Cargando…

Hot-spots of HIV infection in Cameroon: a spatial analysis based on Demographic and Health Surveys data

BACKGROUND: The Human Immunodeficiency Virus(HIV) infection prevalence in Cameroon has decreased from [Formula: see text] in 2004 to [Formula: see text] in 2018. However, this decrease in prevalence does not show disparities especially in terms of spatial or geographical pattern. Efficient control a...

Descripción completa

Detalles Bibliográficos
Autores principales: Sandie, Arsène Brunelle, Tchatchueng Mbougua, Jules Brice, Nlend, Anne Esther Njom, Thiam, Sokhna, Nono, Betrand Fesuh, Fall, Ndèye Awa, Senghor, Diarra Bousso, Sylla, El Hadji Malick, Faye, Cheikh Mbacké
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8981942/
https://www.ncbi.nlm.nih.gov/pubmed/35379192
http://dx.doi.org/10.1186/s12879-022-07306-5
_version_ 1784681708175491072
author Sandie, Arsène Brunelle
Tchatchueng Mbougua, Jules Brice
Nlend, Anne Esther Njom
Thiam, Sokhna
Nono, Betrand Fesuh
Fall, Ndèye Awa
Senghor, Diarra Bousso
Sylla, El Hadji Malick
Faye, Cheikh Mbacké
author_facet Sandie, Arsène Brunelle
Tchatchueng Mbougua, Jules Brice
Nlend, Anne Esther Njom
Thiam, Sokhna
Nono, Betrand Fesuh
Fall, Ndèye Awa
Senghor, Diarra Bousso
Sylla, El Hadji Malick
Faye, Cheikh Mbacké
author_sort Sandie, Arsène Brunelle
collection PubMed
description BACKGROUND: The Human Immunodeficiency Virus(HIV) infection prevalence in Cameroon has decreased from [Formula: see text] in 2004 to [Formula: see text] in 2018. However, this decrease in prevalence does not show disparities especially in terms of spatial or geographical pattern. Efficient control and fight against HIV infection may require targeting hotspot areas. This study aims at presenting a cartography of HIV infection situation in Cameroon using the 2004, 2011 and 2018 Demographic and Health Survey data, and investigating whether there exist spatial patterns of the disease, may help to detect hot-spots. METHODS: HIV biomarkers data and Global Positioning System (GPS) location data were obtained from the Cameroon 2004, 2011, and 2018 Demographic and Health Survey (DHS) after an approved request from the MEASURES Demographic and Health Survey Program. HIV prevalence was estimated for each sampled area. The Moran’s I (MI) test was used to assess spatial autocorrelation. Spatial interpolation was further performed to estimate the prevalence in all surface points. Hot-spots were identified based on Getis–Ord (Gi*) spatial statistics. Data analyses were done in the R software(version 4.1.2), while Arcgis Pro software tools’ were used for all spatial analyses. RESULTS: Generally, spatial autocorrelation of HIV infection in Cameroon was observed across the three time periods of 2004 ([Formula: see text] , [Formula: see text] ), 2011 ([Formula: see text] , [Formula: see text] ) and 2018 ([Formula: see text] , [Formula: see text] ). Subdivisions in which one could find persistent hot-spots for at least two periods including the last period 2018 included: Mbéré, Lom et Djerem, Kadey, Boumba et Ngoko, Haute Sanaga, Nyong et Mfoumou, Nyong et So’o Haut Nyong, Dja et Lobo, Mvila, Vallée du Ntem, Océan, Nyong et Kellé, Sanaga Maritime, Menchum, Dounga Mantung, Boyo, Mezam and Momo. However, Faro et Déo emerged only in 2018 as a subdivision with HIV infection hot-spots. CONCLUSION: Despite the decrease in HIV epidemiology in Cameroon, this study has shown that there are spatial patterns for HIV infection in Cameroon and possible hot-spots have been identified. In its effort to eliminate HIV infection by 2030 in Cameroon, the public health policies may consider these detected HIV hot-spots, while maintaining effective control in other parts of the country.
format Online
Article
Text
id pubmed-8981942
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher BioMed Central
record_format MEDLINE/PubMed
