causes and effects of bullying research paper

• Research article
• Open access
• Published: 14 December 2021

Bullying at school and mental health problems among adolescents: a repeated cross-sectional study

• Håkan Källmén 1 &
• Mats Hallgren   ORCID: orcid.org/0000-0002-0599-2403 2  

Child and Adolescent Psychiatry and Mental Health volume  15 , Article number:  74 ( 2021 ) Cite this article

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To examine recent trends in bullying and mental health problems among adolescents and the association between them.

A questionnaire measuring mental health problems, bullying at school, socio-economic status, and the school environment was distributed to all secondary school students aged 15 (school-year 9) and 18 (school-year 11) in Stockholm during 2014, 2018, and 2020 (n = 32,722). Associations between bullying and mental health problems were assessed using logistic regression analyses adjusting for relevant demographic, socio-economic, and school-related factors.

The prevalence of bullying remained stable and was highest among girls in year 9; range = 4.9% to 16.9%. Mental health problems increased; range = + 1.2% (year 9 boys) to + 4.6% (year 11 girls) and were consistently higher among girls (17.2% in year 11, 2020). In adjusted models, having been bullied was detrimentally associated with mental health (OR = 2.57 [2.24–2.96]). Reports of mental health problems were four times higher among boys who had been bullied compared to those not bullied. The corresponding figure for girls was 2.4 times higher.

Conclusions

Exposure to bullying at school was associated with higher odds of mental health problems. Boys appear to be more vulnerable to the deleterious effects of bullying than girls.

Introduction

Bullying involves repeated hurtful actions between peers where an imbalance of power exists [ 1 ]. Arseneault et al. [ 2 ] conducted a review of the mental health consequences of bullying for children and adolescents and found that bullying is associated with severe symptoms of mental health problems, including self-harm and suicidality. Bullying was shown to have detrimental effects that persist into late adolescence and contribute independently to mental health problems. Updated reviews have presented evidence indicating that bullying is causative of mental illness in many adolescents [ 3 , 4 ].

There are indications that mental health problems are increasing among adolescents in some Nordic countries. Hagquist et al. [ 5 ] examined trends in mental health among Scandinavian adolescents (n = 116, 531) aged 11–15 years between 1993 and 2014. Mental health problems were operationalized as difficulty concentrating, sleep disorders, headache, stomach pain, feeling tense, sad and/or dizzy. The study revealed increasing rates of adolescent mental health problems in all four counties (Finland, Sweden, Norway, and Denmark), with Sweden experiencing the sharpest increase among older adolescents, particularly girls. Worsening adolescent mental health has also been reported in the United Kingdom. A study of 28,100 school-aged adolescents in England found that two out of five young people scored above thresholds for emotional problems, conduct problems or hyperactivity [ 6 ]. Female gender, deprivation, high needs status (educational/social), ethnic background, and older age were all associated with higher odds of experiencing mental health difficulties.

Bullying is shown to increase the risk of poor mental health and may partly explain these detrimental changes. Le et al. [ 7 ] reported an inverse association between bullying and mental health among 11–16-year-olds in Vietnam. They also found that poor mental health can make some children and adolescents more vulnerable to bullying at school. Bayer et al. [ 8 ] examined links between bullying at school and mental health among 8–9-year-old children in Australia. Those who experienced bullying more than once a week had poorer mental health than children who experienced bullying less frequently. Friendships moderated this association, such that children with more friends experienced fewer mental health problems (protective effect). Hysing et al. [ 9 ] investigated the association between experiences of bullying (as a victim or perpetrator) and mental health, sleep disorders, and school performance among 16–19 year olds from Norway (n = 10,200). Participants were categorized as victims, bullies, or bully-victims (that is, victims who also bullied others). All three categories were associated with worse mental health, school performance, and sleeping difficulties. Those who had been bullied also reported more emotional problems, while those who bullied others reported more conduct disorders [ 9 ].

As most adolescents spend a considerable amount of time at school, the school environment has been a major focus of mental health research [ 10 , 11 ]. In a recent review, Saminathen et al. [ 12 ] concluded that school is a potential protective factor against mental health problems, as it provides a socially supportive context and prepares students for higher education and employment. However, it may also be the primary setting for protracted bullying and stress [ 13 ]. Another factor associated with adolescent mental health is parental socio-economic status (SES) [ 14 ]. A systematic review indicated that lower parental SES is associated with poorer adolescent mental health [ 15 ]. However, no previous studies have examined whether SES modifies or attenuates the association between bullying and mental health. Similarly, it remains unclear whether school related factors, such as school grades and the school environment, influence the relationship between bullying and mental health. This information could help to identify those adolescents most at risk of harm from bullying.

To address these issues, we investigated the prevalence of bullying at school and mental health problems among Swedish adolescents aged 15–18 years between 2014 and 2020 using a population-based school survey. We also examined associations between bullying at school and mental health problems adjusting for relevant demographic, socioeconomic, and school-related factors. We hypothesized that: (1) bullying and adolescent mental health problems have increased over time; (2) There is an association between bullying victimization and mental health, so that mental health problems are more prevalent among those who have been victims of bullying; and (3) that school-related factors would attenuate the association between bullying and mental health.

Participants

The Stockholm school survey is completed every other year by students in lower secondary school (year 9—compulsory) and upper secondary school (year 11). The survey is mandatory for public schools, but voluntary for private schools. The purpose of the survey is to help inform decision making by local authorities that will ultimately improve students’ wellbeing. The questions relate to life circumstances, including SES, schoolwork, bullying, drug use, health, and crime. Non-completers are those who were absent from school when the survey was completed (< 5%). Response rates vary from year to year but are typically around 75%. For the current study data were available for 2014, 2018 and 2020. In 2014; 5235 boys and 5761 girls responded, in 2018; 5017 boys and 5211 girls responded, and in 2020; 5633 boys and 5865 girls responded (total n = 32,722). Data for the exposure variable, bullied at school, were missing for 4159 students, leaving 28,563 participants in the crude model. The fully adjusted model (described below) included 15,985 participants. The mean age in grade 9 was 15.3 years (SD = 0.51) and in grade 11, 17.3 years (SD = 0.61). As the data are completely anonymous, the study was exempt from ethical approval according to an earlier decision from the Ethical Review Board in Stockholm (2010-241 31-5). Details of the survey are available via a website [ 16 ], and are described in a previous paper [ 17 ].

Students completed the questionnaire during a school lesson, placed it in a sealed envelope and handed it to their teacher. Student were permitted the entire lesson (about 40 min) to complete the questionnaire and were informed that participation was voluntary (and that they were free to cancel their participation at any time without consequences). Students were also informed that the Origo Group was responsible for collection of the data on behalf of the City of Stockholm.

Study outcome

Mental health problems were assessed by using a modified version of the Psychosomatic Problem Scale [ 18 ] shown to be appropriate for children and adolescents and invariant across gender and years. The scale was later modified [ 19 ]. In the modified version, items about difficulty concentrating and feeling giddy were deleted and an item about ‘life being great to live’ was added. Seven different symptoms or problems, such as headaches, depression, feeling fear, stomach problems, difficulty sleeping, believing it’s great to live (coded negatively as seldom or rarely) and poor appetite were used. Students who responded (on a 5-point scale) that any of these problems typically occurs ‘at least once a week’ were considered as having indicators of a mental health problem. Cronbach alpha was 0.69 across the whole sample. Adding these problem areas, a total index was created from 0 to 7 mental health symptoms. Those who scored between 0 and 4 points on the total symptoms index were considered to have a low indication of mental health problems (coded as 0); those who scored between 5 and 7 symptoms were considered as likely having mental health problems (coded as 1).

Primary exposure

Experiences of bullying were measured by the following two questions: Have you felt bullied or harassed during the past school year? Have you been involved in bullying or harassing other students during this school year? Alternatives for the first question were: yes or no with several options describing how the bullying had taken place (if yes). Alternatives indicating emotional bullying were feelings of being mocked, ridiculed, socially excluded, or teased. Alternatives indicating physical bullying were being beaten, kicked, forced to do something against their will, robbed, or locked away somewhere. The response alternatives for the second question gave an estimation of how often the respondent had participated in bullying others (from once to several times a week). Combining the answers to these two questions, five different categories of bullying were identified: (1) never been bullied and never bully others; (2) victims of emotional (verbal) bullying who have never bullied others; (3) victims of physical bullying who have never bullied others; (4) victims of bullying who have also bullied others; and (5) perpetrators of bullying, but not victims. As the number of positive cases in the last three categories was low (range = 3–15 cases) bully categories 2–4 were combined into one primary exposure variable: ‘bullied at school’.

Assessment year was operationalized as the year when data was collected: 2014, 2018, and 2020. Age was operationalized as school grade 9 (15–16 years) or 11 (17–18 years). Gender was self-reported (boy or girl). The school situation To assess experiences of the school situation, students responded to 18 statements about well-being in school, participation in important school matters, perceptions of their teachers, and teaching quality. Responses were given on a four-point Likert scale ranging from ‘do not agree at all’ to ‘fully agree’. To reduce the 18-items down to their essential factors, we performed a principal axis factor analysis. Results showed that the 18 statements formed five factors which, according to the Kaiser criterion (eigen values > 1) explained 56% of the covariance in the student’s experience of the school situation. The five factors identified were: (1) Participation in school; (2) Interesting and meaningful work; (3) Feeling well at school; (4) Structured school lessons; and (5) Praise for achievements. For each factor, an index was created that was dichotomised (poor versus good circumstance) using the median-split and dummy coded with ‘good circumstance’ as reference. A description of the items included in each factor is available as Additional file 1 . Socio-economic status (SES) was assessed with three questions about the education level of the student’s mother and father (dichotomized as university degree versus not), and the amount of spending money the student typically received for entertainment each month (> SEK 1000 [approximately $120] versus less). Higher parental education and more spending money were used as reference categories. School grades in Swedish, English, and mathematics were measured separately on a 7-point scale and dichotomized as high (grades A, B, and C) versus low (grades D, E, and F). High school grades were used as the reference category.

Statistical analyses

The prevalence of mental health problems and bullying at school are presented using descriptive statistics, stratified by survey year (2014, 2018, 2020), gender, and school year (9 versus 11). As noted, we reduced the 18-item questionnaire assessing school function down to five essential factors by conducting a principal axis factor analysis (see Additional file 1 ). We then calculated the association between bullying at school (defined above) and mental health problems using multivariable logistic regression. Results are presented as odds ratios (OR) with 95% confidence intervals (Cis). To assess the contribution of SES and school-related factors to this association, three models are presented: Crude, Model 1 adjusted for demographic factors: age, gender, and assessment year; Model 2 adjusted for Model 1 plus SES (parental education and student spending money), and Model 3 adjusted for Model 2 plus school-related factors (school grades and the five factors identified in the principal factor analysis). These covariates were entered into the regression models in three blocks, where the final model represents the fully adjusted analyses. In all models, the category ‘not bullied at school’ was used as the reference. Pseudo R-square was calculated to estimate what proportion of the variance in mental health problems was explained by each model. Unlike the R-square statistic derived from linear regression, the Pseudo R-square statistic derived from logistic regression gives an indicator of the explained variance, as opposed to an exact estimate, and is considered informative in identifying the relative contribution of each model to the outcome [ 20 ]. All analyses were performed using SPSS v. 26.0.

Prevalence of bullying at school and mental health problems

Estimates of the prevalence of bullying at school and mental health problems across the 12 strata of data (3 years × 2 school grades × 2 genders) are shown in Table 1 . The prevalence of bullying at school increased minimally (< 1%) between 2014 and 2020, except among girls in grade 11 (2.5% increase). Mental health problems increased between 2014 and 2020 (range = 1.2% [boys in year 11] to 4.6% [girls in year 11]); were three to four times more prevalent among girls (range = 11.6% to 17.2%) compared to boys (range = 2.6% to 4.9%); and were more prevalent among older adolescents compared to younger adolescents (range = 1% to 3.1% higher). Pooling all data, reports of mental health problems were four times more prevalent among boys who had been victims of bullying compared to those who reported no experiences with bullying. The corresponding figure for girls was two and a half times as prevalent.

Associations between bullying at school and mental health problems

Table 2 shows the association between bullying at school and mental health problems after adjustment for relevant covariates. Demographic factors, including female gender (OR = 3.87; CI 3.48–4.29), older age (OR = 1.38, CI 1.26–1.50), and more recent assessment year (OR = 1.18, CI 1.13–1.25) were associated with higher odds of mental health problems. In Model 2, none of the included SES variables (parental education and student spending money) were associated with mental health problems. In Model 3 (fully adjusted), the following school-related factors were associated with higher odds of mental health problems: lower grades in Swedish (OR = 1.42, CI 1.22–1.67); uninteresting or meaningless schoolwork (OR = 2.44, CI 2.13–2.78); feeling unwell at school (OR = 1.64, CI 1.34–1.85); unstructured school lessons (OR = 1.31, CI = 1.16–1.47); and no praise for achievements (OR = 1.19, CI 1.06–1.34). After adjustment for all covariates, being bullied at school remained associated with higher odds of mental health problems (OR = 2.57; CI 2.24–2.96). Demographic and school-related factors explained 12% and 6% of the variance in mental health problems, respectively (Pseudo R-Square). The inclusion of socioeconomic factors did not alter the variance explained.

