Cargando…

Using network analysis to illuminate the intergenerational transmission of adversity

Objective: The effects of maternal exposure to adverse childhood experiences (ACEs) may be transmitted to subsequent generations through various biopsychosocial mechanisms. However, studies tend to focus on exploring one or two focal pathways with less attention paid to links between different pathw...

Descripción completa

Detalles Bibliográficos
Autores principales: Hemady, Chad Lance, Speyer, Lydia Gabriela, Kwok, Janell, Meinck, Franziska, Melendez-Torres, G.J., Fry, Deborah, Auyeung, Bonnie, Murray, Aja Louise
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Taylor & Francis 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9397447/
https://www.ncbi.nlm.nih.gov/pubmed/36016844
http://dx.doi.org/10.1080/20008198.2022.2101347
_version_ 1784772128521846784
author Hemady, Chad Lance
Speyer, Lydia Gabriela
Kwok, Janell
Meinck, Franziska
Melendez-Torres, G.J.
Fry, Deborah
Auyeung, Bonnie
Murray, Aja Louise
author_facet Hemady, Chad Lance
Speyer, Lydia Gabriela
Kwok, Janell
Meinck, Franziska
Melendez-Torres, G.J.
Fry, Deborah
Auyeung, Bonnie
Murray, Aja Louise
author_sort Hemady, Chad Lance
collection PubMed
description Objective: The effects of maternal exposure to adverse childhood experiences (ACEs) may be transmitted to subsequent generations through various biopsychosocial mechanisms. However, studies tend to focus on exploring one or two focal pathways with less attention paid to links between different pathways. Using a network approach, this paper explores a range of core prenatal risk factors that may link maternal ACEs to infant preterm birth (PTB) and low birthweight (LBW). Methods: We used data from the Avon Longitudinal Study of Parents and Children (ALSPAC) (n = 8379) to estimate two mixed graphical network models: Model 1 was constructed using adverse infant outcomes, biopsychosocial and environmental risk factors, forms of ACEs, and sociodemographic factors. In Model 2, ACEs were combined to represent a threshold ACEs score (≥4). Network indices (i.e., shortest path and bridge expected influence [1-step & 2-step]) were estimated to determine the shortest pathway from ACEs to infant outcomes, and to identify the risk factors that are vital in activating other risk factors and adverse outcomes. Results: Network analyses estimated a mutually reinforcing web of childhood and prenatal risk factors, with each risk connected to at least two other risks. Bridge influence indices suggested that childhood physical and sexual abuse and multiple ACEs were highly interconnected to others risks. Overall, risky health behaviours during pregnancy (i.e., smoking & illicit drug use) were identified as ‘active’ risk factors capable of affecting (directly and indirectly) other risk factors and contributing to the persistent activation of the global risk network. These risks may be considered priority candidate targets for interventions to disrupt intergenerational risk transmission. Our study demonstrates the promise of network analysis as an approach for illuminating the intergenerational transmission of adversity in its full complexity. HIGHLIGHTS: We took a network approach to assessing links between ACEs and birth outcomes. ACEs, other prenatal risk factors, and birth outcomes had complex inter-connections. Health behaviours in pregnancy were indicated as optimal intervention targets.
format Online
Article
Text
id pubmed-9397447
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher Taylor & Francis
record_format MEDLINE/PubMed
spelling pubmed-93974472022-08-24 Using network analysis to illuminate the intergenerational transmission of adversity Hemady, Chad Lance Speyer, Lydia Gabriela Kwok, Janell Meinck, Franziska Melendez-Torres, G.J. Fry, Deborah Auyeung, Bonnie Murray, Aja Louise Eur J Psychotraumatol Basic Research Article Objective: The effects of maternal exposure to adverse childhood experiences (ACEs) may be transmitted to subsequent generations through various biopsychosocial mechanisms. However, studies tend to focus on exploring one or two focal pathways with less attention paid to links between different pathways. Using a network approach, this paper explores a range of core prenatal risk factors that may link maternal ACEs to infant preterm birth (PTB) and low birthweight (LBW). Methods: We used data from the Avon Longitudinal Study of Parents and Children (ALSPAC) (n = 8379) to estimate two mixed graphical network models: Model 1 was constructed using adverse infant outcomes, biopsychosocial and environmental risk factors, forms of ACEs, and sociodemographic factors. In Model 2, ACEs were combined to represent a threshold ACEs score (≥4). Network indices (i.e., shortest path and bridge expected influence [1-step & 2-step]) were estimated to determine the shortest pathway from ACEs to infant outcomes, and to identify the risk factors that are vital in activating other risk factors and adverse outcomes. Results: Network analyses estimated a mutually reinforcing web of childhood and prenatal risk factors, with each risk connected to at least two other risks. Bridge influence indices suggested that childhood physical and sexual abuse and multiple ACEs were highly interconnected to others risks. Overall, risky health behaviours during pregnancy (i.e., smoking & illicit drug use) were identified as ‘active’ risk factors capable of affecting (directly and indirectly) other risk factors and contributing to the persistent activation of the global risk network. These risks may be considered priority candidate targets for interventions to disrupt intergenerational risk transmission. Our study demonstrates the promise of network analysis as an approach for illuminating the intergenerational transmission of adversity in its full complexity. HIGHLIGHTS: We took a network approach to assessing links between ACEs and birth outcomes. ACEs, other prenatal risk factors, and birth outcomes had complex inter-connections. Health behaviours in pregnancy were indicated as optimal intervention targets. Taylor & Francis 2022-08-18 /pmc/articles/PMC9397447/ /pubmed/36016844 http://dx.doi.org/10.1080/20008198.2022.2101347 Text en © 2022 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group https://creativecommons.org/licenses/by-nc/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (http://creativecommons.org/licenses/by-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) ), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Basic Research Article
Hemady, Chad Lance
Speyer, Lydia Gabriela
Kwok, Janell
Meinck, Franziska
Melendez-Torres, G.J.
Fry, Deborah
Auyeung, Bonnie
Murray, Aja Louise
Using network analysis to illuminate the intergenerational transmission of adversity
title Using network analysis to illuminate the intergenerational transmission of adversity
title_full Using network analysis to illuminate the intergenerational transmission of adversity
title_fullStr Using network analysis to illuminate the intergenerational transmission of adversity
title_full_unstemmed Using network analysis to illuminate the intergenerational transmission of adversity
title_short Using network analysis to illuminate the intergenerational transmission of adversity
title_sort using network analysis to illuminate the intergenerational transmission of adversity
topic Basic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9397447/
https://www.ncbi.nlm.nih.gov/pubmed/36016844
http://dx.doi.org/10.1080/20008198.2022.2101347
work_keys_str_mv AT hemadychadlance usingnetworkanalysistoilluminatetheintergenerationaltransmissionofadversity
AT speyerlydiagabriela usingnetworkanalysistoilluminatetheintergenerationaltransmissionofadversity
AT kwokjanell usingnetworkanalysistoilluminatetheintergenerationaltransmissionofadversity
AT meinckfranziska usingnetworkanalysistoilluminatetheintergenerationaltransmissionofadversity
AT melendeztorresgj usingnetworkanalysistoilluminatetheintergenerationaltransmissionofadversity
AT frydeborah usingnetworkanalysistoilluminatetheintergenerationaltransmissionofadversity
AT auyeungbonnie usingnetworkanalysistoilluminatetheintergenerationaltransmissionofadversity
AT murrayajalouise usingnetworkanalysistoilluminatetheintergenerationaltransmissionofadversity