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

## TL;DR

This study identifies patterns in dental visit attendance among pregnant women and finds factors that influence their care-seeking behaviors.

## Contribution

The study introduces a clustering approach to identify dental care-seeking patterns and modifiable factors for underserved pregnant women.

## Key findings

- Three clusters of dental visit attendance patterns were identified based on demand and risk.
- Race, age, and mental health conditions like anxiety and depression influenced dental care-seeking behaviors.
- Appointment timing and treatment type were significant determinants of visit attendance.

## Abstract

•This study examined dental care–seeking patterns in 311 underserved pregnant women.•Modifiable factors were identified to improve visit attendance in underserved groups.•Three clusters were identified: low demand/low risk, high demand/high risk, and moderate demand/high risk.•Determinants were race, age, residence, appointment timing, COVID-19 status, and treatment type.•Anxiety, depression, hypertension, and allergies influenced care-seeking behaviors.

This study examined dental care–seeking patterns in 311 underserved pregnant women.

Modifiable factors were identified to improve visit attendance in underserved groups.

Three clusters were identified: low demand/low risk, high demand/high risk, and moderate demand/high risk.

Determinants were race, age, residence, appointment timing, COVID-19 status, and treatment type.

Anxiety, depression, hypertension, and allergies influenced care-seeking behaviors.

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.

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.

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.

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.