Scholarlink

Background <p>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.</p> Methods <p>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.</p> Results <p>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) (<i>P</i> &lt; 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.</p> Conclusion <p>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.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

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

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年1月16日

查看全文
  • He-he Zhao,
  • Dong-hang Luo,
  • Li-ping Fei,
  • Shi Wang,
  • Fang-fang Chen,
  • Qian-qian Qin,
  • Chang Cai,
  • Yi-Chen Jin,
  • Jie Xu,
  • Hou-lin Tang,
  • Fan Lyu

摘要

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.