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Geographically weighted regression of land cover determinants of Plasmodium falciparum transmission in the Ashanti Region of Ghana

Identifieur interne : 001849 ( Ncbi/Merge ); précédent : 001848; suivant : 001850

Geographically weighted regression of land cover determinants of Plasmodium falciparum transmission in the Ashanti Region of Ghana

Auteurs : Lutz Ehlkes [Allemagne] ; Anne Caroline Krefis [Allemagne] ; Benno Kreuels [Allemagne] ; Ralf Krumkamp [Allemagne] ; Ohene Adjei [Ghana] ; Matilda Ayim-Akonor [Ghana] ; Robin Kobbe [Allemagne] ; Andreas Hahn [Allemagne] ; Christof Vinnemeier [Allemagne] ; Wibke Loag [Allemagne] ; Udo Schickhoff [Allemagne] ; Jürgen May [Allemagne]

Source :

RBID : PMC:4192530

Abstract

Background

Malaria is a mosquito-borne parasitic disease that causes severe mortality and morbidity, particularly in Sub-Saharan Africa. As the vectors predominantly bite between dusk and dawn, risk of infection is determined by the abundance of P. falciparum infected mosquitoes in the surroundings of the households. Remote sensing is commonly employed to detect associations between land use/land cover (LULC) and mosquito-borne diseases. Due to challenges in LULC identification and the fact that LULC merely functions as a proxy for mosquito abundance, assuming spatially homogenous relationships may lead to overgeneralized conclusions.

Methods

Data on incidence of P. falciparum parasitaemia were recorded by active and passive follow-up over two years. Nine LULC types were identified through remote sensing and ground-truthing. Spatial associations of LULC and P. falciparum parasitaemia rate were described in a semi-parametric geographically weighted Poisson regression model.

Results

Complete data were available for 878 individuals, with an annual P. falciparum rate of 3.2 infections per person-year at risk. The influences of built-up areas (median incidence rate ratio (IRR): 0.94, IQR: 0.46), forest (median IRR: 0.9, IQR: 0.51), swampy areas (median IRR: 1.15, IQR: 0.88), as well as banana (median IRR: 1.02, IQR: 0.25), cacao (median IRR: 1.33, IQR: 0.97) and orange plantations (median IRR: 1.11, IQR: 0.68) on P. falciparum rate show strong spatial variations within the study area. Incorporating spatial variability of LULC variables increased model performance compared to the spatially homogenous model.

Conclusions

The observed spatial variability of LULC influence in parasitaemia would have been masked by traditional Poisson regression analysis assuming a spatially constant influence of all variables. We conclude that the spatially varying effects of LULC on P. falciparum parasitaemia may in fact be associated with co-factors not captured by remote sensing, and suggest that future studies assess small-scale spatial variation of vegetation to circumvent generalised assumptions on ecological associations that may in fact be artificial.

Electronic supplementary material

The online version of this article (doi:10.1186/1476-072X-13-35) contains supplementary material, which is available to authorized users.


