Zhao Fei, Cheng Shiming, He Guangxue, Huang Fei, Zhang Hui, Xu Biao, Murimwa Tonderayi C, Cheng Jun, Hu Dongmei, Wang Lixia
National Center for Tuberculosis Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China.
School of Public Health, Fudan University, Shanghai, China.
PLoS One. 2013 Dec 19;8(12):e83605. doi: 10.1371/journal.pone.0083605. eCollection 2013.
China is one of the 22 tuberculosis (TB) high-burden countries in the world. As TB is a major public health problem in China, spatial analysis could be applied to detect geographic distribution of TB clusters for targeted intervention on TB epidemics.
Spatial analysis was applied for detecting TB clusters on county-based TB notification data in the national notifiable infectious disease case reporting surveillance system from 2005 to 2011. Two indicators of TB epidemic were used including new sputum smear-positive (SS+) notification rate and total TB notification rate. Global Moran's I by ArcGIS was used to assess whether TB clustering and its trend were significant. SaTScan software that used the retrospective space-time analysis and Possion probability model was utilized to identify geographic areas and time period of potential clusters with notification rates on county-level from 2005 to 2011.
Two indicators of TB notification had presented significant spatial autocorrelation globally each year (p<0.01). Global Moran's I of total TB notification rate had positive trend as time went by (t=6.87, p<0.01). The most likely clusters of two indicators had similar spatial distribution and size in the south-central regions of China from 2006 to 2008, and the secondary clusters in two regions: northeastern China and western China. Besides, the secondary clusters of total TB notification rate had two more large clustering centers in Inner Mongolia, Gansu and Qinghai provinces and several smaller clusters in Shanxi, Henan, Hebei and Jiangsu provinces.
The total TB notification cases clustered significantly in some special areas each year and the clusters trended to aggregate with time. The most-likely and secondary clusters that overlapped among two TB indicators had higher TB burden and risks of TB transmission. These were the focused geographic areas where TB control efforts should be prioritized.
中国是世界上22个结核病高负担国家之一。由于结核病是中国的一个主要公共卫生问题，空间分析可用于检测结核病聚集区的地理分布，以便对结核病流行进行有针对性的干预。
应用空间分析方法，对2005年至2011年国家法定传染病病例报告监测系统中基于县的结核病报告数据进行结核病聚集区检测。使用了两个结核病流行指标，包括新涂阳（SS+）报告率和结核病总报告率。利用ArcGIS软件的全局莫兰指数（Global Moran's I）评估结核病聚集及其趋势是否显著。运用SaTScan软件，采用回顾性时空分析和泊松概率模型，确定2005年至2011年县级报告率的潜在聚集区的地理区域和时间段。
每年结核病报告的两个指标在全球范围内均呈现出显著的空间自相关性（p<0.01）。结核病总报告率的全局莫兰指数随时间呈上升趋势（t=6.87，p<0.01）。2006年至2008年，两个指标最可能的聚集区在中国中南部地区具有相似的空间分布和规模，其次是中国东北地区和西部地区。此外，结核病总报告率的次聚集区在内蒙古、甘肃和青海省还有两个较大的聚集中心，在山西、河南、河北和江苏省有几个较小的聚集区。
每年结核病报告病例在一些特定地区显著聚集，且聚集区有随时间聚集的趋势。两个结核病指标重叠的最可能和次聚集区结核病负担和传播风险较高。这些是结核病防控工作应优先关注的重点地理区域。
