Geo-Spatial Mapping of Tuberculosis Burden in Anambra State, South-East Nigeria

• 作者: Chukwuebuka Immanuel Ugwu Ugochukwu Chukwulobelu Chukwumuanya Igboekwu Nwamaka Emodi Joseph Uche Anumba Samuel Chika Ugwu Chidinma Lilian Ezeobi Vivian Ibeziako Glory Ugochukwu Nwakaogor
• 来源期刊: 结核病研究(英文)
• 学科: 数学
• 关键词: Tuberculosis Transmission Hotspots Disease Surveillance Community Tuberculosis Burden
• 年，卷（期）: 2021,（1）
• 页码范围: 51-62
• 页数: 12页
• 分类号: O17

摘要

<b><span style="font-family:Verdana;">Background:</span></b><span style="font-family:Verdana;"></span><b> </b><span style="font-family:Verdana;">Anambra state in south-east Nigeria is one of the high TB burden states in the country. Despite recent improvements in TB case notification, estimates from the National Prevalence survey suggest that there is still a significant pool of missed TB cases in the state. Although active TB case finding interventions are needed at community level, information on local TB transmission hotspots is lacking. The objective of this study was to map the geo-spatial location of all TB cases detected in the state in 2019. Findings from this secondary data analysis will help to target interventions appropri</span><span style="font-family:Verdana;">ately with a view to achieving better program efficiency. </span><span style="font-family:Verdana;"><b></b></span><b><b><span style="font-family:Verdana;">Method:</span></b><span style="font-family:Verdana;"></span></b><span style="font-family:Verdana;"> A</span><span style="font-family:Verdana;"> de-identified dataset containing descriptive physical addresses of registered TB cases in 2019 was developed. The dataset was then deconstructed and restructured using Structured Query Language in a relational data base environment. The validated dataset was geocoded using ArcGIS server geocode service and validated using python geocoding toolbox, and Google geocoding API. The resultant geocoded dataset was subjected to geo-spatial analysis and the magnitude-per-unit area of the TB cases was calculated using the Kernel Density function. TB case notification rates were also calculated and Choropleth maps were plotted to portray the TB burden as contained in the dataset. <b></b></span><b><b><span style="font-family:Verdana;">Results:</span></b><span style="font-family:Verdana;"></span></b><b> </b><span style="font-family:Verdana;">Five local government are</span><span style="font-family:Verdana;">as <span style="font-family:;" "="">(</span><span style=