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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...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2022
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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. |
format | Online Article Text |
id | pubmed-10263266 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
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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