Evaluation of bias in HIV seroprevalence estimates from national household surveys
Identifieur interne : 002C76 ( Pmc/Corpus ); précédent : 002C75; suivant : 002C77Evaluation of bias in HIV seroprevalence estimates from national household surveys
Auteurs : V. Mishra ; B. Barrere ; R. Hong ; S. KhanSource :
- Sexually Transmitted Infections [ 1368-4973 ] ; 2008.
Abstract
To evaluate HIV seroprevalence estimates from demographic and health surveys (DHS) and AIDS indicator surveys (AIS) for potential bias because of non-response and exclusion of non-household population groups.
Data are from 14 DHS/AIS surveys with HIV testing, conducted during 2003–6. Blood samples were collected and analysed for HIV using standard laboratory and quality control procedures. HIV prevalence among non-tested adults was predicted based on multivariate statistical models of HIV for those who were interviewed and tested, using a common set of predictor variables. Estimates of the size of non-household populations in national censuses were used to assess potential bias because of their exclusion in the household surveys under different assumptions about proportion of adults and HIV prevalence in non-household populations.
Non-tested men had significantly higher predicted HIV prevalence than those tested in eight of the 14 countries, while non-tested women had significantly higher predicted prevalence than those tested in seven of the 14 countries. Effects of non-response were somewhat stronger in lower-prevalence countries. The overall effect of non-response on observed national HIV estimates was small and insignificant in all countries. Estimated effects of exclusion of non-household population groups were generally small, even in concentrated epidemics in India and Cambodia under the scenario that 75% of the non-household population was adults having 20 times greater HIV prevalence than adults in household surveys.
Non-response and the exclusion of non-household population groups tend to have small, insignificant effects on national HIV seroprevalence estimates obtained from household surveys.
Url:
DOI: 10.1136/sti.2008.030411
PubMed: 18647869
PubMed Central: 2569831
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PMC:2569831Le document en format XML
<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">Evaluation of bias in HIV seroprevalence estimates from national household surveys</title><author><name sortKey="Mishra, V" sort="Mishra, V" uniqKey="Mishra V" first="V" last="Mishra">V. Mishra</name></author><author><name sortKey="Barrere, B" sort="Barrere, B" uniqKey="Barrere B" first="B" last="Barrere">B. Barrere</name></author><author><name sortKey="Hong, R" sort="Hong, R" uniqKey="Hong R" first="R" last="Hong">R. Hong</name></author><author><name sortKey="Khan, S" sort="Khan, S" uniqKey="Khan S" first="S" last="Khan">S. Khan</name></author></titleStmt><publicationStmt><idno type="wicri:source">PMC</idno><idno type="pmid">18647869</idno><idno type="pmc">2569831</idno><idno type="url">http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2569831</idno><idno type="RBID">PMC:2569831</idno><idno type="doi">10.1136/sti.2008.030411</idno><date when="2008">2008</date><idno type="wicri:Area/Pmc/Corpus">002C76</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Corpus" wicri:corpus="PMC">002C76</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">Evaluation of bias in HIV seroprevalence estimates from national household surveys</title><author><name sortKey="Mishra, V" sort="Mishra, V" uniqKey="Mishra V" first="V" last="Mishra">V. Mishra</name></author><author><name sortKey="Barrere, B" sort="Barrere, B" uniqKey="Barrere B" first="B" last="Barrere">B. Barrere</name></author><author><name sortKey="Hong, R" sort="Hong, R" uniqKey="Hong R" first="R" last="Hong">R. Hong</name></author><author><name sortKey="Khan, S" sort="Khan, S" uniqKey="Khan S" first="S" last="Khan">S. Khan</name></author></analytic><series><title level="j">Sexually Transmitted Infections</title><idno type="ISSN">1368-4973</idno><idno type="eISSN">1472-3263</idno><imprint><date when="2008">2008</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><sec><title>Objectives:</title><p>To evaluate HIV seroprevalence estimates from demographic and health surveys (DHS) and AIDS indicator surveys (AIS) for potential bias because of non-response and exclusion of non-household population groups.</p></sec><sec><title>Methods:</title><p>Data are from 14 DHS/AIS surveys with HIV testing, conducted during 2003–6. Blood samples were collected and analysed for HIV using standard laboratory and quality control procedures. HIV prevalence among non-tested adults was predicted based on multivariate statistical models of HIV for those who were interviewed and tested, using a common set of predictor variables. Estimates of the size of non-household populations in national censuses were used to assess potential bias because of their exclusion in the household surveys under different assumptions about proportion of adults and HIV prevalence in non-household populations.</p></sec><sec><title>Results:</title><p>Non-tested men had significantly higher predicted HIV prevalence than those tested in eight of the 14 countries, while non-tested women had significantly higher predicted prevalence than those tested in seven of the 14 countries. Effects of non-response were somewhat stronger in lower-prevalence countries. The overall effect of non-response on observed national HIV estimates was small and insignificant in all countries. Estimated effects of exclusion of non-household population groups were generally small, even in concentrated epidemics in India and Cambodia under the scenario that 75% of the non-household population was adults having 20 times greater HIV prevalence than adults in household surveys.</p></sec><sec><title>Conclusions:</title><p>Non-response and the exclusion of non-household population groups tend to have small, insignificant effects on national HIV seroprevalence estimates obtained from household surveys.</p></sec></div></front><back><div1 type="bibliography"><listBibl><biblStruct></biblStruct><biblStruct><analytic><author><name sortKey="Stover, J" uniqKey="Stover J">J Stover</name></author><author><name sortKey="Ghys, Pd" uniqKey="Ghys P">PD Ghys</name></author><author><name sortKey="Walker, N" uniqKey="Walker N">N Walker</name></author></analytic></biblStruct><biblStruct></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, Jt" uniqKey="Boerma J">JT Boerma</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Walker, N" uniqKey="Walker N">N Walker</name></author><author><name sortKey="Grassly, Nc" uniqKey="Grassly N">NC Grassly</name></author><author><name sortKey="Garnett, Gp" uniqKey="Garnett G">GP Garnett</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Garcia Calleja, Jm" uniqKey="Garcia Calleja J">JM García-Calleja</name></author><author><name sortKey="Gouws, E" uniqKey="Gouws E">E Gouws</name></author><author><name sortKey="Ghys, Pd" uniqKey="Ghys P">PD Ghys</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Boerma, Jt" uniqKey="Boerma J">JT Boerma</name></author><author><name sortKey="Ghys, Pd" uniqKey="Ghys P">PD Ghys</name></author><author><name sortKey="Walker, N" uniqKey="Walker N">N Walker</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Buve, A" uniqKey="Buve A">A Buve</name></author><author><name sortKey="Lagarde, E" uniqKey="Lagarde E">E Lagarde</name></author><author><name sortKey="Carael, M" uniqKey="Carael M">M Carael</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Lydie, N" uniqKey="Lydie N">N Lydié</name></author><author><name sortKey="Robinson, Nj" uniqKey="Robinson N">NJ Robinson</name></author><author><name sortKey="Ferry, B" uniqKey="Ferry B">B Ferry</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Lagarde, E" uniqKey="Lagarde E">E Lagarde</name></author><author><name sortKey="Schim Van Der Loeff, M" uniqKey="Schim Van Der Loeff M">M Schim van der Loeff</name></author><author><name sortKey="Enel, C" uniqKey="Enel C">C Enel</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Lurie, Mn" uniqKey="Lurie M">MN Lurie</name></author><author><name sortKey="Williams, Bg" uniqKey="Williams B">BG Williams</name></author><author><name sortKey="Zuma, K" uniqKey="Zuma K">K Zuma</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Faisel, A" uniqKey="Faisel A">A Faisel</name></author><author><name sortKey="Cleland, J" uniqKey="Cleland J">J Cleland</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Zuma, K" uniqKey="Zuma K">K Zuma</name></author><author><name sortKey="Lurie, Mn" uniqKey="Lurie M">MN Lurie</name></author><author><name sortKey="Williams, Bg" uniqKey="Williams B">BG Williams</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Nunn, Aj" uniqKey="Nunn A">AJ Nunn</name></author><author><name sortKey="Wagner, Hu" uniqKey="Wagner H">HU Wagner</name></author><author><name sortKey="Kamali, A" uniqKey="Kamali A">A Kamali</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Coffee, Mp" uniqKey="Coffee M">MP Coffee</name></author><author><name sortKey="Garnett, Gp" uniqKey="Garnett G">GP Garnett</name></author><author><name sortKey="Mlilo, M" uniqKey="Mlilo M">M Mlilo</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Martinez Donate, Ap" uniqKey="Martinez Donate A">AP Martínez-Donate</name></author><author><name sortKey="Rangel, Mg" uniqKey="Rangel M">MG Rangel</name></author><author><name sortKey="Hovell, Mf" uniqKey="Hovell M">MF Hovell</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Mcnaghten, Ad" uniqKey="Mcnaghten A">AD McNaghten</name></author><author><name sortKey="Herold, Jm" uniqKey="Herold J">JM Herold</name></author><author><name sortKey="Dube, Hm" uniqKey="Dube H">HM Dube</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Bignami Van Assche, S" uniqKey="Bignami Van Assche S">S Bignami-Van Assche</name></author><author><name sortKey="Salomon, Ja" uniqKey="Salomon J">JA Salomon</name></author><author><name sortKey="Murray, Cjl" uniqKey="Murray C">CJL Murray</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Obare, F" uniqKey="Obare F">F Obare</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Dandona, R" uniqKey="Dandona R">R Dandona</name></author><author><name sortKey="Dandona, L" uniqKey="Dandona L">L Dandona</name></author><author><name sortKey="Gutierrez, Jp" uniqKey="Gutierrez J">JP Gutierrez</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Sarkar, K" uniqKey="Sarkar