Improving PrEP Availability and Access for Populations at High Risk for HIV: Social Network Tools that Improve Uptake of Innovative Medical Best Practice

项目摘要 公共卫生研究人员对增加使用PrEP的呼吁的第一个倾向是 艾滋病毒高危人群的重点是针对这些地区的患者进行研究和干预 人口。然而，这只是部分解决了这个问题。寻求PrEP的患者 只有当他们的护理提供者知道并愿意开PrEP处方时，医生才会成功。那是 也就是说，我们不能在高危人群中提高PrEP的利用率，除非PrEP是可获得的并可供 他们。因此，我们在R34中的重点是护理提供者。我们建议开发一种新的工具，它使用医疗 索赔数据来推断医生网络，然后使用这些网络来确定如何最好地实施 有针对性的干预措施，鼓励初级保健提供者向患者提供暴露前预防 有感染艾滋病毒的风险。这将增加治疗的可及性和可得性，从而增加 摄取和依从性，对于加强这一高效治疗方式的临床影响至关重要。 我们将使用包含从概率医生网络WE收集的数据的回归模型 根据索赔数据创建以探索PrEP按本地医生子网络开出的群集是否 与传染病专家的联系相关，或与与以下特征相关的地理变异相关 艾滋病的流行，如耻辱。这些模型根据社交网络的统计相关性进行了调整，这将有所帮助 美国实施并理解三个替代进程的相对贡献：社会影响 在医生中，社会性学习(医生教育和信息传播)和非网络 地理上的差异。这些分析的结果将指导确定适当的干预目标 用于由Gilead或感兴趣的第三方进行的教育活动以及确定可能的医生变更 基于社会学习和社会影响过程的基于同伴的干预的代理人。内科医生 我们将根据这些发现，通过在我们创建的网络上进行模拟，来设计和测试目标工具 通过与我们确定为目标和变革推动者的一小群医生的直接接触。

Project Summary The first inclination of public health researchers responding to the call for increasing use of PrEP among populations at high risk for HIV is to focus on research and interventions that target patients in those populations. However, this is only partially addresses the problem. Patients who seek PrEP from their physicians will only be successful if their care providers know about and are willing to prescribe PrEP. That is to say, we cannot increase PrEP utilization in high risk populations unless PrEP is accessible and available to them. Thus, our focus in this R34 is on care providers. We propose to develop a novel tool that uses medical claims data to infer physician networks and then use those networks to determine how to best implement targeted interventions to encourage primary care providers to offer Pre-Exposure Prophylaxis to their patients at risk for HIV. This will increase access and availability of the treatment, thereby increasing the likelihood of uptake and adherence, critical for strengthening the clinical impact of this highly effective treatment modality. We will use regression models that incorporate data gleaned from the probabilistic physician network we create from claims data to explore whether PrEP prescribing clusters by local physician subnetwork, is associated with connections to infectious disease specialists, or to geographic variation related to features of the HIV epidemic, such as stigma. These models, adjusted for social network statistical dependencies, will help us operationalize and understand the relative contribution of three alternative processes: social influence among physicians, social learning (physician education and diffusion of information), and non-network geographic variation. The results of these analyses will guide identification of appropriate intervention targets for education campaigns by Gilead or interested third parties and identification of possible physician ‘change agents’ for peer-based interventions based on social learning and social influence processes. Physician targeting tools will be designed and tested based on these findings via simulations on the network we create and via direct contact with a small group of physicians we have identified as targets and change agents.