Geostatistical methods for disease risk-mapping

研究试图达到什么目的？拟议研究项目的基本目标是通过解决三个具体问题来扩展经典的地质统计学框架[1]，这些问题是正确解释贫困国家疾病流行率调查数据所需的，因为那里不存在全体人口的健康记录。例如，当某些调查使用所谓的方便抽样，因此可能不能代表潜在的高危人口时，应如何合并多项流行率调查的数据，以说明数据质量的差异？2.一个特定社区的零流行率估计值可能是一个偶然发现，也可能是该社区没有疾病/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暴露与风险之间的关系。当暴露只能不精确地测量时，需要认识到这一点，以避免对风险的估计产生偏差。在地统计学背景下，即当暴露和风险在地理上各不相同时，什么是做到这一点的最佳方法？为什么这很重要？决策者将利用我们的方法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，在资源贫乏的情况下，现有数据有许多偏差和局限性，可能妨碍资源的有效分配。将我的研究应用于实际疾病控制将通过精心选择的与直接参与国内公共卫生机构的同事的合作联系来实现。研究与哪些疾病有关？所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疾病，包括马拉维、坦桑尼亚和埃塞俄比亚的疟疾，以及淋巴丝虫病、土壤传播的蠕虫和血吸虫病在所有非洲国家流行。拟议的研究项目将开发高质量的方法，并通过与来自伦敦热带医学与卫生学院，利物浦热带医学学院，哥伦比亚大学国际气候与社会研究所和挪威科技大学的领先专家合作，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，见“宣传计划”、“影响摘要”和“支持案例”。Tawn，J.A.，莫耶德河A.（2002）基于模型的地质统计学。《皇家统计学会杂志》，C辑，47：299-350。[2]海伊，S.I.，格拉，CA，Gething，P.W.，Patil，A.P.，Tatem，A.J.，努尔，上午，Kabaria，C.W.，Manh，B. H.，伊利亚扎，以色列空军，布鲁克，S.，史密斯，D.L.，Moyeed，R.A. Snow，R.W.（2009年）世界疟疾地图：2007年恶性疟原虫地方病。6.第六章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.