Defining vulnerability subgroups among pregnant women using pre-pregnancy information: a latent class analysis
<p><strong>Background</strong> 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...
| Main Authors: | , , , , , , , , |
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| Format: | Journal article |
| Language: | English |
| Published: |
Oxford University Press
2022
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| _version_ | 1797109961822568448 |
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| author | Molenaar, JM van der Meer, L Bertens, LCM de Vries, EF Waelput, AJM Knight, M Steegers, EAP Kiefte-de Jong, JC Struijs, JN |
| author_facet | Molenaar, JM van der Meer, L Bertens, LCM de Vries, EF Waelput, AJM Knight, M Steegers, EAP Kiefte-de Jong, JC Struijs, JN |
| author_sort | Molenaar, JM |
| collection | OXFORD |
| description | <p><strong>Background</strong>
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.</p>
<p><strong>Methods</strong>
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.</p>
<p><strong>Results</strong>
In the study population of 4172 women, we identified five latent classes. The largest ‘healthy and socioeconomically stable’-class [<i>n</i> = 2040 (48.9%)] mostly shared protective factors, such as paid work and positively perceived health. The classes ‘high care utilization’ [<i>n</i> = 485 (11.6%)], ‘socioeconomic vulnerability’ [<i>n</i> = 395 (9.5%)] and ‘psychosocial vulnerability’ [<i>n</i> = 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 [<i>n</i> = 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.</p>
<p><strong>Conclusions</strong>
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.</p> |
| first_indexed | 2024-03-07T07:48:32Z |
| format | Journal article |
| id | oxford-uuid:76953dea-69c2-4e24-9a6c-01caf382af93 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-07T07:48:32Z |
| publishDate | 2022 |
| publisher | Oxford University Press |
| record_format | dspace |
| spelling | oxford-uuid:76953dea-69c2-4e24-9a6c-01caf382af932023-06-28T15:40:28ZDefining vulnerability subgroups among pregnant women using pre-pregnancy information: a latent class analysisJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:76953dea-69c2-4e24-9a6c-01caf382af93EnglishSymplectic ElementsOxford University Press2022Molenaar, JMvan der Meer, LBertens, LCMde Vries, EFWaelput, AJMKnight, MSteegers, EAPKiefte-de Jong, JCStruijs, JN<p><strong>Background</strong> 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.</p> <p><strong>Methods</strong> 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.</p> <p><strong>Results</strong> In the study population of 4172 women, we identified five latent classes. The largest ‘healthy and socioeconomically stable’-class [<i>n</i> = 2040 (48.9%)] mostly shared protective factors, such as paid work and positively perceived health. The classes ‘high care utilization’ [<i>n</i> = 485 (11.6%)], ‘socioeconomic vulnerability’ [<i>n</i> = 395 (9.5%)] and ‘psychosocial vulnerability’ [<i>n</i> = 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 [<i>n</i> = 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.</p> <p><strong>Conclusions</strong> 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.</p> |
| spellingShingle | Molenaar, JM van der Meer, L Bertens, LCM de Vries, EF Waelput, AJM Knight, M Steegers, EAP Kiefte-de Jong, JC Struijs, JN 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 |
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