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Degree of family cohesion and social class are associated with the number of cavitated dental caries in adolescents.

Laio da Costa DutraÉrick Tássio Barbosa NevesLarissa Chaves Morais de LimaMonalisa da Nóbrega Cesarino GomesFranklin Dellano Soares ForteSaul Martins PaivaMauro Henrique Nogueira Guimarães de AbreuFernanda De Morais FerreiraAna Flávia Granville-Garcia
Published in: Brazilian oral research (2020)
The aim of this study was to evaluate the association between number of cavitated dental caries in adolescents and family cohesion, drug use, sociodemographic factors and visits to the dentist. A cross-sectional study was conducted with 746 adolescents aged 15 to 19 years from Campina Grande, Brazil. The parents answered a questionnaire addressing sociodemographic data, and the adolescents answered questionnaires on drug use, type of family cohesion and visits to the dentist. Two examiners underwent training and calibration exercises (K > 0.80) to diagnose dental caries using the Nyvad criteria. A directed acyclic graph was created to select the variables to be controlled in the statistical model. Associations between the independent variables and the outcome were determined using robust Poisson Regression analysis for complex samples (α = 5%). Rate ratios (RR) and 95% confidence intervals (CI) were calculated. The prevalence of dental caries and cavitated lesions among the adolescents was 92.8% and 41.6%, respectively. The following variables remained associated with the number of cavitated lesions in the multivariate analysis: disengaged (RR: 6.30; 95%CI: 1.24-31.88; p = 0.028 ), separated (RR: 4.80; 95%CI: 1.03-22.35; p = 0.046) and connected (RR: 5.23; 95%CI: 1.27-21.59; p = 0.024) levels of family cohesion, and high social class (RR: 0.55; 95%CI: 0.39-0.76; p = 0.001). In conclusion, this paper posits that adolescents with a lower socioeconomic status, and those whose family cohesion was classified as disengaged, separated or connected, had a larger number of cavitated lesions.
Keyphrases
  • young adults
  • physical activity
  • healthcare
  • mental health
  • risk factors
  • machine learning
  • data analysis
  • cross sectional
  • electronic health record
  • convolutional neural network
  • big data