Mathematical modelling and spatial data analysis to inform TB care and control strategies in high TB incidence settings

2013年有900万人患结核病，其中150万人死亡。在早期阶段发现结核病可以最大限度地减少它对人的健康造成的伤害，并减少他们感染的其他人的数量。在结核病较少的富裕国家，任何与结核病患者有过长期接触的人都可能接受检测。结核病发病率高得多的贫穷国家大多依赖于人们在出现结核病症状时自行前往保健中心。对一般人群进行结核病筛查可使病例更早被发现，但成本太高，无法在结核病高发病率国家广泛使用。更好地了解结核病的空间分布将使筛查能够针对效果最大的地区。我将着眼于四个中等或高水平结核病的环境：马拉维的布兰太尔市；马拉维Karonga区；赞比亚;以及南非的西开普省。我将探讨三个主要问题：1)我将确定四种情况下结核病发病率最高的地区，并确定哪些地区的特征与结核病高发病率有关。例如，结核病通常集中在城市中最贫穷、最拥挤的地区和/或艾滋病毒高发地区。2)大多数结核病筛查规划采用两种方法之一：在固定地点（可能定期改变地点）提供快速方便的筛查，或在患者自己家中进行筛查（因为他们与被诊断患有结核病的人住在一起，或者因为筛查正在向一个地区的所有家庭提供）。在前一种情况下，接受筛查的人数可能在靠近筛查地点的人群中最高，并随着距离的增加而下降。然而，对于筛查率随距离下降的速度有多快，或者筛查项目停止后未确诊结核病水平再次上升的速度有多快，人们知之甚少。我将利用在赞比亚和南非完成的两项干预策略试验的数据来调查这些问题。3)诊断为结核病的人的家庭所在地的数据有可能用于制定更具成本效益的结核病干预措施。目前，世界卫生组织建议对与结核病患者住在同一屋檐下的所有人进行检测。然而，对邻居进行测试也可能是有益的，原因有二。首先是结核分枝杆菌。（引起大多数结核病的细菌）具有传染性，因此患者可能传播了结核分枝杆菌。感染或感染结核分枝杆菌是住在附近的人寄来的。其次，艾滋病毒或营养不良等某些因素会增加患结核病的几率，而这些因素往往集中在当地。我将使用上面列出的数据（包括所有被诊断患有结核病的人的日期和家庭地点）来估计如果开展社区筛查本可以更早发现的结核病病例数。我将看看是否可以通过仅筛查具有某些特征的患者（例如艾滋病毒阳性病例）的社区来提高成本效益。最后，我将使用计算机模拟来估计通过筛查可以预防的新结核病例的数量。计划中的研究将使我们更好地了解结核病在高结核病城市和地区的分布如何变化。它还将提高我们对目前防治结核病的干预措施如何在小规模发挥作用的认识，并将提出可用于在资源贫乏环境中减少结核病的新干预措施。

9.0 million people developed tuberculosis (TB) in 2013, with 1.5 million dying. Detecting TB disease at an early stage minimises the harm it causes to a person's health, and reduces the number of other people whom they infect. In richer countries with little TB, anyone who has had extended contact with someone with TB is likely to be tested. Poorer countries with much higher rates of TB mostly rely on people going to health centres themselves when they have symptoms of TB. Screening the general population for TB results in cases being detected earlier, but is too expensive to be widely used in high-TB countries. Developing a better understanding of the spatial distribution of TB will enable screening to be targeted at areas where it will have the greatest effect.I will look at TB in four settings with moderate or high levels of TB: Blantyre city, Malawi; Karonga district, Malawi; Zambia; and Western Cape Province, South Africa. There are three main questions I will explore:1) I will identify areas within the four settings where rates of TB are highest, and determine what characteristics of areas are associated with high levels of TB. For instance, TB is often concentrated in the poorest, most crowded areas of cities, and/or in areas with high rates of HIV.2) Most TB screening programs use one of two approaches: offering quick and convenient screening at a set location (which may change on a regular basis), or screening people at their own homes (either because they live with someone who has been diagnosed with TB, or because screening is being offered to all households in an area). In the former case, uptake of screening is likely to be highest amongst people living close to a screening location, and drop off as the distance increases. Very little is known about how quickly screening rates drop off with distance however, or how quickly levels of undiagnosed TB increase again after a screening program has stopped. I will investigate these questions using data from a completed trial of two intervention strategies in Zambia and South Africa. 3) It is possible that data on the household locations of people diagnosed with TB can be used to develop more cost-effective interventions against TB. At the moment, the World Health Organization recommends testing all people who live in the same house as a TB patient. Testing people in their neighbourhood may also be beneficial however, for two reasons. Firstly, M. tb. (the bacteria that causes most TB disease) is infectious and the patient may therefore have transmitted M. tb. to or caught M. tb. from someone who lives nearby. Secondly, certain factors such as HIV or malnutrition increase the odds of someone having TB, and these factors are often locally clustered. I will use data from the settings listed above (which include the dates and household locations of all people diagnosed with TB) to estimate the number of TB cases that could have been detected earlier if neighbourhood screening had been carried out. I will see if cost-effectiveness can be improved by only screening the neighbourhoods of patients with certain characteristics, for instance HIV positive cases. Finally, I will use computer simulations to estimate the number of new TB cases that could be prevented by screening.The planned research will give us a much better understanding of how the distribution of TB varies within high-TB cities and districts. It also will improve our knowledge of how current interventions against TB work at a small scale, and will suggest new interventions that can be used to reduce TB in resource poor settings.