PURPOSE: Data on adverse childhood experiences (ACEs) among teens is collected using a single informant, a parent-proxy, or teen self-report. Little is known about alignment between these approaches. METHODS: Surveys were administered online to teens ages 15-17 and their parents (n=522 dyads) using the AmeriSpeak panel. We present descriptive statistics on the prevalence and measures agreement for 18 ACEs based on teen self-report and parent-proxy report. We fit multivariable models examining associations between teen and household demographic characteristics and discordance in ACE report. RESULTS: Based on teen-self report and parent-proxy report, cumulative and individual ACE prevalence was overall similar. However, discordance was found in individual ACE reports within teen-parent dyads (discordance ranged: 2.9% - 21.2%). Lowest agreement was among ACEs related to abuse, neglect, and violence victimization and highest among household challenges. Furthermore, parent-teen dyads with LGB+ youth (vs. heterosexual) and Black, Hispanic, and multiracial or another race (vs. White) youth were more likely to have discordant responses among several ACEs. CONCLUSIONS: Surveillance and programmatic efforts should consider the type of ACE and the reporter when using data to inform prevention strategies. Teen self-report for abuse, neglect, and violence victimization and community challenges ACEs are particularly important to capture.

INTRODUCTION: Among U.S. high-school students, interpersonal violence and victimizations often occur on school property. The presence of a weapon can increase the potential for injury and death resulting from interpersonal conflict. This study examines the associations between exposure to school violence and weapon carrying on school property among U.S. high-school students. METHODS: Data from the 2017 and 2019 national Youth Risk Behavior Surveys were combined (N=28,442) and analyzed in 2022. Multivariable logistic regression models were used to calculate sex-stratified, adjusted (for race/ethnicity, grade, sexual identity, current substance use, suicidal thoughts, and history of concussion) prevalence ratios. Prevalence ratios were considered statistically significant if 95% CIs did not include 1.0. RESULTS: Male students (4.7%) were more likely than female students (1.8%) to report carrying a weapon at school during the 30 days preceding the survey. Weapon carrying at school was more prevalent among students who were threatened or injured with a weapon at school (male students, adjusted prevalence ratio=3.45; female students, adjusted prevalence ratio=3.90), among students who were involved in a physical fight at school (male students, adjusted prevalence ratio=3.44; female students, adjusted prevalence ratio=3.72), among students who missed school because they did not feel safe (male students, adjusted prevalence ratio=1.98; female students, adjusted prevalence ratio=2.97), and among male students who were bullied at school (adjusted prevalence ratio=1.72) than among students who did not experience school violence. CONCLUSIONS: Increased emphasis on safe and supportive school environments, where all types of interpersonal violence are less likely to occur, and increased access to programs and services to promote mental health, prevent violence, and deter weapon use are needed.

Community violence, including homicides involving firearms, is a significant public health concern. From 2019 to 2020, firearm-related homicides increased by 39% for youths and young adults aged 10-24 years, and rates of suicide by firearm increased by approximately 15% among the same age group. Findings from the nationally representative 2021 Youth Risk Behavior Survey were used to analyze disparities and correlates of witnessing community violence and gun carrying among a nationally representative sample of high school students. Chi-square tests and logistic regression accounting for the complex sampling of the survey were used to assess demographic differences by student sex, race and ethnicity, age, and sexual identity in ever witnessing community violence, gun carrying in the past 12 months, and their associations with substance use and suicide risk. Measures of substance use included current binge drinking and marijuana use and lifetime prescription opioid misuse and illicit drug use. Suicide risk included seriously considered attempting suicide and attempted suicide in the past 12 months. Overall, approximately 20% of students witnessed community violence and 3.5% of students carried a gun. American Indian or Alaska Native, Black, and Hispanic students were more likely to witness community violence and to report carrying a gun than their White peers. Males were more likely to witness community violence and carry a gun than females. Lesbian, gay, or bisexual students were more likely to witness community violence than their heterosexual peers. Also, witnessing community violence consistently was associated with increased odds of gun carrying, substance use, and suicide risk for both males and females and when comparing Black, White, and Hispanic students. These findings highlight the importance of comprehensive violence prevention strategies that incorporate health equity to mitigate the effects of violence exposure on substance use and suicide risk among youths.

