Mapping HIV prevalence using population and antenatal sentinel-based HIV surveys: a multi-stage approach
Identifieur interne : 000953 ( Pmc/Curation ); précédent : 000952; suivant : 000954Mapping HIV prevalence using population and antenatal sentinel-based HIV surveys: a multi-stage approach
Auteurs : Samuel Manda [Afrique du Sud] ; Lieketseng Masenyetse [Afrique du Sud] ; Bo Cai [États-Unis] ; Renate Meyer [Nouvelle-Zélande]Source :
- Population Health Metrics [ 1478-7954 ] ; 2015.
Abstract
Sound public health policy on HIV/AIDS depends on accurate prevalence and incidence statistics for the epidemic at both local and national levels. However, HIV statistics derived from epidemiological extrapolation models and data sources have a number of limitations that may lead to under- or overestimation of the epidemic. Thus, adjustment techniques need to be employed to correctly estimate the size of the HIV burden.
A multi-stage methodological approach is proposed to obtain HIV statistics at subnational levels by combining nationally population-based and antenatal clinic HIV data.
The stages range from computing inverse probability weighting (IPW) for consenting to HIV testing, to HIV status prediction modelling, to the recently developed Bayesian multivariate spatial models to jointly model and map multiple HIV risks.
The 2010 Malawi Demographic and Health Survey (MDHS 2010) and the 2010 Malawi Antenatal Clinic (ANC 2010) Sentinel HIV data were used for analyses. Gender, residence, employment, marital status, ethnicity, condom use, and multiple sex partners were considered when estimating HIV prevalence.
The observed MDHS 2010 HIV prevalence among people aged 15–49 years was 10.15 %, with 95 % confidence interval (CI) of (9.66, 10.67 %). The ANC 2010 site HIV prevalence had a median of 10.63 %, with 95 % CI ranging from 1.85–24.09 %. The MDHS 2010 prevalence was 10.61 % (9.9, 11.33 %) and 10.19 % (9.69, 10.71 %) using the HIV weight and IPW, respectively. After predicting the HIV status for the non-tested subjects, the overall MDHS 2010 HIV prevalence was 11.05 % (10.80, 11.30 %). Higher HIV prevalence rates were observed in the mostly Southern districts, where poverty and population density levels are also comparatively high. The excess risk attributable to ANC HIV was much larger in the central-eastern and northern parts of the country.
Inverse Probability Weighting combined with an appropriate HIV prediction model can be a useful tool to correct for non-response to HIV testing, especially if the number of tested individuals is very minimal at subnational levels. In populations where most know their HIV status, population-based HIV prevalence estimates can be heavily biased. High-coverage antenatal clinics’ surveillance HIV data would then be the only important HIV data information sources.
Url:
DOI: 10.1186/s12963-015-0055-z
PubMed: 26336361
PubMed Central: 4557594
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<author><name sortKey="Manda, Samuel" sort="Manda, Samuel" uniqKey="Manda S" first="Samuel" last="Manda">Samuel Manda</name>
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<author><name sortKey="Meyer, Renate" sort="Meyer, Renate" uniqKey="Meyer R" first="Renate" last="Meyer">Renate Meyer</name>
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<author><name sortKey="Meyer, Renate" sort="Meyer, Renate" uniqKey="Meyer R" first="Renate" last="Meyer">Renate Meyer</name>
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<front><div type="abstract" xml:lang="en"><sec><title>Background</title>
<p>Sound public health policy on HIV/AIDS depends on accurate prevalence and incidence statistics for the epidemic at both local and national levels. However, HIV statistics derived from epidemiological extrapolation models and data sources have a number of limitations that may lead to under- or overestimation of the epidemic. Thus, adjustment techniques need to be employed to correctly estimate the size of the HIV burden.</p>
