Modeling Health System Infectious Disease Data

卫生系统传染病数据建模

• 批准号: 7168833
• 负责人: Richard Platt
• 金额: $ 56.31万
• 依托单位: HARVARD PILGRIM HEALTH CARE, INC.
• 依托单位国家: 美国
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-02-01 至 2011-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7168833
• 关键词: Accident and Emergency department; Address; Algorithms; Ambulatory Care; Anthrax disease; Antibiotic Resistance; Antimicrobial Resistance; Area; Argentina; Beds; Behavioral; Bioterrorism; Boston; California; Case Study; Characteristics; Cities; Clinic; Communicable Diseases; Complement; Complex; Computing Methodologies; Coughing; Country; County; Data; Data Set; Databases; Detection; Diagnosis; Diagnostic tests; Diarrhea; Dimensions; Disease; Disease Outbreaks; Disease Resistance; Drug Prescriptions; Early Diagnosis; Electronic Health Record; Electronics; Emerging Communicable Diseases; Fever; Health Personnel; Health Planning; Health Services; Health care facility; Health system; Healthcare; Hospital Referrals; Hospitalization; Hospitals; Human; Individual; Infection; Influenza; Informatics; Investigation; Knowledge; Laboratories; Laboratory Diagnosis; Learning; Location; Massachusetts; Measures; Medical Surveillance; Methods; Microbe; Microbiology; Modeling; Monitor; Nature; Noise; Numbers; Operative Surgical Procedures; Outcome; Patients; Pattern; Performance; Pharmaceutical Preparations; Pharmacy facility; Phenotype; Public Health; Range; Readiness; Records; Registries; Reporting; Research; Research Personnel; Resistance profile; Resolution; Scanning; Signal Transduction; Simulate; Space Models; Specificity; Specimen; Statistical Models; Symptoms; Syndrome; System; Test Result; Testing; Time; Vaccination; Variant; Visit; Vomiting; Week; Woman; Work; Zip Code; base; beta-Lactamase; care seeking; day; design; disease transmission; infectious disease model; interest; methicillin resistant Staphylococcus aureus; metropolitan; microbial disease; pathogen; programs; respiratory; statistics; syndromic surveillance; transmission process; vigilance; ward

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.

描述（申请人提供）：早期发现新出现病原体引起的生物恐怖主义和传染病疫情对于公共卫生非常重要，以便及时实施控制措施。自动收集的自动化卫生服务数据，包括微生物实验室检测、门诊护理和急诊科就诊、住院、诊断检测和处方药数据，可能对疾病爆发检测非常有用。然而，只要监测的结果已经处于某种基线水平，不需要采取行动，就需要数学、计算和统计模型来实施这种系统。例如，如果有适当的信号检测方法，通过识别因咳嗽和发烧而寻求治疗的病人数量异常，就可能加快炭疽生物恐怖主义袭击的识别。 在这个项目中，我们将开发用于早期发现传染病爆发和在发现后监测爆发的模型。这包括：（一）描述利用相关卫生服务的人数在时间和地理上的自然变化的模型，以便调整季节和星期几等影响;（二）不同的时空畸变检测模型，当爆发时将产生信号。这些模型将应用于不同的地理尺度，从单个医院的单个病房到整个国家，以及从发烧等非常一般的症状到特定的微生物疾病菌株和从一种细菌物种迁移到另一种细菌物种的抗菌素耐药性谱的不同数据特异性。我们将在两个覆盖400多万人的健康计划（马萨诸塞州的哈佛朝圣者医疗保健和北方加州凯撒医疗机构）、一家大型美国转诊医院（布里格姆和妇女医院）、一个全州（马萨诸塞州）MRSA登记处和一个由55家医院组成的监测抗生素耐药性的国家（阿根廷）联盟中开发和测试我们的新方法和模型。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对模型和方法进行评估。