Adolescence is an important period of risk for substance use initiation and substance use-related adverse outcomes. To examine youth substance use trends and patterns, CDC analyzed data from the 2009-2019 Youth Risk Behavior Survey. This report presents estimated prevalence of current (i.e., previous 30-days) marijuana use, prescription opioid misuse, alcohol use, and binge drinking and lifetime prevalence of marijuana, synthetic marijuana, cocaine, methamphetamine, heroin, injection drug use, and prescription opioid misuse among U.S. high school students. Logistic regression and Joinpoint analyses were used to assess 2009-2019 trends. Prevalence of current and lifetime substance use by demographics, frequency of use, and prevalence of co-occurrence of selected substances among students reporting current prescription opioid misuse are estimated using 2019 data. Multivariable logistic regression analysis was used to determine demographic and substance use correlates of current prescription opioid misuse. Current alcohol, lifetime cocaine, methamphetamine, heroin, and injection drug use decreased during 2009-2019. Lifetime use of synthetic marijuana (also called synthetic cannabinoids) decreased during 2015-2019. Lifetime marijuana use increased during 2009-2013 and then decreased during 2013-2019. In 2019, 29.2% reported current alcohol use, 21.7% current marijuana use, 13.7% current binge drinking, and 7.2% current prescription opioid misuse. Substance use varied by sex, race/ethnicity, grade, and sexual minority status (lesbian, gay, or bisexual). Use of other substances, particularly current use of alcohol (59.4%) and marijuana (43.5%), was common among students currently misusing prescription opioids. Findings highlight opportunities for expanding evidence-based prevention policies, programs, and practices that aim to reduce risk factors and strengthen protective factors related to youth substance use, in conjunction with ongoing initiatives for combating the opioid crisis.

Adolescence is a critical phase of development and is frequently a period of initiating and engaging in risky behaviors, including alcohol and other substance use. The COVID-19 pandemic and associated stressors might have affected adolescent involvement in these behaviors. To examine substance use patterns and understand how substance use among high school students changed before and during the COVID-19 pandemic, CDC analyzed data from the nationally representative Youth Risk Behavior Survey. This report presents estimated prevalences among high school students of current (i.e., previous 30 days) alcohol use, marijuana use, binge drinking, and prescription opioid misuse and lifetime alcohol, marijuana, synthetic marijuana, inhalants, ecstasy, cocaine, methamphetamine, heroin, and injection drug use and prescription opioid misuse. Trends during 2009-2021 were assessed using logistic regression and joinpoint regression analyses. Changes in substance use from 2019 to 2021 were assessed using prevalence differences and prevalence ratios, stratified by demographic characteristics. Prevalence of substance use measures by sexual identity and current co-occurring substance use were estimated using 2021 data. Substance use prevalence declined during 2009-2021. From 2019 to 2021, the prevalence of current alcohol use, marijuana use, and binge drinking and lifetime use of alcohol, marijuana, and cocaine and prescription opioid misuse decreased; lifetime inhalant use increased. In 2021, substance use varied by sex, race and ethnicity, and sexual identity. Approximately one third of students (29%) reported current use of alcohol or marijuana or prescription opioid misuse; among those reporting current substance use, approximately 34% used two or more substances. Widespread implementation of tailored evidence-based policies, programs, and practices likely to reduce risk factors for adolescent substance use and promote protective factors might further decrease substance use among U.S. high school students and is urgently needed in the context of the changing marketplaces for alcohol beverage products and other drugs (e.g., release of high-alcohol beverage products and increased availability of counterfeit pills containing fentanyl).

