Millennium development goal 6 and HIV infection in Zambia: what can we learn from successive household surveys?
Identifieur interne : 001040 ( Ncbi/Merge ); précédent : 001039; suivant : 001041Millennium development goal 6 and HIV infection in Zambia: what can we learn from successive household surveys?
Auteurs : Ngianga-Bakwin Kandala ; Paul Brodish ; Bates Buckner ; Susan Foster ; Nyovani MadiseSource :
- AIDS (London, England) [ 0269-9370 ] ; 2011.
Descripteurs français
- KwdFr :
- Adolescent, Adulte, Adulte d'âge moyen, Caractéristiques familiales, Enquêtes de santé (), Facteurs de risque, Facteurs socioéconomiques, Femelle, Humains, Infections à VIH (ethnologie), Infections à VIH (épidémiologie), Jeune adulte, Mâle, Prévalence, Surveillance sentinelle, Zambie (épidémiologie).
- MESH :
- ethnologie : Infections à VIH.
- épidémiologie : Infections à VIH, Zambie.
- Adolescent, Adulte, Adulte d'âge moyen, Caractéristiques familiales, Enquêtes de santé, Facteurs de risque, Facteurs socioéconomiques, Femelle, Humains, Jeune adulte, Mâle, Prévalence, Surveillance sentinelle.
- Wicri :
- geographic : Zambie.
English descriptors
- KwdEn :
- MESH :
- geographic , epidemiology : Zambia.
- epidemiology : HIV Infections.
- ethnology : HIV Infections.
- statistics & numerical data : Health Surveys.
- Adolescent, Adult, Family Characteristics, Female, Humans, Male, Middle Aged, Prevalence, Risk Factors, Sentinel Surveillance, Socioeconomic Factors, Young Adult.
Abstract
Geographic location represents an ecological measure of HIV status and is a strong predictor of HIV prevalence. Given the complex nature of location effects, there is limited understanding of their impact on policies to reduce HIV prevalence.
Participants were 3,949 and 10,874 respondents from two consecutive Zambia Demographic and Health Surveys from 2001/7 (mean age for men and women: 30.3 and 27.7 years, HIV prevalence 14.3% in 2001/2002; 30.3 and 28.0 years, HIV prevalence of 14.7% in 2007). A Bayesian geo-additive mixed model based on Markov Chain Monte Carlo techniques was used to map the change in the spatial distribution of HIV/AIDS prevalence at the provincial level during the six-year period, accounting for important risk factors.
Overall HIV/AIDS prevalence changed little over the 6-year period, but the mapping of residual spatial effects at the provincial level suggested different regional patterns. A pronounced change in odds ratios in Lusaka and Copperbelt provinces in 2001/2 and in Lusaka and Central provinces in 2007 were observed following adjustment for spatial autocorrelation. Western province went from a lower prevalence area in 2001 (13.4%) to a higher prevalence area in 2007 (17.3%). Southern province went from the highest prevalence area in 2001 (17.3%) to a lower prevalence area in 2007 (15.9%).
Findings from two consecutive surveys corroborate the Zambian government’s effort to achieve MDG 6. The novel finding of increased prevalence in Western province warrants further investigation. Spatially-adjusted provincial-level HIV/AIDS prevalence maps are a useful tool for informing policies to achieve MDG 6 in Zambia.
