Abstract
Introduction:
The impact of tooth loss during adolescence, often a consequence of dental caries or traumatic injuries, extends beyond physical discomfort to significantly affect social interactions and self-esteem. Despite the established associations between poor oral health and various forms of social disadvantage, the potential for tooth loss to be associated with bullying victimization remains inadequately investigated.
Objectives:
This study aims to investigate the causal association between tooth loss and bullying victimization among adolescents, using longitudinal data from Australian children.
Methods:
We analyzed data from 4,476 children from wave 3 (aged 8 to 9 y) to wave 6 (aged 14 to 15 y) of the Longitudinal Study of Australian Children. Tooth loss was assessed through parent-reported dental extractions due to decay or trauma. Bullying victimization was measured through parent-reported incidents of school bullying. A fixed effects regression model was employed to control for time-invariant confounding and estimate the impact of tooth loss on bullying. Sensitivity analyses, including age restriction, lagged effects, and a negative control, were conducted to validate the findings.
Results:
The fixed effects model revealed that adolescents experiencing tooth loss had 42% higher odds of being bullied at school (odds ratio, 1.42; 95% CI, 1.15 to 1.77), after adjusting for household income, disability, and maternal education. Sensitivity analyses supported the robustness of these findings, demonstrating a consistent effect of tooth loss on bullying victimization.
Conclusion:
By leveraging longitudinal data, this study quantifies the within-individual association between tooth loss and bullying victimization during adolescence, underscoring the importance of preventing dental caries and trauma impacts. These findings suggest that tooth loss may be a modifiable determinant of bullying risk during adolescence, with potential implications for long-term mental health outcomes.
Knowledge Transfer Statement:
This study demonstrates a 42% higher risk of school bullying among adolescents who experience tooth loss due to decay or trauma. These findings emphasize the critical need for clinicians to prioritize preventive dental care and early intervention to reduce the social impacts of poor oral health. Policy makers should incorporate oral health interventions alongside antibullying initiatives in schools to mitigate the cascading effects of bullying on mental health and social well-being in adolescents.
Introduction
Oral health is a vital component of overall well-being, with conditions such as dental caries, periodontal disease, and traumatic dental injuries having far-reaching effects beyond physical discomfort. These issues often lead to tooth loss, which is not just a marker of neglected oral health but also a determinant of social functioning (Gerritsen et al. 2010; Haag et al. 2017). While tooth loss is less common in adolescents as compared with adults, its impact can be particularly pronounced in this age group due to heightened sensitivity to social judgment and peer interaction and acceptance. In Australia, tooth loss attributed to caries affects 1.6% of children aged 12 to 14 y, and approximately 14% experience tooth loss because of traumatic injuries (Do and John Spencer 2016). From a quality-of-life perspective, tooth loss, regardless of its cause, negatively affects people across the life span, including adolescence when the social ramifications of tooth loss may be magnified.
Adolescence is a pivotal developmental stage bridging childhood and adulthood, characterized by significant social, physiologic, and psychological changes. Adolescents are particularly vulnerable to bullying, a form of trauma that can have profound and lasting effects on physical and mental health (Laski 2015; Viner et al. 2015; Arseneault 2018). These events are usually repeated and unwanted, and they exhibit strong power imbalances (Volk et al. 2014). Bullying may be physical (hitting or kicking), verbal (name-calling and teasing), or relational (social exclusion, rumor spreading; Olweus 1994). Within Australia, 28% of year 8 students (aged 12 to 13 y) report experiencing bullying monthly and 8% weekly (Thomson et al. 2021). Bullying victimization is linked to mental health problems, including depression, anxiety, and suicidal behaviors, as well as tobacco and illicit drug use (Arseneault et al. 2010; Ford et al. 2017; Moore et al. 2017). There is also evidence that bullying victimization is associated with adverse physical outcomes, such as overweight/obesity and inflammation (Olweus 1994; Sutin et al. 2016; Baldwin et al. 2018; Priest et al. 2019).
