The Association of Cyberbullying with Major Depressive Disorders among Bangladeshi Female Adolescents: Findings from the Bangladesh Adolescent Health and Wellbeing Survey 2019-20

The Association of Cyberbullying with Major Depressive Disorders among Bangladeshi Female Adolescents: Findings from the Bangladesh Adolescent Health and Wellbeing Survey 2019-20 | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The Association of Cyberbullying with Major Depressive Disorders among Bangladeshi Female Adolescents: Findings from the Bangladesh Adolescent Health and Wellbeing Survey 2019-20 Syed Toukir Ahmed Noor, Md. Fakrul Islam, Md Sabbir Hossain, Raisha Binte Islam, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6274564/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 12 Aug, 2025 Read the published version in BMC Psychiatry → Version 1 posted 13 You are reading this latest preprint version Abstract Background Cyberbullying refers to the act of using digital technology to engage in bullying. It involves the intentional use of the internet to demean or denigrate individuals, and it has been linked to substantial psychological distress globally. Despite its increasing prevalence, there remains a gap in nationwide research on its association with the mental well-being of female adolescents in Bangladesh. Objective The main objective of this study was to understand the association of cyberbullying with major depressive disorder (MDD) among female adolescents in Bangladesh. Methods We used secondary data from the 2019–20 Bangladesh Adolescent Health and Wellbeing Survey (BAHWS), a nationally representative survey. A multiple logistic regression model was used to identify the possible association between cyberbullying and MDD. Findings: Among the 4,984 female adolescents surveyed, 8% (95% CI: 7–9) reported experiencing cyberbullying within the past 12 months, and 12% (95% CI: 11–14) were found to have MDD. Among those who had experienced cyberbullying, the prevalence of MDD was 31%. Additionally, a dose‒response relationship was observed, where the burden of MDD increases with the frequency of cyberbullying experiences. Compared with those who did not experience cyberbullying, adolescents who faced any form of cyberbullying in the past 12 months were almost four times more likely (AOR: 3.97, 95% CI: 3.12–5.05) to have MDD. Conclusion Cyberbullying is a significant issue among female adolescents in Bangladesh, with a higher risk of MDD linked to increased exposure. Accessible reporting mechanisms and timely counselling through teachers or healthcare providers can help reduce the long-term mental health impact of cyberbullying. Clinical trial number: not applicable. Cyberbullying Major depressive disorder Female adolescents Bangladesh Mental health Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Use of digital technology to intentionally harm others through words, images, or videos, often via social media, apps, and messaging platforms [ 1 , 2 ]. Globally, it is a growing public health concern [ 3 ]. This is often accomplished via apps, blogs, instant messaging, chat rooms, and social media platforms such as Facebook, Instagram, and Twitter [ 4 ]. Females, particularly adolescents, are more likely to experience cyberbullying than males are [ 5 , 6 ]. This vulnerability is partly due to their increased social media use, greater exposure to relational aggression, and societal pressures on appearance, which can lead to body shame and unrealistic beauty standards [ 7 – 9 ]. Studies have shown that cyberbullying has severe psychological and physical consequences, with female adolescents being disproportionately affected [ 5 , 10 , 11 ]. A review of 57 studies across 17 countries revealed that females experience higher rates of cyberbullying victimisation and its negative effects, such as major depressive disorder (MDD), than males do [ 12 ]. Persistent sadness, negativity, and boredom in once-enjoyable activities are signs of MDD [ 13 ]. Common symptoms include difficulty concentrating, changes in appetite and sleep patterns, low energy, feelings of worthlessness, and, in severe cases, suicidal thoughts. Adolescents with MDD are at heightened risk for long-term academic, social, and emotional difficulties, as well as poor physical health outcomes [ 14 , 15 ]. The consequences of untreated MDD during adolescence often persist into adulthood, affecting overall quality of life and productivity [ 16 ]. Owing to heightened emotional sensitivity during this developmental stage, females, especially adolescents, are more likely to be emotionally impacted by negative online interactions, increasing their vulnerability to the psychological consequences of cyberbullying [ 3 , 17 ]. Adolescents, who make up one-third of internet users globally [ 18 ], are particularly vulnerable to the growing issue of cyberbullying. As online interactions become more prevalent, so do the risks associated with cyberbullying, which has emerged as a significant concern [ 19 ]. In Europe, one in six school-aged students has experienced cyberbullying [ 20 ], and in the UK, one in three students report being bullied on various online platforms [ 21 ]. The problem extends across Asia, with countries such as China (70%), Singapore (58%), India (53%), Malaysia (33%), and Pakistan (26%) reporting high rates of cyberbullying [ 22 ]. The consequences are severe, as studies have shown that 93% of victims experience negative mental health effects, including sadness, hopelessness, stress, and emotional distress [ 23 – 25 ]. According to a study conducted in Bangladesh, at least 32% of adolescents between the ages of 10- and 17-years’ experience cyberbullying, online abuse, and digital harassment [ 26 ]. Another study reported that 64% of women aged 15–35 years experienced online violence, predominantly on Facebook (48%) and Messenger (35%) (Action Aid Bangladesh, 2022). Furthermore, among those who had experienced cyberbullying, 27% had some form of psychiatric disorder [ 28 ]. According to research conducted on school-aged teenagers during the COVID-19 epidemic, 40% of individuals who were victims of cyberbullying also experienced MDD [ 29 ]. There is increasing concern about the impact of cyberbullying on MDD, especially among female adolescents. While existing studies in Bangladesh have provided valuable insights, they have focused predominantly on localised samples from specific schools and colleges [ 28 , 30 , 31 ]. As a result, there is a lack of nationally representative research exploring the relationship between cyberbullying and adolescent mental health. These smaller-scale studies may not fully capture regional variations or the broader experiences of adolescents across diverse sociocultural and economic backgrounds. To the best of our knowledge, no prior study in Bangladesh has examined the association between cyberbullying and MDD among female adolescents using nationally representative data. Therefore, this study utilises data from the nationally representative Bangladesh Adolescent Health and Wellbeing Survey (BAHWS) 2019–20 to understand the association between cyberbullying and MDD among adolescents, particularly females, in Bangladesh. Methods Data source, sampling technique, and study population In this study, we used secondary data from the Bangladesh Adolescent Health and Wellbeing Survey (BAHWS) 2019-20, a nationally representative cross-sectional survey carried out between July 25, 2019, and January 10, 2020. The sampling strategy for the BAHWS was based on a two-stage stratified sample of households. The first stage involved the selection of primary sampling units (PSUs) from the prepared lists, whereas the second stage involved the selection of households from these PSUs. The adolescents were subsequently selected from each stratum. This sampling technique ensured the representativeness and reliability of the survey data; more details of the sampling techniques are available in the report [ 32 ]. The survey utilised a sample consisting of 72,800 households, from which 67,093 households (98%) were successfully interviewed. The participants included 4,926 ever-married female adolescents (97% response rate), 7,800 unmarried female adolescents (94% response rate), and 5,523 unmarried male adolescents (85% response rate) aged 15–19 years. These adolescents were interviewed via three distinct individual questionnaires. Finally, 4984 (weighted) female adolescents, 2962 unmarried and 2022 married, who participated in the cyberbullying question (Fig. 1 ), were included in our analysis. [Insert Fig. 1 here] Study variables and measurements Outcome variables The assessment of MDD in adolescents was conducted via the Patient Health Questionnaire (PHQ-9). It is a widely used, self-reported screening tool consisting of nine items assessing the presence or absence of depressive symptoms over the past two weeks [ 33 ]. Higher scores indicate more severe symptoms. The cumulative score, ranging from 0–27, delineates the following severity levels: no depression (scores 0–4), mild (scores 5–9), moderate (scores 10–14), moderately severe (scores 15–19), and severe (scores 20–27) [ 34 ]. In this study, a score of 10 or above indicated MDD (coded as 1), whereas a score of less than 10 denoted the absence of MDD (coded as 0) [ 35 ]. The Cronbach's alpha value of the PHQ-9 scale in our study was 0.80, indicating good internal consistency. The frequency distributions for the PHQ-9 items are given in Appendix Table 1 . Exposure variable The exposure variable of this study was the experience of cyberbullying among adolescents. This was measured in the survey asking participants, "Has anyone used mobile/internet to bother/harass you at least once in the last 12 months?" If the respondents answered "yes" to this initial question, they were further asked, "How often did this bother you via mobile phone/internet?" This two-tiered approach allowed for the identification of adolescents who had experienced cyberbullying and provided insight into the frequency of such incidents. Confounders This study used several confounding variables, including socioeconomic and demographic factors and regional-level characteristics. With respect to socioeconomic and demographic factors, we considered the age of the respondents (15–19), marital status (never married, ever married), and current employment status (no, yes). Education level was categorised into years of schooling (≥ 5, 6–9 years, 10+). Furthermore, socioeconomic status was assessed through the wealth index (poor, middle, rich) and ownership of a mobile phone (no, yes). Social media use was included when Facebook usage was considered (no, yes). Regional-level characteristics included the area of residence (rural, urban) and the division of residence, covering Barisal, Chittagong, Dhaka, Khulna, Rajshahi, Rangpur, Sylhet, and Mymensingh [ 36 , 37 ]. Statistical analysis In this study, Stata version 17.0 (StataCorp LP, College Station, Texas) and R version (4.3.0) were used to clean, recode and analyse the data. To ensure the survey's representativeness, we used sample weights for weighting [ 38 ]. Additionally, we followed the Strengthening the Reporting of Observational studies in Epidemiology (STROBE) cross-sectional reporting guidelines [ 39 ]. Exploratory data analysis was conducted with a 95% confidence interval (CI) to examine the characteristics of the dataset. This involved reporting frequencies and percentages for the study variables. Additionally, we used bar charts and maps for graphical representation to visualise the data. Additionally, the chi-square test was performed to identify the bivariate relationship between the study variables and MDD. Finally, multivariable logistic regression was used to understand the associations of MDD with cyberbullying and other predictors. Multicollinearity was assessed via the variance