Url:
DOI: 10.1097/QAD.0b013e328340fe0f
PubMed: 21099671
PubMed Central: 3145216
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Given the complex nature of location effects, there is limited understanding of their impact on policies to reduce HIV prevalence.</p></sec><sec sec-type="methods" id="S2"><title>Methods</title><p id="P2">Participants were 3,949 and 10,874 respondents from two consecutive Zambia Demographic and Health Surveys from 2001/7 (mean age for men and women: 30.3 and 27.7 years, HIV prevalence 14.3% in 2001/2002; 30.3 and 28.0 years, HIV prevalence of 14.7% in 2007). A Bayesian geo-additive mixed model based on Markov Chain Monte Carlo techniques was used to map the change in the spatial distribution of HIV/AIDS prevalence at the provincial level during the six-year period, accounting for important risk factors.</p></sec><sec id="S3"><title>Findings</title><p id="P3">Overall HIV/AIDS prevalence changed little over the 6-year period, but the mapping of residual spatial effects at the provincial level suggested different regional patterns. A pronounced change in odds ratios in Lusaka and Copperbelt provinces in 2001/2 and in Lusaka and Central provinces in 2007 were observed following adjustment for spatial autocorrelation. Western province went from a lower prevalence area in 2001 (13.4%) to a higher prevalence area in 2007 (17.3%). Southern province went from the highest prevalence area in 2001 (17.3%) to a lower prevalence area in 2007 (15.9%).</p></sec><sec id="S4"><title>Interpretation</title><p id="P4">Findings from two consecutive surveys corroborate the Zambian government’s effort to achieve MDG 6. The novel finding of increased prevalence in Western province warrants further investigation. Spatially-adjusted provincial-level HIV/AIDS prevalence maps are a useful tool for informing policies to achieve MDG 6 in Zambia.</p></sec></div></front></TEI><double pmid="21099671"><pmc><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">Millennium development goal 6 and HIV infection in Zambia: what can we learn from successive household surveys?</title><author><name sortKey="Kandala, Ngianga Bakwin" sort="Kandala, Ngianga Bakwin" uniqKey="Kandala N" first="Ngianga-Bakwin" last="Kandala">Ngianga-Bakwin Kandala</name></author><author><name sortKey="Brodish, Paul" sort="Brodish, Paul" uniqKey="Brodish P" first="Paul" last="Brodish">Paul Brodish</name></author><author><name sortKey="Buckner, Bates" sort="Buckner, Bates" uniqKey="Buckner B" first="Bates" last="Buckner">Bates Buckner</name></author><author><name sortKey="Foster, Susan" sort="Foster, Susan" uniqKey="Foster S" first="Susan" last="Foster">Susan Foster</name></author><author><name sortKey="Madise, Nyovani" sort="Madise, Nyovani" uniqKey="Madise N" first="Nyovani" last="Madise">Nyovani Madise</name></author></titleStmt><publicationStmt><idno type="wicri:source">PMC</idno><idno type="pmid">21099671</idno><idno type="pmc">3145216</idno><idno type="url">http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3145216</idno><idno type="RBID">PMC:3145216</idno><idno type="doi">10.1097/QAD.0b013e328340fe0f</idno><date when="2011">2011</date><idno type="wicri:Area/Pmc/Corpus">001201</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Corpus" wicri:corpus="PMC">001201</idno><idno type="wicri:Area/Pmc/Curation">001200</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Curation">001200</idno><idno type="wicri:Area/Pmc/Checkpoint">002169</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Checkpoint">002169</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">Millennium development goal 6 and HIV infection in Zambia: what can we learn from successive household surveys?