No prospective studies have examined the prevalence, antecedents or concurrent characteristics associated with self-harm in non-treatment-seeking primary school-aged children.
In this cohort study from Melbourne, Australia we assessed 1239 children annually from age 8–9 years (wave 1) to 11–12 years (wave 4) on a range of health, social, educational and family measures. Past-year self-harm was assessed at wave 4. We estimated the prevalence of self-harm and used multivariable logistic regression to examine associations with concurrent and antecedent factors.
28 participants (3% of the 1059 with self-harm data; 18 girls [3%], 10 boys [2%]) reported self-harm at age 11–12 years. Antecedent (waves 1–3) predictors of self-harm were: persistent symptoms of depression (sex-age-socioeconomic status adjusted odds ratio [aOR]: 7.8; 95% confidence intervals [CI] 2.6 to 24) or anxiety (aOR: 5.1; 95%CI 2.1 to 12), frequent bullying victimisation (aOR: 24.6; 95%CI 3.8 to 158), and recent alcohol consumption (aOR: 2.9; 95%CI 1.2 to 7.1). Concurrent (wave 4) associations with self-harm were: having few friends (aOR: 8.7; 95%CI 3.2 to 24), poor emotional control (aOR: 4.2; 95%CI 1.9 to 9.6), antisocial behaviour (theft—aOR: 3.1; 95%CI 1.2 to 7.9; carrying a weapon—aOR: 6.9; 95%CI 3.1 to 15), and being in mid-puberty (aOR: 6.5; 95%CI 1.5 to 28) or late/post-puberty (aOR: 14.4; 95%CI 2.9 to 70).
Citation: Borschmann R, Mundy LK, Canterford L, Moreno-Betancur M, Moran PA, Allen NB, et al. (2020) Self-harm in primary school-aged children: Prospective cohort study. PLoS ONE 15(11): e0242802. https://doi.org/10.1371/journal.pone.0242802
Editor: Danuta Wasserman, Karolinska Institutet, SWEDEN
Received: August 18, 2020; Accepted: November 9, 2020; Published: November 30, 2020
Copyright: © 2020 Borschmann et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: All relevant data are within the manuscript and its Supporting Information files.
Funding: This research was supported by a project grant from Australia’s National Health and Medical Research Council (NHMRC; #1010018), the Australian Rotary Health and the Victorian Department of Education and Training. MCRI research is supported by the Victorian Government’s Operational Infrastructure Program. RB is supported by an NHMRC Early Career Fellowship (#1104644). LM is partially supported by a grant from the Invergowrie Foundation. GCP is supported by an NHMRC Senior Principal Research Fellowship. LD is supported by an NHMRC Senior Principal Research Fellowship (#1135991) and NIDA NIH grant R01 DA044170-02. PM is supported by the NIHR Biomedical Research Centre at University Hospitals Bristol NHS Foundation Trust and the University of Bristol. The National Drug and Alcohol Research Centre is supported by funding from the Australian Government Department of Health under the Drug and Alcohol Program. The funding sources had no further role in study design, in the collection, analysis or interpretation of data, in the writing of the report, or in the decision to submit the paper for publication.
Competing interests: The authors have declared that no competing interests exist.
Longitudinal and case-control studies have established that self-harm (defined as intentional self-poisoning or self-injury, irrespective of the motive or the extent of suicidal intent ) during adolescence is a risk factor for numerous adverse clinical and social outcomes [2–4], including suicide . As such, the occurrence of self-harm in pre-teen children is a particularly concerning event [5, 6]. Yet to date, knowledge about self-harm in young people derives almost exclusively from adolescent populations [7–12], and few studies have examined self-harm in non-treatment-seeking young people aged 12 years or under [13–18]. This is a key period in the life course when the first symptoms of common mental disorders, such as depression and anxiety, begin to emerge [19, 20]. As such, no prevalence estimates of self-harm in primary school-aged children in the community have been published . Much of what we know comes from treatment-seeking samples of adolescents [5, 10, 21–23], and the degree to which these findings are applicable to the general population remains unknown. Additionally, almost nothing is known about the characteristics of primary school-aged children who engage in self-harm, particularly in relation to pubertal stage . This is important because pubertal stage has been linked to differing levels of risk for the onset of mental disorders and early substance abuse .