inflation factor (VIF). The strength of these relationships was demonstrated via the adjusted odds ratio (AOR) with a 95% CI. Results Sociodemographic characteristics In this study, among the 4984 respondents, approximately 59% of the adolescents were unmarried, whereas 41% were married, with an equal distribution across age groups ranging from 15–19 years. Most respondents were not currently working (91%), and the highest proportion (54%) had between 6 and 9 years of schooling. The wealth index revealed that approximately 43% of the respondents were categorised as poor, 22% as middle, and 36% as rich. Mobile phone ownership was reported by 35% of the respondents; 17% used Facebook, and 8.0% of the respondents had experienced any form of cyberbullying in the last 12 months. Most respondents resided in rural areas (70.5%), with the remainder residing in urban areas (29.5%). The regional distribution indicated that the highest percentages of respondents were from Dhaka (24.5%) and Chittagong (19.3%) (Table 1 ). Table 1 Descriptive statistics of the study variables among female adolescents in Bangladesh (n = 4,984). Variable Weighted frequency (n) Weighted percentage (%) Total 4984 100 Age of respondents 15 1014 20.3 16 1105 22.2 17 1032 20.7 18 1041 20.9 19 792 15.9 Marital status Never Married 2962 59.4 Ever married 2022 40.6 Currently Working No 4548 91.2 Yes 437 8.8 Years of schooling ≤ 5 years 709 14.2 6 to 9 years 2698 54.2 10 + years 1575 31.6 Wealth index Poor 2128 42.7 Middle 1087 21.8 Rich 1768 35.5 Own personal mobile phone No 3267 65.5 Yes 1717 34.5 Used Facebook on the mobile phone No 4155 83.4 Yes 829 16.6 Experienced cyberbullying in the last 12 months No 4366 87.6 Yes 618 12.4 Area of residence Rural 3512 70.5 Urban 1472 29.5 Division 332 6.7 Barisal 963 19.3 Chittagong 1222 24.5 Dhaka 543 10.9 Khulna 649 13 Rajshahi 589 11.8 Rangpur 300 6 Sylhet 386 7.7 Mymensingh 4366 87.6 [Insert Table 1 here] Relationships between the incidence of MDD and other factors In this study, the overall prevalence of MDD among female adolescents was 12.4% (95% CI: 11.3–13.6%). In terms of age, the highest prevalence of MDD was observed among 19-year-olds (17.2%). Marital status indicated that ever-married individuals had a greater prevalence of MDD (14.3%) than never-married individuals did (11.1%). In terms of employment status, those currently working had a greater prevalence of MDD (16.1%) than did those not working (12%). With respect to education, respondents with less than 5 years of schooling had the highest prevalence of MDD (18%). Mobile phone ownership and Facebook use were associated with a higher MDD prevalence, with mobile phone owners at 13.7% and Facebook users at 16.1%. Urban residents also reported a greater prevalence of MDD (14%) than did rural residents (11.7%). Regionally, the highest MDD incidence was observed in Mymensingh (17.7%), whereas the lowest was in Rajshahi (7.4%) (Table 2 ). Table 2 Distribution of MDD by sociodemographic and behavioral characteristics among female adolescents in Bangladesh (n = 4,984). Variables Major depressive symptoms (MDD) p value No Yes n (row %) n (row %) Total 87.6 (86.4, 88.7) 12.4 (11.3, 13.6) Age of respondents 15 89.7 (87.5, 91.6) 10.3 (8.4, 12.5) 0.001 16 89.4 (86.4, 91.8) 10.6 (8.2, 13.6) 17 86.4 (83.8, 88.7) 13.6 (11.3, 16.2) 18 88.4 (86.0, 90.4) 11.6 (9.6, 14.0) 19 82.8 (79.7, 85.5) 17.2 (14.5, 20.3) Marital status Never Married 88.9 (87.3, 90.2) 11.1 (9.8, 12.7) 0.002 Ever married 85.7 (84.0, 87.3) 14.3 (12.7, 16.0) Currently Working No 88 (86.7, 89.1) 12 (10.9, 13.3) 0.028 Yes 83.9 (79.8, 87.4) 16.1 (12.6, 20.2) Years of schooling ≤ 5 years 82 (79.0, 84.6) 18 (15.4, 21.0) < 0.001 6 to 9 years 88.7 (87.0, 90.1) 11.3 (9.9, 13.0) 10 + years 88.3 (86.4, 90.0) 11.7 (10.0, 13.6) Wealth index Poor 86.1 (84.0, 87.9) 13.9 (12.1, 16.0) 0.073 Middle 88.7 (86.5, 90.7) 11.3 (9.3, 13.5) Rich 88.8 (86.7, 90.6) 11.2 (9.4, 13.3) Own personal mobile phone No 88.3 (86.8, 89.6) 11.7 (10.4, 13.2) 0.068 Yes 86.3 (84.4, 88.0) 13.7 (12.0, 15.6) Used Facebook on the mobile phone No 88.3 (87.1, 89.5) 11.7 (10.5, 12.9) < 0.001 Yes 83.9 (80.9, 86.4) 16.1 (13.6, 19.1) Area of residence Rural 88.3 (87.0, 89.4) 11.7 (10.6, 13.0) 0.118 Urban 86 (82.9, 88.6) 14 (11.4, 17.1) Division Barisal 85.6 (81.0, 89.2) 14.4 (10.8, 19.0) 0.004 Chittagong 85.9 (81.6, 89.4) 14.1 (10.6, 18.4) Dhaka 88.8 (86.6, 90.7) 11.2 (9.3, 13.4) Khulna 87.2 (83.7, 90.0) 12.8 (10.0, 16.3) Rajshahi 92.6 (90.2, 94.4) 7.4 (5.6, 9.8) Rangpur 87.9 (84.3, 90.7) 12.1 (9.3, 15.7) Sylhet 86.4 (83.0, 89.2) 13.6 (10.8, 17.0) Mymensingh 82.3 (77.8, 86.0) 17.7 (14.0, 22.2) [Insert Table 2 here] Association of experiencing cyberbullying with MDD Figure 2 illustrates the relationship between cyberbullying and the prevalence of MDD. Compared with those who did not experience any form of cyberbullying (10.7%), those who experienced cyberbullying in the last 12 months had a significantly greater prevalence of MDD (31.6%). The figure further reveals a dose‒response relationship between the frequency of experiencing cyberbullying and MDD. The prevalence of MDD increases with the frequency of cyberbullying encounters. Those who faced cyberbullying once or twice represented 23.2% of the participants with MDD, 29.3% with 2–4 cases, and 38.2% with five or more cases. [Insert Fig. 2 here] The logistic regression model revealed a significant association between cyberbullying and MDD in both the crude and adjusted models (Table 3 ). Compared with those who did not experience cyberbullying, individuals who experienced cyberbullying were 3.85 times more likely to have MDD (COR: 3.85, 95% CI: 2.96, 5.01). After adjusting for various demographic and socioeconomic variables, the associations remained significant. The adjusted model revealed that individuals who experienced cyberbullying are nearly four times more likely to have MDD than those who did not experience cyberbullying (AOR: 3.97, 95% CI: 3.12, 5.05). Table 3 Unadjusted and adjusted associations of MDD with experiencing cyberbullying in the last 12 months among female adolescents in Bangladesh (n = 4,984). Variables COR p value AOR p value Experienced cyberbullying No ref ref Yes 3.85 (2.96 to 5.01) < 0.001 3.97 (3.12 to 5.05) < 0.001 Adjusted for the following variables: age, marital status, employment status, years of schooling, wealth index, mobile phone ownership, Facebook use, area of residence, and division. [Insert Table 3 here] Prevalence and Perpetrator Profiles of Cyberbullying by Marital Status In this study, cyberbullying was slightly more prevalent among unmarried individuals (8.5%) than among those who were ever married (7.3%). The perpetrator profiles revealed that friends/classmates were more common perpetrators among never-married individuals (14.1%) than among ever-married individuals (6.6%). In contrast, unknown persons are the predominant perpetrators for both groups, with 81.1% among never-married individuals and 79.2% among ever-married individuals. Another notable perpetrator group was neighbours, where 8.4% of never-married and 7.7% of ever-married adolescents experienced cyberbullying by neighbours. Moreover, 6.6% of the ever-married adolescents had experienced cyberbullying by their husbands/in-laws in the past twelve months (Fig. 3 ). [Insert Fig. 3 here] Regional variation in the prevalence of cyberbullying The experience of cyberbullying also varies by division, as shown in Fig. 4 . Barisal reported the highest percentage of cyberbullying, at 12.9%, followed by Rangpur, at 10.7%, and Mymensingh, at 9.6%. In contrast, Sylhet had the lowest reported percentage at 3.7%. [Insert Fig. 4 here] Discussion This study examined the association between cyberbullying and MDD among female adolescents via nationally representative data from the BAHWS 2019–20. This study highlights a significant association between experiencing cyberbullying and MDD among female adolescents in Bangladesh. The respondents who experienced cyberbullying were found to have notably greater odds of MDD than those who did not face any form of cyberbullying within 12 months. Additionally, the study revealed a dose‒response relationship, where the likelihood of MDD increased with the frequency of cyberbullying incidents. In this study, we found that a notable proportion of female adolescents in Bangladesh had MDD. This finding aligns with a previous study in Bangladesh, where a study reported a point prevalence of depressive symptoms of 15% among females aged 13–19 years [ 40 ]. Additionally, another nationwide cross-sectional study reported a slightly lower estimate, with 7% of female adolescents (10–19 years) experiencing moderate to severe depression in Bangladesh [ 41 ]. Our estimate is nationally representative, covering a diverse range of regions across Bangladesh. This broad scope provides a more generalised prevalence of MDD among female adolescents than do studies focused on specific populations or geographic areas. The study also revealed that approximately one in every ten female adolescents experienced cyberbullying. A recent systematic review including 63 studies from different countries indicated that the prevalence of cyberbullying victimisation among adolescents ranges from 14–58% [ 3 ]. However, a previous study in Bangladesh, conducted among school-going adolescents in Dhaka city, reported a much higher prevalence of 34% among female adolescents [ 28 ]. This discrepancy can be explained by the specific context of previous studies [ 28 , 29 ], which focused on a single school in a moderately prosperous urban area of Dhaka where internet access was widespread and where most adolescents attended school. In contrast, our study considered a broader population across various regions of Bangladesh, including areas with less access to the internet, areas surrounded by stigma, areas lacking awareness, etc., which may have led to a lower overall prevalence. This study revealed a significant association between cyberbullying victimisation and MDD in female adolescents. Notably, individuals who experienced cyberbullying were more likely to have MDD. This finding was consistent with previous research showing that cyberbullying is a significant predictor of depressive symptoms among adolescents. A cohort study conducted in India revealed that cyberbullying victimisation is linked to a greater risk of depression and suicidal ideation, with victims being almost two times more likely to experience depressive symptoms [ 10 ]. Additionally, a meta-analysis utilising 57 studies from 17 countries revealed a significant positive relationship between cyberbullying victimisation and depression [ 12 ]. Another study on adolescents from Australia and Switzerland revealed that even after adjusting for the risk of traditional bullying victimisation, victims of cyberbullying had significantly greater levels of depressive symptoms [ 42 ]. Above all, adolescents are more vulnerable to the psychological effects of cyberbullying because of their developmental stage and reliance on peer relationships. Unlike traditional bullying, cyberbullying can affect a victim's personal life at any time, creating ongoing stress and anxiety [ 43 ]. Bauman and colleagues reported that cyberbullying often has a more severe impact than traditional bullying does because it can occur 24/7 and reach a wide audience, intensifying feelings of helplessness and humiliation [ 44 ]. Our study also revealed a dose‒response relationship between the frequency of cyberbullying incidents and the prevalence of MDD among female adolescents, indicating that increased incidents of cyberbullying are correlated with a greater burden of MDD. This finding was consistent with a previous study conducted in North Carolina, USA, which revealed a dose‒response relationship between increased vulnerability in mental health and increased levels of cyberbullying