</title><author><name sortKey="Kandala, Ngianga Bakwin" sort="Kandala, Ngianga Bakwin" uniqKey="Kandala N" first="Ngianga-Bakwin" last="Kandala">Ngianga-Bakwin Kandala</name></author><author><name sortKey="Brodish, Paul" sort="Brodish, Paul" uniqKey="Brodish P" first="Paul" last="Brodish">Paul Brodish</name></author><author><name sortKey="Buckner, Bates" sort="Buckner, Bates" uniqKey="Buckner B" first="Bates" last="Buckner">Bates Buckner</name></author><author><name sortKey="Foster, Susan" sort="Foster, Susan" uniqKey="Foster S" first="Susan" last="Foster">Susan Foster</name></author><author><name sortKey="Madise, Nyovani" sort="Madise, Nyovani" uniqKey="Madise N" first="Nyovani" last="Madise">Nyovani Madise</name></author></analytic><series><title level="j">AIDS (London, England)</title><idno type="ISSN">0269-9370</idno><idno type="eISSN">1473-5571</idno><imprint><date when="2011">2011</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><sec id="S1"><title>Background</title><p id="P1">Geographic location represents an ecological measure of HIV status and is a strong predictor of HIV prevalence. Given the complex nature of location effects, there is limited understanding of their impact on policies to reduce HIV prevalence.</p></sec><sec sec-type="methods" id="S2"><title>Methods</title><p id="P2">Participants were 3,949 and 10,874 respondents from two consecutive Zambia Demographic and Health Surveys from 2001/7 (mean age for men and women: 30.3 and 27.7 years, HIV prevalence 14.3% in 2001/2002; 30.3 and 28.0 years, HIV prevalence of 14.7% in 2007). A Bayesian geo-additive mixed model based on Markov Chain Monte Carlo techniques was used to map the change in the spatial distribution of HIV/AIDS prevalence at the provincial level during the six-year period, accounting for important risk factors.</p></sec><sec id="S3"><title>Findings</title><p id="P3">Overall HIV/AIDS prevalence changed little over the 6-year period, but the mapping of residual spatial effects at the provincial level suggested different regional patterns. A pronounced change in odds ratios in Lusaka and Copperbelt provinces in 2001/2 and in Lusaka and Central provinces in 2007 were observed following adjustment for spatial autocorrelation. Western province went from a lower prevalence area in 2001 (13.4%) to a higher prevalence area in 2007 (17.3%). Southern province went from the highest prevalence area in 2001 (17.3%) to a lower prevalence area in 2007 (15.9%).</p></sec><sec id="S4"><title>Interpretation</title><p id="P4">Findings from two consecutive surveys corroborate the Zambian government’s effort to achieve MDG 6. The novel finding of increased prevalence in Western province warrants further investigation. Spatially-adjusted provincial-level HIV/AIDS prevalence maps are a useful tool for informing policies to achieve MDG 6 in Zambia.</p></sec></div></front></TEI></pmc><pubmed><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">Millennium development goal 6 and HIV infection in Zambia: what can we learn from successive household surveys?</title><author><name sortKey="Kandala, Ngianga Bakwin" sort="Kandala, Ngianga Bakwin" uniqKey="Kandala N" first="Ngianga-Bakwin" last="Kandala">Ngianga-Bakwin Kandala</name><affiliation wicri:level="1"><nlm:affiliation>University of Warwick, Clinical Sciences Research Institute, Coventry, UK. N-B.Kandala@warwick.ac.uk</nlm:affiliation><country xml:lang="fr">Royaume-Uni</country><wicri:regionArea>University of Warwick, Clinical Sciences Research Institute, Coventry</wicri:regionArea><wicri:noRegion>Coventry</wicri:noRegion></affiliation></author><author><name sortKey="Brodish, Paul" sort="Brodish, Paul" uniqKey="Brodish P" first="Paul" last="Brodish">Paul Brodish</name></author><author><name sortKey="Buckner, Bates" sort="Buckner, Bates" uniqKey="Buckner B" first="Bates" last="Buckner">Bates Buckner</name></author><author><name sortKey="Foster, Susan" sort="Foster, Susan" uniqKey="Foster S" first="Susan" last="Foster">Susan Foster</name></author><author><name sortKey="Madise, Nyovani" sort="Madise, Nyovani" uniqKey="Madise N" first="Nyovani" last="Madise">Nyovani Madise</name></author></titleStmt><publicationStmt><idno type="wicri:source">PubMed</idno><date when="2011">2011</date><idno type="RBID">pubmed:21099671</idno><idno type="pmid">21099671</idno><idno type="doi">10.1097/QAD.0b013e328340fe0f</idno><idno type="wicri:Area/PubMed/Corpus">001798</idno><idno type="wicri:explorRef" wicri:stream="PubMed" wicri:step="Corpus" wicri:corpus="PubMed">001798</idno><idno type="wicri:Area/PubMed/Curation">001798</idno><idno type="wicri:explorRef" wicri:stream="PubMed" wicri:step="Curation">001798</idno><idno type="wicri:Area/PubMed/Checkpoint">001798</idno><idno type="wicri:explorRef" wicri:stream="Checkpoint" wicri:step="PubMed">001798</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">Millennium development goal 6 and HIV infection in Zambia: what can we learn from successive household surveys?