The relationship between physical appearance and bullying is well established, with visible differences such as obesity and disabilities often serving as triggers for peer victimization (Volk et al. 2014). In this context, it is plausible that tooth loss—due to its impact on facial appearance and speech—could increase the risk of bullying, particularly in adolescence when peer acceptance is crucial. Preliminary research has begun to explore this relationship, with studies linking poor oral health to bullying. For example, a cross-sectional study on 8- to 10-y old children from Brazil reported a positive association between untreated dental caries and bullying and victimization (Barasuol et al. 2017). In Australia, a longitudinal study identified an association between dental problems and bullying among Aboriginal and Torres Strait Islander children (Islam et al. 2022). However, neither of the 2 studies investigated tooth loss as a cause of bullying, nor did either investigate causal effects by leveraging a high-quality longitudinal design.
This study aims to address these gaps by exploring the relationship between tooth loss and bullying among adolescents, using data from the Longitudinal Study of Australian Children (LSAC). By leveraging longitudinal cohort data, we seek to quantify the impact of tooth loss on the likelihood of bullying victimization, thereby highlighting the broader social implications of oral health during adolescence. Understanding this relationship is critical for designing interventions that can mitigate the negative outcomes of bullying and poor oral health, with far-reaching implications for mental and physical well-being.
Methods
Study Design and Analytic Framework
This study draws on data from “Growing Up in Australia: The Longitudinal Study of Australian Children,” a nationally representative study designed to assess a range of influences on the developmental and health trajectories of Australian children. Initiated in 2003, LSAC follows 2 cohorts: the B cohort (infants aged 0 to 1 y at inception) and the K cohort (children aged 4 to 5 y at inception). LSAC’s data collection occurs biennially, involving direct interviews with the children and their parents, self-completed questionnaires, direct physical measurements, and cognitive testing. The study rigorously adheres to methodological standards that ensure high data quality and representativeness across various Australian demographics (for details, see Dunstan 2022). Our analysis focuses on waves 3 to 6 of the K cohort, during which the children were aged 8 to 15 y, providing a dynamic frame to observe changes across early and middle adolescence.
Our primary analytic approach was individual fixed effects logistic regression, which is well-suited for causal inference by using longitudinal observational data. Fixed effects models estimate the effect of changes in exposure on change in outcome within individuals over time, thereby removing all potential confounding by time-invariant characteristics—whether observed (e.g., sex, ethnicity) or unobserved (e.g., temperament, baseline oral hygiene practices). Rather than comparing children with and without tooth loss, the fixed effects model asks the following question: When a given child loses a tooth, does that child’s odds of being bullied change as compared with not having lost a tooth?
A directed acyclic graph illustrating the analytic framework is presented in Figure 1.
Directed acyclic graph illustrating the analytic framework. The diagram shows hypothesized causal relationships between tooth loss (exposure) and bullying victimization (outcome), including time-varying confounders (household income, maternal education, disability), which were adjusted for in the fixed effects models. Time-invariant characteristics are inherently controlled for through within-person fixed effects estimation.
Participants
Participants in this study were drawn from the K cohort, consisting initially of 4,983 children. This analysis included data spanning wave 3 (ages 8 to 9 y) to wave 6 (ages 14 to 15 y) involving 4,476 children and 15,993 person-wave observations. Children with missing data on key variables (exposure, outcome, and covariates) at any wave were excluded, resulting in an analytic sample of 4,427 children (15,150 observations).
Because fixed effects estimation relies on within-individual change, only children who experienced variation in exposure and outcome across waves contributed to the fixed effects regression. This reduced the analytic sample for the main models to 1,957 individuals contributing 7,171 person-wave observations. The construction of the analytic sample and reasons for exclusion are shown in Figure 2.
Participant flowchart (STROBE) for inclusion in fixed effects regression analysis. Children were drawn from the K cohort of the Longitudinal Study of Australian Children, with data available across waves 3 to 6 (ages 8 to 15 y). Of 4,476 eligible participants, exclusions were made for missing exposure, outcome, or covariate data across waves, resulting in an initial analytic sample of 4,427 children (15,150 observations). An additional 2,470 children were excluded due to a lack of within-person variation in exposure or outcome, as required by fixed effects regression. The final analytic sample included 1,957 adolescents contributing 7,171 person-wave observations.
Measurements
Exposure: Tooth Loss due to Decay or Accident
Tooth loss was assessed from waves 3 to 6 by asking parents, “In the last two years has the study child ever had any of the following problems with his/her teeth?” The exposure group consisted of individuals answering “yes” to either “teeth pulled because of dental decay” or “teeth pulled because of accident.” The variable was coded as binary (0, no; 1, yes).