victimisation among adolescents [ 45 ]. Additionally, a series of prospective longitudinal studies analysing large, population-based community samples published between 1960 and 2015 confirmed the existence of this dose‒response relationship [ 46 ]. Remarkably, a study using data from the British National Child Development cohort revealed that the long-term effects of the dose‒response relationship between bullying victimisation and mental health persist well into adulthood, with negative psychological outcomes still evident up to 50 years of age [ 47 ]. Continuous bullying may interfere with the development of healthy coping mechanisms in adolescents, lower self-esteem, and create feelings of loneliness and worthlessness, all of which increase risk and violence [ 48 ]. Additionally, persistent bullying undermines social support networks, as bullied adolescents may struggle to trust peers and adults, reducing perceived support from parents, teachers, and friends [ 43 ]. This lack of social support can lead to feelings of rejection and loneliness, further deteriorating mental health [ 49 ]. The sociocultural context of Bangladesh further amplifies the psychological burden of cyberbullying on female adolescents. In a society where issues related to female autonomy, honour, and social reputation are deeply embedded, online harassment can have severe consequences beyond mental distress [ 50 , 51 ]. Victims often face social stigma, victim blaming, and reputational harm, discouraging them from reporting incidents or seeking support [ 52 ]. Additionally, gender norms in Bangladesh dictate stricter expectations for female behaviour, making young women particularly vulnerable to harassment, blackmail, and cyber-exploitation, which can intensify feelings of depression and anxiety [ 53 ]. Limited digital literacy among parents and guardians further restricts adolescent girls from receiving the guidance and intervention needed to navigate online harassment safely [ 54 ]. These cultural factors increase the long-term consequences of cyberbullying, emphasising the urgent need for gender-sensitive interventions, stronger legal protections, and mental health support systems tailored to the Bangladeshi context. Moreover, in Bangladesh, the term “cyberbullying” is not explicitly mentioned in any existing laws. However, cyberbullying and cyber harassment are addressed under several legal frameworks, including the Information and Communication Technology (ICT) Act of 2006, the Pornography Control Act of 2012, and the Digital Security Act (DSA) of 2018 [ 52 ]. Under these laws, various forms of cyberbullying, such as identity fraud, the dissemination of harmful content, and blackmail using private material, are punishable offences [ 55 ]. Punishments can include imprisonment for up to 14 years and fines of up to 10 core BDTs or both for serious offences such as hacking and illegal data sharing [ 56 ]. While the DSA-2018 provided necessary options for addressing online threats, it faced widespread criticism for overreach and the potential for misuse, particularly regarding nonbailable offences [ 57 ]. In response to these concerns, the Cyber Security Act (CSA) of 2023 was introduced as a replacement for DSA-2018 [ 58 ]. The new Cyber Security Act of 2023 reduced the number of nonbailable offences from 14 sections under the DSA-18 to just four sections while still empowering law enforcement officials to act against crimes [ 59 ]. For example, under the CSA-2023, police inspectors are authorised to conduct searches and arrests without a warrant in specific cases, maintaining a robust legal framework for tackling cyber-related offences [ 59 ]​. In terms of reporting incidents, victims of cyberbullying have several options. The Bangladesh Police Cybercrime Unit offers a hotline at + 8801730336431 and allows for complaints via email at [email protected] [ 60 ]. In addition, the Cyber Police Centre (CID), Bangladesh Police, accepts reports via its official Facebook page​. The government has also launched the National Helpline Centre for Violence Against Women and Children (109), where victims can seek assistance [ 59 ]. These mechanisms aim to ensure quick responses to complaints of online harassment, including cyberbullying [ 60 ]. However, despite the presence of these laws and reporting mechanisms, victims and their families are often reluctant to report cases of cyberbullying openly or promptly [ 61 ], and awareness of how to report cyberbullying and access protection remains limited [ 55 ]. Social stigmas, fear of backlash, and a lack of awareness of legal protections frequently deter them from seeking justice [ 62 ]. This gap suggests that educational campaigns through schools, social media, and community programs are essential for informing people about the legal protections available and the ethical use of technology. Building awareness about the psychological impacts of cyberbullying, along with teaching digital responsibility, can establish a safer online environment [ 63 ]. Strengths and Limitations of the Study This study has several notable strengths. It draws on data from a large, nationally representative sample of 4,984 female adolescents in Bangladesh, providing a reliable picture of cyberbullying among adolescents. Additionally, this is the first study to examine the prevalence of cyberbullying victimisation and its association with MDD among female adolescents in Bangladesh, which ensures that the findings are generalizable to a broader population. A unique aspect of this study is the identification of a dose‒response relationship between the frequency of cyberbullying and MDD, a relationship that has not been explored in previous studies in Bangladesh, further adding to the study’s novelty and significance. Furthermore, MDD was assessed via the PHQ-9 scale, a widely validated and accepted tool for measuring depressive symptoms, enhancing the reliability and accuracy of mental health findings. However, this study has several limitations. The cross-sectional nature of the data prevents us from establishing a direct causal link between cyberbullying and MDD; we can demonstrate only a strong association. Since the data rely on self-reports, there might be some recall bias, with adolescents potentially underreporting their experiences of cyberbullying owing to social desirability or forgetting details. Another limitation is that the survey considered only females aged 15–19 years, whereas the adolescent age group is generally defined as those aged 10–19 years. This creates a gap, as younger adolescents who may also have experienced cyberbullying and MDD were not included in the study. Recommendations On the basis of the findings of this study, interventions targeting mental health among female adolescents should focus on addressing cyberbullying and promoting healthy social media usage. It requires a multifaceted approach involving parents, schools, policymakers, and digital platforms. Effective policies and programs are needed to educate adolescents about the dangers of cyberbullying, promote positive online behaviour, and provide support for those who are affected. One critical aspect highlighted in this study is the importance of promptly reporting cyberbullying incidents. A delay in reporting can allow the psychological harm caused by cyberbullying to escalate, as observed through the dose‒response relationship between the frequency of cyberbullying and MDD. Educating adolescents, parents, and educators on recognising the signs of cyberbullying and ensuring a smooth reporting process can empower victims to seek help without hesitation. Furthermore, strengthening mental health services for victims of cyberbullying can help mitigate psychological impacts, such as depression and anxiety. Collaboration among policymakers, educational institutions, and law enforcement agencies will be crucial to creating a safer digital space. Schools should implement anti-bullying policies, arrange awareness programs, and provide resources for mental health support. Parents should be encouraged to monitor their children's online activities and maintain open communication about their experiences with cyberbullying. Therefore, overall, efforts to prevent and mitigate cyberbullying should be prioritised, including education on online safety and the creation of supportive online and offline environments. Additionally, initiatives to promote positive mental health habits, such as reducing excessive social media use and encouraging healthy lifestyle choices, should be implemented. Conclusion In Bangladesh, nearly one in ten female adolescents experience cyberbullying, and a substantial proportion of them suffer from MDD. This study revealed a strong association between experiencing cyberbullying and MDD among female adolescents. These findings highlight the urgent need for targeted efforts to reduce cyberbullying, increase awareness of the importance of promptly reporting incidents, and establish mental health support systems in vulnerable regions. Such measures are crucial for safeguarding the well-being of female adolescents and mitigating the long-term psychological impacts of cyberbullying. It is also important to increase awareness of Bangladesh's cyberbullying regulations, such as the Cyber Security Act of 2023. Declarations Ethics approval and consent to participate As this study used publicly available secondary data, which can be accessed upon reasonable request, no Ethics Committee or Institutional Review Board approval was needed. However, surveys involving human participants were reviewed and approved by the icddr,b ERC. Written informed consent to participate was provided by the participants' legal guardians or next of kin. Availability of data and materials The BAHWS data are publicly available at this link: https://dataverse.unc.edu/dataset.xhtml?persistentId=doi:10.15139/S3/DVEI9A. Competing interests The authors declare that they have no competing interests. Funding This study was funded by the Department of Foreign Affairs, Trade and Development (DFATD), Canada, through Advancing Sexual and Reproduction Health and Rights (AdSEARCH), Grant number: SGDE-EDRMS-#9926532, Purchase Order 7428855, Project P007358. Authors' contributions Conceptualization, S.T.A.N., M.F.I., and S.R.; data curation, S.T.A.N. and M.F.I.; formal analysis, S.T.A.N.; investigation, R.B. and S.R.; methodology, S.T.A.N. and M.S.H.; supervision, R.B. and S.R.; validation, S.R.; writing – original draft, S.T.A.N., M.S.I., and M.S.H.; writing – review and editing, R.B.I., R.B., S.M.N., S.I.S., and S.R. All authors have read and agreed to the published version of the manuscript. Acknowledgements icddr,b is grateful to the Government of Bangladesh and Canada for providing core/unrestricted support. References Bauman S. Cyberbullying: a Virtual Menace. Cyberbullying - A menace. 2009. Englander E, Donnerstein E, Kowalski R, Lin CA, Parti K. Defining Cyberbullying. Pediatrics [Internet]. 2017 [cited 2023 Nov 23];140:S148–51. Available from: https://pubmed.ncbi.nlm.nih.gov/29093051/ Zhu C, Huang S, Evans R, Zhang W. 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Available from: https://www.jmir.org/2018/4/e129 Extremera N, Quintana-Orts C, Mérida-López S, Rey L. Cyberbullying victimization, self-esteem and suicidal ideation in adolescence: Does emotional intelligence play a buffering role? Front Psychol [Internet]. 2018 [cited 2024 Jul 6];9:335398. Available from: www.frontiersin.org. Nixon CL. Current perspectives: the impact of cyberbullying on adolescent health. Adolesc Health Med Ther [Internet]. 2014 [cited 2024 Jul 6];5:143–58. Available from: https://www.dovepress.com/current-perspectives-the-impact-of-cyberbullying-on-adolescent-health-peer-reviewed-fulltext-article-AHMT UNICEF. UNICEF calls for concerted action to prevent bullying and harassment for the 32 per cent of children online in Bangladesh [Internet]. 