</title><author><name sortKey="Kandala, Ngianga Bakwin" sort="Kandala, Ngianga Bakwin" uniqKey="Kandala N" first="Ngianga-Bakwin" last="Kandala">Ngianga-Bakwin Kandala</name><affiliation wicri:level="1"><nlm:affiliation>University of Warwick, Clinical Sciences Research Institute, Coventry, UK. N-B.Kandala@warwick.ac.uk</nlm:affiliation><country xml:lang="fr">Royaume-Uni</country><wicri:regionArea>University of Warwick, Clinical Sciences Research Institute, Coventry</wicri:regionArea><wicri:noRegion>Coventry</wicri:noRegion></affiliation></author><author><name sortKey="Brodish, Paul" sort="Brodish, Paul" uniqKey="Brodish P" first="Paul" last="Brodish">Paul Brodish</name></author><author><name sortKey="Buckner, Bates" sort="Buckner, Bates" uniqKey="Buckner B" first="Bates" last="Buckner">Bates Buckner</name></author><author><name sortKey="Foster, Susan" sort="Foster, Susan" uniqKey="Foster S" first="Susan" last="Foster">Susan Foster</name></author><author><name sortKey="Madise, Nyovani" sort="Madise, Nyovani" uniqKey="Madise N" first="Nyovani" last="Madise">Nyovani Madise</name></author></analytic><series><title level="j">AIDS (London, England)</title><idno type="eISSN">1473-5571</idno><imprint><date when="2011" type="published">2011</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Adolescent</term><term>Adult</term><term>Family Characteristics</term><term>Female</term><term>HIV Infections (epidemiology)</term><term>HIV Infections (ethnology)</term><term>Health Surveys (statistics & numerical data)</term><term>Humans</term><term>Male</term><term>Middle Aged</term><term>Prevalence</term><term>Risk Factors</term><term>Sentinel Surveillance</term><term>Socioeconomic Factors</term><term>Young Adult</term><term>Zambia (epidemiology)</term></keywords><keywords scheme="KwdFr" xml:lang="fr"><term>Adolescent</term><term>Adulte</term><term>Adulte d'âge moyen</term><term>Caractéristiques familiales</term><term>Enquêtes de santé ()</term><term>Facteurs de risque</term><term>Facteurs socioéconomiques</term><term>Femelle</term><term>Humains</term><term>Infections à VIH (ethnologie)</term><term>Infections à VIH (épidémiologie)</term><term>Jeune adulte</term><term>Mâle</term><term>Prévalence</term><term>Surveillance sentinelle</term><term>Zambie (épidémiologie)</term></keywords><keywords scheme="MESH" type="geographic" qualifier="epidemiology" xml:lang="en"><term>Zambia</term></keywords><keywords scheme="MESH" qualifier="epidemiology" xml:lang="en"><term>HIV Infections</term></keywords><keywords scheme="MESH" qualifier="ethnologie" xml:lang="fr"><term>Infections à VIH</term></keywords><keywords scheme="MESH" qualifier="ethnology" xml:lang="en"><term>HIV Infections</term></keywords><keywords scheme="MESH" qualifier="statistics & numerical data" xml:lang="en"><term>Health Surveys</term></keywords><keywords scheme="MESH" qualifier="épidémiologie" xml:lang="fr"><term>Infections à VIH</term><term>Zambie</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Adolescent</term><term>Adult</term><term>Family Characteristics</term><term>Female</term><term>Humans</term><term>Male</term><term>Middle Aged</term><term>Prevalence</term><term>Risk Factors</term><term>Sentinel Surveillance</term><term>Socioeconomic Factors</term><term>Young Adult</term></keywords><keywords scheme="MESH" xml:lang="fr"><term>Adolescent</term><term>Adulte</term><term>Adulte d'âge moyen</term><term>Caractéristiques familiales</term><term>Enquêtes de santé</term><term>Facteurs de risque</term><term>Facteurs socioéconomiques</term><term>Femelle</term><term>Humains</term><term>Jeune adulte</term><term>Mâle</term><term>Prévalence</term><term>Surveillance sentinelle</term></keywords><keywords scheme="Wicri" type="geographic" xml:lang="fr"><term>Zambie</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Geographic location represents an ecological measure of HIV status and is a strong predictor of HIV prevalence. Given the complex nature of location effects, there is limited understanding of their impact on policies to reduce HIV prevalence.</div></front></TEI></pubmed></double></record>Pour manipuler ce document sous Unix (Dilib)
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