Developing Children's Oral Health Assessment Toolkits Using Machine Learning Algorithm.

ObjectivesEvaluating children's oral health status and treatment needs is challenging. We aim to build oral health assessment toolkits to predict Children's Oral Health Status Index (COHSI) score and referral for treatment needs (RFTN) of oral health. Parent and Child toolkits consist of short-form survey items (12 for children and 8 for parents) with and without children's demographic information (7 questions) to predict the child's oral health status and need for treatment.MethodsData were collected from 12 dental practices in Los Angeles County from 2015 to 2016. We predicted COHSI score and RFTN using random Bootstrap samples with manually introduced Gaussian noise together with machine learning algorithms, such as Extreme Gradient Boosting and Naive Bayesian algorithms (using R). The toolkits predicted the probability of treatment needs and the COHSI score with percentile (ranking). The performance of the toolkits was evaluated internally and externally by residual mean square error (RMSE), correlation, sensitivity and specificity.ResultsThe toolkits were developed based on survey responses from 545 families with children aged 2 to 17 y. The sensitivity and specificity for predicting RFTN were 93% and 49% respectively with the external data. The correlation(s) between predicted and clinically determined COHSI was 0.88 (and 0.91 for its percentile). The RMSEs of the COHSI toolkit were 4.2 for COHSI (and 1.3 for its percentile).ConclusionsSurvey responses from children and their parents/guardians are predictive for clinical outcomes. The toolkits can be used by oral health programs at baseline among school populations. The toolkits can also be used to quantify differences between pre- and post-dental care program implementation. The toolkits' predicted oral health scores can be used to stratify samples in oral health research.Knowledge transfer statementThis study creates the oral health toolkits that combine self- and proxy- reported short forms with children's demographic characteristics to predict children's oral health and treatment needs using Machine Learning algorithms. The toolkits can be used by oral health programs at baseline among school populations to quantify differences between pre and post dental care program implementation. The toolkits can also be used to stratify samples according to the treatment needs and oral health status

A Review on the Oral Health Impacts of Acculturation

The impact of acculturation on systemic health has been extensively investigated and is regarded as an important explanatory factor for health disparity. However, information is limited and fragmented on the oral health implications of acculturation. This study aimed to review the current evidence on the oral health impact of acculturation. Papers were retrieved from five electronic databases. Twenty-seven studies were included in this review. Their scientific quality was rated and key findings were summarized. Seventeen studies investigated the impacts of acculturation on the utilization of dental services; among them, 16 reported positive associations between at least one acculturation indicator and use of dental services. All 15 studies relating acculturation to oral diseases (dental caries and periodontal disease) suggested better oral health among acculturated individuals. Evidence is lacking to support that better oral health of acculturated immigrants is attributable to their improved dental attendance. Further researches involving other oral health behaviors and diseases and incorporating refined acculturation scales are needed. Prospective studies will facilitate the understanding on the trajectory of immigrants’ oral health along the acculturation continuum

Child and adolescent perceptions of oral health over the life course

Purpose: To elicit perceptions of oral health in children and adolescents as an initial step in the development of oral health item banks for the Patient-Reported Oral Health Outcomes Measurement Information System project. Methods: We conducted focus groups with ethnically, socioeconomically, and geographically diverse youth (8–12, 13–17 years) to identify perceptions of oral health status. We performed content analysis, including a thematic and narrative analysis, to identify important themes. Results: We identified three unique themes that the youth associated with their oral health status: (1) understanding the value of maintaining good oral health over the life course, with respect to longevity and quality of life in the adult years; (2) positive association between maintaining good oral health and interpersonal relationships at school, and dating, for older youth; and (3) knowledge of the benefits of orthodontic treatment to appearance and positive self-image, while holding a strong view as to the discomfort associated with braces. Conclusions: The results provide valuable information about core domains for the oral health item banks to be developed and generated content for new items to be developed and evaluated with cognitive interviews and in a field test