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<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">Recent HIV prevalence trends among pregnant women and all women in sub-Saharan Africa: implications for HIV estimates</title><author><name sortKey="Eaton, Jeffrey W" sort="Eaton, Jeffrey W" uniqKey="Eaton J" first="Jeffrey W." last="Eaton">Jeffrey W. Eaton</name><affiliation><nlm:aff id="aff1">Department of Infectious Disease Epidemiology, Imperial College London, London, United Kingdom</nlm:aff></affiliation></author><author><name sortKey="Rehle, Thomas M" sort="Rehle, Thomas M" uniqKey="Rehle T" first="Thomas M." last="Rehle">Thomas M. Rehle</name><affiliation><nlm:aff id="aff2">Human Sciences Research Council</nlm:aff></affiliation><affiliation><nlm:aff id="aff3">Centre for Infectious Disease Epidemiology and Research, School of Public Health and Family Medicine, University of Cape Town, Cape Town, South Africa</nlm:aff></affiliation></author><author><name sortKey="Jooste, Sean" sort="Jooste, Sean" uniqKey="Jooste S" first="Sean" last="Jooste">Sean Jooste</name><affiliation><nlm:aff id="aff2">Human Sciences Research Council</nlm:aff></affiliation></author><author><name sortKey="Nkambule, Rejoice" sort="Nkambule, Rejoice" uniqKey="Nkambule R" first="Rejoice" last="Nkambule">Rejoice Nkambule</name><affiliation><nlm:aff id="aff4">Ministry of Health, Government of the Kingdom of Swaziland, Mbabane, Swaziland</nlm:aff></affiliation></author><author><name sortKey="Kim, Andrea A" sort="Kim, Andrea A" uniqKey="Kim A" first="Andrea A." last="Kim">Andrea A. Kim</name><affiliation><nlm:aff id="aff5">Centers for Disease Control and Prevention Kenya, Nairobi, Kenya</nlm:aff></affiliation></author><author><name sortKey="Mahy, Mary" sort="Mahy, Mary" uniqKey="Mahy M" first="Mary" last="Mahy">Mary Mahy</name><affiliation><nlm:aff id="aff6">Strategic Information and Evidence Department, UNAIDS, Geneva, Switzerland.</nlm:aff></affiliation></author><author><name sortKey="Hallett, Timothy B" sort="Hallett, Timothy B" uniqKey="Hallett T" first="Timothy B." last="Hallett">Timothy B. Hallett</name><affiliation><nlm:aff id="aff1">Department of Infectious Disease Epidemiology, Imperial College London, London, United Kingdom</nlm:aff></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PMC</idno><idno type="pmid">25406753</idno><idno type="pmc">4247272</idno><idno type="url">http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4247272</idno><idno type="RBID">PMC:4247272</idno><idno type="doi">10.1097/QAD.0000000000000412</idno><date when="2014">2014</date><idno type="wicri:Area/Pmc/Corpus">002114</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Corpus" wicri:corpus="PMC">002114</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">Recent HIV prevalence trends among pregnant women and all women in sub-Saharan Africa: implications for HIV estimates</title><author><name sortKey="Eaton, Jeffrey W" sort="Eaton, Jeffrey W" uniqKey="Eaton J" first="Jeffrey W." last="Eaton">Jeffrey W. Eaton</name><affiliation><nlm:aff id="aff1">Department of Infectious Disease Epidemiology, Imperial College London, London, United Kingdom</nlm:aff></affiliation></author><author><name sortKey="Rehle, Thomas M" sort="Rehle, Thomas M" uniqKey="Rehle T" first="Thomas M." last="Rehle">Thomas M. Rehle</name><affiliation><nlm:aff id="aff2">Human Sciences Research Council</nlm:aff></affiliation><affiliation><nlm:aff id="aff3">Centre for Infectious Disease Epidemiology and Research, School of Public Health and Family Medicine, University of Cape Town, Cape Town, South Africa</nlm:aff></affiliation></author><author><name sortKey="Jooste, Sean" sort="Jooste, Sean" uniqKey="Jooste S" first="Sean" last="Jooste">Sean Jooste</name><affiliation><nlm:aff id="aff2">Human Sciences Research Council</nlm:aff></affiliation></author><author><name sortKey="Nkambule, Rejoice" sort="Nkambule, Rejoice" uniqKey="Nkambule R" first="Rejoice" last="Nkambule">Rejoice Nkambule</name><affiliation><nlm:aff id="aff4">Ministry of Health, Government of the Kingdom of Swaziland, Mbabane, Swaziland</nlm:aff></affiliation></author><author><name sortKey="Kim, Andrea A" sort="Kim, Andrea A" uniqKey="Kim A" first="Andrea A." last="Kim">Andrea A. Kim</name><affiliation><nlm:aff id="aff5">Centers for Disease Control and Prevention Kenya, Nairobi, Kenya</nlm:aff></affiliation></author><author><name sortKey="Mahy, Mary" sort="Mahy, Mary" uniqKey="Mahy M" first="Mary" last="Mahy">Mary Mahy</name><affiliation><nlm:aff id="aff6">Strategic Information and Evidence Department, UNAIDS, Geneva, Switzerland.</nlm:aff></affiliation></author><author><name sortKey="Hallett, Timothy B" sort="Hallett, Timothy B" uniqKey="Hallett T" first="Timothy B." last="Hallett">Timothy B. Hallett</name><affiliation><nlm:aff id="aff1">Department of Infectious Disease Epidemiology, Imperial College London, London, United Kingdom</nlm:aff></affiliation></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="2014">2014</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><sec><title>Objectives:</title><p>National population-wide HIV prevalence and incidence trends in sub-Saharan Africa (SSA) are indirectly estimated using HIV prevalence measured among pregnant women attending antenatal clinics (ANC), among other data. We evaluated whether recent HIV prevalence trends among pregnant women are representative of general population trends.</p></sec><sec><title>Design:</title><p>Serial population-based household surveys in 13 SSA countries.</p></sec><sec sec-type="methods"><title>Methods:</title><p>We calculated HIV prevalence trends among all women aged 15–49 years and currently pregnant women between surveys conducted from 2003 to 2008 (period 1) and 2009 to 2012 (period 2). Log-binomial regression was used to test for a difference in prevalence trend between the two groups. Prevalence among pregnant women was age-standardized to represent the age distribution of all women.</p></sec><sec sec-type="results"><title>Results:</title><p>Pooling data for all countries, HIV prevalence declined among pregnant women from 6.5 [95% confidence interval (CI) 5.3–7.9%] to 5.3% (95% CI 4.2–6.6%) between periods 1 and 2, whereas it remained unchanged among all women at 8.4% (95% CI 8.0–8.9%) in period 1 and 8.3% (95% CI 7.9–8.8%) in period 2. Prevalence declined by 18% (95% CI −9–38%) more in pregnant women than nonpregnant women. Estimates were similar in Western, Eastern, and Southern regions of SSA; none were statistically significant (<italic>P</italic> > 0.05). HIV prevalence decreased significantly among women aged 15–24 years while increasing significantly among women 35–49 years, who represented 29% of women but only 15% of pregnant women. Age-standardization of prevalence in pregnant women did not reconcile the discrepant trends because at older ages prevalence was lower among pregnant women than nonpregnant women.</p></sec><sec sec-type="conclusion"><title>Conclusion:</title><p>As HIV prevalence in SSA has shifted toward older, less-fertile women, HIV prevalence among pregnant women has declined more rapidly than prevalence in women overall. Interpretation of ANC prevalence data to inform national HIV estimates should account for both age-specific fertility patterns and HIV-related sub-fertility.</p></sec></div></front><back><div1 type="bibliography"><listBibl><biblStruct></biblStruct><biblStruct><analytic><author><name sortKey="Ortblad, Kf" uniqKey="Ortblad K">KF Ortblad</name></author><author><name sortKey="Lozano, R" uniqKey="Lozano R">R Lozano</name></author><author><name sortKey="Murray, Cjl" uniqKey="Murray C">CJL Murray</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Asamoah Odei, E" uniqKey="Asamoah Odei E">E Asamoah-Odei</name></author><author><name sortKey="Garcia Calleja, Jm" uniqKey="Garcia Calleja J">JM Garcia Calleja</name></author><author><name sortKey="Boerma, Jt" uniqKey="Boerma J">JT Boerma</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Fylkesnes, K" uniqKey="Fylkesnes K">K Fylkesnes</name></author><author><name sortKey="Ndhlovu, Z" uniqKey="Ndhlovu Z">Z Ndhlovu</name></author><author><name sortKey="Kasumba, K" uniqKey="Kasumba K">K Kasumba</name></author><author><name sortKey="Mubanga Musonda, R" uniqKey="Mubanga Musonda R">R Mubanga Musonda</name></author><author><name sortKey="Sichone, M" uniqKey="Sichone M">M Sichone</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Kigadye, Rm" uniqKey="Kigadye R">RM Kigadye</name></author><author><name sortKey="Klokke, A" uniqKey="Klokke A">A Klokke</name></author><author><name sortKey="Nicoll, A" uniqKey="Nicoll A">A Nicoll</name></author><author><name sortKey="Nyamuryekung, Km" uniqKey="Nyamuryekung K">KM Nyamuryekung’e</name></author><author><name sortKey="Borgdorff, M" uniqKey="Borgdorff M">M Borgdorff</name></author><author><name sortKey="Barongo, L" uniqKey="Barongo L">L Barongo</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Glynn, Jr" uniqKey="Glynn J">JR Glynn</name></author><author><name sortKey="Buve, A" uniqKey="Buve A">A Buvé</name></author><author><name sortKey="Carael, M" uniqKey="Carael M">M Caraël</name></author><author><name sortKey="Musonda, Rm" uniqKey="Musonda R">RM Musonda</name></author><author><name sortKey="Kahindo, M" uniqKey="Kahindo M">M Kahindo</name></author><author><name sortKey="Macauley, I" uniqKey="Macauley I">I Macauley</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Brown, T" uniqKey="Brown T">T Brown</name></author><author><name sortKey="Bao, L" uniqKey="Bao L">L Bao</name></author><author><name sortKey="Raftery, Ae" uniqKey="Raftery A">AE Raftery</name></author><author><name sortKey="Salomon, Ja" uniqKey="Salomon J">JA Salomon</name></author><author><name sortKey="Baggaley, Rf" uniqKey="Baggaley R">RF Baggaley</name></author><author><name sortKey="Stover, J" uniqKey="Stover J">J Stover</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Brown, T" uniqKey="Brown T">T Brown</name></author><author><name sortKey="Bao, L" uniqKey="Bao L">L Bao</name></author><author><name sortKey="Eaton, Jw" uniqKey="Eaton J">JW Eaton</name></author><author><name sortKey="Hogan, Dr" uniqKey="Hogan D">DR Hogan</name></author><author><name sortKey="Mahy, M" uniqKey="Mahy M">M Mahy</name></author><author><name sortKey="Marsh, K" uniqKey="Marsh K">K Marsh</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Alkema, L" uniqKey="Alkema L">L Alkema</name></author><author><name sortKey="Raftery, Ae" uniqKey="Raftery A">AE Raftery</name></author><author><name sortKey="Brown, T" uniqKey="Brown