Optimal targeting for individual and population-level TB prevention

项目总结/摘要 在同一社区内，结核病风险可能因以下因素的差异而相差几个数量级： 感染暴露和免疫能力，结核病控制在很大程度上取决于针对性服务， 那些最危险的人。疾病预防控制中心和其他机构描述的优先群体捕获了主要的结核病风险 因素，但这些广泛的类别包括许多结核病风险低的个体，并排除其他人， 会从筛查中受益我们的长期目标是提供个性化和本地定制的 结核病风险和干预效果的证据，以优化结核病预防服务。在先前的工作中，我们有 证明了估计小人群结核病风险的可行性，在目标1中，我们将 通过贝叶斯证据合成，结合 结核病病例和人口规模、潜伏感染（LTBI）患病率以及 最近感染的病例。这种分析将使我们能够产生个性化的风险预测 更有针对性的预防服务，并为患者提供有关风险的量化信息， 脸上这一规定适用的患者数量相当可观--约占美国居民总数的一半 已经进行了LTBI测试，并且在那些测试阳性的人中，只有一半开始治疗。这表示 大量的人面临着关于LTBI测试和治疗的决定。目标2将直接解决 这些问题，创建高度分类的成本，危害和LTBI的好处的估计 测试和治疗。为此，我们将构建一个LTBI筛选的马尔可夫微观模拟模型， 治疗使用这个模型，我们将估计长期患者水平的结果，包括结核病的变化 风险、生存率、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 一种工具，用于为患者和临床医生提供及时、有效和个性化的信息， 治疗结果。我们还将进行分析并开发一个配套工具， 为用户定义的目标群体进行LTBI筛查的影响和成本效益， 方案决策。为了扩大这些工具的覆盖面和影响力，我们将对它们进行调整， 结核病发病率低于20/10万的国家。在目标3中，我们将开发一个传输动态 一个模拟模型，用于预测广泛的结核病控制方案（包括但不限于 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， 一个基于网络的界面将使用户能够具体说明设想方案并将结果可视化。通过识别 如何最有效地部署当前和新的干预措施来改善健康，这项研究 解决了NIH的卫生经济学研究的最高优先领域，并直接回应 需要计算工具和模型，以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