K">K Sarkar</name></author><author><name sortKey="Bal, B" uniqKey="Bal B">B Bal</name></author><author><name sortKey="Mukherjee, R" uniqKey="Mukherjee R">R Mukherjee</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Chimphambano, C" uniqKey="Chimphambano C">C Chimphambano</name></author><author><name sortKey="Komolafe, Oo" uniqKey="Komolafe O">OO Komolafe</name></author><author><name sortKey="Muula, As" uniqKey="Muula A">AS Muula</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Dolan, K" uniqKey="Dolan K">K Dolan</name></author><author><name sortKey="Kite, B" uniqKey="Kite B">B Kite</name></author><author><name sortKey="Black, E" uniqKey="Black E">E Black</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Wolitski, Rj" uniqKey="Wolitski R">RJ Wolitski</name></author><author><name sortKey="Kidder, Dp" uniqKey="Kidder D">DP Kidder</name></author><author><name sortKey="Fenton, Ka" uniqKey="Fenton K">KA Fenton</name></author></analytic></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct></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">Sex Transm Infect</journal-id><journal-id journal-id-type="publisher-id">sti</journal-id><journal-title-group><journal-title>Sexually Transmitted Infections</journal-title></journal-title-group><issn pub-type="ppub">1368-4973</issn><issn pub-type="epub">1472-3263</issn><publisher><publisher-name>BMJ Publishing Group</publisher-name><publisher-loc>BMA House, Tavistock Square, London, WC1H 9JR</publisher-loc></publisher></journal-meta><article-meta><article-id pub-id-type="pmid">18647869</article-id><article-id pub-id-type="pmc">2569831</article-id><article-id pub-id-type="publisher-id">st30411</article-id><article-id pub-id-type="doi">10.1136/sti.2008.030411</article-id><article-categories><subj-group subj-group-type="heading"><subject>Supplement</subject></subj-group></article-categories><title-group><article-title>Evaluation of bias in HIV seroprevalence estimates from national household surveys</article-title><alt-title alt-title-type="running-head">Supplement</alt-title></title-group><contrib-group><contrib contrib-type="author"><name><surname>Mishra</surname><given-names>V</given-names></name></contrib><contrib contrib-type="author"><name><surname>Barrere</surname><given-names>B</given-names></name></contrib><contrib contrib-type="author"><name><surname>Hong</surname><given-names>R</given-names></name></contrib><contrib contrib-type="author"><name><surname>Khan</surname><given-names>S</given-names></name></contrib></contrib-group><aff><addr-line>Macro International Inc, Calverton, Maryland, USA</addr-line></aff><author-notes><corresp>Correspondence to: Dr Vinod Mishra, Demographic and Health Research Division, Macro International Inc, 11785 Beltsville Drive, Calverton, Maryland 20705, USA; <email>vinod.mishra@macrointernational.com</email></corresp></author-notes><pub-date pub-type="collection"><year>2008</year></pub-date><pub-date pub-type="ppub"><month>8</month><year>2008</year></pub-date><pub-date pub-type="epub"><day>22</day><month>7</month><year>2008</year></pub-date><pub-date pub-type="pmc-release"><day>22</day><month>7</month><year>2008</year></pub-date><pmc-comment> PMC Release delay is 0 months and 0 days and was based on the
<volume>84</volume><issue>Suppl_1</issue><issue-title>Improved data, methods and tools for the 2007 HIV and AIDS estimates and projections</issue-title><fpage>i63</fpage><lpage>i70</lpage><history><date date-type="accepted"><day>15</day><month>5</month><year>2008</year></date></history><permissions><copyright-statement>© Mishra et al 2008</copyright-statement><copyright-year>2008</copyright-year><license license-type="open-access" xlink:href="http://creativecommons.org/licenses/by/2.0/"><license-p>This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</license-p></license></permissions><abstract><sec><title>Objectives:</title><p>To evaluate HIV seroprevalence estimates from demographic and health surveys (DHS) and AIDS indicator surveys (AIS) for potential bias because of non-response and exclusion of non-household population groups.</p></sec><sec><title>Methods:</title><p>Data are from 14 DHS/AIS surveys with HIV testing, conducted during 2003–6. Blood samples were collected and analysed for HIV using standard laboratory and quality control procedures. HIV prevalence among non-tested adults was predicted based on multivariate statistical models of HIV for those who were interviewed and tested, using a common set of predictor variables. Estimates of the size of non-household populations in national censuses were used to assess potential bias because of their exclusion in the household surveys under different assumptions about proportion of adults and HIV prevalence in non-household populations.</p></sec><sec><title>Results:</title><p>Non-tested men had significantly higher predicted HIV prevalence than those tested in eight of the 14 countries, while non-tested women had significantly higher predicted prevalence than those tested in seven of the 14 countries. Effects of non-response were somewhat stronger in lower-prevalence countries. The overall effect of non-response on observed national HIV estimates was small and insignificant in all countries. Estimated effects of exclusion of non-household population groups were generally small, even in concentrated epidemics in India and Cambodia under the scenario that 75% of the non-household population was adults having 20 times greater HIV prevalence than adults in household surveys.</p></sec><sec><title>Conclusions:</title><p>Non-response and the exclusion of non-household population groups tend to have small, insignificant effects on national HIV seroprevalence estimates obtained from household surveys.</p></sec></abstract></article-meta></front><body><p>In countries with generalised epidemics, national estimates of HIV prevalence levels and trends in the adult population are generally derived indirectly from HIV surveillance among pregnant women attending selected antenatal clinics.<xref ref-type="bibr" rid="b1">1</xref> <xref ref-type="bibr" rid="b2">2</xref> Recently, HIV seroprevalence data have also been collected in national population-based surveys, such as the demographic and health surveys and AIDS indicators surveys.<xref ref-type="bibr" rid="b3">3</xref> These surveys have enabled direct estimates of population HIV prevalence.<xref ref-type="bibr" rid="b4">4</xref> <xref ref-type="bibr" rid="b5">5</xref></p><p>A major challenge for the surveys is potential bias as a result of non-response.<xref ref-type="bibr" rid="b4">4</xref> <xref ref-type="bibr" rid="b6">6</xref><sup>–</sup><xref ref-type="bibr" rid="b8">8</xref> Some eligible respondents may be absent at the time of the survey while others may be incapacitated or refuse to participate. The survey estimates of HIV prevalence may be biased to the extent that non-responders have different HIV prevalence levels than the responders. There is much evidence that mobility, which is one of the reasons for absence at the time of the survey, tends to be associated higher-risk sexual behaviours<xref ref-type="bibr" rid="b9">9</xref><sup>–</sup><xref ref-type="bibr" rid="b11">11</xref> and risk of sexually transmitted infections,<xref ref-type="bibr" rid="b12">12</xref> <xref ref-type="bibr" rid="b13">13</xref> including HIV infection.<xref ref-type="bibr" rid="b9">9</xref><sup>–</sup><xref ref-type="bibr" rid="b11">11</xref> <xref ref-type="bibr" rid="b14">14</xref> But some studies have failed to find an association between mobility and risk of HIV infection.<xref ref-type="bibr" rid="b15">15</xref> <xref ref-type="bibr" rid="b16">16</xref> There is limited, inconclusive research on how refusal to participate in population-based surveys is associated with risky sexual behaviours.<xref ref-type="bibr" rid="b6">6</xref> <xref ref-type="bibr" rid="b17">17</xref> In a recent study that included an assessment of non-response bias in five countries, Mishra <italic>et al</italic><xref ref-type="bibr" rid="b4">4</xref> concluded that non-responders tend to have somewhat higher HIV prevalence, but this bias has no significant effects on national seroprevalence estimates. Other previous studies have also failed to establish that population-based surveys significantly downwardly bias national HIV seroprevalence estimates.<xref ref-type="bibr" rid="b17">17</xref><sup>–</sup><xref ref-type="bibr" rid="b19">19</xref></p><p>Another major challenge for the surveys is potential bias because of the exclusion of non-household population groups. Survey estimates may be biased to the extent that people residing in institutions (such as brothels, prisons, hostels, military/police barracks, long-term care homes) or those who are homeless have different HIV prevalence levels than those living in households and included in the survey sample. While there is considerable evidence that some of the institutional populations (such as brothels<xref ref-type="bibr" rid="b20">20</xref> <xref ref-type="bibr" rid="b21">21</xref> and prisons<xref ref-type="bibr" rid="b22">22</xref> <xref ref-type="bibr" rid="b23">23</xref>) and the homeless<xref ref-type="bibr" rid="b24">24</xref> tend to have higher risk of HIV infection, there is no previous empirical research to examine how exclusion of non-household population groups might impact national prevalence estimates based on household samples.</p><p>In this study, we expand the analysis of non-response bias in HIV seroprevalence estimates to 14 demographic and health surveys (DHS) and AIDS indicator surveys (AIS). Additionally, in five surveys with varying levels of HIV prevalence, we evaluate potential bias in national seroprevalence estimates because of exclusion of non-household population groups.</p><sec sec-type="methods" id="s2"><title>METHODS</title><p>This study uses data from 14 nationally representative surveys of adult women and men, conducted during 2003 and 2006. Eleven of these surveys were DHS: Burkina Faso, Cambodia, Cameroon, Ethiopia, Ghana, India, Kenya, Lesotho, Malawi, Rwanda, Zimbabwe; and three were AIS: Cote d’Ivoire, Tanzania, Uganda. All these surveys included HIV testing and HIV serostatus data were linked to respondents’ socioeconomic and behavioural characteristics. Dried blood spot samples were collected (venous blood in Uganda) and analysed for HIV using standard laboratory and quality control procedures and internationally accepted ethical standards.