Geostatistical methods for disease risk-mapping

这项研究试图达到什么目的？拟议研究项目的基本目标是通过提及正确解释贫穷国家疾病流行调查数据所需的三个具体问题来扩展经典地统计学框架[1]，因为贫穷国家没有所有人口的健康记录1。应如何合并来自多个流行率调查的数据，以解释数据质量的差异，例如，当一些调查使用所谓的便利抽样，因此可能不能代表潜在的风险人群？2.特定社区的零流行率估计可以是一个偶然发现，也可以是该社区没有疾病/感染的必然结果。如何分析流行率数据以识别这两种不同的现象，并正确解释可能包含这两种零的数据？3.任何流行病学研究的一个关键目标是了解接触和风险之间的关系。当暴露只能被不精确地测量时，需要认识到这一点，以避免对风险估计的偏差。在地质统计学的背景下，即当接触和风险在地理上不同时，最好的方法是什么？为什么这很重要？政策制定者将使用我们的方法来更好地为疾病控制方案的实施提供信息，并最有效地利用现有资源。然而，在资源匮乏的情况下，现有数据存在许多偏见和局限性，这可能会阻碍资源的有效分配。我的研究将通过与直接参与国内公共卫生机构的同事精心挑选的合作联系，将我的研究应用于实际的疾病控制。这项研究与哪些疾病相关？开发的方法将广泛适用于任何传染病。然而，我们的具体应用将是疟疾和被忽视的热带病，包括马拉维、坦桑尼亚和埃塞俄比亚的疟疾，以及所有非洲国家的淋巴丝虫病、土壤传播蠕虫和血吸虫病。如何实现这一目标？拟议的研究项目将开发高质量的方法学，并通过与来自伦敦热带医学与卫生学院、利物浦热带医学院、哥伦比亚大学国际气候与社会研究所和挪威科技大学的知名专家合作，将其应用于重要的公共卫生问题。在研究金期间，将在网上提供开放源码的统计软件和实质性调查结果。关于我打算如何实现这些目标的更多细节，请参阅《沟通计划》、《影响总结》和《支持案例》。参考资料[1]Digger，P.J.，Tawn，J.A.，Moyeed，R.A.(2002)基于模型的地统计学。[2]Hay，S.I.，Guera，C.A.，Gething，P.W.，Patil，A.P.，Tatem，A.J.，Noor，A.M.，Kabaria，C.W.，Manh，B.H.，Elyazar，I.R.F.，Brooker，S.，Smith，D.L.，Moyeed，R.A.和Snow，R.W.(2009)世界疟疾地图：2007年恶性疟原虫流行。公共科学图书馆医学6。e1000048。

What is the research trying to achieve?The fundamental aim of the proposed research project is to extend the classical geostatistical framework [1] by adressing three specific issues that are needed for the correct interpretation of data from disease prevalence surveys in poor countries, where health records for the whole population do not exist.1. How should data from multiple prevalence surveys be combined in order to account for data-quality variation, for example when some of the surveys uses so-called convenience sampling and may therefore not be representative of the underlying population at risk? 2. A zero prevalence estimate in a particular community can be either a chance finding, or a necessary consequence of the community being disease/infection-free. How can prevalence data be analysed to recognize these two different phenomena, and to correctly interpret data that may contain both kinds of zeros?3. A key aim in any epidemiological study is to understand the relationship between exposure and risk. When exposure can only be measured imprecisely, this needs to be recognized to avoid biasing estimated of risk. What is the best way to do this in a geostatistical setting, i.e. when both exposure and risk vary geographically?Why is this important?Policy makers will use our methodology to better inform the implementation of disease control programmes and make the most efficient use of available resources. Yet in resource poor settings the available data have numerous biases and limitations, which could hinder effective allocation of resources. The application of my research to practical disease control will be achieved via carefully chosen collaborative links with colleagues who are directly involved with in-country public health agencies. To which diseases is the research relevant?The developed methodology will be broadly applicable to any infectious disease. However, our specific applications will be in malaria and neglected tropical diseases, including malaria in Malawi, Tanzania and Ethiopia, and lymphatic filariasis, soil-transmitted helminths and schistosomiasis in all of the African countries where these are endemic.How will the aim be achieved?The proposed research project will develop high-quality methodology and apply this to important public health problems through collaborations with leading experts from London School of Tropical Medicine & Hygiene, Liverpool School of Tropical Medicine, International Institute for Climate and Society at Columbia University, and the Norwegian University of Science and Technology. During the fellowship open-source statistical software and substantive findings will be made available online. More details on how I intend to pursue these objectives are given in "Communications plan", "Impact summary" and "Case for support".References[1] Diggle, P. J., Tawn, J. A., Moyeed, R. A. (2002) Model-based geostatistics. Journal of the Royal Statistical Society, Series C, 47:299-350.[2] Hay, S.I., Guerra, C.A., Gething, P.W., Patil, A.P., Tatem, A.J., Noor, A.M., Kabaria, C.W., Manh, B.H., Elyazar, I.R.F., Brooker, S., Smith, D.L., Moyeed, R.A. and Snow, R.W. (2009) A world malaria map: Plasmodium falciparum endemicity in 2007. PLoS Medicine 6. e1000048.