When injecting drug use is not dangerous: the role of networks in HIV prevention

说明（由申请人提供）：注射吸毒者中艾滋病毒流行的特点往往是传播迅速和疾病负担高，这使这一群体成为艾滋病毒预防和护理的重要优先群体。通过注射风险传播艾滋病毒感染往往遵循一种共同和可预测的模式，少数未被发现的感染在达到某种稳定的流行状态之前刺激迅速上升。这一模式在大多数注射吸毒者流行病中仍然相当一致，但注射行为和潜在风险网络的变化可能影响艾滋病毒感染的速度和传播。了解这些重要因素如何相互作用以改变艾滋病毒传播动态，可以通过针对传播的关键驱动因素网络的那些组成部分，带来更有效的干预设计和更有效的交付。一个阐明这些相互作用的机会出现在菲律宾宿务注射吸毒者中新出现的艾滋病毒流行病的异质性中。虽然在宿雾市，注射吸毒者中的艾滋病毒流行率迅速增长并稳定在近50%，但在邻近的曼达韦市，这一比例仍然很低（3.5%）。考虑到这两个城市的地理位置接近，我们本以为艾滋病毒感染会迅速使这两个城市的IDU人口饱和，但数据显示并非如此。为了了解导致这一不寻常的艾滋病流行差异的原因，我们建议研究这两个城市的注射危险行为和IDU网络结构。我们假设宿务市和曼达维市的注射吸毒者网络是有联系的，但由于每个城市的网络结构和注射行为的差异，艾滋病毒的传播动态有所不同。为了评估这些影响，我们将分析使用受访者驱动抽样收集的艾滋病毒监测数据，以比较两个城市的网络结构（目的1）。将利用同一监测样本中检测到的病例的HIV核苷酸序列进行系统发育分析，以确定传播聚集群（目标2）。确定的群集将覆盖在风险网络和行为数据上，以描述每个城市检测到的感染的行为和网络特征。最后，我们将综合这些数据来建立一个数学模型，并复制在宿务和曼达韦观察到的艾滋病毒传播动态（目标3）。利用该模型，我们将进行模拟，以量化个人风险行为（如共用针头流行率和注射频率）和网络措施（包括规模、密度、聚类和距离）对艾滋病毒感染传播的相对贡献。了解网络结构如何影响艾滋病毒传播，并确定其网络位置可能启动下游艾滋病毒感染链的重要个体，可以为我们可以针对的行为类型和个体提供见解，以减缓或阻止艾滋病毒向更广泛的注射吸毒者人群的传播。

DESCRIPTION (provided by applicant): HIV epidemics among injecting drug users (IDUs) are often characterized by rapid spread and high disease burden, which make this group an important priority for HIV prevention and care. Transmission of HIV infection through injecting risk often follows a common and predictable pattern, where a few undetected infections stimulate a rapid rise before reaching some steady state prevalence. This pattern remains quite consistent across most epidemics among injecting drug users, but variation in injecting behaviors and the underlying risk network may influence the speed and spread of HIV infection. Understanding how these important factors interact to alter HIV transmission dynamics can bring about more effective intervention design and more efficient delivery through targeting those components of the network that are key drivers of transmission. One opportunity to elucidate these interactions has arisen in the heterogeneity of emerging HIV epidemics among injecting drug users in Cebu, the Philippines. While HIV prevalence among IDUs has grown rapidly and stabilized at almost 50% in Cebu City, it remains quite low (3.5%) in neighboring Mandaue City. Given their geographic proximity, we would expect HIV infection to quickly saturate the IDU population across both cities, but the data suggest otherwise. To understand the circumstances that precipitated this unusual contrast in HIV prevalence, we propose to study the injecting risk behaviors and IDU network structures in these two cities. We hypothesize that the networks of IDUs in Cebu City and Mandaue City are linked, but HIV transmission dynamics vary because of differences in network structure and injecting behaviors in each city. To assess these effects, we will analyze HIV surveillance data, collected using respondent-driven sampling, to compare the network structures across the two cities (Aim 1). Phylogenetic analyses using HIV nucleotide sequences from cases detected in the same surveillance sample will be analyzed to identify transmission clusters (Aim 2). The clusters identified will be overlaid on the risk network and with behavioral data to describe behavioral and network characteristics of the infections detected in each city. Finally, we will synthesize these data to build a mathematical model and replicate the HIV transmission dynamics observed in Cebu and Mandaue (Aim 3). Using the model, we will conduct simulations to quantify the relative contributions of individual risk behaviors (such as needle sharing prevalence and injecting frequency) and network measures (including size, density, clustering, and distance) on the spread of HIV infection. Understanding how network structures impact HIV transmission, and identifying important individuals, whose network position could initiate a chain of downstream HIV infections, can provide insight on the types of behaviors and individuals we can target to slow or stop the spread of HIV to the broader injecting drug user population.