Geo-Spatial Mapping of Tuberculosis Burden in Anambra State, South-East 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 How to cite this paper: Ugwu, C.I., Chukwulobelu, U., Igboekwu, C., Emodi, N., Anumba, J.U., Ugwu, S.C., Ezeobi, C.L., Ibeziako, V. and Nwakaogor, G.U. (2021) Geo-Spatial Mapping of Tuberculosis Burden in Anambra State, South-East Nigeria. Journal of Tuberculosis Research, 9, 51-62. https://doi.org/10.4236/jtr.2021.91004 Received: February 11, 2021 Accepted: March 28, 2021 Published: March 31, 2021 Copyright © 2021 by author(s) and Scientific Research Publishing Inc. This work is licensed under the Creative Commons Attribution International License (CC BY 4.0). http://creativecommons.org/licenses/by/4.0/ Open Access


Introduction
Tuberculosis (TB) is an infectious disease usually caused by "Mycobacterium tuberculosis" (MTB) bacteria. Tuberculosis generally affects the lungs, but can also affect other parts of the body [1] [2] [3]. Most infections show no symptoms in which case it is known as latent tuberculosis. About 10% of latent infections progress to active disease which, if left untreated, kills about half of those affected [4] [5]. The classic symptoms of active TB are a chronic cough with blood-containing mucus, fever, and night sweats. It was historically called consumption cough due to the weight loss. Infection of other organs causes a wide range of symptoms.
Despite increasing treatment success rates, tuberculosis (TB) continues to spread worldwide. The World Health Organization (WHO) estimates that, in 2015, 10.4 million people developed TB and more than 1 million died by the disease [6], with the burden of TB distributed unevenly in the world. The highest incidence rate of TB is being reported in Africa [7]. The United Nations' (UN) Sustainable Development Goals (SDGs) and WHO's End TB Strategy have a common target of ending the global TB epidemic by 2030 [8]. To achieve this ambitious target, there is a need to narrow TB interventions with consideration of the geographical inequalities prevalent in the areas with high TB burden. This approach also helps in increasing access to preventive, diagnostic and treatment services for those at the highest risk of TB. In resource-limited and high-TB burden countries such as Nigeria, regarded as one of the TB high burden Countries by WHO [9], identifying hotspot areas for targeted interventions is particularly important to optimize use of available resources and to increase efficiencies in the delivery of TB services [10]. This particular study is set in Anambra state located in the South-east region of Nigeria and is also regarded as one of the high TB burden states within the region. Several community-based active surveillance interventions have been implemented with varying degrees of success in the state. It is hoped that this Geospatial analysis of TB hotspots will help to increase the yield of TB surveillance activities in the state. Spatial analytics of prevalence in epidemiology at national and subnational levels is useful in identifying vulnerable populations and geographic areas of TB [11]. Understanding the local dynamics in TB prevalence across geographic regions is effective in designing and implementing planned interventions focused on TB control [12]. TB data with spatial foot prints can help to plan the allocation of resources available for TB control in many settings to achieve better efficiency.
Aim and Objectives: The objective of this study was to map the geo-spatial location of all drug-susceptible and drug-resistant TB cases detected in 2019 in Anambra state, Nigeria and by comparing the case notification rates (cases per 100,000 population) across the Local government areas, map the TB hotspots in the state. Findings from this secondary data analysis will help to target interventions appropriately with a view to achieving better program efficiency.

Study Area
Anambra is a state in southeastern part of Nigeria

Ethical Approval
Permission to conduct this secondary analysis on an already existing dataset was sought and obtained from the Research ethics unit of the directorate for planning research and statistics of the Anambra state ministry of health. The dataset was anonymized as personal identifiers were removed before the permission for use was granted.

Geocoding
The datasets were deconstructed, and restructured using Structured Query Lan- where i = 1, ···, n are the input points. Only include points in the sum if they are within the radius distance of the (x, y) location; pop i is the population field value of point i, which is an optional parameter; dist i is the distance between point i and the (x, y) location.
Furthermore, Kernel Density was used to calculate the density of point features (cases) around each output raster cell. This calculation was done to determine the magnitude-per-unit area from the point features (TB Cases) using a kernel function to fit a smoothly tapered surface to each point or polyline. For the point data in this study, the summation of densities of all points within a bandwidth around the focus point was calculated using the formula below:

Result
A total of 2411 physical addresses of DS-TB cases were geocoded to obtain corresponding geographic coordinates. Of these cases, 2295 corresponding coordinates fell into the area of study (Anambra LGAs) based on the set geocoding parameters with a hit rate of 95.2% (shown in Figure 1). With respect to the DR-TB cases, 133 addresses were geocoded; of these, 124 fell into the area of study (Anambra LGAs) with a hit rate of 93.2% based on the geocoding parameters.
The case notification rate calculations are summarized in Table 1 below  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.
The childhood TB burden displayed in Figure 4 portrays a slightly different picture. Idemili North LGA is the area with the highest burden of TB amongst the 0 -14 age group. This is followed in density by Ogbaru, Onitsha north and south LGAs. Although the four LGAs were part of the LGAs with the highest burden in the adult category, Idemili North has a higher burden than the others.

Discussion
This study found that about 5% of all DS-TB patients and nearly 7% of DR-TB

Conclusion
To interrupt community transmission of TB, Community-based active surveillance interventions are needed. However, these interventions are known to be capital intensive as the high number needed to test to diagnose a case of TB in the general population and other logistics challenges tend to increase the cost. A prior understanding of geo-spatial density of the missing TB cases in high-burden resource-limited settings portends better program efficiency in resource use. Although our findings have shown that urban and peri-urban slum areas with high population density harbour most of the transmission hotspots in our state, more research is needed to understand the recency of these transmissions using real time data as well as the exact mechanisms through which these high burden areas have come to be. Disease control programs in our settings and similar are likely to benefit from such efforts that improve understanding of geographical distribution of cases.