Factors influencing late HIV presentation in China: results from logistic regression and Bayesian network analyses.

BACKGROUND

Late presentation (LP) of HIV infection remains a major challenge to epidemic control, leading to advanced immunodeficiency, poorer treatment outcomes, and ongoing transmission before diagnosis. Despite expanded testing and awareness efforts, a considerable proportion of people living with HIV (PLHIV) in China are still diagnosed late.

METHODS

This study analyzed 386,704 newly reported HIV cases (2019–2022) from the National HIV/AIDS Comprehensive Response Information Management System (CRIMS). Logistic regression was used to identify significant predictors of LP, and a Bayesian network was constructed to model the complex interrelationships among variables.

RESULTS

Logistic regression identified several factors associated with LP of HIV. Key factors included being male (aOR = 1.3), over 60 (aOR = 3.36), Han ethnicity (aOR = 1.16), education at below senior high school (aOR = 1.1), being a farmer or worker (OR = 1.04), transient population (aOR = 1.18), engaging in homosexual transmission (aOR = 1.1), being examined at other institutions (hospitals) (aOR = 1.27), having non-marital partners(lifetime history), and a history of STDs (aOR = 1.03) (P < 0.05). Bayesian network analysis revealed that age, gender, and sample sources were the key factors associated with LP of HIV. Among them, age played a central role in the model, directly influencing occupation, transmission routes, education level, transient status, and non-marital partners. Ethnicity indirectly affected LP through occupation, while education influenced LP indirectly by shaping occupation, non-marital partners, and transient status. In addition, a history of STDs not only directly affected sample sources but also indirectly increased the risk of LP through transient population status.

CONCLUSION

This mixed-model approach demonstrated that demographic, behavioral, and structural factors jointly contribute to LP in China through complex associative pathways. Integrating logistic and Bayesian frameworks provides a more comprehensive understanding of HIV diagnostic delays, informing precision-targeted testing and intervention strategies.