OBJECTIVE: Opioid use disorder (OUD) affects approximately 5.6 million people in the United States annually, yet rates of the use of effective medication for OUD (MOUD) treatment are low. We conducted an observational cohort study from August 2017 through May 2021, the MOUD Study, to better understand treatment engagement and factors that may influence treatment experiences and outcomes. In this article, we describe the study design, data collected, and treatment outcomes. METHODS: We recruited adult patients receiving OUD treatment at US outpatient facilities for the MOUD Study. We collected patient-level data at 5 time points (baseline to 18 months) via self-administered questionnaires and health record data. We collected facility-level data via questionnaires administered to facility directors at 2 time points. Across 16 states, 62 OUD treatment facilities participated, and 1974 patients enrolled in the study. We summarized descriptive data on the characteristics of patients and OUD treatment facilities and selected treatment outcomes. RESULTS: Approximately half of the 62 facilities were private, nonprofit organizations; 62% focused primarily on substance use treatment; and 20% also offered mental health services. Most participants were receiving methadone (61%) or buprenorphine (32%) and were predominately non-Hispanic White (68%), aged 25-44 years (62%), and female (54%). Compared with patient-reported estimates at baseline, 18-month estimates suggested that rates of abstinence increased (55% to 77%), and rates of opioid-related overdoses (7% to 2%), emergency department visits (9% to 4%), and arrests (15% to 7%) decreased. CONCLUSIONS: Our results demonstrated the benefits of treatment retention not only on abstinence from opioid use but also on other quality-of-life metrics, with data collected during an extended period. The MOUD Study produced rich, multilevel data that can lay the foundation for an evidence base to inform OUD treatment and support improvement of care and patient outcomes.

During the COVID-19 pandemic, an urgent need existed for near-real-time data collection to better understand how individual beliefs and behaviors, state and local policies, and organizational practices influenced health outcomes. We describe the processes, methods, and lessons learned during the development and pilot testing of an innovative rapid data collection process we developed to inform decision-making during the COVID-19 public health emergency. We used a fully integrated mixed-methods approach to develop a structured process for triangulating quantitative and qualitative data from traditional (cross-sectional surveys, focus groups) and nontraditional (social media listening) sources. Respondents included students, parents, teachers, and key school personnel (eg, nurses, administrators, mental health providers). During the pilot phase (February-June 2021), data from 12 cross-sectional and sector-based surveys (n = 20 302 participants), 28 crowdsourced surveys (n = 26 820 participants), 10 focus groups (n = 64 participants), and 11 social media platforms (n = 432 754 503 responses) were triangulated with other data to support COVID-19 mitigation in schools. We disseminated findings through internal dashboards, triangulation reports, and policy briefs. This pilot demonstrated that triangulating traditional and nontraditional data sources can provide rapid data about barriers and facilitators to mitigation implementation during an evolving public health emergency. Such a rapid feedback and continuous improvement model can be tailored to strengthen response efforts. This approach emphasizes the value of nimble data modernization efforts to respond in real time to public health emergencies.

BACKGROUND: The school environment, consisting of the physical environment and social-emotional climate (SEC), plays a crucial role in both student and employee health; however, there is a lack of recent literature synthesizing school environment interventions in K-12 settings. We describe updated evidence about school environment interventions to support K-12 student and employee health in the United States. METHODS: A 2-phase search included a review of reviews (2010-2018), followed by a search for individual articles (2010-2020) that targeted school physical environment and/or SEC to address physical activity (PA) and/or nutrition. We also investigated how nutrition and PA interventions with an SEC component improves social-emotional and/or mental health outcomes. Because research on dietary and PA behaviors for school employees is limited; we included studies on other worksites (eg, hospitals and offices) to provide insight for school employees. FINDINGS: We identified 40 articles describing 40 unique studies and 45 interventions and grouped them by intervention type. Physical environment interventions demonstrated significant and positive nutrition and PA behavioral outcomes for students; outcomes among employees were mixed. Interventions with SEC components reported improvements in some mental health outcomes. IMPLICATIONS: The school environment can affect dietary and PA behaviors as well as mental health for students and employees. CONCLUSIONS: Establishing healthy school environments can support student and employee PA, dietary behaviors, and mental health.