Url:
DOI: 10.1186/1476-072X-13-35
PubMed: 25270342
PubMed Central: 4192530

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PMC:4192530

Le document en format XML

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<title xml:lang="en" level="a" type="main">Geographically weighted regression of land cover determinants of
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<author>
<name sortKey="Ehlkes, Lutz" sort="Ehlkes, Lutz" uniqKey="Ehlkes L" first="Lutz" last="Ehlkes">Lutz Ehlkes</name>
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<nlm:aff id="Aff1">Bernhard-Nocht-Institute for Tropical Medicine, Research Group Infectious Disease Epidemiology, Hamburg, Germany</nlm:aff>
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<name sortKey="Krefis, Anne Caroline" sort="Krefis, Anne Caroline" uniqKey="Krefis A" first="Anne Caroline" last="Krefis">Anne Caroline Krefis</name>
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<nlm:aff id="Aff1">Bernhard-Nocht-Institute for Tropical Medicine, Research Group Infectious Disease Epidemiology, Hamburg, Germany</nlm:aff>
<country xml:lang="fr">Allemagne</country>
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<name sortKey="Kreuels, Benno" sort="Kreuels, Benno" uniqKey="Kreuels B" first="Benno" last="Kreuels">Benno Kreuels</name>
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<nlm:aff id="Aff1">Bernhard-Nocht-Institute for Tropical Medicine, Research Group Infectious Disease Epidemiology, Hamburg, Germany</nlm:aff>
<country xml:lang="fr">Allemagne</country>
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<name sortKey="Ayim Akonor, Matilda" sort="Ayim Akonor, Matilda" uniqKey="Ayim Akonor M" first="Matilda" last="Ayim-Akonor">Matilda Ayim-Akonor</name>
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<name sortKey="Vinnemeier, Christof" sort="Vinnemeier, Christof" uniqKey="Vinnemeier C" first="Christof" last="Vinnemeier">Christof Vinnemeier</name>
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<country xml:lang="fr">Allemagne</country>
<wicri:regionArea>First Department for Internal Medicine, Division for Tropical Medicine, University Medical Centre Hamburg-Eppendorf, Hamburg</wicri:regionArea>
<placeName>
<settlement type="city">Hambourg</settlement>
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<name sortKey="Loag, Wibke" sort="Loag, Wibke" uniqKey="Loag W" first="Wibke" last="Loag">Wibke Loag</name>
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<nlm:aff id="Aff1">Bernhard-Nocht-Institute for Tropical Medicine, Research Group Infectious Disease Epidemiology, Hamburg, Germany</nlm:aff>
<country xml:lang="fr">Allemagne</country>
<wicri:regionArea>Bernhard-Nocht-Institute for Tropical Medicine, Research Group Infectious Disease Epidemiology, Hamburg</wicri:regionArea>
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<settlement type="city">Hambourg</settlement>
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</placeName>
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<name sortKey="Schickhoff, Udo" sort="Schickhoff, Udo" uniqKey="Schickhoff U" first="Udo" last="Schickhoff">Udo Schickhoff</name>
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<nlm:aff id="Aff8">Department of Geography, University of Hamburg, Hamburg, Germany</nlm:aff>
<country xml:lang="fr">Allemagne</country>
<wicri:regionArea>Department of Geography, University of Hamburg, Hamburg</wicri:regionArea>
<placeName>
<settlement type="city">Hambourg</settlement>
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<name sortKey="May, Jurgen" sort="May, Jurgen" uniqKey="May J" first="Jürgen" last="May">Jürgen May</name>
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<nlm:aff id="Aff1">Bernhard-Nocht-Institute for Tropical Medicine, Research Group Infectious Disease Epidemiology, Hamburg, Germany</nlm:aff>