2019 [cited 2024 Jul 7]. Available from: https://www.unicef.org/bangladesh/en/press-releases/unicef-calls-concerted-action-prevent-bullying-and-harassment-32-cent-children ActionAid Bangladesh. 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(Bangladesh), Data for Impact (Project), International Centre for Diarrhoeal Disease Research B, USAID/Bangladesh. Bangladesh adolescent health and wellbeing survey, 2019-20: final report [Internet]. 2021 [cited 2024 Jul 5]. Available from: https://www.data4impactproject.org/wp-content/uploads/2022/02/BAHWS-2021_Final_TR-20-432-D41_Rev-Feb-2022.pdf Kroenke K, Spitzer RL, Williams JBW. The PHQ-9: Validity of a brief depression severity measure. J Gen Intern Med. 2001;16. Raza S, Banik R, Noor STA, Sayeed A, Saha A, Jahan E et al. Anxiety and depression among reproductive-aged women in Bangladesh: burden, determinants, and care-seeking practices based on a nationally representative demographic and health survey. Arch Womens Ment Health [Internet]. 2025; Available from: https://doi.org/10.1007/s00737-025-01564-3 Noor STA, Yeasar S, Siddique S, Banik R, Raza S, Prevalence. Determinants and Wealth-Related Inequality of Anxiety and Depression Symptoms Among Reproductive-Aged Women (15–49 Years) in Nepal: An Analysis of Nationally Representative Nepal Demographic and Health Survey Data 2022. Depress Anxiety [Internet]. 2025;2025:9942669. Available from: https://doi.org/10.1155/da/9942669 Streatfield AJ, Rahman MM, Khan S, Haider MM, Rahman M, Nahar Q et al. What shapes attitudes on gender roles among adolescents in Bangladesh. Front Public Health. 2023;11. Rahman M, Jamil K, Nahar Q, Chakraborty N, Haider MM, Khan S. Factors that provide protection against intimate partner physical violence among married adolescents in Bangladesh. Front Public Health. 2023;11. Dey D, Haque MS, Islam MM, Aishi UI, Shammy SS, Mayen MSA et al. The proper application of logistic regression model in complex survey data: a systematic review. BMC Med Res Methodol [Internet]. 2025;25:15. Available from: https://doi.org/10.1186/s12874-024-02454-5 von Elm E, Altman DG, Egger M, Pocock SJ, Gøtzsche PC, Vandenbroucke JP. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) Statement: Guidelines for reporting observational studies. International Journal of Surgery [Internet]. 2014;12:1495–9. Available from: https://www.sciencedirect.com/science/article/pii/S174391911400212X Nasreen HE, Alam MA, Edhborg M. Prevalence and Associated Factors of Depressive Symptoms Among Disadvantaged Adolescents: Results from a Population-Based Study in Bangladesh. J Child Adolesc Psychiatr Nurs. 2016;29. Mridha MK, Hossain MM, Khan MSA, Hanif AAM, Hasan M, Mitra D et al. Prevalence and associated factors of depression among adolescent boys and girls in Bangladesh: Findings from a nationwide survey. BMJ Open. 2021. Perren S, Dooley J, Shaw T, Cross D. Bullying in school and cyberspace: Associations with depressive symptoms in Swiss and Australian adolescents. Child Adolesc Psychiatry Ment Health. 2010;4. Qiqi C. Reactions of adolescent cyber bystanders toward different victims of cyberbullying: the role of parental rearing behaviors. BMC Psychol [Internet]. 2024;12:377. Available from: https://doi.org/10.1186/s40359-024-01879-3 Bauman S, Toomey RB, Walker JL. Associations among bullying, cyberbullying, and suicide in high school students. J Adolesc. 2013;36. Evans CBR, Smokowski PR, Cotter KL. Cumulative bullying victimization: An investigation of the dose-response relationship between victimization and the associated mental health outcomes, social supports, and school experiences of rural adolescents. Child Youth Serv Rev. 2014;44. Klomek AB, Sourander A, Elonheimo H. Bullying by peers in childhood and effects on psychopathology, suicidality, and criminality in adulthood. Lancet Psychiatry. 2015. Takizawa R, Maughan B, Arseneault L. Adult health outcomes of childhood bullying victimization: Evidence from a five-decade longitudinal British birth cohort. Am J Psychiatry. 2014;171. Armitage R. Bullying in children: Impact on child health. BMJ Paediatr Open. 2021. Evans M, Fisher EB. Social Isolation and Mental Health: The Role of Nondirective and Directive Social Support. Community Ment Health J. 2022;58. Klose H, Jebin L. I pretend to be an ideal woman just to keep their mouths shut’: Bangladeshi women’s contestation of abuse through social media platforms. Inf Technol Dev. 2024;30:246–63. Kabeer N. Between affiliation and autonomy: navigating pathways of women’s empowerment and gender justice in rural Bangladesh. Dev Change. 2011;42:499–528. Asha IA, Kabir T. Legal Transformation In The Digital Era: Regulatory Adaptation And Challenges In Bangladesh. Indian J Integr Res Law. 2024;4:416–37. Nadim M, Fladmoe A. Silencing Women? Gender and Online Harassment. Soc Sci Comput Rev. 2021;39. Soldatova GU, Rasskazova EI. Adolescent safety on the internet: Risks, coping with problems and parental mediation. Russian Educ Soc. 2016;58:133–62. Babu K-E-K. September. The Reality of Cyber Security in Bangladesh, Relevant Laws, Drawbacks and Challenges. Cybersecurity in the Age of Smart Societies: Proceedings of the 14th International Conference on Global Security, Safety and Sustainability, London, 2022. Springer; 2023. pp. 89–104. The Business Standard. Cyber Security Act to replace DSA with no jail for defamation | The Business Standard [Internet]. 2023 [cited 2024 Oct 26]. Available from: https://www.tbsnews.net/bangladesh/digital-security-act-be-scrapped-replaced-new-cyber-law-678282 Rahman MA, Rashid H-O. Digital Security Act and investigative journalism in Bangladesh: a critical analysis. CenRaPS J Social Sci. 2020;2:216–36. Dhaka Tribune. Parliament passes Cyber Security Bill 2023 [Internet]. 2023 [cited 2024 Oct 23]. Available from: https://www.dhakatribune.com/bangladesh/325228/parliament-passes-cyber-security-bill-2023 The Daily Star. Cyber bullying | The Daily Star [Internet]. 2024 [cited 2024 Oct 23]. Available from: https://www.thedailystar.net/law-our-rights/your-advocate/cyber-bullying-1586431 Dhaka Tribune. Cyberbullying: How to report crimes online? [Internet]. 2020 [cited 2024 Oct 23]. Available from: https://www.dhakatribune.com/bangladesh/220544/cyberbullying-how-to-report-crimes-online Sheikh MMR, Hossan MR, Menih H. Cyberbullying victimization and perpetration among university students in Bangladesh: Prevalence, impact and help-seeking practices. J Sch Violence. 2023;22. Biswas A, A STUDY ON CYBERBULLYING AGAINST. WOMEN: DIGITAL BANGLADESH PERSPECTIVES. JOURNAL OF LEGAL STUDIES AND RESEARCH. 2023;9. Tozzo P, Cuman O, Moratto E, Caenazzo L. Family and Educational Strategies for Cyberbullying Prevention: A Systematic Review. Int J Environ Res Public Health. 2022. Additional Declarations No competing interests reported. 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3","display":"","copyAsset":false,"role":"figure","size":58331,"visible":true,"origin":"","legend":"\u003cp\u003ePrevalence of experiencing cyberbullying and the profiles of perpetrators stratified by marital status (n=4,984).\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6274564/v1/ec984dbc0b94c8a206431f1c.png"},{"id":81191298,"identity":"cc4eb942-7efe-45c1-a232-5ccba7302e93","added_by":"auto","created_at":"2025-04-23 09:16:13","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":87964,"visible":true,"origin":"","legend":"\u003cp\u003ePrevalence of experiencing cyberbullying by administrative division among female adolescents in Bangladesh 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Globally, it is a growing public health concern [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. This is often accomplished via apps, blogs, instant messaging, chat rooms, and social media platforms such as Facebook, Instagram, and Twitter [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Females, particularly adolescents, are more likely to experience cyberbullying than males are [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. This vulnerability is partly due to their increased social media use, greater exposure to relational aggression, and societal pressures on appearance, which can lead to body shame and unrealistic beauty standards [\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eStudies have shown that cyberbullying has severe psychological and physical consequences, with female adolescents being disproportionately affected [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. A review of 57 studies across 17 countries revealed that females experience higher rates of cyberbullying victimisation and its negative effects, such as major depressive disorder (MDD), than males do [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Persistent sadness, negativity, and boredom in once-enjoyable activities are signs of MDD [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Common symptoms include difficulty concentrating, changes in appetite and sleep patterns, low energy, feelings of worthlessness, and, in severe cases, suicidal thoughts. Adolescents with MDD are at heightened risk for long-term academic, social, and emotional difficulties, as well as poor physical health outcomes [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. The consequences of untreated MDD during adolescence often persist into adulthood, affecting overall quality of life and productivity [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Owing to heightened emotional sensitivity during this developmental stage, females, especially adolescents, are more likely to be emotionally impacted by negative online interactions, increasing their vulnerability to the psychological consequences of cyberbullying [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAdolescents, who make up one-third of internet users globally [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], are particularly vulnerable to the growing issue of cyberbullying. As online interactions become more prevalent, so do the risks associated with cyberbullying, which has emerged as a significant concern [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. In Europe, one in six school-aged students has experienced cyberbullying [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], and in the UK, one in three students report being bullied on various online platforms [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. The problem extends across Asia, with countries such as China (70%), Singapore (58%), India (53%), Malaysia (33%), and Pakistan (26%) reporting high rates of cyberbullying [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. The consequences are severe, as studies have shown that 93% of victims experience negative mental health effects, including sadness, hopelessness, stress, and emotional distress [\u003cspan additionalcitationids=\"CR24\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAccording to a study conducted in Bangladesh, at least 32% of adolescents between the ages of 10- and 17-years\u0026rsquo; experience cyberbullying, online abuse, and digital harassment [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Another study reported that 64% of women aged 15\u0026ndash;35 years experienced online violence, predominantly on Facebook (48%) and Messenger (35%) (Action Aid Bangladesh, 2022). Furthermore, among those who had experienced cyberbullying, 27% had some form of psychiatric disorder [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. According to research conducted on school-aged teenagers during the COVID-19 epidemic, 40% of individuals who were victims of cyberbullying also experienced MDD [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThere is increasing concern about the impact of cyberbullying on MDD, especially among female adolescents. While existing studies in Bangladesh have provided valuable insights, they have focused predominantly on localised samples from specific schools and colleges [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. As a result, there is a lack of nationally representative research exploring the relationship between cyberbullying and adolescent mental health. These smaller-scale studies may not fully capture regional variations or the broader experiences of adolescents across diverse sociocultural and economic backgrounds.