Our findings indicate that mental health problems increased among Swedish adolescents between 2014 and 2020, while the prevalence of bullying at school remained stable (< 1% increase), except among girls in year 11, where the prevalence increased by 2.5%. As previously reported [ 5 , 6 ], mental health problems were more common among girls and older adolescents. These findings align with previous studies showing that adolescents who are bullied at school are more likely to experience mental health problems compared to those who are not bullied [ 3 , 4 , 9 ]. This detrimental relationship was observed after adjustment for school-related factors shown to be associated with adolescent mental health [ 10 ].

A novel finding was that boys who had been bullied at school reported a four-times higher prevalence of mental health problems compared to non-bullied boys. The corresponding figure for girls was 2.5 times higher for those who were bullied compared to non-bullied girls, which could indicate that boys are more vulnerable to the deleterious effects of bullying than girls. Alternatively, it may indicate that boys are (on average) bullied more frequently or more intensely than girls, leading to worse mental health. Social support could also play a role; adolescent girls often have stronger social networks than boys and could be more inclined to voice concerns about bullying to significant others, who in turn may offer supports which are protective [ 21 ]. Related studies partly confirm this speculative explanation. An Estonian study involving 2048 children and adolescents aged 10–16 years found that, compared to girls, boys who had been bullied were more likely to report severe distress, measured by poor mental health and feelings of hopelessness [ 22 ].

Other studies suggest that heritable traits, such as the tendency to internalize problems and having low self-esteem are associated with being a bully-victim [ 23 ]. Genetics are understood to explain a large proportion of bullying-related behaviors among adolescents. A study from the Netherlands involving 8215 primary school children found that genetics explained approximately 65% of the risk of being a bully-victim [ 24 ]. This proportion was similar for boys and girls. Higher than average body mass index (BMI) is another recognized risk factor [ 25 ]. A recent Australian trial involving 13 schools and 1087 students (mean age = 13 years) targeted adolescents with high-risk personality traits (hopelessness, anxiety sensitivity, impulsivity, sensation seeking) to reduce bullying at school; both as victims and perpetrators [ 26 ]. There was no significant intervention effect for bullying victimization or perpetration in the total sample. In a secondary analysis, compared to the control schools, intervention school students showed greater reductions in victimization, suicidal ideation, and emotional symptoms. These findings potentially support targeting high-risk personality traits in bullying prevention [ 26 ].

The relative stability of bullying at school between 2014 and 2020 suggests that other factors may better explain the increase in mental health problems seen here. Many factors could be contributing to these changes, including the increasingly competitive labour market, higher demands for education, and the rapid expansion of social media [ 19 , 27 , 28 ]. A recent Swedish study involving 29,199 students aged between 11 and 16 years found that the effects of school stress on psychosomatic symptoms have become stronger over time (1993–2017) and have increased more among girls than among boys [ 10 ]. Research is needed examining possible gender differences in perceived school stress and how these differences moderate associations between bullying and mental health.

Strengths and limitations

Strengths of the current study include the large participant sample from diverse schools; public and private, theoretical and practical orientations. The survey included items measuring diverse aspects of the school environment; factors previously linked to adolescent mental health but rarely included as covariates in studies of bullying and mental health. Some limitations are also acknowledged. These data are cross-sectional which means that the direction of the associations cannot be determined. Moreover, all the variables measured were self-reported. Previous studies indicate that students tend to under-report bullying and mental health problems [ 29 ]; thus, our results may underestimate the prevalence of these behaviors.

In conclusion, consistent with our stated hypotheses, we observed an increase in self-reported mental health problems among Swedish adolescents, and a detrimental association between bullying at school and mental health problems. Although bullying at school does not appear to be the primary explanation for these changes, bullying was detrimentally associated with mental health after adjustment for relevant demographic, socio-economic, and school-related factors, confirming our third hypothesis. The finding that boys are potentially more vulnerable than girls to the deleterious effects of bullying should be replicated in future studies, and the mechanisms investigated. Future studies should examine the longitudinal association between bullying and mental health, including which factors mediate/moderate this relationship. Epigenetic studies are also required to better understand the complex interaction between environmental and biological risk factors for adolescent mental health [ 24 ].

Availability of data and materials

Data requests will be considered on a case-by-case basis; please email the corresponding author.

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Acknowledgements

Authors are grateful to the Department for Social Affairs, Stockholm, for permission to use data from the Stockholm School Survey.

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HK conceived the study and analyzed the data (with input from MH). HK and MH interpreted the data and jointly wrote the manuscript. All authors read and approved the final manuscript.

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Additional file 1..

Principal factor analysis description.

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Källmén, H., Hallgren, M. Bullying at school and mental health problems among adolescents: a repeated cross-sectional study. Child Adolesc Psychiatry Ment Health 15 , 74 (2021). https://doi.org/10.1186/s13034-021-00425-y

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Bullying: What We Know Based On 40 Years of Research

APA journal examines science aimed at understanding causes, prevention

WASHINGTON — A special issue of American Psychologist ® provides a comprehensive review of over 40 years of research on bullying among school age youth, documenting the current understanding of the complexity of the issue and suggesting directions for future research.

“The lore of bullies has long permeated literature and popular culture. Yet bullying as a distinct form of interpersonal aggression was not systematically studied until the 1970s. Attention to the topic has since grown exponentially,” said Shelley Hymel, PhD, professor of human development, learning and culture at the University of British Columbia, a scholarly lead on the special issue along with Susan M. Swearer, PhD, professor of school psychology at the University of Nebraska-Lincoln. “Inspired by the 2011 U.S. White House Conference on Bullying Prevention, this collection of articles documents current understanding of school bullying.”

The special issue consists of an introductory overview  (PDF, 90KB) by Hymel and Swearer, co-directors of the Bullying Research Network, and five articles on various research areas of bullying including the long-term effects of bullying into adulthood, reasons children bully others, the effects of anti-bullying laws and ways of translating research into anti-bullying practice.

Articles in the issue:

Long-Term Adult Outcomes of Peer Victimization in Childhood and Adolescence: Pathways to Adjustment and Maladjustment  (PDF, 122KB) by Patricia McDougall, PhD, University of Saskatchewan, and Tracy Vaillancourt, PhD, University of Ottawa.

The experience of being bullied is painful and difficult. Its negative impact — on academic functioning, physical and mental health, social relationships and self-perceptions — can endure across the school years. But not every victimized child develops into a maladjusted adult. In this article, the authors provide an overview of the negative outcomes experienced by victims through childhood and adolescence and sometimes into adulthood. They then analyze findings from prospective studies to identify factors that lead to different outcomes in different people, including in their biology, timing, support systems and self-perception.

Patricia McDougall can be contacted by email or by phone at (306) 966-6203.

A Relational Framework for Understanding Bullying: Developmental Antecedents and Outcomes  (PDF, 151KB) by Philip Rodkin, PhD, and Dorothy Espelage, PhD, University of Illinois, Urbana-Champaign, and Laura Hanish, PhD, Arizona State University.

How do you distinguish bullying from aggression in general? In this review, the authors describe bullying from a relationship perspective. In order for bullying to be distinguished from other forms of aggression, a relationship must exist between the bully and the victim, there must be an imbalance of power between the two and it must take place over a period of time. “Bullying is perpetrated within a relationship, albeit a coercive, unequal, asymmetric relationship characterized by aggression,” wrote the authors. Within that perspective, the image of bullies as socially incompetent youth who rely on physical coercion to resolve conflicts is nothing more than a stereotype. While this type of “bully-victim” does exist and is primarily male, the authors describe another type of bully who is more socially integrated and has surprisingly high levels of popularity among his or her peers. As for the gender of victims, bullying is just as likely to occur between boys and girls as it is to occur in same-gender groups.  

Dorothy Espelage can be contacted by email or by phone at (217) 333-9139.

Translating Research to Practice in Bullying Prevention  (PDF, 157KB) by Catherine Bradshaw, PhD, University of Virginia.

This paper reviews the research and related science to develop a set of recommendations for effective bullying prevention programs. From mixed findings on existing programs, the author identifies core elements of promising prevention approaches (e.g., close playground supervision, family involvement, and consistent classroom management strategies) and recommends a three-tiered public health approach that can attend to students at all risk levels. However, the author notes, prevention efforts must be sustained and integrated to effect change. 

Catherine Bradshaw can be contacted by email or by phone at (434) 924-8121.

Law and Policy on the Concept of Bullying at School  (PDF, 126KB) by Dewey Cornell, PhD, University of Virginia, and Susan Limber, PhD, Clemson University.

Since the shooting at Columbine High School in 1999, all states but one have passed anti-bullying laws, and multiple court decisions have made schools more accountable for peer victimization. Unfortunately, current legal and policy approaches, which are strongly rooted in laws regarding harassment and discrimination, do not provide adequate protection for all bullied students. In this article, the authors provide a review of the legal framework underpinning many anti-bullying laws and make recommendations on best practices for legislation and school policies to effectively address the problem of bullying.

Dewey Cornell can be contacted by email or by phone at (434) 924-0793.

Understanding the Psychology of Bullying: Moving Toward a Social-Ecological Diathesis-Stress Model by Susan Swearer, PhD, University of Nebraska-Lincoln, and Shelley Hymel, PhD, University of British Columbia.

Children’s involvement in bullying varies across roles and over time. A student may be victimized by classmates but bully a sibling at home. Bullying is a complex form of interpersonal aggression that can be both a one-on-one process and a group phenomenon. It negatively affects not only the victim, but the bully and witnesses as well. In this paper, the authors suggest an integrated model for examining bullying and victimization that recognizes the complex and dynamic nature of bullying across multiple settings over time.

Susan Swearer  can be contacted by email or by phone at (402) 472-1741. Shelley Hymel can be contacted by email or by phone at (604) 822-6022.

Copies of articles are also available from APA Public Affairs , (202) 336-5700.

The American Psychological Association, in Washington, D.C., is the largest scientific and professional organization representing psychology in the United States. APA's membership includes more than 122,500 researchers, educators, clinicians, consultants and students. Through its divisions in 54 subfields of psychology and affiliations with 60 state, territorial and Canadian provincial associations, APA works to advance the creation, communication and application of psychological knowledge to benefit society and improve people's lives.

Jim Sliwa (202) 336-5707

Preventing Bullying Through Science, Policy, and Practice (2016)

Chapter: 1 introduction, 1 introduction.

Bullying, long tolerated by many as a rite of passage into adulthood, is now recognized as a major and preventable public health problem, one that can have long-lasting consequences ( McDougall and Vaillancourt, 2015 ; Wolke and Lereya, 2015 ). Those consequences—for those who are bullied, for the perpetrators of bullying, and for witnesses who are present during a bullying event—include poor school performance, anxiety, depression, and future delinquent and aggressive behavior. Federal, state, and local governments have responded by adopting laws and implementing programs to prevent bullying and deal with its consequences. However, many of these responses have been undertaken with little attention to what is known about bullying and its effects. Even the definition of bullying varies among both researchers and lawmakers, though it generally includes physical and verbal behavior, behavior leading to social isolation, and behavior that uses digital communications technology (cyberbullying). This report adopts the term “bullying behavior,” which is frequently used in the research field, to cover all of these behaviors.

Bullying behavior is evident as early as preschool, although it peaks during the middle school years ( Currie et al., 2012 ; Vaillancourt et al., 2010 ). It can occur in diverse social settings, including classrooms, school gyms and cafeterias, on school buses, and online. Bullying behavior affects not only the children and youth who are bullied, who bully, and who are both bullied and bully others but also bystanders to bullying incidents. Given the myriad situations in which bullying can occur and the many people who may be involved, identifying effective prevention programs and policies is challenging, and it is unlikely that any one approach will be ap-

propriate in all situations. Commonly used bullying prevention approaches include policies regarding acceptable behavior in schools and behavioral interventions to promote positive cultural norms.

STUDY CHARGE

Recognizing that bullying behavior is a major public health problem that demands the concerted and coordinated time and attention of parents, educators and school administrators, health care providers, policy makers, families, and others concerned with the care of children, a group of federal agencies and private foundations asked the National Academies of Sciences, Engineering, and Medicine to undertake a study of what is known and what needs to be known to further the field of preventing bullying behavior. The Committee on the Biological and Psychosocial Effects of Peer Victimization:

Lessons for Bullying Prevention was created to carry out this task under the Academies’ Board on Children, Youth, and Families and the Committee on Law and Justice. The study received financial support from the Centers for Disease Control and Prevention (CDC), the Eunice Kennedy Shriver National Institute of Child Health and Human Development, the Health Resources and Services Administration, the Highmark Foundation, the National Institute of Justice, the Robert Wood Johnson Foundation, Semi J. and Ruth W. Begun Foundation, and the Substance Abuse and Mental Health Services Administration. The full statement of task for the committee is presented in Box 1-1 .

Although the committee acknowledges the importance of this topic as it pertains to all children in the United States and in U.S. territories, this report focuses on the 50 states and the District of Columbia. Also, while the committee acknowledges that bullying behavior occurs in the school

environment for youth in foster care, in juvenile justice facilities, and in other residential treatment facilities, this report does not address bullying behavior in those environments because it is beyond the study charge.

CONTEXT FOR THE STUDY

This section of the report highlights relevant work in the field and, later in the chapter under “The Committee’s Approach,” presents the conceptual framework and corresponding definitions of terms that the committee has adopted.