<country xml:lang="fr">Allemagne</country>
<wicri:regionArea>Bernhard-Nocht-Institute for Tropical Medicine, Research Group Infectious Disease Epidemiology, Hamburg</wicri:regionArea>
<placeName>
<settlement type="city">Hambourg</settlement>
<region type="land" nuts="2">Hambourg</region>
</placeName>
</affiliation>
<affiliation wicri:level="1">
<nlm:aff id="Aff3">German Center for Infection Research (DZIF), Hamburg-Borstel-Lübeck, Kragujevac, Germany</nlm:aff>
<country xml:lang="fr">Allemagne</country>
<wicri:regionArea>German Center for Infection Research (DZIF), Hamburg-Borstel-Lübeck, Kragujevac</wicri:regionArea>
<wicri:noRegion>Kragujevac</wicri:noRegion>
<wicri:noRegion>Kragujevac</wicri:noRegion>
<wicri:noRegion>Kragujevac</wicri:noRegion>
</affiliation>
</author>
</analytic>
<series>
<title level="j">International Journal of Health Geographics</title>
<idno type="eISSN">1476-072X</idno>
<imprint>
<date when="2014">2014</date>
</imprint>
</series>
</biblStruct>
</sourceDesc>
</fileDesc>
<profileDesc>
<textClass></textClass>
</profileDesc>
</teiHeader>
<front>
<div type="abstract" xml:lang="en">
<sec>
<title>Background</title>
<p>Malaria is a mosquito-borne parasitic disease that causes severe mortality and morbidity, particularly in Sub-Saharan Africa. As the vectors predominantly bite between dusk and dawn, risk of infection is determined by the abundance of
<italic>P. falciparum</italic>
infected mosquitoes in the surroundings of the households. Remote sensing is commonly employed to detect associations between land use/land cover (LULC) and mosquito-borne diseases. Due to challenges in LULC identification and the fact that LULC merely functions as a proxy for mosquito abundance, assuming spatially homogenous relationships may lead to overgeneralized conclusions.</p>
</sec>
<sec>
<title>Methods</title>
<p>Data on incidence of
<italic>P. falciparum</italic>
parasitaemia were recorded by active and passive follow-up over two years. Nine LULC types were identified through remote sensing and ground-truthing. Spatial associations of LULC and
<italic>P. falciparum</italic>
parasitaemia rate were described in a semi-parametric geographically weighted Poisson regression model.</p>
</sec>
<sec>
<title>Results</title>
<p>Complete data were available for 878 individuals, with an annual
<italic>P. falciparum</italic>
rate of 3.2 infections per person-year at risk. The influences of built-up areas (median incidence rate ratio (IRR): 0.94, IQR: 0.46), forest (median IRR: 0.9, IQR: 0.51), swampy areas (median IRR: 1.15, IQR: 0.88), as well as banana (median IRR: 1.02, IQR: 0.25), cacao (median IRR: 1.33, IQR: 0.97) and orange plantations (median IRR: 1.11, IQR: 0.68) on
<italic>P. falciparum</italic>
rate show strong spatial variations within the study area. Incorporating spatial variability of LULC variables increased model performance compared to the spatially homogenous model.</p>
</sec>
<sec>
<title>Conclusions</title>
<p>The observed spatial variability of LULC influence in parasitaemia would have been masked by traditional Poisson regression analysis assuming a spatially constant influence of all variables. We conclude that the spatially varying effects of LULC on
<italic>P. falciparum</italic>
parasitaemia may in fact be associated with co-factors not captured by remote sensing, and suggest that future studies assess small-scale spatial variation of vegetation to circumvent generalised assumptions on ecological associations that may in fact be artificial.</p>
</sec>
<sec>
<title>Electronic supplementary material</title>
<p>The online version of this article (doi:10.1186/1476-072X-13-35) contains supplementary material, which is available to authorized users.</p>
</sec>
</div>
</front>
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<pmc article-type="research-article">
<pmc-dir>properties open_access</pmc-dir>
<front>
