Online sexual victimization in youth: predictors and cross-sectional associations with depressive symptoms

Online sexual victimization in youth: predictors and cross-sectional associations with depressive... Abstract Aim The aim was to analyze (i) the prevalence of online unwanted sexual solicitation (USS) victimization, (ii) predictors of online USS and (iii) the associations between online USS and depressive symptoms in Swedish pupils in grades 7–9. Methods An electronic questionnaire was disseminated in 2011 in schools in a municipality in the northern part of Sweden. Total n = 1193 (boys n = 566; girls n = 627). Logistic regression models were fitted to test the cross-sectional associations between predictors of online USS and depressive symptoms, respectively. Results One third of girls and every fifth boy reported online USS victimization. In boys, predictors associated with online USS were offline bullying and sexual harassment victimization. Only offline sexual harassment victimization was associated with online USS in girls. Girls victimized by online USS had about twice the likelihood to report depressive symptoms compared to non-victimized girls. There were no associations between online USS and depressive symptoms in boys. While offline bullying was associated with depressive symptoms in both genders, offline sexual harassment victimization increased the likelihood to report depressive symptoms in girls only. Conclusions Online USS was common among Swedish youth, particularly among girls. Schools, parents and internet safety educators should look at co-occurrence of different forms of victimization as offline victimization was a predictor of online USS. Online USS was associated with depressive symptoms in girls and may hence be a factor driving gender inequity in mental health in youth. Introduction Internet and computer/smartphone/tablet-mediated communications have opened up new opportunities for increased interaction with peers.1 Young people can communicate in near real time with friends, connect with new friends, seek information, or visit civic or political websites, as well as create their own websites or blogs/vlogs. It can thus be seen as a forum for the development of social subjecthood, where young people can use the Internet as an enabler of e.g. positive sexual relations.2 Nevertheless, the Internet has also opened new ways to victimization such as cyber bullying,3 online sexual harassment,4 internet harassment5 and online unwanted sexual solicitation (USS).6,7 The Internet has presented an increased opportunity for groomers to find victims, and groomers are skilled in building trust in children and young people.8 In the past 15 years, there has been increasing concern about online USS, and it has become an additional harassment problem for young people, in addition to offline victimization as well as other forms of harassment online.4 Adolescent victimization rates vary between 13% and 61% depending on how USS is measured,4–6,9 and this remains an issue for increased mobilization. Previous studies show that there is some inconsistency regarding co-occurrence of different forms of victimization. Espelage and Holt10 found that bully victims report a high level of peer sexual harassment as well as higher levels of physical dating violence. There is also some support for the presence of co-occurrence between different forms of online harassment such as internet harassment and online USS.11 Turner, Shattuck12 have shown that about 18% of youth are poly-victims, meaning that they are likely to have been victimized by different victimization types, as well as in multiple settings (school, home, community). Other studies did not find support for an overlap between being bullied at school and Internet harassment.13 Although Mitchell and Jones14 found that there are co-occurrences between many forms of victimization, they did not find an elevated risk for online USS among those experiencing sexual harassment or peer victimization offline. Despite these inconsistent findings, we consider it worthwhile taking into account offline bullying and sexual harassment victimization when assessing predictors of online USS. Research has found that an unknown adult as an online perpetrator is rare and that a substantial percentage of the solicitations come from peers.15 Hence, it is worthwhile studying whether offline harassment is associated with online USS. Offline bullying and sexual harassment victimization have been shown to be associated with poor mental health in youth.16–19 Nonetheless, there are only a few studies, showing an association between online USS and poor mental health4,20 and further research is needed. Sweden is one of the countries with the highest levels of computer, smartphone and internet-usage in the world.21 The majority of Swedish young people have access to a computer or tablet, and smartphones are common.21 Ninety percent of Swedish 14–16-year-olds report interacting on social media online.21 It is important to know more about the risks that children and young people face online, and even more urgently, from a public health perspective, we need to deepen the understanding of the consequences of online USS victimization for mental health. Depressive symptoms have been shown to both precede and be a consequence of offline sexual harassment,16 and to our knowledge there is no previous Swedish study on online USS and its associations to depressive symptoms in this age group. Aim The aim of this study was 3-fold: (i) to analyze the prevalence of online USS victimization, (ii) to analyze predictors of online USS and finally (iii) to analyze the associations between online USS and depressive symptoms in a sample of Swedish pupils in grades 7–9 (age 14–16). Methods Context For the purposes of the current cross-sectional study, we used survey data from 2011 from a Swedish sample of 14–16 year-olds. The survey contained information about different factors associated with mental health. The municipality in which the data collection was conducted is situated in the northern part of Sweden and is of medium size with a population of about 60 000. The economic base of the municipality is comprised of a focus on tourism and small- and medium-sized enterprises. At the time of data collection, children in Sweden started compulsory school in the year they reached the age of 7 and attendance was compulsory up to the age of 16. Participants and procedure Written informed consent was obtained from parents and pupils. Pupils were informed about the aim of the study and told that they could withdraw their participation at any time. All procedures performed in this study were in accordance with the ethical standards of the regional research committee and with the 1964 Helsinki declaration and its later amendments, and approved by Umeå Regional Ethical Review Board (Ref. no.: 09-179M). The study population comprised 1193 pupils (566 boys, 627 girls) in grades 7–9 (14–16-year-olds). An electronic questionnaire was distributed to pupils in all nine public schools and one independent school via the pupils’ school e-mail addresses in January 2011, it and was filled in on computers during school hours. All schools had at least one member of staff supervising the pupils in order to answer any questions and to make sure that all pupils could complete the questionnaire in privacy. The response rate was 80%. The non-respondents differed between the schools by 10–40%, mainly due to the school administrations’ lack of commitment in providing adequate time and space to fill in the questionnaire in different classes. Measures Outcome variable The Center for Epidemiological Studies Depression Scale (CES-D) was used to measure depressive symptoms. The CES-D has been validated among adolescents22 and was developed for epidemiological and screening purposes.22,23 This scale has a range of 0–60, with higher scores indicating higher levels of depressive symptoms. The commonly used23 cut-off of ≥16 was used. Independent variable Online USS was defined as requests to engage in sexual activities, and sexual talk, and to give personal sexual information or to meet offline24 and it was estimated based on four questions derived from Mitchell and Jones14 and Mitchell, Jones24 with reference to the last 6 months. However, we did not specify that such solicitation had to be by an adult. If the pupils answered ‘at least once’ or more to any of the four questions, we considered it as online USS and coded it as risk = 1. Covariates Socio-demography Family structure. Pupils were asked: ‘Who do you live with?’ Response options were: ‘both mother and father’; ‘sometimes with mother’; ‘sometimes with father’; ‘mother with new partner’; ‘father with new partner’; ‘mother’; ‘father’; ‘somebody else’. Pupils indicating that they did not live with both their mother and their father were coded as risk = 1. Personal relative affluence. Pupils were asked the following question: ‘Thinking about your situation over the last 6 months, have you had as much money as your friends in order to do the same things as them?’ Response options were: ‘always’; ‘often’; ‘sometimes’; ‘rarely’; ‘never’. Pupils not indicating ‘always’ or ‘often’ were scored as risk = 1. Parental immigrant background. Pupils were asked: ‘In what country was your mother/father born?’ Response options were: ‘Sweden’; ‘in another country’; ‘I don’t know’. Pupils were categorized as having parental immigrant background if their father, their mother or both were born abroad and were hence coded as risk= 1. Social support Parental support. Respondents were asked: ‘Do you usually talk about most things with your mother/father?’ Response options were: ‘always’; ‘often’; ‘sometimes’; ‘rarely’; ‘never’; ‘don’t have or don’t see mother/father.’ A low degree of parental support was scored as 2 if they did not talk about almost everything ‘always’ or ‘often’ with both their mother and father. A medium degree of parental support was scored as 1 if respondents did not talk about almost everything ‘always’ or ‘often’ with either their mother or their father. Peer support. Pupils indicated how often, compared to their friends and peers, they experience the following: Does it ever happen that you are alone when you don’t want to be?; Do you have as many friends as you would like?; Do you sometimes feel left out by your peer group? ‘Always’ and ‘often’ was contrasted to ‘sometimes’, ‘rarely’, or ‘never’ and were regarded as having a low degree of peer support and being at risk = 1. Offline victimization Bullying. Respondents were asked: ‘It sometimes happens that other pupils tease, fight with somebody or shut somebody out. Has that happened to you in the past 6 months?’ Response options were: ‘yes, most of the time’; ‘yes, several times’; ‘yes, a couple of times’; ‘yes, once’; ‘no, never.’ Pupils indicating that it had happen once or more often in the last six were scored as being bullied = 1. Sexual harassment. The sexual harassment index was derived from Gruber and Fineran25 and consisted of 14 questions relating to sexual harassment over the previous 6 months. An example of physical harassment was: touched, grabbed or pinched you in a sexual manner; an example of public display: publicly commented on how attractive or unattractive you are; and examples of verbal/name calling: called you a lesbian, fag or similar words. Pupils indicating that this had happened to them at some point over the past 6 months were scored as being sexually harassed = 1. Data analysis Gender differences in all variables were analyzed using chi-square statistics with an alpha level of <0.05. As the chi-square statistics showed gender differences in several items, we decided to conduct gender-separate analyses in the next step. Logistic regression models were fitted to the data in order to assess associations between predictors of online USS and predictors of depressive symptoms respectively. The logistic regression analyses of predictors for depressive symptoms were developed in three steps (Models 1–3) in order to analyze the relative impact of online USS, socio-demographics, social support and offline victimization. Model 3 shows the independent contribution of each variable while controlling for all other predictors including school year. 95% confidence intervals (CIs) were used. No variables showed multi-collinearity with no VIF ≥2. Results Online USS victimization As shown in table 1, 35.5% of girls reported some online USS victimization compared to 19.9% of boys (P = <0.001). Being asked to meet offline was the most common type of online USS in both genders, while being asked to do something sexual was least common. Girls also reported being victims of offline bullying (girls, 34.5%; boys, 26.3%, P = 0.003) and sexual harassment (girls, 51.6%; boys, 45.1%, P = 0.036) more often than boys did, and depressive symptoms were almost twice as prevalent in girls compared to boys (girls, 44.3%; boys, 23.0%, P = <0.001). Table 1 Descriptive statistics by gender   Boys % (n)  Girls % (n)  P-value (95%)  Socio-demography      School year                7  29.9 (169)  31.3 (196)  0.102          8  30.9 (175)  35.2 (221)          9  39.2 (222)  33.5 (210)          Not living with both parents  37.8 (214)  37.9 (237)  0.969          Not having as much money as friends  17.5 (99)  20.0 (124)  0.302          Parental immigrant background  12.9 (72)  16.2 (101)  0.111  Social support      Parental support high  42.6 (193)  35.1 (196)  <0.001      Medium  18.8 (85)  36.3 (203)      Low  38.6 (175)  28.6 (160)        Low peer support  22.5 (108)  25.5 (149)  0.268  Offline victimization            Any bullying  26.3 (130)  34.5 (205)  0.003      Any sexual harassment  45.1 (223)  51.6 (296)  0.036  Unwanted online sexual solicitation (USS)      Has anyone tried to get you to talk about sex when you did not want to?  11.7 (57)  18.7 (110)  0.006      Has anyone asked you personal questions when you did not want them to, such as what your body looks like or sexual things you have done?  13.7 (67)  21.3 (126)  0.001      Has anyone asked you to do something sexual that you did not want to do?  10.7 (52)  14.7 (87)  0.046      Has anyone you don’t know asked you to meet offline?  14.6 (71)  25.6 (151)  <0.001      Any USS  19.9 (96)  35.5 (205)  <0.001  Mental health outcome            CES-D ≥16  23.0 (123)  44.3 (273)  <0.001    Boys % (n)  Girls % (n)  P-value (95%)  Socio-demography      School year                7  29.9 (169)  31.3 (196)  0.102          8  30.9 (175)  35.2 (221)          9  39.2 (222)  33.5 (210)          Not living with both parents  37.8 (214)  37.9 (237)  0.969          Not having as much money as friends  17.5 (99)  20.0 (124)  0.302          Parental immigrant background  12.9 (72)  16.2 (101)  0.111  Social support      Parental support high  42.6 (193)  35.1 (196)  <0.001      Medium  18.8 (85)  36.3 (203)      Low  38.6 (175)  28.6 (160)        Low peer support  22.5 (108)  25.5 (149)  0.268  Offline victimization            Any bullying  26.3 (130)  34.5 (205)  0.003      Any sexual harassment  45.1 (223)  51.6 (296)  0.036  Unwanted online sexual solicitation (USS)      Has anyone tried to get you to talk about sex when you did not want to?  11.7 (57)  18.7 (110)  0.006      Has anyone asked you personal questions when you did not want them to, such as what your body looks like or sexual things you have done?  13.7 (67)  21.3 (126)  0.001      Has anyone asked you to do something sexual that you did not want to do?  10.7 (52)  14.7 (87)  0.046      Has anyone you don’t know asked you to meet offline?  14.6 (71)  25.6 (151)  <0.001      Any USS  19.9 (96)  35.5 (205)  <0.001  Mental health outcome            CES-D ≥16  23.0 (123)  44.3 (273)  <0.001  Table 1 Descriptive statistics by gender   Boys % (n)  Girls % (n)  P-value (95%)  Socio-demography      School year                7  29.9 (169)  31.3 (196)  0.102          8  30.9 (175)  35.2 (221)          9  39.2 (222)  33.5 (210)          Not living with both parents  37.8 (214)  37.9 (237)  0.969          Not having as much money as friends  17.5 (99)  20.0 (124)  0.302          Parental immigrant background  12.9 (72)  16.2 (101)  0.111  Social support      Parental support high  42.6 (193)  35.1 (196)  <0.001      Medium  18.8 (85)  36.3 (203)      Low  38.6 (175)  28.6 (160)        Low peer support  22.5 (108)  25.5 (149)  0.268  Offline victimization            Any bullying  26.3 (130)  34.5 (205)  0.003      Any sexual harassment  45.1 (223)  51.6 (296)  0.036  Unwanted online sexual solicitation (USS)      Has anyone tried to get you to talk about sex when you did not want to?  11.7 (57)  18.7 (110)  0.006      Has anyone asked you personal questions when you did not want them to, such as what your body looks like or sexual things you have done?  13.7 (67)  21.3 (126)  0.001      Has anyone asked you to do something sexual that you did not want to do?  10.7 (52)  14.7 (87)  0.046      Has anyone you don’t know asked you to meet offline?  14.6 (71)  25.6 (151)  <0.001      Any USS  19.9 (96)  35.5 (205)  <0.001  Mental health outcome            CES-D ≥16  23.0 (123)  44.3 (273)  <0.001    Boys % (n)  Girls % (n)  P-value (95%)  Socio-demography      School year                7  29.9 (169)  31.3 (196)  0.102          8  30.9 (175)  35.2 (221)          9  39.2 (222)  33.5 (210)          Not living with both parents  37.8 (214)  37.9 (237)  0.969          Not having as much money as friends  17.5 (99)  20.0 (124)  0.302          Parental immigrant background  12.9 (72)  16.2 (101)  0.111  Social support      Parental support high  42.6 (193)  35.1 (196)  <0.001      Medium  18.8 (85)  36.3 (203)      Low  38.6 (175)  28.6 (160)        Low peer support  22.5 (108)  25.5 (149)  0.268  Offline victimization            Any bullying  26.3 (130)  34.5 (205)  0.003      Any sexual harassment  45.1 (223)  51.6 (296)  0.036  Unwanted online sexual solicitation (USS)      Has anyone tried to get you to talk about sex when you did not want to?  11.7 (57)  18.7 (110)  0.006      Has anyone asked you personal questions when you did not want them to, such as what your body looks like or sexual things you have done?  13.7 (67)  21.3 (126)  0.001      Has anyone asked you to do something sexual that you did not want to do?  10.7 (52)  14.7 (87)  0.046      Has anyone you don’t know asked you to meet offline?  14.6 (71)  25.6 (151)  <0.001      Any USS  19.9 (96)  35.5 (205)  <0.001  Mental health outcome            CES-D ≥16  23.0 (123)  44.3 (273)  <0.001  Predictors for online USS victimization Table 2 shows that in boys, the only predictors associated with online USS wre offline bullying (95% CI: 1.58–5.33) and sexual harassment victimization (95% CI: 2.78–10.36). While offline sexual harassment victimization was also significantly associated with online USS in girls (95% CI: 3.68–8.87), bullying was not. Table 2 Associations between predictors and online USS by gender   Boys   Girls   Predictor  Unadjusted OR (95% CI)  Adjusteda OR (95% CI)  Unadjusted OR (95% CI)  Adjusteda OR (95% CI)  Divorced parents  1.17 (0.74–1.84)  0.77 (0.41–1.45)  1.34 (0.94–1.90)  1.16 (0.75–1.77)  Low personal affluence  1.96 (1.14–3.38)  1.41 (0.67–2.96)  1.31 (0.87–2.00)  0.79 (0.48–1.32)  Parental immigrant background  2.91 (1.62–5.23)  1.82 (0.83–3.92)  0.80 (0.49–1.31)  0.57 (0.32–1.05)  Medium parental support  1.82 (0.90–3.67)  0.70 (0.27–1.82)  1.74 (1.10–2.77)  1.31 (0.77–2.22)  Low parental support  2.11 (1.20–3.72)  1.50 (0.77–2.93)  2.54 (1.60–4.03)  1.68 (0.98–2.87)  Low peer support  1.97 (1.17–3.31)  1.23 (0.61–2.45)  1.44 (0.98–2.12)  1.15 (0.72–1.84)  Any offline bullying  4.12 (2.55–6.65)  2.90 (1.58–5.33)  1.54 (1.08–2.20)  1.23 (0.80–1.91)  Any offline sexual harassment  6.36 (3.68–11.01)  5.37 (2.78–10.36)  6.18 (4.13–9.24)  5.72 (3.68–8.87)    Boys   Girls   Predictor  Unadjusted OR (95% CI)  Adjusteda OR (95% CI)  Unadjusted OR (95% CI)  Adjusteda OR (95% CI)  Divorced parents  1.17 (0.74–1.84)  0.77 (0.41–1.45)  1.34 (0.94–1.90)  1.16 (0.75–1.77)  Low personal affluence  1.96 (1.14–3.38)  1.41 (0.67–2.96)  1.31 (0.87–2.00)  0.79 (0.48–1.32)  Parental immigrant background  2.91 (1.62–5.23)  1.82 (0.83–3.92)  0.80 (0.49–1.31)  0.57 (0.32–1.05)  Medium parental support  1.82 (0.90–3.67)  0.70 (0.27–1.82)  1.74 (1.10–2.77)  1.31 (0.77–2.22)  Low parental support  2.11 (1.20–3.72)  1.50 (0.77–2.93)  2.54 (1.60–4.03)  1.68 (0.98–2.87)  Low peer support  1.97 (1.17–3.31)  1.23 (0.61–2.45)  1.44 (0.98–2.12)  1.15 (0.72–1.84)  Any offline bullying  4.12 (2.55–6.65)  2.90 (1.58–5.33)  1.54 (1.08–2.20)  1.23 (0.80–1.91)  Any offline sexual harassment  6.36 (3.68–11.01)  5.37 (2.78–10.36)  6.18 (4.13–9.24)  5.72 (3.68–8.87)  a Also adjusted for school year. Table 2 Associations between predictors and online USS by gender   Boys   Girls   Predictor  Unadjusted OR (95% CI)  Adjusteda OR (95% CI)  Unadjusted OR (95% CI)  Adjusteda OR (95% CI)  Divorced parents  1.17 (0.74–1.84)  0.77 (0.41–1.45)  1.34 (0.94–1.90)  1.16 (0.75–1.77)  Low personal affluence  1.96 (1.14–3.38)  1.41 (0.67–2.96)  1.31 (0.87–2.00)  0.79 (0.48–1.32)  Parental immigrant background  2.91 (1.62–5.23)  1.82 (0.83–3.92)  0.80 (0.49–1.31)  0.57 (0.32–1.05)  Medium parental support  1.82 (0.90–3.67)  0.70 (0.27–1.82)  1.74 (1.10–2.77)  1.31 (0.77–2.22)  Low parental support  2.11 (1.20–3.72)  1.50 (0.77–2.93)  2.54 (1.60–4.03)  1.68 (0.98–2.87)  Low peer support  1.97 (1.17–3.31)  1.23 (0.61–2.45)  1.44 (0.98–2.12)  1.15 (0.72–1.84)  Any offline bullying  4.12 (2.55–6.65)  2.90 (1.58–5.33)  1.54 (1.08–2.20)  1.23 (0.80–1.91)  Any offline sexual harassment  6.36 (3.68–11.01)  5.37 (2.78–10.36)  6.18 (4.13–9.24)  5.72 (3.68–8.87)    Boys   Girls   Predictor  Unadjusted OR (95% CI)  Adjusteda OR (95% CI)  Unadjusted OR (95% CI)  Adjusteda OR (95% CI)  Divorced parents  1.17 (0.74–1.84)  0.77 (0.41–1.45)  1.34 (0.94–1.90)  1.16 (0.75–1.77)  Low personal affluence  1.96 (1.14–3.38)  1.41 (0.67–2.96)  1.31 (0.87–2.00)  0.79 (0.48–1.32)  Parental immigrant background  2.91 (1.62–5.23)  1.82 (0.83–3.92)  0.80 (0.49–1.31)  0.57 (0.32–1.05)  Medium parental support  1.82 (0.90–3.67)  0.70 (0.27–1.82)  1.74 (1.10–2.77)  1.31 (0.77–2.22)  Low parental support  2.11 (1.20–3.72)  1.50 (0.77–2.93)  2.54 (1.60–4.03)  1.68 (0.98–2.87)  Low peer support  1.97 (1.17–3.31)  1.23 (0.61–2.45)  1.44 (0.98–2.12)  1.15 (0.72–1.84)  Any offline bullying  4.12 (2.55–6.65)  2.90 (1.58–5.33)  1.54 (1.08–2.20)  1.23 (0.80–1.91)  Any offline sexual harassment  6.36 (3.68–11.01)  5.37 (2.78–10.36)  6.18 (4.13–9.24)  5.72 (3.68–8.87)  a Also adjusted for school year. Associations with depressive symptoms Online USS was independently associated with depressive symptoms in girls in all models (table 4), while in boys this association disappeared when we adjusted for social support and offline victimization (table 3). As shown in table 4, adjusting for offline victimization rendered a relatively large reduction in the odds ratio (OR) for online USS in girls (OR: 2.12, 95% CI: 1.35–3.33). Table 3 Associations between online USS and depressive symptoms in boys Boys   Predictor  Unadjusted OR (95% CI)  Adjusteda OR (95% CI)       Model 1  Model 2  Model 3  Any USS  3.37 (2.04–5.55)  2.39 (1.36–4.20)  1.57 (0.80–3.07)  1.01 (0.48–2.13)  Divorced parents  1.63 (1.08–2.44)  1.53 (0.92–2.56)  1.25 (0.70–2.24)  1.31 (0.72–2.41)  Low personal affluence  4.33 (2.69–6.98)  4.47 (2.54–7.85)  4.42 (2.37–8.21)  3.97 (2.05–7.70)  Parental immigrant background  3.88 (2.28–6.60)  3.90 (2.07–7.33)  3.88 (1.85–8.13)  3.49 (1.63–7.46)  Medium parental support  3.61 (1.86–7.01)    2.37 (1.05–5.36)  2.15 (0.90–5.13)  Low parental support  2.42 (1.35–4.35)    1.62 (0.82–3.25)  1.62 (0.79–3-31)  Low peer support  3.57 (2.21–5.79)    2.93 (1.61–5.35)  2.44 (1.29–4.60)  Any offline bullying  3.81 (2.41–6.02)      2.42 (1.28–4.61)  Any offline sexual harassment  2.94 (1.88–4.61)      1.74 (0.92–3.28)  Boys   Predictor  Unadjusted OR (95% CI)  Adjusteda OR (95% CI)       Model 1  Model 2  Model 3  Any USS  3.37 (2.04–5.55)  2.39 (1.36–4.20)  1.57 (0.80–3.07)  1.01 (0.48–2.13)  Divorced parents  1.63 (1.08–2.44)  1.53 (0.92–2.56)  1.25 (0.70–2.24)  1.31 (0.72–2.41)  Low personal affluence  4.33 (2.69–6.98)  4.47 (2.54–7.85)  4.42 (2.37–8.21)  3.97 (2.05–7.70)  Parental immigrant background  3.88 (2.28–6.60)  3.90 (2.07–7.33)  3.88 (1.85–8.13)  3.49 (1.63–7.46)  Medium parental support  3.61 (1.86–7.01)    2.37 (1.05–5.36)  2.15 (0.90–5.13)  Low parental support  2.42 (1.35–4.35)    1.62 (0.82–3.25)  1.62 (0.79–3-31)  Low peer support  3.57 (2.21–5.79)    2.93 (1.61–5.35)  2.44 (1.29–4.60)  Any offline bullying  3.81 (2.41–6.02)      2.42 (1.28–4.61)  Any offline sexual harassment  2.94 (1.88–4.61)      1.74 (0.92–3.28)  a Also adjusted for school year. Table 3 Associations between online USS and depressive symptoms in boys Boys   Predictor  Unadjusted OR (95% CI)  Adjusteda OR (95% CI)       Model 1  Model 2  Model 3  Any USS  3.37 (2.04–5.55)  2.39 (1.36–4.20)  1.57 (0.80–3.07)  1.01 (0.48–2.13)  Divorced parents  1.63 (1.08–2.44)  1.53 (0.92–2.56)  1.25 (0.70–2.24)  1.31 (0.72–2.41)  Low personal affluence  4.33 (2.69–6.98)  4.47 (2.54–7.85)  4.42 (2.37–8.21)  3.97 (2.05–7.70)  Parental immigrant background  3.88 (2.28–6.60)  3.90 (2.07–7.33)  3.88 (1.85–8.13)  3.49 (1.63–7.46)  Medium parental support  3.61 (1.86–7.01)    2.37 (1.05–5.36)  2.15 (0.90–5.13)  Low parental support  2.42 (1.35–4.35)    1.62 (0.82–3.25)  1.62 (0.79–3-31)  Low peer support  3.57 (2.21–5.79)    2.93 (1.61–5.35)  2.44 (1.29–4.60)  Any offline bullying  3.81 (2.41–6.02)      2.42 (1.28–4.61)  Any offline sexual harassment  2.94 (1.88–4.61)      1.74 (0.92–3.28)  Boys   Predictor  Unadjusted OR (95% CI)  Adjusteda OR (95% CI)       Model 1  Model 2  Model 3  Any USS  3.37 (2.04–5.55)  2.39 (1.36–4.20)  1.57 (0.80–3.07)  1.01 (0.48–2.13)  Divorced parents  1.63 (1.08–2.44)  1.53 (0.92–2.56)  1.25 (0.70–2.24)  1.31 (0.72–2.41)  Low personal affluence  4.33 (2.69–6.98)  4.47 (2.54–7.85)  4.42 (2.37–8.21)  3.97 (2.05–7.70)  Parental immigrant background  3.88 (2.28–6.60)  3.90 (2.07–7.33)  3.88 (1.85–8.13)  3.49 (1.63–7.46)  Medium parental support  3.61 (1.86–7.01)    2.37 (1.05–5.36)  2.15 (0.90–5.13)  Low parental support  2.42 (1.35–4.35)    1.62 (0.82–3.25)  1.62 (0.79–3-31)  Low peer support  3.57 (2.21–5.79)    2.93 (1.61–5.35)  2.44 (1.29–4.60)  Any offline bullying  3.81 (2.41–6.02)      2.42 (1.28–4.61)  Any offline sexual harassment  2.94 (1.88–4.61)      1.74 (0.92–3.28)  a Also adjusted for school year. Table 4 Associations between online USS and depressive symptoms in girls Girls   Predictor  Unadjusted OR (95% CI)  Adjusteda OR (95% CI)       Model 1  Model 2  Model 3  Any USS  3.36 (2.35–4.80)  3.42 (2.34–4.99)  2.95 (1.96–4.43)  2.12 (1.35–3.33)  Divorced parents  1.85 (1.33–2.57)  1.55 (1.07–2.27)  1.47 (0.98–2.23)  1.49 (0.96–2.31)  Low personal affluence  3.51 (2.30–5.34)  3.34 (2.11–5.30)  2.93 (1.78–4.84)  2.27 (1.35–3.83)  Parental immigrant background  1.40 (0.91–2.14)  1.61 (0.97–2.66)  1.47 (0.85–2.55)  1.35 (0.75–2.42)  Medium parental support  1.71 (1.10–2.65)    1.11 (0.67–1.84)  1.02 (0.60–1.72)  Low parental support  3.13 (2.01–4.87)    2.05 (1.23–3.42)  2.07 (1.21–3.53)  Low peer support  3.54 (2.39–5.25)    3.07 (1.96–4.79)  2.58 (1.60–4.15)  Any offline bullying  2.78 (1.96–3.95)      2.23 (1.43–3.49)  Any offline sexual harassment  3.60 (2.53–5.12)      2.23 (1.43–3.47)  Girls   Predictor  Unadjusted OR (95% CI)  Adjusteda OR (95% CI)       Model 1  Model 2  Model 3  Any USS  3.36 (2.35–4.80)  3.42 (2.34–4.99)  2.95 (1.96–4.43)  2.12 (1.35–3.33)  Divorced parents  1.85 (1.33–2.57)  1.55 (1.07–2.27)  1.47 (0.98–2.23)  1.49 (0.96–2.31)  Low personal affluence  3.51 (2.30–5.34)  3.34 (2.11–5.30)  2.93 (1.78–4.84)  2.27 (1.35–3.83)  Parental immigrant background  1.40 (0.91–2.14)  1.61 (0.97–2.66)  1.47 (0.85–2.55)  1.35 (0.75–2.42)  Medium parental support  1.71 (1.10–2.65)    1.11 (0.67–1.84)  1.02 (0.60–1.72)  Low parental support  3.13 (2.01–4.87)    2.05 (1.23–3.42)  2.07 (1.21–3.53)  Low peer support  3.54 (2.39–5.25)    3.07 (1.96–4.79)  2.58 (1.60–4.15)  Any offline bullying  2.78 (1.96–3.95)      2.23 (1.43–3.49)  Any offline sexual harassment  3.60 (2.53–5.12)      2.23 (1.43–3.47)  a Also adjusted for school year. Table 4 Associations between online USS and depressive symptoms in girls Girls   Predictor  Unadjusted OR (95% CI)  Adjusteda OR (95% CI)       Model 1  Model 2  Model 3  Any USS  3.36 (2.35–4.80)  3.42 (2.34–4.99)  2.95 (1.96–4.43)  2.12 (1.35–3.33)  Divorced parents  1.85 (1.33–2.57)  1.55 (1.07–2.27)  1.47 (0.98–2.23)  1.49 (0.96–2.31)  Low personal affluence  3.51 (2.30–5.34)  3.34 (2.11–5.30)  2.93 (1.78–4.84)  2.27 (1.35–3.83)  Parental immigrant background  1.40 (0.91–2.14)  1.61 (0.97–2.66)  1.47 (0.85–2.55)  1.35 (0.75–2.42)  Medium parental support  1.71 (1.10–2.65)    1.11 (0.67–1.84)  1.02 (0.60–1.72)  Low parental support  3.13 (2.01–4.87)    2.05 (1.23–3.42)  2.07 (1.21–3.53)  Low peer support  3.54 (2.39–5.25)    3.07 (1.96–4.79)  2.58 (1.60–4.15)  Any offline bullying  2.78 (1.96–3.95)      2.23 (1.43–3.49)  Any offline sexual harassment  3.60 (2.53–5.12)      2.23 (1.43–3.47)  Girls   Predictor  Unadjusted OR (95% CI)  Adjusteda OR (95% CI)       Model 1  Model 2  Model 3  Any USS  3.36 (2.35–4.80)  3.42 (2.34–4.99)  2.95 (1.96–4.43)  2.12 (1.35–3.33)  Divorced parents  1.85 (1.33–2.57)  1.55 (1.07–2.27)  1.47 (0.98–2.23)  1.49 (0.96–2.31)  Low personal affluence  3.51 (2.30–5.34)  3.34 (2.11–5.30)  2.93 (1.78–4.84)  2.27 (1.35–3.83)  Parental immigrant background  1.40 (0.91–2.14)  1.61 (0.97–2.66)  1.47 (0.85–2.55)  1.35 (0.75–2.42)  Medium parental support  1.71 (1.10–2.65)    1.11 (0.67–1.84)  1.02 (0.60–1.72)  Low parental support  3.13 (2.01–4.87)    2.05 (1.23–3.42)  2.07 (1.21–3.53)  Low peer support  3.54 (2.39–5.25)    3.07 (1.96–4.79)  2.58 (1.60–4.15)  Any offline bullying  2.78 (1.96–3.95)      2.23 (1.43–3.49)  Any offline sexual harassment  3.60 (2.53–5.12)      2.23 (1.43–3.47)  a Also adjusted for school year. While offline bullying was associated with depressive symptoms in both genders (95% CI: girls, 1.43–3.49; boys, 1.28–4.61), offline sexual harassment victimization increased the likelihood to report depressive symptoms in girls only (95% CI: 1.43–3.47; table 4). Discussion The results show that it was common for pupils aged 14–16 years to be victims of online USS, and girls to a greater extent than boys. Victimization seems to be evenly distributed among pupils from different social backgrounds. The higher proportion of girls among the victims is in line with previous studies conducted in the US26 and in Denmark.4 Even if girls were more exposed to online USS, the frequency with which boys were victims should not be disregarded. However, it is important to consider the gendered pattern of online USS, as the literature shows that boys and men are perpetrators to a far greater degree than girls and women.27,28 The prevalence of online USS in the present study is much higher compared to some studies.4,26 In a Swedish context, these findings seems to be in line with previous findings by the Swedish National Council for Crime Prevention27 which showed that 50% of 15–17 year-old girls and 20% of boys had been exposed to USS online. The most important factor associated with online USS victimization was offline victimization. Sexual harassment was strongly associated with victimization among both genders, while bullying was significant for boys only. The associations between sexual harassment victimization and poor mental health outcomes in adolescence have been firmly established.16–19 In contrast, information on associations between online (USS) and mental health is scarce. We aimed to fill this gap by using cross-sectional data in a Swedish cohort of 14–16 year-old adolescents. Girls who reported any online USS were more than twice as likely to report high levels of depressive symptoms, which is in contrast to the results of Ybarra et al.20 who found a similar probability among boys but not among girls. Furthermore, while offline bullying was associated with depressive symptoms in both genders, offline sexual harassment victimization increased the likelihood to report depressive symptoms in girls only, which is in line with previous studies.16,17 Adjusting for offline victimization produced a relatively large reduction in the OR for online USS in girls, though the association remained significant. Different directional pathways between offline sexual harassment and depressive symptoms has been shown to drive some of the inequity in depressive symptoms between adolescent boys and girls,16 and based on the results of the current study, we suggest that this may be the case for online USS as well. The concept of online USS can be contested. In an offline situation, the same items would be called sexual harassment or abuse, and there is a risk of diminishing the problem if we keep referring to it as ‘solicitation’. If some of the perpetrators offline are the same peers who are perpetrators online, the Internet is an extended forum for such behaviour and should be named as such. Also, the gendered aspects of online USS should be highlighted. The Internet both reflects and refracts the broader pattern of unequal social power in society, and online practices follow the same axes of social stratification as offline.2 As suggested by Mitchell, Finkelhor,29 prevention and intervention should target a broader range of behaviours and experiences rather than focusing on the Internet component exclusively. The authors29 also conclude that Internet safety educators need to appreciate that many online victims may be at risk not because they are naive about the Internet, but