# Differences in Labor Progress, Care Received During Labor, and Provider-Patient Communication and Decision-Making Quality among Low-Risk Black vs. White Nulliparous Women with Spontaneous Labor Onset

> **NIH NIH R21** · VANDERBILT UNIVERSITY · 2021 · $255,541

## Abstract

PROJECT SUMMARY
The disparity in primary cesarean birth rates between low-risk Black and White nulliparous women with a term,
single, vertex fetus (NTSV) in the United States is greater than ever before. This is concerning because NTSV
women enter hospitals with similar baseline risks for cesarean birth. Safely lowering the cesarean rate,
eliminating disparities, and achieving health equity for all groups are ongoing national priorities. It is unknown
why disparities in cesarean rates exist between races but differences in labor progress, provider-level practice,
or provider-patient communication and decision making quality may be contributory. Dystocia (slow, abnormal
progression of labor) is the indication for half of cesareans among NTSV women, yet this diagnostic category
remains poorly defined and provides a key opportunity to safely reduce primary cesarean births. There is
contradictory evidence regarding whether Black and White NTSV women have similar dilation rates during labor.
If racial differences in labor progress exist, this would have major implications for diagnosing dystocia and clinical
determinations for performing cesarean births. Furthermore, it is unclear if provider application of American
College of Obstetricians and Gynecologists and the Society for Maternal-Fetal Medicine guidelines for safely
preventing primary cesareans are applied differently based on maternal race. Finally, disparate provider-patient
communications and decision making as well as provider implicit bias may contribute to higher cesarean rates
for Black NTSV women although this has not yet been studied. The purpose of this study is to compare Black
and White NTSV women with spontaneous labor onset on (1) labor progress, (2) care received during labor,
and (3) indicators of provider-patient communication and decision making quality. Retrospective and cross-
sectional data will be collected for this study. Labor and birth data will be retrospectively collected from Black
and White NTSV women who birthed at Vanderbilt University Medical Center following a pregnancy with
spontaneous labor onset since 2015 (n ≈ 7,150). For cross-sectional data collection, Black (n = 140) and White
(n = 140) women who birthed following a NTSV pregnancy with spontaneous labor onset will be recruited and
complete measures during their postpartum hospitalizations. Regression methods (polynomial, multiple logistic,
and linear) and will primarily be used to test hypotheses. Labor attendant type, maternal and pregnancy
characteristics, and common labor interventions will be model covariates. If study hypotheses are supported,
specific guidelines for assessing progress and diagnosing dystocia based on race may be necessary for closing
the cesarean rate disparity. Moreover, study findings will be the first to describe Black vs. White provider-patient
communication and decision making quality in an obstetrical setting at a high-volume, academic medical center.
Findings from this R21 st...

## Key facts

- **NIH application ID:** 10218513
- **Project number:** 1R21MD015159-01A1
- **Recipient organization:** VANDERBILT UNIVERSITY
- **Principal Investigator:** JEREMY L NEAL
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $255,541
- **Award type:** 1
- **Project period:** 2021-04-12 → 2023-01-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10218513

## Citation

> US National Institutes of Health, RePORTER application 10218513, Differences in Labor Progress, Care Received During Labor, and Provider-Patient Communication and Decision-Making Quality among Low-Risk Black vs. White Nulliparous Women with Spontaneous Labor Onset (1R21MD015159-01A1). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10218513. Licensed CC0.

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