Geostatistical methods for disease risk-mapping

疾病风险绘图的地统计学方法

• 批准号: MR/M015297/1
• 负责人: Emanuele Giorgi
• 金额: $ 32.7万
• 依托单位: Lancaster University
• 依托单位国家: 英国
• 项目类别: Fellowship
• 财政年份: 2015
• 资助国家: 英国
• 起止时间: 2015 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=MR%2FM015297%2F1
• 关键词: Geostatistical; methods; disease; risk; mapping

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.

这项研究想要达到什么目的？拟议的研究项目的根本目的是通过解决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的地质统计框架，因为贫穷国家没有全民的健康记录。如何将多个流行病学调查的数据合并起来，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时？2. 某一特定社区的零流行率估计可能是偶然发现，也可能是该社区无疾病/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？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有偏差。在地质统计学背景下，即当暴露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？决策者将利用我们的方法更好地为疾病控制规划的实施提供信息，并最有效地利用现有资源。然而，在资源贫乏的情况下，现有的数据有许多偏差和局限性，这可能妨碍资源的有效分配。将我的研究应用于实际的疾病控制，将通过与直接参与国内公共卫生机构的同事精心选择的合作联系来实现。这项研究与哪些疾病有关？拟定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，以及所有流行这些疾病的非洲国家的淋巴丝虫病、土壤传播蠕虫病和血吸虫病。如何实现这一目标？拟议的研究项目将开发高质量的方法，并通过与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主要专家合作，将其应用于重要的公共卫生问题。在研究金期间，将在网上提供开源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，请参见“传播计划”、“影响摘要”和“支持案例”。参考文献[b] Diggle， P. J., Tawn， J. A., Moyeed， R. A.（2002）基于模型的地质统计学。[j] .中国科学：自然科学学报，2011,27 (1):1 -3Hay, 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.和Snow， R.W.(2009) 2007年世界疟疾地图：恶性疟原虫流行情况。公共科学图书馆医学6。e1000048。

项目成果

Spatio-temporal modelling of weekly malaria incidence in children under 5 for early epidemic detection in Mozambique.

• DOI: 10.1038/s41598-018-27537-4
• 发表时间: 2018-06-18
• 期刊: Scientific reports
• 影响因子: 4.6
• 作者: Colborn KL;Giorgi E;Monaghan AJ;Gudo E;Candrinho B;Marrufo TJ;Colborn JM
• 通讯作者: Colborn JM

MOESM3 of Geostatistical modelling of the association between malaria and child growth in Africa

MOESM3 非洲疟疾与儿童生长之间关系的地统计模型

• DOI: 10.6084/m9.figshare.5927257
• 发表时间: 2018
• 作者: Amoah B

MOESM5 of Geostatistical modelling of the association between malaria and child growth in Africa

MOESM5 非洲疟疾与儿童生长之间关系的地统计模型

• DOI: 10.6084/m9.figshare.5927290
• 发表时间: 2018
• 作者: Amoah B

Geostatistical modelling of the association between malaria and child growth in Africa.

• DOI: 10.1186/s12942-018-0127-y
• 发表时间: 2018-02-27
• 期刊: International journal of health geographics
• 影响因子: 4.9
• 作者: Amoah B;Giorgi E;Heyes DJ;van Burren S;Diggle PJ
• 通讯作者: Diggle PJ

MOESM2 of Geostatistical modelling of the association between malaria and child growth in Africa

MOESM2 非洲疟疾与儿童生长之间关系的地统计模型

• DOI: 10.6084/m9.figshare.5927242
• 发表时间: 2018
• 作者: Amoah B