Assessing the spatial structure of the association between attendance at preschool and children’s developmental vulnerabilities in Queensland, Australia

Areed, Wala Draidi; Price, Aiden; Arnett, Kathryn; Thompson, Helen; Malseed, Reid; Mengersen, Kerrie

doi:10.1371/journal.pone.0285409

Cited by 3 publications

(5 citation statements)

References 65 publications

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“…Two approaches were considered to determine cluster membership of the observed data. The first method, known as Dahl's approach [10], involves computing the membership matrices for each iteration, denoted as B (1) , . .…”

Section: Cluster Configurationsmentioning

confidence: 99%

“…, M with B (c) (i, j) = 1, indicating that observations i and j belong to the same cluster in the cth iteration. An empirical estimate of the probability that locations i and j are in the same cluster is given by the average of B (1) , . .…”

Section: Cluster Configurationsmentioning

confidence: 99%

“…Physical, social, emotional, speech and language, and communication skills are the five critical domains of growth [25]. Development domains have been used in previous research to classify children into subgroups that describe their development using an unsupervised clustering algorithm [1,20,52]. While BGWR offers a more robust framework for spatial regression, there remains a challenge in identifying and interpreting localized patterns or homogeneity within the spatial data.…”

Section: Introductionmentioning

confidence: 99%

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Spatial statistical machine learning models to assess the relationship between development vulnerabilities and educational factors in children in Queensland, Australia

et al. 2022

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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.

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Section: Cluster Configurationsmentioning

confidence: 99%

Section: Cluster Configurationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Spatial statistical machine learning models to assess the relationship between development vulnerabilities and educational factors in children in Queensland, Australia

et al. 2022

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“…Williamson et al [24] explored social and emotional developmental vulnerability. Our recent study in Queensland, Australia [25] analyzed spatial structures of preschool attendance and developmental vulnerabilities. Dea et al [26] compared early children's development outcomes in metropolitan areas.…”

Section: Introductionmentioning

confidence: 99%

Where are the vulnerable children? Identification and comparison of clusters of young children with health and developmental vulnerabilities across Queensland

Draidi Areed,

Price,

Arnett

et al. 2024

PLoS ONE

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This study aimed to better understand the vulnerability of children in their first year of school, aged between 5 years 5 months and 6 years 6 months, based on five health and development domains. Identification of subgroups of children within these domains can lead to more targeted policies to reduce these vulnerabilities. The focus of this study was to determine clusters of geographical regions with high and low proportions of vulnerable children in Queensland, Australia. This was achieved by carrying out a K-means analysis 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 made publicly available via an interactive dashboard application developed in R Shiny.

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“…Physical, social, emotional, speech and language, and communication skills are the five critical domains of growth [24]. Development domains have been used in previous research to classify children into subgroups that describe their development using an unsupervised clustering algorithm [25][26][27]. While BGWR offers a more robust framework for spatial regression, there remains a challenge in identifying and interpreting localized patterns or homogeneity within the spatial data.…”

Section: Introductionmentioning

confidence: 99%

Bayesian cluster geographically weighted regression for spatial heterogeneous data

Draidi Areed,

Price,

Thompson

et al. 2024

R. Soc. Open Sci.

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Spatial statistical models are commonly used in geographical scenarios to ensure spatial variation is captured effectively. However, spatial models and cluster algorithms can be complicated and expensive. One of these algorithms is geographically weighted regression (GWR) which was proposed in the geography literature to allow relationships in a regression model to vary over space. In contrast to traditional linear regression models, which have constant regression coefficients over space, regression coefficients are estimated locally at spatially referenced data points with GWR. The motivation for the adaption of GWR is the idea that a set of constant regression coefficients cannot adequately capture spatially varying relationships between covariates and an outcome variable. GWR has been applied widely in diverse fields, such as ecology, forestry, epidemiology, neurology and astronomy. While frequentist GWR gives us point estimates and confidence intervals, Bayesian GWR enriches our understanding by including prior knowledge and providing probability distributions for parameters and predictions of interest. This paper pursues three main objectives. First, it introduces covariate effect clustering by integrating a Bayesian geographically weighted regression (BGWR) with a post-processing step that includes Gaussian mixture model and the Dirichlet process mixture model. Second, this paper examines situations in which a particular covariate holds significant importance in one region but not in another in the Bayesian framework. Lastly, it addresses computational challenges in existing BGWR, leading to enhancements in Markov chain Monte Carlo estimation suitable for large spatial datasets. The efficacy of the proposed method is demonstrated using simulated data and is further validated in a case study examining children’s development domains in Queensland, Australia, using data provided by Children’s Health Queensland and Australia’s Early Development Census.

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Assessing the spatial structure of the association between attendance at preschool and children’s developmental vulnerabilities in Queensland, Australia

Cited by 3 publications

References 65 publications

Spatial statistical machine learning models to assess the relationship between development vulnerabilities and educational factors in children in Queensland, Australia

Spatial statistical machine learning models to assess the relationship between development vulnerabilities and educational factors in children in Queensland, Australia

Where are the vulnerable children? Identification and comparison of clusters of young children with health and developmental vulnerabilities across Queensland

Bayesian cluster geographically weighted regression for spatial heterogeneous data

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