</sec>
<sec><title>Methods</title>
<p>A multi-stage methodological approach is proposed to obtain HIV statistics at subnational levels by combining nationally population-based and antenatal clinic HIV data.</p>
<p>The stages range from computing inverse probability weighting (IPW) for consenting to HIV testing, to HIV status prediction modelling, to the recently developed Bayesian multivariate spatial models to jointly model and map multiple HIV risks.</p>
<p>The 2010 Malawi Demographic and Health Survey (MDHS 2010) and the 2010 Malawi Antenatal Clinic (ANC 2010) Sentinel HIV data were used for analyses. Gender, residence, employment, marital status, ethnicity, condom use, and multiple sex partners were considered when estimating HIV prevalence.</p>
</sec>
<sec><title>Results</title>
<p>The observed MDHS 2010 HIV prevalence among people aged 15–49 years was 10.15 %, with 95 % confidence interval (CI) of (9.66, 10.67 %). The ANC 2010 site HIV prevalence had a median of 10.63 %, with 95 % CI ranging from 1.85–24.09 %. The MDHS 2010 prevalence was 10.61 % (9.9, 11.33 %) and 10.19 % (9.69, 10.71 %) using the HIV weight and IPW, respectively. After predicting the HIV status for the non-tested subjects, the overall MDHS 2010 HIV prevalence was 11.05 % (10.80, 11.30 %). Higher HIV prevalence rates were observed in the mostly Southern districts, where poverty and population density levels are also comparatively high. The excess risk attributable to ANC HIV was much larger in the central-eastern and northern parts of the country.</p>
</sec>
<sec><title>Conclusions</title>
<p>Inverse Probability Weighting combined with an appropriate HIV prediction model can be a useful tool to correct for non-response to HIV testing, especially if the number of tested individuals is very minimal at subnational levels. In populations where most know their HIV status, population-based HIV prevalence estimates can be heavily biased. High-coverage antenatal clinics’ surveillance HIV data would then be the only important HIV data information sources.</p>
</sec>
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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">Popul Health Metr</journal-id>
<journal-id journal-id-type="iso-abbrev">Popul Health Metr</journal-id>
<journal-title-group><journal-title>Population Health Metrics</journal-title>
</journal-title-group>
<issn pub-type="epub">1478-7954</issn>
<publisher><publisher-name>BioMed Central</publisher-name>
<publisher-loc>London</publisher-loc>
</publisher>
</journal-meta>
<article-meta><article-id pub-id-type="pmid">26336361</article-id>
<article-id pub-id-type="pmc">4557594</article-id>
<article-id pub-id-type="publisher-id">55</article-id>
<article-id pub-id-type="doi">10.1186/s12963-015-0055-z</article-id>
<article-categories><subj-group subj-group-type="heading"><subject>Research</subject>
</subj-group>
</article-categories>
<title-group><article-title>Mapping HIV prevalence using population and antenatal sentinel-based HIV surveys: a multi-stage approach</article-title>
</title-group>
<contrib-group><contrib contrib-type="author" corresp="yes"><name><surname>Manda</surname>
<given-names>Samuel</given-names>
</name>
<address><email>samuel.manda@mrc.ac.za</email>
</address>
<xref ref-type="aff" rid="Aff1"></xref>
<xref ref-type="aff" rid="Aff2"></xref>
</contrib>
<contrib contrib-type="author"><name><surname>Masenyetse</surname>
<given-names>Lieketseng</given-names>
</name>
<xref ref-type="aff" rid="Aff1"></xref>
</contrib>
<contrib contrib-type="author"><name><surname>Cai</surname>
<given-names>Bo</given-names>
</name>
<xref ref-type="aff" rid="Aff3"></xref>
</contrib>
<contrib contrib-type="author"><name><surname>Meyer</surname>
<given-names>Renate</given-names>
</name>
<xref ref-type="aff" rid="Aff4"></xref>
</contrib>
<aff id="Aff1"><label></label>
Biostatistics Unit, South African Medical Research Council, Pretoria, South Africa</aff>
<aff id="Aff2"><label></label>
