Trajectories/Predictors of Oral Health-Related Quality of Life to Early Adulthood

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PROJECT SUMMARY/ABSTRACT Healthcare costs continue to grow exponentially in the United States and oral diseases remain one of the top 10 categories in terms of personal health care expenditures. To tackle the rising costs of care and minimize unnecessary treatment, there is increasing emphasis on patient-centered care, by including patient perceptions and health-related quality of life assessments as important health outcomes in medical and dental research. Oral health conditions have physical and psychological effects on individuals and influence their quality of life - how they grow, look, speak, chew, and socialize. Addressing Oral Health-Related Quality of Life (OHRQoL) is important and will help improve the quality of care, minimize oral health disparities, improve patient satisfaction and overall quality of life, and reduce costs. Despite the importance of OHRQoL, there have been few longitudinal and no trajectory studies of OHRQoL in adolescence/young adulthood. Ideally, such studies would identify longitudinal factors and patterns/trajectories to more fully understand development of OHRQoL as individuals enter adulthood. To adequately address the challenges of longitudinal data and create a predictive model capturing the many important trajectory determinants, it is necessary to use a high-performing algorithm like machine learning, a type of artificial intelligence. Our study will be the first to develop machine learning tools for prediction of OHRQoL using longitudinal data. We chose machine learning because it can accommodate the high-dimensional data to accurately predict individuals’ OHRQoL trajectories. We will leverage unique longitudinal data from our Iowa Fluoride Study, with data from subjects followed from birth to age 23 years. OHRQoL trajectories will be defined using three dependent variables measured at ages 17, 19, and 23: 1) Child Perception Questionnaire, 2) global oral health, and 3) visual analog quality of life scores. Due to the complexity and high dimensionality of the data, we will use unsupervised machine learning (K-means for longitudinal data) and supervised machine learning (LASSO regression, random forest and extreme gradient-boosting model) for the trajectory analysis and outcome predictions, respectively. The specific aims of the study will be to 1) determine the OHRQoL trajectories from late adolescence to young adulthood using unsupervised machine learning, and 2) identify predictors of trajectory group membership using supervised machine learning. The study will contribute significantly to our knowledge of adolescents’/young adults’ OHRQoL trajectories and determinants. The outcomes will set the stage for clinicians and policymakers to transition to a care model that is more patient-centered, which will improve oral health outcomes, reduce oral health disparities, reduce costs, and increase patient satisfaction. Our research will introduce and showcase the usefulness of machine learning in oral health research. Long term, we will develop a web-based application that clinicians and policymakers can use to better design interventions and treatments to suit the oral health needs of individuals and populations.