PDF(Pubmed)
Abstract：
BACKGROUND: Sexual health influencers (SHIs) are individuals actively sharing sexual health information with their peers, and they play an important role in promoting HIV care services, including the secondary distribution of HIV self-testing (SD-HIVST). Previous studies used a 6-item empirical leadership scale to identify SHIs. However, this approach may be biased as it does not consider individuals\' social networks.
OBJECTIVE: This study used a quasi-experimental study design to evaluate how well a newly developed machine learning (ML) model identifies SHIs in promoting SD-HIVST compared to SHIs identified by a scale whose validity had been tested before.
METHODS: We recruited participants from BlueD, the largest social networking app for gay men in China. Based on their responses to the baseline survey, the ML model and scale were used to identify SHIs, respectively. This study consisted of 2 rounds, differing in the upper limit of the number of HIVST kits and peer-referral links that SHIs could order and distribute (first round ≤5 and second round ≤10). Consented SHIs could order multiple HIV self-testing (HIVST) kits and generate personalized peer-referral links through a web-based platform managed by a partnered gay-friendly community-based organization. SHIs were encouraged to share additional kits and peer-referral links with their social contacts (defined as \"alters\"). SHIs would receive US $3 incentives when their corresponding alters uploaded valid photographic testing results to the same platform. Our primary outcomes included (1) the number of alters who conducted HIVST in each group and (2) the number of newly tested alters who conducted HIVST in each. We used negative binomial regression to examine group differences during the first round (February-June 2021), the second round (June-November 2021), and the combined first and second rounds, respectively.
RESULTS: In January 2021, a total of 1828 men who have sex with men (MSM) completed the survey. Overall, 393 SHIs (scale=195 and ML model=198) agreed to participate in SD-HIVST. Among them, 229 SHIs (scale=116 and ML model=113) ordered HIVST on the web. Compared with the scale group, SHIs in the ML model group motivated more alters to conduct HIVST (mean difference [MD] 0.88, 95% CI 0.02-2.22; adjusted incidence risk ratio [aIRR] 1.77, 95% CI 1.07-2.95) when we combined the first and second rounds. Although the mean number of newly tested alters was slightly higher in the ML model group than in the scale group, the group difference was insignificant (MD 0.35, 95% CI -0.17 to -0.99; aIRR 1.49, 95% CI 0.74-3.02).
CONCLUSIONS: Among Chinese MSM, SHIs identified by the ML model can motivate more individuals to conduct HIVST than those identified by the scale. Future research can focus on how to adapt the ML model to encourage newly tested individuals to conduct HIVST.
BACKGROUND: Chinese Clinical Trials Registry ChiCTR2000039632; https://www.chictr.org.cn/showprojEN.html?proj=63068.
UNASSIGNED: RR2-10.1186/s12889-021-11817-2.
OBJECTIVE: This study used a quasi-experimental study design to evaluate how well a newly developed machine learning (ML) model identifies SHIs in promoting SD-HIVST compared to SHIs identified by a scale whose validity had been tested before.
METHODS: We recruited participants from BlueD, the largest social networking app for gay men in China. Based on their responses to the baseline survey, the ML model and scale were used to identify SHIs, respectively. This study consisted of 2 rounds, differing in the upper limit of the number of HIVST kits and peer-referral links that SHIs could order and distribute (first round ≤5 and second round ≤10). Consented SHIs could order multiple HIV self-testing (HIVST) kits and generate personalized peer-referral links through a web-based platform managed by a partnered gay-friendly community-based organization. SHIs were encouraged to share additional kits and peer-referral links with their social contacts (defined as \"alters\"). SHIs would receive US $3 incentives when their corresponding alters uploaded valid photographic testing results to the same platform. Our primary outcomes included (1) the number of alters who conducted HIVST in each group and (2) the number of newly tested alters who conducted HIVST in each. We used negative binomial regression to examine group differences during the first round (February-June 2021), the second round (June-November 2021), and the combined first and second rounds, respectively.
RESULTS: In January 2021, a total of 1828 men who have sex with men (MSM) completed the survey. Overall, 393 SHIs (scale=195 and ML model=198) agreed to participate in SD-HIVST. Among them, 229 SHIs (scale=116 and ML model=113) ordered HIVST on the web. Compared with the scale group, SHIs in the ML model group motivated more alters to conduct HIVST (mean difference [MD] 0.88, 95% CI 0.02-2.22; adjusted incidence risk ratio [aIRR] 1.77, 95% CI 1.07-2.95) when we combined the first and second rounds. Although the mean number of newly tested alters was slightly higher in the ML model group than in the scale group, the group difference was insignificant (MD 0.35, 95% CI -0.17 to -0.99; aIRR 1.49, 95% CI 0.74-3.02).
CONCLUSIONS: Among Chinese MSM, SHIs identified by the ML model can motivate more individuals to conduct HIVST than those identified by the scale. Future research can focus on how to adapt the ML model to encourage newly tested individuals to conduct HIVST.
BACKGROUND: Chinese Clinical Trials Registry ChiCTR2000039632; https://www.chictr.org.cn/showprojEN.html?proj=63068.
