TITLE:
Geo-Spatial Mapping of Tuberculosis Burden in Anambra State, South-East Nigeria
AUTHORS:
Chukwuebuka Immanuel Ugwu, Ugochukwu Chukwulobelu, Chukwumuanya Igboekwu, Nwamaka Emodi, Joseph Uche Anumba, Samuel Chika Ugwu, Chidinma Lilian Ezeobi, Vivian Ibeziako, Glory Ugochukwu Nwakaogor
KEYWORDS:
Tuberculosis Transmission Hotspots, Disease Surveillance, Community Tuberculosis Burden
JOURNAL NAME:
Journal of Tuberculosis Research,
Vol.9 No.1,
March
31,
2021
ABSTRACT: 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.