Historical Context

Bullying behavior was first characterized in the scientific literature as part of the childhood experience more than 100 years ago in “Teasing and Bullying,” published in the Pedagogical Seminary ( Burk, 1897 ). The author described bullying behavior, attempted to delineate causes and cures for the tormenting of others, and called for additional research ( Koo, 2007 ). Nearly a century later, Dan Olweus, a Swedish research professor of psychology in Norway, conducted an intensive study on bullying ( Olweus, 1978 ). The efforts of Olweus brought awareness to the issue and motivated other professionals to conduct their own research, thereby expanding and contributing to knowledge of bullying behavior. Since Olweus’s early work, research on bullying has steadily increased (see Farrington and Ttofi, 2009 ; Hymel and Swearer, 2015 ).

Over the past few decades, venues where bullying behavior occurs have expanded with the advent of the Internet, chat rooms, instant messaging, social media, and other forms of digital electronic communication. These modes of communication have provided a new communal avenue for bullying. While the media reports linking bullying to suicide suggest a causal relationship, the available research suggests that there are often multiple factors that contribute to a youth’s suicide-related ideology and behavior. Several studies, however, have demonstrated an association between bullying involvement and suicide-related ideology and behavior (see, e.g., Holt et al., 2015 ; Kim and Leventhal, 2008 ; Sourander, 2010 ; van Geel et al., 2014 ).

In 2013, the Health Resources and Services Administration of the U.S. Department of Health and Human Services requested that the Institute of Medicine 1 and the National Research Council convene an ad hoc planning committee to plan and conduct a 2-day public workshop to highlight relevant information and knowledge that could inform a multidisciplinary

___________________

1 Prior to 2015, the National Academy of Medicine was known as the Institute of Medicine.

road map on next steps for the field of bullying prevention. Content areas that were explored during the April 2014 workshop included the identification of conceptual models and interventions that have proven effective in decreasing bullying and the antecedents to bullying while increasing protective factors that mitigate the negative health impact of bullying. The discussions highlighted the need for a better understanding of the effectiveness of program interventions in realistic settings; the importance of understanding what works for whom and under what circumstances, as well as the influence of different mediators (i.e., what accounts for associations between variables) and moderators (i.e., what affects the direction or strength of associations between variables) in bullying prevention efforts; and the need for coordination among agencies to prevent and respond to bullying. The workshop summary ( Institute of Medicine and National Research Council, 2014c ) informs this committee’s work.

Federal Efforts to Address Bullying and Related Topics

Currently, there is no comprehensive federal statute that explicitly prohibits bullying among children and adolescents, including cyberbullying. However, in the wake of the growing concerns surrounding the implications of bullying, several federal initiatives do address bullying among children and adolescents, and although some of them do not primarily focus on bullying, they permit some funds to be used for bullying prevention purposes.

The earliest federal initiative was in 1999, when three agencies collaborated to establish the Safe Schools/Healthy Students initiative in response to a series of deadly school shootings in the late 1990s. The program is administered by the U.S. Departments of Education, Health and Human Services, and Justice to prevent youth violence and promote the healthy development of youth. It is jointly funded by the Department of Education and by the Department of Health and Human Services’ Substance Abuse and Mental Health Services Administration. The program has provided grantees with both the opportunity to benefit from collaboration and the tools to sustain it through deliberate planning, more cost-effective service delivery, and a broader funding base ( Substance Abuse and Mental Health Services Administration, 2015 ).

The next major effort was in 2010, when the Department of Education awarded $38.8 million in grants under the Safe and Supportive Schools (S3) Program to 11 states to support statewide measurement of conditions for learning and targeted programmatic interventions to improve conditions for learning, in order to help schools improve safety and reduce substance use. The S3 Program was administered by the Safe and Supportive Schools Group, which also administered the Safe and Drug-Free Schools and Communities Act State and Local Grants Program, authorized by the

1994 Elementary and Secondary Education Act. 2 It was one of several programs related to developing and maintaining safe, disciplined, and drug-free schools. In addition to the S3 grants program, the group administered a number of interagency agreements with a focus on (but not limited to) bullying, school recovery research, data collection, and drug and violence prevention activities ( U.S. Department of Education, 2015 ).

A collaborative effort among the U.S. Departments of Agriculture, Defense, Education, Health and Human Services, Interior, and Justice; the Federal Trade Commission; and the White House Initiative on Asian Americans and Pacific Islanders created the Federal Partners in Bullying Prevention (FPBP) Steering Committee. Led by the U.S. Department of Education, the FPBP works to coordinate policy, research, and communications on bullying topics. The FPBP Website provides extensive resources on bullying behavior, including information on what bullying is, its risk factors, its warning signs, and its effects. 3 The FPBP Steering Committee also plans to provide details on how to get help for those who have been bullied. It also was involved in creating the “Be More than a Bystander” Public Service Announcement campaign with the Ad Council to engage students in bullying prevention. To improve school climate and reduce rates of bullying nationwide, FPBP has sponsored four bullying prevention summits attended by education practitioners, policy makers, researchers, and federal officials.

In 2014, the National Institute of Justice—the scientific research arm of the U.S. Department of Justice—launched the Comprehensive School Safety Initiative with a congressional appropriation of $75 million. The funds are to be used for rigorous research to produce practical knowledge that can improve the safety of schools and students, including bullying prevention. The initiative is carried out through partnerships among researchers, educators, and other stakeholders, including law enforcement, behavioral and mental health professionals, courts, and other justice system professionals ( National Institute of Justice, 2015 ).

In 2015, the Every Student Succeeds Act was signed by President Obama, reauthorizing the 50-year-old Elementary and Secondary Education Act, which is committed to providing equal opportunities for all students. Although bullying is neither defined nor prohibited in this act, it is explicitly mentioned in regard to applicability of safe school funding, which it had not been in previous iterations of the Elementary and Secondary Education Act.

The above are examples of federal initiatives aimed at promoting the

2 The Safe and Drug-Free Schools and Communities Act was included as Title IV, Part A, of the 1994 Elementary and Secondary Education Act. See http://www.ojjdp.gov/pubs/gun_violence/sect08-i.html [October 2015].

3 For details, see http://www.stopbullying.gov/ [October 2015].

healthy development of youth, improving the safety of schools and students, and reducing rates of bullying behavior. There are several other federal initiatives that address student bullying directly or allow funds to be used for bullying prevention activities.

Definitional Context

The terms “bullying,” “harassment,” and “peer victimization” have been used in the scientific literature to refer to behavior that is aggressive, is carried out repeatedly and over time, and occurs in an interpersonal relationship where a power imbalance exists ( Eisenberg and Aalsma, 2005 ). Although some of these terms have been used interchangeably in the literature, peer victimization is targeted aggressive behavior of one child against another that causes physical, emotional, social, or psychological harm. While conflict and bullying among siblings are important in their own right ( Tanrikulu and Campbell, 2015 ), this area falls outside of the scope of the committee’s charge. Sibling conflict and aggression falls under the broader concept of interpersonal aggression, which includes dating violence, sexual assault, and sibling violence, in addition to bullying as defined for this report. Olweus (1993) noted that bullying, unlike other forms of peer victimization where the children involved are equally matched, involves a power imbalance between the perpetrator and the target, where the target has difficulty defending him or herself and feels helpless against the aggressor. This power imbalance is typically considered a defining feature of bullying, which distinguishes this particular form of aggression from other forms, and is typically repeated in multiple bullying incidents involving the same individuals over time ( Olweus, 1993 ).

Bullying and violence are subcategories of aggressive behavior that overlap ( Olweus, 1996 ). There are situations in which violence is used in the context of bullying. However, not all forms of bullying (e.g., rumor spreading) involve violent behavior. The committee also acknowledges that perspective about intentions can matter and that in many situations, there may be at least two plausible perceptions involved in the bullying behavior.

A number of factors may influence one’s perception of the term “bullying” ( Smith and Monks, 2008 ). Children and adolescents’ understanding of the term “bullying” may be subject to cultural interpretations or translations of the term ( Hopkins et al., 2013 ). Studies have also shown that influences on children’s understanding of bullying include the child’s experiences as he or she matures and whether the child witnesses the bullying behavior of others ( Hellström et al., 2015 ; Monks and Smith, 2006 ; Smith and Monks, 2008 ).

In 2010, the FPBP Steering Committee convened its first summit, which brought together more than 150 nonprofit and corporate leaders,

researchers, practitioners, parents, and youths to identify challenges in bullying prevention. Discussions at the summit revealed inconsistencies in the definition of bullying behavior and the need to create a uniform definition of bullying. Subsequently, a review of the 2011 CDC publication of assessment tools used to measure bullying among youth ( Hamburger et al., 2011 ) revealed inconsistent definitions of bullying and diverse measurement strategies. Those inconsistencies and diverse measurements make it difficult to compare the prevalence of bullying across studies ( Vivolo et al., 2011 ) and complicate the task of distinguishing bullying from other types of aggression between youths. A uniform definition can support the consistent tracking of bullying behavior over time, facilitate the comparison of bullying prevalence rates and associated risk and protective factors across different data collection systems, and enable the collection of comparable information on the performance of bullying intervention and prevention programs across contexts ( Gladden et al., 2014 ). The CDC and U.S. Department of Education collaborated on the creation of the following uniform definition of bullying (quoted in Gladden et al., 2014, p. 7 ):

Bullying is any unwanted aggressive behavior(s) by another youth or group of youths who are not siblings or current dating partners that involves an observed or perceived power imbalance and is repeated multiple times or is highly likely to be repeated. Bullying may inflict harm or distress on the targeted youth including physical, psychological, social, or educational harm.

This report noted that the definition includes school-age individuals ages 5-18 and explicitly excludes sibling violence and violence that occurs in the context of a dating or intimate relationship ( Gladden et al., 2014 ). This definition also highlighted that there are direct and indirect modes of bullying, as well as different types of bullying. Direct bullying involves “aggressive behavior(s) that occur in the presence of the targeted youth”; indirect bullying includes “aggressive behavior(s) that are not directly communicated to the targeted youth” ( Gladden et al., 2014, p. 7 ). The direct forms of violence (e.g., sibling violence, teen dating violence, intimate partner violence) can include aggression that is physical, sexual, or psychological, but the context and uniquely dynamic nature of the relationship between the target and the perpetrator in which these acts occur is different from that of peer bullying. Examples of direct bullying include pushing, hitting, verbal taunting, or direct written communication. A common form of indirect bullying is spreading rumors. Four different types of bullying are commonly identified—physical, verbal, relational, and damage to property. Some observational studies have shown that the different forms of bullying that youths commonly experience may overlap ( Bradshaw et al., 2015 ;

Godleski et al., 2015 ). The four types of bullying are defined as follows ( Gladden et al., 2014 ):

• Physical bullying involves the use of physical force (e.g., shoving, hitting, spitting, pushing, and tripping).
• Verbal bullying involves oral or written communication that causes harm (e.g., taunting, name calling, offensive notes or hand gestures, verbal threats).
• Relational bullying is behavior “designed to harm the reputation and relationships of the targeted youth (e.g., social isolation, rumor spreading, posting derogatory comments or pictures online).”
• Damage to property is “theft, alteration, or damaging of the target youth’s property by the perpetrator to cause harm.”

In recent years, a new form of aggression or bullying has emerged, labeled “cyberbullying,” in which the aggression occurs through modern technological devices, specifically mobile phones or the Internet ( Slonje and Smith, 2008 ). Cyberbullying may take the form of mean or nasty messages or comments, rumor spreading through posts or creation of groups, and exclusion by groups of peers online.

While the CDC definition identifies bullying that occurs using technology as electronic bullying and views that as a context or location where bullying occurs, one of the major challenges in the field is how to conceptualize and define cyberbullying ( Tokunaga, 2010 ). The extent to which the CDC definition can be applied to cyberbullying is unclear, particularly with respect to several key concepts within the CDC definition. First, whether determination of an interaction as “wanted” or “unwanted” or whether communication was intended to be harmful can be challenging to assess in the absence of important in-person socioemotional cues (e.g., vocal tone, facial expressions). Second, assessing “repetition” is challenging in that a single harmful act on the Internet has the potential to be shared or viewed multiple times ( Sticca and Perren, 2013 ). Third, cyberbullying can involve a less powerful peer using technological tools to bully a peer who is perceived to have more power. In this manner, technology may provide the tools that create a power imbalance, in contrast to traditional bullying, which typically involves an existing power imbalance.

A study that used focus groups with college students to discuss whether the CDC definition applied to cyberbullying found that students were wary of applying the definition due to their perception that cyberbullying often involves less emphasis on aggression, intention, and repetition than other forms of bullying ( Kota et al., 2014 ). Many researchers have responded to this lack of conceptual and definitional clarity by creating their own measures to assess cyberbullying. It is noteworthy that very few of these

definitions and measures include the components of traditional bullying—i.e., repetition, power imbalance, and intent ( Berne et al., 2013 ). A more recent study argues that the term “cyberbullying” should be reserved for incidents that involve key aspects of bullying such as repetition and differential power ( Ybarra et al., 2014 ).

Although the formulation of a uniform definition of bullying appears to be a step in the right direction for the field of bullying prevention, there are some limitations of the CDC definition. For example, some researchers find the focus on school-age youth as well as the repeated nature of bullying to be rather limiting; similarly the exclusion of bullying in the context of sibling relationships or dating relationships may preclude full appreciation of the range of aggressive behaviors that may co-occur with or constitute bullying behavior. As noted above, other researchers have raised concerns about whether cyberbullying should be considered a particular form or mode under the broader heading of bullying as suggested in the CDC definition, or whether a separate defintion is needed. Furthermore, the measurement of bullying prevalence using such a definiton of bullying is rather complex and does not lend itself well to large-scale survey research. The CDC definition was intended to inform public health surveillance efforts, rather than to serve as a definition for policy. However, increased alignment between bullying definitions used by policy makers and researchers would greatly advance the field. Much of the extant research on bullying has not applied a consistent definition or one that aligns with the CDC definition. As a result of these and other challenges to the CDC definition, thus far there has been inconsistent adoption of this particular definition by researchers, practitioners, or policy makers; however, as the definition was created in 2014, less than 2 years is not a sufficient amount of time to assess whether it has been successfully adopted or will be in the future.

