness behaviors. Moreover, responses that the information was encountered by accident suggest that there is an untapped need for online support to avoid gun-related harm to oneself or others. Limitations This study has limitations. All measures were self-reported and subject to recall error, misclassification, and social desirability bias, particularly given the stigmatized nature of some behaviors. Underreporting may have resulted in conservative prevalence estimates, and differential misclassification could have attenuated associations. Online gun-related harm seeking was assessed as lifetime exposure without a defined reference period. Therefore, older participants had greater cumulative opportunity to search, thus age differences may reflect exposure time rather than true developmental variation. Future studies should use defined time frames or age-standardized exposure windows. The cross-sectional design precludes causal inference. The panel excludes institutionalized populations, and measures did not assess recency or frequency of searches. Future research should examine timing and intensity of exposure in relation to life events and evolving digital contexts. 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Specific Items Measuring Exposure to Gun Violence, Threats, and Risky Gun Access jamanetwopen-e267715-s001.pdf (178.2KB, pdf) Supplement 2. 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