Tuberculosis in households with infectious cases in Kampala city: Harnessing health data science for new insights on TB transmission and treatment response (DS-IAFRICA-TB)

摘要： 结核病（TB）在乌干达很流行，与艾滋病毒/结核病合并感染已经很高的负担重叠。而 在乌干达首都坎帕拉市，几乎所有的医院结核病病例都有明显的结核病症状， 通过主动病例发现发现的未确诊结核病患者中，有更多人没有结核病症状; 此外，坎帕拉结核病的宿主风险因素与与结核病相关的风险因素无法区分。 环境使这一情况进一步复杂化的是，乌干达的抗结核治疗失败率较高， 与全球估计数相比，这一比例高出几个数量级（17%对10%）。这些结核病特有的挑战描述了 只有一小部分的复杂性，潜在的疾病，特别是在地方性环境中的高负担， 艾滋病毒/艾滋病的数据科学方法，特别是人工智能（AI）和/或机器学习算法，可以 揭示了结核病的宿主、病原体和环境的复杂性， 迄今为止，难以用常规方法来解释或预测。在这个提议中，我们将利用 健康数据科学和阐明结核病在家庭中传播的潜在因素，以及抗结核病 治疗失败。我们将利用Makerere的计算基础设施和可用的人口统计， 结核病患者及其接触者的临床和实验室数据集，并开发人工智能/机器学习 识别：（1）基线（第0个月）时痰和/或培养物在第2个月时未转化的患者 2和5，因此有结核病治疗失败的风险，（2）有发展成结核病风险的索引结核病病例的接触者 家庭结核病，以及接触者谁可能耐结核感染，尽管持续和/或 多次暴露于M.肺结核在一个家庭实现这些目标提供了必要的证据， 数据科学方法在早期识别潜在结核病病例和高成本患者方面是有效的， 有助于阻止结核病在社区的传播。

Abstract: Tuberculosis (TB) is prevalent in Uganda, and overlaps with an already high burden of HIV/TB coinfection. While almost all hospital-based TB cases in Kampala city, the capital of Uganda, have clear TB symptoms, 30% or more of the people with undiagnosed TB, identified through active case finding, are asymptomatic for TB; moreover, the host risk factors for TB in Kampala cannot be distinguished from risk factors associated with the environment. Complicating this further is the fact that anti-TB treatment failure rates are higher in Uganda by several order of magnitude, compared to global estimates (17% vs. 10%). These TB-specific challenges depict only a fraction of the complexity underlying the disease, especially in endemic settings with a high burden of HIV/AIDS. Data science methods, especially Artificial Intelligence (AI) and/or Machine Learning algorithms, can unravel such complexity and untangle factors of the host, pathogen and environment underlying TB, which hitherto, have been difficult to explain or predict with conventional approaches. In this proposal, we will harness health data science and elucidate factors underlying transmission of TB in a household, as well as anti-TB treatment failure. We will leverage the computational infrastructure at Makerere, and available demographic, clinical and laboratory data sets from TB patients and their contacts, and develop AI/Machine Learning algorithms that identify: (1) Patients at baseline (month 0) who would not sputum and/or culture convert at months 2 and 5, hence are at risk of failing TB treatment, (2) Contacts of index-TB cases who are at risk of developing household TB disease, as well as contacts who could be resistant to TB infection despite persistent and/or multiple exposure to M. tuberculosis in a household. Answering these aims provides the required evidence that data science methods are effective at early identification of potential TB cases and high-cost patients, hence contribute to halting of TB transmission in the community.