THE COMMITTEE’S APPROACH

This report builds on the April 2014 workshop, summarized in Building Capacity to Reduce Bullying: Workshop Summary ( Institute of Medicine and National Research Council, 2014c ). The committee’s work was accomplished over an 18-month period that began in October 2014, after the workshop was held and the formal summary of it had been released. The study committee members represented expertise in communication technology, criminology, developmental and clinical psychology, education, mental health, neurobiological development, pediatrics, public health, school administration, school district policy, and state law and policy. (See Appendix E for biographical sketches of the committee members and staff.) The committee met three times in person and conducted other meetings by teleconferences and electronic communication.

Information Gathering

The committee conducted an extensive review of the literature pertaining to peer victimization and bullying. In some instances, the committee drew upon the broader literature on aggression and violence. The review began with an English-language literature search of online databases, including ERIC, Google Scholar, Lexis Law Reviews Database, Medline, PubMed, Scopus, PsycInfo, and Web of Science, and was expanded as literature and resources from other countries were identified by committee members and project staff as relevant. The committee drew upon the early childhood literature since there is substantial evidence indicating that bullying involvement happens as early as preschool (see Vlachou et al., 2011 ). The committee also drew on the literature on late adolescence and looked at related areas of research such as maltreatment for insights into this emerging field.

The committee used a variety of sources to supplement its review of the literature. The committee held two public information-gathering sessions, one with the study sponsors and the second with experts on the neurobiology of bullying; bullying as a group phenomenon and the role of bystanders; the role of media in bullying prevention; and the intersection of social science, the law, and bullying and peer victimization. See Appendix A for the agendas for these two sessions. To explore different facets of bullying and give perspectives from the field, a subgroup of the committee and study staff also conducted a site visit to a northeastern city, where they convened four stakeholder groups comprised, respectively, of local practitioners, school personnel, private foundation representatives, and young adults. The site visit provided the committee with an opportunity for place-based learning about bullying prevention programs and best practices. Each focus group was transcribed and summarized thematically in accordance with this report’s chapter considerations. Themes related to the chapters are displayed throughout the report in boxes titled “Perspectives from the Field”; these boxes reflect responses synthesized from all four focus groups. See Appendix B for the site visit’s agenda and for summaries of the focus groups.

The committee also benefited from earlier reports by the National Academies of Sciences, Engineering, and Medicine through its Division of Behavioral and Social Sciences and Education and the Institute of Medicine, most notably:

• Reducing Risks for Mental Disorders: Frontiers for Preventive Intervention Research ( Institute of Medicine, 1994 )
• Community Programs to Promote Youth Development ( National Research Council and Institute of Medicine, 2002 )
• Deadly Lessons: Understanding Lethal School Violence ( National Research Council and Institute of Medicine, 2003 )
• Preventing Mental, Emotional, and Behavioral Disorders Among Young People: Progress and Possibilities ( National Research Council and Institute of Medicine, 2009 )
• The Science of Adolescent Risk-Taking: Workshop Report ( Institute of Medicine and National Research Council, 2011 )
• Communications and Technology for Violence Prevention: Workshop Summary ( Institute of Medicine and National Research Council, 2012 )
• Building Capacity to Reduce Bullying: Workshop Summary ( Institute of Medicine and National Research Council, 2014c )
• The Evidence for Violence Prevention across the Lifespan and Around the World: Workshop Summary ( Institute of Medicine and National Research Council, 2014a )
• Strategies for Scaling Effective Family-Focused Preventive Interventions to Promote Children’s Cognitive, Affective, and Behavioral Health: Workshop Summary ( Institute of Medicine and National Research Council, 2014b )
• Investing in the Health and Well-Being of Young Adults ( Institute of Medicine and National Research Council, 2015 )

Although these past reports and workshop summaries address various forms of violence and victimization, this report is the first consensus study by the National Academies of Sciences, Engineering, and Medicine on the state of the science on the biological and psychosocial consequences of bullying and the risk and protective factors that either increase or decrease bullying behavior and its consequences.

Terminology

Given the variable use of the terms “bullying” and “peer victimization” in both the research-based and practice-based literature, the committee chose to use the current CDC definition quoted above ( Gladden et al., 2014, p. 7 ). While the committee determined that this was the best definition to use, it acknowledges that this definition is not necessarily the most user-friendly definition for students and has the potential to cause problems for students reporting bullying. Not only does this definition provide detail on the common elements of bullying behavior but it also was developed with input from a panel of researchers and practitioners. The committee also followed the CDC in focusing primarily on individuals between the ages of 5 and 18. The committee recognizes that children’s development occurs on a continuum, and so while it relied primarily on the CDC defini-

tion, its work and this report acknowledge the importance of addressing bullying in both early childhood and emerging adulthood. For purposes of this report, the committee used the terms “early childhood” to refer to ages 1-4, “middle childhood” for ages 5 to 10, “early adolescence” for ages 11-14, “middle adolescence” for ages 15-17, and “late adolescence” for ages 18-21. This terminology and the associated age ranges are consistent with the Bright Futures and American Academy of Pediatrics definition of the stages of development. 4

A given instance of bullying behavior involves at least two unequal roles: one or more individuals who perpetrate the behavior (the perpetrator in this instance) and at least one individual who is bullied (the target in this instance). To avoid labeling and potentially further stigmatizing individuals with the terms “bully” and “victim,” which are sometimes viewed as traits of persons rather than role descriptions in a particular instance of behavior, the committee decided to use “individual who is bullied” to refer to the target of a bullying instance or pattern and “individual who bullies” to refer to the perpetrator of a bullying instance or pattern. Thus, “individual who is bullied and bullies others” can refer to one who is either perpetrating a bullying behavior or a target of bullying behavior, depending on the incident. This terminology is consistent with the approach used by the FPBP (see above). Also, bullying is a dynamic social interaction ( Espelage and Swearer, 2003 ) where individuals can play different roles in bullying interactions based on both individual and contextual factors.

The committee used “cyberbullying” to refer to bullying that takes place using technology or digital electronic means. “Digital electronic forms of contact” comprise a broad category that may include e-mail, blogs, social networking Websites, online games, chat rooms, forums, instant messaging, Skype, text messaging, and mobile phone pictures. The committee uses the term “traditional bullying” to refer to bullying behavior that is not cyberbullying (to aid in comparisons), recognizing that the term has been used at times in slightly different senses in the literature.

Where accurate reporting of study findings requires use of the above terms but with senses different from those specified here, the committee has noted the sense in which the source used the term. Similarly, accurate reporting has at times required use of terms such as “victimization” or “victim” that the committee has chosen to avoid in its own statements.

4 For details on these stages of adolescence, see https://brightfutures.aap.org/Bright%20Futures%20Documents/3-Promoting_Child_Development.pdf [October 2015].

ORGANIZATION OF THE REPORT

This report is organized into seven chapters. After this introductory chapter, Chapter 2 provides a broad overview of the scope of the problem.

Chapter 3 focuses on the conceptual frameworks for the study and the developmental trajectory of the child who is bullied, the child who bullies, and the child who is bullied and also bullies. It explores processes that can explain heterogeneity in bullying outcomes by focusing on contextual processes that moderate the effect of individual characteristics on bullying behavior.

Chapter 4 discusses the cyclical nature of bullying and the consequences of bullying behavior. It summarizes what is known about the psychosocial, physical health, neurobiological, academic-performance, and population-level consequences of bullying.

Chapter 5 provides an overview of the landscape in bullying prevention programming. This chapter describes in detail the context for preventive interventions and the specific actions that various stakeholders can take to achieve a coordinated response to bullying behavior. The chapter uses the Institute of Medicine’s multi-tiered framework ( National Research Council and Institute of Medicine, 2009 ) to present the different levels of approaches to preventing bullying behavior.

Chapter 6 reviews what is known about federal, state, and local laws and policies and their impact on bullying.

After a critical review of the relevant research and practice-based literatures, Chapter 7 discusses the committee conclusions and recommendations and provides a path forward for bullying prevention.

The report includes a number of appendixes. Appendix A includes meeting agendas of the committee’s public information-gathering meetings. Appendix B includes the agenda and summaries of the site visit. Appendix C includes summaries of bullying prevalence data from the national surveys discussed in Chapter 2 . Appendix D provides a list of selected federal resources on bullying for parents and teachers. Appendix E provides biographical sketches of the committee members and project staff.

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Espelage, D.L., and Swearer, S.M. (2003). Research on school bullying and victimization: What have we learned and where do we go from here? School Psychology Review, 32 (3), 365-383.

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Bullying has long been tolerated as a rite of passage among children and adolescents. There is an implication that individuals who are bullied must have "asked for" this type of treatment, or deserved it. Sometimes, even the child who is bullied begins to internalize this idea. For many years, there has been a general acceptance and collective shrug when it comes to a child or adolescent with greater social capital or power pushing around a child perceived as subordinate. But bullying is not developmentally appropriate; it should not be considered a normal part of the typical social grouping that occurs throughout a child's life.

Although bullying behavior endures through generations, the milieu is changing. Historically, bulling has occurred at school, the physical setting in which most of childhood is centered and the primary source for peer group formation. In recent years, however, the physical setting is not the only place bullying is occurring. Technology allows for an entirely new type of digital electronic aggression, cyberbullying, which takes place through chat rooms, instant messaging, social media, and other forms of digital electronic communication.

Composition of peer groups, shifting demographics, changing societal norms, and modern technology are contextual factors that must be considered to understand and effectively react to bullying in the United States. Youth are embedded in multiple contexts and each of these contexts interacts with individual characteristics of youth in ways that either exacerbate or attenuate the association between these individual characteristics and bullying perpetration or victimization. Recognizing that bullying behavior is a major public health problem that demands the concerted and coordinated time and attention of parents, educators and school administrators, health care providers, policy makers, families, and others concerned with the care of children, this report evaluates the state of the science on biological and psychosocial consequences of peer victimization and the risk and protective factors that either increase or decrease peer victimization behavior and consequences.

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Bullying: issues and challenges in prevention and intervention

• Published: 12 August 2023
• Volume 43 , pages 9270–9279, ( 2024 )

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• Muhammad Waseem   ORCID: orcid.org/0000-0001-8720-955X 1 , 2 , 3 &
• Amanda B. Nickerson 4  

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Bullying is a public health issue that persists and occurs across several contexts. In this narrative review, we highlight issues and challenges in addressing bullying prevention. Specifically, we discuss issues related to defining, measuring, and screening for bullying. These include discrepancies in the interpretation and measurement of power imbalance, repetition of behavior, and perceptions of the reporter. The contexts of bullying, both within and outside of the school setting (including the online environment), are raised as an important issue relevant for identification and prevention. The role of medical professionals in screening for bullying is also noted. Prevention and intervention approaches are reviewed, and we highlight the need and evidence for social architectural interventions that involve multiple stakeholders, including parents, in these efforts. Areas in need are identified, such as understanding and intervening in cyberbullying, working more specifically with perpetrators as a heterogeneous group, and providing more intensive interventions for the most vulnerable youth who remain at risk despite universal prevention efforts.

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Updated Perspectives on Linking School Bullying and Related Youth Violence Research to Effective Prevention Strategies

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Bullying in Elementary Schools: Its Causes and Effects on Students.

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" EFFECTS OF BULLYING " A RESEARCH STUDY

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Long-term effects of bullying

Dieter wolke.

1 Department of Psychology and Division of Mental Health and Wellbeing, University of Warwick, Coventry, UK

Suzet Tanya Lereya

2 Department of Psychology, University of Warwick, Coventry, UK

Bullying is the systematic abuse of power and is defined as aggressive behaviour or intentional harm-doing by peers that is carried out repeatedly and involves an imbalance of power. Being bullied is still often wrongly considered as a ‘normal rite of passage’. This review considers the importance of bullying as a major risk factor for poor physical and mental health and reduced adaptation to adult roles including forming lasting relationships, integrating into work and being economically independent. Bullying by peers has been mostly ignored by health professionals but should be considered as a significant risk factor and safeguarding issue.

Definition and epidemiology

Bullying is the systematic abuse of power and is defined as aggressive behaviour or intentional harm-doing by peers that is carried out repeatedly and involves an imbalance of power , either actual or perceived, between the victim and the bully. 1 Bullying can take the form of direct bullying, which includes physical and verbal acts of aggression such as hitting, stealing or name calling, or indirect bullying, which is characterised by social exclusion (eg, you cannot play with us, you are not invited, etc) and rumour spreading. 2–4 Children can be involved in bullying as victims and bullies, and also as bully/victims, a subgroup of victims who also display bullying behaviour. 5 6 Recently there has been much interest in cyberbullying, which can be broadly defined as any bullying which is performed via electronic means, such as mobile phones or the internet. One in three children report having been bullied at some point in their lives, and 10–14% experience chronic bullying lasting for more than 6 months. 7 8 Between 2% and 5% are bullies and a similar number are bully/victims in childhood/adolescence. 9 Rates of cyberbullying are substantially lower at around 4.5% for victims and 2.8% for perpetrators (bullies and bully/victims), with up to 90% of the cyber-bullying victims also being traditionally (face to face) bullied. 10 Being bullied by peers is the most frequent form of abuse encountered by children, much higher than abuse by parents or other adult perpetrators 11 ( box 1 ).