<xref ref-type="bibr" rid="b25">25</xref> HIV test results were linked anonymously to the characteristics and behaviours of the survey respondents.</p><p>In most surveys, nationally representative samples of women age 15–49 and men age 15–59 were tested for HIV. The only exceptions are Uganda where women age 15–59 were tested; Tanzania, Cote d’Ivoire and Cambodia where men age 15–49 were tested, and India, Kenya, Malawi and Zimbabwe where men age 15–54 were tested. In the 14 countries included in this analysis, the numbers eligible for HIV testing ranged from 3305 males (15–59) and 3758 females (15–49) in Lesotho to 64 175 males (15–54) and 62 182 females (15–49) in India.</p><sec id="s2a"><title>Analysis of bias because of non-response</title><p>To estimate the extent of non-response bias and its potential impact on the observed HIV rates in the 14 countries with linked data, all eligible respondents were divided into four groups: (1) interviewed and tested; (2) not interviewed but tested; (3) interviewed, not tested; and (4) not interviewed, not tested. Eligibility for individual interview and HIV testing was based on de facto population.</p><p>To evaluate the effect of non-response bias on the survey estimates, HIV prevalence is predicted among non-responding adults (groups 3 and 4) based on multivariate models of HIV for those who were interviewed and tested (group 1), using a common set of predictor variables. A logistic regression model is used, after accounting for clustering in the survey design, to calculate predicted HIV prevalence separately for the “not interviewed, not tested” and “interviewed, not tested” groups. Predictions for the “not interviewed, not tested” group are based on a limited set of variables (only from the household questionnaire), but predictions for the “interviewed, not tested” group additionally use several individual sociodemographic and behavioural characteristics of the respondents, as collected in the survey (see footnotes to <xref ref-type="table" rid="U9G-84-S1-0063-t02">table 2</xref>).</p><table-wrap id="U9G-84-S1-0063-t02" position="float"><label>Table 2</label><caption><title>Predicted HIV prevalence among the non-respondents and adjusted HIV prevalence estimate for all eligible males and females, DHS/AIS countries with linked HIV testing data, 2003–6</title></caption><table frame="hsides" rules="groups"><colgroup span="1"><col align="left" span="1"></col><col align="center" span="1"></col><col align="center" span="1"></col><col align="center" span="1"></col><col align="center" span="1"></col><col align="center" span="1"></col><col align="center" span="1"></col><col align="center" span="1"></col><col align="center" span="1"></col></colgroup><thead><tr><td rowspan="3" align="left" colspan="1">Country</td><td rowspan="3" align="left" colspan="1">Observed HIV prevalence among tested respondents</td><td colspan="6" align="left" rowspan="1">Predicted HIV prevalence among non-tested respondents</td><td rowspan="3" align="left" colspan="1">Adjusted prevalence among all eligible respondents</td></tr><tr><td colspan="2" align="left" rowspan="1">Interview status</td><td colspan="3" align="left" rowspan="1">Reason not tested</td><td rowspan="2" align="left" colspan="1">Total non-tested</td></tr><tr><td align="left" rowspan="1" colspan="1">Interviewed</td><td align="left" rowspan="1" colspan="1">Not interviewed</td><td align="left" rowspan="1" colspan="1">Refused</td><td align="left" rowspan="1" colspan="1">Absent</td><td align="left" rowspan="1" colspan="1">Other/ missing</td></tr></thead><tbody><tr><td align="left" rowspan="1" colspan="1"><bold>Burkina Faso 2003</bold></td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td></tr><tr><td align="left" rowspan="1" colspan="1">Male (15–59)</td><td align="char" rowspan="1" colspan="1">1.94</td><td align="char" rowspan="1" colspan="1">2.68</td><td align="char" rowspan="1" colspan="1">2.48</td><td align="char" rowspan="1" colspan="1">2.91</td><td align="char" rowspan="1" colspan="1">2.52</td><td align="char" rowspan="1" colspan="1">2.11</td><td align="char" rowspan="1" colspan="1">2.57*</td><td align="char" rowspan="1" colspan="1">2.02</td></tr><tr><td align="left" rowspan="1" colspan="1">Female (15–49)</td><td align="char" rowspan="1" colspan="1">1.83</td><td align="char" rowspan="1" colspan="1">3.56</td><td align="char" rowspan="1" colspan="1">2.30</td><td align="char" rowspan="1" colspan="1">3.71</td><td align="char" rowspan="1" colspan="1">2.35</td><td align="char" rowspan="1" colspan="1">2.78</td><td align="char" rowspan="1" colspan="1">3.15*</td><td align="char" rowspan="1" colspan="1">1.94</td></tr><tr><td align="left" rowspan="1" colspan="1"><bold>Cambodia 2005</bold></td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td></tr><tr><td align="left" rowspan="1" colspan="1">Male (15–49)</td><td align="char" rowspan="1" colspan="1">0.62</td><td align="char" rowspan="1" colspan="1">1.07</td><td align="char" rowspan="1" colspan="1">0.79</td><td align="char" rowspan="1" colspan="1">1.09</td><td align="char" rowspan="1" colspan="1">0.82</td><td align="char" rowspan="1" colspan="1">0.38</td><td align="char" rowspan="1" colspan="1">0.88</td><td align="char" rowspan="1" colspan="1">0.64</td></tr><tr><td align="left" rowspan="1" colspan="1">Female (15–49)</td><td align="char" rowspan="1" colspan="1">0.61</td><td align="char" rowspan="1" colspan="1">1.49</td><td align="char" rowspan="1" colspan="1">0.61</td><td align="char" rowspan="1" colspan="1">1.41</td><td align="char" rowspan="1" colspan="1">0.54</td><td align="char" rowspan="1" colspan="1">0.53</td><td align="char" rowspan="1" colspan="1">1.02</td><td align="char" rowspan="1" colspan="1">0.63</td></tr><tr><td align="left" rowspan="1" colspan="1"><bold>Cameroon 2004</bold></td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td></tr><tr><td align="left" rowspan="1" colspan="1">Male (15–59)</td><td align="char" rowspan="1" colspan="1">3.91</td><td align="char" rowspan="1" colspan="1">5.17</td><td align="char" rowspan="1" colspan="1">5.10</td><td align="char" rowspan="1" colspan="1">5.44</td><td align="char" rowspan="1" colspan="1">5.00</td><td align="char" rowspan="1" colspan="1">3.71</td><td align="char" rowspan="1" colspan="1">5.13*</td><td align="char" rowspan="1" colspan="1">4.04</td></tr><tr><td align="left" rowspan="1" colspan="1">Female (15–49)</td><td align="char" rowspan="1" colspan="1">6.75</td><td align="char" rowspan="1" colspan="1">8.73</td><td align="char" rowspan="1" colspan="1">8.24</td><td align="char" rowspan="1" colspan="1">8.72</td><td align="char" rowspan="1" colspan="1">8.87</td><td align="char" rowspan="1" colspan="1">7.12</td><td align="char" rowspan="1" colspan="1">8.51*</td><td align="char" rowspan="1" colspan="1">6.90</td></tr><tr><td align="left" rowspan="1" colspan="1"><bold>Cote d’Ivoire 2005</bold></td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td></tr><tr><td align="left" rowspan="1" colspan="1">Male (15–49)</td><td align="char" rowspan="1" colspan="1">2.86</td><td align="char" rowspan="1" colspan="1">3.39</td><td align="char" rowspan="1" colspan="1">3.21</td><td align="char" rowspan="1" colspan="1">3.22</td><td align="char" rowspan="1" colspan="1">3.48</td><td align="char" rowspan="1" colspan="1">2.29</td><td align="char" rowspan="1" colspan="1">3.29</td><td align="char" rowspan="1" colspan="1">2.98</td></tr><tr><td align="left" rowspan="1" colspan="1">Female (15–49)</td><td align="char" rowspan="1" colspan="1">6.40</td><td align="char" rowspan="1" colspan="1">6.89</td><td align="char" rowspan="1" colspan="1">7.73</td><td align="char" rowspan="1" colspan="1">7.15</td><td align="char" rowspan="1" colspan="1">7.93</td><td align="char" rowspan="1" colspan="1">6.05</td><td align="char" rowspan="1" colspan="1">7.29*</td><td align="char" rowspan="1" colspan="1">6.64</td></tr><tr><td align="left" rowspan="1" colspan="1"><bold>Ethiopia 2005</bold></td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td></tr><tr><td align="left" rowspan="1" colspan="1">Male (15–59)</td><td align="char" rowspan="1" colspan="1">0.92</td><td align="char" rowspan="1" colspan="1">1.44</td><td align="char" rowspan="1" colspan="1">1.23</td><td align="char" rowspan="1" colspan="1">1.44</td><td align="char" rowspan="1" colspan="1">1.30</td><td align="char" rowspan="1" colspan="1">0.87</td><td align="char" rowspan="1" colspan="1">1.34*</td><td align="char" rowspan="1" colspan="1">0.99</td></tr><tr><td align="left" rowspan="1" colspan="1">Female (15–49)</td><td align="char" rowspan="1" colspan="1">1.86</td><td align="char" rowspan="1" colspan="1">3.46</td><td align="char" rowspan="1" colspan="1">3.23</td><td align="char" rowspan="1" colspan="1">3.50</td><td align="char" rowspan="1" colspan="1">4.07</td><td align="char" rowspan="1" colspan="1">1.62</td><td align="char" rowspan="1" colspan="1">3.39*</td><td align="char" rowspan="1" colspan="1">2.06</td></tr><tr><td align="left" rowspan="1" colspan="1"><bold>Ghana 2003</bold></td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td></tr><tr><td align="left" rowspan="1" colspan="1">Male (15–59)</td><td align="char" rowspan="1" colspan="1">1.66</td><td align="char" rowspan="1" colspan="1">2.14</td><td align="char" rowspan="1" colspan="1">1.62</td><td align="char" rowspan="1" colspan="1">2.27</td><td align="char" rowspan="1" colspan="1">1.50</td><td align="char" rowspan="1" colspan="1">2.40</td><td align="char" rowspan="1" colspan="1">1.98</td><td align="char" rowspan="1" colspan="1">1.72</td></tr><tr><td align="left" rowspan="1" colspan="1">Female (15–49)</td><td align="char" rowspan="1" colspan="1">2.70</td><td align="char" rowspan="1" colspan="1">2.97</td><td align="char" rowspan="1" colspan="1">2.46</td><td align="char" rowspan="1" colspan="1">3.10</td><td align="char" rowspan="1" colspan="1">2.40</td><td align="char" rowspan="1" colspan="1">2.56</td><td align="char" rowspan="1" colspan="1">2.77</td><td align="char" rowspan="1" colspan="1">2.70</td></tr><tr><td align="left" rowspan="1" colspan="1"><bold>India 2005–6</bold></td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td></tr><tr><td align="left" rowspan="1" colspan="1">Male (15–54)</td><td align="char" rowspan="1" colspan="1">0.35</td><td align="char" rowspan="1" colspan="1">0.44</td><td align="char" rowspan="1" colspan="1">0.53</td><td align="char" rowspan="1" colspan="1">0.44</td><td