Mixed methods studies of human disease that combine surveillance, biomarker, and qualitative data can help elucidate what drives epidemiological trends. Viral genetic data are rarely coupled with other types of data due to legal and ethical concerns about patient privacy. We developed a novel approach to integrate phylogenetic and qualitative methods in order to better target HIV prevention efforts. The overall aim of our mixed methods study was to characterize HIV transmission clusters. We combined surveillance data with HIV genomic data to identify cases whose viruses share enough similarities to suggest a recent common source of infection or participation in linked transmission chains. Cases were recruited through a multi-phase process to obtain consent for recruitment to semi-structured interviews. Through linkage of viral genetic sequences with epidemiological data, we identified individuals in large transmission clusters, which then served as a sampling frame for the interviews. In this article, we describe the multi-phase process and the limitations and challenges encountered. Our approach contributes to the mixed methods research field by demonstrating that phylogenetic analysis and surveillance data can be harnessed to generate a sampling frame for subsequent qualitative data collection, using an explanatory sequential design. The process we developed also respected protections of patient confidentiality. The novel method we devised may offer an opportunity to implement a sampling frame that allows for the recruitment and interview of individuals in high-transmission clusters to better understand what contributes to spread of other infectious diseases, including COVID-19.

结合监测，生物标志物和定性数据的人类疾病混合方法研究可以帮助阐明推动流行病学趋势的因素。由于对患者隐私的法律和道德关注，病毒遗传数据很少与其他类型的数据结合使用。我们开发了一种整合系统发育和定性方法的新方法，以便更好地针对HIV预防工作。我们混合方法研究的总体目标是表征HIV传播群。我们将监测数据与HIV基因组数据结合起来，以鉴定病毒具有足够相似性的病例，以表明最近的常见感染源或参与了链接的传播链。案例是通过多阶段流程招募的，以获得征募同意参加半结构化访谈。通过将病毒遗传序列与流行病学数据联系起来，我们确定了大型传播群中的个体，然后将其用作访谈的抽样框架。在本文中，我们描述了多阶段过程以及遇到的局限性和挑战。我们的方法通过证明系统发育分析和监视数据可以利用解释性顺序设计证明可以利用系统发育分析和监视数据生成用于后续定性数据收集的采样框架，从而为混合方法研究领域做出了贡献。我们开发的流程还尊重对患者机密性的保护。