Higgs Brandon W, Mohtashemi Mojdeh, Grinsdale Jennifer, Kawamura L Masae
MITRE Corporation, McLean, Virginia, United States of America.
PLoS One. 2007 Dec 12;2(12):e1284. doi: 10.1371/journal.pone.0001284.
San Francisco has the highest rate of tuberculosis (TB) in the U.S. with recurrent outbreaks among the homeless and marginally housed. It has been shown for syndromic data that when exact geographic coordinates of individual patients are used as the spatial base for outbreak detection, higher detection rates and accuracy are achieved compared to when data are aggregated into administrative regions such as zip codes and census tracts. We examine the effect of varying the spatial resolution in the TB data within the San Francisco homeless population on detection sensitivity, timeliness, and the amount of historical data needed to achieve better performance measures.
We apply a variation of space-time permutation scan statistic to the TB data in which a patient's location is either represented by its exact coordinates or by the centroid of its census tract. We show that the detection sensitivity and timeliness of the method generally improve when exact locations are used to identify real TB outbreaks. When outbreaks are simulated, while the detection timeliness is consistently improved when exact coordinates are used, the detection sensitivity varies depending on the size of the spatial scanning window and the number of tracts in which cases are simulated. Finally, we show that when exact locations are used, smaller amount of historical data is required for training the model.
Systematic characterization of the spatio-temporal distribution of TB cases can widely benefit real time surveillance and guide public health investigations of TB outbreaks as to what level of spatial resolution results in improved detection sensitivity and timeliness. Trading higher spatial resolution for better performance is ultimately a tradeoff between maintaining patient confidentiality and improving public health when sharing data. Understanding such tradeoffs is critical to managing the complex interplay between public policy and public health. This study is a step forward in this direction.
旧金山的结核病发病率在美国最高，在无家可归者和居住条件差的人群中反复爆发疫情。对于症状数据而言，已表明将个体患者的确切地理坐标用作疫情检测的空间基础时，与将数据汇总到邮政编码和人口普查区等行政区时相比，能实现更高的检测率和准确性。我们研究了旧金山无家可归人群结核病数据中空间分辨率的变化对检测敏感性、及时性以及实现更好性能指标所需历史数据量的影响。
我们将时空排列扫描统计量的一种变体应用于结核病数据，其中患者的位置要么由其确切坐标表示，要么由其普查区的质心表示。我们表明，当使用确切位置来识别实际结核病疫情时，该方法的检测敏感性和及时性通常会提高。当模拟疫情时，虽然使用确切坐标时检测及时性始终得到改善，但检测敏感性会因空间扫描窗口的大小以及模拟病例所在普查区的数量而异。最后，我们表明，当使用确切位置时，训练模型所需的历史数据量较少。
结核病病例时空分布的系统特征描述可广泛有益于实时监测，并指导结核病疫情的公共卫生调查，以确定何种空间分辨率水平可提高检测敏感性和及时性。为了获得更好的性能而提高空间分辨率，最终是在共享数据时维护患者隐私与改善公共卫生之间进行权衡。理解这种权衡对于管理公共政策与公共卫生之间的复杂相互作用至关重要。本研究朝着这个方向迈出了一步。