align="char" rowspan="1" colspan="1">0.51</td><td align="char" rowspan="1" colspan="1">0.58</td><td align="char" rowspan="1" colspan="1">0.50*</td><td align="char" rowspan="1" colspan="1">0.38</td></tr><tr><td align="left" rowspan="1" colspan="1">Female (15–49)</td><td align="char" rowspan="1" colspan="1">0.22</td><td align="char" rowspan="1" colspan="1">0.25</td><td align="char" rowspan="1" colspan="1">0.32</td><td align="char" rowspan="1" colspan="1">0.24</td><td align="char" rowspan="1" colspan="1">0.30</td><td align="char" rowspan="1" colspan="1">0.36</td><td align="char" rowspan="1" colspan="1">0.28*</td><td align="char" rowspan="1" colspan="1">0.23</td></tr><tr><td align="left" rowspan="1" colspan="1"><bold>Kenya 2003</bold></td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td></tr><tr><td align="left" rowspan="1" colspan="1">Male (15–54)</td><td align="char" rowspan="1" colspan="1">4.71</td><td align="char" rowspan="1" colspan="1">4.47</td><td align="char" rowspan="1" colspan="1">5.81</td><td align="char" rowspan="1" colspan="1">4.83</td><td align="char" rowspan="1" colspan="1">5.54</td><td align="char" rowspan="1" colspan="1">4.28</td><td align="char" rowspan="1" colspan="1">5.11</td><td align="char" rowspan="1" colspan="1">4.81</td></tr><tr><td align="left" rowspan="1" colspan="1">Female (15–49)</td><td align="char" rowspan="1" colspan="1">8.70</td><td align="char" rowspan="1" colspan="1">6.82</td><td align="char" rowspan="1" colspan="1">9.24</td><td align="char" rowspan="1" colspan="1">7.19</td><td align="char" rowspan="1" colspan="1">8.00</td><td align="char" rowspan="1" colspan="1">7.59</td><td align="char" rowspan="1" colspan="1">7.46*</td><td align="char" rowspan="1" colspan="1">8.45</td></tr><tr><td align="left" rowspan="1" colspan="1"><bold>Lesotho 2004</bold></td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td></tr><tr><td align="left" rowspan="1" colspan="1">Male (15–59)</td><td align="char" rowspan="1" colspan="1">18.94</td><td align="char" rowspan="1" colspan="1">19.12</td><td align="char" rowspan="1" colspan="1">19.18</td><td align="char" rowspan="1" colspan="1">18.94</td><td align="char" rowspan="1" colspan="1">18.32</td><td align="char" rowspan="1" colspan="1">20.66</td><td align="char" rowspan="1" colspan="1">19.15</td><td align="char" rowspan="1" colspan="1">19.01</td></tr><tr><td align="left" rowspan="1" colspan="1">Female (15–49)</td><td align="char" rowspan="1" colspan="1">26.37</td><td align="char" rowspan="1" colspan="1">25.17</td><td align="char" rowspan="1" colspan="1">24.54</td><td align="char" rowspan="1" colspan="1">25.70</td><td align="char" rowspan="1" colspan="1">23.80</td><td align="char" rowspan="1" colspan="1">23.72</td><td align="char" rowspan="1" colspan="1">25.00</td><td align="char" rowspan="1" colspan="1">26.09</td></tr><tr><td align="left" rowspan="1" colspan="1"><bold>Malawi 2004</bold></td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td></tr><tr><td align="left" rowspan="1" colspan="1">Male (15–54)</td><td align="char" rowspan="1" colspan="1">10.23</td><td align="char" rowspan="1" colspan="1">9.53</td><td align="char" rowspan="1" colspan="1">11.37</td><td align="char" rowspan="1" colspan="1">9.44</td><td align="char" rowspan="1" colspan="1">12.74</td><td align="char" rowspan="1" colspan="1">9.31</td><td align="char" rowspan="1" colspan="1">10.20</td><td align="char" rowspan="1" colspan="1">10.22</td></tr><tr><td align="left" rowspan="1" colspan="1">Female (15–49)</td><td align="char" rowspan="1" colspan="1">13.32</td><td align="char" rowspan="1" colspan="1">12.14</td><td align="char" rowspan="1" colspan="1">12.68</td><td align="char" rowspan="1" colspan="1">12.02</td><td align="char" rowspan="1" colspan="1">13.01</td><td align="char" rowspan="1" colspan="1">13.28</td><td align="char" rowspan="1" colspan="1">12.24</td><td align="char" rowspan="1" colspan="1">12.99</td></tr><tr><td align="left" rowspan="1" colspan="1"><bold>Rwanda 2005</bold></td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td></tr><tr><td align="left" rowspan="1" colspan="1">Male (15–59)</td><td align="char" rowspan="1" colspan="1">2.24</td><td align="char" rowspan="1" colspan="1">3.00</td><td align="char" rowspan="1" colspan="1">3.16</td><td align="char" rowspan="1" colspan="1">4.42</td><td align="char" rowspan="1" colspan="1">2.87</td><td align="char" rowspan="1" colspan="1">3.26</td><td align="char" rowspan="1" colspan="1">3.09*</td><td align="char" rowspan="1" colspan="1">2.28</td></tr><tr><td align="left" rowspan="1" colspan="1">Female (15–49)</td><td align="char" rowspan="1" colspan="1">3.61</td><td align="char" rowspan="1" colspan="1">5.74</td><td align="char" rowspan="1" colspan="1">3.84</td><td align="char" rowspan="1" colspan="1">5.21</td><td align="char" rowspan="1" colspan="1">4.53</td><td align="char" rowspan="1" colspan="1">4.75</td><td align="char" rowspan="1" colspan="1">4.64</td><td align="char" rowspan="1" colspan="1">3.64</td></tr><tr><td align="left" rowspan="1" colspan="1"><bold>Uganda 2004–5</bold></td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td></tr><tr><td align="left" rowspan="1" colspan="1">Male (15–59)</td><td align="char" rowspan="1" colspan="1">5.15</td><td align="char" rowspan="1" colspan="1">3.88</td><td align="char" rowspan="1" colspan="1">4.52</td><td align="char" rowspan="1" colspan="1">3.87</td><td align="char" rowspan="1" colspan="1">4.41</td><td align="char" rowspan="1" colspan="1">5.16</td><td align="char" rowspan="1" colspan="1">4.28*</td><td align="char" rowspan="1" colspan="1">5.03</td></tr><tr><td align="left" rowspan="1" colspan="1">Female (15–59)</td><td align="char" rowspan="1" colspan="1">7.29</td><td align="char" rowspan="1" colspan="1">6.24</td><td align="char" rowspan="1" colspan="1">7.01</td><td align="char" rowspan="1" colspan="1">6.58</td><td align="char" rowspan="1" colspan="1">6.86</td><td align="char" rowspan="1" colspan="1">5.92</td><td align="char" rowspan="1" colspan="1">6.58*</td><td align="char" rowspan="1" colspan="1">7.22</td></tr><tr><td align="left" rowspan="1" colspan="1"><bold>Tanzania 2003</bold></td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td></tr><tr><td align="left" rowspan="1" colspan="1">Male (15–49)</td><td align="char" rowspan="1" colspan="1">6.26</td><td align="char" rowspan="1" colspan="1">6.84</td><td align="char" rowspan="1" colspan="1">7.38</td><td align="char" rowspan="1" colspan="1">6.99</td><td align="char" rowspan="1" colspan="1">7.37</td><td align="char" rowspan="1" colspan="1">5.45</td><td align="char" rowspan="1" colspan="1">7.04*</td><td align="char" rowspan="1" colspan="1">6.44</td></tr><tr><td align="left" rowspan="1" colspan="1">Female (15–49)</td><td align="char" rowspan="1" colspan="1">7.70</td><td align="char" rowspan="1" colspan="1">8.40</td><td align="char" rowspan="1" colspan="1">7.20</td><td align="char" rowspan="1" colspan="1">8.36</td><td align="char" rowspan="1" colspan="1">7.29</td><td align="char" rowspan="1" colspan="1">6.94</td><td align="char" rowspan="1" colspan="1">8.11</td><td align="char" rowspan="1" colspan="1">7.77</td></tr><tr><td align="left" rowspan="1" colspan="1"><bold>Zimbabwe 2005–6</bold></td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td></tr><tr><td align="left" rowspan="1" colspan="1">Male (15–54)</td><td align="char" rowspan="1" colspan="1">14.75</td><td align="char" rowspan="1" colspan="1">15.28</td><td align="char" rowspan="1" colspan="1">17.38</td><td align="char" rowspan="1" colspan="1">15.79</td><td align="char" rowspan="1" colspan="1">16.67</td><td align="char" rowspan="1" colspan="1">19.05</td><td align="char" rowspan="1" colspan="1">16.35*</td><td align="char" rowspan="1" colspan="1">15.28</td></tr><tr><td align="left" rowspan="1" colspan="1">Female (15–49)</td><td align="char" rowspan="1" colspan="1">21.12</td><td align="char" rowspan="1" colspan="1">19.90</td><td align="char" rowspan="1" colspan="1">21.38</td><td align="char" rowspan="1" colspan="1">20.06</td><td align="char" rowspan="1" colspan="1">21.48</td><td align="char" rowspan="1" colspan="1">20.71</td><td align="char" rowspan="1" colspan="1">20.51</td><td align="char" rowspan="1" colspan="1">20.97</td></tr></tbody></table><table-wrap-foot><fn id="nt101"><p>*Significantly different from the observed prevalence at p<0.05.</p></fn><fn id="nt102"><p>Variables for predicting HIV prevalence in the “not interviewed, not tested” group included age, education, wealth index, residence and geographic region.</p></fn><fn id="nt103"><p>Additional variables for predicting HIV in the “interviewed, not tested” group included: marital status; childbirth in last five years (women only); work status; media exposure; ethnicity; religion; circumcision (men only); STI or STI symptoms in the last 12 months; alcohol use at last sex in the last 12 months; number of sex partners in the last 12 months; cigarette smoking/tobacco use; age at first sex; number of lifetime sexual partners; number of sexual partners in the last 12 months; condom use at last sex in the last 12 months; higher-risk sex (sex with a non-marital, non-cohabiting partner) in the last 12 months; knowledge of prevention methods (abstinence, being faithful and condom use); attitudes towards people living with HIV (PLHIV). Woman’s ability to negotiate safer sex with spouse; woman’s participation in household decision-making (women only); number of medical injections in the last 12 months; duration of stay in current place of residence; number of times slept away in the last 12 months (men only); away (from usual place of residence) for more than one month in the last 12 months (men only); and previously tested for HIV. The list of additional variables used varied slightly from country to country, depending on the availability of information.</p></fn></table-wrap-foot></table-wrap><p>Multivariate analyses used Stata version 9.0. Analysis was carried out separately for males and females for each country. Adjusted HIV prevalence was calculated as a weighted average of observed prevalence among those who were tested and predicted prevalence in the two groups of non-tested respondents. Sampling weights were applied in accordance with standard DHS procedures. We used HIV sampling weights for the tested, individual sampling weights for the “interviewed, not tested”, and household sampling weights for the “not interviewed, not tested” groups, respectively.