Y Wang1,2,3, R D Hays4,5,6, M Marcus2, C A Maida2,7, J Shen2, D Xiong1,2, I D Coulter2,6, S Y Lee8, V W Spolsky2, J J Crall2, H Liu1,2,5. 1. Department of Biostatistics, Fielding School of Public Health, University of California, Los Angeles, CA, USA. 2. Division of Public Health and Community Dentistry, School of Dentistry, University of California, Los Angeles, CA, USA. 3. Urban Health Collaborative, Dornsife School of Public Health, Drexel University, Philadelphia, PA, USA. 4. Department of Health Policy and Management, Fielding School of Public Health, University of California, Los Angeles, CA, USA. 5. Department of Medicine, Division of General Internal Medicine and Health Services Research, David Geffen School of Medicine, University of California, Los Angeles, CA, USA. 6. RAND Corporation, Santa Monica, CA, USA. 7. Division of Oral Biology and Medicine, School of Dentistry, University of California, Los Angeles, CA, USA. 8. Division of Constitutive & Regenerative Sciences, Section of Restorative Dentistry, School of Dentistry, University of California, Los Angeles, CA, USA.
Abstract
OBJECTIVES: Evaluating 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. METHODS: Data 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. RESULTS: The 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). CONCLUSIONS: Survey 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 STATEMENT: This 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.
OBJECTIVES: Evaluating 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. METHODS: Data 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. RESULTS: The 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). CONCLUSIONS: Survey 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 STATEMENT: This 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.
Entities:
Keywords:
health services research; patient reported outcome measures; proxy; psychometrics; self report; surveys and questionnaires
Authors: Yan Wang; Ron Hays; Marvin Marcus; Carl Maida; Jie Shen; Di Xiong; Steve Lee; Vladimir Spolsky; Ian Coulter; James Crall; Honghu Liu Journal: Int J Paediatr Dent Date: 2019-01-24 Impact factor: 3.455
Authors: Honghu Liu; Ron D Hays; Marvin Marcus; Ian Coulter; Carl Maida; Francisco Ramos-Gomez; Jie Shen; Yan Wang; Vladimir Spolsky; Steve Lee; Li Cai; James Crall Journal: BMC Oral Health Date: 2016-09-15 Impact factor: 2.757
Authors: Erik von Elm; Douglas G Altman; Matthias Egger; Stuart J Pocock; Peter C Gøtzsche; Jan P Vandenbroucke Journal: PLoS Med Date: 2007-10-16 Impact factor: 11.069
Authors: Francisco Ramos-Gomez; Marvin Marcus; Carl A Maida; Yan Wang; Janni J Kinsler; Di Xiong; Steve Y Lee; Ron D Hays; Jie Shen; James J Crall; Honghu Liu Journal: Dent J (Basel) Date: 2021-12-01