Bullying screener

• Are threatened or blackmailed or have their things stolen
• Are insulted or get called nasty names
• Have nasty tricks played on them/are subject to ridicule
• Are hit, shoved around or beaten up
• Get deliberately left out of get-togethers, parties, trips or groups
• Have others ignore them, not wanting to be their friend anymore, or not wanting them around in their group
• Have nasty lies, rumours or stories told about them
• Have their private email, instant mail or text messages forwarded to someone else or have them posted where others can see them
• Have rumours spread about them online
• Get threatening or aggressive emails, instant messages or text messages
• Have embarrassing pictures posted online without their permission

(Answered for A, B, and C separately on this 4-point scale)

• Not much (1–3 times)
• Quite a lot (more than 4 times)
• A lot (at least once a week)

Victims : Happened to them: quite a lot/a lot; did to others: never/not much

Bully/victims : Happened to them: quite a lot/a lot; did to others: quite a lot/a lot

Bullies : Happened to them: never/not much; did to others: quite a lot/a lot

Adapted from refs 3 8 12 13

Bullying is not conduct disorder

Bullying is found in all societies, including modern hunter-gatherer societies and ancient civilisations. It is considered an evolutionary adaptation, the purpose of which is to gain high status and dominance, 14 get access to resources, secure survival, reduce stress and allow for more mating opportunities. 15 Bullies are often bi-strategic, employing both bullying and also acts of aggressive ‘prosocial’ behaviour to enhance their own position by acting in public and making the recipient dependent as they cannot reciprocate. 16 Thus, pure bullies (but not bully/victims or victims) have been found to be strong, highly popular and to have good social and emotional understanding. 17 Hence, bullies most likely do not have a conduct disorder. Moreover, unlike conduct disorder, bullies are found in all socioeconomic 18 and ethnic groups. 12 In contrast, victims have been described as withdrawn, unassertive, easily emotionally upset, and as having poor emotional or social understanding, 17 19 while bully/victims tend to be aggressive, easily angered, low on popularity, frequently bullied by their siblings 20 and come from families with lower socioeconomic status (SES), 18 similar to children with conduct disorder.

How bullies operate

Bullying occurs in settings where individuals do not have a say concerning the group they want to be in. This is the situation for children in school classrooms or at home with siblings, and has been compared to being ‘caged’ with others. In an effort to establish a social network or hierarchy, bullies will try to exert their power with all children. Those who have an emotional reaction (eg, cry, run away, are upset) and have nobody or few to stand up for them, are the repeated targets of bullies. Bullies may get others to join in (laugh, tease, hit, spread rumours) as bystanders or even as henchmen (bully/victims). It has been shown that conditions that foster higher density and greater hierarchies in classrooms (inegalitarian conditions), 21 at home 22 or even in nations, 23 increase bullying 24 and the stability of bullying victimisation over time. 25

Adverse consequences of being bullied

Until fairly recently, most studies on the effects of bullying were cross-sectional or just included brief follow-up periods, making it impossible to identify whether bullying is the cause or consequence of health problems. Thus, this review focuses mostly on prospective studies that were able to control for pre-existing health conditions, family situation and other exposures to violence (eg, family violence) in investigating the effects of being involved in bullying on subsequent health, self-harm and suicide, schooling, employment and social relationships.

Childhood and adolescence (6–17 years)

A fully referenced summary of the consequences of bullying during childhood and adolescence on prospectively studied outcomes up to the age of 17 years is shown in table 1 . Children who were victims of bullying have been consistently found to be at higher risk for common somatic problems such as colds, or psychosomatic problems such as headaches, stomach aches or sleeping problems, and are more likely to take up smoking. 39 40 Victims have also been reported to more often develop internalising problems and anxiety disorder or depression disorder. 31 Genetically sensitive designs allowed comparison of monozygotic twins who are genetically identical and live in the same households but were discordant for experiences of bullying. Internalising problems was found to have increased over time only in those who were bullied, 32 providing strong evidence that bullying rather than other factors explains increases in internalising problems. Furthermore, victims of bullying are at significantly increased risk of self-harm or thinking about suicide in adolescence. 43 44 Furthermore, being bullied in primary school has been found to both predict borderline personality symptoms 30 and psychotic experiences, such as hallucinations or delusions, by adolescence. 37 Where investigated, those who were either exposed to several forms of bullying or were bullied over long periods of time (chronic bullying) tended to show more adverse effects. 31 37 In contrast to the consistently moderate to strong relationships with somatic and mental health outcomes, the association between being bullied and poor academic functioning has not been as strong as expected. 51 A meta-analysis only indicated a small negative effect of victimisation on mostly concurrent academic performance and the effects differed whether bullying was self-reported or by peers or teachers. 47 Those studies that distinguished between victims and bully/victims usually reported that bully/victims had a slightly higher risk for somatic and mental health problems than pure victims. 41 52 Furthermore, most studies considered bullies and bully/victims together; however, as outlined above, the two roles are quite different with bullies often highly competent manipulators and ringleaders, while bully/victims are described as impulsive and poor in regulating their emotions. 53 We know little about the mental health outcomes of bullies in childhood, but there are some suggestions that they may also be at slightly increased risk of depression or self-harm, 33 45 however, less so than victims. Similarly, the relationship between being a bully and somatic health is weaker than in bully/victims, 39 or bullies have even been found to be healthier and stronger than children not involved in bullying. 41 Bullying perpetration has been found to increase the risk of offending in adolescence; 54 however, the analysis did not distinguish between bullies and bully/victims and did not include information about poly-victimisation (eg, being maltreated by parents). Bullies were also more likely to display delinquent behaviour and perpetrate dating violence by eighth grade. 50

Table 1

Consequences of involvement in bullying behaviour in childhood and adolescence on outcomes assessed up to 17 years of age

Childhood to adulthood (18–50 years)

Children who were victims of bullying have been consistently found to be at higher risk for internalising problems, in particular diagnoses of anxiety disorder 55 and depression 9 in young adulthood and middle adulthood (18–50 years of age) ( table 2 ). 56 Furthermore, victims were at increased risk for displaying psychotic experiences at age 18 8 and having suicidal ideation, attempts and completed suicides. 56 Victims were also reported to have poor general health, 65 including more bodily pain, headaches and slower recovery from illnesses. 57 Moreover, victimised children were found to have lower educational qualifications, be worse at financial management 57 and to earn less than their peers even at age 50. 56 69 Victims were also reported to have more trouble making or keeping friends and to be less likely to live with a partner and have social support. No association between substance use, anti-social behaviour and victimisation was found. The studies that distinguished between victims and bully/victims showed that usually bully/victims had a slightly higher risk for anxiety, depression, psychotic experiences, suicide attempts and poor general health than pure victims. 9 They also had even lower educational qualifications and trouble keeping a job and honouring financial obligations. 57 65 In contrast to pure victims, bully/victims were at increased risk for displaying anti-social behaviour and were more likely to become a young parent. 62 70 71 Again, we know less about pure bullies, but where studied, they were not found to be at increased risk for any mental or general health problems. Indeed, they were healthier than their peers, emotionally and physically. 9 57 However, pure bullies may be more deviant and more likely to be less educated and to be unemployed. 65 They have also been reported to be more likely to display anti-social behaviour, and be charged with serious crime, burglary or illegal drug use. 58 59 66 However, many of these effects on delinquency may disappear when other adverse family circumstances are controlled for. 57

Table 2

Consequences of involvement in bullying behaviour in childhood/adolescence on outcomes in young adulthood and adulthood (18–50 years)

The findings from prospective child, adolescent and adult outcome studies are summarised in figure 1 .

The impact of being bullied on functioning in teenagers and adulthood.

The carefully controlled prospective studies reviewed here provide a converging picture of the long-term effects of being bullied in childhood. First, the effects of being bullied extend beyond the consequences of other childhood adversity and adult abuse. 9 In fact, when compared to the experience of having been placed into care in childhood, the effects of frequent bullying were as detrimental 40 years later 56 ! Second, there is a dose–effect relationship between being victimised by peers and outcomes in adolescence and adulthood. Those who were bullied more frequently, 56 more severely (ie, directly and indirectly) 31 or more chronically (ie, over a longer period of time 8 ) have worse outcomes. Third, even those who stopped being bullied during school age showed some lingering effects on their health, self-worth and quality of life years later compared to those never bullied 72 but significantly less than those who remained victims for years (chronic victims). Fourth, where victims and bully/victims have been considered separately, bully/victims seem to show the poorest outcomes concerning mental health, economic adaptation, social relationships and early parenthood. 8 9 62 70 Lastly, studies that distinguished between bullies and bully/victims found few adverse effects of being a pure bully on adult outcomes. This is consistent with a view that bullies are highly sophisticated social manipulators who are callous and show little empathy. 73

There are a variety of potential routes by which being victimised may affect later life outcomes. Being bullied may alter physiological responses to stress, 74 interact with a genetic vulnerability such as variation in the serotonin transporter (5-HTT) gene, 75 or affect telomere length (ageing) or the epigenome. 76 Altered HPA-axis activity and altered cortisol responses may increase the risk for developing mental health problems 77 and also increase susceptibility to illness by interfering with immune responses. 78 In contrast, bullying may also differentially affect normal chronic inflammation and associated health problems that can persist into adulthood. 64 Chronically raised C-reactive protein (CRP) levels, a marker of low-grade systemic inflammation in the body, increase the risk of cardiovascular diseases, metabolic disorders and mental health problems such as depression. 79 Blood tests revealed that CRP levels in the blood of bullied children increased with the number of times they were bullied. Additional blood tests carried out on the children after they had reached 19 and 21 years of age revealed that those who were bullied as children had CRP levels more than twice as high as bullies, while bullies had CRP levels lower than those who were neither bullies nor victims ( figure 2 ). Thus, bullying others appears to have a protective effect consistent with studies showing lower inflammation for individuals with higher socioeconomic status 80 and studies with non-human primates showing health benefits for those higher in the social hierarchy. 81 The clear implication of these findings is that both ends of the continuum of social status in peer relationships are important for inflammation levels and health status.

Adjusted mean young adult C-reactive protein (CRP) levels (mg/L) based on childhood/adolescent bullying status. These values are adjusted for baseline CRP levels as well as other CRP-related covariates. All analyses used robust SEs to account for repeated observations (reproduced from Copeland et al 64 ).

Furthermore, experiences of threat by peers may alter cognitive responses to threatening situations. 82 Both altered stress responses and altered social cognition (eg, being hypervigilant to hostile cues 38 ) and neurocircuitry 83 related to bullying exposure may affect social relationships with parents, friends and co-workers. Finally, victimisation, in particular of bully/victims, affects schooling and has been found to be associated with school absenteeism. In the UK alone, over 16 000 young people aged 11–15 are estimated to be absent from state school with bullying as the main reason, and 78 000 are absent where bullying is one of the reasons given for absence. 84 The risk of failure to complete high school or college in chronic victims or bully/victims increases the risk of poorer income and job performance. 57

Summary and implications

Childhood bullying has serious effects on health, resulting in substantial costs for individuals, their families and society at large. In the USA, it has been estimated that preventing high school bullying results in lifetime cost benefits of over $1.4 million per individual. 85 In the UK alone, over 16 000 young people aged 11–15 are estimated to be absent from state school with bullying as the main reason, and 78 000 are absent where bullying is one of the reasons given for absence. 86 Many bullied children suffer in silence, and are reluctant to tell their parents or teachers about their experiences, for fear of reprisals or because of shame. 87 Up to 50% of children say they would rarely, or never, tell their parents, while between 35% and 60% would not tell their teacher. 11

Considering this evidence of the ill effects of being bullied and the fact that children will have spent much more time with their peers than their parents by the time they reach 18 years of age, it is more than surprising that childhood bullying is not at the forefront as a major public health concern. 88 Children are hardly ever asked about their peer relationships by health professionals. This may be because health professionals are poorly educated about bullying and find it difficult to raise the subject or deal with it. 89 However, it is important considering that many children abstain from school due to bullying and related health problems and being bullied throws a long shadow over their lives. To prevent violence against the self (eg, self-harm) and reduce mental and somatic health problems, it is imperative for health practitioners to address bullying.

Contributors: DW conceived the review, produced the first draft and revised it critically; STL contributed to the literature research and writing, and critically reviewed and approved the final version of the manuscript.

Funding: This review was partly supported by the Economic and Social Research Council (ESRC) grant ES/K003593/1.

Competing interests: None.

Provenance and peer review: Commissioned; externally peer reviewed.

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• Comparative oral...