INTRODUCTION: The COVID-19 pandemic has had unprecedented socioeconomic and health impacts in the U.S. This study examined racial/ethnic and school poverty status differences in the relationship between parent job loss, experiences with hunger, and indicators of mental health problems among public high-school students nationwide during the COVID-19 pandemic. METHODS: The study analyzed data from the Adolescent Behaviors and Experiences Survey, conducted in January-June 2021. The Adolescent Behaviors and Experiences Survey was a 1-time, cross-sectional, online survey that used a stratified, 3-stage cluster sample to obtain a nationally representative sample of high-school students in the U.S. This study was limited to public-school students (n=7,379). RESULTS: Among public high-school students nationwide, 36.9% experienced poor mental health during the pandemic, and during the past year, 43.9% experienced persistent feelings of sadness or hopelessness, 19.8% seriously considered attempting suicide, and 9.1% attempted suicide. Parent job loss and having gone hungry were associated with indicators of mental health problems overall and across racial/ethnic groups and school poverty status levels. CONCLUSIONS: Students who experience parent job loss and hunger are likely to also experience poor mental health and may be at higher risk for suicide.

Newly released 2019 Youth Risk Behavior Surveillance System data and the Center for Disease Control and Prevention's (CDC)'2019 Youth Risk Behavior Survey Data Summary and Trends Report show that US adolescents continue to suffer from poor mental health and suicidality at alarming rates. These data alone would be cause for concern, but the COVID-19 pandemic has the potential to further erode adolescent mental health, particularly for those whose mental health was poor prior to the pandemic. Given the status of adolescent mental health prior to COVID-19 and the impact of COVID-19, health professionals and schools must partner together now to mitigate potentially deleterious health, mental health and education impacts for children and adolescents.

In 2014, the then director of the Centers for Disease Control and Prevention (CDC), Dr Thomas Frieden, published an article in the American Journal of Public Health that detailed 6 components necessary for effective public health program implementation.1 Successful implementation of proven interventions requires overcoming barriers, including, as Frieden notes, “limited and unstable funding, lack of automatic means to track and improve performance, workforce limitations, and insufficient political commitment.”1(p17) The article suggests that successful, sustainable implementation is linked to 6 key components: (1) innovations in science, medicine, evaluation, or systems; (2) a rigorously established technical package of evidence-based interventions; (3) management, including rigorous monitoring and evaluation, of interventions and effective human resource management; (4) partnerships, particularly between different levels of government agencies (local, state, federal) and between government agencies and nongovernmental organizations; (5) communication, using timely, effective, and sustained messages, particularly through new technology, to facilitate conversations and disseminate messages; and (6) political commitment, which is both bolstered by and bolsters the prior 5 components.1 | This special issue of the Journal of Public Health Management & Practice includes implementation science and practice examples that build on this foundation and illustrate a systems approach to application of these components within programs of CDC’s National Center for Injury Prevention and Control (CDC’s Injury Center) (Table). Many, if not all of the articles, address more than 1 component; indeed, this is the intent: effective programs include all 6 components.1 All of the programs featured in the special issue required implementation of evidence-based strategies (component 2). However, none of the articles discuss this component in detail, primarily because extensive literature already exists for evidence-based programs, and the Injury Center has already developed several technical packages (https://www.cdc.gov/violenceprevention/pub/technical-packages.html) that specifically and thoroughly address this component. Thus, this special issue is devoted to the other 5 components: the systems that are critical to the successful implementation and coordination of the technical packages, for without these systems, technical packages are less likely to yield a population-level impact.