UNASSIGNED: RR2-10.1186/s12889-021-11817-2.
摘要：
背景：性健康影响者（SHIs）是与同龄人积极分享性健康信息的个人，它们在促进艾滋病毒护理服务方面发挥着重要作用，包括艾滋病毒自我检测的二次分布（SD-HIVST）。先前的研究使用6项经验领导量表来识别SHIs。然而,这种方法可能有偏见，因为它没有考虑个人的社交网络。
目的：本研究使用准实验研究设计来评估新开发的机器学习（ML）模型识别SHIs在促进SD-HIVST方面的效果，与之前测试过的量表所确定的SHIs相比。
方法：我们招募了来自BlueD的参与者，中国最大的男同性恋者社交网络应用程序。根据他们对基线调查的反应，ML模型和尺度用于识别SHIs，分别。这项研究由2轮组成，HIVST试剂盒数量的上限以及SHIs可以订购和分发的同行转诊链接的上限不同(第一轮≤5,第二轮≤10).同意的SHIs可以订购多种HIV自检（HIVST）试剂盒，并通过由同性恋友好社区组织合作管理的基于网络的平台生成个性化的同行推荐链接。鼓励SHI与其社交联系人共享其他工具包和同伴推荐链接（定义为“更改”）。当相应的变更人员将有效的摄影测试结果上传到同一平台时，SHIs将获得3美元的奖励。我们的主要结果包括（1）在每组中进行HIVST的改变人数和（2）在每组中进行HIVST的新测试改变人数。我们使用负二项回归来检查第一轮(2021年2月至6月)的组差异，第二轮（2021年6月至11月），以及合并的第一轮和第二轮，分别。
结果：2021年1月，共有1828名与男性发生性关系的男性（MSM）完成了调查。总的来说,393名SHIs(尺度=195,ML模子=198)同意参加SD-HIVST。其中,229SHI（比例=116，ML模型=113）在网上订购了HIVST。与量表组相比，当我们合并第一轮和第二轮时，ML模型组中的SHIs促使更多的人改变进行HIVST（平均差异[MD]0.88，95％CI0.02-2.22；调整后的发生率风险比[aIRR]1.77，95％CI1.07-2.95）。尽管ML模型组的新测试改变的平均数略高于量表组，组间差异无统计学意义（MD0.35，95％CI-0.17至-0.99；aIRR1.49，95％CI0.74-3.02）。
结论：在中国MSM中，ML模型识别的SHI可以激励比量表识别的更多的人进行HIVST。未来的研究可以集中在如何调整ML模型，以鼓励新测试的个体进行HIVST。
背景：中国临床试验注册ChiCTR2000039632;https://www.chictr.org.cn/showprojEN.html?proj=63068。
■RR2-10.1186/s12889-021-11817-2。
目的：本研究使用准实验研究设计来评估新开发的机器学习（ML）模型识别SHIs在促进SD-HIVST方面的效果，与之前测试过的量表所确定的SHIs相比。
方法：我们招募了来自BlueD的参与者，中国最大的男同性恋者社交网络应用程序。根据他们对基线调查的反应，ML模型和尺度用于识别SHIs，分别。这项研究由2轮组成，HIVST试剂盒数量的上限以及SHIs可以订购和分发的同行转诊链接的上限不同(第一轮≤5,第二轮≤10).同意的SHIs可以订购多种HIV自检（HIVST）试剂盒，并通过由同性恋友好社区组织合作管理的基于网络的平台生成个性化的同行推荐链接。鼓励SHI与其社交联系人共享其他工具包和同伴推荐链接（定义为“更改”）。当相应的变更人员将有效的摄影测试结果上传到同一平台时，SHIs将获得3美元的奖励。我们的主要结果包括（1）在每组中进行HIVST的改变人数和（2）在每组中进行HIVST的新测试改变人数。我们使用负二项回归来检查第一轮(2021年2月至6月)的组差异，第二轮（2021年6月至11月），以及合并的第一轮和第二轮，分别。
结果：2021年1月，共有1828名与男性发生性关系的男性（MSM）完成了调查。总的来说,393名SHIs(尺度=195,ML模子=198)同意参加SD-HIVST。其中,229SHI（比例=116，ML模型=113）在网上订购了HIVST。与量表组相比，当我们合并第一轮和第二轮时，ML模型组中的SHIs促使更多的人改变进行HIVST（平均差异[MD]0.88，95％CI0.02-2.22；调整后的发生率风险比[aIRR]1.77，95％CI1.07-2.95）。尽管ML模型组的新测试改变的平均数略高于量表组，组间差异无统计学意义（MD0.35，95％CI-0.17至-0.99；aIRR1.49，95％CI0.74-3.02）。
结论：在中国MSM中，ML模型识别的SHI可以激励比量表识别的更多的人进行HIVST。未来的研究可以集中在如何调整ML模型，以鼓励新测试的个体进行HIVST。
背景：中国临床试验注册ChiCTR2000039632;https://www.chictr.org.cn/showprojEN.html?proj=63068。
■RR2-10.1186/s12889-021-11817-2。
