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Determinants of orthodontic treatment need and demand: a cross-sectional path model study

Journal article
Authors J. T. Bayat
J. Huggare
Bengt Mohlin
N. Akrami
Published in European Journal of Orthodontics
Volume 39
Issue 1
Pages 85-91
ISSN 0141-5387
Publication year 2017
Published at Institute of Odontology
Pages 85-91
Language en
Keywords quality-of-life, traumatic injuries, self-esteem, malocclusion, adolescents, prevalence, children, schoolchildren, validity, swedish, Dentistry, Oral Surgery & Medicine
Subject categories Surgical research, Dentistry


Objectives: To put forward a model predicting orthodontic treatment need and demand. Furthermore, to explore how much of the variance in treatment demand could be explained by a set of self-assessed measures, and how these measures relate to professionally assessed treatment need. Subjects and methods: One hundred and fifty adolescents, aged 13 years, completed a questionnaire which included a set of self-assessed measures dealing with self-esteem, such as dental and global self-esteem, various aspects of malocclusion, such as perceived malocclusion and perceived functional limitation, and treatment demand. Treatment need was assessed by Dental Health Component of the Index of Orthodontic Treatment Need grading. Path analysis was used to examine the relations between the measures and if they could predict treatment need and demand. Results: The measures proved to be reliable and inter-correlated. Path analysis revealed that the proposed model had good fit to the data, providing a test of the unique effect of all included measures on treatment need and demand. The model explained 33% of the variance in treatment demand and 22% of the variance in treatment need. Limitations: The specific age group could affect the generalizability of the findings. Moreover, although showing good fit to data, the final model is based on a combination of theoretical reasoning and semi-explorative approach. Conclusions: The proposed model displays the unique effect of each included measure on treatment need and demand, explaining a large proportion of the variance in perceived treatment demand and professionally assessed treatment need. The model would hopefully lead to improved and more cost-efficient predictions of treatment need and demand.

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