发表机构
School of Computer Science and Applied Mathematics, University of the Witwatersrand; Department of Mathematics and Applied Mathematics, University of Johannesburg; National Institute for Theoretical and Computational Sciences (NITheCS)（威特沃特斯兰德大学计算机科学与应用数学学院; 约翰内斯堡大学数学与应用数学系; 国家理论与计算科学研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究结合事件史分析与轨道理论，分析南非31081名儿童数据，发现母亲迁移在结构上关键而统计不显著，母亲死亡是主要预测因子，三变量子系统可恢复主要发现并暴露高风险集群。
AI 中文摘要
理解早期儿童死亡需要能够同时捕捉统计显著的风险因素和个体生命轨迹结构的方法。事件史分析（EHA）估计协变量与死亡风险之间的关联，但可能掩盖罕见或高维的风险配置。轨道理论（OT）将个体表示为组合状态空间中的精确轨迹，保留完整的多元结构。我们使用这两种方法分析了来自Agincourt健康与人口监测系统（南非，1998-2008年）的31,081名儿童的12个变量的纵向数据。我们展示了仅靠EHA无法做到的三个方面。首先，母亲迁移在导致儿童死亡的转变动态中具有结构性的关键作用，尽管在EHA中统计上不显著。其次，母亲的难民身份虽然在统计上显著，但不产生轨迹动态，并正确地被排除在任何结构简化之外。第三，一个三变量子系统（儿童状态、母亲状态、母亲迁移）在最多48个状态的缩减状态空间中恢复了EHA的主要发现，同时暴露了回归分析无法看到的小型高风险集群。母亲死亡是EHA的主要预测因子；母亲迁移组织了87%可观察到的进入儿童死亡状态的顺序转变。这些并不矛盾：母亲死亡很少作为前置状态出现，因为其作用是终末性的而非顺序性的。早期死亡率可以同时在两个层面上解读——统计上通过效应估计，结构上通过观察到的转变。
英文摘要
Understanding early-life child mortality requires methods that capture both statistically significant risk factors and the structure of individual life trajectories. Event History Analysis (EHA) estimates associations between covariates and mortality risk but may obscure rare or high-dimensional configurations of risk. Orbit Theory (OT) represents individuals as exact trajectories in a combinatorial state space, preserving full multivariate structure. We analyse longitudinal data across 12 variables for 31,081 children from the Agincourt Health and Demographic Surveillance System (South Africa, 1998-2008) using both methods. We show three things EHA alone cannot. First, maternal migration is structurally pivotal in the transition dynamics leading to child death despite being statistically non-significant in EHA. Second, maternal refugee status, while statistically significant, generates no trajectory dynamics and is correctly excluded from any structural reduction. Third, a three-variable subsystem (child status, mother status, mother migration) recovers EHA's principal findings within a reduced state space of at most 48 states while exposing small high-risk clusters invisible to regression-based analysis. Maternal death is the dominant EHA predictor; maternal migration organises 87% of the observable sequential transitions into child-death states. These are not contradictory: maternal death rarely appears as a preceding state because its role is terminal rather than sequential. Early-life mortality can be read at two levels simultaneously - statistically, through effect estimates, and structurally, through observed transitions.
Comments21 pages, 9 figures
