Modeling Health System Infectious Disease Data

描述(申请人提供)：及早发现由新出现的病原体引起的生物恐怖主义和传染病暴发对公共卫生非常重要，以便迅速实施控制措施。常规收集的自动化医疗服务数据，包括微生物学实验室测试、门诊护理和急诊科就诊、住院治疗、诊断测试和处方药数据，可能对疾病爆发检测非常有用。然而，只要受监测的结果已经存在于不需要采取行动的某一基线水平，就需要数学、计算和统计模型来实施这类系统。例如，如果有适当的信号检测方法，可以通过识别寻求治疗咳嗽和发烧的异常数量的患者来加速识别炭疽生物恐怖袭击。 在这个项目中，我们将开发模型，以便及早发现传染病暴发，并在发现疫情后对其进行监测。这包括(I)描述使用相关保健服务的人数的自然、时间和地理变化的模型，以便根据季节和周中的影响进行调整；以及(Ii)不同的时空像差检测模型，该模型将在疫情发生时产生信号。这些模型将应用于不同的地理范围，从一家医院的个别病房到整个国家，以及从非常普遍的症状(如发烧)到特定的微生物疾病菌株和从一个细菌物种迁移到另一个细菌物种的抗菌素耐药性图谱的不同数据特异性。我们将在覆盖400多万人的两个医疗计划(马萨诸塞州的哈佛朝圣者医疗保健计划和北加州凯撒永久医疗计划)、一家美国大型转诊医院(布里格姆和妇女医院)、一个全州(马萨诸塞州)MRSA注册机构以及一个由55家医院组成的国家(阿根廷)联盟中开发和测试我们的新方法和模型。这些模型和方法将使用这些卫生系统的历史数据和基于不同传染病传播动力学模型的模拟数据进行评估。

DESCRIPTION (provided by applicant): Early detection of bioterrorism and infectious disease outbreaks caused by emerging pathogens is very important for public health, to allow prompt implementation of control measures. Routinely collected, automated health services data, including microbiology laboratory tests, ambulatory care and emergency department visits, hospitalizations, diagnostic tests, and prescription drug data could potentially be very useful for disease outbreak detection. However, mathematical, computational and statistical models are needed to implement such systems whenever the outcomes under surveillance already exist at some baseline level that does not require action. For example, if appropriate signal detection methods were available, identification of an anthrax bioterrorism attack might be accelerated through recognition of an unusual number of patients seeking care for cough and fever. In this project, we will develop models for the early detection of infectious disease outbreaks and for monitoring an outbreak after it has been detected. This includes (i) models describing the natural temporal and geographical variation in the number of people utilizing the health services of interest, in order to adjust for e.g. seasonal and day-of-week effects and (ii) different space-time aberration detection models that will generate a signal when an outbreak have occurred. These models will be applied at different geographical scales, from individual wards of a single hospital to a whole country, as well as for different data specificity from very general symptoms such as fever to specific microbial disease strains and antimicrobial resistance profiles that migrate from one bacterial species to another. We will develop and test our new methods and models in two health plans (Harvard Pilgrim Health Care in Massachusetts, and Kaiser Permanente Northern California) that cover over 4 million people, a single large US referral hospital (Brigham and Women's), a statewide (Massachusetts) registry of MRSA, and a national (Argentine) consortium of 55 hospitals that monitors antibiotic resistance. The models and methods will be evaluated using both historical data from these health systems and simulated data based on different infectious disease transmission dynamics models.