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

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 a...

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Autores principales: Draidi Areed, Wala, Price, Aiden, Arnett, Kathryn, Mengersen, Kerrie
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9710117/
https://www.ncbi.nlm.nih.gov/pubmed/36451182
http://dx.doi.org/10.1186/s12889-022-14541-7
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author Draidi Areed, Wala
Price, Aiden
Arnett, Kathryn
Mengersen, Kerrie
author_facet Draidi Areed, Wala
Price, Aiden
Arnett, Kathryn
Mengersen, Kerrie
author_sort Draidi Areed, Wala
collection PubMed
description 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12889-022-14541-7.
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spelling pubmed-97101172022-12-01 Spatial statistical machine learning models to assess the relationship between development vulnerabilities and educational factors in children in Queensland, Australia Draidi Areed, Wala Price, Aiden Arnett, Kathryn Mengersen, Kerrie BMC Public Health Research 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12889-022-14541-7. BioMed Central 2022-11-30 /pmc/articles/PMC9710117/ /pubmed/36451182 http://dx.doi.org/10.1186/s12889-022-14541-7 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Draidi Areed, Wala
Price, Aiden
Arnett, Kathryn
Mengersen, Kerrie
Spatial statistical machine learning models to assess the relationship between development vulnerabilities and educational factors in children in Queensland, Australia
title Spatial statistical machine learning models to assess the relationship between development vulnerabilities and educational factors in children in Queensland, Australia
title_full Spatial statistical machine learning models to assess the relationship between development vulnerabilities and educational factors in children in Queensland, Australia
title_fullStr Spatial statistical machine learning models to assess the relationship between development vulnerabilities and educational factors in children in Queensland, Australia
title_full_unstemmed Spatial statistical machine learning models to assess the relationship between development vulnerabilities and educational factors in children in Queensland, Australia
title_short Spatial statistical machine learning models to assess the relationship between development vulnerabilities and educational factors in children in Queensland, Australia
title_sort spatial statistical machine learning models to assess the relationship between development vulnerabilities and educational factors in children in queensland, australia
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9710117/
https://www.ncbi.nlm.nih.gov/pubmed/36451182
http://dx.doi.org/10.1186/s12889-022-14541-7
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