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Family Impacts of Severe Dental Caries among Children in the United Kingdom.

Rawan AbedEduardo BernabéEllie Heidari
Published in: International journal of environmental research and public health (2019)
The aim of this study was to evaluate the family impacts of severe dental caries among children. Data from 3859 school-age children (5-, 8-, 12- and 15-year-olds) who participated in the 2013 Children's Dental Health Survey, a national cross-sectional survey in England, Wales and Northern Ireland, were used. Severe dental caries was defined as having at least one tooth with pulpal involvement, ulceration, fistula, or abscess (PUFA). Family impacts were measured using seven items of the Family Impact Scale (FIS). The association between severe dental caries and family impacts was assessed in logistic regression models, adjusting for child's age, gender, and country of residence; parent's marital status, education, and job classification; and area deprivation. Severe dental caries among children showed a significant negative impact on family life (Odds Ratio: 6.00; 95% Confidence Interval: 3.34-10.78). Parents of children with severe dental caries had greater odds of taking time off work (OR: 2.75; 95% CI: 1.16-6.54), reporting the child needed more attention (OR: 4.08; 95% CI: 2.15-7.75), feeling guilty (OR: 6.32; 95% CI: 3.26-12.26), feeling stressed (OR: 7.34; 95% CI: 4.15-12.99), having normal activities disrupted (OR: 5.78; 95% CI: 2.71-12.34), and having sleep disrupted (OR: 4.94; 95% CI: 2.78-8.76). Having severe dental caries was not associated with financial difficulties in the family (OR: 1.64; 95% CI: 0.49-5.51). The observed association between severe dental caries and family impacts was independent of child and family sociodemographic characteristics. The findings underscore the importance of preventive interventions to avoid severe dental caries in children and subsequently reduce negative impacts on their family life.
Keyphrases
  • young adults
  • early onset
  • healthcare
  • machine learning
  • drug induced
  • emergency department
  • deep learning
  • functional connectivity
  • big data
  • electronic health record
  • resting state