Urban malaria mapping: a new methodology to assess spatio-temporal trends

说明(申请人提供)：这项研究的目标是在现有的空间流行病学统计方法文献的基础上，设计一种时空疾病绘图方法，以评估针对两个主要问题的疾病控制干预措施。首先，该方法将足够灵活，可以同时容纳纵向和横截面数据。其次，该方法需要允许结合多种协变量来控制混杂。具体地说，我们提出了一种新的时空贝叶斯分层建模方法，其中分析了个体数据和横截面数据，并考虑了解决丢失数据的替代方法。我们对具有空间和非空间背景影响的纵向数据使用贝叶斯分层模型，使我们能够包括空间上连续的发病率估计，并有助于减少对个体感染状况的混杂影响。在这个模型中，疟疾感染的概率是通过线性预测来建模的，该线性预测是协变量(包括干预条件以及社会、行为、经济和生态方面)和随机影响的函数。我们将使用坦桑尼亚达累斯萨拉姆城市疟疾控制计划(UMCP)期间收集的数据来测试和验证所提出的方法方法。我们之所以选择UMCP，是因为该项目自2004年3月推出以来，已经建立了一个高质量的数据库。UMCP覆盖了该市73个区中的15个，总面积56平方公里，居民超过61万人。该方案于2004年开始收集基线数据，以指导干预措施并促进今后的方案评估。2004至2008年间，共有64,537人接受了采访，每个人都通过显微镜检查了疟疾感染情况。关于疟疾控制干预措施，自2006年3月以来，在3个病房开始使用微生物杀幼虫剂，2007年5月扩大到9个病房，2008年4月扩大到所有15个病房。同时进行的城市疟疾防治工作包括2007-2008年在该市两个排水沟开展的以社区为基础的环境管理干预试点工作，以及2007年1月采用青蒿素为基础的联合疗法作为治疗疟疾的一线药物。我们期望，在这一项目成功完成后，我们将取得两项成果。首先，从方法论的角度来看，我们将拥有一种统计方法，它可以用于一系列具有空间和时间成分的应用程序，并将不同类型的数据结合在一起。其次，从公共政策的角度，我们将对UMCP进行全面评估，找出该计划中可以改进的差距，对媒介生物控制构成挑战的领域，以及对计划调整和扩大的建议。第一个成果将扩大目前关于空间流行病学的文献，并促进空间统计在各种应用中的使用(公共卫生只是其中之一)。第二个结果将直接影响UMCP目前的活动，并最终为其他城市考虑类似坦桑尼亚的控制计划提供证据。 公共卫生相关性：拟议的研究将建立在当前时空疾病图谱文献的基础上，考虑感染状况的个人水平纵向模型，然后利用链接的联合横断面模型建立该模型。它有可能在结合个人和聚合数据的研究中得到各种应用，这些数据是纵向收集的，但也可以在多个横断面调查中使用。该模型的应用还将对坦桑尼亚达累斯萨拉姆的城市疟疾控制计划进行全面评估，找出可以改进的差距、对控制构成挑战的领域，以及对计划调整和扩大的建议。

DESCRIPTION (provided by applicant): The goal of this research is to build upon the available literature on statistical methods in spatial epidemiology to devise a space-time disease mapping approach to evaluate disease control interventions addressing two main issues. First, the approach will be flexible enough to accommodate both longitudinal and cross-sectional data. Second, the approach needs to allow for the incorporation of a multitude of covariates to control for confounding. Specifically, we propose a novel spatio-temporal Bayesian hierarchical modeling approach where both individual and cross-sectional data are analyzed, considering alternative ways to address missing data. Our use of Bayesian Hierarchical models for the longitudinal data with spatial and non-spatial contextual effects allows us to include spatially contiguous incidence estimates, and help to reduce confounding effects on the individual infection status. In this model, the probability of malaria infection is modeled via a linear predictor that is a function of covariates (including intervention terms, as well as social, behavior, economic, and ecological aspects), and random effects. We will test and validate the proposed methodological approach using data collected during the Urban Malaria Control Program (UMCP) in Dar es Salaam, Tanzania. We chose the UMCP because the program has assembled a good quality database since its launch in March 2004. The UMCP covers 15 of the 73 wards of the city, encompassing a total area of 56 km2 and more than 610,000 residents. The program started with baseline data collection in 2004, in order to guide interventions and facilitate future program evaluation. A total of 64,537 individuals were interviewed between 2004 and 2008, and each was tested for a malaria infection through microscopy. Regarding interventions for malaria control, since March 2006, the use of microbial larvicides was introduced in three wards, expanded to nine wards in May 2007, and to all 15 wards in April 2008. Concurrent urban malaria control efforts included a pilot community-based environmental management intervention undertook in two drains in the city in 2007-8, and the introduction of Artemisinin-based combination therapy as first line drug for the treatment of malaria treatment in January 2007. We expect that upon the successful completion of this project we will deliver two outcomes. First, from a methodological point of view, we will have a statistical approach that could be used in a range of applications that have spatial and temporal components, and that combine different types of data. Second, from a public policy point of view, we will produce a comprehensive evaluation of the UMCP, and identify gaps in the program that could be improved, areas that impose challenges for vector control, and recommendations for program tuning and scaling-up. The first outcome will expand the current literature on spatial epidemiology, and facilitate the use of spatial statistics in a variety of applications (public health being just one of them). The second outcome will directly impact current activities of the UMCP, and ultimately provide evidence for other cities considering control programs similar to the Tanzanian effort. PUBLIC HEALTH RELEVANCE: The proposed study will build on the current literature of spatio-temporal disease mapping, considering an individual level longitudinal model for infection status, and then build on that with a linked joint cross-sectional model. It has the potential to allow a variety of applications in studies that combine individual and aggregated data, collected longitudinally but also in multiple cross-sectional surveys. An application of the model will also produce a comprehensive evaluation of the Urban Malaria Control Program in Dar es Salaam, Tanzania, and identify gaps that could be improved, areas that impose challenges for control, and recommendations for program tuning and scaling-up.