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

提高艾滋病毒高危人群的 PrEP 可用性和可及性：提高创新医疗最佳实践采用的社交网络工具

• 批准号: 9410896
• 负责人: Harold D. Green
• 金额: $ 23.95万
• 依托单位: RAND CORPORATION
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-08-16 至 2020-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9410896
• 关键词: AIDS prevention; Address; Adherence; Affect; Awareness; Behavior; Caring; Characteristics; Clinical; Communicable Diseases; Communication; Communities; Country; Data; Dependency; Diffusion; Education; Epidemic; Exhibits; Familiarity; Geographic Factor; Glean; Growth; HIV; HIV Infections; Health; Health Personnel; Incidence; Information Dissemination; Institute of Medicine (U.S.); Insurance; Intervention; Knowledge; Lesbian Gay Bisexual; Location; Medical; Minority; Modality; Modeling; Network-based; Pathway Analysis; Patient Representative; Patient risk; Patients; Pharmaceutical Preparations; Physicians; Population; Positioning Attribute; Prevention; Prevention strategy; Preventive treatment; Primary Health Care; Privatization; Process; Provider; Public Health; Recommendation; Reporting; Research; Research Personnel; Risk; Rural; Series; Sexual and Gender Minorities; Shapes; Social Network; Specialist; Structure; Surveys; Testing; base; contextual factors; design; effective therapy; genital herpes; geographic difference; high risk; high risk population; improved; innovation; insight; interest; men who have sex with men; novel; peer; pre-exposure prophylaxis; response; screening; simulation; social; social learning; social stigma; tool; transgender; uptake

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.

项目概要 公共卫生研究人员响应增加 PrEP 使用的呼吁的第一反应是 艾滋病毒高危人群的重点是针对这些人群的患者进行研究和干预 人口。然而，这仅部分解决了问题。向其寻求 PrEP 的患者 只有当他们的护理人员了解并愿意开 PrEP 处方时，医生才会成功。那是 也就是说，我们无法提高高危人群中 PrEP 的利用率，除非 PrEP 可供获取并可供使用 他们。因此，我们在此 R34 中的重点是护理提供者。我们建议开发一种利用医疗的新工具 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用性，从而增加治疗的可能性 吸收和坚持对于加强这种高效治疗方式的临床影响至关重要。 我们将使用回归模型，该模型结合了从我们的概率医生网络收集的数据。 根据索赔数据创建，以探索当地医生子网络的 PrEP 处方集群是否是 与传染病专家的联系有关，或与与传染病特征有关的地理差异有关 艾滋病毒流行，例如耻辱。这些模型根据社交网络统计依赖性进行调整，将有助于 我们实施并理解三个替代过程的相对贡献： 社会影响力 医生之间、社会学习（医生教育和信息传播）和非网络 地理差异。这些分析的结果将指导确定适当的干预目标 用于吉利德或感兴趣的第三方的教育活动以及确定可能的医生“改变” 代理人基于社会学习和社会影响过程进行基于同伴的干预。医师 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 通过与一小群医生的直接接触，我们确定了目标和变革推动者。