背景:在斯威士兰,就像在许多艾滋病毒/结核病高负担环境中一样,没有关于结核病病例的家庭位置的信息来确定结核病发病率增加的地区,限制了有针对性的干预措施的发展。数据来自\"Butimba\",结核病REACH活跃病例发现项目,进行了重新分析,以深入了解Mbabane周围结核病例的位置,斯威士兰.
目的:该项目旨在确定结核病负担较高的地理区域,为积极的病例发现工作提供参考。
方法:Butimba实施了家庭接触追踪;获得基于地标的,非正式方向,索引案例房屋,这里定义为相对位置。使用MicrosoftExcel模糊查找功能将相对位置与人口普查枚举区域(已知位置参考区域)进行匹配。在403个相对位置中,在388例(96%)中检测到计数区域参考。每个人口普查枚举区域的结核病病例和每个Tinkhundla的活跃病例发现者,一个地方政府区域,是使用地理信息系统绘制的,QGIS2.16。
结果:UrbanTinkhundla预计占大多数病例;然而,在调整人口后,病例密度最高的是在Tinkhundla农村地区。目前分配给Mbabane周围7个Tinkhundla的活跃病例发现者的数量与TB病例总数(Spearmanrho=-0.57,p=0.17)或人口调整的TB病例(Spearmanrho=0.14,p=0.75)之间没有相关性。
结论:在高负担环境中降低TB发病率需要新的分析方法来研究TB病例的位置。我们证明了将相对位置与更精确的地理区域联系起来的可行性,为国家结核病计划资源分配提供数据驱动的指导。与斯威士兰国家结核病控制计划合作,该分析强调了更好地将积极病例发现国家战略与结核病疾病负担相结合的机会.
BACKGROUND: In Swaziland, as in many high HIV/TB burden settings, there is not information available regarding the household location of TB cases for identifying areas of increased TB incidence, limiting the development of targeted interventions. Data from \"Butimba\", a TB REACH active
case finding project, was re-analyzed to provide insight into the location of TB cases surrounding Mbabane, Swaziland.
OBJECTIVE: The project aimed to identify geographical areas with high TB burdens to inform active
case finding efforts.
METHODS: Butimba implemented household contact tracing; obtaining landmark based, informal directions, to index
case homes, defined here as relative locations. The relative locations were matched to census enumeration areas (known location reference areas) using the Microsoft Excel Fuzzy Lookup function. Of 403 relative locations, an enumeration area reference was detected in 388 (96%). TB cases in each census enumeration area and the active
case finders in each Tinkhundla, a local governmental region, were mapped using the geographic information system, QGIS 2.16.
RESULTS: Urban Tinkhundla predictably accounted for most cases; however, after adjusting for population, the highest density of cases was found in rural Tinkhundla. There was no correlation between the number of active
case finders currently assigned to the 7 Tinkhundla surrounding Mbabane and the total number of TB cases (Spearman rho = -0.57, p = 0.17) or the population adjusted TB cases (Spearman rho = 0.14, p = 0.75) per Tinkhundla.
CONCLUSIONS: Reducing TB incidence in high-burden settings demands novel analytic approaches to study TB case locations. We demonstrated the feasibility of linking relative locations to more precise geographical areas, enabling data-driven guidance for National Tuberculosis Programs\' resource allocation. In collaboration with the Swazi National Tuberculosis Control Program, this analysis highlighted opportunities to better align the active case finding national strategy with the TB disease burden.