Climate change concerns impact on young Australians’ psychological distress and outlook for the future

Aims: Climate change is escalating and will disproportionately affect young people. Research on the mental health consequences of worry or concerns related to climate change are so far limited. This study aims to evaluate the extent of climate change concern in young people aged 15–19, its association with various demographic factors and its impact on psychological distress and future outlook. Understanding the impact of climate concerns on young people's mental wellbeing is crucial for identifying effective measures and building resilience. Methods: Climate concerns, psychological distress, and future outlook were measured in the 2022 Mission Australia Youth Survey, Australia's largest annual population-wide survey of young people aged 15 to 19 (N = 18,800). Multinomial logistic regression models were used to map factors associated with climate concerns and assess whether climate concerns are associated with psychological distress and future outlook. Results: One in four young people reported feeling very or extremely concerned about climate change. Climate concerns were higher among individuals identifying as female or gender diverse, or who self-reported a mental health condition. After controlling for confounding factors, we found those who were very or extremely concerned about climate change to be more likely to have high psychological distress than those not at all concerned (Relative risk ratio (RRR) = 1.81; 95% CI: 1.56–2.11), and more likely to have a negative future outlook (RRR = 1.52; 95% CI: 1.27–1.81). These associations were stronger among participants who reported to be gender diverse, Indigenous or from outer-regional/remote areas. Conclusion: This study identified associations between climate concerns, psychological distress, and future outlook among young people. Immediate attention from research and policy sectors to support climate change education, communication strategies and targeted interventions is urgently required to mitigate long-term impacts on young people's wellbeing.</p

Automated and manual hippocampal segmentation techniques: Comparison of results, reproducibility and clinical applicability

Background: Imaging techniques used to measure hippocampal atrophy are key to understanding the clinical progression of Alzheimer's disease (AD). Various semi-automated hippocampal segmentation techniques are available and require human expert input to learn how to accurately segment new data. Our goal was to compare 1) the performance of our automated hippocampal segmentation technique relative to manual segmentations, and 2) the performance of our automated technique when provided with a training set from two different raters. We also explored the ability of hippocampal volumes obtained using manual and automated hippocampal segmentations to predict conversion from MCI to AD. Methods: We analyzed 161 1.5 T T1-weighted brain magnetic resonance images (MRI) from the ADCS Donepezil/Vitamin E clinical study. All subjects carried a diagnosis of mild cognitive impairment (MCI). Three different segmentation outputs (one produced by manual tracing and two produced by a semi-automated algorithm trained with training sets developed by two raters) were compared using single measure intraclass correlation statistics (smICC). The radial distance method was used to assess each segmentation technique's ability to detect hippocampal atrophy in 3D. We then compared how well each segmentation method detected baseline hippocampal differences between MCI subjects who remained stable (MCInc) and those who converted to AD (MCIc) during the trial. Our statistical maps were corrected for multiple comparisons using permutation-based statistics with a threshold of p < .01. Results: Our smICC analyses showed significant agreement between the manual and automated hippocampal segmentations from rater 1 [right smICC = 0.78 (95%CI 0.72–0.84); left smICC = 0.79 (95%CI 0.72–0.85)], the manual segmentations from rater 1 versus the automated segmentations from rater 2 [right smICC = 0.78 (95%CI 0.7–0.84); left smICC = 0.78 (95%CI 0.71–0.84)], and the automated segmentations of rater 1 versus rater 2 [right smICC = 0.97 (95%CI 0.96–0.98); left smICC = 0.97 (95%CI 0.96–0.98)]. All three segmentation methods detected significant CA1 and subicular atrophy in MCIc compared to MCInc at baseline (manual: right pcorrected = 0.0112, left pcorrected = 0.0006; automated rater 1: right pcorrected = 0.0318, left pcorrected = 0.0302; automated rater 2: right pcorrected = 0.0029, left pcorrected = 0.0166). Conclusions: The hippocampal volumes obtained with a fast semi-automated segmentation method were highly comparable to the ones obtained with the labor-intensive manual segmentation method. The AdaBoost automated hippocampal segmentation technique is highly reliable allowing the efficient analysis of large data sets. Keywords: Automated segmentation, Manual segmentation, Radial distance mapping, Magnetic resonance imaging, Mild cognitive impairment, Alzheimer's diseas