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

BACKGROUND: Early 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 r...

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Autores principales: Molenaar, J M, van der Meer, L, Bertens, L C M, de Vries, E F, Waelput, A J M, Knight, M, Steegers, E A P, Kiefte-de Jong, J C, Struijs, J N
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10263266/
https://www.ncbi.nlm.nih.gov/pubmed/36515418
http://dx.doi.org/10.1093/eurpub/ckac170
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author Molenaar, J M
van der Meer, L
Bertens, L C M
de Vries, E F
Waelput, A J M
Knight, M
Steegers, E A P
Kiefte-de Jong, J C
Struijs, J N
author_facet Molenaar, J M
van der Meer, L
Bertens, L C M
de Vries, E F
Waelput, A J M
Knight, M
Steegers, E A P
Kiefte-de Jong, J C
Struijs, J N
author_sort Molenaar, J M
collection PubMed
description BACKGROUND: Early 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. METHODS: We 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. RESULTS: In 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. CONCLUSIONS: Co-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.
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spelling pubmed-102632662023-06-15 Defining vulnerability subgroups among pregnant women using pre-pregnancy information: a latent class analysis Molenaar, J M van der Meer, L Bertens, L C M de Vries, E F Waelput, A J M Knight, M Steegers, E A P Kiefte-de Jong, J C Struijs, J N Eur J Public Health Social Determinants BACKGROUND: Early 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. METHODS: We 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. RESULTS: In 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. CONCLUSIONS: Co-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. Oxford University Press 2022-12-14 /pmc/articles/PMC10263266/ /pubmed/36515418 http://dx.doi.org/10.1093/eurpub/ckac170 Text en © The Author(s) 2022. Published by Oxford University Press on behalf of the European Public Health Association. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Social Determinants
Molenaar, J M
van der Meer, L
Bertens, L C M
de Vries, E F
Waelput, A J M
Knight, M
Steegers, E A P
Kiefte-de Jong, J C
Struijs, J N
Defining vulnerability subgroups among pregnant women using pre-pregnancy information: a latent class analysis
title Defining vulnerability subgroups among pregnant women using pre-pregnancy information: a latent class analysis
title_full Defining vulnerability subgroups among pregnant women using pre-pregnancy information: a latent class analysis
title_fullStr Defining vulnerability subgroups among pregnant women using pre-pregnancy information: a latent class analysis
title_full_unstemmed Defining vulnerability subgroups among pregnant women using pre-pregnancy information: a latent class analysis
title_short Defining vulnerability subgroups among pregnant women using pre-pregnancy information: a latent class analysis
title_sort defining vulnerability subgroups among pregnant women using pre-pregnancy information: a latent class analysis
topic Social Determinants
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10263266/
https://www.ncbi.nlm.nih.gov/pubmed/36515418
http://dx.doi.org/10.1093/eurpub/ckac170
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