T">T Brown</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Rehle, Tm" uniqKey="Rehle T">TM Rehle</name></author><author><name sortKey="Hallett, Tb" uniqKey="Hallett T">TB Hallett</name></author><author><name sortKey="Shisana, O" uniqKey="Shisana O">O Shisana</name></author><author><name sortKey="Pillay Van Wyk, V" uniqKey="Pillay Van Wyk V">V Pillay-van Wyk</name></author><author><name sortKey="Zuma, K" uniqKey="Zuma K">K Zuma</name></author><author><name sortKey="Carrara, H" uniqKey="Carrara H">H Carrara</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Mahy, M" uniqKey="Mahy M">M Mahy</name></author><author><name sortKey="Garcia Calleja, Jm" uniqKey="Garcia Calleja J">JM Garcia-Calleja</name></author><author><name sortKey="Marsh, Ka" uniqKey="Marsh K">KA Marsh</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Zaba, Bw" uniqKey="Zaba B">BW Zaba</name></author><author><name sortKey="Carpenter, Lm" uniqKey="Carpenter L">LM Carpenter</name></author><author><name sortKey="Boerma, Tj" uniqKey="Boerma T">TJ Boerma</name></author><author><name sortKey="Gregson, S" uniqKey="Gregson S">S Gregson</name></author><author><name sortKey="Nakiyingi, J" uniqKey="Nakiyingi J">J Nakiyingi</name></author><author><name sortKey="Urassa, M" uniqKey="Urassa M">M Urassa</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="Pillay Van Wyk, V" uniqKey="Pillay Van Wyk V">V Pillay-van Wyk</name></author></analytic></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="Labadarios, D" uniqKey="Labadarios D">D Labadarios</name></author><author><name sortKey="Jooste, S" uniqKey="Jooste S">S Jooste</name></author><author><name sortKey="Davids, A" uniqKey="Davids A">A Davids</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Bicego, Gt" uniqKey="Bicego G">GT Bicego</name></author><author><name sortKey="Nkambule, R" uniqKey="Nkambule R">R Nkambule</name></author><author><name sortKey="Peterson, I" uniqKey="Peterson I">I Peterson</name></author><author><name sortKey="Reed, J" uniqKey="Reed J">J Reed</name></author><author><name sortKey="Donnell, D" uniqKey="Donnell D">D Donnell</name></author><author><name sortKey="Ginindza, H" uniqKey="Ginindza H">H Ginindza</name></author></analytic></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct><analytic><author><name sortKey="Lumley, T" uniqKey="Lumley T">T Lumley</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Maher, D" uniqKey="Maher D">D Maher</name></author><author><name sortKey="Biraro, S" uniqKey="Biraro S">S Biraro</name></author><author><name sortKey="Hosegood, V" uniqKey="Hosegood V">V Hosegood</name></author><author><name sortKey="Isingo, R" uniqKey="Isingo R">R Isingo</name></author><author><name sortKey="Lutalo, T" uniqKey="Lutalo T">T Lutalo</name></author><author><name sortKey="Mushati, P" uniqKey="Mushati P">P Mushati</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Marsh, K" uniqKey="Marsh K">K Marsh</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Zaba, B" uniqKey="Zaba B">B Zaba</name></author><author><name sortKey="Boerma, T" uniqKey="Boerma T">T Boerma</name></author><author><name sortKey="White, R" uniqKey="White R">R White</name></author></analytic></biblStruct><biblStruct></biblStruct><biblStruct><analytic><author><name sortKey="Eaton, Jw" uniqKey="Eaton J">JW Eaton</name></author><author><name sortKey="Garnett, Gp" uniqKey="Garnett G">GP Garnett</name></author><author><name sortKey="Takavarasha, Fr" uniqKey="Takavarasha F">FR Takavarasha</name></author><author><name sortKey="Mason, Pr" uniqKey="Mason P">PR Mason</name></author><author><name sortKey="Robertson, L" uniqKey="Robertson L">L Robertson</name></author><author><name sortKey="Schumacher, Cm" uniqKey="Schumacher C">CM Schumacher</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Ferrand, Ra" uniqKey="Ferrand R">RA Ferrand</name></author><author><name sortKey="Corbett, El" uniqKey="Corbett E">EL Corbett</name></author><author><name sortKey="Wood, R" uniqKey="Wood R">R Wood</name></author><author><name sortKey="Hargrove, J" uniqKey="Hargrove J">J Hargrove</name></author><author><name sortKey="Ndhlovu, Ce" uniqKey="Ndhlovu C">CE Ndhlovu</name></author><author><name sortKey="Cowan, Fm" uniqKey="Cowan F">FM Cowan</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Zaba, B" uniqKey="Zaba B">B Zaba</name></author><author><name sortKey="Gregson, S" uniqKey="Gregson S">S Gregson</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Lewis, Jjc" uniqKey="Lewis J">JJC Lewis</name></author><author><name sortKey="Ronsmans, C" uniqKey="Ronsmans C">C Ronsmans</name></author><author><name sortKey="Ezeh, A" uniqKey="Ezeh A">A Ezeh</name></author><author><name sortKey="Gregson, S" uniqKey="Gregson S">S Gregson</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Chen, W J" uniqKey="Chen W">W-J Chen</name></author><author><name sortKey="Walker, N" uniqKey="Walker N">N Walker</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Mujugira, A" uniqKey="Mujugira A">A Mujugira</name></author><author><name sortKey="Heffron, R" uniqKey="Heffron R">R Heffron</name></author><author><name sortKey="Celum, C" uniqKey="Celum C">C Celum</name></author><author><name sortKey="Mugo, N" uniqKey="Mugo N">N Mugo</name></author><author><name sortKey="Nakku Joloba, E" uniqKey="Nakku Joloba E">E Nakku-Joloba</name></author><author><name sortKey="Baeten, Jm" uniqKey="Baeten J">JM Baeten</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Koal, T" uniqKey="Koal T">T Koal</name></author><author><name sortKey="Burhenne, H" uniqKey="Burhenne H">H Burhenne</name></author><author><name sortKey="Romling, R" uniqKey="Romling R">R Römling</name></author><author><name sortKey="Svoboda, M" uniqKey="Svoboda M">M Svoboda</name></author><author><name sortKey="Resch, K" uniqKey="Resch K">K Resch</name></author><author><name sortKey="Kaever, V" uniqKey="Kaever V">V Kaever</name></author></analytic></biblStruct><biblStruct></biblStruct><biblStruct><analytic><author><name sortKey="Simkhada, B" uniqKey="Simkhada B">B Simkhada</name></author><author><name sortKey="Teijlingen, Er" uniqKey="Teijlingen E">ER Teijlingen</name></author><author><name sortKey="Porter, M" uniqKey="Porter M">M Porter</name></author><author><name sortKey="Simkhada, P" uniqKey="Simkhada P">P Simkhada</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Mrisho, M" uniqKey="Mrisho M">M Mrisho</name></author><author><name sortKey="Schellenberg, Ja" uniqKey="Schellenberg J">JA Schellenberg</name></author><author><name sortKey="Mushi, Ak" uniqKey="Mushi A">AK Mushi</name></author><author><name sortKey="Obrist, B" uniqKey="Obrist B">B Obrist</name></author><author><name sortKey="Mshinda, H" uniqKey="Mshinda H">H Mshinda</name></author><author><name sortKey="Tanner, M" uniqKey="Tanner M">M Tanner</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Gouws, E" uniqKey="Gouws E">E Gouws</name></author><author><name sortKey="Mishra, V" uniqKey="Mishra V">V Mishra</name></author><author><name sortKey="Fowler, Tb" uniqKey="Fowler T">TB Fowler</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Montana, Ls" uniqKey="Montana L">LS Montana</name></author><author><name sortKey="Mishra, V" uniqKey="Mishra V">V Mishra</name></author><author><name sortKey="Hong, R" uniqKey="Hong R">R Hong</name></author></analytic></biblStruct></listBibl></div1></back></TEI><pmc article-type="research-article"><pmc-dir>properties open_access</pmc-dir>
<front><journal-meta><journal-id journal-id-type="nlm-ta">AIDS</journal-id><journal-id journal-id-type="iso-abbrev">AIDS</journal-id><journal-id journal-id-type="publisher-id">AIDS</journal-id><journal-title-group><journal-title>AIDS (London, England)</journal-title></journal-title-group><issn pub-type="ppub">0269-9370</issn><issn pub-type="epub">1473-5571</issn><publisher><publisher-name>Lippincott Williams & Wilkins</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="pmid">25406753</article-id><article-id pub-id-type="pmc">4247272</article-id><article-id pub-id-type="doi">10.1097/QAD.0000000000000412</article-id><article-id pub-id-type="art-access-id">00012</article-id><article-categories><subj-group subj-group-type="heading"><subject>The 2013/14 UNAIDS Estimates Methods: Extending The Scope and Granularity of HIV Estimates</subject></subj-group></article-categories><title-group><article-title>Recent HIV prevalence trends among pregnant women and all women in sub-Saharan Africa: implications for HIV estimates</article-title></title-group><contrib-group><contrib contrib-type="author"><name><surname>Eaton</surname><given-names>Jeffrey W.</given-names></name><xref ref-type="aff" rid="aff1"><sup>a</sup></xref></contrib><contrib contrib-type="author"><name><surname>Rehle</surname><given-names>Thomas M.</given-names></name><xref ref-type="aff" rid="aff2"><sup>b</sup></xref><xref ref-type="aff" rid="aff3"><sup>c</sup></xref></contrib><contrib contrib-type="author"><name><surname>Jooste</surname><given-names>Sean</given-names></name><xref ref-type="aff" rid="aff2"><sup>b</sup></xref></contrib><contrib contrib-type="author"><name><surname>Nkambule</surname><given-names>Rejoice</given-names></name><xref ref-type="aff" rid="aff4"><sup>d</sup></xref></contrib><contrib contrib-type="author"><name><surname>Kim</surname><given-names>Andrea A.</given-names></name><xref ref-type="aff" rid="aff5"><sup>e</sup></xref></contrib><contrib contrib-type="author"><name><surname>Mahy</surname><given-names>Mary</given-names></name><xref ref-type="aff" rid="aff6"><sup>f</sup></xref></contrib><contrib contrib-type="author"><name><surname>Hallett</surname><given-names>Timothy B.</given-names></name><xref ref-type="aff" rid="aff1"><sup>a</sup></xref></contrib></contrib-group><aff id="aff1"><label>a</label>Department of Infectious Disease Epidemiology, Imperial College London, London, United Kingdom</aff><aff id="aff2"><label>b</label>Human Sciences Research Council</aff><aff id="aff3"><label>c</label>Centre for Infectious Disease Epidemiology and Research, School of Public Health and Family Medicine, University of Cape Town, Cape Town, South Africa</aff><aff id="aff4"><label>d</label>Ministry of Health, Government of the Kingdom of Swaziland, Mbabane, Swaziland</aff><aff id="aff5"><label>e</label>Centers for Disease Control and Prevention Kenya, Nairobi, Kenya</aff><aff id="aff6"><label>f</label>Strategic Information and Evidence Department, UNAIDS, Geneva, Switzerland.