because they also face complicated victimization problems offline. Helweg–Larsen, Schütt4 use a wider definition of online victimization than the one used in the present study that includes any type of harassment, and suggest that those exposed to parental violence or sexual abuse were associated with being online victims. This supports the theory that vulnerable youth display an Internet behaviour that puts them at risk of unknown people who approach them on the internet. Nevertheless, it is also possible that most of the online victims are harassed offline by peers, and that the internet thus has provided harassers with an opportunity for continued perpetration after school hours. Jones, Mitchell26 suggest that a high prevalence of online harassing behaviour mirrors the increasing possibility of harassing others as a consequence of the new digital techniques. Considering the fact that the odds ratio for online USS decreased considerably in girls when we adjusted for offline bullying and sexual harassment victimization, schools, parents and internet safety educators need to consider the co-occurrence of other forms of victimization. Although Mitchell, Finkelhor29 found that there was co-occurrence between many forms of victimization, they did not find an elevated risk for online USS among those experiencing sexual harassment or peer victimization offline. However, unlike the current study, they included questions on other forms of sexual victimization such as rape, which rendered the association between sexual harassment and online USS insignificant. Even if some of the perpetrators are the same age as the victims, and possibly even peers at school, we also have to be aware of the likelihood that some of them are groomers who may manipulate young people and become increasingly aggressive.8 As many as 30% of Swedish 15-year-olds have been contacted by a person they believe to be an unknown adult.27 This shows that the high prevalence in this study must be taken seriously, and that we have to know more about the perpetrators, as well as how to protect young people from grooming victimization. It is thus of great importance that schools in Sweden continue working to increase Internet literacy and Internet safety skills among pupils. Methodological considerations As this study is cross-sectional, findings should be interpreted with caution, as we cannot infer evidence of causal relationship between data. Answering questions about sexual solicitation can be sensitive, and it is possible that the prevalence of victimization in this study is underestimated. The high response rate is an advantage, however, as is the data collection method. Our definition of online USS is different from Mitchell et al.,30 who say that online USS must be perpetrated by an adult. There was no information available regarding the perpetrator in our data and the question was not framed in a way that would exclude (or include) different types of perpetrators. We argue that the generalizability of these results extends to adolescents in grades 7–9 in Sweden, particularly outside the main metropolitan areas. Some time has passed since the data collection was conducted and Internet behaviour is most likely to have change with the rapid growth of e.g. social media. Conclusions and implications for future research This study shows that online USS was common among Swedish youth in grades 7–9, particularly among girls, but that online USS was associated with depressive symptoms in girls only. Considering the effects of offline bullying and sexual harassment victimization in the adjusted models, future studies should delve deeper into the patterns of co-occurrence and poly-victimization, including other types of violence. Acknowledgements The authors would like to extend our gratitude to participating pupils and schools. Funding The data collection of this study was supported by the Public Health Agency of Sweden [HFÅ2008/212]. Conflicts of interest: None declared. Key points Online USS was common among Swedish youth, particularly among girls. Offline victimization was a predictor of online USS and stakeholders should address the issue of co-occurrence of different forms of victimization. Online USS was associated with depressive symptoms in girls and may hence be a factor driving gender inequity in mental health in youth. References 1 Livingstone S, Bober M, Helsper EJ. Active participation or just more information? Inform Commun Soc  2005; 8: 287– 314. Google Scholar CrossRef Search ADS   2 Brickell C. Sexuality, power and the sociology of the Internet. Curr Sociol  2012; 60: 28– 44. Google Scholar CrossRef Search ADS   3 Wang J, Iannotti R, Nansel T. School bullying among US adolescents: physical, verbal, relational and cyber. J Adoles Health  2009; 45: 368– 75. Google Scholar CrossRef Search ADS   4 Helweg-Larsen K, Schütt N, Larsen H. Predictors and protective factors for adolescent Internet victimization: results from a 2008 nationwide Danish youth survey. Acta Paediatrica  2012; 101: 533– 9. Google Scholar CrossRef Search ADS PubMed  5 Montiel I, Carbonell E, Pereda N. Multiple online victimization of Spanish adolescents: results from a community sample. Child Abuse Neglect  2016; 52: 123– 34. Google Scholar CrossRef Search ADS PubMed  6 Chang F-C, Chiu C-H, Miao N-F, et al.   Predictors of unwanted exposure to online pornography and online sexual solicitation of youth. J Health Psychol  2016; 21: 1107– 18. Google Scholar CrossRef Search ADS PubMed  7 Schulz A, Bergen E, Schuhmann P, et al.   Online sexual solicitation of minors. J Res Crime Delinquency  2016; 53: 165– 88. Google Scholar CrossRef Search ADS   8 Whittle H, Hamilton-Giachritsis C, Beech A, Collings G. A review of young people’s vulnerabilities to online grooming. Aggression Violent Behav  2013; 18: 135– 46. Google Scholar CrossRef Search ADS   9 Villacampa C, Gómez M. Online child sexual grooming. Int Rev Victimol  2017; 23: 105– 21. Google Scholar CrossRef Search ADS   10 Espelage DL, Holt MK. Dating violence & sexual harassment across the bully-victim continuum among middle and high school students. J Youth Adolescence  2007; 36: 799– 811. Google Scholar CrossRef Search ADS   11 Ybarra ML, Espelage DL, Mitchell KJ. The co-occurrence of Internet harassment and unwanted sexual solicitation victimization and perpetration: associations with psychosocial indicators. J Adoles Health  2007; 41: S31– 41. Google Scholar CrossRef Search ADS   12 Turner HA, Shattuck A, Finkelhor D, Hamby S. Polyvictimization and youth violence exposure across contexts. J Adoles Health  2016; 58: 208– 14. Google Scholar CrossRef Search ADS   13 Ybarra ML, Diener-West M, Leaf PJ. Examining the overlap in Internet harassment and school bullying: implications for school intervention. J Adoles Health  2007; 41: S42– 50. Google Scholar CrossRef Search ADS   14 Mitchell KJ, Jones LM. Youth Internet Safety (YISS) Study: Methodological Report. Durham: University of New Hampshire, 2011. 15 Wolak J, Finkelhor D, Mitchell K, Ybarra M. Online ‘predators’ and their victims: myths, realities, and implications for prevention and treatment. Am Psychol  2008; 63: 111– 28. Google Scholar CrossRef Search ADS PubMed  16 Zetterström Dahlqvist H, Landstedt E, Young R, Gillander Gådin K. Dimensions of peer sexual harassment victimization and depressive symptoms in adolescence: a longitudinal cross-lagged study in a Swedish sample. J Youth Adoles  2016; 45: 858– 73. Google Scholar CrossRef Search ADS   17 Gruber JE, Fineran S. Comparing the impact of bullying and sexual harassment victimization on the mental and physical health of adolescents. Sex Roles  2008; 59: 1– 13. Google Scholar CrossRef Search ADS   18 Abada T, Hou F, Ram B. The effects of harassment and victimization on self-rated health and mental health among Canadian adolescents. Soc Sci Med  2008; 67: 557– 67. Google Scholar CrossRef Search ADS PubMed  19 Rinehart S, Espelage D, Bub K. Longitudinal effects of gendered harassment perpetration and victimization on mental health outcomes in adolescence. J Interpers Violence  2017;0:088626051772374, 0886260517723746. 20 Ybarra ML, Leaf J, Diener-West M. Sex differences in youth-reported depressive symptomatology and unwanted internet sexual solicitation. J Med Internet Res  2004; 6: e5. Google Scholar CrossRef Search ADS PubMed  21 Statens M. Youth and Medias. [Unga och medier]. Available at: https://www.iis.se/fakta/ungar-och-medier-2017/ (29 January 2018, date last accessed), Stockholm, 2017. 22 Radloff SL. The use of the center for epidemiologic studies depression scale in adolescents and young adults. J Youth Adolescence  1991; 20: 149– 66. Google Scholar CrossRef Search ADS   23 Radloff SL. The CES-D scale: a self-report depression scale for research in the general population. Appl Psychol Measure  1977; 1: 385– 401. Google Scholar CrossRef Search ADS   24 Mitchell KJ, Jones LM, Finkelhor D, Wolak J. Understanding the decline in unwanted online sexual solicitations for U.S. youth 2000-2010: findings from three youth Internet safety surveys. Child Abuse Neglect  2013; 37: 1225– 36. Google Scholar CrossRef Search ADS PubMed  25 Gruber JE, Fineran S. The impact of bullying and sexual harassment on middle and high school girls. Violence Against Women  2007; 13: 627– 43. Google Scholar CrossRef Search ADS PubMed  26 Jones LM, Mitchell KJ, Finkelhor D. Trends in youth Internet victimization: findings from three youth Internet safety surveys 2000–2010. J Adoles Health  2012; 50: 179– 86. Google Scholar CrossRef Search ADS   27 Swedish National Council for Crime Prevention. Adults’ Sexual Contacts with Children on the Internet: Prevalence, Characteristics and Measures. [Vuxnas sexuella kontakter med barn via Internet: Omfattning, karaktär, åtgärder]. Available at: https://www.bra.se/download/18.cba82f7130f475a2f180008790/2007_11_vuxnas_sexuella_kontakter_med_barn.pdf (30 January 2018, date last accessed), Swedish National Council for Crime Prevention (Brå), 2007. Report No.: 11. 28 Wang J, Iannotti R, Luk J, Nansel T. Co-occurrence of victimization from five subtypes of bullying: physical, verbal, social exclusion, spreading rumors, and cyber. J Pediatric Psychol  2010; 35: 1103– 12. Google Scholar CrossRef Search ADS   29 Mitchell KJ, Finkelhor D, Wolak J, et al.   Youth Internet victimization in a broader victimization context. J Adolescent Health  2011; 48: 128– 34. Google Scholar CrossRef Search ADS   30 Mitchell KJ, Finkelhor D, Wolak J. Risk factors for and impact of online sexual solicitation of youth. JAMA  2001; 285: 3011– 4. Google Scholar CrossRef Search ADS PubMed  © The Author(s) 2018. Published by Oxford University Press on behalf of the European Public Health Association. All rights reserved. This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/about_us/legal/notices) http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png The European Journal of Public Health Oxford University Press

Online sexual victimization in youth: predictors and cross-sectional associations with depressive symptoms

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Oxford University Press
Copyright
© The Author(s) 2018. Published by Oxford University Press on behalf of the European Public Health Association. All rights reserved.
