US Army Medical Research Directorate-Kenya (USAMRD-K)/Kenya Medical Research Institute, Kisumu, School of Public Health and Community Development, Maseno University, Kisumu, Kenya, INDEPTH Network, Accra, Ghana.
School of Public Health and Community Development, Maseno University, Kisumu, Kenya.
Int J Tuberc Lung Dis. 2019 Mar 1;23(3):363-370. doi: 10.5588/ijtld.18.0245.
Effective management of tuberculosis (TB) and reduction of TB incidence relies on knowledge of where, when and to what degree the disease is present.
In a retrospective cross-sectional study, we analysed the spatial distribution of notified TB incidence from 1 January 2012 and 31 December 2015 in Siaya and Kisumu Counties, Western Kenya. TB data were obtained from the Division of Leprosy, Tuberculosis and Lung Disease, Nairobi, Kenya, as part of an approved TB case detection study. Cases were linked to their corresponding geographic location using physical address identifiers. Spatial analysis techniques were used to examine the spatial and temporal patterns of TB. Assessment of spatial clustering was carried out following Moran's method of spatial autocorrelation and the Getis-Ord Gi* statistic.
The notified TB incidence varied from 638.0 to 121.4 per 100 000 at the small area level. Spatial analysis identified 16 distinct geographic regions with high TB incidence clustering (GiZScore 2.58, < 0.01). There was a positive correlation between population density and TB incidence that was statistically significant (rs = 0.5739, = 0.0001).
The present study presents an opportunity for targeted interventions in the identified subepidemics to supplement measures aimed at the general population.
结核病（TB）的有效管理和发病率的降低依赖于对疾病发生的地点、时间和程度的了解。
在一项回顾性的横断面研究中，我们分析了 2012 年 1 月 1 日至 2015 年 12 月 31 日期间肯尼亚西部锡亚亚县和基苏木县报告的结核病发病率的空间分布情况。结核病数据来自肯尼亚内罗毕麻风、结核病和肺部疾病司，是经批准的结核病病例检出研究的一部分。通过物理地址标识符将病例与相应的地理位置联系起来。使用空间分析技术来检查结核病的时空模式。采用莫兰（Moran's）方法的空间自相关和 Getis-Ord Gi*统计量评估空间聚类。
小地区层面的报告结核病发病率从 638.0 到 121.4/100000 不等。空间分析确定了 16 个具有高结核病发病率聚集的独特地理区域（GiZScore 2.58， < 0.01）。人口密度与结核病发病率之间存在正相关关系，具有统计学意义（rs = 0.5739， = 0.0001）。
本研究为在确定的次流行地区进行有针对性的干预提供了机会，以补充针对一般人群的措施。