BACKGROUND: Childhood exposure to adverse experiences has been associated with adult asthma, smoking, sexually transmitted disease, obesity, substance use, depression, and sleep disturbances. Conceptualizing bullying as an adverse childhood experience, 2011 Youth Risk Behavior Survey (YRBS) data were used to examine the relationship between in-person and electronic bullying victimization among US high school students and health risk behaviors and conditions related to violence, substance use, sexual risk, overweight and physical activity, sleep, and asthma. METHODS: Data were from the 2011 national YRBS among students who answered questions about in-person and electronic bullying (N = 13,846). The YRBS is a biennial, nationally representative survey of students in grades 9-12 (overall response rate = 71%). Logistic regression analyses, stratified by sex and controlling for race/ethnicity and grade, examined the association between bullying victimization and health risk behaviors or conditions. RESULTS: Rates of victimization varied; 9.4% of students reported being bullied in-person and electronically, 10.8% only bullied in-person, 6.8% only electronically bullied, and 73.0% uninvolved. Bullying was associated with nearly all health risk behaviors and conditions studied. CONCLUSION: Assessing the broad functioning and behaviors of victims of bullying could enable educators and health practitioners to intervene early and promote the long-term health of youth.

IMPORTANCE: Bullying is the most widespread form of peer aggression in schools. In an effort to address school bullying, 49 states have passed antibullying statutes. Despite the ubiquity of these policies, there has been limited empirical examination of their effectiveness in reducing students' risk of being bullied. OBJECTIVE: To evaluate the effectiveness of antibullying legislation in reducing students' risk of being bullied and cyberbullied, using data from 25 states in the United States. DESIGN, SETTING, AND PARTICIPANTS: A cross-sectional observational study was conducted using a population-based survey of 63635 adolescents in grades 9 to 12 from 25 states participating in the 2011 Youth Risk Behavior Surveillance System study (September 2010-December 2011). Data on antibullying legislation were obtained from the US Department of Education (DOE), which commissioned a systematic review of state laws in 2011. The report identified 16 key components that were divided into the following 4 broad categories: purpose and definition of the law, district policy development and review, school district policy components (eg, responsibilities for reporting bullying incidents), and additional components (eg, how policies are communicated). Policy variables from 25 states were linked to individual-level data from the Youth Risk Behavior Surveillance System on experiencing bullying and cyberbullying. Analyses were conducted between March 1, 2014, and December 1, 2014. EXPOSURE: State antibullying legislation. MAIN OUTCOMES AND MEASURES: Exposure to bullying and cyberbullying in the past 12 months. RESULTS: There was substantial variation in the rates of bullying and cyberbullying across states. After controlling for relevant state-level confounders, students in states with at least 1 DOE legislative component in the antibullying law had a 24% (95% CI, 15%-32%) reduced odds of reporting bullying and 20% (95% CI, 9%-29%) reduced odds of reporting cyberbullying compared with students in states whose laws had no DOE legislative components. Three individual components of antibullying legislation were consistently associated with decreased odds of exposure to both bullying and cyberbullying: statement of scope, description of prohibited behaviors, and requirements for school districts to develop and implement local policies. CONCLUSIONS AND RELEVANCE: Antibullying policies may represent effective intervention strategies for reducing students' risk of being bullied and cyberbullied in schools.

OBJECTIVE: To examine the association of media violence exposure and physical aggression in fifth graders across 3 media types. METHODS: We analyzed data from a population-based, cross-sectional survey of 5,147 fifth graders and their parents in 3 US metropolitan areas. We used multivariable linear regression and report partial correlation coefficients to examine associations between children's exposure to violence in television/film, video games, and music (reported time spent consuming media and reported frequency of violent content: physical fighting, hurting, shooting, or killing) and the Problem Behavior Frequency Scale. RESULTS: Child-reported media violence exposure was associated with physical aggression after multivariable adjustment for sociodemographics, family and community violence, and child mental health symptoms (partial correlation coefficients: TV, 0.17; video games, 0.15; music, 0.14). This association was significant and independent for television, video games, and music violence exposure in a model including all 3 media types (partial correlation coefficients: TV, 0.11; video games, 0.09; music, 0.09). There was a significant positive interaction between media time and media violence for video games and music but not for television. Effect sizes for the association of media violence exposure and physical aggression were greater in magnitude than for most of the other examined variables. CONCLUSIONS: The association between physical aggression and media violence exposure is robust and persistent; the strength of this association of media violence may be at least as important as that of other factors with physical aggression in children, such as neighborhood violence, home violence, child mental health, and male gender.