</aff><author-notes><corresp>Correspondence to Jeffrey W. Eaton, St Mary's Campus, Norfolk Place, London W2 1PG, United Kingdom. Tel: +44 20 7594 3007; fax: +44 20 7402 3927; e-mail: <email>jeffrey.eaton@imperial.ac.uk</email></corresp></author-notes><pub-date pub-type="ppub"><month>11</month><year>2014</year></pub-date><pub-date pub-type="epub"><day>20</day><month>11</month><year>2014</year></pub-date><volume>28</volume><issue>4</issue><fpage>S507</fpage><lpage>S514</lpage><history><date date-type="received"><day>8</day><month>6</month><year>2014</year></date><date date-type="rev-recd"><day>4</day><month>7</month><year>2014</year></date><date date-type="accepted"><day>15</day><month>7</month><year>2014</year></date></history><permissions><copyright-statement>© 2014 Wolters Kluwer Health | Lippincott Williams & Wilkins</copyright-statement><copyright-year>2014</copyright-year><license license-type="open-access" xlink:href="http://creativecommons.org/licenses/by-nc-nd/4.0/"><license-p>This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License, where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially. <ext-link ext-link-type="uri" xlink:href="http://creativecommons.org/licenses/by-nc-nd/4.0">http://creativecommons.org/licenses/by-nc-nd/4.0</ext-link></license-p></license></permissions><self-uri xlink:type="simple" xlink:href="aids-28-s507.pdf"></self-uri><abstract><sec><title>Objectives:</title><p>National population-wide HIV prevalence and incidence trends in sub-Saharan Africa (SSA) are indirectly estimated using HIV prevalence measured among pregnant women attending antenatal clinics (ANC), among other data. We evaluated whether recent HIV prevalence trends among pregnant women are representative of general population trends.</p></sec><sec><title>Design:</title><p>Serial population-based household surveys in 13 SSA countries.</p></sec><sec sec-type="methods"><title>Methods:</title><p>We calculated HIV prevalence trends among all women aged 15–49 years and currently pregnant women between surveys conducted from 2003 to 2008 (period 1) and 2009 to 2012 (period 2). Log-binomial regression was used to test for a difference in prevalence trend between the two groups. Prevalence among pregnant women was age-standardized to represent the age distribution of all women.</p></sec><sec sec-type="results"><title>Results:</title><p>Pooling data for all countries, HIV prevalence declined among pregnant women from 6.5 [95% confidence interval (CI) 5.3–7.9%] to 5.3% (95% CI 4.2–6.6%) between periods 1 and 2, whereas it remained unchanged among all women at 8.4% (95% CI 8.0–8.9%) in period 1 and 8.3% (95% CI 7.9–8.8%) in period 2. Prevalence declined by 18% (95% CI −9–38%) more in pregnant women than nonpregnant women. Estimates were similar in Western, Eastern, and Southern regions of SSA; none were statistically significant (<italic>P</italic> > 0.05). HIV prevalence decreased significantly among women aged 15–24 years while increasing significantly among women 35–49 years, who represented 29% of women but only 15% of pregnant women. Age-standardization of prevalence in pregnant women did not reconcile the discrepant trends because at older ages prevalence was lower among pregnant women than nonpregnant women.</p></sec><sec sec-type="conclusion"><title>Conclusion:</title><p>As HIV prevalence in SSA has shifted toward older, less-fertile women, HIV prevalence among pregnant women has declined more rapidly than prevalence in women overall. Interpretation of ANC prevalence data to inform national HIV estimates should account for both age-specific fertility patterns and HIV-related sub-fertility.</p></sec></abstract><kwd-group><title>Keywords</title><kwd>antenatal clinic surveillance</kwd><kwd>HIV prevalence</kwd><kwd>household survey</kwd><kwd>pregnant women</kwd><kwd>sub-Saharan Africa</kwd></kwd-group><custom-meta-group><custom-meta><meta-name>OPEN-ACCESS</meta-name><meta-value>TRUE</meta-value></custom-meta></custom-meta-group></article-meta></front><body><sec sec-type="intro"><title>Introduction</title><p>The establishment of sentinel surveillance of HIV prevalence among pregnant women attending antenatal clinics (ANC) in the early 1990s in countries with generalized HIV epidemics in sub-Saharan Africa [<xref rid="R1" ref-type="bibr">1</xref>] enabled HIV to become one of the better-quantified disease burdens in global health [<xref rid="R2" ref-type="bibr">2</xref>]. These prevalence surveys are repeated in the same clinics every 1–2 years and HIV prevalence estimates derived from women attending sentinel ANC have been used as a proxy measure to estimate the levels and trends in HIV prevalence among all adults aged 15–49 years using mathematical models [<xref rid="R3" ref-type="bibr">3</xref>–<xref rid="R6" ref-type="bibr">6</xref>]. In addition, data from these surveys are used to indirectly derive estimates of HIV incidence using mathematical models that incorporate other information about survival after HIV infection and antiretroviral use [<xref rid="R7" ref-type="bibr">7</xref>,<xref rid="R8" ref-type="bibr">8</xref>].</p><p>Since 2001, national household surveys including HIV testing, such as the Demographic and Health Surveys (DHS) and AIDS Indicator Surveys (AIS), have provided statistically representative estimates of population-wide adult HIV prevalence, including men and nonpregnant women. Based on the assumption that household prevalence surveys provide an unbiased estimate of HIV prevalence in the general population, these data have been used to calibrate the level of HIV prevalence estimates derived from ANC sentinel surveillance [<xref rid="R9" ref-type="bibr">9</xref>]. But, because such household HIV prevalence surveys are less frequent and, until recently, few countries had conducted more than one, ANC surveillance has continued to be relied upon as the primary information source for estimating the HIV epidemic trend in the general population.</p><p>The veracity of estimates derived from ANC prevalence data thus relies on the assumption that HIV prevalence trends among women attending ANC are representative of HIV trends among all adults aged 15–49 years. Two recent epidemiologic phenomena particularly motivate investigation of this assumption: recent declines in HIV incidence being especially great among younger adults [<xref rid="R10" ref-type="bibr">10</xref>,<xref rid="R11" ref-type="bibr">11</xref>] and the scale-up of antiretroviral treatment, which has resulted in treated persons living with HIV to older ages than they might have previously. Both changes could have shifted the age-distribution of prevalent HIV infections to older age groups, who are less fertile and thus less represented in ANC surveillance [<xref rid="R12" ref-type="bibr">12</xref>].</p><p>We pool data from serial cross-sectional household surveys in sub-Saharan Africa to compare recent trends in HIV prevalence between all women and women currently pregnant at the time of the survey.</p></sec><sec sec-type="methods"><title>Methods</title><sec><title>Data</title><p>We used nationally representative household-based HIV prevalence survey data from countries in sub-Saharan Africa with at least two surveys in which women's HIV serostatus could be linked to her self-reported pregnancy status at the time of the survey. All surveys were conducted using a stratified two-stage cluster design. In most countries, these consist of DHS and AIS [<xref rid="R13" ref-type="bibr">13</xref>]. For South Africa, we used data from the HSRC National HIV Prevalence, Incidence, and Behaviour Surveys [<xref rid="R14" ref-type="bibr">14</xref>,<xref rid="R15" ref-type="bibr">15</xref>]. For Swaziland, we compared data from the 2007 DHS to estimates from the 2011 Swaziland HIV Incidence Measurement Survey [<xref rid="R16" ref-type="bibr">16</xref>]. In Swaziland, data were for women aged 18–49 years, the sampling frame for the latter survey. In all other countries, we restricted analysis to women aged 15–49 years, the age-range for which ANC prevalence trend is assumed to be representative in the Joint United Nations Programme on HIV/AIDS (UNAIDS) global HIV estimates [<xref rid="R8" ref-type="bibr">8</xref>].</p><p>To analyze HIV trends over time, we divided surveys into two periods with each country having one survey in each period. All surveys during the first period were conducted between 2003 and 2008 and all surveys during the second period between 2009 and 2012. In countries with more than two available surveys (Kenya, South Africa, Tanzania), we used the two most recent surveys. Table <xref ref-type="table" rid="T1">1</xref> summarizes the countries, year conducted, and sample sizes for each of the surveys included in the analysis.</p><p>We combined data from multiple countries into SSA regions according to standard UNAIDS classifications (Western, Eastern, Southern), and all countries together. For pooled multicountry analyses, survey sampling weights were de-normalized using the population size for women aged 15–49 years in 2012 from the UN World Population Prospects 2012 [<xref rid="R17" ref-type="bibr">17</xref>]. Thus, regional and continental pooled statistics and trends are representative of the aggregated population of the countries included in the analysis.</p></sec><sec><title>Statistical analysis</title><p>We calculated the percentage of women currently pregnant at the time of the survey. We calculated HIV prevalence among all women and currently pregnant women in each time period. Ninety five percentage (95%) confidence intervals for proportions were calculated using the logit method. Prevalence trends among all women aged 15–49 years and men aged 15–49 years were also compared.</p><p>We tested for a difference in recent HIV prevalence trends between pregnant and not currently pregnant women by estimating a log-binomial regression model with an interaction between survey period and pregnancy status at the time of the survey. Country-level fixed-effects were included in regressions to adjust for different fertility levels across countries:<inline-graphic xlink:href="aids-28-s507-i001.jpg"></inline-graphic></p><p>The regression was also estimated separately for women in age groups 15–24 years, 25–34 years, and 35–49 years to evaluate prevalence differences between pregnant and nonpregnant women in each age group. We standardized the age distributions of the two prevalence measures by re-weighting the 5-year age-specific prevalence among pregnant women according to the age distribution of all women in the general population.