We addressed these notable gaps in the literature by analysing data from a prospective cohort study in Victoria, Australia: the Childhood to Adolescence Transition Study (CATS) . We aimed to describe the cross-sectional correlates and antecedents of self-harm in primary school-aged children. In the absence of comparable data in the published literature on which to formulate specific a priori causal hypotheses, we conducted an exploratory analysis of CATS data to generate the first health, social and demographic profile of self-harm in this population.
Study population, design and procedure
Data for this study were drawn from CATS , a longitudinal cohort study with a broad focus on health, education and social adjustment as children make the transition from childhood to adolescence. Full details about the study design are reported elsewhere. In brief, 2 289 students (aged 8–9 years) from a stratified random sample of 43 primary schools in Melbourne, Australia were invited to participate and 1 239 (54%) were recruited through informed parental consent. Participants were followed up annually from wave 1 (aged 8–9 years) to wave 4 (aged 11–12 years), with measures comprising student, parent and teacher questionnaires. Participation rates are presented in S1 Fig and further information about the measurement of depressive symptoms, anxiety symptoms, emotional control and overall wellbeing is located in the Supplementary Material under ‘Methodology’. Full details of the study design and protocol have been published elsewhere .
At wave 1, parents indicated their highest level of completed education and their child’s country of birth and Indigenous status. Socioeconomic status (SES) was calculated from each student’s home postcode using the 2011 Socio-Economic Index For Areas (SEIFA) Index of Relative Socio-economic Advantage and Disadvantage (IRSAD) . Adjustment variables were age (measured at wave 4 student questionnaire completion, centred around 12.0 years) and sex.
At wave 4, participants were asked the following question: “In the past 12 months have you ever hurt yourself on purpose or done anything that might have harmed you or even killed you?” If participants responded “yes”, they were then asked to describe what they did. Free-text from this follow-up question was screened for self-harm using a coding system adapted from a recent large-scale epidemiological study of self-harm . Self-harm was defined as any behaviour fitting into one of five categories: (1) cutting/burning, (2) self-poisoning, (3) self-battering, (4) non-recreational risk-taking (e.g., reckless behaviour near traffic), or (5) other self-harm.
Mental health and wellbeing.
At each of waves 1–4, the presence of any depressive symptoms was measured using an adapted version of the Short Mood and Feelings Questionnaire (SMFQ) . Anxiety symptoms were assessed using an adapted version of the Spence Children’s Anxiety Scale (SCAS) . Emotional control was measured using four items from the International Youth Development Study (IYDS) [28–30]. Overall wellbeing was measured using the PedsQL General Wellbeing Scale .
Participants were asked: “Do you have a group of friends?” with response options of ‘not many’, ‘some’, or ‘lots’. At waves 2–4, participants were also asked: “How often do you argue or fall out with your friends?” Peer victimisation was measured using selected items from the Gatehouse Bullying Scale  which assessed both overt (e.g., teasing) and covert (e.g., social exclusion) victimisation. An additional question on cyberbullying was included in waves 3 and 4.
Pubertal development was assessed at wave 4 using the Pubertal Development Scale (PDS) . An overall pubertal development score was created to categorise participants as either “pre-pubertal/early pubertal”, “mid-pubertal” or “late pubertal/post-pubertal” .
Academic performance and truancy.
Teachers provided an overall rating of each participant’s abilities in English and mathematics using items adapted from the Longitudinal Study of Australian Children (LSAC) . At wave 4, participants were asked whether they had deliberately skipped a lesson or left school without permission during the previous year.
Participants were asked at wave 4: “Have you had more than a sip or taste of alcohol over the past 12 months?”
For each of the measures listed above, a summary measure representing history of prior exposure was derived using data from waves 1–3 to indicate whether participants reported the outcome (e.g., presence of depressive symptoms, alcohol consumption, good general wellbeing) at no previous waves, at one wave only, or at ≥2 waves.