\u003c/p\u003e \u003cp\u003eTo the best of our knowledge, no prior study in Bangladesh has examined the association between cyberbullying and MDD among female adolescents using nationally representative data. Therefore, this study utilises data from the nationally representative Bangladesh Adolescent Health and Wellbeing Survey (BAHWS) 2019\u0026ndash;20 to understand the association between cyberbullying and MDD among adolescents, particularly females, in Bangladesh.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData source, sampling technique, and study population\u003c/h2\u003e \u003cp\u003eIn this study, we used secondary data from the Bangladesh Adolescent Health and Wellbeing Survey (BAHWS) 2019-20, a nationally representative cross-sectional survey carried out between July 25, 2019, and January 10, 2020.\u003c/p\u003e \u003cp\u003eThe sampling strategy for the BAHWS was based on a two-stage stratified sample of households. The first stage involved the selection of primary sampling units (PSUs) from the prepared lists, whereas the second stage involved the selection of households from these PSUs. The adolescents were subsequently selected from each stratum. This sampling technique ensured the representativeness and reliability of the survey data; more details of the sampling techniques are available in the report [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe survey utilised a sample consisting of 72,800 households, from which 67,093 households (98%) were successfully interviewed. The participants included 4,926 ever-married female adolescents (97% response rate), 7,800 unmarried female adolescents (94% response rate), and 5,523 unmarried male adolescents (85% response rate) aged 15\u0026ndash;19 years. These adolescents were interviewed via three distinct individual questionnaires. Finally, 4984 (weighted) female adolescents, 2962 unmarried and 2022 married, who participated in the cyberbullying question (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), were included in our analysis.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e[Insert Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e here]\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStudy variables and measurements\u003c/h3\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eOutcome variables\u003c/h2\u003e \u003cp\u003eThe assessment of MDD in adolescents was conducted via the Patient Health Questionnaire (PHQ-9). It is a widely used, self-reported screening tool consisting of nine items assessing the presence or absence of depressive symptoms over the past two weeks [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Higher scores indicate more severe symptoms. The cumulative score, ranging from 0\u0026ndash;27, delineates the following severity levels: no depression (scores 0\u0026ndash;4), mild (scores 5\u0026ndash;9), moderate (scores 10\u0026ndash;14), moderately severe (scores 15\u0026ndash;19), and severe (scores 20\u0026ndash;27) [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. In this study, a score of 10 or above indicated MDD (coded as 1), whereas a score of less than 10 denoted the absence of MDD (coded as 0) [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. The Cronbach's alpha value of the PHQ-9 scale in our study was 0.80, indicating good internal consistency. The frequency distributions for the PHQ-9 items are given in \u003cb\u003eAppendix Table\u0026nbsp;1\u003c/b\u003e.\u003c/p\u003e \n\u003ch3\u003eExposure variable\u003c/h3\u003e\n\u003cp\u003eThe exposure variable of this study was the experience of cyberbullying among adolescents. This was measured in the survey asking participants, \"Has anyone used mobile/internet to bother/harass you at least once in the last 12 months?\" If the respondents answered \"yes\" to this initial question, they were further asked, \"How often did this bother you via mobile phone/internet?\" This two-tiered approach allowed for the identification of adolescents who had experienced cyberbullying and provided insight into the frequency of such incidents.\u003c/p\u003e\n\u003ch3\u003eConfounders\u003c/h3\u003e\n\u003cp\u003eThis study used several confounding variables, including socioeconomic and demographic factors and regional-level characteristics. With respect to socioeconomic and demographic factors, we considered the age of the respondents (15\u0026ndash;19), marital status (never married, ever married), and current employment status (no, yes). Education level was categorised into years of schooling (\u0026ge;\u0026thinsp;5, 6\u0026ndash;9 years, 10+). Furthermore, socioeconomic status was assessed through the wealth index (poor, middle, rich) and ownership of a mobile phone (no, yes). Social media use was included when Facebook usage was considered (no, yes). Regional-level characteristics included the area of residence (rural, urban) and the division of residence, covering Barisal, Chittagong, Dhaka, Khulna, Rajshahi, Rangpur, Sylhet, and Mymensingh [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e].\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eIn this study, Stata version 17.0 (StataCorp LP, College Station, Texas) and R version (4.3.0) were used to clean, recode and analyse the data. To ensure the survey's representativeness, we used sample weights for weighting [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Additionally, we followed the Strengthening the Reporting of Observational studies in Epidemiology (STROBE) cross-sectional reporting guidelines [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eExploratory data analysis was conducted with a 95% confidence interval (CI) to examine the characteristics of the dataset. This involved reporting frequencies and percentages for the study variables. Additionally, we used bar charts and maps for graphical representation to visualise the data. Additionally, the chi-square test was performed to identify the bivariate relationship between the study variables and MDD. Finally, multivariable logistic regression was used to understand the associations of MDD with cyberbullying and other predictors. Multicollinearity was assessed via the variance inflation factor (VIF). The strength of these relationships was demonstrated via the adjusted odds ratio (AOR) with a 95% CI.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eSociodemographic characteristics\u003c/h2\u003e \u003cp\u003eIn this study, among the 4984 respondents, approximately 59% of the adolescents were unmarried, whereas 41% were married, with an equal distribution across age groups ranging from 15\u0026ndash;19 years. Most respondents were not currently working (91%), and the highest proportion (54%) had between 6 and 9 years of schooling. The wealth index revealed that approximately 43% of the respondents were categorised as poor, 22% as middle, and 36% as rich. Mobile phone ownership was reported by 35% of the respondents; 17% used Facebook, and 8.0% of the respondents had experienced any form of cyberbullying in the last 12 months. Most respondents resided in rural areas (70.5%), with the remainder residing in urban areas (29.5%). The regional distribution indicated that the highest percentages of respondents were from Dhaka (24.5%) and Chittagong (19.3%) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \n\u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescriptive statistics of the study variables among female adolescents in Bangladesh (n\u0026thinsp;=\u0026thinsp;4,984).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWeighted frequency (n)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWeighted percentage (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4984\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge of respondents\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e792\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMarital status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever Married\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2962\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e59.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEver married\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCurrently Working\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4548\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e91.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e437\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eYears of schooling\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;5 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e709\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6 to 9 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2698\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e54.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u0026thinsp;+\u0026thinsp;years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1575\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWealth index\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMiddle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1087\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRich\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1768\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOwn personal mobile phone\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3267\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e65.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1717\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eUsed Facebook on the mobile phone\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4155\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e83.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e829\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eExperienced cyberbullying in the last 12 months\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4366\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e87.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e618\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eArea of residence\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3512\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e70.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1472\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDivision\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e332\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBarisal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e963\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChittagong\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1222\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDhaka\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e543\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKhulna\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e649\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRajshahi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e589\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRangpur\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSylhet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e386\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMymensingh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4366\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e87.