</p></sec><sec id="s2b"><title>Analysis of bias because of exclusion of non-household population</title><p>In five of the countries (Cambodia, India, Ghana, Uganda and Lesotho), we examine potential bias because of exclusion of non-household population groups on the survey estimates of HIV prevalence for adults age 15–49. These countries were chosen to represent countries at varying levels of HIV prevalence.</p><p>For this purpose, we obtained national estimates of the size of household population, size of non-household population (including both institutional and homeless), total population, the annual population growth rate and the proportion of adults age 15–49 in the total population in each country.<xref ref-type="bibr" rid="b26">26</xref><sup>–</sup><xref ref-type="bibr" rid="b30">30</xref> Using the annual growth rate, the household, non-household and total population sizes were projected to the DHS survey year. Next, using the proportion of adults in the total population, numbers of adults in the household, non-household and total population were estimated for the survey year. Adults are more likely to live in institutions and be homeless than children or elderly, but information on the age structure of the non-household population was not readily available from census in most cases. We therefore used different assumptions about the proportion of adults in the non-household population and the level of HIV prevalence among non-household adults to estimate overall HIV prevalence among all adults in each country (accounting for exclusion of non-household population groups).</p><p>We estimated the potential impact of excluding non-household population groups under the following three scenarios:</p><list list-type="bullet"><list-item><p><italic>Scenario A (baseline):</italic> The proportion of adults (15–49) in the non-household population is the same as in the census population; and HIV prevalence among non-household adults is the same as the prevalence among adults in the household survey.</p></list-item><list-item><p><italic>Scenario B:</italic> The proportion of adults (15–49) in the non-household population is 66.67%; and the HIV prevalence among the non-household adults is 10 times in India and Cambodia, five times in Ghana, two times in Uganda, 1.5 times in Lesotho that of the prevalence among adults in the household survey.</p></list-item><list-item><p><italic>Scenario C:</italic> The proportion of adults (15–49) in the non-household population is 75.00%; and the HIV prevalence among the non-household adults is 20 times in India and Cambodia, 10 times in Ghana, four times in Uganda, two times in Lesotho that of the prevalence among adults in the household survey.</p></list-item></list></sec></sec><sec id="s3"><title>RESULTS</title><p>HIV prevalence among adults (15–49) in the 14 countries ranged from less than 1% in India and Cambodia to 23.2% in Lesotho. Despite large HIV prevalence differences among the surveys, fairly consistent patterns of HIV infection are observed by age, sex and urban/rural residence (data not shown).</p><sec id="s3a"><title>Estimates of bias because of non-response</title><p>Household response rates were very high in all surveys (93% or higher) (<xref ref-type="table" rid="U9G-84-S1-0063-t01">table 1</xref>). Response rates for the individual interview were also above 90% in most surveys. Individual interview response rates for females ranged from 90% in Cote d’Ivoire and Zimbabwe to 98% in Rwanda. Individual interview response rates for males were lower than for females in all 14 countries, and ranged from a low of 82% in Zimbabwe to 97% in Rwanda.</p><table-wrap id="U9G-84-S1-0063-t01" position="float"><label>Table 1</label><caption><title>Response rates for household interview, individual interview and HIV testing by sex, and reason for HIV non-response, DHS/AIS countries with HIV testing, 2003–6</title></caption><table frame="hsides" rules="groups"><colgroup span="1"><col align="left" span="1"></col><col align="center" span="1"></col><col align="center" span="1"></col><col align="center" span="1"></col><col align="center" span="1"></col><col align="center" span="1"></col><col align="center" span="1"></col><col align="center" span="1"></col></colgroup><thead><tr><td rowspan="2" align="left" colspan="1">Country, sex (age)</td><td rowspan="2" align="left" colspan="1">Household response rate</td><td rowspan="2" align="left" colspan="1">Number eligible for individual interview and HIV testing</td><td rowspan="2" align="left" colspan="1">Individual response rate</td><td rowspan="2" align="left" colspan="1">HIV response rate</td><td colspan="3" align="left" rowspan="1">Reason for HIV non-response</td></tr><tr><td align="left" rowspan="1" colspan="1">Refused</td><td align="left" rowspan="1" colspan="1">Absent</td><td align="left" rowspan="1" colspan="1">Other/ missing</td></tr></thead><tbody><tr><td align="left" rowspan="1" colspan="1"><bold>Burkina Faso 2003</bold></td><td align="left" rowspan="1" colspan="1">99.4</td><td align="char" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td></tr><tr><td align="left" rowspan="1" colspan="1">Male (15–59)</td><td align="left" rowspan="1" colspan="1"></td><td align="char" rowspan="1" colspan="1">3984</td><td align="left" rowspan="1" colspan="1">90.5</td><td align="left" rowspan="1" colspan="1">85.8</td><td align="left" rowspan="1" colspan="1">5.9</td><td align="left" rowspan="1" colspan="1">5.1</td><td align="left" rowspan="1" colspan="1">3.2</td></tr><tr><td align="left" rowspan="1" colspan="1">Female (15–49)</td><td align="left" rowspan="1" colspan="1"></td><td align="char" rowspan="1" colspan="1">4575</td><td align="left" rowspan="1" colspan="1">96.7</td><td align="left" rowspan="1" colspan="1">92.3</td><td align="left" rowspan="1" colspan="1">4.0</td><td align="left" rowspan="1" colspan="1">2.0</td><td align="left" rowspan="1" colspan="1">1.7</td></tr><tr><td align="left" rowspan="1" colspan="1"><bold>Cambodia 2005</bold></td><td align="left" rowspan="1" colspan="1">98.0</td><td align="char" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td></tr><tr><td align="left" rowspan="1" colspan="1">Male (15–49)</td><td align="left" rowspan="1" colspan="1"></td><td align="char" rowspan="1" colspan="1">7229</td><td align="left" rowspan="1" colspan="1">93.1</td><td align="left" rowspan="1" colspan="1">90.3</td><td align="left" rowspan="1" colspan="1">3.7</td><td align="left" rowspan="1" colspan="1">5.1</td><td align="left" rowspan="1" colspan="1">0.9</td></tr><tr><td align="left" rowspan="1" colspan="1">Female (15–49)</td><td align="left" rowspan="1" colspan="1"></td><td align="char" rowspan="1" colspan="1">8638</td><td align="left" rowspan="1" colspan="1">97.2</td><td align="left" rowspan="1" colspan="1">95.1</td><td align="left" rowspan="1" colspan="1">2.7</td><td align="left" rowspan="1" colspan="1">1.5</td><td align="left" rowspan="1" colspan="1">0.6</td></tr><tr><td align="left" rowspan="1" colspan="1"><bold>Cameroon 2004</bold></td><td align="left" rowspan="1" colspan="1">97.6</td><td align="char" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td></tr><tr><td align="left" rowspan="1" colspan="1">Male (15–59)</td><td align="left" rowspan="1" colspan="1"></td><td align="char" rowspan="1" colspan="1">5676</td><td align="left" rowspan="1" colspan="1">93.0</td><td align="left" rowspan="1" colspan="1">89.8</td><td align="left" rowspan="1" colspan="1">5.2</td><td align="left" rowspan="1" colspan="1">3.5</td><td align="left" rowspan="1" colspan="1">1.5</td></tr><tr><td align="left" rowspan="1" colspan="1">Female (15–49)</td><td align="left" rowspan="1" colspan="1"></td><td align="char" rowspan="1" colspan="1">5703</td><td align="left" rowspan="1" colspan="1">94.5</td><td align="left" rowspan="1" colspan="1">92.1</td><td align="left" rowspan="1" colspan="1">5.1</td><td align="left" rowspan="1" colspan="1">1.5</td><td align="left" rowspan="1" colspan="1">1.3</td></tr><tr><td align="left" rowspan="1" colspan="1"><bold>Cote d’Ivoire 2005</bold></td><td align="left" rowspan="1" colspan="1">95.5</td><td align="char" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td></tr><tr><td align="left" rowspan="1" colspan="1">Male (15–49)</td><td align="left" rowspan="1" colspan="1"></td><td align="char" rowspan="1" colspan="1">5148</td><td align="left" rowspan="1" colspan="1">87.5</td><td align="left" rowspan="1" colspan="1">75.8</td><td align="left" rowspan="1" colspan="1">13.9</td><td align="left" rowspan="1" colspan="1">9.2</td><td align="left" rowspan="1" colspan="1">1.1</td></tr><tr><td align="left" rowspan="1" colspan="1">Female (15–49)</td><td align="left" rowspan="1" colspan="1"></td><td align="char" rowspan="1" colspan="1">5772</td><td align="left" rowspan="1" colspan="1">89.8</td><td align="left" rowspan="1" colspan="1">78.7</td><td align="left" rowspan="1" colspan="1">14.5</td><td align="left" rowspan="1" colspan="1">5.2</td><td align="left" rowspan="1" colspan="1">1.6</td></tr><tr><td align="left" rowspan="1" colspan="1"><bold>Ethiopia 2005</bold></td><td align="left" rowspan="1" colspan="1">98.6</td><td align="char" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td></tr><tr><td align="left" rowspan="1" colspan="1">Male (15–59)</td><td align="left" rowspan="1" colspan="1"></td><td align="char" rowspan="1" colspan="1">6778</td><td align="left" rowspan="1" colspan="1">89.0</td><td align="left" rowspan="1" colspan="1">75.4</td><td align="left" rowspan="1" colspan="1">15.0</td><td align="left" rowspan="1" colspan="1">7.8</td><td align="left" rowspan="1" colspan="1">1.9</td></tr><tr><td align="left" rowspan="1" colspan="1">Female (15–49)</td><td align="left" rowspan="1" colspan="1"></td><td align="char" rowspan="1" colspan="1">7142</td><td align="left" rowspan="1" colspan="1">95.4</td><td align="left" rowspan="1" colspan="1">83.2</td><td align="left" rowspan="1" colspan="1">13.3</td><td align="left" rowspan="1" colspan="1">2.3</td><td align="left" rowspan="1" colspan="1">1.3</td></tr><tr><td align="left" rowspan="1" colspan="1"><bold>Ghana 2003</bold></td><td align="left" rowspan="1" colspan="1">98.7</td><td align="char" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td></tr><tr><td align="left" rowspan="1" colspan="1">Male (15–59)</td><td align="left" rowspan="1" colspan="1"></td><td align="char" rowspan="1" colspan="1">5345</td><td align="left" rowspan="1" colspan="1">93.8</td><td align="left" rowspan="1" colspan="1">80.0</td><td