All variables had less than 15% missing data, except for the prior ‘poor emotional control’ exposure (37.9% missing), and the prior ‘below average language/literacy’ exposure (16.1% missing). Missing data were handled using multiple imputation. A total of 50 complete data sets were imputed using multiple imputation by chained equations . Logistic regression was used to impute the binary variables, and ordinal logistic regression was used to impute variables with >3 categories, in each case including all other analysis and auxiliary variables (details below) as predictors. To investigate the association between each antecedent (waves 1–3) and concurrent (wave 4) characteristic with self-harm at wave 4, we used separate logistic regression models within a generalised estimating equations framework to account for clustering by school (exchangeable correlation structure within school at wave 4, and robust standard errors). Each model was run twice to obtain estimates of each association (along with confidence interval [CI] and Wald-test p-value) that were unadjusted (univariable model), and then adjusted for sex, age and SEIFA quintile (multivariable model). Finally, sensitivity analyses were conducted to obtain estimates using available full case data (minimal dataset available at: https://figshare.com/articles/dataset/CATS_dataset_PLOSONE_2020_09_30_dta/13174328).
The recruited sample (n = 1 239) contained a slightly lower proportion of boys than girls (46% boys; 54% girls) compared with census data for 8–9 year-old schoolchildren across the state of Victoria (51% boys; 49% girls) . Participants scored slightly higher than the Australian population average for SES (mean SEIFA = 1 012, standard deviation [SD] = 67 vs. M = 1 000, SD = 100) . A higher percentage of participants identified as Indigenous compared with all schoolchildren of the same age in Victoria (5% vs. 1%) . At wave 4, 1 067 of the 1 239 recruited students (86.1%) completed the student questionnaire and this is the sample we used for the study (see Table 1).
Self-harm data were available for 1 059 participants (96.4% of all 1 067) participants at wave 4. Twenty-eight participants (2.6%) reported past-year self-harm, of whom 18 (64.3%) were females and 10 (35.7%) were males. Ten (35.7%) reported self-battering, 8 (28.6%) reported cutting, 2 (7.1%) reported choking/hanging, 2 (7.1%) reported scratching, and one (3.6%) reported jumping from a height. A further five participants (17.9%) did not specify the nature of their self-harm. Table 1 shows descriptive estimates (obtained via multiple imputation) of the characteristics of the whole sample (n = 1 067): 47.5% were males and 52.5% were females, with a mean age of 11.9 years (SD = 0.39, range 10.7 to 13.4 years) at wave 4.
Table 2 displays unadjusted and adjusted estimates of the associations between participants’ demographic, health, social, educational, and family-related factors at wave 4 and self-harm at wave 4 (obtained via multiple imputation). These cross-sectional results indicated that the presence of depressive symptoms, being in mid-puberty or late/post-puberty, and experiencing difficulties with peer relationships were most strongly associated with self-harm at wave 4. Estimates obtained from available case analysis (conducted as a sensitivity analysis) were similar and are displayed in S1 Table.
Table 3 displays the estimated associations between health, social, academic and family characteristics in waves 1–3 and self-harm at wave 4 (obtained via multiple imputation). A similar pattern of variables that were concurrent correlates of self-harm at wave 4 were also antecedents of self-harm. Importantly, the presence of these characteristics at two or more waves was typically associated with larger adjusted odds ratios than the presence at one wave only. Estimates obtained from available cases analysis (conducted as a sensitivity analysis) were similar and are displayed in S2 Table.
In this prospective cohort study examining the prevalence, correlates and antecedents of self-harm in a population-based sample of children in Melbourne, Australia, we found that 3% of 11-12-year-olds had self-harmed during the previous 12 months. To our knowledge, these are the first prevalence estimates of self-harm among a community-dwelling sample of primary school-aged children internationally. Our findings suggest that mental health, puberty and peer relationships are most strongly associated with self-harm among primary school-aged children. Participants who had few friends, and those who had experienced bullying victimisation, were seven and 24 times more likely to have self-harmed at age 11–12 years, respectively. Additionally, participants were experienced frequent social exclusion by their peers, and those who had been teased frequently, were four and 15 times more likely to have self-harmed, respectively. These findings add weight to the predictive impact of bullying victimisation on self-harm in young people [37, 38] and demonstrate that this impact is observable at a considerably younger age than has previously been reported. In a recent prospective multicentre study from Europe examining life events as risk factors for self-harm in adolescents aged 14–15 years, Kaess et al.  demonstrated that prior bullying victimisation was associated with the first onset of self-harm at 12 month follow-up. Our finding also supports the conclusion of a recent meta-analysis which demonstrated an association between bullying and cyberbullying victimisation and self-harm in young people . In terms of mental health, participants who self-harmed were more than seven times more likely to experience depressive symptoms and five times more likely to experience anxiety than their peers who had not self-harmed.