Outcome: Parent-Reported Bullying Victimization
Bullying victimization was assessed at each wave through parents responding to the question “In the last 12 months, has your child been bullied at school?” Interviewers were instructed to clarify that “at school” includes travel to and from school and that “bullied” was left to the respondent to interpret. This wording was consistent across all waves included in the analysis (waves 3 to 6). While this item is not part of a formally validated bullying scale, it was developed through LSAC’s standard instrument design process, which involved expert consultation and cognitive testing (Dunstan 2022). This parent-reported item has been used in several LSAC-based studies to examine bullying-related outcomes and trajectories (e.g., Sutin et al. 2016; Walters 2021). This variable was similarly coded as binary (0, no; 1, yes).
Covariates
The analysis incorporated several key covariates to control for time-varying confounding that influences tooth loss and bullying victimization. Household income (continuous variable centered on the mean) was used as a proxy for socioeconomic status, recognizing its impact on health access and social dynamics relevant to bullying. Maternal education (categorical variable: 1, year 8 or below; 2, years 9 to 11; 3, year 12 or certificate; 4, bachelor degree and above) was included to reflect the educational and socioeconomic environment of the home. Additionally, the disability status of the child (binary variable: 0, no; 1, yes) was considered to account for increased vulnerability to dental problems and bullying.
These covariates were selected a priori by theoretical relevance. Additional time-varying covariates were considered but not included if they lacked conceptual clarity as confounders (i.e., common causes of tooth loss and bullying). Variables that do not vary over time, such as sex and language spoken at home, were not included in the model, as they are inherently adjusted for in fixed effects estimation.
Statistical Analysis
Descriptive statistics of the study sample (proportion, mean and standard deviation) were estimated at baseline, adjusting for sampling weights. To estimate the within-individual association between tooth loss and bullying, we used fixed effects logistic regression models. These models included the time-varying covariates previously described and accounted for repeated observations within individuals. Age was not included as an explicit covariate owing to collinearity with survey wave; however, age-related trends were implicitly addressed through the panel structure and the within-individual design.
In addition, we conducted 3 sensitivity analyses to assess robustness, temporality, and specificity:
Age restriction: Analysis restricted the sample to children aged ≥10 y (i.e., waves 4 to 6) to reduce the impact on bullying due to natural transition from deciduous to permanent dentition. Notably, for the majority of the population, most permanent teeth have already erupted prior to 11 y of age.
Lagged effects: To ascertain the temporal sequence, tooth loss data from the previous wave were used to predict bullying in the following wave, ensuring that the exposure preceded the outcome (i.e., if bullying was measured at time t, then tooth loss was measured at time t – 1).
Negative control: Analyses including dental restorations, which are unlikely to affect bullying, served as negative controls (Lipsitch et al. 2010) to further validate the specificity of the observed effects between tooth loss and bullying.
All analyses were conducted in STATA/MP version 17.1 (StataCorp).
Results
At baseline, 10% (443 of 4,331) of the study children had lost teeth due to decay (n = 265, 6%) or accident (n = 202, 5%). When compared with their peers who had not lost teeth, children who had lost teeth were more likely to come from households of lower socioeconomic status, as indicated by maternal education attainment and household income; they were also more likely to live in regional or remote areas. These demographic details are presented in Table 1. Missing values for key demographic and health indicators were minimal.
Baseline Characteristics of Study Participants by Tooth Loss Status (N = 4,331).
Table 2 presents the results from the fixed effects regression. This provides insight into the potential causal effects of change in tooth loss (i.e., losing teeth between survey waves) on change in the risk of being bullied in a subsequent wave. By comparing individuals with themselves over time, the model controls for all unobserved time-invariant confounders (e.g., ethnicity and gender). Individuals without variations in exposure and outcome were excluded, leading to the model sample size of 1,957 adolescents (7,171 data points). The fixed effects model estimated that adolescents had 42% higher odds of being bullied at school in waves when they had lost a tooth as compared with waves when they had not (odds ratio [OR], 1.42; 95% CI, 1.15 to 1.77).
