The relationship between malocclusion, orthodontic treatment, and oral health–related quality of life (OHRQOL) is complicated, with some traits, such as increased overjet, having a potentially greater adverse effect on an adolescent’s OHRQOL. The aim of this study was to evaluate the impact of malocclusion and orthodontic treatment on OHRQOL in adolescents presenting with Class II Division 1 malocclusion and explore the relationship between OHRQOL using a condition-specific and generic instrument and occlusal outcome.
Two groups of adolescents were recruited from a United Kingdom university hospital: a pretreatment group of adolescents with Class II Division 1 malocclusion and a treated (posttreatment) group whose Class II Division 1 malocclusion had been corrected. Self-reported OHRQOL was assessed using the malocclusion impact questionnaire (MIQ) and the short form of Child Oral Health Impact Profile questionnaires. Occlusion severity and outcome were assessed using Peer Assessment Rating scores.
A total of 241 participants (106 male; 135 female) were recruited. MIQ scores differed significantly between the pretreatment and posttreatment groups, with scores being 11.35 times lower posttreatment than pretreatment, after adjusting for age and sex (95% confidence interval, −17.28 to −5.42; P <0.001). Females had higher total MIQ scores by 2.6 (95% confidence interval, 0.38 to 4.82), which was statistically significant ( P = 0.022). There was a moderate correlation between MIQ and Peer Assessment Rating scores, but this relationship strengthened when omitting the global MIQ questions (Spearman’s correlation coefficient, 0.59).
Increased overjet was associated with impaired OHRQOL using a condition-specific measure. A deeper understanding of associations between malocclusion, orthodontic treatment, and OHRQOL would benefit from longitudinal evaluation.
Malocclusion can negatively impact an adolescent’s oral health–related quality of life (OHRQOL).
A condition-specific instrument can better detect impact on OHRQOL than a generic one.
Increased overjet and female sex were associated with poorer OHRQOL scores.
Longitudinal evaluation of malocclusion, orthodontic treatment, and OHRQOL is needed.
Malocclusion in young people has been linked to bullying and teasing, as well as impaired oral health–related quality of life (OHRQOL) and social and emotional well-being. Bullying and negative comments related to dentofacial appearance are also prevalent among school-aged peers on social media platforms. Teasing relating to variations in what is considered a socially accepted and esthetic norm can have a significant negative impact on adolescents. In a similar fashion to certain personal attributes, such as height and weight, it is, therefore, possible that certain malocclusion traits have a greater adverse effect on OHRQOL than others. For instance, there is evidence that young people with Class II Division 1 malocclusion and increased overjet are more susceptible to bullying, as well as accidental damage arising from traumatic injuries to their teeth. , In addition, patients with missing teeth and spaced dentitions or increased overjet have been found to have significantly poorer OHRQOL than their counterparts with less significant malocclusion, , suggesting that the impact of specific malocclusions needs further investigation.
In the United Kingdom (UK), the most recent Children’s Dental Health Survey (2013) revealed that 35% of 12-year-olds and 28% of 15-year-olds were embarrassed to smile or laugh because of their teeth, and 37% of 12-year-olds and 20% of 15-year-olds had an unmet need for orthodontic treatment. If not corrected in adolescence, malocclusion may therefore continue to adversely affect quality of life in the long term. It has been suggested that by improving appearance and function, orthodontic treatment leads to enhanced OHRQOL in young people, particularly in the domains of emotional and social well-being and might be considered integral for optimal oral health.
A number of instruments involving generic measures of OHRQOL have been designed to assess the impact of oral conditions on children from their perspective, such as the Child Perception Questionnaire (CPQ11-14 and CPQ8-10), the Child Oral Impacts on Daily Performances and the Child Oral Health Impact Profile (COHIP). These have been validated for use in children with a variety of oral conditions and have been used extensively in the literature. A generic measure of OHRQOL is useful to understand the impact of dental status on children’s health and well-being; however, their appropriateness and relevance specifically to orthodontic patients have been questioned. , A malocclusion-specific measure was thus recently developed and validated in the UK, aiming to evaluate specifically the impact of malocclusion on young people’s OHRQOL. , The malocclusion impact questionnaire (MIQ) has been advocated as an outcome measure to assess the benefits of treatment and possibly, combined with a normative needs assessment, to determine treatment need. However, further evaluation of this measure was also recommended to confirm its responsiveness and generalizability.