OBJECTIVES: We examined the associations between 2 measures of sexual orientation and 4 suicide risk outcomes (SROs) from pooled local Youth Risk Behavior Surveys. METHODS: We aggregated data from 5 local Youth Risk Behavior Surveys from 2001 to 2009. We defined sexual minority youths (SMYs) by sexual identity (lesbian, gay, bisexual) and sex of sexual contacts (same- or both-sex contacts). Survey logistic regression analyses controlled for a wide range of suicide risk factors and sample design effects. RESULTS: Compared with non-SMYs, all SMYs had increased odds of suicide ideation; bisexual youths, gay males, and both-sex contact females had greater odds of suicide planning; all SMYs, except same-sex contact males, had increased odds of suicide attempts; and lesbians, bisexuals, and both-sex contact youths had increased odds of medically serious attempts. Unsure males had increased odds of suicide ideation compared with heterosexual males. Not having sexual contact was protective of most SROs among females and of medically serious attempts among males. CONCLUSIONS: Regardless of sexual orientation measure used, most SMY subgroups had increased odds of all SROs. However, many factors are associated with SROs.

Several recent studies have found an association between bullying and depression [1] or bullying and suicide-related behaviors [2], and one study found evidence consistent with a causal link, at least for girls [3,4]. These studies, in conjunction with extensive media coverage of the deaths by suicide of several young people who were victims of bullying, led the Centers for Disease Control and Prevention (CDC) to convene an expert panel focusing on the relationship between bullying involvement and suicide-related behaviors. The purpose of the panel, held in September 2010, was to synthesize the latest research about the relationship between youth involvement in bullying (youth who bully, youth who are bullied, and those who bully and are bullied) and suicide-related behaviors (attempts, fatalities, and risk factors associated with suicide, such as depression). Experts on the topics of bullying and suicide presented their research about the relationship between these two behaviors; their work is contained in this supplement. The panel and this special issue provide clarity around the complicated issues of bullying and suicide among youth. Three key themes emerged: (1) bullying among youth is a significant public health problem; it is prevalent and frequently has detrimental effects; (2) there is a strong association between bullying and suicide-related behaviors, but this relationship is often mediated by other factors, including depression and delinquency; and (3) there are public health strategies that can be applied to the prevention of bullying and suicide.

BACKGROUND: This study investigated associations of violence-related behaviors with physical activity (PA)-related behaviors among U.S. high school students. METHODS: Data from the 2009 national Youth Risk Behavior Survey, a cross-sectional survey of a nationally representative sample of 9th-12th grade students, were analyzed. Sex-stratified, adjusted odds ratios (aORs) and 95% confidence intervals (CIs) were estimated for associations between violence-related behaviors and being physically active for ≥60 minutes daily, sports participation, TV watching for ≥3 hours/day, and video game/computer use for ≥3 hours/day. RESULTS: Among male students, at-school bullying victimization was negatively associated with daily PA (aOR: 0.72; 95%CI: 0.58-0.87) and sports participation; skipping school because of safety concerns was positively associated with video game/computer use (1.42; 1.01-2.00); and physical fighting was positively associated with daily PA. Among female students, at-school bullying victimization and skipping school because of safety concerns were both positively associated with video game/computer use (1.46; 1.19-1.79 and 1.60; 1.09-2.34, respectively), and physical fighting at school was negatively associated with sports participation and positively associated with TV watching. CONCLUSIONS: Bullying victimization emerged as a potentially important risk factor for insufficient PA. Schools should consider the role of violence in initiatives designed to promote PA.

