ArticleFrontiers in public health2026
Population mobility and disease progression in people living with HIV: a machine learning analysis of a 10-year dynamic cohort.
Article in Frontiers in public health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: 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. Methods: A dynamic longitudinal cohort ( Results: 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 Conclusion: 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.
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