Comparative oral monotherapy of psilocybin, lysergic acid diethylamide, 3,4-methylenedioxymethamphetamine, ayahuasca, and escitalopram for depressive symptoms: systematic review and Bayesian network meta-analysis

• Related content
• Peer review
• Tien-Wei Hsu , doctoral researcher 1 2 3 ,
• Chia-Kuang Tsai , associate professor 4 ,
• Yu-Chen Kao , associate professor 5 6 ,
• Trevor Thompson , professor 7 ,
• Andre F Carvalho , professor 8 ,
• Fu-Chi Yang , professor 4 ,
• Ping-Tao Tseng , assistant professor 9 10 11 12 ,
• Chih-Wei Hsu , assistant professor 13 ,
• Chia-Ling Yu , clinical pharmacist 14 ,
• Yu-Kang Tu , professor 15 16 ,
• 1 Department of Psychiatry, E-DA Dachang Hospital, I-Shou University, Kaohsiung, Taiwan
• 2 Department of Psychiatry, E-DA Hospital, I-Shou University, Kaohsiung, Taiwan
• 3 Graduate Institute of Clinical Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung, Taiwan
• 4 Department of Neurology, Tri-Service General Hospital, National Defense Medical Centre, Taipei, Taiwan
• 5 Department of Psychiatry, National Defense Medical Centre, Taipei, Taiwan
• 6 Department of Psychiatry, Beitou Branch, Tri-Service General Hospital, Taipei, Taiwan
• 7 Centre for Chronic Illness and Ageing, University of Greenwich, London, UK
• 8 IMPACT (Innovation in Mental and Physical Health and Clinical Treatment) Strategic Research Centre, School of Medicine, Barwon Health, Deakin University, Geelong, VIC, Australia
• 9 Institute of Biomedical Sciences, National Sun Yat-sen University, Kaohsiung, Taiwan
• 10 Department of Psychology, College of Medical and Health Science, Asia University, Taichung, Taiwan
• 11 Prospect Clinic for Otorhinolaryngology and Neurology, Kaohsiung, Taiwan
• 12 Institute of Precision Medicine, National Sun Yat-sen University, Kaohsiung, Taiwan
• 13 Department of Psychiatry, Kaohsiung Chang Gung Memorial Hospital and Chang Gung University College of Medicine, Kaohsiung, Taiwan
• 14 Department of Pharmacy, Chang Gung Memorial Hospital Linkou, Taoyuan, Taiwan
• 15 Institute of Health Data Analytics and Statistics, College of Public Health, National Taiwan University, Taipei, Taiwan
• 16 Department of Dentistry, National Taiwan University Hospital, Taipei, Taiwan
• Correspondence to: C-S Liang lcsyfw{at}gmail.com
• Accepted 20 June 2024

Objective To evaluate the comparative effectiveness and acceptability of oral monotherapy using psychedelics and escitalopram in patients with depressive symptoms, considering the potential for overestimated effectiveness due to unsuccessful blinding.

Design Systematic review and Bayesian network meta-analysis.

Data sources Medline, Cochrane Central Register of Controlled Trials, Embase, PsycINFO, ClinicalTrial.gov, and World Health Organization’s International Clinical Trials Registry Platform from database inception to 12 October 2023.

Eligibility criteria for selecting studies Randomised controlled trials on psychedelics or escitalopram in adults with depressive symptoms. Eligible randomised controlled trials of psychedelics (3,4-methylenedioxymethamphetamine (known as MDMA), lysergic acid diethylamide (known as LSD), psilocybin, or ayahuasca) required oral monotherapy with no concomitant use of antidepressants.

Data extraction and synthesis The primary outcome was change in depression, measured by the 17-item Hamilton depression rating scale. The secondary outcomes were all cause discontinuation and severe adverse events. Severe adverse events were those resulting in any of a list of negative health outcomes including, death, admission to hospital, significant or persistent incapacity, congenital birth defect or abnormality, and suicide attempt. Data were pooled using a random effects model within a Bayesian framework. To avoid estimation bias, placebo responses were distinguished between psychedelic and antidepressant trials.

Results Placebo response in psychedelic trials was lower than that in antidepression trials of escitalopram (mean difference −3.90 (95% credible interval −7.10 to −0.96)). Although most psychedelics were better than placebo in psychedelic trials, only high dose psilocybin was better than placebo in antidepression trials of escitalopram (mean difference 6.45 (3.19 to 9.41)). However, the effect size (standardised mean difference) of high dose psilocybin decreased from large (0.88) to small (0.31) when the reference arm changed from placebo response in the psychedelic trials to antidepressant trials. The relative effect of high dose psilocybin was larger than escitalopram at 10 mg (4.66 (95% credible interval 1.36 to 7.74)) and 20 mg (4.69 (1.64 to 7.54)). None of the interventions was associated with higher all cause discontinuation or severe adverse events than the placebo.

Conclusions Of the available psychedelic treatments for depressive symptoms, patients treated with high dose psilocybin showed better responses than those treated with placebo in the antidepressant trials, but the effect size was small.

Systematic review registration PROSPERO, CRD42023469014.

Introduction

Common psychedelics belong to two classes: classic psychedelics, such as psilocybin, lysergic acid diethylamide (known as LSD), and ayahuasca; and entactogens, such as 3,4-methylenedioxymethamphetamine (MDMA). 1 Several randomised controlled trials have shown efficacy of psychedelics for people with clinical depression. 2 3 The proposed mechanism of its fast and persistent antidepressant effects is to promote structural and functional neuroplasticity through the activation of intracellular 5-HT 2A receptors in the cortical neurons. 4 Additionally, the increased neuroplasticity was associated with psychedelic’s high affinity directly binding to brain derived neurotrophic factor receptor TrkB, indicating a dissociation between the hallucinogenic and plasticity promoting effects of psychedelics. 5 A meta-analysis published in 2023 reported that the standardised mean difference of psychedelics for depression reduction ranged from 1.37 to 3.12, 2 which are considered large effect sizes. 6 Notably, the standardised mean difference of antidepressant trials is approximately 0.3 (a small effect size). 7 8

Although modern randomised controlled trials involving psychedelics usually use a double blinded design, the subjective effects of these substances can compromise blinding. 9 Unsuccessful blinding may lead to differing placebo effects between the active and control groups, potentially introducing bias into the estimation of relative treatment effects. 10 Concerns have arisen regarding the overestimated effect sizes of psychedelics due to the issues of blinding and response expectancy. 9 Psychedelic treatment is usually administered with psychological support or psychotherapy, and thereby the isolated pharmacological effects of psychedelics remain to be determined. 2 Surprisingly, on 1 July 2023, Australia approved psilocybin for the treatment of depression 11 ; the first country to classify psychedelics as a medicine at a national level.

To date, only one double blind, head-to-head randomised controlled trial has directly compared a psychedelic drug (psilocybin) with an antidepressant drug (escitalopram) for patients with major depressive disorder. 12 This randomised controlled trial reported that psilocybin showed a better efficacy than escitalopram on the 17 item Hamilton depression rating scale (HAMD-17).

We aimed to assess the comparative effectiveness and acceptability of oral monotherapy with psychedelics and escitalopram in patients experiencing depressive symptoms. Given that unsuccessful blinding can potentially lead to a reduced placebo response in psychedelic trials, we distinguished between the placebo responses in psychedelic and antidepressant trials. We also investigated the differences in patient responses between people who received extremely low dose psychedelics as a placebo and those who received a placebo in the form of a fake pill, such as niacin, in psilocybin trials. 13 14 Our study allowed for a relative effect assessment of psychedelics compared with placebo responses observed in antidepressant trials.

The study protocol was registered with PROSPERO (CRD42023469014). We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) extension statement for reporting systematic reviews incorporating network meta-analysis (NMA) (appendix 1). 15

Data sources and searches

A comprehensive search of the Medline, Cochrane Central Register of Controlled Trials (CENTRAL), Embase, PsycINFO, ClinicalTrial.gov, and World Health Organization’s International Clinical Trials Registry Platform databases were performed without language restrictions from database inception to 12 October 2023. We also searched the grey literature and reviewed reference lists of the included studies and related systematic reviews. 2 3

Study selection

Eligible studies were randomised controlled trials with parallel group or crossover designs. We included: (i) adults (≥18 years) with clinically diagnosed depression (eg, major depressive disorder, bipolar disorder, or other psychiatric disorders with comorbid clinical depression) or life threatening diagnoses and terminal illness with depressive symptoms; and (ii) adults with assessment of treatment response (preapplication/postapplication) using standard, validated, and internationally recognised instruments, such as HAMD-17. The outcome of interest was the change in depressive symptoms at the end of treatment compared with the controls, and we only extracted data from the first phase of crossover randomised controlled trials to avoid carry-over effects. Eligible psychedelic randomised controlled trials (including psilocybin, lysergic acid diethylamide, MDMA, and ayahuasca without dosage limit) required oral monotherapy without the concomitant use of antidepressants. For escitalopram, we included only fixed dose randomised controlled trials that compared at least two arms with different doses of oral form escitalopram (maximum dose of 20 mg/day) with placebo because psychedelic therapies usually use a fixed dose study design. We also included randomised controlled trials that evaluated psychedelic monotherapy compared with escitalopram monotherapy. We excluded follow-up studies and studies with healthy volunteers. We also excluded conference abstracts, editorials, reviews, meta-analyses, case reports, and case series, as well as publications reporting duplicate data. We did not consider ketamine because this drug is usually administered parenterally and is not a classic psychedelic. 16 Screening and selection of the studies were performed independently by two authors. Discrepancies in study inclusion were resolved by deliberation among the reviewer pairs or with input from a third author. Appendix 2 shows the complete search strategies, and appendix 3 presents the reasons for exclusion.

Definition of outcomes, data extraction, and risk of bias assessment

The primary outcome was change in depressive symptoms from baseline (continuous outcome), as measured by a validated rating scale, such as HAMD-17. When multiple measurement tools were used, they were selected in the following order: the HAMD-17, Montgomery-Åsberg depression rating scale, and Beck depression inventory (second edition). To improve interpretability, all extracted depression scores were converted to corresponding HAMD-17 scores using a validated method. 17 We used a conservative correlation coefficient of 0.5 or other statistics (eg, t statistics) to calculate the standard deviation of change from baseline when unreported. 18 The secondary outcomes were all cause discontinuation and severe adverse events (categorical outcomes). Severe adverse events were classified as those resulting in any of a list of negative health outcomes including, death, admission to hospital, significant or persistent incapacity, congenital birth defect or abnormality, and suicide attempt. Outcome data were extracted from original intention-to-treat or last observation carrying forward analysis, as well as from estimates of mixed-effect models for repeated measures.

Two authors independently extracted and reviewed the data, each being reviewed by another author. WebPlot Digitizer ( https://automeris.io/WebPlotDigitizer/ ) was used to extract numerical data from the figures. Two authors independently used the Cochrane randomised trial risk of bias tool (version 2.0) to assess the risk of bias in the included trials, and discrepancies were resolved by consensus. 19

Data synthesis

To estimate the relative effect between two interventions, we computed mean difference on the basis of change values (with 95% credible interval) for continuous outcomes (change in depressive symptoms) and odds ratios for categorical outcomes (all cause discontinuation and severe adverse event). To assess the clinical significance of the relative effect, we evaluated whether the mean difference exceeded the minimal important difference, which is estimated to be 3 points for HAMD-17. 20 We defined high, low, and extremely low doses of the included psychedelics as follows: (i) psilocybin: high dose (≥20 mg), extremely low dose (1-3 mg), low dose (other range); and (ii) MDMA: high dose (≥100 mg), extremely low dose (≤40 mg), low dose (other range). Escitalopram was divided into escitalopram 10 mg and escitalopram ≥20 mg. In previous clinical trials, a dose of 1 mg of psilocybin or a dose range of 1-3 mg/70 kg were used as an active control because these doses were believed not to produce significant psychedelic effects. 21 22 A dose of 5 mg/70 kg can produce noticeable psychedelic effects. 22 In many two arm psilocybin trials, the psilocybin dose in the active group typically falls within the range of 20-30 mg. 12 21 23 24 In a three arm trial, 25 mg was defined as high dose, and 10 mg was considered a moderate dose. 21 Another clinical trial also defined 0.215 mg/kg of psilocybin as a moderate dose for the active group. 25 Therefore, we used 20 mg and 3 mg as the boundaries for grouping psilocybin doses; when the dosage was calculated per kilogram in the study, we converted it to per 70 kg. For MDMA, in two trials with three arms, 125 mg was defined as high dose, and 30-40 mg was defined as active control. 26 27 Thus, we used 100 mg and 40 mg as the boundaries for grouping MDMA doses.

We conducted random effects network meta-analysis and meta-analysis within a Bayesian framework. 28 29 Previous meta-analyses considered all control groups as a common comparator; however, concerns have been raised regarding the overestimated effect sizes of psychedelics because of unsuccessful blinding and poor placebo response. 9 Therefore, we treated the three treatments as distinct interventions: the placebo response observed in psychedelic trials, the placebo response observed in antidepressant escitalopram trials, and extremely low dose psychedelics (ie, psilocybin and MDMA). We calculated the relative effects of all interventions compared with these three groups, indicating the following three conditions: (1) the treatment response of placebo response in the psychedelic trials is assumed to be lower than that of placebo response in antidepressant trials because of unsuccessful blinding. 9 As such, the relative effects compared with placebo response in the psychedelic trials represented potential overestimated effect sizes. (2) the placebo response in antidepressant trials is assumed to be the placebo response in antidepressant trials with adequate blinding, therefore, the relative effects compared with placebo response in antidepressant trials represents effect sizes in trials with adequate blinding. (3) Psychedelic drugs are usually administered with psychotherapy 13 or psychological support, 14 the relative effects of psychedelics compared with extremely low dose psychedelics might eliminate the concomitant effects from psychotherapeutic support, approximating so-called pure pharmacological effects.

