Evaluating Portfolio Interventions for HIV Incidence Reduction in the United States: Development of a Novel Agent-Based Decision-Analytic Model for Dynamic Evaluations of Interventions

评估美国减少艾滋病毒发病率的组合干预措施：开发基于代理的新型决策分析模型，用于干预措施的动态评估

• 批准号: 9411456
• 负责人: Chaitra Gopalappa
• 金额: $ 30.65万
• 依托单位: UNIVERSITY OF MASSACHUSETTS AMHERST
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-07-15 至 2022-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9411456
• 关键词: AIDS prevention; AIDS/HIV problem; Acquired Immunodeficiency Syndrome; Address; Age; Algorithmic Analysis; Algorithms; Behavior Therapy; Big Data; Biological Neural Networks; Caring; Centers for Disease Control and Prevention (U.S.); Chronic; Collaborations; Comorbidity; Computer software; Cost of Illness; Data; Data Analyses; Data Collection; Data Set; Data Sources; Decision Analysis; Development; Diagnosis; Disease; Disease Outbreaks; Disease model; Ebola Hemorrhagic Fever; Emerging Communicable Diseases; Epidemic; Epidemiology; Evaluation; Event; Foundations; Funding; Future; Goals; HIV; HIV Infections; HIV risk; Health; Health Policy; Highly Active Antiretroviral Therapy; Incidence; Individual; Infection; Intervention; Investments; Java; Knowledge; Lead; Letters; Life; Markov Chains; Massachusetts; Mathematics; Measures; Methodology; Methods; Modeling; Needle Sharing; New Agents; Outcome; Persons; Population; Population Analysis; Population Dynamics; Prevention strategy; Property; Proxy; Resources; Risk; Statistical Models; Structure; Syndrome; System; Testing; Time; United States; Universities; Update; advanced disease; algorithmic methodologies; antiretroviral therapy; base; behavior measurement; burden of illness; cost effective; disorder prevention; effective intervention; health economics; high risk; improved; innovation; intervention program; knowledge base; life time cost; mathematical model; migration; model design; models and simulation; mortality; multilevel analysis; novel; population migration; power analysis; programs; real time model; respiratory; scale up; simulation; software development; surveillance data; therapy design; transmission process; trend; user friendly software

Project Summary/Abstract The number of people living with the human immunodeficiency virus (HIV) in the United States has been gradually increasing from 800,000 in the late 1990's to 1.2 million by 2011. This is partially due to HIV-infected persons living longer and closer to normal life years on highly-active antiretroviral therapy (ART) treatment for HIV. However, the number of persons becoming newly infected with HIV has not decreased in recent years, it has been stable at almost 50,000 persons each year since the late 1990's. While mortality due to acquired immune deficiency syndrome (AIDS) related causes have been decreasing because of effective ART treatment, it is estimated that persons with HIV could be at a higher risk of certain non-communicable diseases that could lead to mortality. Further, the lifetime costs of treating an HIV-infected person on ART is very high, ranging from USD 250,000 to USD 400,000. Thus, it is important to identify optimal investment strategies for the prevention of new infections, which will reduce future HIV-related disease burden and costs. The overall goal of the project is to identify population-specific cost-effective combinations of care and behavioral intervention measures (intervention portfolios) that would help reduce new infections. The U.S. National HIV/AIDS Strategy (NHAS), 2015, proposes a goal of a 25% reduction in new infections by year 2020 compared to 2010. The analyses from this proposal would inform the development of a national strategy for achieving the NHAS goal for 2020 and similar such strategies in the future. It specifically proposes to advance theoretical concepts for the development of novel structure and algorithms for individual-level simulation of contact dynamics for disease spread (Aim 1), construction of a new agent-based decision-analytic model for dynamic evaluations of HIV interventions in the US (Aim 2), and development and implementation of new algorithms for evaluation and identification of optimal intervention portfolios for HIV prevention in the US (Aim 3). In this age of `big data' and computational power for analyzing these data, development of innovative methodologies for simulating the complicated dynamics of disease spread, and integrating disparate data sources to derive significant information that otherwise cannot be inferred through any of the data sources independently, could significantly improve use of decision-analytic models for evaluation of national strategies for disease prevention. Models can also further inform data collection for more accurate design of models and intervention analyses in the future. The theoretical knowledge gained through Aim 1 could also be foundational for the development of new methodologies for real-time decision-analyses during outbreak of emerging infectious diseases, similar to previous disease outbreaks such as Ebola-virus Disease or the Middle-Eastern Respiratory Syndrome.

项目摘要/摘要 在美国，患有人类免疫缺陷病毒（HIV）的人数逐渐逐渐 从1990年代末的80万增加到2011年的120万。这部分是由于艾滋病毒感染者的生活 在高度活跃的抗逆转录病毒疗法（ART）治疗HIV的情况下，较长且更接近正常的寿命。但是， 近年来，新近感染艾滋病毒的人的数量尚未减少，几乎稳定 自1990年代后期以来，每年有50,000人。而因获得免疫缺陷综合征（AIDS）而导致的死亡率 由于有效的艺术治疗，相关原因一直在减少，据估计，艾滋病毒患者可能处于 某些可能导致死亡率的某些非传染性疾病的风险更高。此外，治疗的一生成本 艾滋病毒感染者的艺术很高，从25万美元到40万美元不等。因此，重要的是要识别 预防新感染的最佳投资策略，这将减轻未来与HIV相关的疾病负担 和成本。 该项目的总体目标是确定特定人群的护理和行为组合 干预措施（干预投资组合）将有助于减少新的感染。美国国家艾滋病毒/艾滋病 战略（NHAS），2015年，提议的目标是，到2020年，与2010年相比，到2020年，新感染降低了25％。 该提案的分析将为实现2020年NHA目标的国家战略的制定提供信息 以及将来的类似策略。它专门提议推进理论概念以开发 用于疾病传播的接触动力学单个级别模拟的新型结构和算法（AIM 1）， 建造一个新的基于代理的决策分析模型，用于美国艾滋病毒干预措施的动态评估（AIM 2），以及用于评估和确定最佳干预措施的新算法的开发和实施 美国预防HIV的投资组合（AIM 3）。在这个“大数据”和计算能力的时代，用于分析这些 数据，开发创新方法，用于模拟疾病扩散的复杂动态以及 集成不同的数据源以获取重要信息，否则无法通过任何一个 独立的数据源可以显着改善决策分析模型评估国家的使用 预防疾病的策略。模型还可以进一步告知数据收集，以更准确地设计模型和 干预分析将来。通过AIM 1获得的理论知识也可以是基础 在新兴传染病爆发期间开发实时决策新方法的新方法， 类似于以前的疾病暴发，例如埃博拉病毒病或中东呼吸道综合征。