Mapping the spatial variability of HIV infection in Sub-Saharan Africa: Effective information for localized HIV prevention and control
Identifieur interne : 000552 ( Main/Exploration ); précédent : 000551; suivant : 000553Mapping the spatial variability of HIV infection in Sub-Saharan Africa: Effective information for localized HIV prevention and control
Auteurs : Diego F. Cuadros [États-Unis] ; Jingjing Li [États-Unis] ; Adam J. Branscum [États-Unis] ; Adam Akullian [États-Unis] ; Peng Jia [Pays-Bas] ; Elizabeth N. Mziray [États-Unis] ; Frank Tanser [Afrique du Sud]Source :
- Scientific Reports [ 2045-2322 ] ; 2017.
Abstract
Under the premise that in a resource-constrained environment such as Sub-Saharan Africa it is not possible to do everything, to everyone, everywhere, detailed geographical knowledge about the HIV epidemic becomes essential to tailor programmatic responses to specific local needs. However, the design and evaluation of national HIV programs often rely on aggregated national level data. Against this background, here we proposed a model to produce high-resolution maps of intranational estimates of HIV prevalence in Kenya, Malawi, Mozambique and Tanzania based on spatial variables. The HIV prevalence maps generated highlight the stark spatial disparities in the epidemic within a country, and localize areas where both the burden and drivers of the HIV epidemic are concentrated. Under an era focused on optimal allocation of evidence-based interventions for populations at greatest risk in areas of greatest HIV burden, as proposed by the Joint United Nations Programme on HIV/AIDS (UNAIDS) and the United States President’s Emergency Plan for AIDS Relief (PEPFAR), such maps provide essential information that strategically targets geographic areas and populations where resources can achieve the greatest impact.
Url:
DOI: 10.1038/s41598-017-09464-y
PubMed: 28831171
PubMed Central: 5567213
Affiliations:
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However, the design and evaluation of national HIV programs often rely on aggregated national level data. Against this background, here we proposed a model to produce high-resolution maps of intranational estimates of HIV prevalence in Kenya, Malawi, Mozambique and Tanzania based on spatial variables. The HIV prevalence maps generated highlight the stark spatial disparities in the epidemic within a country, and localize areas where both the burden and drivers of the HIV epidemic are concentrated. Under an era focused on optimal allocation of evidence-based interventions for populations at greatest risk in areas of greatest HIV burden, as proposed by the Joint United Nations Programme on HIV/AIDS (UNAIDS) and the United States President’s Emergency Plan for AIDS Relief (PEPFAR), such maps provide essential information that strategically targets geographic areas and populations where resources can achieve the greatest impact.</p></div></front><back><div1 type="bibliography"><listBibl><biblStruct><analytic><author><name sortKey="Zulu, Lc" uniqKey="Zulu L">LC Zulu</name></author><author><name sortKey="Kalipeni, E" uniqKey="Kalipeni E">E Kalipeni</name></author><author><name sortKey="Johannes, E" uniqKey="Johannes E">E Johannes</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Cuadros, Df" uniqKey="Cuadros D">DF Cuadros</name></author><author><name sortKey="Awad, Sf" uniqKey="Awad S">SF Awad</name></author><author><name sortKey="Abu Raddad, Lj" 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Njeuhmeli</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Tanser, F" uniqKey="Tanser F">F Tanser</name></author><author><name sortKey="B Rnighausen, T" uniqKey="B Rnighausen T">T Bärnighausen</name></author><author><name sortKey="Grapsa, E" uniqKey="Grapsa E">E Grapsa</name></author><author><name sortKey="Zaidi, J" uniqKey="Zaidi J">J Zaidi</name></author><author><name sortKey="Newell, M L" uniqKey="Newell M">M-L Newell</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Tanser, F" uniqKey="Tanser F">F Tanser</name></author><author><name sortKey="Lesueur, D" uniqKey="Lesueur D">D LeSueur</name></author><author><name sortKey="Solarsh, G" uniqKey="Solarsh G">G Solarsh</name></author><author><name sortKey="Wilkinson, D" uniqKey="Wilkinson D">D Wilkinson</name></author></analytic></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct></listBibl></div1></back></TEI><affiliations><list><country><li>Afrique du Sud</li><li>Pays-Bas</li><li>États-Unis</li></country><region><li>District de Columbia</li></region></list><tree><country name="États-Unis"><noRegion><name sortKey="Cuadros, Diego F" sort="Cuadros, Diego F" uniqKey="Cuadros D" first="Diego F." last="Cuadros">Diego F. Cuadros</name></noRegion><name sortKey="Akullian, Adam" sort="Akullian, Adam" uniqKey="Akullian A" first="Adam" last="Akullian">Adam Akullian</name><name sortKey="Branscum, Adam J" sort="Branscum, Adam J" uniqKey="Branscum A" first="Adam J." last="Branscum">Adam J. Branscum</name><name sortKey="Cuadros, Diego F" sort="Cuadros, Diego F" uniqKey="Cuadros D" first="Diego F." last="Cuadros">Diego F. Cuadros</name><name sortKey="Li, Jingjing" sort="Li, Jingjing" uniqKey="Li J" first="Jingjing" last="Li">Jingjing Li</name><name sortKey="Mziray, Elizabeth N" sort="Mziray, Elizabeth N" uniqKey="Mziray E" first="Elizabeth N." last="Mziray">Elizabeth N. Mziray</name></country><country name="Pays-Bas"><noRegion><name sortKey="Jia, Peng" sort="Jia, Peng" uniqKey="Jia P" first="Peng" last="Jia">Peng Jia</name></noRegion></country><country name="Afrique du Sud"><noRegion><name sortKey="Tanser, Frank" sort="Tanser, Frank" uniqKey="Tanser F" first="Frank" last="Tanser">Frank Tanser</name></noRegion><name sortKey="Tanser, Frank" sort="Tanser, Frank" uniqKey="Tanser F" first="Frank" last="Tanser">Frank Tanser</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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