align="left" rowspan="1" colspan="1">10.3</td><td align="left" rowspan="1" colspan="1">7.5</td><td align="left" rowspan="1" colspan="1">2.2</td></tr><tr><td align="left" rowspan="1" colspan="1">Female (15–49)</td><td align="left" rowspan="1" colspan="1"></td><td align="char" rowspan="1" colspan="1">5949</td><td align="left" rowspan="1" colspan="1">95.7</td><td align="left" rowspan="1" colspan="1">89.3</td><td align="left" rowspan="1" colspan="1">5.3</td><td align="left" rowspan="1" colspan="1">3.7</td><td align="left" rowspan="1" colspan="1">1.8</td></tr><tr><td align="left" rowspan="1" colspan="1"><bold>India 2005–6</bold></td><td align="left" rowspan="1" colspan="1">92.9</td><td align="char" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td></tr><tr><td align="left" rowspan="1" colspan="1">Male (15–54)</td><td align="left" rowspan="1" colspan="1"></td><td align="char" rowspan="1" colspan="1">64 175</td><td align="left" rowspan="1" colspan="1">86.5</td><td align="left" rowspan="1" colspan="1">78.1</td><td align="left" rowspan="1" colspan="1">6.9</td><td align="left" rowspan="1" colspan="1">12.1</td><td align="left" rowspan="1" colspan="1">3.0</td></tr><tr><td align="left" rowspan="1" colspan="1">Female (15–49)</td><td align="left" rowspan="1" colspan="1"></td><td align="char" rowspan="1" colspan="1">62 182</td><td align="left" rowspan="1" colspan="1">93.6</td><td align="left" rowspan="1" colspan="1">85.0</td><td align="left" rowspan="1" colspan="1">8.1</td><td align="left" rowspan="1" colspan="1">4.5</td><td align="left" rowspan="1" colspan="1">2.4</td></tr><tr><td align="left" rowspan="1" colspan="1"><bold>Kenya 2003</bold></td><td align="left" rowspan="1" colspan="1">96.3</td><td align="char" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td></tr><tr><td align="left" rowspan="1" colspan="1">Male (15–54)</td><td align="left" rowspan="1" colspan="1"></td><td align="char" rowspan="1" colspan="1">4183</td><td align="left" rowspan="1" colspan="1">85.5</td><td align="left" rowspan="1" colspan="1">70.3</td><td align="left" rowspan="1" colspan="1">12.8</td><td align="left" rowspan="1" colspan="1">13.9</td><td align="left" rowspan="1" colspan="1">2.9</td></tr><tr><td align="left" rowspan="1" colspan="1">Female (15–49)</td><td align="left" rowspan="1" colspan="1"></td><td align="char" rowspan="1" colspan="1">4303</td><td align="left" rowspan="1" colspan="1">94.0</td><td align="left" rowspan="1" colspan="1">76.3</td><td align="left" rowspan="1" colspan="1">14.5</td><td align="left" rowspan="1" colspan="1">6.3</td><td align="left" rowspan="1" colspan="1">2.8</td></tr><tr><td align="left" rowspan="1" colspan="1"><bold>Lesotho 2004</bold></td><td align="left" rowspan="1" colspan="1">97.4</td><td align="char" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td></tr><tr><td align="left" rowspan="1" colspan="1">Male (15–59)</td><td align="left" rowspan="1" colspan="1"></td><td align="char" rowspan="1" colspan="1">3305</td><td align="left" rowspan="1" colspan="1">84.6</td><td align="left" rowspan="1" colspan="1">68.0</td><td align="left" rowspan="1" colspan="1">16.6</td><td align="left" rowspan="1" colspan="1">8.7</td><td align="left" rowspan="1" colspan="1">6.8</td></tr><tr><td align="left" rowspan="1" colspan="1">Female (15–49)</td><td align="left" rowspan="1" colspan="1"></td><td align="char" rowspan="1" colspan="1">3758</td><td align="left" rowspan="1" colspan="1">94.2</td><td align="left" rowspan="1" colspan="1">80.7</td><td align="left" rowspan="1" colspan="1">12.1</td><td align="left" rowspan="1" colspan="1">2.9</td><td align="left" rowspan="1" colspan="1">4.3</td></tr><tr><td align="left" rowspan="1" colspan="1"><bold>Malawi 2004</bold></td><td align="left" rowspan="1" colspan="1">97.8</td><td align="char" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td></tr><tr><td align="left" rowspan="1" colspan="1">Male (15–54)</td><td align="left" rowspan="1" colspan="1"></td><td align="char" rowspan="1" colspan="1">3797</td><td align="left" rowspan="1" colspan="1">85.9</td><td align="left" rowspan="1" colspan="1">63.3</td><td align="left" rowspan="1" colspan="1">24.6</td><td align="left" rowspan="1" colspan="1">9.3</td><td align="left" rowspan="1" colspan="1">2.8</td></tr><tr><td align="left" rowspan="1" colspan="1">Female (15–49)</td><td align="left" rowspan="1" colspan="1"></td><td align="char" rowspan="1" colspan="1">4071</td><td align="left" rowspan="1" colspan="1">94.9</td><td align="left" rowspan="1" colspan="1">70.4</td><td align="left" rowspan="1" colspan="1">24.0</td><td align="left" rowspan="1" colspan="1">2.5</td><td align="left" rowspan="1" colspan="1">3.1</td></tr><tr><td align="left" rowspan="1" colspan="1"><bold>Rwanda 2005</bold></td><td align="left" rowspan="1" colspan="1">99.6</td><td align="char" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td></tr><tr><td align="left" rowspan="1" colspan="1">Male (15–59)</td><td align="left" rowspan="1" colspan="1"></td><td align="char" rowspan="1" colspan="1">4959</td><td align="left" rowspan="1" colspan="1">97.2</td><td align="left" rowspan="1" colspan="1">95.6</td><td align="left" rowspan="1" colspan="1">0.4</td><td align="left" rowspan="1" colspan="1">3.3</td><td align="left" rowspan="1" colspan="1">0.8</td></tr><tr><td align="left" rowspan="1" colspan="1">Female (15–49)</td><td align="left" rowspan="1" colspan="1"></td><td align="char" rowspan="1" colspan="1">5837</td><td align="left" rowspan="1" colspan="1">98.1</td><td align="left" rowspan="1" colspan="1">97.3</td><td align="left" rowspan="1" colspan="1">0.3</td><td align="left" rowspan="1" colspan="1">2.0</td><td align="left" rowspan="1" colspan="1">0.5</td></tr><tr><td align="left" rowspan="1" colspan="1"><bold>Tanzania 2003</bold></td><td align="left" rowspan="1" colspan="1">98.5</td><td align="char" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td></tr><tr><td align="left" rowspan="1" colspan="1">Male (15–49)</td><td align="left" rowspan="1" colspan="1"></td><td align="char" rowspan="1" colspan="1">6196</td><td align="left" rowspan="1" colspan="1">91.3</td><td align="left" rowspan="1" colspan="1">77.1</td><td align="left" rowspan="1" colspan="1">14.9</td><td align="left" rowspan="1" colspan="1">7.0</td><td align="left" rowspan="1" colspan="1">1.1</td></tr><tr><td align="left" rowspan="1" colspan="1">Female (15–49)</td><td align="left" rowspan="1" colspan="1"></td><td align="char" rowspan="1" colspan="1">7154</td><td align="left" rowspan="1" colspan="1">95.9</td><td align="left" rowspan="1" colspan="1">83.5</td><td align="left" rowspan="1" colspan="1">12.9</td><td align="left" rowspan="1" colspan="1">2.8</td><td align="left" rowspan="1" colspan="1">0.8</td></tr><tr><td align="left" rowspan="1" colspan="1"><bold>Uganda 2004–5</bold></td><td align="left" rowspan="1" colspan="1">96.8</td><td align="char" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td></tr><tr><td align="left" rowspan="1" colspan="1">Male (15–59)</td><td align="left" rowspan="1" colspan="1"></td><td align="char" rowspan="1" colspan="1">9905</td><td align="left" rowspan="1" colspan="1">89.1</td><td align="left" rowspan="1" colspan="1">83.8</td><td align="left" rowspan="1" colspan="1">5.8</td><td align="left" rowspan="1" colspan="1">8.8</td><td align="left" rowspan="1" colspan="1">1.6</td></tr><tr><td align="left" rowspan="1" colspan="1">Female (15–59)</td><td align="left" rowspan="1" colspan="1"></td><td align="char" rowspan="1" colspan="1">11 454</td><td align="left" rowspan="1" colspan="1">94.5</td><td align="left" rowspan="1" colspan="1">89.3</td><td align="left" rowspan="1" colspan="1">5.2</td><td align="left" rowspan="1" colspan="1">4.0</td><td align="left" rowspan="1" colspan="1">1.6</td></tr><tr><td align="left" rowspan="1" colspan="1"><bold>Zimbabwe 2005–6</bold></td><td align="left" rowspan="1" colspan="1">95.0</td><td align="char" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td></tr><tr><td align="left" rowspan="1" colspan="1">Male (15–54)</td><td align="left" rowspan="1" colspan="1"></td><td align="char" rowspan="1" colspan="1">8761</td><td align="left" rowspan="1" colspan="1">81.9</td><td align="left" rowspan="1" colspan="1">63.4</td><td align="left" rowspan="1" colspan="1">21.0</td><td align="left" rowspan="1" colspan="1">12.9</td><td align="left" rowspan="1" colspan="1">2.7</td></tr><tr><td align="left" rowspan="1" colspan="1">Female (15–49)</td><td align="left" rowspan="1" colspan="1"></td><td align="char" rowspan="1" colspan="1">9870</td><td align="left" rowspan="1" colspan="1">90.2</td><td align="left" rowspan="1" colspan="1">75.9</td><td align="left" rowspan="1" colspan="1">15.3</td><td align="left" rowspan="1" colspan="1">6.4</td><td align="left" rowspan="1" colspan="1">2.3</td></tr></tbody></table></table-wrap><p>Response rates for HIV testing were lower than those for individual interview in all cases. In seven of the 14 countries, the difference in the response rates for individual interview and for HIV testing was greater than 10 percentage points for both males and females. The highest differences were observed in Malawi, where the response rate for HIV testing was 23 percentage points lower for males and 25 percentage points lower for females than the corresponding response rates for individual interview. On the other hand, Rwanda had the smallest differences between the individual interview and HIV testing response rates of about 2 percentage points for males and 1 percentage point for females.</p><p>HIV response rates for males were lowest in Malawi and Zimbabwe (63%), followed by Lesotho (68%) and Kenya (70%). The highest male HIV response rates were in Rwanda (96%), followed by Cambodia and Cameroon (90% each). Similar to individual interview response rates, HIV response rates for females were considerably higher than for males in all countries. Female HIV response rates ranged from 70% in Malawi to 97% in Rwanda, and were above 90% also in Cameroon, Burkina Faso and Cambodia.</p><p>Refusal was a more important reason for HIV non-response than absence in all countries for women (except in Rwanda) and in nine of the 14 countries for men. In Rwanda, very few women or men refused testing. In all countries, men were much more likely than women to be absent for testing. In 12 of the 14 countries, the HIV non-response rate because of absence was two to four times greater for men than for women.