Over the past few years, there has been increasing emphasis on evaluating the success of treatment from the patient’s perspective with patient-reported outcome measures (PROMs) in addition to normative measures, as many traditional orthodontic measures rely solely on clinician-derived assessments of orthodontic treatment need and/or outcome. , Peer Assessment Rating (PAR) scoring is an example of such measure, as it enables a standardized objective assessment of orthodontic treatment outcome to be made. It is a reliable and valid weighted index, which can be used to calculate the degree of improvement in patients with orthodontic issues using pretreatment and posttreatment casts of teeth. The PAR index is an already established quality indicator for orthodontic treatment as set out by the National Health Service England’s Dental Assurance Framework in the UK. However, the importance of evaluating patients’ opinions is increasingly recognized, and a number of studies investigating the effects of malocclusion and orthodontic treatment have now been published using PROMs.
The aim of this study was therefore to evaluate the impact of Class II Division1 malocclusion and orthodontic treatment on adolescent’s OHRQOL. The objectives were to assess the relationship between normative measures (using PAR scores) and PROMs using both condition-specific (MIQ) and generic OHRQOL questionnaire (short form Child Oral Health Impact Profile [COHIP(SF)]) scores and to identify any factors that may influence young people’s MIQ scores.
Material and methods
Ethical approval for this cross-sectional study was obtained from the Health Research Authority and the London-Chelsea Research Ethics Committee (reference no. 17/LO/1884). The participant sample was identified and recruited prospectively from treatment and posttreatment review clinics in the orthodontic department at the Institute of Dentistry, Barts Health NHS Trust.
A group of adolescents having completed comprehensive orthodontic treatment to address Class II Division 1 malocclusion (presenting overjet >6 mm; index of orthodontic treatment need 4a and above) aged 10-18 years were recruited, with no restrictions placed on the type of treatment modality received. A control group of adolescents aged 10-16 years with Class II Division 1 malocclusion who had been accepted for orthodontic treatment in an NHS hospital but had not commenced any active orthodontic treatment was also recruited. Patients with craniofacial syndromes, cleft lip and/or palate, severe hypodontia, or complex medical history and those refusing to consent were excluded. Similarly, patients having undergone interceptive or combined orthognathic-surgical treatment were also excluded.
Participants who met our inclusion criteria were approached at their routine clinic appointment and invited to participate in the study. Potential participants were given an information sheet about the study and had an opportunity to read the information and ask any questions at that time. Those consenting to participate were asked to complete both the MIQ and COHIP(SF) questionnaires anonymously. Each recruited participant was then assigned a study number, and a note was made of their corresponding start or end of treatment study model number. These were subsequently blindly PAR scored according to UK weightings by a calibrated orthodontist A.T.).
On the basis of previous research relating to MIQ development and validation, , a total of 174 participants was required to detect a minimum difference of 3 MIQ scores between the 2 groups with a power of 80% at the 0.05 level of statistical significance. To compensate for a dropout rate of 30% because of incomplete records, the final number needed to be recruited was 226, which would also allow multivariate linear regression with adjustment for relevant covariates.
Descriptive statistics of the participant sample were calculated. The COHIP(SF) questionnaire outcomes were reverse-scored to allow all OHRQOL questions to score in a similar direction, with higher scores indicating poorer OHRQOL. The correlation between the total MIQ, COHIP, and PAR scores for the 2 groups was assessed using Spearman rank correlation coefficients and plotted on scatterplots. Linear regression was carried out to assess the influence of independent factors (age, sex, socioeconomic status based on a postcode-derived index of multiple deprivation (IMD) score, and malocclusion severity as assessed by PAR score) on OHRQOL using the condition-specific MIQ scores as the dependent variable. To ensure intrarater reliability for the study model PAR scoring, Cohen’s Kappa statistic was calculated on 20 sets of study models selected at random and re-measured after at least a 4-week interval.
A total of 241 participants (106 male; 135 female) were recruited into the study over 12 months (December 2017 to December 2018), with 121 in the pretreatment (Class II Division 1 malocclusion) group and 120 participants in the posttreatment group. All but 1 of the approached participants volunteered to take part in the study, with the 1 adolescent declining because of time constraints ( Fig 1 ). The mean age of the recruited participants was 14 years (standard deviation, 2.35). From the recruited participant sample, there were 178 complete records with no missing data for either questionnaire, no missing PAR scores and/or no missing IMD data ( Table I ).
|Characteristics||Total (n = 241)||Nonmissing||Missing|
|MIQ (n = 233)||COHIP (n = 209)||PAR (n = 211)||MIQ (n = 8)||COHIP (n = 32)||PAR (n = 30)|
|Mean (SD)||14 (2.35)||14 (2.36)||14 (2.33)||14 (2.32)||15 (2.10)||14 (2.56)||15 (2.37)|
|Mean (SD)||3.4 (2.05)||3.5 (2.07)||3.5 (2.07)||3.4 (2.09)||2.3 (1.16)||3.2 (1.94)||3.5 (1.77)|