Modeling and Simulation to Support Epidemiological Decision-Making in Healthcare Settings

支持医疗机构中流行病学决策的建模和仿真

• 批准号: 10800785
• 负责人: MATTHEW H SAMORE
• 金额: $ 60万
• 依托单位: UNIVERSITY OF UTAH
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-08-01 至 2024-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10800785
• 关键词: Modeling; Simulation; Support; Epidemiological; Decision

Our proposal assembles an exceptional group of researchers to join the Modeling Infectious Diseases in Healthcare (MInD) network. The University of Utah will serve as the hub, with Harvard School of Public Health operating as a major node. We propose a program of research that will use models and data to make significant contributions to the body of evidence that informs prevention and control of healthcare-associated infections due to antibiotic-resistant pathogens. In the CDC’s 2019 Antibiotic Resistance Threat Report, it was estimated that the burden of resistant infections, including Clostridioides difficile, exceeded 3 million cases per year, with approximately 48,000 deaths. Two of the urgent threats listed in CDC’s Threat Report – carbapenem-resistant Enterobacteriaceae and carbapenem resistant Acinetobacter – and four of the serious threats – extended spectrum beta lactamase-producing Enterobacteriaceae, methicillin-resistant Staphylococcus aureus, vancomycin resistant enterococci, and multi-drug resistant Pseudomonas aeruginosa – are directly addressed in our proposal. Our proposal includes two tightly interwoven and complementary projects that tackle key controversies in infection control and antibiotic stewardship, highlighting the role that dynamic models can play in healthcare epidemiology. Project 1 uses the distinction between horizontal and vertical infection control strategies as a framework to systematically evaluate interventions to prevent transmission, ranging from the level of a hospital room to the region level, with multiple connected facilities. Project 1 powerfully leverages the model, data, and results generated by the CDC-funded "Granular Modeling – Simulating the Transmission of Healthcare- Associated Infections in Hospitals and Control Strategies" project. For Aim 1.1, we will use our agent based model, which has detailed representation of transmission components, to simulate trials of horizontal interventions. For Aim 1.2 we will use a blend of mathematical theory and data analysis to assess the facility- level impact of vertical control strategies across a range of surveillance and cohorting scenarios. For Aim 1.3 we will use a multi-facility framework to model the effects of combinations of horizontal and vertical interventions across multiple pathogens and perform cost-effectiveness analyses that account for indirect population-level effects due to transmission reduction. Project 2 leverages the immense data resources associated with the VA health system’s electronic health records to examine the effect of antibiotic selection pressure on antibiotic resistance profiles for a broad set of high priority pathogens. We will test hypotheses derived from evolutionary models about the impact of co-selection and about the temporal relationships between changes in antibiotic use and antibiotic resistance. We will also apply multivariate time series methods and empirical dynamic modeling to forecast and explain trends in resistance. Our goal is to improve understanding of why some forms of bacterial resistance are decreasing, others are increasing, and others are at relative equilibrium.

我们的计划召集了一组特殊的研究人员加入到传染病建模中 医疗(心理)网络。犹他大学将作为中心，哈佛大学公共卫生学院 作为主要节点运行。我们提出了一个研究计划，将使用模型和数据来 对为预防和控制与医疗保健相关的疾病提供信息的证据的重大贡献 由抗生素耐药病原体引起的感染。在疾控中心2019年抗生素耐药性威胁报告中， 据估计，包括艰难梭状芽胞杆菌在内的耐药感染的负担每年超过300万例 去年，约有48,000人死亡。CDC的威胁报告中列出的两个紧急威胁- 耐碳青霉烯类肠杆菌科和耐碳青霉烯类不动杆菌--以及四种严重的 威胁-产超广谱β-内酰胺酶的肠杆菌科细菌，耐甲氧西林 金黄色葡萄球菌、万古霉素耐药肠球菌和多重耐药铜绿假单胞菌 -在我们的提案中直接涉及。 我们的提案包括两个紧密交织和互补的项目，以解决 感染控制和抗生素管理，强调动态模型在医疗保健中的作用 流行病学。项目1使用水平和垂直感染控制策略之间的区别作为 系统评价预防传播干预措施的框架，范围从医院一级 房间通向区域级别，有多个相连的设施。项目1有效地利用了模型、数据和 美国疾病控制与预防中心资助的“细粒度建模--模拟医疗的传播-- 医院中的相关感染和控制策略“项目。对于Aim 1.1，我们将使用我们的代理基于 模型，其中有详细的变速箱部件的表示，以模拟水平试验 干预措施。对于AIM 1.2，我们将使用数学理论和数据分析相结合的方法来评估设施- 垂直控制战略在一系列监测和队列情景中的水平影响。对于AIM 1.3 我们将使用多设施框架来模拟水平和垂直组合的影响 跨多个病原体的干预措施，并执行成本效益分析，以间接 由于传播减少而产生的人口水平效应。项目2利用了海量的数据资源 与退伍军人健康系统的电子健康记录相关联，以检查抗生素选择的效果 对一系列高优先级病原体的抗生素耐药性图谱的压力。我们将检验假设 源自关于共同选择的影响和关于时间关系的进化模型 抗生素使用的变化和抗生素耐药性之间的关系。我们还将应用多变量时间序列 预测和解释耐药性趋势的方法和经验动态模型。我们的目标是改进 了解为什么某些形式的细菌耐药性正在减少，另一些正在增加，而另一些正在 处于相对平衡状态。