Dataset Information

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

SUBMITTER: Eastwood JG

PROVIDER: S-EPMC3921342 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

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

Eastwood John G JG Jalaludin Bin B BB Kemp Lynn A LA Phung Hai N HN

SpringerPlus 20140127

<h4>Background</h4>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 covariat ...[more]

PMID: 24555171

Similar Datasets

Project description:BACKGROUND:Emerging resistance to anti-malarial drugs has led malaria researchers to investigate what covariates (parasite and host factors) are associated with resistance. In this regard, investigation of how covariates impact malaria parasites clearance is often performed using a two-stage approach in which the WWARN Parasite Clearance Estimator or PCE is used to estimate parasite clearance rates and then the estimated parasite clearance is regressed on the covariates. However, the recently developed Bayesian Clearance Estimator instead leads to more accurate results for hierarchial regression modelling which motivated the authors to implement the method as an R package, called "bhrcr". METHODS:Given malaria parasite clearance profiles of a set of patients, the "bhrcr" package performs Bayesian hierarchical regression to estimate malaria parasite clearance rates along with the effect of covariates on them in the presence of "lag" and "tail" phases. In particular, the model performs a linear regression of the log clearance rates on covariates to estimate the effects within a Bayesian hierarchical framework. All posterior inferences are obtained by a "Markov Chain Monte Carlo" based sampling scheme which forms the core of the package. RESULTS:The "bhrcr" package can be utilized to study malaria parasite clearance data, and specifically, how covariates affect parasite clearance rates. In addition to estimating the clearance rates and the impact of covariates on them, the "bhrcr" package provides tools to calculate the WWARN PCE estimates of the parasite clearance rates as well. The fitted Bayesian model to the clearance profile of each individual, as well as the WWARN PCE estimates, can also be plotted by this package. CONCLUSIONS:This paper explains the Bayesian Clearance Estimator for malaria researchers including describing the freely available software, thus making these methods accessible and practical for modelling covariates' effects on parasite clearance rates.

Project description:BACKGROUND: The impact of socio-environmental factors on suicide has been examined in many studies. Few of them, however, have explored these associations from a spatial perspective, especially in assessing the association between meteorological factors and suicide. This study examined the association of meteorological and socio-demographic factors with suicide across small areas over different time periods. METHODS: Suicide, population and socio-demographic data (e.g., population of Aboriginal and Torres Strait Islanders (ATSI), and unemployment rate (UNE) at the Local Government Area (LGA) level were obtained from the Australian Bureau of Statistics for the period of 1986 to 2005. Information on meteorological factors (rainfall, temperature and humidity) was supplied by Australian Bureau of Meteorology. A Bayesian Conditional Autoregressive (CAR) Model was applied to explore the association of socio-demographic and meteorological factors with suicide across LGAs. RESULTS: In Model I (socio-demographic factors), proportion of ATSI and UNE were positively associated with suicide from 1996 to 2000 (Relative Risk (RR)ATSI = 1.0107, 95% Credible Interval (CI): 1.0062-1.0151; RRUNE = 1.0187, 95% CI: 1.0060-1.0315), and from 2001 to 2005 (RRATSI = 1.0126, 95% CI: 1.0076-1.0176; RRUNE = 1.0198, 95% CI: 1.0041-1.0354). Socio-Economic Index for Area (SEIFA) and IND, however, had negative associations with suicide between 1986 and 1990 (RRSEIFA = 0.9983, 95% CI: 0.9971-0.9995; RRATSI = 0.9914, 95% CI: 0.9848-0.9980). Model II (meteorological factors): a 1°C higher yearly mean temperature across LGAs increased the suicide rate by an average by 2.27% (95% CI: 0.73%, 3.82%) in 1996-2000, and 3.24% (95% CI: 1.26%, 5.21%) in 2001-2005. The associations between socio-demographic factors and suicide in Model III (socio-demographic and meteorological factors) were similar to those in Model I; but, there is no substantive association between climate and suicide in Model III. CONCLUSIONS: Proportion of Aboriginal and Torres Strait Islanders, unemployment and temperature appeared to be statistically associated with of suicide incidence across LGAs among all selected variables, especially in recent years. The results indicated that socio-demographic factors played more important roles than meteorological factors in the spatial pattern of suicide incidence.

Dataset Information

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

Background

Methods

Results

Conclusions

Publications

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

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets