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ArticleMaternal and child health journal2019

Patient-, Hospital-, and Neighborhood-Level Factors Associated with Severe Maternal Morbidity During Childbirth: A Cross-Sectional Study in New York State 2013-2014.

Jean Guglielminotti, Ruth Landau, Cynthia A Wong, Guohua Li

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Article in Maternal and child health journal, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
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28citing papers in PubMed, 1 pooled it
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1 · What the graph read from it

What it found

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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.

2 · The registry

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3 · Its place in the literature

Who cites it

28 citing papers in PubMed, 1 synthesis or guideline pooled it.

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Jean GuglielminottiDepartment of Anesthesiology, Columbia University College of Physicians and Surgeons, 622 West 168th Street, PH5-505, New York, NY, 10032, USA. jg3481@cumc.columbia.edu.ORCID http://orcid.org/0000-0002-8699-0724
Ruth LandauDepartment of Anesthesiology, Columbia University College of Physicians and Surgeons, 622 West 168th Street, PH5-505, New York, NY, 10032, USA.
Cynthia A WongDepartment of Anesthesia, University of Iowa Carver College of Medicine, 200 Hawkins Dr., JCP 6618, Iowa City, IA, 52242, USA.
Guohua LiDepartment of Anesthesiology, Columbia University College of Physicians and Surgeons, 622 West 168th Street, PH5-505, New York, NY, 10032, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background The incidence of severe maternal morbidity (SMM) during childbirth is increasing in the United States. A better characterization of risk factors for SMM may identify targets for improving maternal outcomes. This study aims to characterize patient-, hospital-, and neighborhood-level factors associated with SMM during childbirth. Methods SMM during childbirth was identified in the 2013-2014 State Inpatients Database for New York. Hospital and neighborhood characteristics were abstracted from the American Hospital Association and the Area Health Resources files. Multilevel modeling was used to identify factors associated with SMM, with and without blood transfusion, and its between-hospital variation. Results 403,116 delivery-related discharges from 139 hospitals were analyzed; 1557 (0.39%) recorded SMM without blood transfusion. In the final multilevel model, 7 patient-level factors were associated with a greater than fourfold increase in the risk of SMM: pulmonary hypertension, postpartum hemorrhage, placenta accreta, chronic kidney disease, cardiac conduction disorders, emergent cesarean delivery, and preeclampsia. Three hospital-level factors were associated with SMM: proportion of non-White patients, proportion of Medicaid beneficiaries, and coding intensity. No neighborhood-level factors were predictive of SMM. The proportion of variation in SMM explained by the model was 23.5 with 23.2% related to patient-level factors. The model explained 55% of the between-hospital variation, primarily related to patient-level factors. Similar results were observed for SMM with blood transfusion. Conclusions This study confirms the association between patient-level factors and SMM. It identifies patient-level factors as the major driver of between-hospital variation in SMM. Efforts to improve maternal outcomes should target patient-level factors.

Indexed as

Geographic MappingAdultCross-Sectional StudiesFemaleHumansMaternal HealthMorbidityMultivariate AnalysisNew YorkParturitionPregnancyPregnancy ComplicationsRisk FactorsChildbirthHospitalsMaternal morbidityNeigborhood

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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.