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Factors associated with the number of filled teeth in adolescents from public schools: a cohort study.

Rafaela Oliveira PileccoLeonardo da Silva GodoisMarília Cunha MaronezeFernanda Ruffo OrtizThiago Machado Ardenghi
Published in: Brazilian oral research (2020)
This study aimed to assess the association of demographic conditions, socioeconomic status, clinical variables, and psychosocial factors with the number of filled teeth in adolescents from public schools. This cohort study comprised 1,134 12-year-old adolescents enrolled in public schools in Santa Maria, Brazil, in 2012. They were followed-up in 2014, where 743 individuals were reassessed (follow-up rate of 65.52%) for the number of filled teeth. Data were collected via dental examinations and structured interviews. Demographic and socioeconomic characteristics were collected from parents or legal guardians. The psychosocial factor comprised students' subjective measurement of happiness (Brazilian version of the Subjective Happiness Scale - SHS). Dental examinations were performed to assess the number of filled teeth through decay, missing, and filled teeth index (DMF-T). Unadjusted and adjusted Poisson regression analyses were performed to assess the association between baseline variables and filled teeth at follow-up. The number of filled teeth in 2012 and 2014 were 193 (17.02%) and 235 (31.63%), respectively. The incidence of filled teeth in 2014 was 42 (5.65%). Adolescents with untreated dental caries, those who visited the dentist in the last 6 months, those that exhibited being happier, and those who had filled teeth at baseline were associated with a higher number of filled teeth at follow-up. We conclude that the number of filled teeth in adolescents was influenced by clinical and psychosocial factors, emphasizing the need to focus on oral health policies in individuals with higher disease burden and those who feel psychologically inferior.
Keyphrases
  • young adults
  • mental health
  • physical activity
  • cone beam computed tomography
  • oral health
  • healthcare
  • public health
  • emergency department
  • machine learning
  • deep learning