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003cp\u003e[Insert Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e here]\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eRelationships between the incidence of MDD and other factors\u003c/h2\u003e \u003cp\u003eIn this study, the overall prevalence of MDD among female adolescents was 12.4% (95% CI: 11.3\u0026ndash;13.6%). In terms of age, the highest prevalence of MDD was observed among 19-year-olds (17.2%). Marital status indicated that ever-married individuals had a greater prevalence of MDD (14.3%) than never-married individuals did (11.1%). In terms of employment status, those currently working had a greater prevalence of MDD (16.1%) than did those not working (12%). With respect to education, respondents with less than 5 years of schooling had the highest prevalence of MDD (18%). Mobile phone ownership and Facebook use were associated with a higher MDD prevalence, with mobile phone owners at 13.7% and Facebook users at 16.1%. Urban residents also reported a greater prevalence of MDD (14%) than did rural residents (11.7%). Regionally, the highest MDD incidence was observed in Mymensingh (17.7%), whereas the lowest was in Rajshahi (7.4%) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDistribution of MDD by sociodemographic and behavioral characteristics among female adolescents in Bangladesh (n\u0026thinsp;=\u0026thinsp;4,984).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eMajor depressive symptoms (MDD)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003ep value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003en (row %)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003en (row %)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e87.6 (86.4, 88.7)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e12.4 (11.3, 13.6)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge of respondents\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e89.7 (87.5, 91.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.3 (8.4, 12.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e89.4 (86.4, 91.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.6 (8.2, 13.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e86.4 (83.8, 88.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13.6 (11.3, 16.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e88.4 (86.0, 90.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11.6 (9.6, 14.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e82.8 (79.7, 85.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17.2 (14.5, 20.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMarital status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever Married\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e88.9 (87.3, 90.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11.1 (9.8, 12.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEver married\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e85.7 (84.0, 87.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14.3 (12.7, 16.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCurrently Working\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e88 (86.7, 89.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12 (10.9, 13.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e83.9 (79.8, 87.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16.1 (12.6, 20.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eYears of schooling\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;5 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e82 (79.0, 84.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18 (15.4, 21.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6 to 9 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e88.7 (87.0, 90.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11.3 (9.9, 13.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u0026thinsp;+\u0026thinsp;years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e88.3 (86.4, 90.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11.7 (10.0, 13.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWealth index\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e86.1 (84.0, 87.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13.9 (12.1, 16.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.073\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMiddle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e88.7 (86.5, 90.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11.3 (9.3, 13.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRich\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e88.8 (86.7, 90.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11.2 (9.4, 13.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOwn personal mobile phone\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e88.3 (86.8, 89.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11.7 (10.4, 13.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.068\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e86.3 (84.4, 88.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13.7 (12.0, 15.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eUsed Facebook on the mobile phone\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e88.3 (87.1, 89.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11.7 (10.5, 12.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e83.9 (80.9, 86.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16.1 (13.6, 19.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eArea of residence\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e88.3 (87.0, 89.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11.7 (10.6, 13.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.118\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e86 (82.9, 88.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14 (11.4, 17.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDivision\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBarisal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e85.6 (81.0, 89.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14.4 (10.8, 19.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"7\" rowspan=\"8\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChittagong\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e85.9 (81.6, 89.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14.1 (10.6, 18.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDhaka\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e88.8 (86.6, 90.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11.2 (9.3, 13.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKhulna\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e87.2 (83.7, 90.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12.8 (10.0, 16.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRajshahi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e92.6 (90.2, 94.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.4 (5.6, 9.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRangpur\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e87.9 (84.3, 90.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12.1 (9.3, 15.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSylhet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e86.4 (83.0, 89.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13.6 (10.8, 17.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMymensingh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e82.3 (77.8, 86.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17.7 (14.0, 22.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e[Insert Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e here]\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eAssociation of experiencing cyberbullying with MDD\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e illustrates the relationship between cyberbullying and the prevalence of MDD. Compared with those who did not experience any form of cyberbullying (10.7%), those who experienced cyberbullying in the last 12 months had a significantly greater prevalence of MDD (31.6%). The figure further reveals a dose‒response relationship between the frequency of experiencing cyberbullying and MDD. The prevalence of MDD increases with the frequency of cyberbullying encounters. Those who faced cyberbullying once or twice represented 23.2% of the participants with MDD, 29.3% with 2\u0026ndash;4 cases, and 38.2% with five or more cases.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e[Insert Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e here]\u003c/p\u003e \u003cp\u003eThe logistic regression model revealed a significant association between cyberbullying and MDD in both the crude and adjusted models (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Compared with those who did not experience cyberbullying, individuals who experienced cyberbullying were 3.85 times more likely to have MDD (COR: 3.85, 95% CI: 2.96, 5.01). After adjusting for various demographic and socioeconomic variables, the associations remained significant. The adjusted model revealed that individuals who experienced cyberbullying are nearly four times more likely to have MDD than those who did not experience cyberbullying (AOR: 3.97, 95% CI: 3.12, 5.05).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUnadjusted and adjusted associations of MDD with experiencing cyberbullying in the last 12 months among female adolescents in Bangladesh (n\u0026thinsp;=\u0026thinsp;4,984).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExperienced cyberbullying\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eref\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eref\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.85 (2.96 to 5.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.97 (3.12 to 5.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eAdjusted for the following variables: age, marital status, employment status, years of schooling, wealth index, mobile phone ownership, Facebook use, area of residence, and division.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e[Insert Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e here]\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003ePrevalence and Perpetrator Profiles of Cyberbullying by Marital Status\u003c/h2\u003e \u003cp\u003eIn this study, cyberbullying was slightly more prevalent among unmarried individuals (8.5%) than among those who were ever married (7.3%). The perpetrator profiles revealed that friends/classmates were more common perpetrators among never-married individuals (14.1%) than among ever-married individuals (6.6%). In contrast, unknown persons are the predominant perpetrators for both groups, with 81.1% among never-married individuals and 79.2% among ever-married individuals. Another notable perpetrator group was neighbours, where 8.4% of never-married and 7.7% of ever-married adolescents experienced cyberbullying by neighbours. Moreover, 6.6% of the ever-married adolescents had experienced cyberbullying by their husbands/in-laws in the past twelve months (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e[Insert Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e here]\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eRegional variation in the prevalence of cyberbullying\u003c/h2\u003e \u003cp\u003eThe experience of cyberbullying also varies by division, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. Barisal reported the highest percentage of cyberbullying, at 12.9%, followed by Rangpur, at 10.7%, and Mymensingh, at 9.6%. In contrast, Sylhet had the lowest reported percentage at 3.7%.