</p><p>Non-response rates because of both refusal and absence were much higher in urban areas than in rural areas. Also, the non-response rates were considerably higher among more educated and wealthier respondents. In five of the eight countries, where data on chronically ill adults (seriously ill for three or more months in the past year) were available, response rates were slightly higher among chronically ill adults than among adults who were not chronically ill. There were no clear patterns in the HIV non-response rates by various risk and protective factors (data not shown).</p><p>In most countries, non-tested males and females have higher predicted HIV prevalence than the observed prevalence among those who were tested (<xref ref-type="table" rid="U9G-84-S1-0063-t02">table 2</xref>). In eight of the 14 countries for males and in seven of the 14 countries for females, the predicted prevalence among non-tested individuals is significantly greater than the observed prevalence among those tested. In Uganda for both males and females and in Kenya for females, the predicted prevalence among the non-tested individuals is significantly lower than among those tested.</p><p>Adjusting the observed national HIV estimates from tested males and females by accounting for the predicted rates among the non-tested makes little difference to the observed estimates in most cases (<xref ref-type="fig" rid="U9G-84-S1-0063-f01">fig 1</xref>). Even in countries where predicted prevalence among the non-responders is significantly higher or lower, the adjusted prevalence for all eligible respondents is about the same as the observed prevalence based only on the tested respondents. Although not statistically significant in all 14 countries, the effects of non-response tend to be somewhat greater among lower prevalence countries for both males and females.</p><fig id="U9G-84-S1-0063-f01" position="float"><label>Figure 1</label><caption><title>Ratios of adjusted HIV prevalence among all eligible individuals to observed HIV prevalence among those tested in the surveys</title></caption><graphic xlink:href="U9G-84-S1-0063-f01"></graphic></fig></sec><sec id="s3b"><title>Estimates of bias because of exclusion of non-household population</title><p>Our simulation analyses for India, Cambodia, Ghana, Uganda and Lesotho show that under varying assumptions of much greater HIV prevalence among non-household adults, estimated bias because of exclusion of non-household population groups in national HIV prevalence estimates from household samples tends to be small (<xref ref-type="table" rid="U9G-84-S1-0063-t03">table 3</xref>).</p><table-wrap id="U9G-84-S1-0063-t03" position="float"><label>Table 3</label><caption><title>Potential effects of exclusion of non-household population on the national HIV estimate for adults age 15–49, India, Cambodia, Ghana, Uganda, Lesotho</title></caption><table frame="hsides" rules="groups"><colgroup span="1"><col align="left" span="1"></col><col align="center" span="1"></col><col align="center" span="1"></col><col align="center" span="1"></col><col align="center" span="1"></col><col align="center" span="1"></col><col align="center" span="1"></col><col align="center" span="1"></col></colgroup><thead><tr><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1">Projected population in survey year</td><td align="left" rowspan="1" colspan="1">% population (15–49)</td><td align="left" rowspan="1" colspan="1">Projected population (15–49)</td><td colspan="2" align="left" rowspan="1">HIV prevalence</td><td align="left" rowspan="1" colspan="1">No of HIV+ in population (15–49)</td><td align="left" rowspan="1" colspan="1">Estimated HIV prevalence</td></tr></thead><tbody><tr><td colspan="8" align="left" rowspan="1"><bold>India 2005–6</bold></td></tr><tr><td colspan="8" align="left" rowspan="1"><bold>Scenario A (baseline)</bold></td></tr><tr><td align="left" rowspan="1" colspan="1">Population living in households</td><td align="char" rowspan="1" colspan="1">1 101 648 431</td><td align="left" rowspan="1" colspan="1">53.18%</td><td align="char" rowspan="1" colspan="1">585 804 092</td><td align="left" rowspan="1" colspan="1">Svy Est</td><td align="left" rowspan="1" colspan="1">0.28%</td><td align="char" rowspan="1" colspan="1">1 640 251</td><td align="left" rowspan="1" colspan="1"></td></tr><tr><td align="left" rowspan="1" colspan="1">Non-household population</td><td align="char" rowspan="1" colspan="1">10 538 569</td><td align="left" rowspan="1" colspan="1">53.18%</td><td align="char" rowspan="1" colspan="1">5 603 908</td><td align="left" rowspan="1" colspan="1">NHH% = Svy%</td><td align="left" rowspan="1" colspan="1">0.28%</td><td align="char" rowspan="1" colspan="1">15 691</td><td align="left" rowspan="1" colspan="1"></td></tr><tr><td align="left" rowspan="1" colspan="1">Total population</td><td align="char" rowspan="1" colspan="1">1 112 187 000</td><td align="left" rowspan="1" colspan="1">53.18%</td><td align="char" rowspan="1" colspan="1">591 408 000</td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td><td align="char" rowspan="1" colspan="1">1 655 942</td><td align="left" rowspan="1" colspan="1">0.28%</td></tr><tr><td colspan="8" align="left" rowspan="1"><bold>Scenario B</bold></td></tr><tr><td align="left" rowspan="1" colspan="1">Population living in households</td><td align="char" rowspan="1" colspan="1">1 101 648 431</td><td align="left" rowspan="1" colspan="1">53.05%</td><td align="char" rowspan="1" colspan="1">584 381 936</td><td align="left" rowspan="1" colspan="1">Svy Est</td><td align="left" rowspan="1" colspan="1">0.28%</td><td align="char" rowspan="1" colspan="1">1 636 269</td><td align="left" rowspan="1" colspan="1"></td></tr><tr><td align="left" rowspan="1" colspan="1">Non-household population</td><td align="char" rowspan="1" colspan="1">10 538 569</td><td align="left" rowspan="1" colspan="1">66.67%</td><td align="char" rowspan="1" colspan="1">7 026 064</td><td align="left" rowspan="1" colspan="1">NHH% = 10*Svy%</td><td align="left" rowspan="1" colspan="1">2.80%</td><td align="char" rowspan="1" colspan="1">196 730</td><td align="left" rowspan="1" colspan="1"></td></tr><tr><td align="left" rowspan="1" colspan="1">Total population</td><td align="char" rowspan="1" colspan="1">1 112 187 000</td><td align="left" rowspan="1" colspan="1">53.18%</td><td align="char" rowspan="1" colspan="1">591 408 000</td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td><td align="char" rowspan="1" colspan="1">1 832 999</td><td align="left" rowspan="1" colspan="1">0.31%</td></tr><tr><td colspan="8" align="left" rowspan="1">Scenario C</td></tr><tr><td align="left" rowspan="1" colspan="1">Population living in households</td><td align="char" rowspan="1" colspan="1">1 101 648 431</td><td align="left" rowspan="1" colspan="1">52.97%</td><td align="char" rowspan="1" colspan="1">583 504 073</td><td align="left" rowspan="1" colspan="1">Svy Est</td><td align="left" rowspan="1" colspan="1">0.28%</td><td align="char" rowspan="1" colspan="1">1 633 811</td><td align="left" rowspan="1" colspan="1"></td></tr><tr><td align="left" rowspan="1" colspan="1">Non-household population</td><td align="char" rowspan="1" colspan="1">10 538 569</td><td align="left" rowspan="1" colspan="1">75.00%</td><td align="char" rowspan="1" colspan="1">7 903 927</td><td align="left" rowspan="1" colspan="1">NHH% = 20*Svy%</td><td align="left" rowspan="1" colspan="1">5.60%</td><td align="char" rowspan="1" colspan="1">442 620</td><td align="left" rowspan="1" colspan="1"></td></tr><tr><td align="left" rowspan="1" colspan="1">Total population</td><td align="char" rowspan="1" colspan="1">1 112 187 000</td><td align="left" rowspan="1" colspan="1">53.18%</td><td align="char" rowspan="1" colspan="1">591 408 000</td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td><td align="char" rowspan="1" colspan="1">2 076 431</td><td align="left" rowspan="1" colspan="1">0.35%</td></tr><tr><td colspan="8" align="left" rowspan="1"><bold>Cambodia 2005</bold></td></tr><tr><td colspan="8" align="left" rowspan="1"><bold>Scenario A (baseline)</bold></td></tr><tr><td align="left" rowspan="1" colspan="1">Population living in households</td><td align="char" rowspan="1" colspan="1">13 056 114</td><td align="left" rowspan="1" colspan="1">50.40%</td><td align="char" rowspan="1" colspan="1">6 580 282</td><td align="left" rowspan="1" colspan="1">Svy Est</td><td align="left" rowspan="1" colspan="1">0.62%</td><td align="char" rowspan="1" colspan="1">40 798</td><td align="left" rowspan="1" colspan="1"></td></tr><tr><td align="left" rowspan="1" colspan="1">Non-household population</td><td align="char" rowspan="1" colspan="1">271 833</td><td align="left" rowspan="1" colspan="1">50.40%</td><td align="char" rowspan="1" colspan="1">137 004</td><td align="left" rowspan="1" colspan="1">NHH% = Svy%</td><td align="left" rowspan="1" colspan="1">0.62%</td><td align="char" rowspan="1" colspan="1">849</td><td align="left" rowspan="1" colspan="1"></td></tr><tr><td align="left" rowspan="1" colspan="1">Total population</td><td align="char" rowspan="1" colspan="1">13 327 947</td><td align="left" rowspan="1" colspan="1">50.40%</td><td align="char" rowspan="1" colspan="1">6 717 285</td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td><td align="char" rowspan="1" colspan="1">41 647</td><td align="left" rowspan="1" colspan="1">0.62%</td></tr><tr><td colspan="8" align="left" rowspan="1"><bold>Scenario B</bold></td></tr><tr><td align="left" rowspan="1" colspan="1">Population living in households</td><td align="char" rowspan="1" colspan="1">13 056 114</td><td align="left" rowspan="1" colspan="1">50.06%</td><td align="char" rowspan="1" colspan="1">6 536 054</td><td align="left" rowspan="1" colspan="1">Svy Est</td><td align="left" rowspan="1" colspan="1">0.62%</td><td align="char" rowspan="1" colspan="1">40 524</td><td align="left" rowspan="1" colspan="1"></td></tr><tr><td align="left" rowspan="1" colspan="1">Non-household population</td><td align="char" rowspan="1" colspan="1">271 833</td><td align="left" rowspan="1" colspan="1">66.67%</td><td align="char" rowspan="1" colspan="1">181 231</td><td align="left" rowspan="1" colspan="1">NHH% = 10*Svy%</td><td align="left" rowspan="1" colspan="1">6.20%</td><td align="char" rowspan="1" colspan="1">11 236</td><td align="left" rowspan="1" colspan="1"></td></tr><tr><td align="left" rowspan="1" colspan="1">Total population</td><td align="char" rowspan="1" colspan="1">13 327 947</td><td align="left" rowspan="1" colspan="1">50.40%</td><td