Trends in Spatial Distribution and Characteristics of HIV Clusters in Cross River State, Nigeria
Author(s)
Antor Ndep , Philip Imohi , Frank Eyam , Bernadine Ekpenyong , Michael Egbe , Kenneth Odey , Kingsley Obase , Cajetan Obi , Ikechukwuka Abah , Franklyn Achara ,
Download Full PDF Pages: 29-35 | Views: 380 | Downloads: 104 | DOI: 10.5281/zenodo.5827912
Volume 10 - December 2021 (12)
Abstract
Introduction: Nigeria bears the highest burden of HIV in Sub-Saharan Africa with about 1.9 million of the population infected with the virus. Objectives: This study aimed to describe the spatial distribution and characteristics of HIV clusters in Cross River State, Nigeria. Methods: The study used a retrospective cohort design to track activities of Community ART Management (CAM) teams over a period of eight months (January-August, 2020). The essence was to determine their contribution to HIV counselling and testing (HTS) including index case testing (ICT) in relation to the GIS mapped hotspots in the study area. Areas of high positivity using spatial autocorrelation analysis techniques in the data were defined. Results: Of the 830 hotspots mapped, the Local Government Areas with the largest number of hotspots included Odukpani, 98(12%), Akamkpa, 89(11%) and Akpabuyo, 82(10%). From January-August, 3170 people tested positive to HIV. HIV-positive yield by geospatial clustering outside of the previously mapped hotspots was 2,339. The distribution by Local Government Area (LGA), showed four LGAs with the largest HIV positive clusters; Yakurr with 495(21%), Akamkpa, 436(19%), Akpabuyo, 409(17%), and Bakassi, 336(14%). Geospatial clustering of HIV positives at the 90% confidence by LGA was 353; at the 95% confidence was 588 and at the 99% confidence was 1,398. The largest clusters at 99% CI were Yakurr, 420(30%), Bakassi, 201(14.4%), Akamkpa, 194(14%), and Akpabuyo, 171(12%). Conclusion/Recommendations: HTS and ICT should focus more on these large clusters to maximize efficiency in testing and initiation to HIV care within the state
Keywords
GIS, spatial analysis, HIV/AIDS, clusters
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