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e[Insert Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e here]\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study examined the association between cyberbullying and MDD among female adolescents via nationally representative data from the BAHWS 2019\u0026ndash;20. This study highlights a significant association between experiencing cyberbullying and MDD among female adolescents in Bangladesh. The respondents who experienced cyberbullying were found to have notably greater odds of MDD than those who did not face any form of cyberbullying within 12 months. Additionally, the study revealed a dose‒response relationship, where the likelihood of MDD increased with the frequency of cyberbullying incidents.\u003c/p\u003e \u003cp\u003eIn this study, we found that a notable proportion of female adolescents in Bangladesh had MDD. This finding aligns with a previous study in Bangladesh, where a study reported a point prevalence of depressive symptoms of 15% among females aged 13\u0026ndash;19 years [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Additionally, another nationwide cross-sectional study reported a slightly lower estimate, with 7% of female adolescents (10\u0026ndash;19 years) experiencing moderate to severe depression in Bangladesh [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Our estimate is nationally representative, covering a diverse range of regions across Bangladesh. This broad scope provides a more generalised prevalence of MDD among female adolescents than do studies focused on specific populations or geographic areas.\u003c/p\u003e \u003cp\u003eThe study also revealed that approximately one in every ten female adolescents experienced cyberbullying. A recent systematic review including 63 studies from different countries indicated that the prevalence of cyberbullying victimisation among adolescents ranges from 14\u0026ndash;58% [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. However, a previous study in Bangladesh, conducted among school-going adolescents in Dhaka city, reported a much higher prevalence of 34% among female adolescents [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. This discrepancy can be explained by the specific context of previous studies [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], which focused on a single school in a moderately prosperous urban area of Dhaka where internet access was widespread and where most adolescents attended school. In contrast, our study considered a broader population across various regions of Bangladesh, including areas with less access to the internet, areas surrounded by stigma, areas lacking awareness, etc., which may have led to a lower overall prevalence.\u003c/p\u003e \u003cp\u003eThis study revealed a significant association between cyberbullying victimisation and MDD in female adolescents. Notably, individuals who experienced cyberbullying were more likely to have MDD. This finding was consistent with previous research showing that cyberbullying is a significant predictor of depressive symptoms among adolescents. A cohort study conducted in India revealed that cyberbullying victimisation is linked to a greater risk of depression and suicidal ideation, with victims being almost two times more likely to experience depressive symptoms [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Additionally, a meta-analysis utilising 57 studies from 17 countries revealed a significant positive relationship between cyberbullying victimisation and depression [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Another study on adolescents from Australia and Switzerland revealed that even after adjusting for the risk of traditional bullying victimisation, victims of cyberbullying had significantly greater levels of depressive symptoms [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Above all, adolescents are more vulnerable to the psychological effects of cyberbullying because of their developmental stage and reliance on peer relationships. Unlike traditional bullying, cyberbullying can affect a victim's personal life at any time, creating ongoing stress and anxiety [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Bauman and colleagues reported that cyberbullying often has a more severe impact than traditional bullying does because it can occur 24/7 and reach a wide audience, intensifying feelings of helplessness and humiliation [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOur study also revealed a dose‒response relationship between the frequency of cyberbullying incidents and the prevalence of MDD among female adolescents, indicating that increased incidents of cyberbullying are correlated with a greater burden of MDD. This finding was consistent with a previous study conducted in North Carolina, USA, which revealed a dose‒response relationship between increased vulnerability in mental health and increased levels of cyberbullying victimisation among adolescents [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Additionally, a series of prospective longitudinal studies analysing large, population-based community samples published between 1960 and 2015 confirmed the existence of this dose‒response relationship [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Remarkably, a study using data from the British National Child Development cohort revealed that the long-term effects of the dose‒response relationship between bullying victimisation and mental health persist well into adulthood, with negative psychological outcomes still evident up to 50 years of age [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Continuous bullying may interfere with the development of healthy coping mechanisms in adolescents, lower self-esteem, and create feelings of loneliness and worthlessness, all of which increase risk and violence [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Additionally, persistent bullying undermines social support networks, as bullied adolescents may struggle to trust peers and adults, reducing perceived support from parents, teachers, and friends [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. This lack of social support can lead to feelings of rejection and loneliness, further deteriorating mental health [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe sociocultural context of Bangladesh further amplifies the psychological burden of cyberbullying on female adolescents. In a society where issues related to female autonomy, honour, and social reputation are deeply embedded, online harassment can have severe consequences beyond mental distress [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. Victims often face social stigma, victim blaming, and reputational harm, discouraging them from reporting incidents or seeking support [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. Additionally, gender norms in Bangladesh dictate stricter expectations for female behaviour, making young women particularly vulnerable to harassment, blackmail, and cyber-exploitation, which can intensify feelings of depression and anxiety [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. Limited digital literacy among parents and guardians further restricts adolescent girls from receiving the guidance and intervention needed to navigate online harassment safely [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. These cultural factors increase the long-term consequences of cyberbullying, emphasising the urgent need for gender-sensitive interventions, stronger legal protections, and mental health support systems tailored to the Bangladeshi context.\u003c/p\u003e \u003cp\u003eMoreover, in Bangladesh, the term \u0026ldquo;cyberbullying\u0026rdquo; is not explicitly mentioned in any existing laws. However, cyberbullying and cyber harassment are addressed under several legal frameworks, including the Information and Communication Technology (ICT) Act of 2006, the Pornography Control Act of 2012, and the Digital Security Act (DSA) of 2018 [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. Under these laws, various forms of cyberbullying, such as identity fraud, the dissemination of harmful content, and blackmail using private material, are punishable offences [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. Punishments can include imprisonment for up to 14 years and fines of up to 10 core BDTs or both for serious offences such as hacking and illegal data sharing [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWhile the DSA-2018 provided necessary options for addressing online threats, it faced widespread criticism for overreach and the potential for misuse, particularly regarding nonbailable offences [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. In response to these concerns, the Cyber Security Act (CSA) of 2023 was introduced as a replacement for DSA-2018 [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. The new Cyber Security Act of 2023 reduced the number of nonbailable offences from 14 sections under the DSA-18 to just four sections while still empowering law enforcement officials to act against crimes [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. For example, under the CSA-2023, police inspectors are authorised to conduct searches and arrests without a warrant in specific cases, maintaining a robust legal framework for tackling cyber-related offences [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]​.\u003c/p\u003e \u003cp\u003eIn terms of reporting incidents, victims of cyberbullying have several options. The Bangladesh Police Cybercrime Unit offers a hotline at +\u0026thinsp;8801730336431 and allows for complaints via email at [email protected] [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. In addition, the Cyber Police Centre (CID), Bangladesh Police, accepts reports via its official Facebook page​. The government has also launched the National Helpline Centre for Violence Against Women and Children (109), where victims can seek assistance [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. These mechanisms aim to ensure quick responses to complaints of online harassment, including cyberbullying [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. However, despite the presence of these laws and reporting mechanisms, victims and their families are often reluctant to report cases of cyberbullying openly or promptly [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e], and awareness of how to report cyberbullying and access protection remains limited [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. Social stigmas, fear of backlash, and a lack of awareness of legal protections frequently deter them from seeking justice [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]. This gap suggests that educational campaigns through schools, social media, and community programs are essential for informing people about the legal protections available and the ethical use of technology. Building awareness about the psychological impacts of cyberbullying, along with teaching digital responsibility, can establish a safer online environment [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e].\u003c/p\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eStrengths and Limitations of the Study\u003c/h2\u003e \u003cp\u003eThis study has several notable strengths. It draws on data from a large, nationally representative sample of 4,984 female adolescents in Bangladesh, providing a reliable picture of cyberbullying among adolescents. Additionally, this is the first study to examine the prevalence of cyberbullying victimisation and its association with MDD among female adolescents in Bangladesh, which ensures that the findings are generalizable to a broader population. A unique aspect of this study is the identification of a dose‒response relationship between the frequency of cyberbullying and MDD, a relationship that has not been explored in previous studies in Bangladesh, further adding to the study\u0026rsquo;s novelty and significance. Furthermore, MDD was assessed via the PHQ-9 scale, a widely validated and accepted tool for measuring depressive symptoms, enhancing the reliability and accuracy of mental health findings.