Parental Perception of the Oral Health-Related Quality of Life of Children and Adolescents with Autism Spectrum Disorder (ASD).
Anna Cecília Farias da SilvaTaís de Souza BarbosaMaria Beatriz Duarte GaviãoPublished in: International journal of environmental research and public health (2023)
This study evaluated the parental perception of the oral health-related quality of life (OHRQoL) of children and adolescents with autism spectrum disorder (ASD) and their family functioning. Moreover, sociodemographic factors associated with parental ratings of OHRQoL were assessed. A hundred parents/guardians of children and adolescents aged 6 to 14 years with ASD (ASD group) and 101 unaffected children and adolescents (UCA group) participated. Data collection was carried out using a Google form, containing three sections: (1st) Socioeconomic data and health history; (2nd) Oral health assessment by parental report; (3rd) The short forms of the Parental-Caregiver Perceptions Questionnaire (16-P-CPQ) and the Family Impact Scale (4-FIS). The scores of 16-P-CPQ total and subscales and 4-FIS were significantly higher for the ASD group ( p < 0.02), except for the oral symptoms subscale ( p > 0.05). Older ages (OR = 1.24), brushing 0/1x day (OR = 2.21), teeth grinding (OR = 2.20), gingival bleeding (OR = 3.34), parents with an elementary school degree (OR = 0.314) and family incomes less or equal to the minimum wage (OR = 3.049) were associated with a worse OHRQoL. Parents in the ASD group had a worse perception of QHRQoL when compared to the UCA group. 'Frequency of tooth brushing', 'gingival bleeding', and 'teeth grinding' were predictors of the worst parental perception of their children's OHRQoL. Families with low socioeconomic conditions were more strongly affected by the oral conditions of their children.
Keyphrases
- autism spectrum disorder
- attention deficit hyperactivity disorder
- intellectual disability
- healthcare
- mental health
- physical activity
- atrial fibrillation
- electronic health record
- young adults
- oral health
- public health
- big data
- cross sectional
- machine learning
- climate change
- health information
- social media
- depressive symptoms
- human health