Population mobility and disease progression in people living with HIV: a machine learning analysis of a 10-year...

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 (N = 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 p < 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.