Bayesian spatial modelling of early childhood development in Australian regions

Mu Li,Alice Richardson,Bernard Baffour

doi:10.1186/s12942-020-00237-x

Mu Li, Alice Richardson + Show 1 more

Open Access

https://doi.org/10.1186/s12942-020-00237-x

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Abstract

BackgroundChildren’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.MethodsWe 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.ResultsThere 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.ConclusionSparsely 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.

Highlights

Childhood development is a critically important part of achieving good health outcomes for children through to adulthood
Geographical unit of analysis and analytic sample We focus on the third level of Australian statistical geography (SA3) as they are designed for output of regional data, and are formed by clustering groups of areas that have similar regional characteristics, administrative boundaries or labour markets
We present some results on the performance of the estimation of prevalence of child developmental vulnerability in Australia, through comparing the direct and model-based estimates

Summary

Introduction

Childhood development is a critically important part of achieving good health outcomes for children through to adulthood. At a subnational level there are differences in the developmental vulnerability, and stark differences are exhibited at lower levels of geography This observed geographical difference is due to the fact that vulnerable child development is not relatively common in the population, and as such the sample sizes are usually not large enough to provide reliable estimates for disaggregated analysis. Children’s early development plays a vital role for maintaining healthy lives and influences future outcomes. It is heavily affected by community factors which vary geographically. 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

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