Investigating Transmission Risk and Incidence of Extensively Drug-Resistant Tuberculosis in South Africa

项目总结 结核病(TB)是由结核分枝杆菌引起的，是传染性疾病死亡的主要原因。 全球结核病，2017年有130多万人死于结核病。越来越令人担忧的是广泛传播的 耐药结核病，目前已在100多个国家出现。广泛耐药结核病与高死亡率相关 世界卫生组织(WHO)认为这是一种公共卫生危机。传输现在是 被认为是这一全球流行病的主要驱动力。因此，更好地了解XDR TB在哪里 传输的发生是迫切需要的。虽然以前认为传播主要发生在 在密切接触者中，最近的证据表明，大多数传播可归因于临时接触(简要 社区中的互动)，就像在共享空间和聚集环境中发生的那样，例如学校、公共 交通和市场。目前，我们对偶然接触传播的理解存在很大差距， 包括它最常发生的地方。结核病在室内传播的风险(感染的概率) 可以使用二氧化碳(CO2)测量作为呼吸空气的替代指标来估计空间。发病率，或 人口中新病例的数量也可以突出高传播地区--尽管结核病发病率 表现出空间依赖性，其中传播依赖于与感染者的物理接近 个人的。世卫组织目前用来计算结核病发病率的方法没有考虑到空间相关性 因此，可能无法提供对当地传播的准确估计。我们的主要假设是：1) 偶然接触传播主要发生在聚集环境中，以及2)改进了对当地发病率的估计 可以使用考虑到空间相关性的贝叶斯方法来计算。对于目标1，我们将衡量 收集15个环境中的二氧化碳浓度并计算通风率。地点将从我们的 该组织之前在南非德班进行的广泛耐药结核病传播研究和特定地点的传播风险 将建模为通风率、在场人员数量、位置持续时间和范围的函数 具有传染性的剂量。在目标2中，我们将使用贝叶斯统计方法来估计德班的局部发病率。 此建模分析的数据将来自南非人口普查和我们目前的NIH-R01队列研究 包括诊断出的广泛耐药结核病病例和地理空间信息(例如，家庭邻居和诊断诊所)。 我们将使用考虑空间相关性的自相关回归模型来估计局部发病率。 量化特定地点的传播风险将使有针对性的公共卫生干预措施得以改善 通风和减少在这些地点的传播。改进的局部发病率估计也可以突出显示 需要额外资源的社区。有了这份研究计划和培训计划，申请人将 掌握结合流行病学方法、地理空间分析和贝叶斯建模的多学科技能 同时促进我们对结核病传播动态的了解。这一独特的培训将提供技术和 分析技能使她成为传染病流行病学领域的一名成功的内科科学家。

PROJECT SUMMARY Tuberculosis (TB), caused by Mycobacterium tuberculosis, is the leading cause of mortality from an infectious disease worldwide, with over 1.3 million TB deaths in 2017. Of increasing concern is the spread of extensively drug-resistant (XDR) TB, which is now present in over 100 countries. XDR TB is associated with high mortality rates and has been deemed a public health crisis by the World Health Organization (WHO). Transmission is now recognized as the major driver of this global epidemic. Thus, a better understanding of where XDR TB transmission occurs is urgently required. While transmission was previously thought to occur predominantly among close contacts, recent evidence suggests that most transmission is attributable to casual contact (brief interactions in the community), as would occur in shared spaces and congregate settings such as schools, public transport, and markets. Currently, there are large gaps in our understanding of casual contact transmission, including where it most commonly occurs. TB transmission risk (the probability of becoming infected) in an indoor space can be estimated using carbon dioxide (CO2) measurements as a proxy for rebreathed air. Incidence, or the number of new cases in a population, can also highlight areas of high transmission—although TB incidence demonstrates spatial dependency, wherein transmission is dependent on physical proximity to an infected individual. Current methods used by WHO to calculate TB incidence do not take into account spatial dependency and, thus, may not provide accurate estimates of local transmission. Our overarching hypotheses are: 1) casual contact transmission occurs mainly in congregate settings, and 2) improved estimates of local incidence can be calculated using Bayesian methods that account for spatial dependence. For Aim 1, we will measure CO2 concentrations in 15 congregate settings and calculate ventilation rates. Locations will be chosen from our group’s previous XDR TB transmission study in Durban, South Africa and location-specific risk of transmission will be modeled as a function of ventilation rates, number of individuals present, duration in location, and a range of infectious doses. In Aim 2, we will use Bayesian statistical methods to estimate local incidence in Durban. Data for this modeling analysis will come from the South African census and our current NIH-R01 cohort study including diagnosed XDR TB cases and geospatial information (e.g., home neighborhood and diagnosing clinic). We will estimate local incidence using autocorrelated regression models that account for spatial dependence. Quantifying the location-specific risk of transmission will allow for targeted public health interventions to improve ventilation and reduce transmission at these sites. Improved estimates of local incidence can also highlight neighborhoods requiring additional resources. With this research proposal and training plan, the applicant will gain a multidisciplinary skill set combining epidemiologic methods, geospatial analysis, and Bayesian modeling while advancing our knowledge of TB transmission dynamics. This unique training will provide the technical and analytical skillset for her to become a successful physician-scientist in infectious disease epidemiology.