ArticleReproductive sciences (Thousand Oaks, Calif.)2022
Maternal Morbidity Predicted by an Intersectional Social Determinants of Health Phenotype: A Secondary Analysis of the NuMoM2b Dataset.
Article in Reproductive sciences (Thousand Oaks, Calif.), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
7 citing papers in PubMed, 16 citations in OpenAlex.
- Postpartum Management of Hypertensive Disorders of Pregnancy in Six Large U.S. Hospital Systems: Descriptive Review and Identification of Clinical and Research Gaps.American journal of perinatology · 2025Review
- Respectful maternity care experiences of South Asian refugees in the US: a qualitative study.Frontiers in public health · 2025Article
- Article
- Observational
- Disparities by race/ethnicity in unplanned cesarean birth among healthy nulliparas: a secondary analysis of the nuMoM2b dataset.BMC pregnancy and childbirth · 2023Article
- Identifying High-Need Primary Care Patients Using Nursing Knowledge and Machine Learning Methods.Applied clinical informatics · 2023Article
- Social Determinant Pathways to Hypertensive Disorders of Pregnancy Among Nulliparous U.S. Women.Women's health issues : official publication of the Jacobs Institute of Women's HealthObservational
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 2 institutions in 1 country.
Funding
Abstract
Maternal race, ethnicity and socio-economic position are known to be associated with increased risk for a range of poor pregnancy outcomes, including maternal morbidity and mortality. Previously, researchers seeking to identify the contributing factors focused on maternal behaviors and pregnancy complications. Less understood is the contribution of the social determinants of health (SDoH) in observed differences by race/ethnicity in these key outcomes. In this secondary analysis of the Nulliparous Pregnancy Outcomes Study: Monitoring Mothers-to-Be (nuMoM2b) dataset, latent mixture modeling was used to construct groups of healthy, nulliparous participants with a non-anomalous fetus in a cephalic presentation having a trial of labor (N = 5763) based on SDoH variables. The primary outcome was a composite score of postpartum maternal morbidity. A postpartum maternal morbidity event was experienced by 350 individuals (6.1%). Latent class analysis using SDoH variables revealed six groups of participants, with postpartum maternal morbidity rates ranging from 8.7% to 4.5% across groups (p < 0.001). Two SDoH groups had the highest odds for maternal morbidity. These higher-risk groups were comprised of participants with the lowest income and highest stress and those who had lived in the USA for the shortest periods of time. SDoH phenotype predicted MM outcomes and identified two important, yet distinct groups of pregnant people who were the most likely have a maternal morbidity event.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.