</p><p>Analyses were conducted in R version 3.1.0 [<xref rid="R18" ref-type="bibr">18</xref>] using the ‘survey’ package [<xref rid="R19" ref-type="bibr">19</xref>] to account for the stratified two-stage cluster sampling design of the surveys. R code for reproducing analyses is available at <ext-link ext-link-type="uri" xlink:href="https://github.com/jeffeaton/currpreg-hiv-trends"><italic>https://github.com/jeffeaton/currpreg-hiv-trends</italic></ext-link>.</p></sec></sec><sec sec-type="results"><title>Results</title><sec><title>Age distribution of fertility and changes in age-specific prevalence</title><p>Figure <xref ref-type="fig" rid="F1">1</xref>a compares the age distribution of all women in the population and the age distribution of currently pregnant women. Figure <xref ref-type="fig" rid="F1">1</xref>b illustrates changes in age-specific HIV prevalence among women aged 15–49 years between surveys conducted in period 1 (2003–2008) and period 2 (2008–2012). Younger adult women were disproportionately represented among currently pregnant women. Across all countries, women aged 15–34 years accounted for 85% of pregnant women compared with only 71% of the overall female population. Results were similar in each region: 86 versus 73% in Western Africa, 85 versus 72% in Eastern Africa, and 85 versus 69% in Southern Africa. There was not a statistically significant change in the age distribution of currently pregnant women between periods 1 and 2.</p><fig id="F1" position="float"><label>Fig. 1</label><graphic xlink:href="aids-28-s507-g001"></graphic><p>(a) Age distribution of all women aged 15–49 years and currently pregnant women (both periods combined). (b) Age-specific HIV prevalence among all women aged 15–49 years in period 1 (surveys conducted 2003–2008) and period 2 (surveys 2008–2012). Shaded regions indicate 95% confidence intervals. Note vertical axes are not the same in (b).</p></fig><p>Between periods 1 and 2, HIV prevalence shifted to older ages (Fig. <xref ref-type="fig" rid="F1">1</xref>b). Among women aged 15–24 years, HIV prevalence decreased significantly from 5.1 (95% CI 4.6–5.5%) to 3.6% (95% CI 3.2–3.9%) and among those aged 35–49 years increased significantly from 9.4 (95% CI 8.7–10.2%) to 11.7% (95% CI 10.9–12.7%). This pattern was the same in all three regions (Fig. <xref ref-type="fig" rid="F1">1</xref>b). Taken together, HIV prevalence has declined among the age groups of women who are overrepresented among samples of pregnant women, whereas it increased among age groups underrepresented among samples of pregnant women.</p></sec><sec><title>Prevalence trends between pregnant women and all women</title><p>Figure <xref ref-type="fig" rid="F2">2</xref>a illustrates the HIV prevalence trend in each country among all women aged 15–49 years and currently pregnant women. Point estimates in 11 of 13 countries indicated that between the two most recent surveys HIV prevalence declined more or increased less among currently pregnant women than all women (Fig. <xref ref-type="fig" rid="F2">2</xref>b) Note that the standard errors for the change HIV prevalence among pregnant women are large (Fig. <xref ref-type="fig" rid="F2">2</xref>a; Table S1), and the difference in prevalence trend is not statistically significant in any country. Relative changes in prevalence among adult men aged 15–49 years were similar on average to relative prevalence changes in adult women (Figure S1).</p><fig id="F2" position="float"><label>Fig. 2</label><graphic xlink:href="aids-28-s507-g002"></graphic><p>(a) HIV prevalence trend in all women aged 15–49 years and currently pregnant women in all countries included in the analysis. Note: earlier surveys for Kenya, Tanzania, and South Africa are also plotted, which are not included in pooled analysis. Swaziland illustrates women aged 18–49 years. (b) The relative change [100 × (period 2 prevalence)/(period 1 prevalence) – 1] in prevalence from period 1 to period 2 among all women (horizontal axis) and currently pregnant women. BF, Burkina Faso; CM, Cameroon; CI, Côte d’Ivoire; SN, Senegal; ET, Ethiopia; KE, Kenya; RW, Rwanda; TZ, Tanzania; LS, Lesotho; MW, Malawi; SZ, Swaziland; ZA, South Africa; ZW, Zimbabwe. (c) Comparison of relative change in prevalence among all women and in age-standardized prevalence among pregnant women.</p></fig><p>Pooling all countries, HIV prevalence among currently pregnant women declined from 6.5 (95% CI 5.3–7.9%) to 5.3% (95% CI 4.2–6.6%), a 19% decline, whereas it remained relatively unchanged at 8.4% (95% CI 8.0–8.9%) in period 1 to 8.3% (95% CI 7.9–8.8%) in period 2 among all women aged 15–49 years. In Western Africa, prevalence among pregnant women declined from 4.3 (95% CI 3.4–5.4%) to 2.9% (95% CI 2.2–3.9%) among pregnant women and 4.0 (95% CI 3.7–4.4) to 3.4% (95% CI 3.1–3.7%) among all women, in Eastern Africa from 3.6 (95% CI 2.6–4.9) to 2.9 (95% CI 2.2–3.9) for pregnant women and 4.5 (95% CI 4.1–4.9%) to 4.1% (95% CI 3.8–4.5%) for all women, and in Southern Africa from 17.3% (95% CI 12.8–22.9%) to 16.1% (95% CI 11.6–22.0%) for pregnant women and increased from 20.1% (95% CI 18.8–21.4%) to 20.9% (95% CI 19.6–22.3%) for all women (Table S1).</p><p>Adjusting for country, HIV prevalence was lower among currently pregnant women than nonpregnant women during period 1 [relative risk (RR) 0.89, 95% CI 0.73–1.08; Table <xref ref-type="table" rid="T2">2</xref>]. Based on the estimated interaction between current pregnancy and survey period, prevalence among pregnant women declined 18% more than prevalence among nonpregnant women between periods 1 and 2 (RR 0.82, 0.62–1.09; Table <xref ref-type="table" rid="T2">2</xref>). The same pattern was found in all regions, with the prevalence trend among pregnant women being lower than that for all women by 22, 21, and 13% in Western, Eastern and Southern regions, respectively, although none were statistically significant (Western: RR 0.78, 0.54–1.14; Eastern: 0.79, 0.51–1.22; Southern 0.87; 0.57–1.33).</p></sec><sec><title>Age-standardized prevalence and age-specific prevalence trends among pregnant women</title><p>Based on the discrepancy in the age-distribution of pregnant women compared with all women (Fig. <xref ref-type="fig" rid="F1">1</xref>a), a natural approach to adjusting for the bias in prevalence among pregnant women would be to re-weight age-specific ANC prevalence with the population age distribution. Figure <xref ref-type="fig" rid="F2">2</xref>c illustrates the effect of age-standardizing prevalence among pregnant women on the estimated prevalence trend. Pooling all countries, the relative change in age-standardized prevalence was somewhat more similar to the change in general population prevalence, but not substantially so; age standardized prevalence declined by 17.4% compared with a 18.8% decline in crude prevalence among pregnant women. Standard error estimates for age-standardized prevalence among pregnant women were large, similar to the standard errors for crude prevalence among pregnant women (Table S1), preventing formal comparison of trends in age-standardized prevalence.</p><p>Furthermore, age-standardization increased the extent to which HIV prevalence among pregnant women underestimated HIV prevalence for all women. For example, pooling all countries, in period 1, age-standardization reduced HIV prevalence among currently pregnant women from 6.5 to 5.5%, compared with a prevalence of 8.4% among all women aged 15–49 years (Table S1).</p><p>The increased discrepancy in age-standardized prevalence was explained by examining the age-specific prevalence trends between pregnant women and all women (Fig. <xref ref-type="fig" rid="F3">3</xref>). Among young women aged 15–24 years, levels and HIV prevalence among pregnant women were representative of all women. However, among women aged 25–34 years and 35–49 years, prevalence was lower among pregnant women than all women. Adjusting for country-level differences in fertility, for women 25–34 years, HIV prevalence was 24% (95% CI -4–45%) lower among pregnant women than nonpregnant women in period 2. For women aged 35–49 years, prevalence was 54% (36–79%) lower among pregnant women (data not shown).</p><fig id="F3" position="float"><label>Fig. 3</label><caption><p>Trends in age-specific prevalence among pregnant women and all women by age group and region.</p></caption><graphic xlink:href="aids-28-s507-g003"></graphic></fig></sec></sec><sec sec-type="discussion"><title>Discussion</title><p>Observed reductions in HIV prevalence in ANC surveillance systems may exaggerate the extent of HIV prevalence declines that are occurring in the wider female population. This is because HIV epidemics in sub-Saharan Africa are aging into older age groups of women, who experience lower fertility and thus are likely to be less represented among ANC clinic attendees. This is consistent across all three sub-Saharan regions of Western, Eastern and Southern Africa, and this pattern will be expected to continue and be exacerbated as ART programmes mature and HIV positive patients survive to older ages.</p><p>Point estimates in all three regions suggested that prevalence declined more rapidly among currently pregnant women than all women between surveys conducted from 2003 to 2008 and 2008 to 2012 in the same countries. The downward bias in prevalence among pregnant women compared with not currently pregnant women was around 18% across all countries, but the difference was not statistically significant and CIs for prevalence among pregnant women were large. Using population sizes at the time of each survey to de-normailze sampling weights in pooled analyses rather than standardizing to 2012 population sizes had a negligible effect on the results. Other data sources offer the potential to improve the precision of these estimates, such as population-based HIV cohort studies embedded within demographic surveillance sites [<xref rid="R20" ref-type="bibr">20</xref>] and by comparing national household survey prevalence trends with the actual age-specific ANC HIV prevalence surveillance data, which have larger samples of pregnant women. Mathematical modelling will further elucidate how biases may vary over time, with ART programme scale-up, and across epidemics with different recent and future HIV incidence trends [<xref rid="R21" ref-type="bibr">21</xref>].