Evidence map›Paper›PMID 36515418›Full record

ArticleEuropean journal of public health2023

Defining vulnerability subgroups among pregnant women using pre-pregnancy information: a latent class analysis.

J M Molenaar, L van der Meer, L C M Bertens, E F de Vries, A J M Waelput, M Knight, E A P Steegers, J C Kiefte-de Jong, J N Struijs

Open access · goldAbstract read
In one paragraph

Article in European journal of public health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
3.4field-weighted citation impact, top 7% 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

11 citing papers in PubMed, 18 citations in OpenAlex.

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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

9 authors at 2 institutions in 2 countries.

J M MolenaarDepartment of Quality of Care and Health Economics, National Institute for Public Health and the Environment (RIVM), Centre for Nutrition, Prevention and Health Services, Bilthoven, the Netherlands.ORCID 0000-0002-8025-040X
L van der MeerDepartment of Obstetrics and Gynaecology, Erasmus MC, University Medical Centre, Rotterdam, the Netherlands.ORCID 0000-0002-1880-8329
L C M BertensDepartment of Obstetrics and Gynaecology, Erasmus MC, University Medical Centre, Rotterdam, the Netherlands.ORCID 0000-0003-0897-0709
E F de VriesDepartment of Quality of Care and Health Economics, National Institute for Public Health and the Environment (RIVM), Centre for Nutrition, Prevention and Health Services, Bilthoven, the Netherlands.ORCID 0000-0002-1336-4327
A J M WaelputDepartment of Obstetrics and Gynaecology, Erasmus MC, University Medical Centre, Rotterdam, the Netherlands.ORCID 0000-0002-6266-3622
M KnightDepartment of Public Health and Primary Care/Health Campus The Hague, Leiden University Medical Centre, the Hague, the Netherlands.ORCID 0000-0002-1984-4575
E A P SteegersDepartment of Obstetrics and Gynaecology, Erasmus MC, University Medical Centre, Rotterdam, the Netherlands.ORCID 0000-0001-6658-9274
J C Kiefte-de JongDepartment of Public Health and Primary Care/Health Campus The Hague, Leiden University Medical Centre, the Hague, the Netherlands.ORCID 0000-0002-8136-0918
J N StruijsDepartment of Quality of Care and Health Economics, National Institute for Public Health and the Environment (RIVM), Centre for Nutrition, Prevention and Health Services, Bilthoven, the Netherlands.ORCID 0000-0002-7493-6720
Leiden University · NLErasmus MC · NL

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundEarly detection of vulnerability during or before pregnancy can contribute to optimizing the first 1000 days, a crucial period for children's development and health. We aimed to identify classes of vulnerability among pregnant women in the Netherlands using pre-pregnancy data on a wide range of social risk and protective factors, and validate these classes against the risk of adverse outcomes.

methodsWe conducted a latent class analysis based on 42 variables derived from nationwide observational data sources and self-reported data. Variables included individual, socioeconomic, lifestyle, psychosocial and household characteristics, self-reported health, healthcare utilization, life-events and living conditions. We compared classes in relation to adverse outcomes using logistic regression analyses.

resultsIn the study population of 4172 women, we identified five latent classes. The largest 'healthy and socioeconomically stable'-class [n = 2040 (48.9%)] mostly shared protective factors, such as paid work and positively perceived health. The classes 'high care utilization' [n = 485 (11.6%)], 'socioeconomic vulnerability' [n = 395 (9.5%)] and 'psychosocial vulnerability' [n = 1005 (24.0%)] were characterized by risk factors limited to one specific domain and protective factors in others. Women classified into the 'multidimensional vulnerability'-class [n = 250 (6.0%)] shared multiple risk factors in different domains (psychosocial, medical and socioeconomic risk factors). Multidimensional vulnerability was associated with adverse outcomes, such as premature birth and caesarean section.

conclusionsCo-existence of multiple risk factors in various domains is associated with adverse outcomes for mother and child. Early detection of vulnerability and strategies to improve parental health and well-being might benefit from focussing on different domains and combining medical and social care and support.

Indexed as

Cesarean SectionPregnant PeopleChildFemaleHumansLatent Class AnalysisPregnancyRisk FactorsSocioeconomic Factors

Identifiers

PMID36515418
PMCPMC10263266
OpenAlexW4311401780

What OpenQuestion holds

Textmetadata
LicenceCC BY
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Registered trials

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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.