Evaluating the tuberculosis epidemic in South Africa using a novel ten-year cohort

项目总结： 结核病是全球传染病死亡的主要原因。结核病发病率 目前每年下降2%，实现世界卫生组织的EndTB目标 到2050年消除结核病将需要对我们的结核病控制方法进行重大修改。 以前针对其他疾病的消除策略仅在空间上取得成功 确定疾病发病率的变化，然后采取当地相关干预措施 实施。消除结核病的战略需要纵向结核病队列，包括 详细的空间信息。南非是我们工作的理想环境，第二个国家 全球最高结核病发病率和中央国家卫生实验室服务(NHLS) 常规收集的实验室结果数据库。我们建议制定一项为期十年的国家 结核病队列，能够跟踪患者长达十年，包括空间位置数据 和艾滋病毒状况，使用国家NHL数据。我们假设NHLS数据可以用来 在设施层面确定结核病护理级联中的关键指标，并评估艾滋病毒的影响 扩大治疗规模，并在南非推出结核病流行的新诊断方法。在……里面 目标1a我们将使用概率记录链接在NHL中创建唯一的患者标识 所有结核病患者的数据，使我们能够链接个人的检测结果并跟踪那些确认的 病例的空间和时间，并纳入艾滋病毒检测结果。在目标1b中，我们将确定 结核病护理级联中的关键步骤，并使用临床图表中的数据扩大我们的十年队列 评论。在目标1c中，我们将确定在特定护理级联步骤中存在差距的位置，常见 结核病治疗期间患者的运动模式，并找出潜在的高风险部位 基于分子检验变量的疾病负担。在目标2中，我们将量化这种关系 在设施一级，艾滋病毒治疗水平与结核病发病率之间的关系。在目标3中，我们将评估 GeneXpert超“跟踪”结果与结核病反复发作之间的关联以增强 对这些新的诊断结果的解释。这一贡献意义重大，因为它将 建立全国结核病患者十年队列，对他们进行纵向和空间跟踪 在治疗期间，使当地的公共卫生专业人员能够开发出适合当地的 弥合结核病护理差距的干预措施和了解干预措施的影响 和新的结核病诊断方法。拟议的工作具有创新性，因为它将提供第一个 这种类型的队列在高负担环境中，具有纵向和空间性质 数据能够史无前例地捕捉结核病护理和干预措施的影响。这将是 允许政策制定者针对结核病护理方面的具体差距设计与当地相关的干预措施 和高度流动的人群，防止进一步传播，最终减少疾病和 结核病导致的死亡率。通过使用常规收集的实验室数据，我们的模型添加了最少 财务成本，并可适用于类似的结核病高负担、中等收入环境 大型实验室数据集。

Project Summary: Tuberculosis (TB) is the leading cause of infectious disease deaths globally. With TB incidence currently decreasing by 2% annually, achieving the World Health Organization’s ENDTB goal of TB elimination by 2050 will require a substantial reworking of our TB control approach. Previous elimination strategies for other diseases have only been successful once spatial variation in disease incidence was identified and then locally relevant interventions implemented. TB elimination strategies require longitudinal TB cohorts that incorporate detailed spatial information. South Africa is an ideal setting for our work with both the second highest TB incidence globally and a central National Health Laboratory Service (NHLS) database of routinely-collected laboratory results. We propose to develop a ten-year national TB cohort with the ability to track patients for up to ten years, including spatial location data and HIV status, using national NHLS data. We hypothesize that NHLS data can be used to identify key metrics in the TB care cascade, at the facility level, and evaluate the impact of HIV treatment scale-up and the roll-out of new diagnostics on the TB epidemic in South Africa. In aim 1a we will use probabilistic record linkage to create unique patient identifiers in the NHLS data for all TB patients, allowing us to link an individual’s test results and track those confirmed cases spatially and temporally, and incorporating HIV test results. In aim 1b, we will identify key steps in the TB care cascade and augment our ten-year cohort with data from clinic chart reviews. In aim 1c, we will identify locations with gaps in specific care cascade steps, common patient movement patterns during TB treatment and identify locations with potential higher burden of disease based on molecular test variables. In aim 2 we will quantify the relationship between HIV treatment levels and TB incidence, at the facility level. In aim 3 we will evaluate the association between GeneXpert Ultra “trace” results and repeat TB episodes to enhance the interpretation of these new diagnostic results. This contribution is significant because it will develop a national ten-year cohort of TB patients tracking them longitudinally and spatially during treatment, enabling local public health professionals to develop locally appropriate interventions to close gaps in the TB care cascade and understand the impact of interventions and new TB diagnostics. The proposed work is innovative because it will provide the first cohort of this kind in a high burden setting, with the longitudinal and spatial nature of these data allowing for unprecedented capture of TB care and the impact of interventions. This will allow policy makers to design locally-relevant interventions targeting specific gaps in TB care and highly mobile populations, preventing further spread and ultimately reducing disease and mortality due to TB. By using routinely collected laboratory data, our model adds minimal financial costs and can be adapted for similar TB high-burden, middle-income settings with large laboratory datasets.