Bayesian spatial modelling of early childhood development in Australian regions
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International Journal of Health Geographics Open Access
RESEARCH
Bayesian spatial modelling of early childhood development in Australian regions Mu Li1, Bernard Baffour2 and Alice Richardson3*
Abstract Background: Children’s early development plays a vital role for maintaining healthy lives and influences future outcomes. It is also heavily affected by community factors which vary geographically. Direct methods do not provide a comprehensive picture of this variation, especially for areas with sparse populations and low data coverage. In the context of Australia, the Australian Early Development Census (AEDC) provides a measure of early child development upon school entry. There are two primary aims of this study: (i) provide improved prevalence estimates of children who are considered as developmentally vulnerable in regions across Australia; (ii) ascertain how social-economic disadvantage partly explains the spatial variation. Methods: We used Bayesian spatial hierarchical models with the Socio-economic Indexes for Areas (SEIFA) as a covariate to provide improved estimates of all 335 SA3 regions in Australia. The study included 308,953 children involved in the 2018 AEDC where 21.7% of them were considered to be developmentally vulnerable in at least one domain. There are five domains of developmental vulnerability—physical health and wellbeing; social competence; emotional maturity; language and cognitive skills; and communication and general knowledge. Results: There are significant improvements in estimation of the prevalence of developmental vulnerability through incorporating the socio-economic disadvantage in an area. These improvements persist in all five domains—the largest improvements occurred in the Language and Cognitive Skills domain. In addition, our results reveal that there is an important geographical dimension to developmental vulnerability in Australia. Conclusion: Sparsely populated areas in sample surveys lead to unreliable direct estimates of the relatively small prevalence of child vulnerability. Bayesian spatial modelling can account for the spatial patterns in childhood vulnerability while including the impact of socio-economic disadvantage on geographic variation. Further investigation, using a broader range of covariates, could shed more light on explaining this spatial variation. Keywords: Australian Early Development Census (AEDC), Developmental vulnerability, Bayesian modelling, Spatial smoothing, Socio-economic index Introduction Early childhood development is a critically important part of achieving good health outcomes for children through to adulthood. Furthermore, like most phenomena, the impact of early childhood development on future *Correspondence: [email protected] 3 Statistical Consulting Unit, Australian National University, Canberra 2601, Australia Full list of author information is available at the end of the article
health and social outcomes is influenced by community factors where a person lives [1–3]. While the evidence for geographic variation in
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