Methods to Estimate the Effect of Interventions on the Incidence and Transmission of Tuberculosis

评估干预措施对结核病发病率和传播效果的方法

• 批准号: 9884778
• 负责人: Laura Forsberg White
• 金额: $ 31.84万
• 依托单位: BOSTON UNIVERSITY MEDICAL CAMPUS
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-04-15 至 2022-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9884778
• 关键词: AIDS/HIV problem; Address; Annual Reports; Area; Bayesian Method; Cessation of life; Characteristics; Clinical; Communicable Diseases; Communities; Consensus; Country; Data; Data Analyses; Data Reporting; Data Sources; Databases; Diagnosis; Disease; Drug resistance; Epidemiology; Evaluation; Foundations; Geography; Goals; Heterogeneity; Incidence; Individual; Intervention; Knowledge; Lead; Literature; Measures; Methods; Modeling; Modification; Monitor; Newly Diagnosed; Participant; Pattern; Policies; Policy Maker; Population; Prevalence; Public Health; Publishing; Reporting; Research; Resource Allocation; Resources; Sampling; Statistical Methods; Surveillance Methods; Surveys; Techniques; Time; Treatment Efficacy; Tuberculosis; Work; analytical method; burden of illness; health data; illness length; improved; insight; intervention effect; low income country; meetings; method development; novel; novel strategies; programs; prospective; reproductive; surveillance data; time use; tool; transmission process

Tuberculosis (TB) is the leading cause of infectious disease deaths globally, recently surpassing HIV/AIDS. The WHO has set goals for the eventual elimination of TB, but there are few, if any, reliable tools to monitor progress toward this goal. Additionally our understanding of the spatial and demographic variability of TB transmission and incidence is inadequate, making the allocation of limited resources challenging. This project proposes to develop methods to estimate transmission and incidence of TB in real time using data sources that are widely available. In Aim 1, we will develop improved estimates of the serial interval and duration of disease at time of diagnosis by using existing data (ours and published). These estimates will make use of statistical methods and novel modifications of these approaches to make them relevant to TB. These estimates provide the foundation of monitoring TB, further modeling work to guide policy, and provide valuable insights into TB epidemiology. Aim 2 will focus on developing methods to estimate the reproductive number of TB in real time, providing insight on transmission patterns. We propose to modify methods we and others have developed for diseases with short latency to TB, relying on routinely collected surveillance data. We will extend these methods to estimate heterogeneities in the reproductive number spatially, across demographic characteristics, and according the presence/absence of drug resistance. In Aim 3 we address the challenge of estimating disease incidence. Current estimates are given on a countrywide basis and result from annual reports of newly diagnosed individuals supplied to the WHO by individual countries. This data does not provide granularity in incidence estimates and is hampered by substantial reporting issues, which are not well-understood. We propose a novel Bayesian approach to estimate TB incidence from TB prevalence surveys, using our estimates of duration derived in Aim 1. TB prevalence surveys are detailed cluster sampled surveys with spatial, clinical, and demographic information on all participants. They are performed throughout areas with a high burden of TB. This approach will provide more detailed information on TB incidence and its heterogeneities, allowing for more targeted allocation of resources. We will create R packages to disseminate the methods that we develop in all the aims for widespread use. This work will enable policy makers to better understand the incidence of TB as well as groups and areas that are carrying the highest burden of disease. This knowledge will guide intervention efforts, with the eventual goal of elimination.

结核病 (TB) 是全球传染病死亡的主要原因，最近超过了艾滋病毒/艾滋病。 世界卫生组织设定了最终消除结核病的目标，但几乎没有可靠的工具来监测 朝着这一目标取得进展。此外，我们对结核病的空间和人口变异性的了解 传播和发病率不充分，使得有限资源的分配具有挑战性。这个项目 提议开发方法，利用以下数据源实时估计结核病的传播和发病率： 广泛可用。在目标 1 中，我们将改进对疾病的连续间隔和持续时间的估计 在使用现有数据（我们的和已发布的）进行诊断时。这些估计将利用统计数据 方法以及这些方法的新颖修改，使其与结核病相关。这些估计提供了 监测结核病的基础，进一步建模工作以指导政策，并提供有关结核病的宝贵见解 流行病学。目标 2 将重点开发实时估计结核病繁殖数量的方法， 提供有关传输模式的见解。我们建议修改我们和其他人开发的方法 结核病潜伏期较短的疾病，依赖于常规收集的监测数据。我们将扩展这些 估计跨人口特征的再生数空间异质性的方法， 并根据是否存在耐药性。在目标 3 中，我们解决了估计的挑战 疾病发生率。目前的估计是在全国范围内给出的，并且是根据新的年度报告得出的 各个国家向世界卫生组织提供的确诊个体。该数据不提供粒度 发病率估计，并受到大量报告问题的阻碍，而这些问题尚未得到充分理解。我们 提出一种新的贝叶斯方法，通过结核病患病率调查估计结核病发病率，使用我们的 目标 1 中得出的持续时间估计值。结核病患病率调查是详细的整群抽样调查，其中 所有参与者的空间、临床和人口统计信息。它们在整个区域进行 结核病负担高。该方法将提供有关结核病发病率及其影响的更详细信息 异质性，可以更有针对性地分配资源。我们将创建 R 包来传播 我们开发的方法旨在广泛使用。这项工作将使政策制定者能够更好地 了解结核病的发病率以及疾病负担最重的群体和地区。 这些知识将指导干预工作，最终达到消除的目标。