BACKGROUND: In 2000, the USDHHS released Healthy People 2010 (HP2010), a series of disease prevention and health promotion objectives for the nation. Thirty-nine of these objectives were dedicated to injury prevention and six of these objectives related to adolescents, who were tracked through CDC's National Youth Risk Behavior Survey (YRBS). PURPOSE: This paper uses national YRBS data from 1999 to 2009 to analyze overall and subgroup trends and determine progress toward targets for the following HP2010 objectives: seatbelt use (HP2010 objective 15-19); motorcycle helmet use (15-21); riding with a driver who had been drinking alcohol (26-6); physical fighting (15-38); weapon carrying on school property (15-39); and suicide attempts requiring medical attention (18-2). METHODS: The CDC conducted the national YRBS biennially from 1999 to 2009 and used similar three-stage cluster-sample designs to obtain representative samples of high school students in the U.S. This study was conducted in 2010 and used linear and quadratic time variables simultaneously in logistic regression models while controlling for gender, race/ethnicity, and grade to test for secular trends over time. RESULTS: Only two objectives met their HP2010 targets: riding with a driver who had been drinking alcohol (26-6) and physical fighting (15-38). Progress was seen for four additional objectives and within some subgroups. CONCLUSIONS: Substantial policy and practice changes must occur if the recently released Healthy People 2020 targets are to be met. School-, community-, and state-level policies and programs may be effective tools to prevent injuries and victimizations.

OBJECTIVE: We identified associations between time spent watching television and time spent playing video or computer games or using computers and involvement in interpersonal violence, alcohol and drug use in a nationally representative sample of United States high school students. METHODS: We analyzed data from the 2007 national Youth Risk Behavior Survey. Exposure variables were time spent watching television and time spent playing computer or video games or using computers (hereafter denoted as "computer/video game use") on an average school day; outcome variables included multiple measures assessing involvement in violence and alcohol or drug use. Chi-square tests were used to identify statistically significant associations between each exposure variable and each of the outcome variables. We used logistic regression to obtain crude odds ratios for outcome variables with a significant chi-square p-value and to obtain adjusted odds ratios controlling for sex, race, and grade in school. RESULTS: Overall, 35.4% (95% CI=33.1%-37.7%) of students reported frequent television (TV) use and 24.9% (95% CI=22.9%-27.0%) reported frequent computer/video game use. A number of risk behaviors, including involvement in physical fights and initiation of alcohol use before age 13, were significantly associated with frequent TV use or frequent computer/video game use, even after controlling for sex, race/ethnicity and grade. CONCLUSION: Findings highlight the need for additional research to better understand the mechanisms by which electronic media exposure and health-risk behaviors are associated and for the development of strategies that seek to understand how the content and context (e.g., watching with peers, having computer in common area) of media use influence risk behaviors among youth.

Bullying is an age-old phenomenon. In 1904, Stanley G. Hall, the first president of the American Psychological Association, described bullying behaviors in his book chapter, “Juvenile faults, immoralities, and crimes [1].” In 1945, 50% of sixth-grade boys in Rose Zeligs' study cited bullying as “an annoying social factor [2].” Times have changed and, although bullying remains, its character has changed as well. Young people today live as much in the virtual world as in the “real world.” Aggression experienced remotely may be just as upsetting as aggression experienced in person, especially because the victimization may be “witnessed” by hundreds of peers. The analysis by Mitchell et al [3] in this issue of the Journal of Adolescent Health contributes to a growing body of research that seeks to understand the differences and similarities between in-person and virtual aggression. | The work by Mitchell et al highlights the overlap between in-person and online victimization, raising the following questions: Is online aggression a unique and discrete phenomenon that requires new prevention and intervention strategies? Or, is online aggression a manifestation of in-person aggression? The similarity in intent of the perpetrator (to embarrass, humiliate, or threaten), the co-occurrence of in-person and online victimization, and shared risk and protective factors suggest the latter. The online environment simply provides a more efficient way for young people to aggress. Thus, violence prevention strategies with evidence of effectiveness in reducing in-person aggression may also be effective when applied to online aggression.