ISSN
1101-1262
eISSN
1464-360X
D.O.I.
10.1093/eurpub/cky102
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Abstract

Abstract Aim The aim was to analyze (i) the prevalence of online unwanted sexual solicitation (USS) victimization, (ii) predictors of online USS and (iii) the associations between online USS and depressive symptoms in Swedish pupils in grades 7–9. Methods An electronic questionnaire was disseminated in 2011 in schools in a municipality in the northern part of Sweden. Total n = 1193 (boys n = 566; girls n = 627). Logistic regression models were fitted to test the cross-sectional associations between predictors of online USS and depressive symptoms, respectively. Results One third of girls and every fifth boy reported online USS victimization. In boys, predictors associated with online USS were offline bullying and sexual harassment victimization. Only offline sexual harassment victimization was associated with online USS in girls. Girls victimized by online USS had about twice the likelihood to report depressive symptoms compared to non-victimized girls. There were no associations between online USS and depressive symptoms in boys. While offline bullying was associated with depressive symptoms in both genders, offline sexual harassment victimization increased the likelihood to report depressive symptoms in girls only. Conclusions Online USS was common among Swedish youth, particularly among girls. Schools, parents and internet safety educators should look at co-occurrence of different forms of victimization as offline victimization was a predictor of online USS. Online USS was associated with depressive symptoms in girls and may hence be a factor driving gender inequity in mental health in youth. Introduction Internet and computer/smartphone/tablet-mediated communications have opened up new opportunities for increased interaction with peers.1 Young people can communicate in near real time with friends, connect with new friends, seek information, or visit civic or political websites, as well as create their own websites or blogs/vlogs. It can thus be seen as a forum for the development of social subjecthood, where young people can use the Internet as an enabler of e.g. positive sexual relations.2 Nevertheless, the Internet has also opened new ways to victimization such as cyber bullying,3 online sexual harassment,4 internet harassment5 and online unwanted sexual solicitation (USS).6,7 The Internet has presented an increased opportunity for groomers to find victims, and groomers are skilled in building trust in children and young people.8 In the past 15 years, there has been increasing concern about online USS, and it has become an additional harassment problem for young people, in addition to offline victimization as well as other forms of harassment online.4 Adolescent victimization rates vary between 13% and 61% depending on how USS is measured,4–6,9 and this remains an issue for increased mobilization. Previous studies show that there is some inconsistency regarding co-occurrence of different forms of victimization. Espelage and Holt10 found that bully victims report a high level of peer sexual harassment as well as higher levels of physical dating violence. There is also some support for the presence of co-occurrence between different forms of online harassment such as internet harassment and online USS.11 Turner, Shattuck12 have shown that about 18% of youth are poly-victims, meaning that they are likely to have been victimized by different victimization types, as well as in multiple settings (school, home, community). Other studies did not find support for an overlap between being bullied at school and Internet harassment.13 Although Mitchell and Jones14 found that there are co-occurrences between many forms of victimization, they did not find an elevated risk for online USS among those experiencing sexual harassment or peer victimization offline. Despite these inconsistent findings, we consider it worthwhile taking into account offline bullying and sexual harassment victimization when assessing predictors of online USS. Research has found that an unknown adult as an online perpetrator is rare and that a substantial percentage of the solicitations come from peers.15 Hence, it is worthwhile studying whether offline harassment is associated with online USS. Offline bullying and sexual harassment victimization have been shown to be associated with poor mental health in youth.16–19 Nonetheless, there are only a few studies, showing an association between online USS and poor mental health4,20 and further research is needed. Sweden is one of the countries with the highest levels of computer, smartphone and internet-usage in the world.21 The majority of Swedish young people have access to a computer or tablet, and smartphones are common.21 Ninety percent of Swedish 14–16-year-olds report interacting on social media online.21 It is important to know more about the risks that children and young people face online, and even more urgently, from a public health perspective, we need to deepen the understanding of the consequences of online USS victimization for mental health. Depressive symptoms have been shown to both precede and be a consequence of offline sexual harassment,16 and to our knowledge there is no previous Swedish study on online USS and its associations to depressive symptoms in this age group. Aim The aim of this study was 3-fold: (i) to analyze the prevalence of online USS victimization, (ii) to analyze predictors of online USS and finally (iii) to analyze the associations between online USS and depressive symptoms in a sample of Swedish pupils in grades 7–9 (age 14–16). Methods Context For the purposes of the current cross-sectional study, we used survey data from 2011 from a Swedish sample of 14–16 year-olds. The survey contained information about different factors associated with mental health. The municipality in which the data collection was conducted is situated in the northern part of Sweden and is of medium size with a population of about 60 000. The economic base of the municipality is comprised of a focus on tourism and small- and medium-sized enterprises. At the time of data collection, children in Sweden started compulsory school in the year they reached the age of 7 and attendance was compulsory up to the age of 16. Participants and procedure Written informed consent was obtained from parents and pupils. Pupils were informed about the aim of the study and told that they could withdraw their participation at any time. All procedures performed in this study were in accordance with the ethical standards of the regional research committee and with the 1964 Helsinki declaration and its later amendments, and approved by Umeå Regional Ethical Review Board (Ref. no.: 09-179M). The study population comprised 1193 pupils (566 boys, 627 girls) in grades 7–9 (14–16-year-olds). An electronic questionnaire was distributed to pupils in all nine public schools and one independent school via the pupils’ school e-mail addresses in January 2011, it and was filled in on computers during school hours. All schools had at least one member of staff supervising the pupils in order to answer any questions and to make sure that all pupils could complete the questionnaire in privacy. The response rate was 80%. The non-respondents differed between the schools by 10–40%, mainly due to the school administrations’ lack of commitment in providing adequate time and space to fill in the questionnaire in different classes. Measures Outcome variable The Center for Epidemiological Studies Depression Scale (CES-D) was used to measure depressive symptoms. The CES-D has been validated among adolescents22 and was developed for epidemiological and screening purposes.22,23 This scale has a range of 0–60, with higher scores indicating higher levels of depressive symptoms. The commonly used23 cut-off of ≥16 was used. Independent variable Online USS was defined as requests to engage in sexual activities, and sexual talk, and to give personal sexual information or to meet offline24 and it was estimated based on four questions derived from Mitchell and Jones14 and Mitchell, Jones24 with reference to the last 6 months. However, we did not specify that such solicitation had to be by an adult. If the pupils answered ‘at least once’ or more to any of the four questions, we considered it as online USS and coded it as risk = 1. Covariates Socio-demography Family structure. Pupils were asked: ‘Who do you live with?’ Response options were: ‘both mother and father’; ‘sometimes with mother’; ‘sometimes with father’; ‘mother with new partner’; ‘father with new partner’; ‘mother’; ‘father’; ‘somebody else’. Pupils indicating that they did not live with both their mother and their father were coded as risk = 1. Personal relative affluence. Pupils were asked the following question: ‘Thinking about your situation over the last 6 months, have you had as much money as your friends in order to do the same things as them?’ Response options were: ‘always’; ‘often’; ‘sometimes’; ‘rarely’; ‘never’. Pupils not indicating ‘always’ or ‘often’ were scored as risk = 1. Parental immigrant background. Pupils were asked: ‘In what country was your mother/father born?’ Response options were: ‘Sweden’; ‘in another country’; ‘I don’t know’. Pupils were categorized as having parental immigrant background if their father, their mother or both were born abroad and were hence coded as risk= 1. Social support Parental support. Respondents were asked: ‘Do you usually talk about most things with your mother/father?’ Response options were: ‘always’; ‘often’; ‘sometimes’; ‘rarely’; ‘never’; ‘don’t have or don’t see mother/father.’ A low degree of parental support was scored as 2 if they did not talk about almost everything ‘always’ or ‘often’ with both their mother and father. A medium degree of parental support was scored as 1 if respondents did not talk about almost everything ‘always’ or ‘often’ with either their mother or their father. Peer support. Pupils indicated how often, compared to their friends and peers, they experience the following: Does it ever happen that you are alone when you don’t want to be?; Do you have as many friends as you would like?; Do you sometimes feel left out by your peer group? ‘Always’ and ‘often’ was contrasted to ‘sometimes’, ‘rarely’, or ‘never’ and were regarded as having a low degree of peer support and being at risk = 1. Offline victimization Bullying. Respondents were asked: ‘It sometimes happens that other pupils tease, fight with somebody or shut somebody out. Has that happened to you in the past 6 months?’ Response options were: ‘yes, most of the time’; ‘yes, several times’; ‘yes, a couple of times’; ‘yes, once’; ‘no, never.’ Pupils indicating that it had happen once or more often in the last six were scored as being bullied = 1. Sexual harassment. The sexual harassment index was derived from Gruber and Fineran25 and consisted of 14 questions relating to sexual harassment over the previous 6 months. An example of physical harassment was: touched, grabbed or pinched you in a sexual manner; an example of public display: publicly commented on how attractive or unattractive you are; and examples of verbal/name calling: called you a lesbian, fag or similar words. Pupils indicating that this had happened to them at some point over the past 6 months were scored as being sexually harassed = 1. Data analysis Gender differences in all variables were analyzed using chi-square statistics with an alpha level of <0.05. As the chi-square statistics showed gender differences in several items, we decided to conduct gender-separate analyses in the next step. Logistic regression models were fitted to the data in order to assess associations between predictors of online USS and predictors of depressive symptoms respectively. The logistic regression analyses of predictors for depressive symptoms were developed in three steps (Models 1–3) in order to analyze the relative impact of online USS, socio-demographics, social support and offline victimization. Model 3 shows the independent contribution of each variable while controlling for all other predictors including school year. 95% confidence intervals (CIs) were used. No variables showed multi-collinearity with no VIF ≥2. Results Online USS victimization As shown in table 1, 35.5% of girls reported some online USS victimization compared to 19.9% of boys (P = <0.001). Being asked to meet offline was the most common type of online USS in both genders, while being asked to do something sexual was least common. Girls also reported being victims of offline bullying (girls, 34.5%; boys, 26.3%, P = 0.003) and sexual harassment (girls, 51.6%; boys, 45.1%, P = 0.036) more often than boys did, and depressive symptoms were almost twice as prevalent in girls compared to boys (girls, 44.3%; boys, 23.0%, P = <0.001). Table 1 Descriptive statistics by gender   Boys % (n)  Girls % (n)  P-value (95%)  Socio-demography      School year                7  29.9 (169)  31.3 (196)  0.102          8  30.9 (175)  35.2 (221)          9  39.2 (222)  33.5 (210)          Not living with both parents  37.8 (214)  37.9 (237)  0.969          Not having as much money as friends  17.5 (99)  20.0 (124)  0.302          Parental immigrant background  12.9 (72)  16.2 (101)  0.111  Social support      Parental support high  42.6 (193)  35.1 (196)  <0.001      Medium  18.8 (85)  36.3 (203)      Low  38.6 (175)  28.6 (160)        Low peer support  22.5 (108)  25.5 (149)  0.268  Offline victimization            Any bullying  26.3 (130)  34.5 (205)  0.003      Any sexual harassment  45.1 (223)  51.6 (296)  0.036  Unwanted online sexual solicitation (USS)      Has anyone tried to get you to talk about sex when you did not want to?  11.7 (57)  18.7 (110)  0.006      Has anyone asked you personal questions when you did not want them to, such as what your body looks like or sexual things you have done?  13.7 (67)  21.3 (126)  0.001      Has anyone asked you to do something sexual that you did not want to do?  10.7 (52)  14.7 (87)  0.046      Has anyone you don’t know asked you to meet offline?  14.6 (71)  25.6 (151)  <0.001      Any USS  19.9 (96)  35.5 (205)  <0.001  Mental health outcome            CES-D ≥16  23.0 (123)  44.3 (273)  <0.001    Boys % (n)  Girls % (n)  P-value (95%)  Socio-demography      School year                7  29.9 (169)  31.3 (196)  0.102          8  30.9 (175)  35.2 (221)          9  39.2 (222)  33.5 (210)          Not living with both parents  37.8 (214)  37.9 (237)  0.969          Not having as much money as friends  17.5 (99)  20.0 (124)  0.302          Parental immigrant background  12.9 (72)  16.2 (101)  0.111  Social support      Parental support high  42.6 (193)  35.1 (196)  <0.001      Medium  18.8 (85)  36.3 (203)      Low  38.6 (175)  28.6 (160)        Low peer support  22.5 (108)  25.5 (149)  0.268  Offline victimization            Any bullying  26.3 (130)  34.5 (205)  0.003      Any sexual harassment  45.1 (223)  51.6 (296)  0.036  Unwanted online sexual solicitation (USS)      Has anyone tried to get you to talk about sex when you did not want to?  11.7 (57)  18.7 (110)  0.006      Has anyone asked you personal questions when you did not want them to, such as what your body looks like or sexual things you have done?  13.7 (67)  21.3 (126)  0.001      Has anyone asked you to do something sexual that you did not want to do?  10.7 (52)  14.7 (87)  0.046      Has anyone you don’t know asked you to meet offline?  14.6 (71)  25.6 (151)  <0.001      Any USS  19.9 (96)  35.5 (205)  <0.001  Mental health outcome            CES-D ≥16  23.0 (123)  44.3 (273)  <0.001  Table 1 Descriptive statistics by gender   Boys % (n)  Girls % (n)  P-value (95%)  Socio-demography      School year                7  29.9 (169)  31.3 (196)  0.102          8  30.9 (175)  35.2 (221)          9  39.2 (222)  33.5 (210)          Not living with both parents  37.8 (214)  37.9 (237)  0.969          Not having as much money as friends  17.5 (99)  20.0 (124)  0.302          Parental immigrant background  12.9 (72)  16.2 (101)  0.111  Social support      Parental support high  42.6 (193)  35.1 (196)  <0.001      Medium  18.8 (85)  36.3 (203)      Low  38.6 (175)  28.6 (160)        Low peer support  22.5 (108)  25.5 (149)  0.268  Offline victimization            Any bullying  26.3 (130)  34.5 (205)  0.003      Any sexual harassment  45.1 (223)  51.6 (296)  0.036  Unwanted online sexual solicitation (USS)      Has anyone tried to get you to talk about sex when you did not want to?  11.7 (57)  18.7 (110)  0.006      Has anyone asked you personal questions when you did not want them to, such as what your body looks like or sexual things you have done?  13.7 (67)  21.3 (126)  0.001      Has anyone asked you to do something sexual that you did not want to do?  10.7 (52)  14.7 (87)  0.046      Has anyone you don’t know asked you to meet offline?  14.6 (71)  25.6 (151)  <0.001      Any USS  19.9 (96)  35.5 (205)  <0.001  Mental health outcome            CES-D ≥16  23.0 (123)  44.3 (273)  <0.001    Boys % (n)  Girls % (n)  P-value (95%)  Socio-demography      School year                7  29.9 (169)  31.3 (196)  0.102          8  30.9 (175)  35.2 (221)          9  39.2 (222)  33.5 (210)          Not living with both parents  37.8 (214)  37.9 (237)  0.969          Not having as much money as friends  17.5 (99)  20.0 (124)  0.302          Parental immigrant background  12.9 (72)  16.2 (101)  0.111  Social support      Parental support high  42.6 (193)  35.1 (196)  <0.001      Medium  18.8 (85)  36.3 (203)      Low  38.6 (175)  28.6 (160)        Low peer support  22.5 (108)  25.5 (149)  0.268  Offline victimization            Any bullying  26.3 (130)  34.5 (205)  0.003      Any sexual harassment  45.1 (223)  51.6 (296)  0.036  Unwanted online sexual solicitation (USS)      Has anyone tried to get you to talk about sex when you did not want to?  11.7 (57)  18.7 (110)  0.006      Has anyone asked you personal questions when you did not want them to, such as what your body looks like or sexual things you have done?  13.7 (67)  21.3 (126)  0.001      Has anyone asked you to do something sexual that you did not want to do?  10.7 (52)  14.7 (87)  0.046      Has anyone you don’t know asked you to meet offline?  14.6 (71)  25.6 (151)  <0.001      Any USS  19.9 (96)  35.5 (205)  <0.001  Mental health outcome            CES-D ≥16  23.0 (123)  44.3 (273)  <0.001  Predictors for online USS victimization Table 2 shows that in boys, the only predictors associated with online USS wre offline bullying (95% CI: 1.58–5.33) and sexual harassment victimization (95% CI: 2.78–10.36). While offline sexual harassment victimization was also significantly associated with online USS in girls (95% CI: 3.68–8.87), bullying was not. Table 2 Associations between predictors and online USS by gender   Boys   Girls   Predictor  Unadjusted OR (95% CI)  Adjusteda OR (95% CI)  Unadjusted OR (95% CI)  Adjusteda OR (95% CI)  Divorced parents  1.17 (0.74–1.84)  0.77 (0.41–1.45)  1.34 (0.94–1.90)  1.16 (0.75–1.77)  Low personal affluence  1.96 (1.14–3.38)  1.41 (0.67–2.96)  1.31 (0.87–2.00)  0.79 (0.48–1.32)  Parental immigrant background  2.91 (1.62–5.23)  1.82 (0.83–3.92)  0.80 (0.49–1.31)  0.57 (0.32–1.05)  Medium parental support  1.82 (0.90–3.67)  0.70 (0.27–1.82)  1.74 (1.10–2.77)  1.31 (0.77–2.22)  Low parental support  2.11 (1.20–3.72)  1.50 (0.77–2.93)  2.54 (1.60–4.03)  1.68 (0.98–2.87)  Low peer support  1.97 (1.17–3.31)  1.23 (0.61–2.45)  1.44 (0.98–2.12)  1.15 (0.72–1.84)  Any offline bullying  4.12 (2.55–6.65)  2.90 (1.58–5.33)  1.54 (1.08–2.20)  1.23 (0.80–1.91)  Any offline sexual harassment  6.36 (3.68–11.01)  5.37 (2.78–10.36)  6.18 (4.13–9.24)  5.72 (3.68–8.87)    Boys   Girls   Predictor  Unadjusted OR (95% CI)  Adjusteda OR (95% CI)  Unadjusted OR (95% CI)  Adjusteda OR (95% CI)  Divorced parents  1.17 (0.74–1.84)  0.77 (0.41–1.45)  1.34 (0.94–1.90)  1.16 (0.75–1.77)  Low personal affluence  1.96 (1.14–3.38)  1.41 (0.67–2.96)  1.31 (0.87–2.00)  0.79 (0.48–1.32)  Parental immigrant background  2.91 (1.62–5.23)  1.82 (0.83–3.92)  0.80 (0.49–1.31)  0.57 (0.32–1.05)  Medium parental support  1.82 (0.90–3.67)  0.70 (0.27–1.82)  1.74 (1.10–2.77)  1.31 (0.77–2.22)  Low parental support  2.11 (1.20–3.72)  1.50 (0.77–2.93)  2.54 (1.60–4.03)  1.68 (0.98–2.87)  Low peer support  1.97 (1.17–3.31)  1.23 (0.61–2.45)  1.44 (0.98–2.12)  1.15 (0.72–1.84)  Any offline bullying  4.12 (2.55–6.65)  2.90 (1.58–5.33)  1.54 (1.08–2.20)  1.23 (0.80–1.91)  Any offline sexual harassment  6.36 (3.68–11.01)  5.37 (2.78–10.36)  6.18 (4.13–9.24)  5.72 (3.68–8.87)  a Also adjusted for school year. Table 2 Associations between predictors and online USS by gender   Boys   Girls   Predictor  Unadjusted OR (95% CI)  Adjusteda OR (95% CI)  Unadjusted OR (95% CI)  Adjusteda OR (95% CI)  Divorced parents  1.17 (0.74–1.84)  0.77 (0.41–1.45)  1.34 (0.94–1.90)  1.16 (0.75–1.77)  Low personal affluence  1.96 (1.14–3.38)  1.41 (0.67–2.96)  1.31 (0.87–2.00)  0.79 (0.48–1.32)  Parental immigrant background  2.91 (1.62–5.23)  1.82 (0.83–3.92)  0.80 (0.49–1.31)  0.57 (0.32–1.05)  Medium parental support  1.82 (0.90–3.67)  0.70 (0.27–1.82)  1.74 (1.10–2.77)  1.31 (0.77–2.22)  Low parental support  2.11 (1.20–3.72)  1.50 (0.77–2.93)  2.54 (1.60–4.03)  1.68 (0.98–2.87)  Low peer support  1.97 (1.17–3.31)  1.23 (0.61–2.45)  1.44 (0.98–2.12)  1.15 (0.72–1.84)  Any offline bullying  4.12 (2.55–6.65)  2.90 (1.58–5.33)  1.54 (1.08–2.20)  1.23 (0.80–1.91)  Any offline sexual harassment  6.36 (3.68–11.01)  5.37 (2.78–10.36)  6.18 (4.13–9.24)  5.72 (3.68–8.87)    Boys   Girls   Predictor  Unadjusted OR (95% CI)  Adjusteda OR (95% CI)  Unadjusted OR (95% CI)  Adjusteda OR (95% CI)  Divorced parents  1.17 (0.74–1.84)  0.77 (0.41–1.45)  1.34 (0.94–1.90)  1.16 (0.75–1.77)  Low personal affluence  1.96 (1.14–3.38)  1.41 (0.67–2.96)  1.31 (0.87–2.00)  0.79 (0.48–1.32)  Parental immigrant background  2.91 (1.62–5.23)  1.82 (0.83–3.92)  0.80 (0.49–1.31)  0.57 (0.32–1.05)  Medium parental support  1.82 (0.90–3.67)  0.70 (0.27–1.82)  1.74 (1.10–2.77)  1.31 (0.77–2.22)  Low parental support  2.11 (1.20–3.72)  1.50 (0.77–2.93)  2.54 (1.60–4.03)  1.68 (0.98–2.87)  Low peer support  1.97 (1.17–3.31)  