Mapping HIV prevalence using population and antenatal sentinel-based HIV surveys: a multi-stage approach
Identifieur interne : 001544 ( Main/Exploration ); précédent : 001543; suivant : 001545Mapping 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
Affiliations:
Links toward previous steps (curation, corpus...)
- to stream Pmc, to step Corpus: 000954
- to stream Pmc, to step Curation: 000953
- to stream Pmc, to step Checkpoint: 000B93
- to stream Ncbi, to step Merge: 003294
- to stream Ncbi, to step Curation: 003294
- to stream Ncbi, to step Checkpoint: 003294
- to stream Main, to step Merge: 001545
- to stream Main, to step Curation: 001544
Le document en format XML
<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">Mapping HIV prevalence using population and antenatal sentinel-based HIV surveys: a multi-stage approach</title><author><name sortKey="Manda, Samuel" sort="Manda, Samuel" uniqKey="Manda S" first="Samuel" last="Manda">Samuel Manda</name><affiliation wicri:level="1"><nlm:aff id="Aff1">Biostatistics Unit, South African Medical Research Council, Pretoria, South Africa</nlm:aff><country xml:lang="fr">Afrique du Sud</country><wicri:regionArea>Biostatistics Unit, South African Medical Research Council, Pretoria</wicri:regionArea><wicri:noRegion>Pretoria</wicri:noRegion></affiliation><affiliation wicri:level="1"><nlm:aff id="Aff2">School of Mathematics, Statistics & Computer Science, University of KwaZulu-Natal, Pietermaritzburg, South Africa</nlm:aff><country xml:lang="fr">Afrique du Sud</country><wicri:regionArea>School of Mathematics, Statistics & Computer Science, University of KwaZulu-Natal, Pietermaritzburg</wicri:regionArea><wicri:noRegion>Pietermaritzburg</wicri:noRegion></affiliation></author><author><name sortKey="Masenyetse, Lieketseng" sort="Masenyetse, Lieketseng" uniqKey="Masenyetse L" first="Lieketseng" last="Masenyetse">Lieketseng Masenyetse</name><affiliation wicri:level="1"><nlm:aff id="Aff1">Biostatistics Unit, South African Medical Research Council, Pretoria, South Africa</nlm:aff><country xml:lang="fr">Afrique du Sud</country><wicri:regionArea>Biostatistics Unit, South African Medical Research Council, Pretoria</wicri:regionArea><wicri:noRegion>Pretoria</wicri:noRegion></affiliation></author><author><name sortKey="Cai, Bo" sort="Cai, Bo" uniqKey="Cai B" first="Bo" last="Cai">Bo Cai</name><affiliation wicri:level="1"><nlm:aff id="Aff3">Department of Epidemiology and Biostatistics, University of South Carolina, Columbia, USA</nlm:aff><country xml:lang="fr">États-Unis</country><wicri:regionArea>Department of Epidemiology and Biostatistics, University of South Carolina, Columbia</wicri:regionArea><wicri:noRegion>Columbia</wicri:noRegion></affiliation></author><author><name sortKey="Meyer, Renate" sort="Meyer, Renate" uniqKey="Meyer R" first="Renate" last="Meyer">Renate Meyer</name><affiliation wicri:level="1"><nlm:aff id="Aff4">Department of Statistics, University of Auckland, Auckland, New Zealand</nlm:aff><country xml:lang="fr">Nouvelle-Zélande</country><wicri:regionArea>Department of Statistics, University of Auckland, Auckland</wicri:regionArea><wicri:noRegion>Auckland</wicri:noRegion></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PMC</idno><idno type="pmid">26336361</idno><idno type="pmc">4557594</idno><idno type="url">http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4557594</idno><idno type="RBID">PMC:4557594</idno><idno type="doi">10.1186/s12963-015-0055-z</idno><date when="2015">2015</date><idno type="wicri:Area/Pmc/Corpus">000954</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Corpus" wicri:corpus="PMC">000954</idno><idno type="wicri:Area/Pmc/Curation">000953</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Curation">000953</idno><idno type="wicri:Area/Pmc/Checkpoint">000B93</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Checkpoint">000B93</idno><idno type="wicri:Area/Ncbi/Merge">003294</idno><idno type="wicri:Area/Ncbi/Curation">003294</idno><idno type="wicri:Area/Ncbi/Checkpoint">003294</idno><idno type="wicri:Area/Main/Merge">001545</idno><idno type="wicri:Area/Main/Curation">001544</idno><idno type="wicri:Area/Main/Exploration">001544</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">Mapping HIV prevalence using population and antenatal sentinel-based HIV surveys: a multi-stage approach</title><author><name sortKey="Manda, Samuel" sort="Manda, Samuel" uniqKey="Manda S" first="Samuel" last="Manda">Samuel Manda</name><affiliation wicri:level="1"><nlm:aff id="Aff1">Biostatistics Unit, South African Medical Research Council, Pretoria, South Africa</nlm:aff><country xml:lang="fr">Afrique du Sud</country><wicri:regionArea>Biostatistics Unit, South African Medical Research Council, Pretoria</wicri:regionArea><wicri:noRegion>Pretoria</wicri:noRegion></affiliation><affiliation wicri:level="1"><nlm:aff id="Aff2">School of Mathematics, Statistics & Computer Science, University of KwaZulu-Natal, Pietermaritzburg, South Africa</nlm:aff><country xml:lang="fr">Afrique du Sud</country><wicri:regionArea>School of Mathematics, Statistics & Computer Science, University of KwaZulu-Natal, Pietermaritzburg</wicri:regionArea><wicri:noRegion>Pietermaritzburg</wicri:noRegion></affiliation></author><author><name sortKey="Masenyetse, Lieketseng" sort="Masenyetse, Lieketseng" uniqKey="Masenyetse L" first="Lieketseng" last="Masenyetse">Lieketseng Masenyetse</name><affiliation wicri:level="1"><nlm:aff id="Aff1">Biostatistics Unit, South African Medical Research Council, Pretoria, South Africa</nlm:aff><country xml:lang="fr">Afrique du Sud</country><wicri:regionArea>Biostatistics Unit, South African Medical Research Council, Pretoria</wicri:regionArea><wicri:noRegion>Pretoria</wicri:noRegion></affiliation></author><author><name sortKey="Cai, Bo" sort="Cai, Bo" uniqKey="Cai B" first="Bo" last="Cai">Bo Cai</name><affiliation wicri:level="1"><nlm:aff id="Aff3">Department of Epidemiology and Biostatistics, University of South Carolina, Columbia, USA</nlm:aff><country xml:lang="fr">États-Unis</country><wicri:regionArea>Department of Epidemiology and Biostatistics, University of South Carolina, Columbia</wicri:regionArea><wicri:noRegion>Columbia</wicri:noRegion></affiliation></author><author><name sortKey="Meyer, Renate" sort="Meyer, Renate" uniqKey="Meyer R" first="Renate" last="Meyer">Renate Meyer</name><affiliation wicri:level="1"><nlm:aff id="Aff4">Department of Statistics, University of Auckland, Auckland, New Zealand</nlm:aff><country xml:lang="fr">Nouvelle-Zélande</country><wicri:regionArea>Department of Statistics, University of Auckland, Auckland</wicri:regionArea><wicri:noRegion>Auckland</wicri:noRegion></affiliation></author></analytic><series><title level="j">Population Health Metrics</title><idno type="eISSN">1478-7954</idno><imprint><date when="2015">2015</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><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></div></front><back><div1 type="bibliography"><listBibl><biblStruct><analytic><author><name sortKey="Floyd, S" uniqKey="Floyd S">S Floyd</name></author><author><name sortKey="Molesworth, A" uniqKey="Molesworth A">A Molesworth</name></author><author><name sortKey="Dube, A" uniqKey="Dube A">A Dube</name></author><author><name sortKey="Crampin, Ac" uniqKey="Crampin A">AC Crampin</name></author><author><name sortKey="Houben, R" uniqKey="Houben R">R Houben</name></author><author><name sortKey="Chihana, M" uniqKey="Chihana M">M Chihana</name></author></analytic></biblStruct><biblStruct></biblStruct><biblStruct><analytic><author><name sortKey="Brookmeyer, R" uniqKey="Brookmeyer R">R Brookmeyer</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Boerma, Jt" uniqKey="Boerma J">JT Boerma</name></author><author><name sortKey="Ghys, Dp" uniqKey="Ghys D">DP Ghys</name></author><author><name sortKey="Walker, N" uniqKey="Walker N">N Walker</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Mishra, V" uniqKey="Mishra V">V Mishra</name></author><author><name sortKey="Vaessen, M" uniqKey="Vaessen M">M Vaessen</name></author><author><name sortKey="Boerma, T" uniqKey="Boerma T">T Boerma</name></author><author><name sortKey="Way, A" uniqKey="Way A">A Way</name></author><author><name sortKey="Barrere, B" uniqKey="Barrere B">B Barrère</name></author><author><name sortKey="Cross, A" uniqKey="Cross A">A Cross</name></author></analytic></biblStruct><biblStruct></biblStruct><biblStruct><analytic><author><name sortKey="Shisana, O" uniqKey="Shisana O">O Shisana</name></author><author><name sortKey="Rehle, T" uniqKey="Rehle T">T Rehle</name></author><author><name sortKey="Simbayi, Lc" uniqKey="Simbayi