Effect of Tooth Loss on Bullying Victimization, Estimated by Longitudinal Fixed Effects Model Adjusting for Household Income, Disability, and Maternal Education.
Results from individual fixed effects logistic regression models estimating the within-person association between tooth loss and bullying victimization. Only children with variation in both exposure and outcome contributed to the analytic sample. The models adjust for time-varying covariates: household income (mean centered), maternal education, and disability status. Time-invariant characteristics—including sex, ethnicity, and language spoken at home—are automatically adjusted for by design in fixed effects estimation.
Results from the sensitivity analyses were consistent with the main findings. First, after the sample was restricted from wave 4 (ages 10 to 11 y) to wave 6 (ages 14 to 15 y), the prevalence of tooth loss was 7% (273 of 4,161) at wave 4, with 2% (81 of 4,161) being due to decay and 5% (193 of 4,161) to accidents. In this restricted sample, tooth loss was associated with 1.33-times higher odds of bullying (95% CI, 0.97 to 1.81). Second, in a time-lagged model that ensured temporal sequence by tooth loss preceding bullying, we found that tooth loss was associated with 1.34-times higher odds of bullying (95% CI, 1.02 to 1.76). This analysis strengthened the temporal plausibility of the observed association. Finally, utilizing dental restorations as a negative control, which theoretically should not affect bullying risk, confirmed the specificity of our main findings. No significant association was observed (OR, 1.09; 95% CI, 0.94 to 1.27), reinforcing that our observed effects are specific to tooth loss rather than general dental issues.
Discussion
This study investigated the effect of tooth loss on bullying during adolescence using a well-established population-based cohort data on Australian children. First, a key observation from this study was that tooth loss due to tooth decay or accident was relatively common in this Australian cohort of adolescents, as 1 in 10 experienced it. Second, we found that tooth loss, whether from decay or accidents, was associated with a substantially higher risk of bullying in school-aged children. The effect was strong and consistent across multiple sensitivity analyses. Our findings substantiate the marked negative impacts of poor oral health, tooth loss in particular, on the social development of adolescents that can be caused by associated bullying and victimization.
Integration with Existing Literature
Given the shared role of social determinants of oral health, there is increasing attention to the overlaps between oral health and mental health outcomes (Joury et al. 2023; Kalaigian and Chaffee 2023). However, the links between oral health and mental health outcomes are rarely studied from a causal perspective. Our study suggests that tooth loss may contribute to increased vulnerability to bullying, which in turn may have long-lasting poor mental health impacts on adolescents. Therefore, our finding underscores the critical need to prevent premature tooth loss in young people as a sequalae of dental caries or traumatic dental injuries. Additionally, bullying and victimization due to tooth loss may be particularly intense during adolescence owing to peer pressure (Broutin et al. 2023). Until now, bullying and victimization as impacts of oral disease have primarily been seen as the potential impact of craniofacial features and the role of orthodontic treatment in addressing them (Al-Omari et al. 2014). A study from Brazil found an association between tooth loss and bullying linked to dental appearance (Barasuol et al. 2017). Our study extends this work by examining the association between tooth loss and general school bullying, regardless of its perceived cause. By focusing on overall victimization and using a fixed effects design to control for stable individual traits, our findings may have broader implications for public health and school-based prevention efforts.
Strengths and Limitations
To our knowledge, this is the first longitudinal analysis that uses a nationally representative cohort to estimate the association between adolescent tooth loss and bullying victimization. The use of fixed effects regression is a major strength, as it controls for all time-invariant characteristics, thereby reducing a key source of confounding in observational studies. This analytic approach is disentangles within-person changes in exposure and outcome over time. To assess robustness and strengthen causal interpretation, we conducted multiple sensitivity analyses, including a time-lagged model to establish temporal ordering, a negative-control analysis using dental restorations, and a restriction to older children to address concerns about primary tooth exfoliation. Findings were consistent across these analyses, increasing confidence in the observed association.
