In this study, we constructed a city-level HIV molecular transmission network and explored the effectiveness of molecular network parameters in identifying individuals at high risk of HIV transmission. Our findings revealed that the combined utilization of DC and BC can effectively identify individuals at high risk of HIV transmission while providing a more comprehensive understanding of their characteristics.
The primary objective of this study was to explore the effectiveness of molecular network indicators (DC and BC) in identifying individuals at high risk of HIV transmission. Previous study has indirectly predicted the risk of HIV transmission among the individuals within the network by analyzing the associations of baseline high risk behaviors, such as number of unique sexual partners and insertive unprotected anal intercourse, (p = 0.014 and p = 0.0455, respectively)[8]. In another study, the transmission rate (TR) of molecular clusters was calculated using Bayesian molecular clock phylogenetic inference to estimate the HIV transmission efficiency within large molecular clusters, revealing a median TR of 52.4 per 100 person-years for eight large clusters[22]. These methods were indirect assessments of the risk of HIV transmission for individuals or molecular clusters within a network. In this study, we initially calculated the cumulative probability of RHI within each group. Our findings revealed that the high BC group had the highest contribution to RHI (74.94), followed by the high DC group (48.23) and the high DC + BC group (45.21). This observation aligned with the intuitive understanding that the contribution was proportional to the number of individuals at risk of HIV transmission in each group (201,169, and 92, respectively). However, given limited resources, intervention efficiency becomes more cost-effective when a larger number of RHI can be prevented by intervening with a smaller number of individuals. Our study revealed that individuals in high DC + BC group exhibited the highest average risk of HIV transmission among the three groups, suggesting that targeting interventions towards individuals with high DC and BC may be the most effective approach.
Our results also emphasized the potential benefits of the combined utilization of DC and BC to achieve a more comprehensive understanding of the characteristics associated with individuals at risk of HIV transmission. Our findings revealed that among MSM in the high DC group, a notable characteristics was having a junior high school education or below. This finding aligns with a recently published systematic review that reported a higher HIV prevalence among the illiterate population (16.8%) compared to those with an education in China[23]. Additionally, another recent research highlighted that male sex workers (MSWs) in China tend to have lower levels of education[24]. On the other hand, our study found that unemployment emerged as a risk factor for MSM with high BC. This finding is consistent with the fact that MSWs often face unemployment and engage in sex work or exchange sexual services for financial reasons, which make them as a bridge population involved in interactions with diverse populations[24]. Remarkably, among MSM in the high DC + BC group, all three characteristics (junior high school education or below, unemployment, and high baseline viral load) were found to be significant. Therefore, the combined utilization of DC and BC provided insights into the characteristics of influential individuals (high DC) as well as sheds light on the characteristics of bridge individuals (high BC) within the network.
Another notable advantage of this study is its ability to quantify the risk of HIV transmission of individuals within the network. In reality, obtaining accurate information about the timing of HIV infection presents a significant challenge, making it difficult to determine the direction of HIV transmission between the linked individuals within a network. Consequently, the guidelines rely on defining high-risk clusters based on the behavior or demographic characteristics of individuals within the cluster, and recommend implementing interventions for the entire cluster[25, 26]. In our study, we utilizied HIV-1 LAg-Avidity EIA results and had access to the timing of HIV infection diagnosis. This enable us to infer the direction of HIV transmission among the majority of linked PLWH and calculate their risk of HIV transmission within the network. This advancement significantly improved the resolution for applying molecular networks in guiding precise targeted interventions.
This study still had some limitations. Firstly, our study only focused on three main HIV subtypes, excluding other subtypes or newly emerging URF. Secondly, it is important to acknowledge that not all links between PLWH within the HIV molecular transmission network necessarily represent true transmission relationships.