1.23 (0.61–2.45)  1.44 (0.98–2.12)  1.15 (0.72–1.84)  Any offline bullying  4.12 (2.55–6.65)  2.90 (1.58–5.33)  1.54 (1.08–2.20)  1.23 (0.80–1.91)  Any offline sexual harassment  6.36 (3.68–11.01)  5.37 (2.78–10.36)  6.18 (4.13–9.24)  5.72 (3.68–8.87)  a Also adjusted for school year. Associations with depressive symptoms Online USS was independently associated with depressive symptoms in girls in all models (table 4), while in boys this association disappeared when we adjusted for social support and offline victimization (table 3). As shown in table 4, adjusting for offline victimization rendered a relatively large reduction in the odds ratio (OR) for online USS in girls (OR: 2.12, 95% CI: 1.35–3.33). Table 3 Associations between online USS and depressive symptoms in boys Boys   Predictor  Unadjusted OR (95% CI)  Adjusteda OR (95% CI)       Model 1  Model 2  Model 3  Any USS  3.37 (2.04–5.55)  2.39 (1.36–4.20)  1.57 (0.80–3.07)  1.01 (0.48–2.13)  Divorced parents  1.63 (1.08–2.44)  1.53 (0.92–2.56)  1.25 (0.70–2.24)  1.31 (0.72–2.41)  Low personal affluence  4.33 (2.69–6.98)  4.47 (2.54–7.85)  4.42 (2.37–8.21)  3.97 (2.05–7.70)  Parental immigrant background  3.88 (2.28–6.60)  3.90 (2.07–7.33)  3.88 (1.85–8.13)  3.49 (1.63–7.46)  Medium parental support  3.61 (1.86–7.01)    2.37 (1.05–5.36)  2.15 (0.90–5.13)  Low parental support  2.42 (1.35–4.35)    1.62 (0.82–3.25)  1.62 (0.79–3-31)  Low peer support  3.57 (2.21–5.79)    2.93 (1.61–5.35)  2.44 (1.29–4.60)  Any offline bullying  3.81 (2.41–6.02)      2.42 (1.28–4.61)  Any offline sexual harassment  2.94 (1.88–4.61)      1.74 (0.92–3.28)  Boys   Predictor  Unadjusted OR (95% CI)  Adjusteda OR (95% CI)       Model 1  Model 2  Model 3  Any USS  3.37 (2.04–5.55)  2.39 (1.36–4.20)  1.57 (0.80–3.07)  1.01 (0.48–2.13)  Divorced parents  1.63 (1.08–2.44)  1.53 (0.92–2.56)  1.25 (0.70–2.24)  1.31 (0.72–2.41)  Low personal affluence  4.33 (2.69–6.98)  4.47 (2.54–7.85)  4.42 (2.37–8.21)  3.97 (2.05–7.70)  Parental immigrant background  3.88 (2.28–6.60)  3.90 (2.07–7.33)  3.88 (1.85–8.13)  3.49 (1.63–7.46)  Medium parental support  3.61 (1.86–7.01)    2.37 (1.05–5.36)  2.15 (0.90–5.13)  Low parental support  2.42 (1.35–4.35)    1.62 (0.82–3.25)  1.62 (0.79–3-31)  Low peer support  3.57 (2.21–5.79)    2.93 (1.61–5.35)  2.44 (1.29–4.60)  Any offline bullying  3.81 (2.41–6.02)      2.42 (1.28–4.61)  Any offline sexual harassment  2.94 (1.88–4.61)      1.74 (0.92–3.28)  a Also adjusted for school year. Table 3 Associations between online USS and depressive symptoms in boys Boys   Predictor  Unadjusted OR (95% CI)  Adjusteda OR (95% CI)       Model 1  Model 2  Model 3  Any USS  3.37 (2.04–5.55)  2.39 (1.36–4.20)  1.57 (0.80–3.07)  1.01 (0.48–2.13)  Divorced parents  1.63 (1.08–2.44)  1.53 (0.92–2.56)  1.25 (0.70–2.24)  1.31 (0.72–2.41)  Low personal affluence  4.33 (2.69–6.98)  4.47 (2.54–7.85)  4.42 (2.37–8.21)  3.97 (2.05–7.70)  Parental immigrant background  3.88 (2.28–6.60)  3.90 (2.07–7.33)  3.88 (1.85–8.13)  3.49 (1.63–7.46)  Medium parental support  3.61 (1.86–7.01)    2.37 (1.05–5.36)  2.15 (0.90–5.13)  Low parental support  2.42 (1.35–4.35)    1.62 (0.82–3.25)  1.62 (0.79–3-31)  Low peer support  3.57 (2.21–5.79)    2.93 (1.61–5.35)  2.44 (1.29–4.60)  Any offline bullying  3.81 (2.41–6.02)      2.42 (1.28–4.61)  Any offline sexual harassment  2.94 (1.88–4.61)      1.74 (0.92–3.28)  Boys   Predictor  Unadjusted OR (95% CI)  Adjusteda OR (95% CI)       Model 1  Model 2  Model 3  Any USS  3.37 (2.04–5.55)  2.39 (1.36–4.20)  1.57 (0.80–3.07)  1.01 (0.48–2.13)  Divorced parents  1.63 (1.08–2.44)  1.53 (0.92–2.56)  1.25 (0.70–2.24)  1.31 (0.72–2.41)  Low personal affluence  4.33 (2.69–6.98)  4.47 (2.54–7.85)  4.42 (2.37–8.21)  3.97 (2.05–7.70)  Parental immigrant background  3.88 (2.28–6.60)  3.90 (2.07–7.33)  3.88 (1.85–8.13)  3.49 (1.63–7.46)  Medium parental support  3.61 (1.86–7.01)    2.37 (1.05–5.36)  2.15 (0.90–5.13)  Low parental support  2.42 (1.35–4.35)    1.62 (0.82–3.25)  1.62 (0.79–3-31)  Low peer support  3.57 (2.21–5.79)    2.93 (1.61–5.35)  2.44 (1.29–4.60)  Any offline bullying  3.81 (2.41–6.02)      2.42 (1.28–4.61)  Any offline sexual harassment  2.94 (1.88–4.61)      1.74 (0.92–3.28)  a Also adjusted for school year. Table 4 Associations between online USS and depressive symptoms in girls Girls   Predictor  Unadjusted OR (95% CI)  Adjusteda OR (95% CI)       Model 1  Model 2  Model 3  Any USS  3.36 (2.35–4.80)  3.42 (2.34–4.99)  2.95 (1.96–4.43)  2.12 (1.35–3.33)  Divorced parents  1.85 (1.33–2.57)  1.55 (1.07–2.27)  1.47 (0.98–2.23)  1.49 (0.96–2.31)  Low personal affluence  3.51 (2.30–5.34)  3.34 (2.11–5.30)  2.93 (1.78–4.84)  2.27 (1.35–3.83)  Parental immigrant background  1.40 (0.91–2.14)  1.61 (0.97–2.66)  1.47 (0.85–2.55)  1.35 (0.75–2.42)  Medium parental support  1.71 (1.10–2.65)    1.11 (0.67–1.84)  1.02 (0.60–1.72)  Low parental support  3.13 (2.01–4.87)    2.05 (1.23–3.42)  2.07 (1.21–3.53)  Low peer support  3.54 (2.39–5.25)    3.07 (1.96–4.79)  2.58 (1.60–4.15)  Any offline bullying  2.78 (1.96–3.95)      2.23 (1.43–3.49)  Any offline sexual harassment  3.60 (2.53–5.12)      2.23 (1.43–3.47)  Girls   Predictor  Unadjusted OR (95% CI)  Adjusteda OR (95% CI)       Model 1  Model 2  Model 3  Any USS  3.36 (2.35–4.80)  3.42 (2.34–4.99)  2.95 (1.96–4.43)  2.12 (1.35–3.33)  Divorced parents  1.85 (1.33–2.57)  1.55 (1.07–2.27)  1.47 (0.98–2.23)  1.49 (0.96–2.31)  Low personal affluence  3.51 (2.30–5.34)  3.34 (2.11–5.30)  2.93 (1.78–4.84)  2.27 (1.35–3.83)  Parental immigrant background  1.40 (0.91–2.14)  1.61 (0.97–2.66)  1.47 (0.85–2.55)  1.35 (0.75–2.42)  Medium parental support  1.71 (1.10–2.65)    1.11 (0.67–1.84)  1.02 (0.60–1.72)  Low parental support  3.13 (2.01–4.87)    2.05 (1.23–3.42)  2.07 (1.21–3.53)  Low peer support  3.54 (2.39–5.25)    3.07 (1.96–4.79)  2.58 (1.60–4.15)  Any offline bullying  2.78 (1.96–3.95)      2.23 (1.43–3.49)  Any offline sexual harassment  3.60 (2.53–5.12)      2.23 (1.43–3.47)  a Also adjusted for school year. Table 4 Associations between online USS and depressive symptoms in girls Girls   Predictor  Unadjusted OR (95% CI)  Adjusteda OR (95% CI)       Model 1  Model 2  Model 3  Any USS  3.36 (2.35–4.80)  3.42 (2.34–4.99)  2.95 (1.96–4.43)  2.12 (1.35–3.33)  Divorced parents  1.85 (1.33–2.57)  1.55 (1.07–2.27)  1.47 (0.98–2.23)  1.49 (0.96–2.31)  Low personal affluence  3.51 (2.30–5.34)  3.34 (2.11–5.30)  2.93 (1.78–4.84)  2.27 (1.35–3.83)  Parental immigrant background  1.40 (0.91–2.14)  1.61 (0.97–2.66)  1.47 (0.85–2.55)  1.35 (0.75–2.42)  Medium parental support  1.71 (1.10–2.65)    1.11 (0.67–1.84)  1.02 (0.60–1.72)  Low parental support  3.13 (2.01–4.87)    2.05 (1.23–3.42)  2.07 (1.21–3.53)  Low peer support  3.54 (2.39–5.25)    3.07 (1.96–4.79)  2.58 (1.60–4.15)  Any offline bullying  2.78 (1.96–3.95)      2.23 (1.43–3.49)  Any offline sexual harassment  3.60 (2.53–5.12)      2.23 (1.43–3.47)  Girls   Predictor  Unadjusted OR (95% CI)  Adjusteda OR (95% CI)       Model 1  Model 2  Model 3  Any USS  3.36 (2.35–4.80)  3.42 (2.34–4.99)  2.95 (1.96–4.43)  2.12 (1.35–3.33)  Divorced parents  1.85 (1.33–2.57)  1.55 (1.07–2.27)  1.47 (0.98–2.23)  1.49 (0.96–2.31)  Low personal affluence  3.51 (2.30–5.34)  3.34 (2.11–5.30)  2.93 (1.78–4.84)  2.27 (1.35–3.83)  Parental immigrant background  1.40 (0.91–2.14)  1.61 (0.97–2.66)  1.47 (0.85–2.55)  1.35 (0.75–2.42)  Medium parental support  1.71 (1.10–2.65)    1.11 (0.67–1.84)  1.02 (0.60–1.72)  Low parental support  3.13 (2.01–4.87)    2.05 (1.23–3.42)  2.07 (1.21–3.53)  Low peer support  3.54 (2.39–5.25)    3.07 (1.96–4.79)  2.58 (1.60–4.15)  Any offline bullying  2.78 (1.96–3.95)      2.23 (1.43–3.49)  Any offline sexual harassment  3.60 (2.53–5.12)      2.23 (1.43–3.47)  a Also adjusted for school year. While offline bullying was associated with depressive symptoms in both genders (95% CI: girls, 1.43–3.49; boys, 1.28–4.61), offline sexual harassment victimization increased the likelihood to report depressive symptoms in girls only (95% CI: 1.43–3.47; table 4). Discussion The results show that it was common for pupils aged 14–16 years to be victims of online USS, and girls to a greater extent than boys. Victimization seems to be evenly distributed among pupils from different social backgrounds. The higher proportion of girls among the victims is in line with previous studies conducted in the US26 and in Denmark.4 Even if girls were more exposed to online USS, the frequency with which boys were victims should not be disregarded. However, it is important to consider the gendered pattern of online USS, as the literature shows that boys and men are perpetrators to a far greater degree than girls and women.27,28 The prevalence of online USS in the present study is much higher compared to some studies.4,26 In a Swedish context, these findings seems to be in line with previous findings by the Swedish National Council for Crime Prevention27 which showed that 50% of 15–17 year-old girls and 20% of boys had been exposed to USS online. The most important factor associated with online USS victimization was offline victimization. Sexual harassment was strongly associated with victimization among both genders, while bullying was significant for boys only. The associations between sexual harassment victimization and poor mental health outcomes in adolescence have been firmly established.16–19 In contrast, information on associations between online (USS) and mental health is scarce. We aimed to fill this gap by using cross-sectional data in a Swedish cohort of 14–16 year-old adolescents. Girls who reported any online USS were more than twice as likely to report high levels of depressive symptoms, which is in contrast to the results of Ybarra et al.20 who found a similar probability among boys but not among girls. Furthermore, while offline bullying was associated with depressive symptoms in both genders, offline sexual harassment victimization increased the likelihood to report depressive symptoms in girls only, which is in line with previous studies.16,17 Adjusting for offline victimization produced a relatively large reduction in the OR for online USS in girls, though the association remained significant. Different directional pathways between offline sexual harassment and depressive symptoms has been shown to drive some of the inequity in depressive symptoms between adolescent boys and girls,16 and based on the results of the current study, we suggest that this may be the case for online USS as well. The concept of online USS can be contested. In an offline situation, the same items would be called sexual harassment or abuse, and there is a risk of diminishing the problem if we keep referring to it as ‘solicitation’. If some of the perpetrators offline are the same peers who are perpetrators online, the Internet is an extended forum for such behaviour and should be named as such. Also, the gendered aspects of online USS should be highlighted. The Internet both reflects and refracts the broader pattern of unequal social power in society, and online practices follow the same axes of social stratification as offline.2 As suggested by Mitchell, Finkelhor,29 