</p><p>Modelling will also quantify the potential magnitude of biases in HIV incidence estimates resulting from downward biases in prevalence among pregnant women. National epidemic estimates for HIV incidence are generated by fitting a model to HIV prevalence trends in ANC data and using information about natural HIV survival and ART uptake to determine what previous incidence would be required to match the observed prevalence. Thus, overestimating declines in HIV prevalence based on trends in ANC data could also overstate the extent to which HIV incidence has declined. Because recent prevalence patterns represent the accumulation of HIV incidence and mortality from several years previous, the relationship between the magnitude of bias in prevalence trends and incidence trends over the same period may vary across epidemics and biases in prevalence trends in recent years may affect estimates of HIV incidence in earlier periods.</p><p>HIV prevalence trends among women aged 15–24 years who were currently pregnant were very similar to trends for all women aged 15–24 years. This is consistent with previous research finding that prevalence trends among young women attending ANC were representative of prevalence trends in all young women [<xref rid="R11" ref-type="bibr">11</xref>,<xref rid="R22" ref-type="bibr">22</xref>], which justified the recommendation of using ANC prevalence among women aged 15–24 years as proxy indicator for recent incidence trends [<xref rid="R23" ref-type="bibr">23</xref>]. This may be inappropriate going forward because an increasing proportion of prevalence in young adults will be accounted for by long-term survivors of mother-to-child HIV transmission [<xref rid="R24" ref-type="bibr">24</xref>,<xref rid="R25" ref-type="bibr">25</xref>], particularly as paediatric ART programmes mature.</p><p>Among older adult women, however, prevalence in pregnant women was significantly lower than in the general population. The implication of this is that simply re-weighting age-specific prevalence among pregnant women to represent the age distribution of the general population may not resolve the downward bias in HIV prevalence trends among pregnant women. One reason for the lower prevalence is thought to be the lower fertility among HIV-positive pregnant women at older ages [<xref rid="R26" ref-type="bibr">26</xref>–<xref rid="R28" ref-type="bibr">28</xref>]. To the extent that this subfertility is due to the effects of HIV infection, antiretroviral treatment may ameliorate HIV-related subfertility [<xref rid="R29" ref-type="bibr">29</xref>]. Thus, with high levels of ART coverage, prevalence among older pregnant women may again become more similar to general population prevalence.</p><p>In light of the different age patterns of prevalence among pregnant women and the general population and potential future changes owing to ART, we recommend that HIV prevalence surveillance among antenatal clinic attendees should be stratified by age and whether HIV-positive women were on ART prior to the pregnancy or initiated ART during their pregnancy. The latter will enable appropriate adjustment for the effects of ART on fertility when interpreting future ANC prevalence trends. Ascertainment of ART status could be done anonymously using biomarker testing for antiretrovirals on blood samples from the first ANC visit [<xref rid="R15" ref-type="bibr">15</xref>,<xref rid="R30" ref-type="bibr">30</xref>], or biomarker testing could be used to confirm self-reported ART status. These recommendations for reporting ANC prevalence also apply to sentinel surveillance in pregnant women based on HIV testing for prevention of mother-to-child transmission (PMTCT), which must additionally ensure that women who opt out of PMTCT testing because of prior knowledge of HIV status are reflected in PMTCT surveillance reporting and that information on ART status be collected for such women [<xref rid="R31" ref-type="bibr">31</xref>].</p><p>This analysis did not consider factors beyond pregnancy that could influence the representativeness of HIV prevalence trends among ANC attendees. These could include changing patterns in ANC attendance over time or systematic differences in ANC attendance according to factors that are also correlated with risk of HIV infection, such as education, socioeconomic status, parity, or contraceptive use [<xref rid="R6" ref-type="bibr">6</xref>,<xref rid="R32" ref-type="bibr">32</xref>,<xref rid="R33" ref-type="bibr">33</xref>]. Although we found that HIV prevalence among pregnant women was lower than among all women, at the national level, HIV prevalence derived from ANC surveillance has tended to overestimate general population prevalence [<xref rid="R34" ref-type="bibr">34</xref>]. Reasons for this apparent discrepancy include: general population prevalence includes men who tend to have a lower prevalence than women [<xref rid="R6" ref-type="bibr">6</xref>], sites initially chosen for ANC surveillance may have been in higher prevalence areas [<xref rid="R35" ref-type="bibr">35</xref>], and potential systematic differences among women who attend public antenatal clinics as opposed to those who attend private facilities or do not attend any health facility for their prenatal care.</p><p>Sentinel surveillance of HIV prevalence among pregnant women attending antenatal care has been invaluable for quantifying HIV epidemics in sub-Saharan Africa and will continue to be useful for informing HIV estimates. However, naïve interpretation of prevalence trends among pregnant women as representative of all women aged 15–49 years may lead to downward biases in model-derived estimates of HIV prevalence and incidence in the general population. To correct for this bias, HIV incidence and prevalence estimates in the general population should account for both the age-pattern of fertility and the effects of HIV and ART on fertility when interpreting trends in ANC prevalence.</p></sec><sec><title>Acknowledgements</title><p>We thank Dr Kimberly Powers (University of North Carolina) and attendees of the HIV Modelling Consortium meeting on HIV Incidence Declines (18–19 September 2013, London: <ext-link ext-link-type="uri" xlink:href="http://www.hivmodelling.org/projects/investigating-reports-hiv-incidence-decline">http://www.hivmodelling.org/projects/investigating-reports-hiv-incidence-decline</ext-link>) and the UNAIDS Reference Group on Estimates, Modelling, and Projections meeting on new approaches for generating estimates (24–25 April 2014, Seattle: <ext-link ext-link-type="uri" xlink:href="http://www.epidem.org/future-considerations-for-epp-spectrum-and-new-approaches-for-generating-estimates">http://www.epidem.org/future-considerations-for-epp-spectrum-and-new-approaches-for-generating-estimates</ext-link>) for helpful discussions and feedback on earlier versions of this work.</p><p>J.W.E. and T.B.H. received funding for this study from the Bill and Melinda Gates Foundation through a grant to the HIV Modelling Consortium. T.B.H. is a principal investigator of the UNAIDS Reference Group on Estimates, Modelling, and Projections, which receives a grant from UNAIDS to advise on methods for creating HIV estimates. The findings and conclusions in this article are those of the authors and do not necessarily represent the official position of the US Centers for Disease Control and Prevention.</p><sec><title>Conflicts of interest</title><p>There are no conflicts of interest</p></sec></sec><sec sec-type="supplementary-material"><title>Supplementary Material</title><supplementary-material content-type="local-data" id="SD1"><caption><title>Supplemental Digital Content</title></caption><media mimetype="document" mime-subtype="doc" xlink:href="aids-28-s507-s001.doc" xlink:type="simple" id="d35e506" position="anchor"></media></supplementary-material></sec></body><back><ref-list><title>References</title><ref id="R1"><label>1.</label><mixed-citation publication-type="book"><collab>World Health Organization Global Programme on AIDS</collab>. <source>Unlinked anonymous screening for public health surveillance of HIV infections: proposed international guidelines</source>. <year>1989</year><comment><ext-link ext-link-type="uri" xlink:href="http://apps.who.int/iris/bitstream/10665/64004/1/GPA_SFI_89.3.pdf">http://apps.who.int/iris/bitstream/10665/64004/1/GPA_SFI_89.3.pdf</ext-link></comment><date-in-citation>[Accessed 13 September 2012]</date-in-citation></mixed-citation></ref><ref id="R2"><label>2.</label><mixed-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Ortblad</surname><given-names>KF</given-names></name><name><surname>Lozano</surname><given-names>R</given-names></name><name><surname>Murray</surname><given-names>CJL</given-names></name></person-group><article-title>The burden of HIV: insights from the Global Burden of Disease Study 2010</article-title>. <source><italic>AIDS</italic></source><year>2013</year>; <volume>27</volume>:<fpage>2003</fpage>–<lpage>2017</lpage>.<pub-id pub-id-type="pmid">23660576</pub-id></mixed-citation></ref><ref id="R3"><label>3.</label><mixed-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Asamoah-Odei</surname><given-names>E</given-names></name><name><surname>Garcia Calleja</surname><given-names>JM</given-names></name><name><surname>Boerma</surname><given-names>JT</given-names></name></person-group><article-title>HIV prevalence and trends in sub-Saharan Africa: no decline and large subregional differences</article-title>. <source><italic>Lancet</italic></source><year>2004</year>; <volume>364</volume>:<fpage>35</fpage>–<lpage>40</lpage>.<pub-id pub-id-type="pmid">15234854</pub-id></mixed-citation></ref><ref id="R4"><label>4.</label><mixed-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Fylkesnes</surname><given-names>K</given-names></name><name><surname>Ndhlovu</surname><given-names>Z</given-names></name><name><surname>Kasumba</surname><given-names>K</given-names></name><name><surname>Mubanga Musonda</surname><given-names>R</given-names></name><name><surname>Sichone</surname><given-names>M</given-names></name></person-group><article-title>Studying dynamics of the HIV epidemic: population-based data compared with sentinel surveillance in Zambia</article-title>. <source><italic>AIDS</italic></source><year>1998</year>; <volume>12</volume>:<fpage>1227</fpage>–<lpage>1234</lpage>.<pub-id pub-id-type="pmid">9677172</pub-id></mixed-citation></ref><ref id="R5"><label>5.</label><mixed-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Kigadye</surname><given-names>RM</given-names></name><name><surname>Klokke</surname><given-names>A</given-names></name><name><surname>Nicoll</surname><given-names>A</given-names></name><name><surname>Nyamuryekung’e</surname><given-names>KM</given-names></name><name><surname>Borgdorff</surname><given-names>M</given-names></name><name><surname>Barongo</surname><given-names>L</given-names></name><etal></etal></person-group><article-title>Sentinel surveillance for HIV-1 among pregnant women in a developing country: 3 years’ experience and comparison with a population serosurvey</article-title>. <source><italic>AIDS</italic></source><year>1993</year>; <volume>7</volume>:<fpage>849</fpage>–<lpage>855</lpage>.