L">LC Simbayi</name></author><author><name sortKey="Zuma, K" uniqKey="Zuma K">K Zuma</name></author><author><name sortKey="Jooste, S" uniqKey="Jooste S">S Jooste</name></author><author><name sortKey="Zungu, N" uniqKey="Zungu N">N Zungu</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Stover, J" uniqKey="Stover J">J Stover</name></author><author><name sortKey="Brown, T" uniqKey="Brown T">T Brown</name></author><author><name sortKey="Marston, M" uniqKey="Marston M">M Marston</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Johnson, L" uniqKey="Johnson L">L Johnson</name></author></analytic></biblStruct><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></biblStruct><biblStruct></biblStruct><biblStruct><analytic><author><name sortKey="Fitzgerald, J" uniqKey="Fitzgerald J">J Fitzgerald</name></author><author><name sortKey="Gottshalk, P" uniqKey="Gottshalk P">P Gottshalk</name></author><author><name sortKey="Moffitt, R" uniqKey="Moffitt R">R Moffitt</name></author></analytic></biblStruct><biblStruct></biblStruct><biblStruct><analytic><author><name sortKey="Knorr Held, L" uniqKey="Knorr Held L">L Knorr-Held</name></author><author><name sortKey="Best, Ng" uniqKey="Best N">NG Best</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Downing, A" uniqKey="Downing A">A Downing</name></author><author><name sortKey="Forman, D" uniqKey="Forman D">D Forman</name></author><author><name sortKey="Gilthorpe, Ms" uniqKey="Gilthorpe M">MS Gilthorpe</name></author><author><name sortKey="Edwards, Kl" uniqKey="Edwards K">KL Edwards</name></author><author><name sortKey="Manda, Som" uniqKey="Manda S">SOM Manda</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Manda, So" uniqKey="Manda S">SO Manda</name></author><author><name sortKey="Feltbower, Rg" uniqKey="Feltbower R">RG Feltbower</name></author><author><name sortKey="Gilthorpe, Ms" uniqKey="Gilthorpe M">MS Gilthorpe</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Cressie, N" uniqKey="Cressie N">N Cressie</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Manda, Som" uniqKey="Manda S">SOM Manda</name></author><author><name sortKey="Feltbower, Rg" uniqKey="Feltbower R">RG Feltbower</name></author><author><name sortKey="Gilthorpe, Ms" uniqKey="Gilthorpe M">MS Gilthorpe</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Pettit" uniqKey="Pettit">Pettit</name></author></analytic></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct><analytic><author><name sortKey="Obare, F" uniqKey="Obare F">F Obare</name></author></analytic></biblStruct></listBibl></div1></back></TEI><affiliations><list><country><li>Afrique du Sud</li><li>Nouvelle-Zélande</li><li>États-Unis</li></country></list><tree><country name="Afrique du Sud"><noRegion><name sortKey="Manda, Samuel" sort="Manda, Samuel" uniqKey="Manda S" first="Samuel" last="Manda">Samuel Manda</name></noRegion><name sortKey="Manda, Samuel" sort="Manda, Samuel" uniqKey="Manda S" first="Samuel" last="Manda">Samuel Manda</name><name sortKey="Masenyetse, Lieketseng" sort="Masenyetse, Lieketseng" uniqKey="Masenyetse L" first="Lieketseng" last="Masenyetse">Lieketseng Masenyetse</name></country><country name="États-Unis"><noRegion><name sortKey="Cai, Bo" sort="Cai, Bo" uniqKey="Cai B" first="Bo" last="Cai">Bo Cai</name></noRegion></country><country name="Nouvelle-Zélande"><noRegion><name sortKey="Meyer, Renate" sort="Meyer, Renate" uniqKey="Meyer R" first="Renate" last="Meyer">Renate Meyer</name></noRegion></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
EXPLOR_STEP=$WICRI_ROOT/Wicri/Sante/explor/SidaSubSaharaV1/Data/Main/Exploration
HfdSelect -h $EXPLOR_STEP/biblio.hfd -nk 001544 | SxmlIndent | more
Ou
HfdSelect -h $EXPLOR_AREA/Data/Main/Exploration/biblio.hfd -nk 001544 | SxmlIndent | more
Pour mettre un lien sur cette page dans le réseau Wicri
{{Explor lien
|wiki= Wicri/Sante
|area= SidaSubSaharaV1
|flux= Main
|étape= Exploration
|type= RBID
|clé= PMC:4557594
|texte= Mapping HIV prevalence using population and antenatal sentinel-based HIV surveys: a multi-stage approach
}}
Pour générer des pages wiki
HfdIndexSelect -h $EXPLOR_AREA/Data/Main/Exploration/RBID.i -Sk "pubmed:26336361" \
| HfdSelect -Kh $EXPLOR_AREA/Data/Main/Exploration/biblio.hfd \
| NlmPubMed2Wicri -a SidaSubSaharaV1
| This area was generated with Dilib version V0.6.32. |  |