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

城市疟疾绘图：评估时空趋势的新方法

• 批准号: 8235779
• 负责人: MARCIA C. CASTRO
• 金额: $ 7.73万
• 依托单位: HARVARD SCHOOL OF PUBLIC HEALTH
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-04-01 至 2014-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8235779
• 关键词: Address; Africa South of the Sahara; Area; Artemisinins; Bacillus thuringiensis; Cereals; Cities; Combined Modality Therapy; Communities; Cross-Sectional Studies; Data; Data Aggregation; Data Analyses; Data Collection; Databases; Disease; Economics; Epidemiology; Evaluation; Face; Funding; Future; Goals; Impact evaluation; Incidence; Individual; Infection; Intervention; Interview; Joints; Learning; Light; Link; Literature; Malaria; Maps; Methodology; Microscopy; Modeling; Morbidity - disease rate; Outcome; Pharmaceutical Preparations; Prevalence; Probability; Program Evaluation; Public Health Applications Research; Public Policy; Recommendation; Research; Social Behavior; Social Environment; Statistical Methods; Tanzania; Testing; Time; Urbanization; artemisinine; base; disorder control; evidence base; flexibility; improved; microbial; mortality; novel; programs; public health relevance; scale up; statistics; success; transmission process; trend; vector control; ward

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.

描述（由申请人提供）：本研究的目标是基于空间流行病学统计方法的现有文献，设计一种时空疾病绘图方法来评估解决两个主要问题的疾病控制干预措施。首先，该方法足够灵活，可以容纳纵向和横截面数据。其次，该方法需要允许合并多个协变量来控制混杂。具体来说，我们提出了一种新颖的时空贝叶斯分层建模方法，其中分析个体数据和横截面数据，并考虑解决缺失数据的替代方法。我们对具有空间和非空间背景效应的纵向数据使用贝叶斯分层模型，使我们能够包含空间连续的发病率估计，并有助于减少对个体感染状态的混杂影响。在此模型中，疟疾感染的概率通过线性预测变量进行建模，该预测变量是协变量（包括干预项以及社会、行为、经济和生态方面）和随机效应的函数。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。我们选择 UMCP 是因为该计划自 2004 年 3 月启动以来已经建立了一个高质量的数据库。UMCP 覆盖了全市 73 个区中的 15 个区，总面积 56 平方公里，居民超过 61 万。该计划于 2004 年开始收集基线数据，以指导干预措施并促进未来的计划评估。 2004 年至 2008 年间，总共采访了 64,537 人，并通过显微镜对每个人进行了疟疾感染检测。在疟疾控制干预措施方面，自 2006 年 3 月起，在 3 个病区开始使用微生物杀幼虫剂，2007 年 5 月扩大到 9 个病区，2008 年 4 月扩大到全部 15 个病区。同期的城市疟疾控制工作包括 2007 年 8 月在该市的两个排水沟进行试点社区环境管理干预措施，以及引入以青蒿素为基础的联合治疗作为一线药物。用于治疗疟疾 2007 年 1 月接受治疗。我们预计，在成功完成该项目后，我们将取得两个成果。首先，从方法论的角度来看，我们将拥有一种统计方法，可用于具有空间和时间成分并结合不同类型数据的一系列应用程序。其次，从公共政策的角度，我们将对UMCP进行全面评估，找出项目中可以改进的差距、对病媒控制构成挑战的领域以及项目调整和扩大规模的建议。第一个成果将扩展现有的空间流行病学文献，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 当前的活动，并最终为其他城市考虑类似于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，考虑感染状态的个体水平纵向模型，然后在此基础上建立链接的关节横截面模型。它有可能允许在结合个人和汇总数据的研究中进行各种应用，这些数据可以纵向收集，也可以在多个横断面调查中收集。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，并找出可以改进的差距、控制挑战的领域以及计划调整和扩大规模的建议。