Millennium development goal 6 and HIV infection in Zambia: what can we learn from successive household surveys?
Identifieur interne : 001200 ( Pmc/Curation ); précédent : 001199; suivant : 001201Millennium 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.
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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PMC:3145216Le document en format XML
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<author><name sortKey="Kandala, Ngianga Bakwin" sort="Kandala, Ngianga Bakwin" uniqKey="Kandala N" first="Ngianga-Bakwin" last="Kandala">Ngianga-Bakwin Kandala</name>
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<author><name sortKey="Brodish, Paul" sort="Brodish, Paul" uniqKey="Brodish P" first="Paul" last="Brodish">Paul Brodish</name>
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<author><name sortKey="Buckner, Bates" sort="Buckner, Bates" uniqKey="Buckner B" first="Bates" last="Buckner">Bates Buckner</name>
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<author><name sortKey="Foster, Susan" sort="Foster, Susan" uniqKey="Foster S" first="Susan" last="Foster">Susan Foster</name>
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<author><name sortKey="Madise, Nyovani" sort="Madise, Nyovani" uniqKey="Madise N" first="Nyovani" last="Madise">Nyovani Madise</name>
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<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>
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<title-group><article-title>Millennium development goal 6 and HIV infection in Zambia: what can we learn from successive household surveys?</article-title>
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<contrib-group><contrib contrib-type="author"><name><surname>Kandala</surname>
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<contrib contrib-type="author"><name><surname>Brodish</surname>
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<contrib contrib-type="author"><name><surname>Buckner</surname>
<given-names>Bates</given-names>
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<contrib contrib-type="author"><name><surname>Foster</surname>
<given-names>Susan</given-names>
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<contrib contrib-type="author"><name><surname>Madise</surname>
<given-names>Nyovani</given-names>
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<pub-date pub-type="nihms-submitted"><day>14</day>
<month>7</month>
<year>2011</year>
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<pub-date pub-type="ppub"><day>2</day>
<month>1</month>
<year>2011</year>
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<pub-date pub-type="pmc-release"><day>28</day>
<month>7</month>
<year>2011</year>
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<volume>25</volume>
<issue>1</issue>
<fpage>95</fpage>
<lpage>106</lpage>
<abstract><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>
</abstract>
<kwd-group><kwd>MDG 6</kwd>
<kwd>HIV prevalence</kwd>
<kwd>geographic location</kwd>
<kwd>spatial autocorrelation</kwd>
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<funding-group><award-group><funding-source country="United States">National Institute of Child Health & Human Development : NICHD</funding-source>
<award-id>R24 HD050924-05 || HD</award-id>
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