A combined epidemiologic and genomic approach to identify and control transmission events of hospital associated infections

项目总结/摘要 这是一份K 01奖的申请，申请者是林赛基根博士，他是一位传染病流行病学家， 犹他州大学。基冈博士正在确立自己作为一个年轻的研究人员在传染病动力学 处理与传染病传播和控制有关的公共卫生问题。K 01奖项 将提供必要的支持，以实现以下目标，以发展：（1）基因组学方面的专业知识 方法，因为它涉及到流行病学;（2）在生物统计学的专业知识，特别是概率论，网络 模型和贝叶斯统计;（3）与病原体传播相关的医疗流行病学专业知识 在医疗机构（HCF）;（4）和加强研究领导和管理技能。到 为了实现这些目标，Keegan博士组建了一个指导团队，成员包括：Matthew Samore博士 （主要导师），医疗保健流行病学家和医疗保健相关数学建模专家 感染;和马克Lipsitch博士（共同导师），流行病学家和公认的领导者在数学 传染病建模，包括医疗保健相关感染建模，以及Michael Rubin博士 （共同导师），传染病医生和转化科学专家，以支持抗菌 管理、感染预防和领导能力。基冈博士还组建了一个由四名顾问组成的团队 在生物统计学、基因组流行病学、数学建模和医院流行病学方面具有专长。 抗生素耐药性细菌构成了重大的公共卫生威胁，造成超过280万人感染， 美国每年有35,000人死亡。这些感染的负担集中在HCF; 如何控制这些病原体仍然是相当大的争论的根源。根据从一个 在CDC之前的一项研究中，Keegan博士的中心假设是，与医疗保健相关的病原体正在传播， 主要是通过患者脱落和来源不足间接在患者和环境表面之间 控制通过追求以下具体目标，基冈博士将测试她的假设，并制定方法， 适用于未来的数据集（适用于K 01期间未来拟议的R 01应用）。具体目标1，博士。 基冈将检验这一假设，即病原体之间的聚类将有系统发育支持 与环境隔离的和与病人隔离的。在具体目标2中，她将构建和 验证将接触网络数据与病原体基因组数据整合的模型，以概率性地推断 HCF内传输事件的方向。在具体目标3中，她将测试大多数人 患者到患者的传播事件由环境表面介导。 拟议的研究意义重大，因为它解决了改善感染控制的关键障碍： 如果不量化不同病原体传播源的作用， 有意针对关键传输步骤。这项研究是创新的，因为一种方法， 推断细菌传播事件目前并不存在，尽管其在感染控制中至关重要。

PROJECT SUMMARY/ABSTRACT This is an application for a K01 award for Dr. Lindsay Keegan, an infectious disease epidemiologist at the University of Utah. Dr. Keegan is establishing herself as a young investigator in infectious disease dynamics addressing public health relevant questions on the spread and control of infectious diseases. This K01 award will provide the support necessary to accomplish the following goals to develop: (1) expertise in genomic methods as it relates to epidemiology; (2) expertise in biostatistics particularly probability theory, network models, and Bayesian statistics; (3) expertise in healthcare epidemiology as it relates to pathogen transmission within healthcare facilities (HCF); and (4) and strengthen research leadership and management skills. To achieve these goals, Dr. Keegan has assembled a mentoring team comprised of: Dr. Matthew Samore (primary mentor), a healthcare epidemiologist and expert in mathematical modeling of healthcare associated infections; and Dr. Marc Lipsitch (co-mentor), an epidemiologist and a recognized leader in mathematical modeling of infectious diseases including modeling healthcare associated infections, and Dr. Michael Rubin (co-mentor), and infectious disease physician and an expert on translational science to support antimicrobial stewardship and infection prevention and leadership. Dr. Keegan has also assembled a team of four advisors with expertise in biostatistics, genomic epidemiology, mathematical modeling, and hospital epidemiology. Antibiotic resistant bacteria pose a significant public health threat, causing over 2.8 million infections and over 35,000 deaths each year in the United States. The burden of these infections is concentrated within HCFs; and how to control these pathogens remains the source of considerable debate. Based on data collected from a prior CDC study, Dr. Keegan’s central hypothesis is that healthcare associated pathogens are spreading primarily indirectly between patients and environmental surfaces via patient shedding and inadequate source control. By pursuing the following Specific Aims, Dr. Keegan will test her hypotheses and develop methods to apply to future data sets (for a future proposed R01 application during the K01 period). In Specific Aim 1, Dr. Keegan will test the hypothesis that there will be phylogenetic support for clustering between pathogens isolated from the environment and those isolated from patients. In Specific Aim 2 she will construct and validate a model that integrates contact network data with pathogen genomic data to probabilistically infer the direction of transmission events within a HCF. In Specific Aim 3, she will test the hypothesis that the majority of patient-to-patient transmission events are mediated by environmental surfaces. The proposed research is significant because it addresses a critical barrier to improving infection control: without quantifying the role of different sources for pathogen transmission, infection control practices cannot intentionally target the key transmission steps. The proposed research is innovative because a method to infer transmission events for bacteria does not currently exist despite its critical importance in infection control.