Department of Infectious Diseases, Bern University Hospital Inselspital, University of Bern, Switzerland.
Division of Infectious Diseases and Hospital Epidemiology, University Hospital Zurich, Switzerland.
Clin Infect Dis. 2020 Jan 16;70(3):416-424. doi: 10.1093/cid/ciz208.
Separately addressing specific groups of people who share patterns of behavioral change might increase the impact of behavioral interventions to prevent transmission of sexually transmitted infections. We propose a method based on machine learning to assist the identification of such groups among men who have sex with men (MSM).
By means of unsupervised learning, we inferred "behavioral clusters" based on the recognition of similarities and differences in longitudinal patterns of condomless anal intercourse with nonsteady partners (nsCAI) in the HIV Cohort Study over the last 18 years. We then used supervised learning to investigate whether sociodemographic variables could predict cluster membership.
We identified 4 behavioral clusters. The largest behavioral cluster (cluster 1) contained 53% of the study population and displayed the most stable behavior. Cluster 3 (17% of the study population) displayed consistently increasing nsCAI. Sociodemographic variables were predictive for both of these clusters. The other 2 clusters displayed more drastic changes: nsCAI frequency in cluster 2 (20% of the study population) was initially similar to that in cluster 3 but accelerated in 2010. Cluster 4 (10% of the study population) had significantly lower estimates of nsCAI than all other clusters until 2017, when it increased drastically, reaching 85% by the end of the study period.
We identified highly dissimilar behavioral patterns across behavioral clusters, including drastic, atypical changes. The patterns suggest that the overall increase in the frequency of nsCAI is largely attributable to 2 clusters, accounting for a third of the population.
分别针对具有相似行为变化模式的特定人群采取措施，可能会增强预防性传播感染行为干预的效果。我们提出了一种基于机器学习的方法，以协助识别男男性行为者（MSM）中的这些人群。
通过无监督学习，我们根据过去 18 年 HIV 队列研究中与非固定性伴侣（nsCAI）发生无保护肛交的纵向模式识别相似性和差异，推断出“行为聚类”。然后，我们使用有监督学习来研究社会人口统计学变量是否可以预测聚类成员。
我们确定了 4 种行为聚类。最大的行为聚类（聚类 1）包含研究人群的 53%，表现出最稳定的行为。聚类 3（研究人群的 17%）显示出持续增加的 nsCAI。社会人口统计学变量对这两个聚类都具有预测性。其他 2 个聚类则表现出更为剧烈的变化：聚类 2（研究人群的 20%）的 nsCAI 频率最初与聚类 3 相似，但在 2010 年加速。聚类 4（研究人群的 10%）的 nsCAI 频率明显低于所有其他聚类，直到 2017 年急剧增加，在研究期末达到 85%。
我们在行为聚类中发现了高度不同的行为模式，包括急剧的、非典型的变化。这些模式表明，nsCAI 频率的总体增加主要归因于两个聚类，占人口的三分之一。
