Olson Karen L, Bonetti Marco, Pagano Marcello, Mandl Kenneth D
Children's Hospital Informatics Program, Children's Hospital Boston, Boston, Massachusetts, USA.
BMC Med Inform Decis Mak. 2005 Jun 21;5:19. doi: 10.1186/1472-6947-5-19.
Public health departments in the United States are beginning to gain timely access to health data, often as soon as one day after a visit to a health care facility. Consequently, new approaches to outbreak surveillance are being developed. When cases cluster geographically, an analysis of their spatial distribution can facilitate outbreak detection. Our method focuses on detecting perturbations in the distribution of pair-wise distances among all patients in a geographical region. Barring outbreaks, this distribution can be quite stable over time. We sought to exemplify the method by measuring its cluster detection performance, and to determine factors affecting sensitivity to spatial clustering among patients presenting to hospital emergency departments with respiratory syndromes.
The approach was to (1) define a baseline spatial distribution of home addresses for a population of patients visiting an emergency department with respiratory syndromes using historical data; (2) develop a controlled feature set simulation by inserting simulated outbreak data with varied parameters into authentic background noise, thereby creating semisynthetic data; (3) compare the observed with the expected spatial distribution; (4) establish the relative value of different alarm strategies so as to maximize sensitivity for the detection of clustering; and (5) measure factors which have an impact on sensitivity.
Overall sensitivity to detect spatial clustering was 62%. This contrasts with an overall alarm rate of less than 5% for the same number of extra visits when the extra visits were not characterized by geographic clustering. Clusters that produced the least number of alarms were those that were small in size (10 extra visits in a week, where visits per week ranged from 120 to 472), diffusely distributed over an area with a 3 km radius, and located close to the hospital (5 km) in a region most densely populated with patients to this hospital. Near perfect alarm rates were found for clusters that varied on the opposite extremes of these parameters (40 extra visits, within a 250 meter radius, 50 km from the hospital).
Measuring perturbations in the interpoint distance distribution is a sensitive method for detecting spatial clustering. When cases are clustered geographically, there is clearly power to detect clustering when the spatial distribution is represented by the M statistic, even when clusters are small in size. By varying independent parameters of simulated outbreaks, we have demonstrated empirically the limits of detection of different types of outbreaks.
美国公共卫生部门开始能够及时获取健康数据，通常在患者就诊医疗机构一天后就能获取。因此，正在开发新的疫情监测方法。当病例在地理上聚集时，对其空间分布进行分析有助于疫情检测。我们的方法侧重于检测地理区域内所有患者之间成对距离分布的扰动。在没有疫情的情况下，这种分布随时间可能相当稳定。我们试图通过测量其聚类检测性能来例证该方法，并确定影响到医院急诊科就诊的呼吸道综合征患者对空间聚类敏感性的因素。
该方法包括：（1）使用历史数据为到急诊科就诊的呼吸道综合征患者群体定义家庭住址的基线空间分布；（2）通过将具有不同参数的模拟疫情数据插入真实背景噪声中，从而创建半合成数据，开发一个受控特征集模拟；（3）将观察到的空间分布与预期的进行比较；（4）确定不同警报策略的相对价值，以最大限度地提高聚类检测的敏感性；（5）测量对敏感性有影响的因素。
检测空间聚类的总体敏感性为62%。相比之下，当额外就诊没有地理聚类特征时相同数量的额外就诊总体警报率不到5%。产生警报最少的聚类规模较小（一周内有10次额外就诊，每周就诊次数范围为120至472次），分散分布在半径3公里的区域内，并且位于该医院患者人口最密集区域中距离医院较近（5公里）处。对于在这些参数相反极端情况下变化的聚类（40次额外就诊，半径250米内，距离医院50公里），发现警报率近乎完美。
测量点间距离分布中的扰动是检测空间聚类的一种敏感方法。当病例在地理上聚类时，当空间分布由M统计量表示时，即使聚类规模较小，也显然有能力检测到聚类。通过改变模拟疫情的独立参数，我们凭经验证明了不同类型疫情检测的局限性。
