Geospatial clustering of newly diagnosed HIV infected adults in Cross River State helps define new “hotspots”

Authors

  • Michael Egbe Department of HIV/AIDS Research, Enareh Public Health Consultancy, Calabar, Cross River State, Nigeria https://orcid.org/0000-0002-0453-1226
  • Antor O. Ndep Department of Public Health, Faculty of Allied Medical Sciences, University of Calabar, Nigeria https://orcid.org/0000-0002-3273-8195
  • Philip Imohi Department of HIV/AIDS Research, Enareh Public Health Consultancy, Calabar, Cross River State, Nigeria https://orcid.org/0000-0003-3901-4154
  • Kingsley Obase Department of HIV/AIDS Research, Enareh Public Health Consultancy, Calabar, Cross River State, Nigeria https://orcid.org/0000-0002-9527-122X
  • Frank Eyam Department of HIV/AIDS Research, Enareh Public Health Consultancy, Calabar, Cross River State, Nigeria
  • Betta Edu Office of the Commissioner, Cross River State Ministry of Health, Calabar, Nigeria

DOI:

https://doi.org/10.17532/jhsci.2020.912

Keywords:

Hotspots , HIV/AIDS , geographic information system , geospatial , clustering , transactional sex , maritime work

Abstract

Introduction: A hotspot is a geographical location having evidence of high STIs/HIV prevalence, and/or behaviors that put people at high risk of becoming infected. Therefore, Nigeria, with almost two million people living with HIV, could be considered a giant “hotspot.” The main aim was to describe how the geospatial clustering of newly diagnosed HIV-infected adults in Cross River State helps define new “hotspots.”
Methods: Secondary data collected between January 2020 and March 2020, identified and mapped around a presumed hotspot’s radius of influence (ROI), were analyzed using a “Hotspot Analysis” plugin in QGIS software. With a sample size of 3019, both seropositive and seronegative results were geo-referenced and the resultant map was analyzed to determine HIV-positive clusters.
Results: From the 3019 spatial locations mapped, 720 (23.9%) were positive cases. Of these, 328 (45.6%) were thus estimated as being associated with the presumed hotspots. The remaining 392 (54.4%) were positive cases identified outside of the ROI of the originally presumed hotspots. The total number of mapped HIV testing services (HTS) points (both negative and positive cases) within the hotspot ROI was 1319, while those outside of the hotspot ROI were 1700.
Discussion: Collectively, the clustering of the HTS points into various groups indicating that hard-to-reach communities along the borders with the Republic of Cameroon on the east and Akwa Ibom State on the southwest had a majority of the new clusters.
Conclusion: Unique hotspots where social gatherings occur tended to have a wider ROI. Targeted testing in these hard-to-reach communities is recommended.

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Published

2020-06-28

How to Cite

Egbe, M., O. Ndep, A., Imohi, P., Obase, K., Eyam, F., & Edu, B. (2020). Geospatial clustering of newly diagnosed HIV infected adults in Cross River State helps define new “hotspots”. Journal of Health Sciences. https://doi.org/10.17532/jhsci.2020.912

Issue

Section

Research articles