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

调查南非广泛耐药结核病的传播风险和发病率

• 批准号: 10347338
• 负责人: Kristin R. Harrington
• 金额: $ 5.18万
• 依托单位: EMORY UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-02-18 至 2024-02-17
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10347338
• 关键词: Address; Air; Area; Bayesian Method; Bayesian Modeling; Beauty; Biometry; Carbon Dioxide; Categories; Censuses; Cessation of life; Church; Clinic; Cohort Studies; Communicable Diseases; Communities; Country; Data; Data Collection; Dependence; Diagnosis; Dose; Drug resistance; Drug resistance in tuberculosis; Epidemic; Epidemiologic Methods; Epidemiology; Extreme drug resistant tuberculosis; Funding; Gaussian model; Generations; Goals; HIV; Home; Hour; Household; Incidence; Individual; Infection; Infectious Disease Epidemiology; Interruption; Knowledge; Location; Measurement; Measures; Methods; Modeling; Modification; Mycobacterium tuberculosis; Neighborhoods; Notification; Patients; Persons; Physicians; Population; Population Density; Prevention strategy; Probability; Prospective cohort study; Proxy; Public Health; Reporting; Research; Research Proposals; Research Training; Resources; Risk; Schools; Scientist; Site; South Africa; South African; Time; Training; Tuberculosis; United States National Institutes of Health; Visit; World Health Organization; extensive drug resistance; improved; infection risk; mortality; multidisciplinary; novel; pathogen; public health intervention; resistant strain; skills; transmission process; ventilation

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.

项目摘要 由结核分枝杆菌（Mycobacterium tuberculosis）引起的结核病（TB）是由感染性疾病引起的死亡的主要原因。 2017年全球结核病死亡人数超过130万。越来越令人担忧的是， 耐药（XDR）结核病，目前存在于100多个国家。广泛耐药结核病与高死亡率相关 世界卫生组织（WHO）认为这是一场公共卫生危机。传输现在 被认为是这一全球流行病的主要驱动因素。因此，更好地了解XDR TB 传输是迫切需要的。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偶然接触（短暂接触）， 社区中的互动），如在共享空间和聚集环境中发生的，如学校、公共场所、 运输和市场。目前，我们对偶然接触传播的理解存在很大差距， 包括最常发生的地方。室内环境中结核病传播风险（感染的可能性） 可以使用二氧化碳（CO2）测量值作为再呼吸空气的代表来估计空间。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度。 单独的.世卫组织目前用于计算结核病发病率的方法没有考虑空间依赖性 并且因此不能提供对本地传输的精确估计。我们的总体假设是：1） 偶然接触传播主要发生在聚集环境中，2）改进了对当地发病率的估计 可以使用考虑空间依赖性的贝叶斯方法来计算。对于目标1，我们将测量 15个聚集设置中的CO2浓度并计算通风率。地点将从我们的 该小组先前在南非德班进行的广泛耐药结核病传播研究以及特定地点的传播风险 将被建模为通风率、存在的个体数量、位置持续时间和范围的函数 感染剂量。在目标2中，我们将使用贝叶斯统计方法估计德班的当地发病率。 该建模分析的数据将来自南非人口普查和我们目前的NIH-R 01队列研究 包括诊断的XDR TB病例和地理空间信息（例如，家庭邻里和诊断诊所）。 我们将使用考虑空间依赖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有助于采取有针对性的公共卫生干预措施， 通风和减少这些场所的传播。对当地发病率的更好估计也可以强调 社区需要更多的资源。通过本研究计划和培训计划，申请人将 获得一个多学科的技能组合流行病学方法，地理空间分析和贝叶斯建模 同时增进我们对结核病传播动力学的了解。这种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成功的医生兼科学家。