Building Adaptive School-based Interventions for Caries

Summary

Dental caries (tooth decay) is the world's most prevalent noncommunicable disease. Current preventive and therapeutic treatments are available that can be provided in a school-based health service environment. However, not all children respond to care, and school programs can result in under- or over-treatment, leaving children at high risk. The Building Adaptive School-based Interventions for Caries (BASICS) study will identify the optimal sequence and intensity of these treatments to minimize nonresponse, creating adaptive interventions tailored to specific needs in order to personalize school-based dental medicine.

Detailed Description

Dental caries is the world's most prevalent noncommunicable disease, with considerable health disparities found in low-income rural white children and low-income urban Hispanic/Latino and black children. To combat this silent epidemic, school-based caries prevention can increase access to critical dental care, reducing caries risk and mitigating its severe health and socioemotional consequences. However, despite the use of evidence-based interventions, approximately 30% of children participating in school-based caries prevention fail to respond to treatment ("nonresponse"), developing new caries and remaining at high risk for subsequent complications. Additionally, the sustainability of school-based prevention is jeopardized due to prohibitive costs, limitations of the professional workforce, and relying on treatments that are not optimized for individual patient needs. The Building Adaptive School-based Interventions for Caries (BASICS) study will develop and assess personalized, resource-efficient approaches to school caries prevention using adaptive interventions. Evaluated by embedding dynamic treatment regimes (DTR) within a Sequential, Multiple-Assignment, Randomized Trial (SMART) design, investigators will explore a number of hypotheses related to adaptive preventive interventions for caries. First, investigators will test the effects of adaptive interventions on reducing treatment nonresponse using generalized linear mixed models, followed by identifying the best dynamic treatment regime using augmented inverse probability weighted marginal structural models for causal effect estimation. Investigators will subsequently build an optimal adaptive intervention using Q-learning, tailoring treatments to individual patient attributes and responsiveness. Investigators will conclude with a microcosting analysis of the adaptive interventions, ultimately quantifying the intervention costs under different implementation scenarios and taking the first step towards understanding how much payers should reimburse for adaptive interventions. Successful completion of this project will produce resource-efficient adaptive prevention interventions (API) to reduce treatment nonresponse and improve oral health.

Eligibility Criteria

Interventions