Life-course theorists have long posited that the period between childhood and adolescence is a critical developmental period during which social learning and interaction with peers become central developmental functions . Young people begin to enact various social and behavioural strategies during this period and receive prompt feedback about the success (or lack thereof) of the chosen strategies . As such, experiences during this period can have broad-ranging implications for children’s later social development and can be expected to affect many areas of behaviour, from attachment to aggression and sexuality. For participants in our study who experienced persistent difficulties with their peer groups and later self-harmed, it is possible that this self-harm may contribute to further social isolation due to the stigma associated with such behaviour . Other potential mechanisms may also contribute to self-harm in children and adolescence. These include the influence of sex hormones, which act on hippocampal and hypothalamic systems and shift control of affect and cognitive process, may contribute to the onset and rate of self-harm . Potential mechanisms also include factors related to cognitive development, as evidence suggests that many young people who self-harm demonstrate specific deficits in problem-solving abilities . Similarly, difficulties with emotion regulation may precipitate self-harm in young people; participants who reported poor emotional control in our sample were more than four times as likely to have self-harmed. This finding supported those of a recent study by Palmer et al. examining the association between emotion dysregulation and the onset of self-harm in adolescents aged 14–15 years in the UK . Palmer et al. reported that, prior to the onset of first self-harm, participants experienced difficulties regulating their emotions and, specifically, a lack of emotional clarity and poor impulse control. These difficulties in regulating emotions, the authors argued, might help to identify adolescents at increased risk of engaging in self-harm in the future.
In our study we did not assess the presence or degree of suicidal intent when measuring self-harm and were therefore unable to make inferences about the prevalence, antecedents or characteristics associated with non-suicidal self-injury (NSSI). We adopted this approach, in part, due to the many limitations of NSSI as a concept ; first, the definition of NSSI is restricted to methods such as cutting, burning, stabbing, and hitting, thus ensuring that any act of non-suicidal self-poisoning cannot be classified as NSSI . Second, longitudinal research has identified NSSI as one of the most important risk factors for suicide attempts , indicating that intentionality can change over time. Third, as many people use different methods of self-harm on different occasions , it is possible that people may engage in both NSSI and self-poisoning at different times, which would result in an under-ascertainment of self-harming behaviours if the NSSI categorisation alone was used.
Strengths and limitations
Strengths of our study include its prospective design, multiple assessment points across a narrow age range, large population-based sample size, comprehensive measures of emotional and behavioural problems, and inclusion of both males and females. Additionally, we scrutinised all participants’ free-text descriptions of self-reported self-harm events in order to improve case ascertainment. Unlike many previous studies [13, 16, 18], our sample was a non-treatment-seeking sample and participants did not need to have sought medical help for self-harm to have been included. Our study also contained some potential limitations. First, the number of participants who had self-harmed by age 11–12 years was relatively small; however, these participants formed part of a much larger, population-based community cohort for whom we have comprehensive demographic, mental health, social, family composition, and educational data. Second, an active parental consent process was employed at recruitment, with only 54% of parents providing written consent. Third, the sample was skewed towards higher SES and had a higher proportion of Indigenous children than the general Australian population. Fourth, we did not ask about self-harm prior to wave 4 and this may represent a missed opportunity to collect informative data. Fifth, self-harm was measured via self-report and this may have contributed to an under-ascertainment of cases . Finally, we did not assess the presence or degree of suicidal intent when measuring self-harm and were therefore unable to make inferences about the prevalence, antecedents or characteristics associated with NSSI. As discussed above, however, this approach resulted in a larger proportion of all self-harming behaviours being detected.
Primary school-aged children who experience persistent difficulties within their peer group, including bullying, social exclusion and teasing, are at increased risk of self-harm as they progress through the pubertal transition. These peer problems, along with the early onset of puberty, dominate the risk profile for self-harm among children at this age and represent tangible, modifiable risk factors which may benefit from targeted prevention initiatives. The focus of existing intervention efforts aimed at preventing and reducing adolescent self-harm should extend to primary school-aged children, with a particular focus on mental health and peer relationships during the pubertal transition.
