Evidence map›Paper›PMID 37178717›Full record

ArticleAmerican journal of obstetrics & gynecology MFM2023

Preconception and perinatal hospitalizations as indicators of risk for severe maternal morbidity in primiparas.

Audra R Meadows, Howard Cabral, Chia-Ling Liu, Xiaohui Cui, Ndidiamaka Amutah-Onukagha, Hafsatou Diop, Eugene R Declercq

Open access · greenAbstract read
In one paragraph

Article in American journal of obstetrics & gynecology MFM, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact, top 92% of its field
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed, 0 citations in OpenAlex.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors at 4 institutions in 1 country.

Audra R MeadowsDepartment of Obstetrics, Gynecology, and Reproductive Sciences, University of California, San Diego, San Diego, CA (Dr Meadows).
Howard CabralBoston University School of Public Health, Boston, MA (Drs Cabral and Declercq).
Chia-Ling LiuEvalogic Services, Inc, Newton, MA(Dr Liu).
Xiaohui CuiMassachusetts Department of Public Health, Boston, MA (Drs Cui and Diop).
Ndidiamaka Amutah-OnukaghaTufts University School of Medicine, Boston, MA (Dr Amutah-Onukagha).
Hafsatou DiopMassachusetts Department of Public Health, Boston, MA (Drs Cui and Diop).
Eugene R DeclercqBoston University School of Public Health, Boston, MA (Drs Cabral and Declercq). Electronic address: declercq@bu.edu.
Boston University · USMassachusetts Department of Public Health · USTufts University · USUniversity of California San Diego · US

Funding

Reducing Racial Disparities in SMM post COVID19: Assessing the integration of maternal safety bundles and community based doulas to improve outcomes for Black womenR01MD016026 · NIMHD · TUFTS UNIVERSITY BOSTON · PI AMUTAH-ONUKAGHA, NDIDIAMAKA, DECLERCQ, EUGENE R · 2020 to 2024
$3.1M
NIMHD NIH HHS R01 MD016026
6 · The paper itself

Abstract

backgroundSevere maternal morbidity includes unexpected outcomes of labor and delivery that result in significant short- or long-term consequences to a woman's health. A statewide longitudinally linked database was used to examine hospitalization during and before pregnancy for birthing people with severe maternal morbidity at delivery.

objectiveThis study aimed to examine the association between hospital visits during pregnancy and 1 to 5 years before pregnancy and severe maternal morbidity at delivery. STUDY

designThis study was a retrospective, population-based cohort analysis of the Massachusetts Pregnancy to Early Life Longitudinal database between January 1, 2004, and December 31, 2018. Nonbirth hospital visits, including emergency department visits, observational stays, and hospital admissions during pregnancy and 5 years before pregnancy, were identified. The diagnoses for hospitalizations were categorized. We compared medical conditions leading to antecedent, nonbirth hospital visits among primiparous birthing individuals with singleton births with and without severe maternal morbidity, excluding transfusions.

resultsOf 235,398 birthing individuals, 2120 had severe maternal morbidity, a rate of 90.1 cases per 10,000 deliveries, and 233,278 did not have severe maternal morbidity. Compared with 4.3% of patients without severe maternal morbidity, 10.4% of patients with severe maternal morbidity were hospitalized during pregnancy. In multivariable analysis, there was a 31% increased risk of hospital admission during the prenatal period, a 60% increased risk of hospital admission in the year before pregnancy, and a 41% increased risk of hospital admission in 2 to 5 years before pregnancy. Compared with 9.8% of non-Hispanic White birthing people, 14.9% of non-Hispanic Black birthing people with severe maternal morbidity experienced a hospital admission during pregnancy. For those with severe maternal morbidity, prenatal hospitalization was most common for those with endocrine (3.6%) or hematologic (3.3%) conditions, with the largest differences between those with and without severe maternal morbidity for musculoskeletal (relative risk, 9.82; 95% confidence interval, 7.06-13.64) and cardiovascular (relative risk, 9.73; 95% confidence interval, 7.26-13.03) conditions.

conclusionThis study found a strong association between previous nonbirth hospitalizations and the likelihood of severe maternal morbidity at delivery.

Indexed as

EthnicityHospitalizationFemaleHumansParityPregnancyRetrospective StudiesWhitelinked dataperinatal hospitalizationpreconception careracial disparitiessevere maternal morbidity

Identifiers

PMID37178717
PMCPMC10367434
OpenAlexW4376280157

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

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.