<journal-meta>
<journal-id journal-id-type="nlm-ta">Int J Health Geogr</journal-id>
<journal-id journal-id-type="iso-abbrev">Int J Health Geogr</journal-id>
<journal-title-group>
<journal-title>International Journal of Health Geographics</journal-title>
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<issn pub-type="epub">1476-072X</issn>
<publisher>
<publisher-name>BioMed Central</publisher-name>
<publisher-loc>London</publisher-loc>
</publisher>
</journal-meta>
<article-meta>
<article-id pub-id-type="pmid">25270342</article-id>
<article-id pub-id-type="pmc">4192530</article-id>
<article-id pub-id-type="publisher-id">603</article-id>
<article-id pub-id-type="doi">10.1186/1476-072X-13-35</article-id>
<article-categories>
<subj-group subj-group-type="heading">
<subject>Research</subject>
</subj-group>
</article-categories>
<title-group>
<article-title>Geographically weighted regression of land cover determinants of
<italic>Plasmodium falciparum</italic>
transmission in the Ashanti Region of Ghana</article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname>Ehlkes</surname>
<given-names>Lutz</given-names>
</name>
<address>
<email>ehlkes@bni-hamburg.de</email>
</address>
<xref ref-type="aff" rid="Aff1"></xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Krefis</surname>
<given-names>Anne Caroline</given-names>
</name>
<address>
<email>krefis@bni-hamburg.de</email>
</address>
<xref ref-type="aff" rid="Aff1"></xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Kreuels</surname>
<given-names>Benno</given-names>
</name>
<address>
<email>kreuels@bni-hamburg.de</email>
</address>
<xref ref-type="aff" rid="Aff1"></xref>
<xref ref-type="aff" rid="Aff2"></xref>
<xref ref-type="aff" rid="Aff3"></xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Krumkamp</surname>
<given-names>Ralf</given-names>
</name>
<address>
<email>krumkamp@bni-hamburg.de</email>
</address>
<xref ref-type="aff" rid="Aff1"></xref>
<xref ref-type="aff" rid="Aff3"></xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Adjei</surname>
<given-names>Ohene</given-names>
</name>
<address>
<email>oadjei@kathhsp.org</email>
</address>
<xref ref-type="aff" rid="Aff4"></xref>
<xref ref-type="aff" rid="Aff5"></xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Ayim-Akonor</surname>
<given-names>Matilda</given-names>
</name>
<address>
<email>tillymyia02@yahoo.co.uk</email>
</address>
<xref ref-type="aff" rid="Aff6"></xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Kobbe</surname>
<given-names>Robin</given-names>
</name>
<address>
<email>r.kobbe@uke.de</email>
</address>
<xref ref-type="aff" rid="Aff7"></xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Hahn</surname>
<given-names>Andreas</given-names>
</name>
<address>
<email>hahn@bni-hamburg.de</email>
</address>
<xref ref-type="aff" rid="Aff1"></xref>
<xref ref-type="aff" rid="Aff3"></xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Vinnemeier</surname>
<given-names>Christof</given-names>
</name>
<address>
<email>vinnemeier@bni-hamburg.de</email>
</address>
<xref ref-type="aff" rid="Aff2"></xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Loag</surname>
<given-names>Wibke</given-names>
</name>
<address>
<email>loag@bni-hamburg.de</email>
</address>
<xref ref-type="aff" rid="Aff1"></xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Schickhoff</surname>
<given-names>Udo</given-names>
</name>
<address>
<email>schickhoff@geowiss.uni-hamburg.de</email>
</address>
<xref ref-type="aff" rid="Aff8"></xref>
</contrib>
<contrib contrib-type="author" corresp="yes">
<name>
<surname>May</surname>
<given-names>Jürgen</given-names>
</name>
<address>
<email>may@bni-hamburg.de</email>
</address>
<xref ref-type="aff" rid="Aff1"></xref>
<xref ref-type="aff" rid="Aff3"></xref>
</contrib>
<aff id="Aff1">
<label></label>
Bernhard-Nocht-Institute for Tropical Medicine, Research Group Infectious Disease Epidemiology, Hamburg, Germany</aff>
