ArticleBMC infectious diseases2026
Factors influencing late HIV presentation in China: results from logistic regression and Bayesian network analyses.
Article in BMC infectious diseases, 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
backgroundLate 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.
methodsThis 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.
resultsLogistic 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.
conclusionThis 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.
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