spelling pubmed-89819422022-04-06 Hot-spots of HIV infection in Cameroon: a spatial analysis based on Demographic and Health Surveys data Sandie, Arsène Brunelle Tchatchueng Mbougua, Jules Brice Nlend, Anne Esther Njom Thiam, Sokhna Nono, Betrand Fesuh Fall, Ndèye Awa Senghor, Diarra Bousso Sylla, El Hadji Malick Faye, Cheikh Mbacké BMC Infect Dis Research BACKGROUND: The Human Immunodeficiency Virus(HIV) infection prevalence in Cameroon has decreased from [Formula: see text] in 2004 to [Formula: see text] in 2018. However, this decrease in prevalence does not show disparities especially in terms of spatial or geographical pattern. Efficient control and fight against HIV infection may require targeting hotspot areas. This study aims at presenting a cartography of HIV infection situation in Cameroon using the 2004, 2011 and 2018 Demographic and Health Survey data, and investigating whether there exist spatial patterns of the disease, may help to detect hot-spots. METHODS: HIV biomarkers data and Global Positioning System (GPS) location data were obtained from the Cameroon 2004, 2011, and 2018 Demographic and Health Survey (DHS) after an approved request from the MEASURES Demographic and Health Survey Program. HIV prevalence was estimated for each sampled area. The Moran’s I (MI) test was used to assess spatial autocorrelation. Spatial interpolation was further performed to estimate the prevalence in all surface points. Hot-spots were identified based on Getis–Ord (Gi*) spatial statistics. Data analyses were done in the R software(version 4.1.2), while Arcgis Pro software tools’ were used for all spatial analyses. RESULTS: Generally, spatial autocorrelation of HIV infection in Cameroon was observed across the three time periods of 2004 ([Formula: see text] , [Formula: see text] ), 2011 ([Formula: see text] , [Formula: see text] ) and 2018 ([Formula: see text] , [Formula: see text] ). Subdivisions in which one could find persistent hot-spots for at least two periods including the last period 2018 included: Mbéré, Lom et Djerem, Kadey, Boumba et Ngoko, Haute Sanaga, Nyong et Mfoumou, Nyong et So’o Haut Nyong, Dja et Lobo, Mvila, Vallée du Ntem, Océan, Nyong et Kellé, Sanaga Maritime, Menchum, Dounga Mantung, Boyo, Mezam and Momo. However, Faro et Déo emerged only in 2018 as a subdivision with HIV infection hot-spots. CONCLUSION: Despite the decrease in HIV epidemiology in Cameroon, this study has shown that there are spatial patterns for HIV infection in Cameroon and possible hot-spots have been identified. In its effort to eliminate HIV infection by 2030 in Cameroon, the public health policies may consider these detected HIV hot-spots, while maintaining effective control in other parts of the country. BioMed Central 2022-04-04 /pmc/articles/PMC8981942/ /pubmed/35379192 http://dx.doi.org/10.1186/s12879-022-07306-5 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Sandie, Arsène Brunelle
Tchatchueng Mbougua, Jules Brice
Nlend, Anne Esther Njom
Thiam, Sokhna
Nono, Betrand Fesuh
Fall, Ndèye Awa
Senghor, Diarra Bousso
Sylla, El Hadji Malick
Faye, Cheikh Mbacké
Hot-spots of HIV infection in Cameroon: a spatial analysis based on Demographic and Health Surveys data
title Hot-spots of HIV infection in Cameroon: a spatial analysis based on Demographic and Health Surveys data
title_full Hot-spots of HIV infection in Cameroon: a spatial analysis based on Demographic and Health Surveys data
title_fullStr Hot-spots of HIV infection in Cameroon: a spatial analysis based on Demographic and Health Surveys data
title_full_unstemmed Hot-spots of HIV infection in Cameroon: a spatial analysis based on Demographic and Health Surveys data
title_short Hot-spots of HIV infection in Cameroon: a spatial analysis based on Demographic and Health Surveys data
title_sort hot-spots of hiv infection in cameroon: a spatial analysis based on demographic and health surveys data
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8981942/
https://www.ncbi.nlm.nih.gov/pubmed/35379192
http://dx.doi.org/10.1186/s12879-022-07306-5
work_keys_str_mv AT sandiearsenebrunelle hotspotsofhivinfectionincameroonaspatialanalysisbasedondemographicandhealthsurveysdata
AT tchatchuengmbouguajulesbrice hotspotsofhivinfectionincameroonaspatialanalysisbasedondemographicandhealthsurveysdata
AT nlendanneesthernjom hotspotsofhivinfectionincameroonaspatialanalysisbasedondemographicandhealthsurveysdata
AT thiamsokhna hotspotsofhivinfectionincameroonaspatialanalysisbasedondemographicandhealthsurveysdata
AT nonobetrandfesuh hotspotsofhivinfectionincameroonaspatialanalysisbasedondemographicandhealthsurveysdata
AT fallndeyeawa hotspotsofhivinfectionincameroonaspatialanalysisbasedondemographicandhealthsurveysdata
AT senghordiarrabousso hotspotsofhivinfectionincameroonaspatialanalysisbasedondemographicandhealthsurveysdata
AT syllaelhadjimalick hotspotsofhivinfectionincameroonaspatialanalysisbasedondemographicandhealthsurveysdata
AT fayecheikhmbacke hotspotsofhivinfectionincameroonaspatialanalysisbasedondemographicandhealthsurveysdata