<aff id="Aff2">
<label></label>
First Department for Internal Medicine, Division for Tropical Medicine, University Medical Centre Hamburg-Eppendorf, Hamburg, Germany</aff>
<aff id="Aff3">
<label></label>
German Center for Infection Research (DZIF), Hamburg-Borstel-Lübeck, Kragujevac, Germany</aff>
<aff id="Aff4">
<label></label>
Kumasi Centre for Collaborative Research in Tropical Medicine, Kumasi, Ghana</aff>
<aff id="Aff5">
<label></label>
Komfo Anokye Teaching Hospital, Kumasi, Ghana</aff>
<aff id="Aff6">
<label></label>
Animal Health and Food Safety Division, Animal Research Institute, Achimota-Accra, Ghana</aff>
<aff id="Aff7">
<label></label>
Department of Paediatrics, University Medical Centre Hamburg-Eppendorf, Hamburg, Germany</aff>
<aff id="Aff8">
<label></label>
Department of Geography, University of Hamburg, Hamburg, Germany</aff>
</contrib-group>
<pub-date pub-type="epub">
<day>30</day>
<month>9</month>
<year>2014</year>
</pub-date>
<pub-date pub-type="pmc-release">
<day>30</day>
<month>9</month>
<year>2014</year>
</pub-date>
<pub-date pub-type="collection">
<year>2014</year>
</pub-date>
<volume>13</volume>
<elocation-id>35</elocation-id>
<history>
<date date-type="received">
<day>23</day>
<month>5</month>
<year>2014</year>
</date>
<date date-type="accepted">
<day>3</day>
<month>9</month>
<year>2014</year>
</date>
</history>
<permissions>
<copyright-statement>© Ehlkes et al.; licensee BioMed Central Ltd. 2014</copyright-statement>
<license license-type="open-access">
<license-p>This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (
<ext-link ext-link-type="uri" xlink:href="http://creativecommons.org/licenses/by/4.0">http://creativecommons.org/licenses/by/4.0</ext-link>
), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (
<ext-link ext-link-type="uri" xlink:href="http://creativecommons.org/publicdomain/zero/1.0/">http://creativecommons.org/publicdomain/zero/1.0/</ext-link>
) applies to the data made available in this article, unless otherwise stated.</license-p>
</license>
</permissions>
<abstract id="Abs1">
<sec>
<title>Background</title>
<p>Malaria is a mosquito-borne parasitic disease that causes severe mortality and morbidity, particularly in Sub-Saharan Africa. As the vectors predominantly bite between dusk and dawn, risk of infection is determined by the abundance of
<italic>P. falciparum</italic>
infected mosquitoes in the surroundings of the households. Remote sensing is commonly employed to detect associations between land use/land cover (LULC) and mosquito-borne diseases. Due to challenges in LULC identification and the fact that LULC merely functions as a proxy for mosquito abundance, assuming spatially homogenous relationships may lead to overgeneralized conclusions.</p>
</sec>
<sec>
<title>Methods</title>
<p>Data on incidence of
<italic>P. falciparum</italic>
parasitaemia were recorded by active and passive follow-up over two years. Nine LULC types were identified through remote sensing and ground-truthing. Spatial associations of LULC and
<italic>P. falciparum</italic>
parasitaemia rate were described in a semi-parametric geographically weighted Poisson regression model.</p>
</sec>
<sec>
<title>Results</title>
<p>Complete data were available for 878 individuals, with an annual
<italic>P. falciparum</italic>
rate of 3.2 infections per person-year at risk. The influences of built-up areas (median incidence rate ratio (IRR): 0.94, IQR: 0.46), forest (median IRR: 0.9, IQR: 0.51), swampy areas (median IRR: 1.15, IQR: 0.88), as well as banana (median IRR: 1.02, IQR: 0.25), cacao (median IRR: 1.33, IQR: 0.97) and orange plantations (median IRR: 1.11, IQR: 0.68) on
<italic>P. falciparum</italic>
rate show strong spatial variations within the study area. Incorporating spatial variability of LULC variables increased model performance compared to the spatially homogenous model.</p>
