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Mapping a Syndemic of Psychosocial Risks During Pregnancy Using Network Analysis

Overview
Journal Int J Behav Med
Publisher Informa Healthcare
Date 2019 Feb 27
PMID 30805768
Citations 13
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Abstract

Background: Psychosocial risks during pregnancy impact maternal health in resource-limited settings, and HIV-positive women often bear a heavy burden of these factors. This study sought to use network modeling to characterize co-occurring psychosocial risks to maternal and child health among at-risk pregnant women.

Methods: Two hundred pregnant HIV-positive women attending antenatal care in South Africa were enrolled. Measured risk factors included younger age, low income, low education, unemployment, unintended pregnancy, distress about pregnancy, antenatal depression, internalized HIV stigma, violence exposure, and lack of social support. Network analysis between risk factors was conducted in R using mixed graphical modeling. Centrality statistics were examined for each risk node in the network.

Results: In the resulting network, unintended pregnancy was strongly tied to distress about pregnancy. Distress about pregnancy was most central in the network and was connected to antenatal depression and HIV stigma. Unintended pregnancy was also associated with lack of social support, which was itself linked to antenatal depression, HIV stigma, and low income. Finally, antenatal depression was connected to violence exposure.

Conclusions: Our results characterize a network of psychosocial risks among pregnant HIV-positive women. Distress about pregnancy emerged as central to this network, suggesting that unintended pregnancy is particularly distressing in this population and may contribute to further risks to maternal health, such as depression. Prevention of unintended pregnancies and interventions for coping with unplanned pregnancies may be particularly useful where multiple risks intersect. Efforts addressing single risk factors should consider an integrated, multilevel approach to support women during pregnancy.

Trial Registration: ClinicalTrials.gov identifier: NCT03069417.

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