Bayesian hierarchical spatial regression of maternal depressive symptoms in South Western Sydney, Australia

John G Eastwood; Bin B Jalaludin; Lynn A Kemp; Hai N Phung

doi:10.1186/2193-1801-3-55

Bayesian hierarchical spatial regression of maternal depressive symptoms in South Western Sydney, Australia

Springerplus. 2014 Jan 27:3:55. doi: 10.1186/2193-1801-3-55. eCollection 2014.

Authors

John G Eastwood¹, Bin B Jalaludin², Lynn A Kemp², Hai N Phung³

Affiliations

¹ Community Paediatrics, South Western Sydney Local Health District, Locked Mail Bag 7008, Liverpool, BC 1871 New South Wales Australia ; School of Public Health and Community Medicine, The University of New South Wales, Sydney, NSW 2052 Australia ; School of Women's and Children's Health, The University of New South Wales, Sydney, NSW 2052 Australia ; School of Public Health, University of Sydney, Sydney, NSW 2006 Australia ; School of Public Health, Griffith University, Gold Coast, Queensland, 4222 Australia.
² Community Paediatrics, South Western Sydney Local Health District, Locked Mail Bag 7008, Liverpool, BC 1871 New South Wales Australia ; School of Public Health and Community Medicine, The University of New South Wales, Sydney, NSW 2052 Australia.
³ Community Paediatrics, South Western Sydney Local Health District, Locked Mail Bag 7008, Liverpool, BC 1871 New South Wales Australia ; School of Public Health and Community Medicine, The University of New South Wales, Sydney, NSW 2052 Australia ; School of Public Health, Griffith University, Gold Coast, Queensland, 4222 Australia.

Abstract

Background: There is increasing interest in the role played by maternal depression in mediating the effects of adversity during pregnancy and poor infant outcomes. There is also increasing evidence from multilevel regression studies for an association of area-level economic deprivation and poor individual mental health. The purpose of the study reported here is to explore the spatial distribution of postnatal depressive symptoms in South Western Sydney, Australia, and to identify covariate associations that could inform subsequent multilevel studies.

Methods: Mothers (n = 15,389) delivering in 2002 and 2003 were assessed at 2-3 weeks after delivery for risk factors for depressive symptoms. The individual-level binary outcome variables were Edinburgh Depression Scale (EDS) >9 and >12. The association between social, demographic and ecological factors and aggregated outcome variables were investigated using exploratory factor analysis and multivariate hierarchical Bayesian spatial regression. Relative risks from the final EDS >12 regression model were mapped to visualise the contribution from explanatory covariates and residual components.

Results: The exploratory factor analysis identified six factors: neighbourhood adversity, social cohesion, health behaviours, housing quality, social services, and support networks. Variables associated with neighbourhood adversity, social cohesion, social networks, and ethnic diversity were consistently associated with aggregated depressive symptoms. Measures of social disadvantage, lack of social cohesion and lack of social capital were associated with increased depressive symptoms. The association with social disadvantage was not significant when controlling for ethnic diversity and social capital.

Conclusions: The findings support the theoretical proposition that neighbourhood adversity causes maternal psychological distress and depression within the context of social buffers including social networks, social cohesion, and social services. The finding have implications for the distribution of health services including early nurse home visiting which has recently been confirmed to be effective in preventing postnatal depression.

Keywords: Bayesian hierarchical regression; Exploratory factor analysis; Postnatal depression; Social capital; Social cohesion; Spatial epidemiology.