In network meta-analysis, the validity of indirect comparison relies on transitivity assumption. 30 We assessed the transitivity assumption by comparing the distribution of potential effect modifies across treatment comparisons. In addition, we assessed whether the efficacy of escitalopram is similar in placebo controlled randomised controlled trials (escitalopram v placebo response in antidepressant trials) and in the head-to-head randomised controlled trial (psilocybin v escitalopram) using network meta-analysis. 12 Furthermore, we assessed the efficacy of the different placebo responses (placebo response in the psychedelic trials v placebo response in antidepressant trials) as additional proof of transitivity. If the placebo response in antidepressant trials was better than that in the psychedelic trials, the transitivity assumption did not hold when grouping placebo response in antidepressant trials and placebo response in the psychedelic trials together. Finally, for the primary outcome (change in depressive symptoms), network meta-regression analyses were conducted to evaluate the impact of potential effect modifiers, including proportion of men and women in the study, mean age, baseline depression severity, disorder type, and follow-up assessment period. We assumed a common effect on all treatment comparisons for each of the effect modifiers. In other words, all interactions between the treatment comparisons and the effect modifier were constrained to be identical.

We also conducted the following sensitivity analyses: analysing studies of patients with major depressive disorder; excluding studies with a high risk of bias; adjusting for baseline depression severity; and using correlation coefficient of zero (most conservative) to calculate the standard deviation of change from baseline when unreported.

Publication bias was assessed by visual inspection of a comparison adjusted funnel plots. The first funnel plot used placebo response in the psychedelic trials as the comparator. The second funnel plot used placebo response in antidepressant trials as the comparator. The third funnel plot used both placebo response in the psychedelic trials and placebo response in antidepressant trials as comparators simultaneously. Additionally, we conducted the Egger test, Begg test, and Thompson test to examine the asymmetry of the third funnel plot. A previous meta-analysis reported that the standardised mean difference of psychedelics for depression reduction ranged from 1.37 to 3.12. 2 Therefore, we also transformed the effect size of mean difference to standardised mean difference (Hedges’ g) for the primary outcome. The global inconsistency of the network meta-analysis was examined by fitting an unrelated main effects model. Local inconsistency of the network meta-analysis was examined using node splitting methods. 31 Four Markov chains were implemented. 50 000 iterations occurred per chain and the first 20 000 iterations for each chain were discarded as a warm-up. Convergence was assessed by visual inspection of the trace plots of the key parameters for each analysis. The prior settings and convergence results are shown in appendix 4. All statistical analyses were done using R version 4.3.1. The network meta-analysis and pairwise meta-analysis within a Bayesian framework were fitted using the Bayesian statistical software called Stan within the R packages multinma 28 and brms, 29 respectively. The frequentist random effects network meta-analysis, funnel plots, and tests for funnel plot asymmetry were conducted using the R package netmeta. Reasons for protocol changes are in appendix 5.

Assessment certainty of evidence for the primary outcome

The certainty of evidence produced by the network meta-analysis was evaluated using GRADE (Grading of recommendations, assessment, development and evaluation). 32 33 We used a minimally contextualised framework with the value of 3 (minimal important difference) as our decision threshold. The certainty of evidence refers to our certainty that the intervention had, relative to minimal intervention, any clinically minimal important difference. The optimal information size was calculated using a validated method. 32 33 34

Patient and public involvement

Both patients and the public are interested in research on novel depression treatments and their efficacy compared with existing antidepressants. However, due to a scarcity of available funding for recruitment and researcher training, patients and members of the public were not directly involved in the planning or writing of this manuscript. We did speak to patients about the study, and we asked a member of the public to read our manuscript after submission.

Characteristics of included study

After searching the database and excluding duplicated records, we identified 3104 unique potential studies. We then screened the titles and abstracts of these studies for eligibility and excluded 3062 of them, in which 42 studies remained. Twenty six studies were excluded after an assessment of the full text for various reasons (appendix 3). We identified three additional studies through a manual search resulting in total 19 eligible studies (efigure 1). Details of the characteristics of the included studies are shown in etable 1. Protocols of psychological support or psychotherapy with psychedelic treatment are shown in etable 2. Overall, 811 people (mean age of 42.49 years, 54.2% (440/811) were women) were included in psychedelic trials (15 trials), and 1968 participants (mean age of 39.35 years, 62.5% (1230/1968) were women) were included in escitalopram trials (five trials).

Risk of bias of the included studies

No psychedelic study (0/15) had a high overall risk of bias (efigure 2A and efigure 3A). The percentages of studies with high, some concerns, or low risk of bias in the 15 psychedelic trials were as follows: 0% (k=15), 33% (k=5), and 67% (k=10) for randomisation; 0% (k=0), 33% (k=5), and 67% (k=10) for deviations from intended interventions; 0% (k=0), 13% (k=2), and 87% (k=13) for missing outcome data; 0% (k=0), 33% (k=5), and 67% (k=10) for measurements of outcomes; 0% (k=0), 67% (k=1), and 93% (k=14) for selection of reported results. No non-psychedelic studies (0/5) were rated as high risk of bias (efigure 2B and efigure 3B). The percentages of studies with high, some concerns, and low risk of bias in the five non-psychedelic trials were as follows: 0% (k=0), 80% (k=4), and 20% (k=1) for randomisation; 0% (k=0), 100% (k=5), and 0% (k=0) for deviations from intended interventions; 0% (k=0), 80% (k=4), and 20% (k=1) for missing outcome data; 0% (k=0), 80% (k=4), and 20% (k=1) for measurements of outcomes; 0% (k=0), 20% (k=1), and 80% (k=4) for selection of reported results.

Network meta-analysis

In the network structure, all interventions were connected, with two main structures ( fig 1 ). All psychedelics were compared with placebo response in the psychedelic trials, and escitalopram was compared with placebo response in antidepressant trials. A head-to-head comparison of high dose psilocybin and 20 mg escitalopram connected the two main structures. 12

Network structure. LSD=lysergic acid diethylamide; MDMA=3,4-methylenedioxymethamphetamine

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In the main network meta-analysis, all interventions, except for extremely low dose and low dose MDMA, were associated with a larger mean difference exceeding the minimal important difference of 3 points on the HAMD-17 than with placebo response in the psychedelic trials ( fig 2 ). Notably, placebo response in antidepressant trials (3.79 (95% credibile interval 0.77 to 6.80)) and extremely low dose psilocybin (3.96 (0.61 to 7.17)) were better than placebo response in the psychedelic trials, with mean differences exceeding 3 and 95% credibile intervals that did not cross zero. Additionally, in comparison with placebo response in antidepressant trials ( fig 2 ), the relative effects of high dose psilocybin (6.52 (3.19 to 9.57)), escitalopram 10 mg (1.86 (0.21 to 3.50)), and escitalopram 20 mg (1.82 (0.16 to 3.43)) did not cross zero. Only high dose psilocybin resulted in a mean difference that was greater than 3. The standardised mean difference of high dose psilocybin decreased from large (0.88) to small (0.31) when the reference arm was changed from placebo response in the psychedelic trials to placebo response in antidepressant trials.

Forest plots of network meta-analytical estimates v different reference arms by observed placebo response. The dotted line represents the minimal important difference of 3 whereas the red line indicates 0. LSD=lysergic acid diethylamide; MDMA=3,4-methylenedioxymethamphetamine

When compared with extremely low dose psilocybin ( fig 2 ), only the relative effects of high dose psilocybin (6.35 (95% credibile interval 3.41 to 9.21)) and placebo response in the psychedelic trials (−3.96 (−7.17 to −0.61)) showed a larger mean difference exceeding 3, without crossing zero. All relative effects between interventions are showed in efigure 4. Importantly, the mean differences of high dose psilocybin compared with escitalopram 10 mg (4.66 (1.36 to 7.74); standardised mean difference 0.22), escitalopram 20 mg (4.69 (1.64 to 7.54); 0.24), high dose MDMA (4.98 (1.23 to 8.67); 0.32), and low dose psilocybin (4.36 (1.20 to 7.51); 0.32) all exceeded 3 and did not cross zero (efigure 4).

Transitivity assumption

The assessment of transitivity assumption is showed in efigure 5 and efigure 6. We compared the efficacy of escitalopram in the placebo controlled antidepressant trials 8 with that in the head-to-head trial (psilocybin v escitalopram) 12 using network meta-analysis and pairwise meta-analysis. The results of the network meta-analysis showed that the relative effects between these two study designs (0.64 (95% credibile interval −4.41 to 5.40), efigure 6A; 1.94 (−2.66 to 6.14), efigure 6B) included zero, and the mean differences did not exceed 3. Placebo response in antidepressant trials was better than placebo response in the psychedelic trials with a small effect size (3.79 (0.77 to 6.80), standardised mean difference 0.2), and the mean difference exceed 3 ( fig 2 ).

Sensitivity analyses

When including only patients with major depressive disorder, the relative effects of escitalopram 20 mg, escitalopram 10 mg, ayahuasca, and high dose psilocybin were better than placebo response in antidepressant trials, while placebo response in the psychedelic trials was worse than placebo response in antidepressant trials ( fig 3 ). However, only the mean differences for high dose psilocybin (6.82 (95% credibile interval 3.84 to 9.67)), ayahuasca (5.38 (0.02 to 10.61)), and placebo response in the psychedelic trials (−4.00 (−6.87 to −1.13)) exceeded 3. When compared with extremely low dose psilocybin (excluding the effects from concomitant psychotherapeutic support), only the 95% credibile intervals of the relative effects of high dose psilocybin (4.36 (0.54 to 8.27); standardised mean difference 0.30) and placebo response in the psychedelic trials (−6.46 (−10.41 to −2.32), standardised mean difference −0.46) exceeded 3 and did not cross zero ( fig 3 ). All of the relative effects between interventions are showed in efigure 7. Notably, the relative effects of high dose psilocybin compared with escitalopram 10 mg (4.96 (1.97 to 7.82)), escitalopram 20 mg (4.97 (2.19 to 7.64)), and low dose psilocybin (3.82 (0.61 to 7.04)) all exceeded 3 and did not cross zero (efigure 7).

Forest plots of network meta-analytical estimates when considering a population with major depressive disorder

The other three sensitivity analyses showed similar findings with the main analyses: exclusion of studies with high risk of bias (efigure 8); adjustment of baseline depression severity (efigure 9); and use of most conservative correlation coefficient of zero (efigure 10).

All cause discontinuation and severe adverse event

When referencing placebo in psychedelic trials, no interventions were associated with higher risks of all cause discontinuation rate nor severe adverse event rate (efigure 11).

Network meta-regression and publication bias

In network meta-regression analyses, the 95% credibile intervals of the relative effects of the baseline depressive severity, mean age, and percentage of women, crossed zero (etable 3). The results of the statistical tests (Egger, Begg, and Thompson-Sharp tests) for funnel plot asymmetry and visual inspection of funnel plots did not show publication bias (efigure 12). The results of GRADE assessment are provided in the efigure 13. Most of the certainty of evidence for treatment comparisons was moderate or low.

Consistency assumptions

The back calculation methods for all the models (appendix 6) did not show any inconsistencies. The node splitting methods also did not show any inconsistencies (appendix 7).

Principal findings

This network meta-analysis investigated the comparative effectiveness between psychedelics and escitalopram for depressive symptoms. Firstly, we found that the placebo response observed in antidepressant trials was associated with greater effectiveness than that observed in psychedelic trials. Secondly, when compared with placebo responses in antidepressant trials, only escitalopram and high dose psilocybin were associated with greater effectiveness, and only high dose psilocybin exceeded minimal important difference of 3. Notably, the effect size of high dose psilocybin decreased from large to small. Thirdly, among the included psychedelics, only high dose psilocybin was more likely to be better than escitalopram 10 mg or 20 mg, exceeding the minimally important difference of 3. Fourthly, in patients with major depressive disorder, escitalopram, ayahuasca, and high dose psilocybin were associated with greater effectiveness than placebo responses in antidepressant trials; however, only high dose psilocybin was better than extremely low dose psilocybin, exceeding minimal important difference of 3. Taken together, our study findings suggest that among psychedelic treatments, high dose psilocybin is more likely to reach the minimal important difference for depressive symptoms in studies with adequate blinding design, while the effect size of psilocybin was similar to that of current antidepressant drugs, showing a mean standardised mean difference of 0.3. 7

Comparison with other studies

In a randomised controlled trial, treatment response was defined as the response observed in the active arm; placebo response was defined as the response observed in the control (placebo) arm. 10 Treatment response consists of non-specific effects, placebo effect, and true treatment effect; placebo response consisted of non-specific effects and placebo effect. Therefore, when the placebo effect is not the same for the active and control arms within an randomised controlled trial, the estimation of the true treatment effect is biased. For example, in a psychedelic trial, unsuccessful blinding may occur due to the profound subjective effects of psychedelics. This unblinding may lead to high placebo effect in the active arm and low placebo effect in the control arms, and the true treatment effect is overestimated. 10 Without addressing unequal placebo effects within studies, the estimation of meta-analysis and network meta-analysis are biased. 10 However, in most psychedelic trials, blinding was either reported as unsuccessful or not assessed at all. For example, two trials of lysergic acid diethylamide reported unsuccessful blinding, 35 36 whereas the trial of ayahuasca only reported that five of 10 participants misclassified the placebo as ayahuasca. 37 In trials of MDMA, participants' accuracy in guessing which treatment arm they were in ranged from approximately 60-90%. 26 27 38 39 40 In the case of most psilocybin trials, blinding was not assessed, with the exception of the study by Ross and colleagues in 2016. 13 In that study, participants were asked to guess whether the psilocybin or an active control was received, and the correct guessing rate was 97%. In our study, we established several network meta-analysis models addressing this issue, and we found that placebo response in the psychedelic trials was associated with less effectiveness than that in antidepressant trials. Therefore, the effect sizes of psychedelics compared with placebo response observed in psychedelic trials may be overestimated. All of the psychedelics’ 95% credibile intervals of the relative effects crossed zero when compared with the placebo response in antidepressant trials, except for high dose psilocybin.

