Background The health and development of children during their first year of full time school is known to impact their social, emotional, and academic capabilities throughout and beyond early education. Physical health, motor development, social and emotional well-being, learning styles, language and communication, cognitive skills, and general knowledge are all considered to be important aspects of a child’s health and development. It is important for many organisations and governmental agencies to continually improve their understanding of the factors which determine or influence development vulnerabilities among children. This article studies the relationships between development vulnerabilities and educational factors among children in Queensland, Australia. Methods Spatial statistical machine learning models are reviewed and compared in the context of a study of geographic variation in the association between development vulnerabilities and attendance at preschool among children in Queensland, Australia. A new spatial random forest (SRF) model is suggested that can explain more of the spatial variation in data than other approaches. Results In the case study, spatial models were shown to provide a better fit compared to models that ignored the spatial variation in the data. The SRF model was shown to be the only model which can explain all of the spatial variation in each of the development vulnerabilities considered in the case study. The spatial analysis revealed that the attendance at preschool factor has a strong influence on the physical health domain vulnerability and emotional maturity vulnerability among children in their first year of school. Conclusion This study confirmed that it is important to take into account the spatial nature of data when fitting statistical machine learning models. A new spatial random forest model was introduced and was shown to explain more of the spatial variation and provide a better model fit in the case study of development vulnerabilities among children in Queensland. At small-area population level, increased attendance at preschool was strongly associated with reduced physical and emotional development vulnerabilities among children in their first year of school.

Background: Developmental vulnerabilities within children in Queensland have a variety of domains; these domains measure the development of children in their first five years. It is crucial to understand how these domains are grouped, or clustered, with respect to population risk factor profiles. These groups inform policy implementation, which can help to provide assistance to the most vulnerable children across Queensland. Methods: K-means analysis was conducted on data from the Australian Early Development Census and the Australian Bureau of Statistics. The clusters were then compared with respect to their geographic locations and risk factor profiles. The results are presented in this paper and are publicly available via an interactive dashboard application in R Shiny. Results: This study presents a comprehensive clustering analysis for child development vulnerability domains in Queensland. In addition, all of the clustering analyses reveal a strong relationship between developmental vulnerability and socio-economic and remoteness factors. In addition, we found that children who attend preschool and whose primary language is English are, in most cases, in the lowest developmental vulnerability cluster. Conclusion: In this study, the performance of the K-means clustering algorithm has been developed to study the clusters inside child development vulnerabilities when analysing the data at the small area level. Further, R shiny application was created, and the feature of the risk factors in each region was studied.