Optimal targeting for individual and population-level TB prevention

项目摘要/摘要 在同一社区内，结核病风险可能会因不同的 感染暴露和免疫能力，结核病控制在很大程度上依赖于针对性服务 那些风险最大的人。疾控中心和其他机构描述的优先群体捕捉到了主要的结核病风险 因素，但这些广泛的类别包括许多低结核病风险的个人，并排除其他 会从筛查中受益。我们的长期目标是为个人和当地提供量身定制的 结核病风险和干预效果的证据，以优化结核病预防服务。在以前的工作中，我们有 论证了估计小人口群体结核病风险的可行性，在目标1中，我们将 通过贝叶斯证据综合，为美国人口创建结核病风险的细粒度估计 关于结核病病例和人口规模、潜伏感染流行(LTBI)的时间序列数据以及 由于最近的感染而导致的病例。这一分析将使我们能够产生个性化的风险预测 更好地针对预防性服务，并为患者提供有关他们的风险的量化信息 脸。适用这一规定的患者数量相当多--约占所有美国居民的一半 已经进行了LTBI检测，在检测呈阳性的人中，只有一半开始治疗。这表示一个 大量的人面临着关于LTBI检测和治疗的决定。目标2将直接解决 这些问题，创造了对LTBI的成本、危害和好处的高度分类的估计 检测和治疗。为此，我们将构建LTBI筛查的马尔可夫微观模拟模型，并 治疗。使用这个模型，我们将估计患者水平的长期结果，包括结核病的变化。 风险、生存、成本和不良事件。基于这些分析，我们将开发一个用户友好的网站 为患者和临床医生提供提示、验证和个性化定制信息的工具 治疗结果。我们还将进行分析并开发一个配套工具，该工具将报告 为指导目的对用户定义的目标人群进行长期脑损伤筛查的影响和成本效益 计划决策。为了扩大这些工具的覆盖范围和影响，我们将使它们适用于其他工具 结核病发病率低于每10万人20人的国家。在目标3中，我们将开发一种变速箱动力学 用于预测广泛结核病控制方案(包括但不包括)长期结果的模拟模型 仅限于LTBI治疗)和风险因素趋势。该模型将针对多个司法管辖区进行校准， 基于网络的界面将允许用户指定场景并可视化结果。通过识别 这项研究表明，如何最有效地利用现有的和新的干预措施来改善健康状况 解决了NIH最优先的卫生经济学研究领域，并直接回应 需要计算工具和模型来更好地了解和应对传染病风险。 1

PROJECT SUMMARY/ABSTRACT Within the same community, TB risks can differ by several orders of magnitude due to differences in infectious exposure and immune competence, and TB control depends heavily on targeting services to those most at risk. Priority groups described by the CDC and other agencies capture major TB risk factors, but these broad categories include many individuals with low TB risk, and exclude others who would benefit from screening. Our long-term objective is to provide individually- and locally-tailored evidence on TB risks and intervention effects, to optimize TB prevention services. In prior work we have demonstrated the feasibility of estimating TB risks for small population groups, and in Aim 1 we will create granular estimates of TB risk for the US population, via a Bayesian evidence synthesis combining time series data on TB cases and population size, prevalence of latent infection (LTBI), and the fraction of cases due to recent infection. This analysis will allow us to produce individually-tailored risk predictions to better target preventive services, and provide patients with quantitative information on the risks they face. The number of patients to whom this applies is substantial—approximately half of all US residents have been tested for LTBI, and of those testing positive only half initiate treatment. This represents a large number of people facing decisions about LTBI testing and treatment. Aim 2 will directly address these questions, creating highly-disaggregated estimates of the costs, harms, and benefits of LTBI testing and treatment. To do so we will construct a Markov microsimulation model of LTBI screening and treatment. Using this model we will estimate long-term patient-level outcomes, including changes in TB risk, survival, costs, and adverse events. Based on these analyses we will develop a user-friendly web tool to provide patients and clinicians prompt, validated, and individually-tailored information on possible treatment outcomes. We will also conduct analyses and develop a companion tool that will report the impact and cost-effectiveness of LTBI screening for user-defined target groups for the purpose of guiding program decision-making. To increase the reach and impact of these tools we will adapt them for other countries with TB incidence below 20 per 100,000. In Aim 3 we will develop a transmission-dynamic simulation model to predict long-term outcomes for a broad set of TB control options (including but not limited to LTBI treatment) and risk factor trends. The model will be calibrated for multiple jurisdictions, and a web-based interface will allow users to specify scenarios and visualize outcomes. By identifying how current and novel interventions can be most effectively deployed to improve health, this research addresses the NIH’s highest priority area of health economics research, and responds directly to the need for computational tools and models to better understand and respond to infectious disease risks. 1