The comparisons between psychedelics and escitalopram showed that high dose psilocybin was more likely to be better than escitalopram. Psilocybin was usually administered with psychotherapy or psychological support. 13 14 Therefore, the greater effectiveness of psilocybin may be from not only pharmacological effects but also psychotherapeutic support. However, we also found that high doses of psilocybin was associated greater effectiveness than extremely low doses of psilocybin. This effect also indicates that the effectiveness of psilocybin cannot be attributed only to concomitant psychotherapy or psychological support.

In patients with major depressive disorder, ayahuasca, low dose psilocybin, high dose psilocybin, escitalopram 10 mg, and escitalopram 20 mg were associated with greater effectiveness than the placebo response in antidepressant trials . However, when compared with extremely low dose psilocybin, only high dose psilocybin was associated with better effectiveness; the standardised mean difference decreased from 0.38 (compared with placebo response in antidepressant trials) to 0.30 (compared with extremely low dose psilocybin). As such, the effectiveness of psilocybin should be considered with concomitant psychotherapeutic support in people with major depressive disorder. The effect size of high dose psilocybin was similar with antidepressant trials of patients with major depressive disorder showing a mean standardised mean difference of 0.3. 7 8

Strengths and limitations of this study

This study has several strengths. We conducted separate analyses for placebo response in antidepressant trials, placebo response in psychedelic trials, and an extremely low active dose of psychedelics, thereby mitigating the effect of placebo response variations across different studies. This approach allowed us to assess the efficacy of psychedelics more impartially and make relatively unbiased comparisons than if these groups were not separated. This study supported the transitivity assumption of the efficacy of escitalopram in placebo controlled antidepressant trials with that in psilocybin versus escitalopram head-to-head trial, thereby bridging the escitalopram trials and psychedelic trials. We also performed various sensitivity analyses to ensure the validation of our statistical results.

Nevertheless, our study has several limitations. Firstly, we extracted only the acute effects of the interventions. A comparison of the long term effects of psychedelics and escitalopram remains unclear. Secondly, participants in the randomised controlled trials on MDMA were predominantly diagnosed with post-traumatic stress disorder, whereas participants in the randomised controlled trials on escitalopram were patients with major depressive disorder. However, depressive symptoms in post-traumatic stress disorder could be relatively treatment resistant, requiring high doses of psychotropic drugs. 41 Moreover, our study focused not only on major depressive disorder but also on the generalisability of psychedelic treatment for depressive symptoms. Thirdly, although all available studies were included, the sample size of the psychedelic randomised controlled trials was small (k=15). Fourthly, when using extremely low dose psychedelics as a reference group, the relative effect may also eliminate some pharmacological effects because our study found that extremely low dose psychedelics could not be considered a placebo. Fifthly, in network meta-analysis, direct evidence for one treatment comparison may serve as indirect evidence for other treatment comparisons, 42 and biases in the direct evidence might affect estimates of other treatment comparisons. Because the absolute effect of escitalopram in the head-to-head trial (high dose psilocybin v escitalopram 20 mg) 12 was lower than those of placebo controlled trials, the relative effects of high dose psilocybin might be slightly overestimated when compared with other treatments in the current study. We addressed this issue by use of a Bayesian network meta-analysis, distinguishing between placebo response in psychedelic trials and placebo response in antidepressant trials. Specifically, we only considered that the 95% credibile interval of the relative effect between two comparisons did not cross zero. Indeed, the relative effect of escitalopram 20 mg between these two study designs included zero. Finally, our network meta-analysis may not have sufficient statistical power to detect potential publication bias due to the scarcity of trials and participants.

Implications and conclusions

Serotonergic psychedelics, especially high dose psilocybin, appeared to have the potential to treat depressive symptoms. However, study designs may have overestimated the efficacy of psychedelics. Our analysis suggested that the standardised mean difference of high dose psilocybin was similar to that of current antidepressant drugs, showing a small effect size. Improved blinding methods and standardised psychotherapies can help researchers to better estimate the efficacy of psychedelics for depressive symptoms and other psychiatric conditions.

What is already known on this topic

Psychedelic treatment resulted in significant efficacy in treating depressive symptoms and alleviating distress related to life threatening diagnoses and terminal illness

Meta-analyses have reported standardised mean difference of psychedelics for depression reduction ranging from 1.37 to 3.12, while antidepressant trials were approximately 0.3

No network meta-analysis has examined comparative efficacy between psychedelics and antidepressants for depressive symptoms, and effect sizes of psychedelics might be overestimated because of unsuccessful blinding and response expectancies

What this study adds

To avoid estimation bias, placebo responses in psychedelic and antidepressant trials were separated; placebo response in psychedelic trials was lower than that in antidepressant trials

Among all psychedelics studied, only high dose psilocybin was associated with greater effectiveness than placebo response in antidepressant trials (standardised mean difference 0.31)

Among all psychedelics, only high dose psilocybin was associated with greater effectiveness than escitalopram

Ethics statements

Ethical approval.

Not required because this study is an analysis of aggregated identified clinical trial data.

Data availability statement

The data that support the findings of this study are available from the corresponding author (C-SL) upon reasonable request.

Contributors: T-WH and C-KT contributed equally to this work and are joint first authors. Y-KT and C-SL contributed equally to this work and are joint last/corresponding authors. C-SL, T-WH, and Y-KT conceived and designed the study. T-WH, C-KT, C-WH, and P-TT selected the articles, extracted the data, and assess the risk of bias. C-LY did the systemic search. T-WH and C-SL wrote the first draught of the manuscript. TT, AFC, Y-CK, F-CY, and Y-KT interpreted the data and contributed to the writing of the final version of the manuscript. C-KT and T-WH have accessed and verified the data. C-SL and Y-KT were responsible for the decision to submit the manuscript. All authors confirmed that they had full access to all the data in the study and accept responsibility to submit for publication. The corresponding author attests that all listed authors meet authorship criteria and that no others meeting the criteria have been omitted.

Funding: The study was supported by grant from the National Science and Technology Council (NSTC 112-2314-B-016−036-MY2 and NSTC 112-2314-B-002−210-MY3). The funding source had no role in any process of our study.

Competing interests: All authors have completed the ICMJE uniform disclosure form at www.icmje.org/disclosure-of-interest/ and declare: support from National Science and Technology Council for the submitted work; no financial relationships with any organisations that might have an interest in the submitted work in the previous three years; no other relationships or activities that could appear to have influenced the submitted work.

Transparency: The lead author (C-SL) affirms that the manuscript is an honest, accurate, and transparent account of the study being reported; that no important aspects of the study have been omitted; and that any discrepancies from the study as planned

Dissemination to participants and related patient and public communities: Dissemination of the work to the public and clinical community through social media and lectures is planned.

Provenance and peer review: Not commissioned; externally peer reviewed.

This is an Open Access article distributed in accordance with the terms of the Creative Commons Attribution (CC BY 4.0) license, which permits others to distribute, remix, adapt and build upon this work, for commercial use, provided the original work is properly cited. See: http://creativecommons.org/licenses/by/4.0/ .

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Study reveals the benefits and downside of fasting

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Low-calorie diets and intermittent fasting have been shown to have numerous health benefits: They can delay the onset of some age-related diseases and lengthen lifespan, not only in humans but many other organisms.

Many complex mechanisms underlie this phenomenon. Previous work from MIT has shown that one way fasting exerts its beneficial effects is by boosting the regenerative abilities of intestinal stem cells, which helps the intestine recover from injuries or inflammation.

In a study of mice, MIT researchers have now identified the pathway that enables this enhanced regeneration, which is activated once the mice begin “refeeding” after the fast. They also found a downside to this regeneration: When cancerous mutations occurred during the regenerative period, the mice were more likely to develop early-stage intestinal tumors.

“Having more stem cell activity is good for regeneration, but too much of a good thing over time can have less favorable consequences,” says Omer Yilmaz, an MIT associate professor of biology, a member of MIT’s Koch Institute for Integrative Cancer Research, and the senior author of the new study.

Yilmaz adds that further studies are needed before forming any conclusion as to whether fasting has a similar effect in humans.

“We still have a lot to learn, but it is interesting that being in either the state of fasting or refeeding when exposure to mutagen occurs can have a profound impact on the likelihood of developing a cancer in these well-defined mouse models,” he says.

MIT postdocs Shinya Imada and Saleh Khawaled are the lead authors of the paper, which appears today in Nature .

Driving regeneration

For several years, Yilmaz’s lab has been investigating how fasting and low-calorie diets affect intestinal health. In a 2018 study , his team reported that during a fast, intestinal stem cells begin to use lipids as an energy source, instead of carbohydrates. They also showed that fasting led to a significant boost in stem cells’ regenerative ability.

However, unanswered questions remained: How does fasting trigger this boost in regenerative ability, and when does the regeneration begin?

“Since that paper, we’ve really been focused on understanding what is it about fasting that drives regeneration,” Yilmaz says. “Is it fasting itself that’s driving regeneration, or eating after the fast?”

In their new study, the researchers found that stem cell regeneration is suppressed during fasting but then surges during the refeeding period. The researchers followed three groups of mice — one that fasted for 24 hours, another one that fasted for 24 hours and then was allowed to eat whatever they wanted during a 24-hour refeeding period, and a control group that ate whatever they wanted throughout the experiment.

The researchers analyzed intestinal stem cells’ ability to proliferate at different time points and found that the stem cells showed the highest levels of proliferation at the end of the 24-hour refeeding period. These cells were also more proliferative than intestinal stem cells from mice that had not fasted at all.

“We think that fasting and refeeding represent two distinct states,” Imada says. “In the fasted state, the ability of cells to use lipids and fatty acids as an energy source enables them to survive when nutrients are low. And then it’s the postfast refeeding state that really drives the regeneration. When nutrients become available, these stem cells and progenitor cells activate programs that enable them to build cellular mass and repopulate the intestinal lining.”

Further studies revealed that these cells activate a cellular signaling pathway known as mTOR, which is involved in cell growth and metabolism. One of mTOR’s roles is to regulate the translation of messenger RNA into protein, so when it’s activated, cells produce more protein. This protein synthesis is essential for stem cells to proliferate.

The researchers showed that mTOR activation in these stem cells also led to production of large quantities of polyamines — small molecules that help cells to grow and divide.

“In the refed state, you’ve got more proliferation, and you need to build cellular mass. That requires more protein, to build new cells, and those stem cells go on to build more differentiated cells or specialized intestinal cell types that line the intestine,” Khawaled says.

Too much of a good thing

The researchers also found that when stem cells are in this highly regenerative state, they are more prone to become cancerous. Intestinal stem cells are among the most actively dividing cells in the body, as they help the lining of the intestine completely turn over every five to 10 days. Because they divide so frequently, these stem cells are the most common source of precancerous cells in the intestine.

In this study, the researchers discovered that if they turned on a cancer-causing gene in the mice during the refeeding stage, they were much more likely to develop precancerous polyps than if the gene was turned on during the fasting state. Cancer-linked mutations that occurred during the refeeding state were also much more likely to produce polyps than mutations that occurred in mice that did not undergo the cycle of fasting and refeeding.

“I want to emphasize that this was all done in mice, using very well-defined cancer mutations. In humans it’s going to be a much more complex state,” Yilmaz says. “But it does lead us to the following notion: Fasting is very healthy, but if you’re unlucky and you’re refeeding after a fasting, and you get exposed to a mutagen, like a charred steak or something, you might actually be increasing your chances of developing a lesion that can go on to give rise to cancer.”

Yilmaz also noted that the regenerative benefits of fasting could be significant for people who undergo radiation treatment, which can damage the intestinal lining, or other types of intestinal injury. His lab is now studying whether polyamine supplements could help to stimulate this kind of regeneration, without the need to fast.

“This fascinating study provides insights into the complex interplay between food consumption, stem cell biology, and cancer risk,” says Ophir Klein, a professor of medicine at the University of California at San Francisco and Cedars-Sinai Medical Center, who was not involved in the study. “Their work lays a foundation for testing polyamines as compounds that may augment intestinal repair after injuries, and it suggests that careful consideration is needed when planning diet-based strategies for regeneration to avoid increasing cancer risk.”

The research was funded, in part, by Pew-Stewart Scholars Program for Cancer Research award, the MIT Stem Cell Initiative, the Koch Institute Frontier Research Program via the Kathy and Curt Marble Cancer Research Fund, and the Bridge Project, a partnership between the Koch Institute for Integrative Cancer Research at MIT and the Dana-Farber/Harvard Cancer Center.

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A new study led by researchers at MIT suggests that fasting and then refeeding stimulates cell regeneration in the intestines, reports Katharine Lang for Medical News Today . However, notes Lang, researchers also found that fasting “carries the risk of stimulating the formation of intestinal tumors.” 

Prof. Ömer Yilmaz and his colleagues have discovered the potential health benefits and consequences of fasting, reports Max Kozlov for Nature . “There is so much emphasis on fasting and how long to be fasting that we’ve kind of overlooked this whole other side of the equation: what is going on in the refed state,” says Yilmaz.

MIT researchers have discovered how fasting impacts the regenerative abilities of intestinal stem cells, reports Ed Cara for Gizmodo . “The major finding of our current study is that refeeding after fasting is a distinct state from fasting itself,” explain Prof. Ömer Yilmaz and postdocs Shinya Imada and Saleh Khawaled. “Post-fasting refeeding augments the ability of intestinal stem cells to, for example, repair the intestine after injury.” 

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