Our study had several limitations. First, the exposure and the outcome are parent reported; therefore, the potential for dependent misclassification exists. There was also a small loss to follow-up, which may introduce selection bias. In addition, fixed effects models estimate within-person changes and, by design, include only individuals who experience change in the exposure or outcome, which can limit generalizability (Gunasekara et al. 2014). The LSAC data do not include clinical detail on the affected teeth, such as whether they were anterior or posterior or whether they were primary or permanent. Although we focused on extractions attributed to decay or trauma, some children in the mixed dentition stage may have had visible gaps from natural exfoliation (Al-Omari et al. 2014). To partially address this, we conducted a sensitivity analysis restricted to children aged ≥10 y—an age by which most children have transitioned to permanent dentition. This reduces the likelihood that tooth loss reflects normal exfoliation of primary teeth. It is also important to note that trauma, rather than decay, was the more common reason for tooth loss in our sample, which may reflect more visible or socially salient causes. Given the low prevalence of tooth loss due to dental caries in our sample, it was not possible to check whether marked differences in bullying impacts exist based on the cause of tooth loss. Future studies should investigate whether bullying is dependent on the tooth loss site. Bullying was measured as a binary outcome and referred only to in-person school settings; this approach does not capture variation in frequency, severity, or specific forms such as cyberbullying. However, the binary structure was well suited to our fixed effects approach and allowed us to examine within-individual changes in overall bullying victimization in response to changes in tooth loss. Nonetheless, such binary measures are commonly used in education and public health monitoring and have been applied in several studies using the LSAC dataset (Biswas et al. 2022; Chen et al. 2024; Low et al. 2025). Finally, to assess the impact of unmeasured time-varying confounding, we calculated the E-value of 2.16 for our main effect estimate (OR, 1.42; VanderWeele and Ding 2017). This means that an unmeasured confounder would need to be associated with tooth loss and bullying by risk ratios of at least 2.16—independent of measured and time-invariant factors—to fully explain away the observed association. These results provide additional quantitative support for the robustness of our findings.
Research and Policy Implications
Our study has important findings from a policy perspective. Social gradients in oral health outcomes are well established across the life span, including childhood and adolescents, and in dental caries (Schwendicke et al. 2015) and traumatic dental injuries (Comim et al. 2024). Our findings imply that the bullying and victimization due to poor oral health will also disproportionally affect adolescents from households experiencing disadvantage. Dental caries is largely preventable (Selwitz et al. 2007), and traumatic dental injuries and their impacts are modifiable by creating safe environments (Glendor 2009). Inequitable allocation of resources heightens the risk of unsafe environments in particularly deprived areas and schools (Bendo et al. 2009; Glendor 2009). Therefore, to minimize harms of bullying and victimization on adolescents, it is crucial to address the social and environmental determinants that lead to dental caries and traumatic dental injuries. Future studies should consider investigating potential effect modification by social disadvantage in the effect of tooth loss on bullying.
From a research perspective, this study creates an important area of examination regarding oral health impacts on bullying and victimization across the life span. While the current study focuses on adolescents, the findings may similarly translate across age cohorts. Bullying and victimization, while heightened during adolescence, are not unique to this age group. Pathways through which oral conditions affect bullying need to be better understood for designing relevant and timely interventions. Furthermore, bullying and victimization are well-established risk factors of poor mental health. Given the observed association between tooth loss and bullying, this study indicates a potential mediation pathway from tooth loss to mental health, mediated by bullying.
Tooth loss due to dental caries is rare in adolescents, but when we also consider accidents, nearly 1 in 10 children in a relatively advantaged LSAC cohort were affected. This prevalence is concerning as the nature of tooth loss does not shield adolescents from bullying. Thus, while the prevention of dental caries remains crucial, equal emphasis should be placed on preventing traumatic dental injuries. Addressing these factors comprehensively will help mitigate one of the less recognized but potentially potent risks to adolescent well-being.
Author Contributions
Y. Li, contributed to conception, design, data analysis and interpretation, drafted and critically revised the manuscript; G. Tsakos, T. King, contributed to conception, data interpretation, critically revised the manuscript; Z. Ge, contributed to data interpretation, critically revised the manuscript; A. Singh, contributed to conception, design, data acquisition, analysis, and interpretation, drafted and critically revised the manuscript. All authors gave final approval and agree to be accountable for all aspects of the work.
Footnotes
Declaration of Conflicting Interests
The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Funding
The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research is supported by the Australian Research Council Discovery Early Career Research Award (DE230101210; recipient, A.S.)
