Identifying Patterns in Dental Visit Attendance Among Pregnant Women: A Retrospective Cohort Study.

INTRODUCTION

Understanding the factors influencing dental care utilization is crucial for enhancing treatment adherence and outcomes. This study evaluates dental care-seeking patterns among pregnant women in low-income community.

METHODS

The authors analyzed data from 311 pregnant patients and 1,111 visits (2019-2022) synchronized from dental and medical records. The primary outcome was showing up for scheduled dental visits. To identify visit-attending patterns, the authors used a model-based clustering method to cluster longitudinal data with categorical outcomes. A penalized generalized linear mixed-effects model was applied to identify relevant variables for the visit attendance trajectories within each cluster.

RESULTS

The study participants comprised 49.6% Black, 32.2% White, and 12.5% Hispanic women. The majority (89.07%) were holding Medicaid insurance. Among the 1,111 scheduled visits, 432 resulted in no-shows (38.8%), including failed and canceled appointments. The authors identified 3 distinct clusters of visit-attending patterns on the basis of their show-up rates: low demand/low appointment risk (85% attendance), high demand/high appointment risk (57% attendance despite multiple scheduled visits), and moderate demand/high appointment risk (55% attendance with fewer scheduled visits). Various determinants, such as race; age; inner-city residence; appointment timing; the COVID-19 era; type of scheduled dental treatment; and prior medical visits for conditions such as anxiety, depression, hypertension, and allergies, influenced the visit-attending behaviors within each patient group.

CONCLUSIONS

The innovative clustering approach of this study successfully identified dental care-seeking patterns among pregnant women, suggesting its applicability to a broader demographic. Identifying potential modifiable factors that could enhance attendance at dental visits is essential for improving oral healthcare outcomes among underserved pregnant patients.