利用马尔可夫框架建模从青春期前期到早期的自杀意念与行为的时间动态
Modelling temporal dynamics of suicidal ideation and behaviour across pre- to early adolescence using a Markov framework
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中文总结 AI 辅助
本研究基于ABCD研究的11864名9-13岁青少年数据，用时变非齐次马尔可夫框架建模自杀轨迹，发现NSSI者自杀风险更高、轨迹更不稳定，该方法可用于刻画青少年自杀动态。
中文摘要 AI 辅助
理解青少年自杀意念与行为的动态变化，以及与从意念向行为转变相关的因素，对于早期识别、监测和预防至关重要。本研究使用来自青少年脑认知发展（Adolescent Brain Cognitive Development, ABCD）研究的纵向自我报告数据（样本量n=11864，覆盖9至13岁年龄段），构建了一个时变非齐次离散时间马尔可夫链框架，用于对由自杀意念与行为、以及共同报告的非自杀性自伤（non-suicidal self-injury, NSSI）存在与否所定义的8种状态之间的转变进行建模。该框架可推断逐年及多年的转变概率，量化转变的稳定性与不确定性，并对转变可能性进行统计比较。研究识别出了具有结构性但随发育变化的转变模式，包括在报告自杀意念或行为后，恢复至无报告状态的概率普遍较高。与仅报告自杀意念和/或行为的儿童相比，共同报告终身NSSI的儿童表现出独特的轨迹特征，其风险更高、可预测性更低：他们更可能转变为自杀行为或持续处于自杀行为状态，恢复至无报告状态的概率更低，且转变可能性的不确定性更大。这些发现表明，NSSI不仅标志着更高的风险，也标志着青春期前期至早期的轨迹不稳定性增强。通过估计纵向队列数据中的升级、缓解、持续和波动概率，该框架为在大型稀疏心理健康数据集中刻画自杀轨迹动态提供了一种系统、可解释且可扩展的方法，有望为未来青少年心理健康监测、早期预防和时变风险研究提供信息。
英文摘要
Understanding the dynamics of suicidal ideation and behaviour in youth and the factors associated with transitions from thoughts to behaviours is critical for early identification, monitoring, and prevention. Using longitudinal self-report data from the Adolescent Brain Cognitive Development (ABCD) Study (n = 11,864) spanning ages 9 to 13 years, we developed a time-inhomogeneous discrete-time Markov chain framework to model transitions across eight states defined by suicidal ideation and behaviour and the co-reported presence or absence of non-suicidal self-injury (NSSI). The framework enables inference of year-to-year and multi-year transition probabilities, quantification of transition stability and uncertainty, and statistical comparison of transition likelihoods. We identified structured but developmentally changing transition patterns, including a generally high probability of recovery to a no-report state following reports of suicidal ideation or behaviour. Co-reported lifetime NSSI marked a distinct trajectory profile characterized by both greater risk and lower predictability: children with NSSI were more likely to transition to or persist in suicidal behaviour, were less likely to recover to a no-report state, and exhibited greater uncertainty in their transition likelihoods compared to those reporting suicidal ideation and/or behaviour alone. These findings suggest that NSSI marks not only elevated risk but also greater trajectory instability during pre- to early adolescence. By estimating probabilities of escalation, remission, persistence, and fluctuation in longitudinal cohort data, the framework provides a systematic, interpretable, and scalable approach to characterising suicidal trajectory dynamics in large, sparse mental health datasets, with potential to inform future research on monitoring, early prevention, and time-varying risk in youth mental health.
