E D John Eastwood1,2,3,4, Andy Wang5,6,7, Sarah Khanlari8, Alicia Montgomery8,9, Jean Yee Hwa Yang7,10. 1. Child & Family Clinical Services, Community Health, Sydney Local Health District, 24 Liverpool Road, Croydon, NSW, 2132, Australia. John.Eastwood@health.nsw.gov.au. 2. Clinical Services Integration and Population Health, Sydney Local Health District, Camperdown, Australia. John.Eastwood@health.nsw.gov.au. 3. Sydney Institute for Women, Children and their Families, Sydney Local Health District, Camperdown, Australia. John.Eastwood@health.nsw.gov.au. 4. Faculty of Medicine and Health, University of Sydney, Sydney, Australia. John.Eastwood@health.nsw.gov.au. 5. Faculty of Medicine and Health, University of Sydney, Sydney, Australia. 6. Department of Anaesthesia, Royal Prince Alfred Hospital, Sydney Local Health District, Camperdown, Australia. 7. School of Mathematics and Statistics, The University of Sydney, Sydney, Australia. 8. Child & Family Clinical Services, Community Health, Sydney Local Health District, 24 Liverpool Road, Croydon, NSW, 2132, Australia. 9. Sydney Institute for Women, Children and their Families, Sydney Local Health District, Camperdown, Australia. 10. Charles Perkins Centre, The University of Sydney, Sydney, Australia.
Abstract
BACKGROUND: There is increasing awareness that perinatal psychosocial adversity experienced by mothers, children, and their families, may influence health and well-being across the life course. To maximise the impact of population-based interventions for optimising perinatal wellbeing, health services can utilise empirical methods to identify subgroups at highest risk of poor outcomes relative to the overall population. METHODS: This study sought to identify sub-groups using latent class analysis within a population of mothers in Sydney, Australia, based on their differing experience of self-reported indicators of psychosocial adversity. This study sought to identify sub-groups using latent class analysis within a population of mothers in Sydney, Australia, based on their differing experience of self-reported indicators of psychosocial adversity. Subgroup differences in antenatal and postnatal depressive symptoms were assessed using the Edinburgh Postnatal Depression Scale. RESULTS: Latent class analysis identified four distinct subgroups within the cohort, who were distinguished empirically on the basis of their native language, current smoking status, previous involvement with Family-and-Community Services (FaCS), history of child abuse, presence of a supportive partner, and a history of intimate partner psychological violence. One group consisted of socially supported 'local' women who speak English as their primary language (Group L), another of socially supported 'migrant' women who speak a language other than English as their primary language (Group M), another of socially stressed 'local' women who speak English as their primary language (Group Ls), and socially stressed 'migrant' women who speak a language other than English as their primary language (Group Ms.). Compared to local and not socially stressed residents (L group), the odds of antenatal depression were nearly three times higher for the socially stressed groups (Ls OR: 2.87 95%CI 2.10-3.94) and nearly nine times more in the Ms. group (Ms OR: 8.78, 95%CI 5.13-15.03). Antenatal symptoms of depression were also higher in the not socially stressed migrant group (M OR: 1.70 95%CI 1.47-1.97) compared to non-migrants. In the postnatal period, Group M was 1.5 times more likely, while the Ms. group was over five times more likely to experience suboptimal mental health compared to Group L (OR 1.50, 95%CI 1.22-1.84; and OR 5.28, 95%CI 2.63-10.63, for M and Ms. respectively). CONCLUSIONS: The application of empirical subgrouping analysis permits an informed approach to targeted interventions and resource allocation for optimising perinatal maternal wellbeing.
BACKGROUND: There is increasing awareness that perinatal psychosocial adversity experienced by mothers, children, and their families, may influence health and well-being across the life course. To maximise the impact of population-based interventions for optimising perinatal wellbeing, health services can utilise empirical methods to identify subgroups at highest risk of poor outcomes relative to the overall population. METHODS: This study sought to identify sub-groups using latent class analysis within a population of mothers in Sydney, Australia, based on their differing experience of self-reported indicators of psychosocial adversity. This study sought to identify sub-groups using latent class analysis within a population of mothers in Sydney, Australia, based on their differing experience of self-reported indicators of psychosocial adversity. Subgroup differences in antenatal and postnatal depressive symptoms were assessed using the Edinburgh Postnatal Depression Scale. RESULTS: Latent class analysis identified four distinct subgroups within the cohort, who were distinguished empirically on the basis of their native language, current smoking status, previous involvement with Family-and-Community Services (FaCS), history of child abuse, presence of a supportive partner, and a history of intimate partner psychological violence. One group consisted of socially supported 'local' women who speak English as their primary language (Group L), another of socially supported 'migrant' women who speak a language other than English as their primary language (Group M), another of socially stressed 'local' women who speak English as their primary language (Group Ls), and socially stressed 'migrant' women who speak a language other than English as their primary language (Group Ms.). Compared to local and not socially stressed residents (L group), the odds of antenatal depression were nearly three times higher for the socially stressed groups (Ls OR: 2.87 95%CI 2.10-3.94) and nearly nine times more in the Ms. group (Ms OR: 8.78, 95%CI 5.13-15.03). Antenatal symptoms of depression were also higher in the not socially stressed migrant group (M OR: 1.70 95%CI 1.47-1.97) compared to non-migrants. In the postnatal period, Group M was 1.5 times more likely, while the Ms. group was over five times more likely to experience suboptimal mental health compared to Group L (OR 1.50, 95%CI 1.22-1.84; and OR 5.28, 95%CI 2.63-10.63, for M and Ms. respectively). CONCLUSIONS: The application of empirical subgrouping analysis permits an informed approach to targeted interventions and resource allocation for optimising perinatal maternal wellbeing.
Entities:
Keywords:
Depression; Integrated care; Latent class analysis; Perinatal; Stratification
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