Back

Treatment Gaps Among Young Medicaid-Enrolled Children with Tooth Decay in Pediatric Primary Care

Selvaraj, D.; Ronis, S. D.; Albert, J. M.; Rose, J.; Nelson, S.

2026-07-27 dentistry and oral medicine
10.64898/2026.07.23.26357672 medRxiv
Show abstract

Objective: To examine whether Medicaid-enrolled preschoolers with untreated decayed teeth received dental treatment within one year of enrollment and identify the factors associated with a treatment gap. Methods: A retrospective cohort analysis of data from a cluster-randomized trial conducted in 18 community-based pediatric primary care practices in Northeastern Ohio (2017-2022). Treatment receipt was determined using Medicaid claims, with treatment gap defined as fewer teeth with treatment claims than teeth found on baseline exam with decay. Multivariable logistic regression assessed the association of treatment gap with child age, sex, race/ethnicity, caregiver education, and number and location of baseline decayed teeth. Results: Of 766 eligible children, 487 (63.6%) attended the dentist within one year. Among 155/487 (31.8%) with baseline untreated decay, 90/155 (58.1%) had a treatment gap. Odontograms visually showed that decay was concentrated on upper anterior and posterior teeth. A treatment gap was associated with a greater number of decayed posterior teeth (OR = 1.90, 95% CI: 1.60-2.30) and decayed anterior teeth (OR = 2.19, 95% CI: 1.51-3.39), both p < 0.001. Other socio-demographic variables were not significantly associated with a treatment gap. Conclusion: More than half of Medicaid-enrolled children attending well-child visits had a dental treatment gap after 1 year. This pattern may reflect dentists' hesitancy to restore primary teeth nearing exfoliation and needing multiple dental visits to complete needed restorative treatment. To address this gap, non-surgical interventions such as silver diamine fluoride can be applied by pediatric primary care providers to control the bacteria and prevent disease progression.

Matching journals

The top 3 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.