Data Evaluation for Early Disease Outbreak Detection

及早发现疾病有助于及时实施适当的疾病控制措施 暴发，无论是由于生物恐怖主义还是自然发生的病原体。目前存在一系列 关于例如门诊护理和急诊科就诊的自动化卫生系统数据， 住院、诊断测试和药物；此外，这些数据很可能是可用的 随着更多地使用卫生信息技术，这一数字将增加。虽然这样的信息对于 疾病暴发检测，对早期不同数据源的相对优点了解不够 疾病暴发的检测。 在这个项目中，我们将评估和比较不同的卫生服务数据源在早期 疾病暴发检测，包括电话查询、门诊护理访问、急诊科 探视、实验室化验要求和结果、放射学化验、住院、药物处方和药物 配药。作为试验床，我们将使用两个大型综合医疗服务系统(哈佛朝圣者健康中心 CARE/哈佛先锋医疗协会和北加州凯撒永久医疗公司) 关于400多万人的全面电子医疗信息。这意味着我们将拥有 关于完全相同的明确定义的人群的每个健康遭遇数据源的信息，即 对于正确的比较是至关重要的。将使用所有三种统计信号检测对数据源进行评估 由Biosense Initiative选择的算法，加上时空排列扫描统计。后者 自动调整数据中的任何纯时间和纯空间变化，以便数据 比较并不依赖于我们通过统计回归对噪声建模的相对成功 不同数据源的模型。将根据数量来评估不同的数据源， 四种不同方式的信号的及时性、准确性和精确度，(1)信号总数与 预计在没有暴发的零假设下，(Ii)信号与已知疾病之间的一致性 如地方公共卫生部门所定义的暴发，(3)确认或拒绝信号 查看产生信号的个人的后续详细健康信息，(Iv)存在 或者不是信号，当真实数据中添加了模拟疫情时。

Timely implementation of appropriate disease control measures is facilitated by earlier detection of disease outbreaks whether due to bioterrorism or naturally occurring pathogens. There currently exists a range of automated health systems data concerning e.g. ambulatory care and emergency department visits, hospitalizations, diagnostic tests and pharmaceutical drugs; moreover, the availability of these data is likely to increase with greater use of health information technology. While such information could be invaluable for disease outbreak detection, not enough is known about the relative merits of different data sources for early detection of disease outbreaks. In this project we will evaluate and compare the efficacy of different health services data sources for early disease outbreak detection, including telephone inquiries, ambulatory care visits, emergency department visits, laboratory test requests and results, radiology tests, hospitalizations, drug prescriptions and drug dispensings. As test-beds we will use two large integrated health delivery systems (Harvard Pilgrim Health Care / Harvard Vanguard Medical Associates and Kaiser Permanente Northern California) with comprehensive electronic medical information on over four million persons. This means that we will have information about each health encounter data source for exactly the same well-defined population, which is critical for proper comparison. The data sources will be evaluated using all three statistical signal detection algorithms chosen by the BioSense Initiative, plus the space-time permutation scan statistic. The latter automatically adjusts for any purely temporal and purely spatial variation in the data, so that the data comparison does not depend on our relative success at modeling that noise through statistical regression models for different data sources. The different data sources will be evaluated with respect to the number, timeliness, accuracy and precision of signals in four different ways, (i) Total number of signals compared to expect under the null hypothesis of no outbreaks, (ii) Concordance between signals and known disease outbreaks as defined by e.g. local public health departments, (iii) Confirmation or rejection of signals by boking at subsequent detailed health information for those individuals generating the signals, (iv) Presence or not of signals when the real data is spiked with simulated outbreaks.