\u003c/p\u003e \u003cp\u003eHowever, this study has several limitations. The cross-sectional nature of the data prevents us from establishing a direct causal link between cyberbullying and MDD; we can demonstrate only a strong association. Since the data rely on self-reports, there might be some recall bias, with adolescents potentially underreporting their experiences of cyberbullying owing to social desirability or forgetting details. Another limitation is that the survey considered only females aged 15\u0026ndash;19 years, whereas the adolescent age group is generally defined as those aged 10\u0026ndash;19 years. This creates a gap, as younger adolescents who may also have experienced cyberbullying and MDD were not included in the study.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eRecommendations\u003c/h2\u003e \u003cp\u003eOn the basis of the findings of this study, interventions targeting mental health among female adolescents should focus on addressing cyberbullying and promoting healthy social media usage. It requires a multifaceted approach involving parents, schools, policymakers, and digital platforms. Effective policies and programs are needed to educate adolescents about the dangers of cyberbullying, promote positive online behaviour, and provide support for those who are affected. One critical aspect highlighted in this study is the importance of promptly reporting cyberbullying incidents. A delay in reporting can allow the psychological harm caused by cyberbullying to escalate, as observed through the dose‒response relationship between the frequency of cyberbullying and MDD. Educating adolescents, parents, and educators on recognising the signs of cyberbullying and ensuring a smooth reporting process can empower victims to seek help without hesitation. Furthermore, strengthening mental health services for victims of cyberbullying can help mitigate psychological impacts, such as depression and anxiety. Collaboration among policymakers, educational institutions, and law enforcement agencies will be crucial to creating a safer digital space. Schools should implement anti-bullying policies, arrange awareness programs, and provide resources for mental health support. Parents should be encouraged to monitor their children's online activities and maintain open communication about their experiences with cyberbullying. Therefore, overall, efforts to prevent and mitigate cyberbullying should be prioritised, including education on online safety and the creation of supportive online and offline environments. Additionally, initiatives to promote positive mental health habits, such as reducing excessive social media use and encouraging healthy lifestyle choices, should be implemented.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn Bangladesh, nearly one in ten female adolescents experience cyberbullying, and a substantial proportion of them suffer from MDD. This study revealed a strong association between experiencing cyberbullying and MDD among female adolescents. These findings highlight the urgent need for targeted efforts to reduce cyberbullying, increase awareness of the importance of promptly reporting incidents, and establish mental health support systems in vulnerable regions. Such measures are crucial for safeguarding the well-being of female adolescents and mitigating the long-term psychological impacts of cyberbullying. It is also important to increase awareness of Bangladesh's cyberbullying regulations, such as the Cyber Security Act of 2023.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eEthics approval and consent to participate\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAs this study used publicly available secondary data, which can be accessed upon reasonable request, no Ethics Committee or Institutional Review Board approval was needed. However, surveys involving human participants were reviewed and approved by the icddr,b ERC. Written informed consent to participate was provided by the participants' legal guardians or next of kin.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAvailability of data and materials\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe BAHWS data are publicly available at this link: https://dataverse.unc.edu/dataset.xhtml?persistentId=doi:10.15139/S3/DVEI9A.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eCompeting interests\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eFunding\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was funded by the Department of Foreign Affairs, Trade and Development (DFATD), Canada, through Advancing Sexual and Reproduction Health and Rights (AdSEARCH), Grant number: SGDE-EDRMS-#9926532, Purchase Order 7428855, Project P007358.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization, S.T.A.N., M.F.I., and S.R.; data curation, S.T.A.N. and M.F.I.; formal analysis, S.T.A.N.; investigation, R.B. and S.R.; methodology, S.T.A.N. and M.S.H.; supervision, R.B. and S.R.; validation, S.R.; writing – original draft, S.T.A.N., M.S.I., and M.S.H.; writing – review and editing, R.B.I., R.B., S.M.N., S.I.S., and S.R. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAcknowledgements\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eicddr,b is grateful to the Government of Bangladesh and Canada for providing core/unrestricted support.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBauman S. Cyberbullying: a Virtual Menace. Cyberbullying - A menace. 2009.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEnglander E, Donnerstein E, Kowalski R, Lin CA, Parti K. Defining Cyberbullying. Pediatrics [Internet]. 2017 [cited 2023 Nov 23];140:S148\u0026ndash;51. 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Cyberbullying: How to report crimes online? [Internet]. 2020 [cited 2024 Oct 23]. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.dhakatribune.com/bangladesh/220544/cyberbullying-how-to-report-crimes-online\u003c/span\u003e\u003cspan address=\"https://www.dhakatribune.com/bangladesh/220544/cyberbullying-how-to-report-crimes-online\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSheikh MMR, Hossan MR, Menih H. Cyberbullying victimization and perpetration among university students in Bangladesh: Prevalence, impact and help-seeking practices. J Sch Violence. 2023;22.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBiswas A, A STUDY ON CYBERBULLYING AGAINST. WOMEN: DIGITAL BANGLADESH PERSPECTIVES. JOURNAL OF LEGAL STUDIES AND RESEARCH. 2023;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTozzo P, Cuman O, Moratto E, Caenazzo L. Family and Educational Strategies for Cyberbullying Prevention: A Systematic Review. Int J Environ Res Public Health. 2022.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-psychiatry","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bpsy","sideBox":"Learn more about [BMC Psychiatry](http://bmcpsychiatry.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bpsy/default.aspx","title":"BMC Psychiatry","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Cyberbullying, Major depressive disorder, Female adolescents, Bangladesh, Mental health","lastPublishedDoi":"10.21203/rs.3.rs-6274564/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6274564/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eCyberbullying refers to the act of using digital technology to engage in bullying. It involves the intentional use of the internet to demean or denigrate individuals, and it has been linked to substantial psychological distress globally. Despite its increasing prevalence, there remains a gap in nationwide research on its association with the mental well-being of female adolescents in Bangladesh.\u003c/p\u003e\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eThe main objective of this study was to understand the association of cyberbullying with major depressive disorder (MDD) among female adolescents in Bangladesh.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe used secondary data from the 2019\u0026ndash;20 Bangladesh Adolescent Health and Wellbeing Survey (BAHWS), a nationally representative survey. A multiple logistic regression model was used to identify the possible association between cyberbullying and MDD.\u003c/p\u003e\u003ch2\u003eFindings:\u003c/h2\u003e \u003cp\u003eAmong the 4,984 female adolescents surveyed, 8% (95% CI: 7\u0026ndash;9) reported experiencing cyberbullying within the past 12 months, and 12% (95% CI: 11\u0026ndash;14) were found to have MDD. Among those who had experienced cyberbullying, the prevalence of MDD was 31%. Additionally, a dose‒response relationship was observed, where the burden of MDD increases with the frequency of cyberbullying experiences. Compared with those who did not experience cyberbullying, adolescents who faced any form of cyberbullying in the past 12 months were almost four times more likely (AOR: 3.97, 95% CI: 3.12\u0026ndash;5.05) to have MDD.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eCyberbullying is a significant issue among female adolescents in Bangladesh, with a higher risk of MDD linked to increased exposure. Accessible reporting mechanisms and timely counselling through teachers or healthcare providers can help reduce the long-term mental health impact of cyberbullying.\u003c/p\u003e\u003ch2\u003eClinical trial number:\u003c/h2\u003e \u003cp\u003enot applicable.\u003c/p\u003e","manuscriptTitle":"The Association of Cyberbullying with Major Depressive Disorders among Bangladeshi Female Adolescents: Findings from the Bangladesh Adolescent Health and Wellbeing Survey 2019-20","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-23 09:08:07","doi":"10.21203/rs.3.rs-6274564/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-06-23T11:46:28+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-18T11:37:12+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"148994246620234290597282837842610288586","date":"2025-05-26T07:01:44+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"226824970052323171883426814474271049299","date":"2025-05-22T05:58:08+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-13T18:03:18+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-10T20:09:24+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"283269936928489617119775134539328087307","date":"2025-04-06T13:40:08+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"166744184242961819867381039855907079534","date":"2025-04-02T15:14:30+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-04-01T23:59:26+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-03-27T10:48:28+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-03-25T15:04:41+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-03-25T15:01:54+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Psychiatry","date":"2025-03-21T06:09:19+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-psychiatry","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bpsy","sideBox":"Learn more about [BMC Psychiatry](http://bmcpsychiatry.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bpsy/default.aspx","title":"BMC Psychiatry","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1e95429b-3d1a-47f1-a997-dfcdf04f5716","owner":[],"postedDate":"April 23rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-08-18T16:06:10+00:00","versionOfRecord":{"articleIdentity":"rs-6274564","link":"https://doi.org/10.1186/s12888-025-07234-z","journal":{"identity":"bmc-psychiatry","isVorOnly":false,"title":"BMC Psychiatry"},"publishedOn":"2025-08-12 15:57:55","publishedOnDateReadable":"August 12th, 2025"},"versionCreatedAt":"2025-04-23 09:08:07","video":"","vorDoi":"10.1186/s12888-025-07234-z","vorDoiUrl":"https://doi.org/10.1186/s12888-025-07234-z","workflowStages":[]},"version":"v1","identity":"rs-6274564","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6274564","identity":"rs-6274564","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}