Mapping malaria incidence using health facility surveillance data in Uganda

使用乌干达卫生设施监测数据绘制疟疾发病率图

• 批准号: 10404909
• 负责人: Adrienne Epstein
• 金额: $ 2.15万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-06-01 至 2021-10-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10404909
• 关键词: Address; Africa South of the Sahara; Area; Bayesian Modeling; Beds; Case Study; Catchment Area; Cessation of life; Climate; Communicable Diseases; Communication; Competence; Consumption; Country; Cross-Sectional Studies; Culicidae; Data; Data Collection; Data Reporting; Disease; Epidemiologic Methods; Epidemiology; Evaluation; Future; Geography; Goals; Gold; Health Services Accessibility; Health care facility; Health system; Incidence; Individual; Infectious Disease Epidemiology; Insecticides; Intervention; Knowledge; Laboratories; Link; Location; Longitudinal cohort; Malaria; Management Information Systems; Maps; Measurement; Measures; Mentorship; Methods; Mission; Modeling; Morbidity - disease rate; National Institute of Allergy and Infectious Disease; Parasites; Patients; Population; Population Density; Population Sizes; Prevalence; Public Health; Public Health Practice; Research; Research Personnel; Residual state; Resolution; Resource Allocation; Risk; Sentinel; Site; Statistical Methods; Statistical Models; Testing; Time; Training; Travel; Uganda; Variant; Work; World Health Organization; accurate diagnosis; base; burden of illness; career; cost; health management; high risk; high risk population; improved; individual patient; infectious disease model; insight; malaria transmission; mortality; residence; skills; spatial epidemiology; spatiotemporal; statistics; surveillance data; time use; tool; transmission process

PROJECT SUMMARY In sub-Saharan Africa, malaria is a major cause of morbidity and mortality with an estimated 200 million cases and 405,000 deaths in 2017. Effective surveillance of malaria cases is essential for tracking disease burden, allowing for targeted interventions, and evaluating the effect of control efforts. The current method for conducting malaria surveillance is through the health management information system (HMIS), which relies on accurate diagnosis and reporting of cases. Unfortunately, neither are performed consistently, often compromising the quality of these data. Furthermore, even in the presence of high quality HMIS data, quantifying an accurate denominator for malaria incidence rates is ambiguous because catchment areas around health facilities are not well defined. Without measures of malaria incidence over time, high transmission regions cannot accurately measure malaria morbidity and therefore are unable to effectively target high risk groups and track progress toward goals. The goal of this proposal is to leverage quality health facility surveillance data to generate high resolution, accurate risk maps of malaria incidence over time in Uganda, the country with the fifth largest malaria burden globally. Since 2006, high quality individual-level patient data has been collected in 34 sentinel health facilities across Uganda as part of the Uganda Malaria Surveillance Project, with nearly 100% laboratory testing of all individuals suspected of having malaria and information on patient geographic residence. The study will define catchment areas around health facilities to generate a population denominator for incidence rates around these sentinel sites (Aim 1) and identify environmental, demographic, and intervention predictors of malaria incidence around the 34 sites over time (Aim 2). This information will be used to build a statistical model to predict incidence and generate monthly maps of malaria risk in all 60,791 villages across Uganda (Aim 3). The proposed work will provide valuable insights on the burden of malaria in Uganda, allowing for long-term evaluation of recent control interventions including mass distributions of insecticide-treated mosquito nets and indoor residual spraying of insecticides. Knowledge gained from this research will directly advance the NIAID’s mission of reducing malaria morbidity and mortality with the ultimate goal of eradication. The proposed training, guided by an excellent mentorship team, will enhance the applicant’s epidemiologic and statistical methods, research competency, and content expertise needed for her career as a future independent academic researcher. In particular, the applicant will gain necessary skills in geospatial analyses, infectious disease epidemiologic methods, and communication of findings to improve both scientific knowledge and public health practice.

项目摘要 在撒哈拉以南非洲，疟疾是发病率和死亡率的主要原因，估计有2亿人死于疟疾。 2017年有405，000人死亡。有效监测疟疾病例对追踪疾病至关重要 负担，允许有针对性的干预措施，并评估控制工作的效果。目前的方法 通过卫生管理信息系统进行疟疾监测，该系统依赖于 准确诊断和报告病例。不幸的是，两者都没有始终如一地执行， 从而影响这些数据的质量。此外，即使存在高质量的HMIS数据， 对疟疾发病率的准确分母进行量化并不明确， 卫生设施周围没有很好的界定。如果不衡量疟疾发病率随时间的变化， 传播地区无法准确衡量疟疾发病率，因此无法有效地 以高风险群体为目标，跟踪目标的进展情况。 该提案的目标是利用高质量的卫生设施监测数据， 乌干达是世界第五大疟疾发病国， 全球疟疾负担。自2006年以来，在34个哨点收集了高质量的个人水平患者数据。 作为乌干达疟疾监测项目的一部分， 对所有疑似疟疾患者进行检测，并提供患者居住地信息。的 研究将确定卫生设施周围的集水区，以产生发病率的人口分母 这些哨点周围的发病率（目标1），并确定环境，人口和干预预测因素 随着时间的推移，34个地点的疟疾发病率下降（目标2）。这些信息将用于建立统计 预测发病率并生成乌干达所有60，791个村庄疟疾风险月度地图的模型 (Aim 3）。拟议的工作将提供关于乌干达疟疾负担的宝贵见解， 对近期控制干预措施的长期评价，包括大规模分发经杀虫剂处理的蚊子 蚊帐和室内滞留喷洒杀虫剂。从这项研究中获得的知识将直接推动 NIAID的使命是降低疟疾发病率和死亡率，最终实现消灭疟疾的目标。的 建议的培训，由一个优秀的导师团队指导，将提高申请人的流行病学和 统计方法，研究能力和内容专业知识需要她的职业生涯作为一个未来 独立学术研究员。特别是，申请人将获得必要的地理空间分析技能， 传染病流行病学方法，并交流研究结果，以提高科学知识 和公共卫生实践。