S1 Fig. “Participant recruitment and retention across waves 1–4 of the Childhood to Adolescence Transition Study (CATS)”.
S1 File. Additional information about the recruitment procedure and measurement of depressive symptoms, anxiety symptoms, emotional control, and overall wellbeing.
S1 Table. “Cross-sectional associations between participant characteristics at wave 4 (age 11–12 years) and self-harm reported at wave 4 (available case analysis)”.
The authors would like to thank all the families and schools who have participated in this study. We would like to thank all staff and volunteers involved in data collection and processing at MCRI. Permission was granted from the Victorian Department of Education and Training and the Catholic Education Office Melbourne to recruit through their schools.
- 1. Hawton K, Saunders KE, O'Connor RC. Self-harm and suicide in adolescents. The Lancet. 2012;379(9834):2373–82.
- 2. Mars B, Heron J, Crane C, Hawton K, Lewis G, Macleod J, et al. Clinical and social outcomes of adolescent self harm: population based birth cohort study. BMJ. 2014;349:g5954. pmid:25335825
- 3. Moran P, Coffey C, Romaniuk H, Degenhardt L, Borschmann R, Patton G. Substance use in adulthood following adolescent self‐harm: a population‐based cohort study. Acta Psychiatrica Scandinavica. 2015;131(1):61–8. pmid:24954250
- 4. Borschmann R, Becker D, Coffey C, Spry E, Moreno-Betancur M, Moran P, et al. 20-year outcomes in adolescents who self-harm: a population-based cohort study. The Lancet Child & Adolescent Health. 2017. pmid:30169168
- 5. Olfson M, Gameroff MJ, Marcus SC, Greenberg T, Shaffer D. Emergency treatment of young people following deliberate self-harm. Archives of General Psychiatry. 2005;62(10):1122–8. pmid:16203957
- 6. Sourander A, Aromaa M, Pihlakoski L, Haavisto A, Rautava P, Helenius H, et al. Early predictors of deliberate self-harm among adolescents. A prospective follow-up study from age 3 to age 15. Journal of affective disorders. 2006;93(1):87–96. pmid:16563519
- 7. Hawton K, James A. Suicide and deliberate self harm in young people. Bmj. 2005;330(7496):891–4. pmid:15831877
- 8. Madge N, Hewitt A, Hawton K, Wilde EJd, Corcoran P, Fekete S, et al. Deliberate self‐harm within an international community sample of young people: comparative findings from the Child & Adolescent Self‐harm in Europe (CASE) Study. Journal of child Psychology and Psychiatry. 2008;49(6):667–77. pmid:18341543
- 9. Moran P, Coffey C, Romaniuk H, Olsson C, Borschmann R, Carlin JB, et al. The natural history of self-harm during adolescence and young adulthood: population-based cohort study. The Lancet. 2012;379(9812):236–43. pmid:22100201
- 10. Hawton K, Harriss L. Deliberate self‐harm by under‐15‐year‐olds: characteristics, trends and outcome. Journal of Child Psychology and Psychiatry. 2008;49(4):441–8. pmid:18081755
- 11. Patton GC, Hemphill SA, Beyers JM, Bond L, Toumbourou JW, McMORRIS BJ, et al. Pubertal stage and deliberate self-harm in adolescents. Journal of the American Academy of Child & Adolescent Psychiatry. 2007;46(4):508–14. pmid:17420686
- 12. Mars B, Heron J, Klonsky ED, Moran P, O'Connor RC, Tilling K, et al. Predictors of future suicide attempt among adolescents with suicidal thoughts or non-suicidal self-harm: a population-based birth cohort study. The Lancet Psychiatry. 2019. pmid:30879972
- 13. Krishnakumar P, Geeta M, Gopalan A. Deliberate self-poisoning in children. Indian pediatrics. 2005;42(6):582. pmid:15995274
- 14. Bem D, Connor C, Palmer C, Channa S, Birchwood M. Frequency and preventative interventions for non-suicidal self-injury and suicidal behaviour in primary school-age children: a scoping review protocol. BMJ open. 2017;7(7):e017291. pmid:28698352
- 15. Geulayov G, Casey D, McDonald KC, Foster P, Pritchard K, Wells C, et al. Incidence of suicide, hospital-presenting non-fatal self-harm, and community-occurring non-fatal self-harm in adolescents in England (the iceberg model of self-harm): a retrospective study. The Lancet Psychiatry. 2018;5(2):167–74. pmid:29246453
- 16. Morgan C, Webb RT, Carr MJ, Kontopantelis E, Green J, Chew-Graham CA, et al. Incidence, clinical management, and mortality risk following self harm among children and adolescents: cohort study in primary care. bmj. 2017;359:j4351. pmid:29046278
- 17. Resch F, Parzer P, Brunner R, Group BS. Self-mutilation and suicidal behaviour in children and adolescents: prevalence and psychosocial correlates: results of the BELLA study. European Child & Adolescent Psychiatry. 2008;17(1):92–8. pmid:19132308
- 18. Studart-Bottó P, Martins-Junior DF, Sarmento S, Argolo L, Galvão-de-Almeida A, Miranda-Scippa Â. Self-injurious behavior and related mortality in children under 10 years of age: a retrospective health record study in Brazil. Brazilian Journal of Psychiatry. 2019(AHEAD).
