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Evaluating the impact of Hazelwood mine fire event on students' educational development with Bayesian interrupted
Caroline X Gao1,2,3, Jonathan C Broder1, Sam Brilleman1
1School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia.
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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