Yan Xiangyu, Lu Zuhong, Zhang Bo, Li Yongjie, Tang Wenjun, Zhang Lingling, Jia Zhongwei
School of Public Health, Peking University, Beijing, China.
National Institute on Drug Dependence, Peking University, Beijing, China.
JMIR Mhealth Uhealth. 2020 Feb 19;8(2):e14457. doi: 10.2196/14457.
BACKGROUND: Traditional partner notification methods have been implemented for HIV-infected patients, as well as HIV treatment, in order to identify people at risk of HIV infection, especially men who have sex with men (MSM), since they are more likely to have casual sex partners. These traditional methods have some limitations. OBJECTIVE: Our study focused on developing an mHealth app to improve partner notification in practice for MSM; the study then focused on evaluating the effects of the app. METHODS: We developed an mHealth app with different modules using Java and HTML5 and tested it in an MSM community to prevent HIV transmission. The HIV incidence stratified by different follow-up periods were calculated. Poisson regression and social networks were used to estimate the risk ratios and to identify the connection among MSM, respectively. RESULTS: In addition to the partner notification module, which is the kernel of the app, we developed a test result self-query module to enable MSM to get their approved test results in a timely manner, a prompt and warning module to alert users to protect themselves from high-risk conditions, and a health education module to teach users more skills regarding HIV/AIDS prevention. Over a 1-year duration, a total of 3186 MSM used the app, of which 678 had at least two HIV test results since becoming app users; they were included in the final analysis. Among 678 users, a total of 6473 self-queries and 623 partner notifications were recorded, which identified 180 social networks of MSM app users. Those who used the partner notification function were more likely to have self-queries (P<.001). The 678 MSM app users covered 296.47 person-years and contributed to 20 HIV seroconversions; the cumulative HIV infection incidence was estimated as 6.75 per 100 person-years (95% CI 4.38-10.01). We found that the longer the app was used, the lower the HIV incidence (>5 months vs ≤5 months: 2.22 per 100 person-years vs 6.99 per 100 person-years; risk ratio 0.32, 95% CI 0.12- 0.87). CONCLUSIONS: The app developed in this study is consistent with the World Health Organization's sensitivity and confidentiality recommendations; it has the potential to reduce the risk of HIV infection among MSM.
背景:针对感染艾滋病毒的患者以及艾滋病毒治疗,已经实施了传统的性伴通知方法,以识别有感染艾滋病毒风险的人群,尤其是男男性行为者(MSM),因为他们更有可能有临时性伴。这些传统方法存在一些局限性。 目的:我们的研究重点是开发一款移动健康应用程序,以改善MSM在实际中的性伴通知情况;然后该研究重点评估该应用程序的效果。 方法:我们使用Java和HTML5开发了一个具有不同模块的移动健康应用程序,并在一个MSM社区进行了测试,以预防艾滋病毒传播。计算了不同随访期分层的艾滋病毒发病率。分别使用泊松回归和社交网络来估计风险比并识别MSM之间的联系。 结果:除了作为应用程序核心的性伴通知模块外,我们还开发了一个检测结果自我查询模块,使MSM能够及时获取其经批准的检测结果,一个提示和警告模块,提醒用户保护自己免受高风险情况的影响,以及一个健康教育模块,向用户传授更多关于艾滋病毒/艾滋病预防的技能。在1年的时间里,共有3186名MSM使用了该应用程序,其中678人自成为应用程序用户以来至少有两次艾滋病毒检测结果;他们被纳入最终分析。在678名用户中,共记录了6473次自我查询和623次性伴通知,确定了180个MSM应用程序用户的社交网络。使用性伴通知功能的人更有可能进行自我查询(P<0.001)。678名MSM应用程序用户涵盖296.47人年,促成了20例艾滋病毒血清转化;累积艾滋病毒感染发病率估计为每100人年6.75例(95%CI 4.38 - 10.01)。我们发现应用程序使用时间越长,艾滋病毒发病率越低(>5个月与≤5个月:每100人年2.22例与每100人年6.99例;风险比0.32,95%CI 0.12 - 0.87)。 结论:本研究中开发的应用程序符合世界卫生组织的敏感性和保密性建议;它有可能降低MSM中艾滋病毒感染的风险。
JMIR Mhealth Uhealth. 2020-2-19
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