- 19. Kessler RC, Berglund P, Demler O, Jin R, Merikangas KR, Walters EE. Lifetime prevalence and age-of-onset distributions of DSM-IV disorders in the National Comorbidity Survey Replication. Archives of general psychiatry. 2005;62(6):593–602. pmid:15939837
- 20. Patton GC, Olsson C, Bond L, Toumbourou JW, Carlin JB, Hemphill SA, et al. Predicting female depression across puberty: a two-nation longitudinal study. Journal of the American Academy of Child & Adolescent Psychiatry. 2008;47(12):1424–32. pmid:18978636
- 21. Hawton K, Harriss L. Deliberate Self-Harm in Young People: Characteristics and Subsequent Mortality in a 20-Year Cohort of Patients Presenting to Hospital.[CME]. Journal of Clinical Psychiatry. 2007;68(10):1574. pmid:17960975
- 22. Hawton K, Harriss L. The changing gender ratio in occurrence of deliberate self-harm across the lifecycle. Crisis. 2008;29(1):4–10. pmid:18389640
- 23. Bennardi M, McMahon E, Corcoran P, Griffin E, Arensman E. Risk of repeated self-harm and associated factors in children, adolescents and young adults. BMC Psychiatry. 2016;16(1):421. pmid:27881107
- 24. Mundy LK, Simmons JG, Allen NB, Viner RM, Bayer JK, Olds T, et al. Study protocol: the Childhood to Adolescence Transition Study (CATS). BMC pediatrics. 2013;13(1):160.
- 25. ABS. Census of Population and Housing: Socio-Economic Indexes for Areas (SEIFA), 2011. Australian Bureau of Statistics. Available at: http://www.abs.gov.au/ausstats/abs@.nsf/mf/2033.0.55.001. 2011.
- 26. Angold A, Costello EJ, Messer SC, Pickles A. Development of a short questionnaire for use in epidemiological studies of depression in children and adolescents. International journal of methods in psychiatric research. 1995.
- 27. Spence SH, Barrett PM, Turner CM. Psychometric properties of the Spence Children’s Anxiety Scale with young adolescents. Journal of anxiety disorders. 2003;17(6):605–25. pmid:14624814
- 28. Arthur MW, Hawkins JD, Pollard JA, Catalano RF, Baglioni Jr A. Measuring risk and protective factors for use, delinquency, and other adolescent problem behaviors: The Communities That Care Youth Survey. Evaluation review. 2002;26(6):575–601. pmid:12465571
- 29. Glaser RR, Horn MLV, Arthur MW, Hawkins JD, Catalano RF. Measurement properties of the Communities That Care® Youth Survey across demographic groups. Journal of Quantitative Criminology. 2005;21(1):73–102.
- 30. Pollard JA, Hawkins JD, Arthur MW. Risk and protection: Are both necessary to understand diverse behavioral outcomes in adolescence? Social work research. 1999;23(3):145–58.