prevention and intervention should target a broader range of behaviours and experiences rather than focusing on the Internet component exclusively. The authors29 also conclude that Internet safety educators need to appreciate that many online victims may be at risk not because they are naive about the Internet, but because they also face complicated victimization problems offline. Helweg–Larsen, Schütt4 use a wider definition of online victimization than the one used in the present study that includes any type of harassment, and suggest that those exposed to parental violence or sexual abuse were associated with being online victims. This supports the theory that vulnerable youth display an Internet behaviour that puts them at risk of unknown people who approach them on the internet. Nevertheless, it is also possible that most of the online victims are harassed offline by peers, and that the internet thus has provided harassers with an opportunity for continued perpetration after school hours. Jones, Mitchell26 suggest that a high prevalence of online harassing behaviour mirrors the increasing possibility of harassing others as a consequence of the new digital techniques. Considering the fact that the odds ratio for online USS decreased considerably in girls when we adjusted for offline bullying and sexual harassment victimization, schools, parents and internet safety educators need to consider the co-occurrence of other forms of victimization. Although Mitchell, Finkelhor29 found that there was co-occurrence between many forms of victimization, they did not find an elevated risk for online USS among those experiencing sexual harassment or peer victimization offline. However, unlike the current study, they included questions on other forms of sexual victimization such as rape, which rendered the association between sexual harassment and online USS insignificant. Even if some of the perpetrators are the same age as the victims, and possibly even peers at school, we also have to be aware of the likelihood that some of them are groomers who may manipulate young people and become increasingly aggressive.8 As many as 30% of Swedish 15-year-olds have been contacted by a person they believe to be an unknown adult.27 This shows that the high prevalence in this study must be taken seriously, and that we have to know more about the perpetrators, as well as how to protect young people from grooming victimization. It is thus of great importance that schools in Sweden continue working to increase Internet literacy and Internet safety skills among pupils. Methodological considerations As this study is cross-sectional, findings should be interpreted with caution, as we cannot infer evidence of causal relationship between data. Answering questions about sexual solicitation can be sensitive, and it is possible that the prevalence of victimization in this study is underestimated. The high response rate is an advantage, however, as is the data collection method. Our definition of online USS is different from Mitchell et al.,30 who say that online USS must be perpetrated by an adult. There was no information available regarding the perpetrator in our data and the question was not framed in a way that would exclude (or include) different types of perpetrators. We argue that the generalizability of these results extends to adolescents in grades 7–9 in Sweden, particularly outside the main metropolitan areas. Some time has passed since the data collection was conducted and Internet behaviour is most likely to have change with the rapid growth of e.g. social media. Conclusions and implications for future research This study shows that online USS was common among Swedish youth in grades 7–9, particularly among girls, but that online USS was associated with depressive symptoms in girls only. Considering the effects of offline bullying and sexual harassment victimization in the adjusted models, future studies should delve deeper into the patterns of co-occurrence and poly-victimization, including other types of violence. Acknowledgements The authors would like to extend our gratitude to participating pupils and schools. Funding The data collection of this study was supported by the Public Health Agency of Sweden [HFÅ2008/212]. Conflicts of interest: None declared. Key points Online USS was common among Swedish youth, particularly among girls. Offline victimization was a predictor of online USS and stakeholders should address the issue of co-occurrence of different forms of victimization. Online USS was associated with depressive symptoms in girls and may hence be a factor driving gender inequity in mental health in youth. References 1 Livingstone S, Bober M, Helsper EJ. Active participation or just more information? Inform Commun Soc  2005; 8: 287– 314. Google Scholar CrossRef Search ADS   2 Brickell C. Sexuality, power and the sociology of the Internet. Curr Sociol  2012; 60: 28– 44. Google Scholar CrossRef Search ADS   3 Wang J, Iannotti R, Nansel T. School bullying among US adolescents: physical, verbal, relational and cyber. J Adoles Health  2009; 45: 368– 75. Google Scholar CrossRef Search ADS   4 Helweg-Larsen K, Schütt N, Larsen H. Predictors and protective factors for adolescent Internet victimization: results from a 2008 nationwide Danish youth survey. Acta Paediatrica  2012; 101: 533– 9. Google Scholar CrossRef Search ADS PubMed  5 Montiel I, Carbonell E, Pereda N. Multiple online victimization of Spanish adolescents: results from a community sample. Child Abuse Neglect  2016; 52: 123– 34. Google Scholar CrossRef Search ADS PubMed  6 Chang F-C, Chiu C-H, Miao N-F, et al.   Predictors of unwanted exposure to online pornography and online sexual solicitation of youth. J Health Psychol  2016; 21: 1107– 18. Google Scholar CrossRef Search ADS PubMed  7 Schulz A, Bergen E, Schuhmann P, et al.   Online sexual solicitation of minors. J Res Crime Delinquency  2016; 53: 165– 88. Google Scholar CrossRef Search ADS   8 Whittle H, Hamilton-Giachritsis C, Beech A, Collings G. A review of young people’s vulnerabilities to online grooming. Aggression Violent Behav  2013; 18: 135– 46. Google Scholar CrossRef Search ADS   9 Villacampa C, Gómez M. Online child sexual grooming. Int Rev Victimol  2017; 23: 105– 21. Google Scholar CrossRef Search ADS   10 Espelage DL, Holt MK. Dating violence & sexual harassment across the bully-victim continuum among middle and high school students. J Youth Adolescence  2007; 36: 799– 811. Google Scholar CrossRef Search ADS   11 Ybarra ML, Espelage DL, Mitchell KJ. The co-occurrence of Internet harassment and unwanted sexual solicitation victimization and perpetration: associations with psychosocial indicators. J Adoles Health  2007; 41: S31– 41. Google Scholar CrossRef Search ADS   12 Turner HA, Shattuck A, Finkelhor D, Hamby S. Polyvictimization and youth violence exposure across contexts. J Adoles Health  2016; 58: 208– 14. Google Scholar CrossRef Search ADS   13 Ybarra ML, Diener-West M, Leaf PJ. Examining the overlap in Internet harassment and school bullying: implications for school intervention. J Adoles Health  2007; 41: S42– 50. Google Scholar CrossRef Search ADS   14 Mitchell KJ, Jones LM. Youth Internet Safety (YISS) Study: Methodological Report. Durham: University of New Hampshire, 2011. 15 Wolak J, Finkelhor D, Mitchell K, Ybarra M. Online ‘predators’ and their victims: myths, realities, and implications for prevention and treatment. Am Psychol  2008; 63: 111– 28. Google Scholar CrossRef Search ADS PubMed  16 Zetterström Dahlqvist H, Landstedt E, Young R, Gillander Gådin K. Dimensions of peer sexual harassment victimization and depressive symptoms in adolescence: a longitudinal cross-lagged study in a Swedish sample. J Youth Adoles  2016; 45: 858– 73. Google Scholar CrossRef Search ADS   17 Gruber JE, Fineran S. Comparing the impact of bullying and sexual harassment victimization on the mental and physical health of adolescents. Sex Roles  2008; 59: 1– 13. Google Scholar CrossRef Search ADS   18 Abada T, Hou F, Ram B. The effects of harassment and victimization on self-rated health and mental health among Canadian adolescents. Soc Sci Med  2008; 67: 557– 67. Google Scholar CrossRef Search ADS PubMed  19 Rinehart S, Espelage D, Bub K. Longitudinal effects of gendered harassment perpetration and victimization on mental health outcomes in adolescence. J Interpers Violence  2017;0:088626051772374, 0886260517723746. 20 Ybarra ML, Leaf J, Diener-West M. Sex differences in youth-reported depressive symptomatology and unwanted internet sexual solicitation. J Med Internet Res  2004; 6: e5. Google Scholar CrossRef Search ADS PubMed  21 Statens M. Youth and Medias. [Unga och medier]. Available at: https://www.iis.se/fakta/ungar-och-medier-2017/ (29 January 2018, date last accessed), Stockholm, 2017. 22 Radloff SL. The use of the center for epidemiologic studies depression scale in adolescents and young adults. J Youth Adolescence  1991; 20: 149– 66. Google Scholar CrossRef Search ADS   23 Radloff SL. The CES-D scale: a self-report depression scale for research in the general population. Appl Psychol Measure  1977; 1: 385– 401. Google Scholar CrossRef Search ADS   24 Mitchell KJ, Jones LM, Finkelhor D, Wolak J. Understanding the decline in unwanted online sexual solicitations for U.S. youth 2000-2010: findings from three youth Internet safety surveys. Child Abuse Neglect  2013; 37: 1225– 36. Google Scholar CrossRef Search ADS PubMed  25 Gruber JE, Fineran S. The impact of bullying and sexual harassment on middle and high school girls. Violence Against Women  2007; 13: 627– 43. Google Scholar CrossRef Search ADS PubMed  26 Jones LM, Mitchell KJ, Finkelhor D. Trends in youth Internet victimization: findings from three youth Internet safety surveys 2000–2010. J Adoles Health  2012; 50: 179– 86. Google Scholar CrossRef Search ADS   27 Swedish National Council for Crime Prevention. Adults’ Sexual Contacts with Children on the Internet: Prevalence, Characteristics and Measures. [Vuxnas sexuella kontakter med barn via Internet: Omfattning, karaktär, åtgärder]. Available at: https://www.bra.se/download/18.cba82f7130f475a2f180008790/2007_11_vuxnas_sexuella_kontakter_med_barn.pdf (30 January 2018, date last accessed), Swedish National Council for Crime Prevention (Brå), 2007. Report No.: 11. 28 Wang J, Iannotti R, Luk J, Nansel T. Co-occurrence of victimization from five subtypes of bullying: physical, verbal, social exclusion, spreading rumors, and cyber. J Pediatric Psychol  2010; 35: 1103– 12. Google Scholar CrossRef Search ADS   29 Mitchell KJ, Finkelhor D, Wolak J, et al.   Youth Internet victimization in a broader victimization context. J Adolescent Health  2011; 48: 128– 34. Google Scholar CrossRef Search ADS   30 Mitchell KJ, Finkelhor D, Wolak J. Risk factors for and impact of online sexual solicitation of youth. JAMA  2001; 285: 3011– 4. Google Scholar CrossRef Search ADS PubMed  © The Author(s) 2018. 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The European Journal of Public HealthOxford University Press

Published: Jun 2, 2018

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