<pub-id pub-id-type="pmid">8363761</pub-id></mixed-citation></ref><ref id="R6"><label>6.</label><mixed-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Glynn</surname><given-names>JR</given-names></name><name><surname>Buvé</surname><given-names>A</given-names></name><name><surname>Caraël</surname><given-names>M</given-names></name><name><surname>Musonda</surname><given-names>RM</given-names></name><name><surname>Kahindo</surname><given-names>M</given-names></name><name><surname>Macauley</surname><given-names>I</given-names></name><etal></etal></person-group><article-title>Factors influencing the difference in HIV prevalence between antenatal clinic and general population in sub-Saharan Africa</article-title>. <source><italic>AIDS</italic></source><year>2001</year>; <volume>15</volume>:<fpage>1717</fpage>–<lpage>1725</lpage>.<pub-id pub-id-type="pmid">11546948</pub-id></mixed-citation></ref><ref id="R7"><label>7.</label><mixed-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Brown</surname><given-names>T</given-names></name><name><surname>Bao</surname><given-names>L</given-names></name><name><surname>Raftery</surname><given-names>AE</given-names></name><name><surname>Salomon</surname><given-names>JA</given-names></name><name><surname>Baggaley</surname><given-names>RF</given-names></name><name><surname>Stover</surname><given-names>J</given-names></name><etal></etal></person-group><article-title>Modelling HIV epidemics in the antiretroviral era: the UNAIDS Estimation and Projection package</article-title>. <source><italic>Sex Transm Infect</italic></source><year>2010</year>; <volume>86</volume><issue>Suppl 2</issue>:<fpage>ii3</fpage>–<lpage>ii10</lpage>.<pub-id pub-id-type="pmid">20929855</pub-id></mixed-citation></ref><ref id="R8"><label>8.</label><mixed-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Brown</surname><given-names>T</given-names></name><name><surname>Bao</surname><given-names>L</given-names></name><name><surname>Eaton</surname><given-names>JW</given-names></name><name><surname>Hogan</surname><given-names>DR</given-names></name><name><surname>Mahy</surname><given-names>M</given-names></name><name><surname>Marsh</surname><given-names>K</given-names></name><etal></etal></person-group><article-title>Improvements in prevalence trend fitting and incidence estimation in EPP 2013</article-title>. <source><italic>AIDS</italic></source><year>2014</year>; <volume>28</volume><issue>Suppl 4</issue>:<fpage>S415</fpage>–<lpage>S425</lpage>.<pub-id pub-id-type="pmid">25406747</pub-id></mixed-citation></ref><ref id="R9"><label>9.</label><mixed-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Alkema</surname><given-names>L</given-names></name><name><surname>Raftery</surname><given-names>AE</given-names></name><name><surname>Brown</surname><given-names>T</given-names></name></person-group><article-title>Bayesian melding for estimating uncertainty in national HIV prevalence estimates</article-title>. <source><italic>Sex Transm Infect</italic></source><year>2008</year>; <volume>84</volume><issue>Suppl 1</issue>:<fpage>i11</fpage>–<lpage>i16</lpage>.<pub-id pub-id-type="pmid">18647860</pub-id></mixed-citation></ref><ref id="R10"><label>10.</label><mixed-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Rehle</surname><given-names>TM</given-names></name><name><surname>Hallett</surname><given-names>TB</given-names></name><name><surname>Shisana</surname><given-names>O</given-names></name><name><surname>Pillay-van Wyk</surname><given-names>V</given-names></name><name><surname>Zuma</surname><given-names>K</given-names></name><name><surname>Carrara</surname><given-names>H</given-names></name><etal></etal></person-group><article-title>A decline in new HIV infections in South Africa: estimating HIV incidence from three national HIV surveys in and 2008</article-title>. <source><italic>PLoS One</italic></source><year>2010</year>; <volume>5</volume>:<fpage>e11094</fpage>.<pub-id pub-id-type="pmid">20559425</pub-id></mixed-citation></ref><ref id="R11"><label>11.</label><mixed-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Mahy</surname><given-names>M</given-names></name><name><surname>Garcia-Calleja</surname><given-names>JM</given-names></name><name><surname>Marsh</surname><given-names>KA</given-names></name></person-group><article-title>Trends in HIV prevalence among young people in generalised epidemics: implications for monitoring the HIV epidemic</article-title>. <source><italic>Sex Transm Infect</italic></source><year>2012</year>; <volume>88</volume><issue>Suppl 2</issue>:<fpage>i65</fpage>–<lpage>i75</lpage>.<pub-id pub-id-type="pmid">23172347</pub-id></mixed-citation></ref><ref id="R12"><label>12.</label><mixed-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Zaba</surname><given-names>BW</given-names></name><name><surname>Carpenter</surname><given-names>LM</given-names></name><name><surname>Boerma</surname><given-names>TJ</given-names></name><name><surname>Gregson</surname><given-names>S</given-names></name><name><surname>Nakiyingi</surname><given-names>J</given-names></name><name><surname>Urassa</surname><given-names>M</given-names></name></person-group><article-title>Adjusting ante-natal clinic data for improved estimates of HIV prevalence among women in sub-Saharan Africa</article-title>. <source><italic>AIDS</italic></source><year>2000</year>; <volume>14</volume>:<fpage>2741</fpage>–<lpage>2750</lpage>.<pub-id pub-id-type="pmid">11125893</pub-id></mixed-citation></ref><ref id="R13"><label>13.</label><mixed-citation publication-type="book"><collab>ICF International</collab>. <source>Demographic and Health Surveys</source>. <year>2014</year><comment><ext-link ext-link-type="uri" xlink:href="https://dhsprogram.com/data/available-datasets.cfm">https://dhsprogram.com/data/available-datasets.cfm</ext-link></comment><date-in-citation>[Accessed 2 Jun 2014]</date-in-citation>.</mixed-citation></ref><ref id="R14"><label>14.</label><mixed-citation publication-type="book"><person-group person-group-type="author"><name><surname>Shisana</surname><given-names>O</given-names></name><name><surname>Rehle</surname><given-names>T</given-names></name><name><surname>Simbayi</surname><given-names>LC</given-names></name><name><surname>Zuma</surname><given-names>K</given-names></name><name><surname>Jooste</surname><given-names>S</given-names></name><name><surname>Pillay-van Wyk</surname><given-names>V</given-names></name><etal></etal></person-group><article-title>South African national HIV prevalence, incidence, behaviour and communication survey 2008: a turning tide amongst teenagers?</article-title>. <publisher-loc>Cape Town</publisher-loc>: <publisher-name>HSRC Press</publisher-name>; <year>2009</year>.</mixed-citation></ref><ref id="R15"><label>15.</label><mixed-citation publication-type="book"><person-group person-group-type="author"><name><surname>Shisana</surname><given-names>O</given-names></name><name><surname>Rehle</surname><given-names>T</given-names></name><name><surname>Simbayi</surname><given-names>LC</given-names></name><name><surname>Labadarios</surname><given-names>D</given-names></name><name><surname>Jooste</surname><given-names>S</given-names></name><name><surname>Davids</surname><given-names>A</given-names></name><etal></etal></person-group><article-title>South African National HIV Prevalence, Incidence and Behaviour Survey, 2012</article-title>. <publisher-loc>Cape Town</publisher-loc>: <publisher-name>HSRC Press</publisher-name>; <year>2014</year>.</mixed-citation></ref><ref id="R16"><label>16.</label><mixed-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Bicego</surname><given-names>GT</given-names></name><name><surname>Nkambule</surname><given-names>R</given-names></name><name><surname>Peterson</surname><given-names>I</given-names></name><name><surname>Reed</surname><given-names>J</given-names></name><name><surname>Donnell</surname><given-names>D</given-names></name><name><surname>Ginindza</surname><given-names>H</given-names></name><etal></etal></person-group><article-title>Recent patterns in population-based HIV prevalence in Swaziland</article-title>. <source><italic>PLoS One</italic></source><year>2013</year>; <volume>8</volume>:<fpage>e77101</fpage>.<pub-id pub-id-type="pmid">24143205</pub-id></mixed-citation></ref><ref id="R17"><label>17.</label><mixed-citation publication-type="book"><collab>Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat</collab>. <source>World Population Prospects: The 2012 Revision</source>. <year>2012</year><comment><ext-link ext-link-type="uri" xlink:href="http://esa.un.org/wpp/ASCII-Data/DISK_NAVIGATION_ASCII.htm">http://esa.un.org/wpp/ASCII-Data/DISK_NAVIGATION_ASCII.htm</ext-link></comment><date-in-citation>[Accessed 17 March 2014]</date-in-citation>.</mixed-citation></ref><ref id="R18"><label>18.</label><mixed-citation publication-type="book"><collab>R Core Team</collab>. <source>R: A Language and Environment for Statistical Computing</source>. <year>2014</year><comment><ext-link ext-link-type="uri" xlink:href="http://www.r-project.org/">http://www.r-project.org/</ext-link></comment><date-in-citation>[Accessed 24 June 2014]</date-in-citation>.</mixed-citation></ref><ref id="R19"><label>19.</label><mixed-citation publication-type="book"><person-group person-group-type="author"><name><surname>Lumley</surname><given-names>T</given-names></name></person-group><article-title>Complex surveys: a guide to analysis using R</article-title>. <publisher-loc>Hoboken, NJ</publisher-loc>: <publisher-name>John Wiley & Sons</publisher-name>; <year>2010</year>.</mixed-citation></ref><ref id="R20"><label>20.</label><mixed-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Maher</surname><given-names>D</given-names></name><name><surname>Biraro</surname><given-names>S</given-names></name><name><surname>Hosegood</surname><given-names>V</given-names></name><name><surname>Isingo</surname><given-names>R</given-names></name><name><surname>Lutalo</surname><given-names>T</given-names></name><name><surname>Mushati</surname><given-names>P</given-names></name><etal></etal></person-group><article-title>Translating global health research aims into action: the example of the ALPHA network</article-title>. <source><italic>Trop Med Int Heal</italic></source><year>2010</year>; <volume>15</volume>:<fpage>321</fpage>–<lpage>328</lpage>.