- 31. Varni JW, Seid M, Rode CA. The PedsQL™: measurement model for the pediatric quality of life inventory. Medical care. 1999:126–39. pmid:10024117
- 32. Bond L, Wolfe S, Tollit M, Butler H, Patton G. A comparison of the Gatehouse Bullying Scale and the Peer Relations Questionnaire for students in secondary school. Journal of School Health. 2007;77(2):75–9. pmid:17222158
- 33. Petersen AC, Crockett L, Richards M, Boxer A. A self-report measure of pubertal status: Reliability, validity, and initial norms. Journal of Youth and Adolescence. 1988;17(2):117–33. pmid:24277579
- 34. Zubrick S, Rogers H, Nicholson J, Sanson A, Strazdins L. The Longitudinal Study of Australian Children; Review and Revision of the Key Research Questions. Available at: https://growingupinaustralia.gov.au/sites/default/files/key-research-questions-april-15.pdf. 2015.
- 35. Royston P, White IR. Multiple imputation by chained equations (MICE): implementation in Stata. J Stat Softw. 2011;45(4):1–20.
- 36. ABS. 4221.0—Schools, Australia, 2013. The Australian Bureau of Statistics. Available at http://www.abs.gov.au/AUSSTATS/abs@.nsf/DetailsPage/4221.02013. 2014.
- 37. Fisher HL, Moffitt TE, Houts RM, Belsky DW, Arseneault L, Caspi A. Bullying victimisation and risk of self harm in early adolescence: longitudinal cohort study. BMJ. 2012;344:e2683. pmid:22539176
- 38. Kaess M, Eppelmann L, Brunner R, Parzer P, Resch F, Carli V, et al. Life events predicting the first onset of adolescent direct self-injurious behavior—a prospective multicenter study. Journal of adolescent health. 2020;66(2):195–201. pmid:31677986
- 39. Heerde JA, Hemphill SA. Are bullying perpetration and victimization associated with adolescent deliberate self-harm? A meta-analysis. Archives of suicide research. 2019;23(3):353–81. pmid:29791272
- 40. Del Giudice M, Angeleri R, Manera V. The juvenile transition: A developmental switch point in human life history. Developmental Review. 2009;29(1):1–31.
- 41. Ellis BJ, Del Giudice M, Dishion TJ, Figueredo AJ, Gray P, Griskevicius V, et al. The evolutionary basis of risky adolescent behavior: implications for science, policy, and practice. Developmental psychology. 2012;48(3):598. pmid:22122473
- 42. Evans E, Hawton K, Rodham K. In what ways are adolescents who engage in self-harm or experience thoughts of self-harm different in terms of help-seeking, communication and coping strategies? Journal of Adolescence. 2005;28:573–87. pmid:16022890
- 43. Townsend E, Hawton K, Altman DG, Arensman E, Gunnell D, Hazell P, et al. The efficacy of problem-solving treatments after deliberate self-harm: meta-analysis of randomized controlled trials with respect to depression, hopelessness and improvement in problems. Psychological medicine. 2001;31(6):979–88. pmid:11513383
- 44. Palmer C, Connor C, Channa S, Lavis A, Leung N, Parsons N, et al. The development of first‐episode direct self‐injurious behavior and association with difficulties in emotional regulation in adolescence. Suicide and Life‐Threatening Behavior. 2019;49(5):1266–80. pmid:30178563
- 45. Kapur N, Cooper J, O'Connor RC, Hawton K. Non-suicidal self-injury v. attempted suicide: new diagnosis or false dichotomy? British Journal of Psychiatry. 2013;202:326–8. pmid:23637107
- 46. Andover MS, Morris BW, Wren A, Bruzzese MEJC, psychiatry a, health m. The co-occurrence of non-suicidal self-injury and attempted suicide among adolescents: distinguishing risk factors and psychosocial correlates. 2012;6(1):11.
- 47. Lilley R, Owens D, Horrocks J, House A, Noble R, Bergen H, et al. Hospital are and repetition follow self-harm: multicentre comparison of self-poisoning and self-injury. British Journal of Psychiatry. 2008;192:440–5.
- 48. de Wilde EJ, Kienhorst I. Suicide Attempts in Adolescence—“Self-Report” and “Other-Report”—Sixth Column in a Series—. Crisis. 1995;16(2):59–62, 5.