Bayesian spatial modelling of early childhood development in Australian regions

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.

A Systematic Review of Areal Units and Adjacency Used in Bayesian Spatial and Spatio-Temporal Conditional Autoregressive Models in Health Research

“…Of the total of 52 studies, 26 (50.0%) were on infectious diseases, 11 (21.1%) on malaria [ 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 ], 8 (15.3%) on Dengue fever [ 40 , 41 , 42 , 43 , 44 , 45 ], 4 (7.6%) on HIV [ 46 , 47 , 48 , 49 ], and 3 (5.7%) on TB [ 50 , 51 , 52 ]. Overall, 10 (19.2%) studies were on chronic diseases [ 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 ], 8 (15.5%) on maternal and child health [ 63 , 64 , 65 , 66 ], 3 (5.7%) on nutrition [ 67 , 68 , 69 ] and 5 (9.6%) were applied to other health-related outcomes [ 58 , 70 , 71 , 72 , 73 ]. Most of the studies (42, 80.7%) used an ecological study design [ 30 , 31 , 33 , 34 , 37 , 38 , ...…”

Predicting child development and school readiness, at age 5, for Aboriginal and non-Aboriginal children in Australia’s Northern Territory

“…For Aboriginal children, we fitted a mixed linear model using total AEDC summary scale scores. For non-Aboriginal children we fitted a multilevel logistic regression with robust standard error [ 71 ] using results for each of the five domains as well as the summary measure ‘being developmentally vulnerable in two or more domains’ (DV2) [ 72 ]. We fitted separate prediction models for Aboriginal and non-Aboriginal children for the following reasons: (i) there were differences in the number and type of predictors we identified in the regression analysis (Appendix II in S1 File ); (ii) there were differences in the distributions of the AEDC measurement; and (iii) the AEDC domains were not equally captured by Aboriginal and non-Aboriginal children [ 71 ].…”

MSM with HIV: Improving prevalence and risk estimates by a Bayesian small area estimation modelling approach for public health service areas in the Netherlands

“…To respond, multiple studies proposed and used different small area estimation (SAE) techniques: From generalized additive models [9], over basic area-level models [10], to Poisson regression models [8]. In this study, taking the Netherlands as an example, we propose a Bayesian solution, which has been shown to be particularly effective and has been applied in several other epidemiological fields [3,[11][12][13], to estimate the posterior distribution (the revised or updated probability of an event occurring after considering new information and other uncertainties in the Bayesian inference [14]) of the prevalence and the risk of MSMHIV.…”