</mixed-citation></ref><ref id="R21"><label>21.</label><mixed-citation publication-type="book"><person-group person-group-type="author"><name><surname>Marsh</surname><given-names>K</given-names></name></person-group><source>Measuring the Utility of Surveillance Data For Monitoring the HIV/AIDS Epidemic In Sub-Saharan Africa. 2011; PhD Thesis</source>. <publisher-name>Imperial College London, United Kingdom</publisher-name>; p. <fpage>355</fpage>.</mixed-citation></ref><ref id="R22"><label>22.</label><mixed-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Zaba</surname><given-names>B</given-names></name><name><surname>Boerma</surname><given-names>T</given-names></name><name><surname>White</surname><given-names>R</given-names></name></person-group><article-title>Monitoring the AIDS epidemic using HIV prevalence data among young women attending antenatal clinics: prospects and problems</article-title>. <source><italic>AIDS</italic></source><year>2000</year>; <volume>14</volume>:<fpage>1633</fpage>–<lpage>1645</lpage>.<pub-id pub-id-type="pmid">10983651</pub-id></mixed-citation></ref><ref id="R23"><label>23.</label><mixed-citation publication-type="book"><collab>United Nations General Assembly Special Session on HIV/AIDS</collab>. <source>Monitoring the Declaration of Commitment on HIV/AIDS: Guidelines On Construction of Core Indicators. Joint United Nations Program on HIV/AIDS</source>. <year>2002</year>; <comment><ext-link ext-link-type="uri" xlink:href="http://www.who.int/hiv/strategic/me/en/isbn9291732389.pdf">http://www.who.int/hiv/strategic/me/en/isbn9291732389.pdf</ext-link></comment><date-in-citation>[Accessed 7 June 2014]</date-in-citation>.</mixed-citation></ref><ref id="R24"><label>24.</label><mixed-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Eaton</surname><given-names>JW</given-names></name><name><surname>Garnett</surname><given-names>GP</given-names></name><name><surname>Takavarasha</surname><given-names>FR</given-names></name><name><surname>Mason</surname><given-names>PR</given-names></name><name><surname>Robertson</surname><given-names>L</given-names></name><name><surname>Schumacher</surname><given-names>CM</given-names></name><etal></etal></person-group><article-title>Increasing adolescent HIV prevalence in Eastern Zimbabwe – evidence of long-term survivors of mother-to-child transmission?</article-title>. <source><italic>PLoS One</italic></source><year>2013</year>; <volume>8</volume>:<fpage>e70447</fpage>.<pub-id pub-id-type="pmid">23950938</pub-id></mixed-citation></ref><ref id="R25"><label>25.</label><mixed-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Ferrand</surname><given-names>RA</given-names></name><name><surname>Corbett</surname><given-names>EL</given-names></name><name><surname>Wood</surname><given-names>R</given-names></name><name><surname>Hargrove</surname><given-names>J</given-names></name><name><surname>Ndhlovu</surname><given-names>CE</given-names></name><name><surname>Cowan</surname><given-names>FM</given-names></name><etal></etal></person-group><article-title>AIDS among older children and adolescents in Southern Africa: projecting the time course and magnitude of the epidemic</article-title>. <source><italic>AIDS</italic></source><year>2009</year>; <volume>23</volume>:<fpage>2039</fpage>–<lpage>2046</lpage>.<pub-id pub-id-type="pmid">19684508</pub-id></mixed-citation></ref><ref id="R26"><label>26.</label><mixed-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Zaba</surname><given-names>B</given-names></name><name><surname>Gregson</surname><given-names>S</given-names></name></person-group><article-title>Measuring the impact of HIV on fertility in Africa</article-title>. <source><italic>AIDS</italic></source><year>1998</year>; <volume>12</volume><issue>Suppl 1</issue>:<fpage>S41</fpage>–<lpage>S50</lpage>.<pub-id pub-id-type="pmid">9677188</pub-id></mixed-citation></ref><ref id="R27"><label>27.</label><mixed-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Lewis</surname><given-names>JJC</given-names></name><name><surname>Ronsmans</surname><given-names>C</given-names></name><name><surname>Ezeh</surname><given-names>A</given-names></name><name><surname>Gregson</surname><given-names>S</given-names></name></person-group><article-title>The population impact of HIV on fertility in sub-Saharan Africa</article-title>. <source><italic>AIDS</italic></source><year>2004</year>; <volume>18</volume><issue>Suppl 2</issue>:<fpage>S35</fpage>–<lpage>43</lpage>.<pub-id pub-id-type="pmid">15319742</pub-id></mixed-citation></ref><ref id="R28"><label>28.</label><mixed-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Chen</surname><given-names>W-J</given-names></name><name><surname>Walker</surname><given-names>N</given-names></name></person-group><article-title>Fertility of HIV-infected women: insights from Demographic and Health Surveys</article-title>. <source><italic>Sex Transm Infect</italic></source><year>2010</year>; <volume>86</volume><issue>Suppl 2</issue>:<fpage>ii22</fpage>–<lpage>ii27</lpage>.<pub-id pub-id-type="pmid">21106511</pub-id></mixed-citation></ref><ref id="R29"><label>29.</label><mixed-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Mujugira</surname><given-names>A</given-names></name><name><surname>Heffron</surname><given-names>R</given-names></name><name><surname>Celum</surname><given-names>C</given-names></name><name><surname>Mugo</surname><given-names>N</given-names></name><name><surname>Nakku-Joloba</surname><given-names>E</given-names></name><name><surname>Baeten</surname><given-names>JM</given-names></name></person-group><article-title>Fertility intentions and interest in early antiretroviral therapy among East African HIV-1-infected individuals in serodiscordant partnerships</article-title>. <source><italic>J Acquir Immune Defic Syndr</italic></source><year>2013</year>; <volume>63</volume>:<fpage>e33</fpage>–<lpage>e35</lpage>.<pub-id pub-id-type="pmid">23574927</pub-id></mixed-citation></ref><ref id="R30"><label>30.</label><mixed-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Koal</surname><given-names>T</given-names></name><name><surname>Burhenne</surname><given-names>H</given-names></name><name><surname>Römling</surname><given-names>R</given-names></name><name><surname>Svoboda</surname><given-names>M</given-names></name><name><surname>Resch</surname><given-names>K</given-names></name><name><surname>Kaever</surname><given-names>V</given-names></name></person-group><article-title>Quantification of antiretroviral drugs in dried blood spot samples by means of liquid chromatography/tandem mass spectrometry</article-title>. <source><italic>Rapid Commun Mass Spectrom</italic></source><year>2005</year>; <volume>19</volume>:<fpage>2995</fpage>–<lpage>3001</lpage>.<pub-id pub-id-type="pmid">16193530</pub-id></mixed-citation></ref><ref id="R31"><label>31.</label><mixed-citation publication-type="book"><collab>UNAIDS/WHO Working Group on Global HIV/AIDS and STI Surveillance</collab>. <source>Guidelines for assessing the utility of data from prevention of mother-to-child transmission (PMTCT) programmes for HIV sentinel surveillance among pregnant women</source>. <year>2013</year>; <comment><ext-link ext-link-type="uri" xlink:href="http://apps.who.int/iris/bitstream/10665/85512/1/9789241505611_eng.pdf">http://apps.who.int/iris/bitstream/10665/85512/1/9789241505611_eng.pdf</ext-link></comment><date-in-citation>[Accessed 30 June 2014]</date-in-citation>.</mixed-citation></ref><ref id="R32"><label>32.</label><mixed-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Simkhada</surname><given-names>B</given-names></name><name><surname>Teijlingen</surname><given-names>ER</given-names></name><name><surname>Porter</surname><given-names>M</given-names></name><name><surname>Simkhada</surname><given-names>P</given-names></name></person-group><article-title>Factors affecting the utilization of antenatal care in developing countries: systematic review of the literature</article-title>. <source><italic>J Adv Nurs</italic></source><year>2008</year>; <volume>61</volume>:<fpage>244</fpage>–<lpage>260</lpage>.<pub-id pub-id-type="pmid">18197860</pub-id></mixed-citation></ref><ref id="R33"><label>33.</label><mixed-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Mrisho</surname><given-names>M</given-names></name><name><surname>Schellenberg</surname><given-names>JA</given-names></name><name><surname>Mushi</surname><given-names>AK</given-names></name><name><surname>Obrist</surname><given-names>B</given-names></name><name><surname>Mshinda</surname><given-names>H</given-names></name><name><surname>Tanner</surname><given-names>M</given-names></name><etal></etal></person-group><article-title>Factors affecting home delivery in rural Tanzania</article-title>. <source><italic>Trop Med Int Health</italic></source><year>2007</year>; <volume>12</volume>:<fpage>862</fpage>–<lpage>872</lpage>.<pub-id pub-id-type="pmid">17596254</pub-id></mixed-citation></ref><ref id="R34"><label>34.</label><mixed-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Gouws</surname><given-names>E</given-names></name><name><surname>Mishra</surname><given-names>V</given-names></name><name><surname>Fowler</surname><given-names>TB</given-names></name></person-group><article-title>Comparison of adult HIV prevalence from national population-based surveys and antenatal clinic surveillance in countries with generalised epidemics: implications for calibrating surveillance data</article-title>. <source><italic>Sex Transm Infect</italic></source><year>2008</year>; <volume>84</volume><issue>Suppl 1</issue>:<fpage>i17</fpage>–<lpage>i23</lpage>.<pub-id pub-id-type="pmid">18647861</pub-id></mixed-citation></ref><ref id="R35"><label>35.</label><mixed-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Montana</surname><given-names>LS</given-names></name><name><surname>Mishra</surname><given-names>V</given-names></name><name><surname>Hong</surname><given-names>R</given-names></name></person-group><article-title>Comparison of HIV prevalence estimates from antenatal care surveillance and population-based surveys in sub-Saharan Africa</article-title>. <source><italic>Sex Transm Infect</italic></source><year>2008</year>; <volume>84</volume><issue>Suppl 1</issue>:<fpage>i78</fpage>–<lpage>i84</lpage>.<pub-id pub-id-type="pmid">18647871</pub-id></mixed-citation></ref></ref-list></back><floats-group><table-wrap id="T1" position="float"><label>Table 1</label><caption><p>Countries and surveys included and percentage of women currently pregnant at the time of survey.</p></caption><table frame="hsides" rules="groups"><thead><tr><td rowspan="1" colspan="1"></td><td rowspan="1" colspan="1"></td><td rowspan="1" colspan="1"></td><td colspan="4" rowspan="1">Period 1</td><td colspan="4" rowspan="1">Period 2</td></tr><tr><td rowspan="1" colspan="1">Country</td><td rowspan="1" colspan="1">Region</td><td rowspan="1" colspan="1">Female 15–49 pop size 2012 (millions)</td><td rowspan="1" colspan="1">Survey year</td><td rowspan="1" colspan="1"><italic>N</italic> females HIV tested 15–49 (unadjusted)</td><td rowspan="1" colspan="1"><italic>N</italic> currently pregnant 15–49 (unweighted)</td><td rowspan="1" colspan="1">% curr. pregnant (SE)</td><td rowspan="1" colspan="1">Survey year</td><td rowspan="1" colspan="1"><italic>N</italic> females HIV tested 15–49 (unadjusted)</td><td rowspan="1" colspan="1"><italic>N</italic> currently pregnant 15–49 (unweighted)</td><td rowspan="1" colspan="1">% curr. pregnant (SE)</td></tr></thead><tbody><tr><td rowspan="1" colspan="1">Burkina Faso</td><td rowspan="1" colspan="1">Western</td><td rowspan="1" colspan="1">3.8</td><td rowspan="1" colspan="1">2003</td><td rowspan="1" colspan="1">4189</td><td rowspan="1" colspan="1">426</td><td rowspan="1" colspan="1">10.1 (0.6)</td><td rowspan="1" colspan="1">2010</td><td rowspan="1" colspan="1">8346</td><td rowspan="1" colspan="1">818</td><td rowspan="1" colspan="1">10.2 (0.4)</td></tr><tr><td rowspan="1" colspan="1">Cameroon</td><td rowspan="1" colspan="1">Western</td><td rowspan="1" colspan="1">5.1</td><td rowspan="1" colspan="1">2004</td><td rowspan="1" colspan="1">5154</td><td rowspan="1" colspan="1">534</td><td rowspan="1" colspan="1">10.5 (0.5)</td><td rowspan="1" colspan="1">2011</td><td rowspan="1" colspan="1">7253</td><td rowspan="1" colspan="1">716</td><td rowspan="1" colspan="1">10.1 (0.5)</td></tr><tr><td rowspan="1" colspan="1">Côte d’Ivoire</td><td rowspan="1" colspan="1">Western</td><td rowspan="1" colspan="1">4.7</td><td rowspan="1" colspan="1">2005</td><td rowspan="1" colspan="1">22681</td><td rowspan="1" colspan="1">1954</td><td rowspan="1" colspan="1">7.9 (0.6)</td><td rowspan="1" colspan="1">2010–2011</td><td rowspan="1" colspan="1">4655</td><td rowspan="1" colspan="1">484</td><td rowspan="1" colspan="1">10.6 (0.6)</td></tr><tr><td rowspan="1" colspan="1">Senegal</td><td rowspan="1" colspan="1">Western</td><td rowspan="1" colspan="1">3.3</td><td rowspan="1" colspan="1">2005</td><td rowspan="1" colspan="1">4466</td><td rowspan="1" colspan="1">394</td><td rowspan="1" colspan="1">7.9 (0.5)</td><td rowspan="1" colspan="1">2010–2011</td><td rowspan="1" colspan="1">5590</td><td rowspan="1" colspan="1">470</td><td rowspan="1" colspan="1">7.7 (0.5)</td></tr><tr><td rowspan="1" colspan="1">Ethiopia</td><td rowspan="1" colspan="1">Eastern</td><td rowspan="1" colspan="1">21.5</td><td rowspan="1" colspan="1">2005</td><td rowspan="1" colspan="1">5942</td><td rowspan="1" colspan="1">474</td><td rowspan="1" colspan="1">8.4 (0.5)</td><td rowspan="1" colspan="1">2011</td><td rowspan="1" colspan="1">15505</td><td rowspan="1" colspan="1">1221</td><td rowspan="1" colspan="1">7.4 (0.3)</td></tr><tr><td rowspan="1" colspan="1">Kenya</td><td rowspan="1" colspan="1">Eastern</td><td rowspan="1" colspan="1">10.5</td><td rowspan="1" colspan="1">2008-09</td><td rowspan="1" colspan="1">3811</td><td rowspan="1" colspan="1">270</td><td rowspan="1" colspan="1">6.6 (0.6)</td><td rowspan="1" colspan="1">2012</td><td rowspan="1" colspan="1">5870</td><td rowspan="1" colspan="1">365</td><td rowspan="1" colspan="1">6.3 (0.4)</td></tr><tr><td rowspan="1" colspan="1">Rwanda</td><td rowspan="1" colspan="1">Eastern</td><td rowspan="1" colspan="1">2.8</td><td rowspan="1" colspan="1">2005</td><td rowspan="1" colspan="1"> 5663</td><td rowspan="1" colspan="1">432</td><td rowspan="1" colspan="1">7.6 (0.4)</td><td rowspan="1" colspan="1">2010</td><td rowspan="1" colspan="1">6952</td><td rowspan="1" colspan="1">478</td><td rowspan="1" colspan="1">7.0 (0.3)</td></tr><tr><td rowspan="1" colspan="1">Tanzania</td><td rowspan="1" colspan="1">Eastern</td><td rowspan="1" colspan="1">10.9</td><td rowspan="1" colspan="1">2007–2008</td><td rowspan="1" colspan="1"> 8711</td><td rowspan="1" colspan="1">770</td><td rowspan="1" colspan="1">9.2 (0.4)</td><td rowspan="1" colspan="1">2011–2012</td><td rowspan="1" colspan="1">10299</td><td rowspan="1" colspan="1">982</td><td rowspan="1" colspan="1">9.3 (0.5)</td></tr><tr><td rowspan="1" colspan="1">Lesotho</td><td rowspan="1" colspan="1">Southern</td><td rowspan="1" colspan="1">0.5</td><td rowspan="1" colspan="1">2004</td><td rowspan="1" colspan="1"> 3020</td><td rowspan="1" colspan="1">198</td><td rowspan="1" colspan="1">6.7 (0.6)</td><td rowspan="1" colspan="1">2009</td><td rowspan="1" colspan="1">3849</td><td rowspan="1" colspan="1">182</td><td rowspan="1" colspan="1">4.6 (0.4)</td></tr><tr><td rowspan="1" colspan="1">Malawi</td><td rowspan="1" colspan="1">Southern</td><td rowspan="1" colspan="1">3.5</td><td rowspan="1" colspan="1">2004</td><td rowspan="1" colspan="1"> 2864</td><td rowspan="1" colspan="1">379</td><td rowspan="1" colspan="1">13.5 (0.8)</td><td rowspan="1" colspan="1">2010</td><td rowspan="1" colspan="1">7396</td><td rowspan="1" colspan="1">698</td><td rowspan="1" colspan="1">9.1 (0.5)</td></tr><tr><td rowspan="1" colspan="1">South Africa</td><td rowspan="1" colspan="1">Southern</td><td rowspan="1" colspan="1">14.1</td><td rowspan="1" colspan="1">2008</td><td rowspan="1" colspan="1"> 4823</td><td rowspan="1" colspan="1">156</td><td rowspan="1" colspan="1">4.7 (0.5)</td><td rowspan="1" colspan="1">2012</td><td rowspan="1" colspan="1">8253</td><td rowspan="1" colspan="1">240</td><td rowspan="1" colspan="1">4.5 (0.5)</td></tr><tr><td rowspan="1" colspan="1">Swaziland<xref ref-type="table-fn" rid="TF1-1"><sup>a</sup></xref></td><td rowspan="1" colspan="1">Southern</td><td rowspan="1" colspan="1">0.3</td><td rowspan="1" colspan="1">2006–2007</td><td rowspan="1" colspan="1"> 3840</td><td rowspan="1" colspan="1">217</td><td rowspan="1" colspan="1">5.6 (0.4)</td><td rowspan="1" colspan="1">2011</td><td rowspan="1" colspan="1">11042</td><td rowspan="1" colspan="1">764</td><td rowspan="1" colspan="1">7.0 (0.3)</td></tr><tr><td rowspan="1" colspan="1">Zimbabwe</td><td rowspan="1" colspan="1">Southern</td><td rowspan="1" colspan="1">3.4</td><td rowspan="1" colspan="1">2005–2006</td><td rowspan="1" colspan="1"> 7494</td><td rowspan="1" colspan="1">494</td><td rowspan="1" colspan="1">6.8 (0.4)</td><td rowspan="1" colspan="1">2010–2011</td><td rowspan="1" colspan="1">7852</td><td rowspan="1" colspan="1">625</td><td rowspan="1" colspan="1">8.3 (0.4)</td></tr><tr><td rowspan="1" colspan="1">Western</td><td rowspan="1" colspan="1"></td><td rowspan="1" colspan="1">17.0</td><td rowspan="1" colspan="1"></td><td rowspan="1" colspan="1">36490</td><td rowspan="1" colspan="1">3308</td><td rowspan="1" colspan="1">9.2 (0.3)</td><td rowspan="1" colspan="1"></td><td rowspan="1" colspan="1">25844</td><td rowspan="1" colspan="1">2488</td><td rowspan="1" colspan="1">9.8 (0.3)</td></tr><tr><td rowspan="1" colspan="1">Eastern</td><td rowspan="1" colspan="1"></td><td rowspan="1" colspan="1">45.6</td><td rowspan="1" colspan="1"></td><td rowspan="1" colspan="1">24127</td><td rowspan="1" colspan="1">1946</td><td rowspan="1" colspan="1">8.1 (0.3)</td><td rowspan="1" colspan="1"></td><td rowspan="1" colspan="1">38626</td><td rowspan="1" colspan="1">3046</td><td rowspan="1" colspan="1">7.6 (0.2)</td></tr><tr><td rowspan="1" colspan="1">Southern</td><td rowspan="1" colspan="1"></td><td rowspan="1" colspan="1">21.9</td><td rowspan="1" colspan="1"></td><td rowspan="1" colspan="1">22041</td><td rowspan="1" colspan="1">1444</td><td rowspan="1" colspan="1">6.8 (0.4)</td><td rowspan="1" colspan="1"></td><td rowspan="1" colspan="1">38392</td><td rowspan="1" colspan="1">2509</td><td rowspan="1" colspan="1">6.1 (0.3)</td></tr><tr><td rowspan="1" colspan="1">All</td><td rowspan="1" colspan="1"></td><td rowspan="1" colspan="1">84.4</td><td rowspan="1" colspan="1"></td><td rowspan="1" colspan="1">82658</td><td rowspan="1" colspan="1">6698</td><td rowspan="1" colspan="1">8.0 (0.2)</td><td rowspan="1" colspan="1"></td><td rowspan="1" colspan="1">102862</td><td rowspan="1" colspan="1">8043</td><td rowspan="1" colspan="1">7.7 (0.2)</td></tr></tbody></table><table-wrap-foot><fn fn-type="other"><p>SE, standard error.</p></fn><fn id="TF1-1"><p><sup>a</sup>Swaziland for 18 to 49-year-old women.</p></fn></table-wrap-foot></table-wrap><table-wrap id="T2" position="float"><label>Table 2</label><caption><p>Log-binomial regression estimating the relationship between HIV prevalence and current pregnancy and survey period.</p></caption><table frame="hsides" rules="groups"><thead><tr><td rowspan="1" colspan="1"></td><td rowspan="1" colspan="1">Western</td><td rowspan="1" colspan="1">Eastern</td><td rowspan="1" colspan="1">Southern</td><td rowspan="1" colspan="1">All</td></tr></thead><tbody><tr><td rowspan="1" colspan="1">Currently pregnant vs. not</td><td rowspan="1" colspan="1">1.04 (0.82, 1.33)</td><td rowspan="1" colspan="1">0.80 (0.59, 1.09)</td><td rowspan="1" colspan="1">0.89 (0.66, 1.20)</td><td rowspan="1" colspan="1">0.89 (0.73, 1.08)</td></tr><tr><td rowspan="1" colspan="1">Period 2 vs. period 1</td><td rowspan="1" colspan="1">0.86 (0.75, 0.98)</td><td rowspan="1" colspan="1">0.93 (0.82, 1.05)</td><td rowspan="1" colspan="1">1.04 (0.95, 1.13)</td><td rowspan="1" colspan="1">0.99 (0.93, 1.06)</td></tr><tr><td rowspan="1" colspan="1">Currently pregnant · period 2<xref ref-type="table-fn" rid="TF2-1"><sup>a</sup></xref></td><td rowspan="1" colspan="1">0.78 (0.54, 1.14)</td><td rowspan="1" colspan="1">0.79 (0.51, 1.22)</td><td rowspan="1" colspan="1">0.87 (0.57, 1.33)</td><td rowspan="1" colspan="1">0.82 (0.62, 1.09)</td></tr></tbody></table><table-wrap-foot><fn fn-type="other"><p>Estimates indicate relative risk ratios (RR) and 95% confidence intervals, adjusted for country.</p></fn><fn id="TF2-1"><p><sup>a</sup>Interaction between current pregnancy and survey period estimates excess prevalence decline in HIV prevalence among currently pregnant women vs. not currently pregnant women.</p></fn></table-wrap-foot></table-wrap></floats-group></pmc></record>Pour manipuler ce document sous Unix (Dilib)
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