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American journal of perinatology

July 27, 2026 · via Europe PMC

Adverse Maternal Outcome Disparities in a Clinically Low-risk Cohort.

Mayne G, Simon NT, Flores K, Lijewski V, Sheeder J, Ramírez IJ, Hurt KJ

Journal ArticleAbstract only — full text under license

AI summary — for doctors, generated from the abstract only

Even among clinically low-risk patients, adverse maternal outcomes differed by race and ethnicity, with non-Hispanic Asian patients having the highest rate.

What was studied

Secondary analysis of 7,691 low-risk patients cared for by midwives or obstetricians at a tertiary hospital (2013-2018), comparing severe maternal morbidity and a broader adverse maternal outcome composite by race/ethnicity and zip code vulnerability.

Key findings

  • AMO, but not SMM, differed by race/ethnicity
  • Non-Hispanic Asian patients had the highest AMO (38%), aOR 2.02 (95% CI 1.63-2.51) vs non-Hispanic White
  • Zip code social vulnerability did not differ by AMO prevalence

Clinical relevance

Aggregate reporting can hide inequities; the composite includes common outcomes like unplanned cesarean, so disaggregated data may reveal different risk patterns.

Who should care

Neonatology

Limitations

Single-institution secondary analysis with a composite outcome that includes common events like unplanned cesarean, and the data are from 2013-2018.

Disaggregating quality data by race and ethnicity may help units spot disparities otherwise masked in low-risk populations.

This is an AI-generated summary of the published abstract, not a clinical recommendation. It may not reflect the full text or subsequent updates — verify against the original source before acting on it.

Abstract

OBJECTIVE

Maternal health in the United States is characterized by persistent racial and ethnic disparities, even among clinically low-risk patients. Health systems often report outcomes in aggregate, a practice that can obscure inequities. We examined adverse maternal outcomes (AMO) among clinically low-risk patients stratified by race/ethnicity and assessed whether area-level social vulnerability differed across geographic patterns of AMO.

STUDY DESIGN

We conducted a secondary analysis of 7,691 patients who received intrapartum care from certified nurse-midwives or obstetricians at a tertiary academic hospital between 2013 and 2018. We evaluated severe maternal morbidity (SMM), adapted from CDC criteria, and AMO which included SMM plus postpartum hemorrhage, intra-amniotic infection, obstetric anal sphincter injury, and unplanned cesarean delivery. We stratified outcomes by mutually exclusive, census-derived racial/ethnic categories based on patient self-report. We used chi-squared, Student's-tests, and one-way ANOVA for comparisons, and logistic regression to assess AMO by racial/ethnic category. We used the Mann-Whitney U test to compare area-level social vulnerability indicators between zip codes with the highest and lowest AMO prevalence, and Geographic Information Systems (GIS) to map the zip code distribution.

RESULTS

AMO, but not SMM, differed by racial/ethnic category. Patients identifying as non-Hispanic Asian had the highest AMO (38%) and more than twice the odds compared with non-Hispanic White patients (aOR: 2.02; 95% CI: 1.63-2.51; < 0.001). Zip code-level social vulnerability indicators did not differ by AMO prevalence.

CONCLUSION

Racial and ethnic disparities in AMO persisted in a clinically low-risk cohort, underscoring the importance of disaggregated reporting to identify inequities that may be masked in aggregate analyses.

KEY POINTS

· Racial disparities exist even among low-risk patients.. · AMO, but not SMM, varied by race/ethnicity.. · Non-Hispanic Asian patients had the highest AMO rates.. · Zip code social vulnerability did not differ by AMO prevalence.. · Disaggregated reporting clarifies disparities..

DOI

10.1055/a-2914-3884

PMID

42508449

Research metadata (titles, abstracts, authors, publication dates) is sourced from Europe PMC, which aggregates content from PubMed/MEDLINE, PubMed Central, and other providers under the U.S. National Library of Medicine (NLM/NCBI). NLM does not claim copyright on abstracts; the original journal publishers or authors may hold rights to the full articles. AI-generated summaries are derived only from the abstract, never the full text, and are always labeled as AI-generated.

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