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Evaluating the impact of Hazelwood mine fire event on students’ educational development with Bayesian interrupted time-series hierarchical meta-regression

BACKGROUND: Environmental disasters such as wildfires, floods and droughts can introduce significant interruptions and trauma to impacted communities. Children and young people can be disproportionately affected with additional educational disruptions. However, evaluating the impact of disasters is...

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Autores principales: Gao, Caroline X., Broder, Jonathan C., Brilleman, Sam, Campbell, Timothy C. H., Berger, Emily, Ikin, Jillian, Smith, Catherine L., Wolfe, Rory, Johnston, Fay, Guo, Yuming, Carroll, Matthew
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9977026/
https://www.ncbi.nlm.nih.gov/pubmed/36857352
http://dx.doi.org/10.1371/journal.pone.0281655
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author Gao, Caroline X.
Broder, Jonathan C.
Brilleman, Sam
Campbell, Timothy C. H.
Berger, Emily
Ikin, Jillian
Smith, Catherine L.
Wolfe, Rory
Johnston, Fay
Guo, Yuming
Carroll, Matthew
author_facet Gao, Caroline X.
Broder, Jonathan C.
Brilleman, Sam
Campbell, Timothy C. H.
Berger, Emily
Ikin, Jillian
Smith, Catherine L.
Wolfe, Rory
Johnston, Fay
Guo, Yuming
Carroll, Matthew
author_sort Gao, Caroline X.
collection PubMed
description BACKGROUND: Environmental disasters such as wildfires, floods and droughts can introduce significant interruptions and trauma to impacted communities. Children and young people can be disproportionately affected with additional educational disruptions. However, evaluating the impact of disasters is challenging due to difficulties in establishing studies and recruitment post-disasters. OBJECTIVES: We aimed to (1) develop a Bayesian model using aggregated school-level data to evaluate the impact of environmental disasters on academic achievement and (2) evaluate the impact of the 2014 Hazelwood mine fire (a six-week fire event in Australia). METHODS: Bayesian hierarchical meta-regression was developed to evaluate the impact of the mine fire using easily accessible aggregated school-level data from the standardised National Assessment Program-Literacy and Numeracy (NAPLAN) test. NAPLAN results and school characteristics (2008–2018) from 69 primary/secondary schools with different levels of mine fire-related smoke exposure were used to estimate the impact of the event. Using an interrupted time series design, the model estimated immediate effects and post-interruption trend differences with full Bayesian statistical inference. RESULTS: Major academic interruptions across NAPLAN domains were evident in high exposure schools in the year post-mine fire (greatest interruption in Writing: 11.09 [95%CI: 3.16–18.93], lowest interruption in Reading: 8.34 [95%CI: 1.07–15.51]). The interruption was comparable to a four to a five-month delay in educational attainment and had not fully recovered after several years. CONCLUSION: Considerable academic delays were found as a result of a mine fire, highlighting the need to provide educational and community-based supports in response to future events. Importantly, this work provides a statistical method using readily available aggregated data to assess the educational impacts in response to other environmental disasters.
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spelling pubmed-99770262023-03-02 Evaluating the impact of Hazelwood mine fire event on students’ educational development with Bayesian interrupted time-series hierarchical meta-regression Gao, Caroline X. Broder, Jonathan C. Brilleman, Sam Campbell, Timothy C. H. Berger, Emily Ikin, Jillian Smith, Catherine L. Wolfe, Rory Johnston, Fay Guo, Yuming Carroll, Matthew PLoS One Research Article BACKGROUND: Environmental disasters such as wildfires, floods and droughts can introduce significant interruptions and trauma to impacted communities. Children and young people can be disproportionately affected with additional educational disruptions. However, evaluating the impact of disasters is challenging due to difficulties in establishing studies and recruitment post-disasters. OBJECTIVES: We aimed to (1) develop a Bayesian model using aggregated school-level data to evaluate the impact of environmental disasters on academic achievement and (2) evaluate the impact of the 2014 Hazelwood mine fire (a six-week fire event in Australia). METHODS: Bayesian hierarchical meta-regression was developed to evaluate the impact of the mine fire using easily accessible aggregated school-level data from the standardised National Assessment Program-Literacy and Numeracy (NAPLAN) test. NAPLAN results and school characteristics (2008–2018) from 69 primary/secondary schools with different levels of mine fire-related smoke exposure were used to estimate the impact of the event. Using an interrupted time series design, the model estimated immediate effects and post-interruption trend differences with full Bayesian statistical inference. RESULTS: Major academic interruptions across NAPLAN domains were evident in high exposure schools in the year post-mine fire (greatest interruption in Writing: 11.09 [95%CI: 3.16–18.93], lowest interruption in Reading: 8.34 [95%CI: 1.07–15.51]). The interruption was comparable to a four to a five-month delay in educational attainment and had not fully recovered after several years. CONCLUSION: Considerable academic delays were found as a result of a mine fire, highlighting the need to provide educational and community-based supports in response to future events. Importantly, this work provides a statistical method using readily available aggregated data to assess the educational impacts in response to other environmental disasters. Public Library of Science 2023-03-01 /pmc/articles/PMC9977026/ /pubmed/36857352 http://dx.doi.org/10.1371/journal.pone.0281655 Text en © 2023 Gao et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Gao, Caroline X.
Broder, Jonathan C.
Brilleman, Sam
Campbell, Timothy C. H.
Berger, Emily
Ikin, Jillian
Smith, Catherine L.
Wolfe, Rory
Johnston, Fay
Guo, Yuming
Carroll, Matthew
Evaluating the impact of Hazelwood mine fire event on students’ educational development with Bayesian interrupted time-series hierarchical meta-regression
title Evaluating the impact of Hazelwood mine fire event on students’ educational development with Bayesian interrupted time-series hierarchical meta-regression
title_full Evaluating the impact of Hazelwood mine fire event on students’ educational development with Bayesian interrupted time-series hierarchical meta-regression
title_fullStr Evaluating the impact of Hazelwood mine fire event on students’ educational development with Bayesian interrupted time-series hierarchical meta-regression
title_full_unstemmed Evaluating the impact of Hazelwood mine fire event on students’ educational development with Bayesian interrupted time-series hierarchical meta-regression
title_short Evaluating the impact of Hazelwood mine fire event on students’ educational development with Bayesian interrupted time-series hierarchical meta-regression
title_sort evaluating the impact of hazelwood mine fire event on students’ educational development with bayesian interrupted time-series hierarchical meta-regression
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9977026/
https://www.ncbi.nlm.nih.gov/pubmed/36857352
http://dx.doi.org/10.1371/journal.pone.0281655
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