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

9.0 2013年，有100万人患上结核病，其中150万人死亡。在早期发现结核病可以最大限度地减少它对一个人的健康造成的伤害，并减少他们感染的其他人的数量。在结核病很少的富裕国家，任何与结核病患者有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主要依靠人们在出现结核病症状时自己去卫生中心。对一般人群进行结核病筛查可以更早地发现病例，但由于过于昂贵，无法在结核病高发国家广泛使用。更好地了解结核病的空间分布将使筛查能够针对效果最好的地区。我将在四个结核病中度或高度流行的地区进行结核病筛查：马拉维的布兰太尔市;马拉维的卡隆加区;赞比亚;南非的西开普省。我将探讨三个主要问题：1）我将确定四个环境中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水平有关。例如，结核病通常集中在城市中最贫穷、最拥挤的地区，和/或艾滋病毒感染率高的地区。2）大多数结核病筛查计划使用以下两种方法之一：在设定的位置提供快速方便的筛选（可能会定期改变），或者在自己家里进行筛查（或者是因为他们与被诊断患有结核病的人住在一起，或者是因为正在向一个地区的所有家庭提供筛查）。在前一种情况下，筛查的接受率可能在居住在筛查地点附近的人中最高，并随着距离的增加而下降。然而，人们对筛查率随着距离的增加而下降的速度有多快，或者在筛查计划停止后，未确诊的结核病水平又会以多快的速度上升，知之甚少。我将调查这些问题使用的数据从一个完成的试验两个干预策略在赞比亚和南非。3)关于被诊断患有结核病的人的家庭位置的数据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所有与结核病患者住在同一所房子里的人进行检测。然而，测试他们附近的人也可能是有益的，原因有两个。首先，M. TB. (the引起大多数结核病的细菌）具有传染性，因此患者可能已传播M。TB.或者说是抓住了M。TB.从附近的某个人那里其次，某些因素，如艾滋病毒或营养不良，增加了某人患结核病的几率，这些因素往往是局部聚集的。我将使用上述环境中的数据（包括所有被诊断患有结核病的人的日期和家庭位置）来估计如果进行了邻里筛查，本可以更早发现的结核病病例数。我会研究只在有某些特徴的病人，例如艾滋病病毒阳性个案的邻近地区进行检查，是否可以提高成本效益。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高发城市和地区的结核病分布情况。它还将提高我们对当前结核病干预措施如何在小规模上发挥作用的认识，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.