Author(s)

Chukwuebuka Immanuel Ugwu¹, Ugochukwu Chukwulobelu², Chukwumuanya Igboekwu¹, Nwamaka Emodi², Joseph Uche Anumba³, Samuel Chika Ugwu⁴, Chidinma Lilian Ezeobi⁵, Vivian Ibeziako⁶, Glory Ugochukwu Nwakaogor⁷

¹World Health Organization, Awka Field Office, Anambra, Nigeria.
²Anambra State Ministry of Health, State Tuberculosis Leprosy and Buruli Ulcer Control Program, Awka, Nigeria.
³Federal Ministry of Health, Neglected Tropical Diseases Control and Elimination Programme, Abuja, Nigeria.
⁴Department of Internal Medicine, Jos University Teaching Hospital, Jos, Nigeria.
⁵CARITAS, Anambra, Nigeria.
⁶World Health Organization, TUB Unit, South-East Zonal Office, Enugu, Nigeria.
⁷Healthy Environment for International Development (HEID), Anambra, Nigeria.

Background: 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 appropriately with a view to achieving better program efficiency. Method: A 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. Results: Five local government areas (LGAs) (Onitsha North, Onitsha South, Idemili North, Nnewi North, Ogbaru) had spots with “Extremely high” burden with two LGAs (Onitsha North and South) accounting for the largest spots. Eight LGAs had spots with “Very high” TB burden. Also, 24 hotspots across the state had “High” TB burden and two LGAs (Orumba North, Orumba South) had only “Low” TB burden areas. Conclusion: Visualizing the heat map of TB patients has helped to identify transmission hotspots that will be targeted for case finding interventions and effort should be made to increase sensitization of the people on certain behavioural attributes that may contribute to contracting Tuberculosis.

Tuberculosis Transmission Hotspots, Disease Surveillance, Community Tuberculosis Burden

Ugwu, C. , Chukwulobelu, U. , Igboekwu, C. , Emodi, N. , Anumba, J. , Ugwu, S. , Ezeobi, C. , Ibeziako, V. and Nwakaogor, G. (2021) Geo-Spatial Mapping of Tuberculosis Burden in Anambra State, South-East Nigeria. Journal of Tuberculosis Research, 9, 51-62. doi: 10.4236/jtr.2021.91004.