A Comprehensive Snapshot of Tuberculosis Transmission in an Urban Ugandan Community

项目摘要 与艾滋病毒和疟疾等其他主要传染病不同，全球防治结核病的进展- 现在是世界上传染病死亡率的主要因素，但进展缓慢得令人不安。一个主要原因是 全球结核病控制工作停滞不前的一个重要原因是，目前的方法主要包括广泛的指导方针， 在高负担的社区很难实施。我们需要的是一种方法， 在可行的、有针对性的结核病控制干预措施之间， 社区一级的疾病负担和传播。我们建议通过以下方式来解决这一知识差距 构建高负担社区流行结核病的详细快照： 活动性结核病病例（包括亚临床结核病或不寻求治疗的患者），使用全基因组测序， 检测社区中的传播网络，并根据什么来描述每一个流行病例 具体的有针对性的干预措施可以更快地诊断和治疗这种情况。这些数据将使我们能够 估计可干预的比例-可干预的流行结核病（和结核病传播潜力）的比例 已经在较早的时间点被一系列可行的、有针对性的干预措施中的每一项所捕获。我们将 联合收割机将这些数据与成本和效率数据（捕获结核病患者的能力与未捕获结核病患者的控制能力）相结合 结核病），以帮助确定哪些结核病干预措施组合最有可能降低结核病患病率， 在给定的预算支出下，我们还将构建一个基于主体的数学模型 以帮助预测这些发现如何适用于具有不同流行病学和经济 特色这项研究将为寻求优先考虑结核病的政策制定者提供关键的证据基础 它还将告知哪些干预措施对人口一级的疾病负担影响最大， 应该在下一代临床试验中优先考虑。在具体目标1中，我们不仅要估计 结核病在乌干达一个界限分明的城市社区的流行率，但也将这种流行率描述为 在群体水平上从未达到的程度-包括传染性的新标志物（例如，咳嗽 频率），甚至可能没有症状的病例之间的传播（在第二波病例发现中）， 以及有针对性的干预措施的可能性。在具体目标2中，我们将估计和 比较与多种有针对性的干预措施相关的结核病可干预部分，从而帮助 根据这些干预措施可能在社区一级产生的影响，确定其优先次序。在具体目标3中，我们将这些 流行病学优先事项与成本效益分析和基于代理的模拟建模，以指导 利用有限的经济资源确定优先事项，并将这些结果推广到各种流行病学 设置.这种创新的方法将使我们能够采取迫切需要的步骤，从简单地测量 结核病的负担，并提供“一刀切”的指导方针，以了解我们如何 采取可行的、有针对性的干预措施，有效减少疾病负担，每次一个社区。

PROJECT SUMMARY Unlike other major infectious diseases such as HIV and malaria, global progress against tuberculosis (TB) – now the world's leading agent of infectious mortality – has been disappointingly slow. A major reason for this stagnation in global TB control efforts is that the current approach consists largely of broad guidelines that are difficult to implement in high-burden communities. What we need is an approach that can help prioritize between feasible, targeted TB control interventions in a way that uses limited resources to maximize impact on disease burden and transmission at the community level. We propose to address this knowledge gap by constructing a detailed snapshot of prevalent TB in a high-burden community: identifying every detectable case of active TB (including those with subclinical TB or not seeking care), using whole-genome sequencing to detect transmission networks in the community, and characterizing every prevalent case in terms of what specific targeted interventions could diagnose and treat that case sooner. These data will enable us to estimate intervenable fractions – the proportion of prevalent TB (and of TB transmission potential) that could have been captured at an earlier time point by each of an array of feasible, targeted interventions. We will combine these data with data on cost and efficiency (ability to capture people with TB versus controls without TB) to help identify what combinations of TB interventions have greatest potential to reduce TB prevalence at the community level for a given budgetary outlay. We will also construct an agent-based mathematical model to help project how these findings might pertain to other settings with different epidemiological and economic characteristics. This research will provide a critical evidence base for policy-makers seeking to prioritize TB interventions with greatest impact on population-level disease burden, and it will also inform what interventions should be prioritized in the next generation of clinical trials. In Specific Aim 1, we will not only estimate the prevalence of TB in a well-circumscribed urban Ugandan community, but also characterize that prevalence to a degree never before achieved at the population level – including novel markers of infectiousness (e.g., cough frequency), transmission between cases that may not even have symptoms (in a second wave of case-finding), and cases' likelihood of being engaged by targeted interventions. In Specific Aim 2, we will estimate and compare the intervenable fraction of TB associated with numerous targeted interventions, thereby helping to prioritize those interventions in terms of their likely community-level impact. In Specific Aim 3, we will link these epidemiological priorities with cost-effectiveness analysis and agent-based simulation modeling, to guide priorities using constrained economic resources and to generalize such findings to a variety of epidemiological settings. This innovative approach will allow us to take an urgently-needed step, from simply measuring the burden of TB disease and offering “one-size-fits-all” guidelines, to understanding how we can prioritize feasible, targeted interventions to effectively reduce that disease burden, one community at a time.