</sec>
<sec>
<title>Conclusions</title>
<p>The observed spatial variability of LULC influence in parasitaemia would have been masked by traditional Poisson regression analysis assuming a spatially constant influence of all variables. We conclude that the spatially varying effects of LULC on
<italic>P. falciparum</italic>
parasitaemia may in fact be associated with co-factors not captured by remote sensing, and suggest that future studies assess small-scale spatial variation of vegetation to circumvent generalised assumptions on ecological associations that may in fact be artificial.</p>
</sec>
<sec>
<title>Electronic supplementary material</title>
<p>The online version of this article (doi:10.1186/1476-072X-13-35) contains supplementary material, which is available to authorized users.</p>
</sec>
</abstract>
<kwd-group xml:lang="en">
<title>Keywords</title>
<kwd>Spatial epidemiology</kwd>
<kwd>Land use</kwd>
<kwd>Land cover</kwd>
<kwd>Malaria risk</kwd>
<kwd>Geographically weighted regression</kwd>
</kwd-group>
<custom-meta-group>
<custom-meta>
<meta-name>issue-copyright-statement</meta-name>
<meta-value>© The Author(s) 2014</meta-value>
</custom-meta>
</custom-meta-group>
</article-meta>
</front>
</pmc>
<affiliations>
<list>
<country>
<li>Allemagne</li>
<li>Ghana</li>
</country>
<region>
<li>Hambourg</li>
</region>
<settlement>
<li>Hambourg</li>
</settlement>
</list>
<tree>
<country name="Allemagne">
<region name="Hambourg">
<name sortKey="Ehlkes, Lutz" sort="Ehlkes, Lutz" uniqKey="Ehlkes L" first="Lutz" last="Ehlkes">Lutz Ehlkes</name>
</region>
<name sortKey="Hahn, Andreas" sort="Hahn, Andreas" uniqKey="Hahn A" first="Andreas" last="Hahn">Andreas Hahn</name>
<name sortKey="Hahn, Andreas" sort="Hahn, Andreas" uniqKey="Hahn A" first="Andreas" last="Hahn">Andreas Hahn</name>
<name sortKey="Kobbe, Robin" sort="Kobbe, Robin" uniqKey="Kobbe R" first="Robin" last="Kobbe">Robin Kobbe</name>
<name sortKey="Krefis, Anne Caroline" sort="Krefis, Anne Caroline" uniqKey="Krefis A" first="Anne Caroline" last="Krefis">Anne Caroline Krefis</name>
<name sortKey="Kreuels, Benno" sort="Kreuels, Benno" uniqKey="Kreuels B" first="Benno" last="Kreuels">Benno Kreuels</name>
<name sortKey="Kreuels, Benno" sort="Kreuels, Benno" uniqKey="Kreuels B" first="Benno" last="Kreuels">Benno Kreuels</name>
<name sortKey="Kreuels, Benno" sort="Kreuels, Benno" uniqKey="Kreuels B" first="Benno" last="Kreuels">Benno Kreuels</name>
<name sortKey="Krumkamp, Ralf" sort="Krumkamp, Ralf" uniqKey="Krumkamp R" first="Ralf" last="Krumkamp">Ralf Krumkamp</name>
<name sortKey="Krumkamp, Ralf" sort="Krumkamp, Ralf" uniqKey="Krumkamp R" first="Ralf" last="Krumkamp">Ralf Krumkamp</name>
<name sortKey="Loag, Wibke" sort="Loag, Wibke" uniqKey="Loag W" first="Wibke" last="Loag">Wibke Loag</name>
<name sortKey="May, Jurgen" sort="May, Jurgen" uniqKey="May J" first="Jürgen" last="May">Jürgen May</name>
<name sortKey="May, Jurgen" sort="May, Jurgen" uniqKey="May J" first="Jürgen" last="May">Jürgen May</name>
<name sortKey="Schickhoff, Udo" sort="Schickhoff, Udo" uniqKey="Schickhoff U" first="Udo" last="Schickhoff">Udo Schickhoff</name>
<name sortKey="Vinnemeier, Christof" sort="Vinnemeier, Christof" uniqKey="Vinnemeier C" first="Christof" last="Vinnemeier">Christof Vinnemeier</name>
</country>
<country name="Ghana">
<noRegion>
<name sortKey="Adjei, Ohene" sort="Adjei, Ohene" uniqKey="Adjei O" first="Ohene" last="Adjei">Ohene Adjei</name>
</noRegion>
<name sortKey="Adjei, Ohene" sort="Adjei, Ohene" uniqKey="Adjei O" first="Ohene" last="Adjei">Ohene Adjei</name>
<name sortKey="Ayim Akonor, Matilda" sort="Ayim Akonor, Matilda" uniqKey="Ayim Akonor M" first="Matilda" last="Ayim-Akonor">Matilda Ayim-Akonor</name>
</country>
</tree>
</affiliations>
</record>

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