School of Mathematics, Statistics & Computer Science, University of KwaZulu-Natal, Pietermaritzburg, South Africa</aff>
<aff id="Aff3"><label></label>
Department of Epidemiology and Biostatistics, University of South Carolina, Columbia, USA</aff>
<aff id="Aff4"><label></label>
Department of Statistics, University of Auckland, Auckland, New Zealand</aff>
</contrib-group>
<pub-date pub-type="epub"><day>2</day>
<month>9</month>
<year>2015</year>
</pub-date>
<pub-date pub-type="pmc-release"><day>2</day>
<month>9</month>
<year>2015</year>
</pub-date>
<pub-date pub-type="collection"><year>2015</year>
</pub-date>
<volume>13</volume>
<elocation-id>22</elocation-id>
<history><date date-type="received"><day>12</day>
<month>12</month>
<year>2014</year>
</date>
<date date-type="accepted"><day>28</day>
<month>7</month>
<year>2015</year>
</date>
</history>
<permissions><copyright-statement>© Manda et al. 2015</copyright-statement>
<license license-type="OpenAccess"><license-p><bold>Open Access</bold>
This article is distributed under the terms of the Creative Commons Attribution 4.0 International 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 you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. 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>Sound public health policy on HIV/AIDS depends on accurate prevalence and incidence statistics for the epidemic at both local and national levels. However, HIV statistics derived from epidemiological extrapolation models and data sources have a number of limitations that may lead to under- or overestimation of the epidemic. Thus, adjustment techniques need to be employed to correctly estimate the size of the HIV burden.</p>
</sec>
<sec><title>Methods</title>
<p>A multi-stage methodological approach is proposed to obtain HIV statistics at subnational levels by combining nationally population-based and antenatal clinic HIV data.</p>
<p>The stages range from computing inverse probability weighting (IPW) for consenting to HIV testing, to HIV status prediction modelling, to the recently developed Bayesian multivariate spatial models to jointly model and map multiple HIV risks.</p>
<p>The 2010 Malawi Demographic and Health Survey (MDHS 2010) and the 2010 Malawi Antenatal Clinic (ANC 2010) Sentinel HIV data were used for analyses. Gender, residence, employment, marital status, ethnicity, condom use, and multiple sex partners were considered when estimating HIV prevalence.</p>
</sec>
<sec><title>Results</title>
<p>The observed MDHS 2010 HIV prevalence among people aged 15–49 years was 10.15 %, with 95 % confidence interval (CI) of (9.66, 10.67 %). The ANC 2010 site HIV prevalence had a median of 10.63 %, with 95 % CI ranging from 1.85–24.09 %. The MDHS 2010 prevalence was 10.61 % (9.9, 11.33 %) and 10.19 % (9.69, 10.71 %) using the HIV weight and IPW, respectively. After predicting the HIV status for the non-tested subjects, the overall MDHS 2010 HIV prevalence was 11.05 % (10.80, 11.30 %). Higher HIV prevalence rates were observed in the mostly Southern districts, where poverty and population density levels are also comparatively high. The excess risk attributable to ANC HIV was much larger in the central-eastern and northern parts of the country.</p>
</sec>
<sec><title>Conclusions</title>
<p>Inverse Probability Weighting combined with an appropriate HIV prediction model can be a useful tool to correct for non-response to HIV testing, especially if the number of tested individuals is very minimal at subnational levels. In populations where most know their HIV status, population-based HIV prevalence estimates can be heavily biased. High-coverage antenatal clinics’ surveillance HIV data would then be the only important HIV data information sources.</p>
</sec>
</abstract>
<custom-meta-group><custom-meta><meta-name>issue-copyright-statement</meta-name>
<meta-value>© The Author(s) 2015</meta-value>
</custom-meta>
</custom-meta-group>
</article-meta>
</front>
</pmc>
</record>
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