艾滋病毒感染者的人口流动和疾病进展：对10年动态队列的机器学习分析
Population mobility and disease progression in people living with HIV: a machine learning analysis of a 10-year dynamic cohort.
摘要(中文译文)
跨区域人口流动对基于居住地的HIV随访体系构成挑战。本研究旨在界定HIV感染者（PLHIV）中的来源异质性（本地新发感染与流入移民），并利用机器学习模型评估其对迁出（空间流动）的预测价值及其与疾病进展的关联。研究基于中国重庆市116个月（2016—2025年）的月度随访数据构建了动态纵向队列（n=5,213）。开发XGBoost模型以预测管理期间的迁出风险，并引入SHAP框架进行特征贡献分析。应用随机生存森林（RSF）模型评估疾病进展（进展至AIDS阶段或死亡）的长期风险，并采用偏依赖图（PDP）剖析核心预测因素的非线性关联。队列包括2,820例本地新发感染病例、1,606例基线现患病例和787例流入移民。XGBoost模型预测迁出风险的受试者工作特征曲线下面积（AUC）为0.849；SHAP分析表明，流入移民属性及特定传播途径（如注射吸毒）是与空间不稳定性相关的最强预测因素。RSF模型对长期进展风险的一致性指数（C-index）为0.7575；Kaplan-Meier曲线显示，流入移民的生存预后显著劣于其他组（log-rank P<0.001）。PDP显示，诊断年龄超过30岁和50岁后疾病进展风险呈阶梯式递增。基于职业的分层分析表明，失业/居家组（53.3%）和农业/流动/体力劳动组（25.5%）的预测进展风险最高。流入移民表现出显著升高的空间不稳定性和临床脆弱性。当前管理体系需要从静态属地化管理模式向跨区域动态协作转变，通过信息互通和精准分层干预来弥合流动期间出现的治疗缺口。
Abstract
Inter-regional population mobility poses challenges to the residence-based HIV follow-up system. This study aimed to define source heterogeneity among PLHIV (local incident versus incoming migrant) and to evaluate its predictive value on out-migration (spatial mobility) and its association with disease progression using machine learning models. A dynamic longitudinal cohort ( = 5,213) was constructed from monthly follow-up data spanning 116 months (2016-2025) in Chongqing, China. An XGBoost model was developed to predict out-migration risk during management, with the SHAP framework introduced for feature contribution analysis. A random survival forest (RSF) model was applied to assess the long-term risk of disease progression (to AIDS stage or death), and partial dependence plots (PDP) were used to dissect the nonlinear associations of core predictors. The cohort comprised 2,820 local incident cases, 1,606 baseline prevalent cases, and 787 incoming migrants. The XGBoost model achieved an area under the receiver operating characteristic curve (AUC) of 0.849 for predicting out-migration risk; SHAP analysis indicated that the incoming migrant attribute and specific transmission routes (such as injection drug use) were the strongest predictors associated with spatial instability. The RSF model yielded a concordance index (C-index) of 0.7575 for long-term progression risk; Kaplan-Meier curves showed that incoming migrants had a significantly worse survival prognosis than the other groups (log-rank < 0.001). PDP revealed a stepwise risk increment in disease progression after 30 and 50 years of age at diagnosis. Occupation-based stratification indicated the highest predicted progression risk among the Unemployed/Homebound (53.3%) and Agricultural/Migrant/Manual (25.5%) groups. Incoming migrants exhibited markedly elevated spatial instability and clinical vulnerability. The current management system needs to evolve from static territorial approaches toward cross-regional dynamic collaboration, employing information interoperability and precise stratified interventions to close treatment gaps that arise during mobility.
如何引用
AMA
Yu Luo, Hong Li, Xiyue Fan, Lu Liu, Xinglan Tan, Wenting Yang. Population mobility and disease progression in people living with HIV: a machine learning analysis of a 10-year dynamic cohort.. Frontiers in public health. 2026; doi:10.3389/fpubh.2026.1856172.
APA
Yu Luo, Hong Li, Xiyue Fan, Lu Liu, Xinglan Tan, Wenting Yang (2026). Population mobility and disease progression in people living with HIV: a machine learning analysis of a 10-year dynamic cohort.. Frontiers in public health. https://doi.org/10.3389/fpubh.2026.1856172
GB/T 7714
Yu Luo, Hong Li, Xiyue Fan, Lu Liu, Xinglan Tan, Wenting Yang. Population mobility and disease progression in people living with HIV: a machine learning analysis of a 10-year dynamic cohort.[J]. Frontiers in public health, 2026 doi:10.3389/fpubh.2026.1856172.
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