align="char" rowspan="1" colspan="1">6 717 285</td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td><td align="char" rowspan="1" colspan="1">51 760</td><td align="left" rowspan="1" colspan="1">0.77%</td></tr><tr><td colspan="8" align="left" rowspan="1"><bold>Scenario C</bold></td></tr><tr><td align="left" rowspan="1" colspan="1">Population living in households</td><td align="char" rowspan="1" colspan="1">13 056 114</td><td align="left" rowspan="1" colspan="1">49.89%</td><td align="char" rowspan="1" colspan="1">6 513 411</td><td align="left" rowspan="1" colspan="1">Svy Est</td><td align="left" rowspan="1" colspan="1">0.62%</td><td align="char" rowspan="1" colspan="1">40 383</td><td align="left" rowspan="1" colspan="1"></td></tr><tr><td align="left" rowspan="1" colspan="1">Non-household population</td><td align="char" rowspan="1" colspan="1">271 833</td><td align="left" rowspan="1" colspan="1">75.00%</td><td align="char" rowspan="1" colspan="1">203 875</td><td align="left" rowspan="1" colspan="1">NHH% = 20*Svy%</td><td align="left" rowspan="1" colspan="1">12.40%</td><td align="char" rowspan="1" colspan="1">25 280</td><td align="left" rowspan="1" colspan="1"></td></tr><tr><td align="left" rowspan="1" colspan="1">Total population</td><td align="char" rowspan="1" colspan="1">13 327 947</td><td align="left" rowspan="1" colspan="1">50.40%</td><td align="char" rowspan="1" colspan="1">6 717 285</td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td><td align="char" rowspan="1" colspan="1">65 664</td><td align="left" rowspan="1" colspan="1">0.98%</td></tr><tr><td colspan="8" align="left" rowspan="1"><bold>Ghana 2003</bold></td></tr><tr><td colspan="8" align="left" rowspan="1"><bold>Scenario A (baseline)</bold></td></tr><tr><td align="left" rowspan="1" colspan="1">Population living in households</td><td align="char" rowspan="1" colspan="1">18 382 204</td><td align="left" rowspan="1" colspan="1">48.60%</td><td align="char" rowspan="1" colspan="1">8 933 751</td><td align="left" rowspan="1" colspan="1">Svy Est</td><td align="left" rowspan="1" colspan="1">2.16%</td><td align="char" rowspan="1" colspan="1">192 969</td><td align="left" rowspan="1" colspan="1"></td></tr><tr><td align="left" rowspan="1" colspan="1">Non-household population</td><td align="char" rowspan="1" colspan="1">19 982</td><td align="left" rowspan="1" colspan="1">48.60%</td><td align="char" rowspan="1" colspan="1">9711</td><td align="left" rowspan="1" colspan="1">NHH% = Svy%</td><td align="left" rowspan="1" colspan="1">2.16%</td><td align="char" rowspan="1" colspan="1">210</td><td align="left" rowspan="1" colspan="1"></td></tr><tr><td align="left" rowspan="1" colspan="1">Total population</td><td align="char" rowspan="1" colspan="1">18 402 186</td><td align="left" rowspan="1" colspan="1">48.60%</td><td align="char" rowspan="1" colspan="1">8 943 462</td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td><td align="char" rowspan="1" colspan="1">193 179</td><td align="left" rowspan="1" colspan="1">2.16%</td></tr><tr><td colspan="8" align="left" rowspan="1"><bold>Scenario B</bold></td></tr><tr><td align="left" rowspan="1" colspan="1">Population living in households</td><td align="char" rowspan="1" colspan="1">18 382 204</td><td align="left" rowspan="1" colspan="1">48.58%</td><td align="char" rowspan="1" colspan="1">8 930 140</td><td align="left" rowspan="1" colspan="1">Svy Est</td><td align="left" rowspan="1" colspan="1">2.16%</td><td align="char" rowspan="1" colspan="1">192 891</td><td align="left" rowspan="1" colspan="1"></td></tr><tr><td align="left" rowspan="1" colspan="1">Non-household population</td><td align="char" rowspan="1" colspan="1">19 982</td><td align="left" rowspan="1" colspan="1">66.67%</td><td align="char" rowspan="1" colspan="1">13 322</td><td align="left" rowspan="1" colspan="1">NHH% = 5*Svy%</td><td align="left" rowspan="1" colspan="1">10.80%</td><td align="char" rowspan="1" colspan="1">1439</td><td align="left" rowspan="1" colspan="1"></td></tr><tr><td align="left" rowspan="1" colspan="1">Total population</td><td align="char" rowspan="1" colspan="1">18 402 186</td><td align="left" rowspan="1" colspan="1">48.60%</td><td align="char" rowspan="1" colspan="1">8 943 462</td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td><td align="char" rowspan="1" colspan="1">194 330</td><td align="left" rowspan="1" colspan="1">2.17%</td></tr><tr><td colspan="8" align="left" rowspan="1"><bold>Scenario C</bold></td></tr><tr><td align="left" rowspan="1" colspan="1">Population living in households</td><td align="char" rowspan="1" colspan="1">18 382 204</td><td align="left" rowspan="1" colspan="1">48.57%</td><td align="char" rowspan="1" colspan="1">8 928 476</td><td align="left" rowspan="1" colspan="1">Svy Est</td><td align="left" rowspan="1" colspan="1">2.16%</td><td align="char" rowspan="1" colspan="1">192 855</td><td align="left" rowspan="1" colspan="1"></td></tr><tr><td align="left" rowspan="1" colspan="1">Non-household population</td><td align="char" rowspan="1" colspan="1">19 982</td><td align="left" rowspan="1" colspan="1">75.00%</td><td align="char" rowspan="1" colspan="1">14 987</td><td align="left" rowspan="1" colspan="1">NHH% = 10*Svy%</td><td align="left" rowspan="1" colspan="1">21.60%</td><td align="char" rowspan="1" colspan="1">3237</td><td align="left" rowspan="1" colspan="1"></td></tr><tr><td align="left" rowspan="1" colspan="1">Total population</td><td align="char" rowspan="1" colspan="1">18 402 186</td><td align="left" rowspan="1" colspan="1">48.60%</td><td align="char" rowspan="1" colspan="1">8 943 462</td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td><td align="char" rowspan="1" colspan="1">196 092</td><td align="left" rowspan="1" colspan="1">2.19%</td></tr><tr><td colspan="8" align="left" rowspan="1"><bold>Uganda 2004–5</bold></td></tr><tr><td colspan="8" align="left" rowspan="1"><bold>Scenario A (baseline</bold>)</td></tr><tr><td align="left" rowspan="1" colspan="1">Population living in households</td><td align="char" rowspan="1" colspan="1">26 172 937</td><td align="left" rowspan="1" colspan="1">42.30%</td><td align="char" rowspan="1" colspan="1">11 071 153</td><td align="left" rowspan="1" colspan="1">Svy Est</td><td align="left" rowspan="1" colspan="1">6.38%</td><td align="char" rowspan="1" colspan="1">706 340</td><td align="left" rowspan="1" colspan="1"></td></tr><tr><td align="left" rowspan="1" colspan="1">Non-household population</td><td align="char" rowspan="1" colspan="1">423 793</td><td align="left" rowspan="1" colspan="1">42.30%</td><td align="char" rowspan="1" colspan="1">179 264</td><td align="left" rowspan="1" colspan="1">NHH% = Svy%</td><td align="left" rowspan="1" colspan="1">6.38%</td><td align="char" rowspan="1" colspan="1">11 437</td><td align="left" rowspan="1" colspan="1"></td></tr><tr><td align="left" rowspan="1" colspan="1">Total population</td><td align="char" rowspan="1" colspan="1">26 596 730</td><td align="left" rowspan="1" colspan="1">42.30%</td><td align="char" rowspan="1" colspan="1">11 250 417</td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td><td align="char" rowspan="1" colspan="1">717 777</td><td align="left" rowspan="1" colspan="1">6.38%</td></tr><tr><td colspan="8" align="left" rowspan="1"><bold>Scenario B</bold></td></tr><tr><td align="left" rowspan="1" colspan="1">Population living in households</td><td align="char" rowspan="1" colspan="1">26 172 937</td><td align="left" rowspan="1" colspan="1">41.91%</td><td align="char" rowspan="1" colspan="1">10 967 874</td><td align="left" rowspan="1" colspan="1">Svy Est</td><td align="left" rowspan="1" colspan="1">6.38%</td><td align="char" rowspan="1" colspan="1">699 750</td><td align="left" rowspan="1" colspan="1"></td></tr><tr><td align="left" rowspan="1" colspan="1">Non-household population</td><td align="char" rowspan="1" colspan="1">423 793</td><td align="left" rowspan="1" colspan="1">66.67%</td><td align="char" rowspan="1" colspan="1">282 542</td><td align="left" rowspan="1" colspan="1">NHH% = 2*Svy%</td><td align="left" rowspan="1" colspan="1">12.76%</td><td align="char" rowspan="1" colspan="1">36 052</td><td align="left" rowspan="1" colspan="1"></td></tr><tr><td align="left" rowspan="1" colspan="1">Total population</td><td align="char" rowspan="1" colspan="1">26 596 730</td><td align="left" rowspan="1" colspan="1">42.30%</td><td align="char" rowspan="1" colspan="1">11 250 417</td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td><td align="char" rowspan="1" colspan="1">735 803</td><td align="left" rowspan="1" colspan="1">6.54%</td></tr><tr><td colspan="8" align="left" rowspan="1"><bold>Scenario C</bold></td></tr><tr><td align="left" rowspan="1" colspan="1">Population living in households</td><td align="char" rowspan="1" colspan="1">26 172 937</td><td align="left" rowspan="1" colspan="1">41.77%</td><td align="char" rowspan="1" colspan="1">10 932 572</td><td align="left" rowspan="1" colspan="1">Svy Est</td><td align="left" rowspan="1" colspan="1">6.38%</td><td align="char" rowspan="1" colspan="1">697 498</td><td align="left" rowspan="1" colspan="1"></td></tr><tr><td align="left" rowspan="1" colspan="1">Non-household population</td><td align="char" rowspan="1" colspan="1">423 793</td><td align="left" rowspan="1" colspan="1">75.00%</td><td align="char" rowspan="1" colspan="1">317 844</td><td align="left" rowspan="1" colspan="1">NHH% = 4*Svy%</td><td align="left" rowspan="1" colspan="1">25.52%</td><td align="char" rowspan="1" colspan="1">81 114</td><td align="left" rowspan="1" colspan="1"></td></tr><tr><td align="left" rowspan="1" colspan="1">Total population</td><td align="char" rowspan="1" colspan="1">26 596 730</td><td align="left" rowspan="1" colspan="1">42.30%</td><td align="char" rowspan="1" colspan="1">11 250 417</td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td><td align="char" rowspan="1" colspan="1">778 612</td><td align="left" rowspan="1" colspan="1">6.92%</td></tr><tr><td colspan="8" align="left" rowspan="1"><bold>Lesotho 2004</bold></td></tr><tr><td colspan="8" align="left" rowspan="1"><bold>Scenario A (baseline)</bold></td></tr><tr><td align="left" rowspan="1" colspan="1">Population living in households</td><td align="char" rowspan="1" colspan="1">1 866 354</td><td align="left" rowspan="1" colspan="1">50.90%</td><td align="char" rowspan="1" colspan="1">949 974</td><td align="left" rowspan="1" colspan="1">Svy Est</td><td align="left" rowspan="1" colspan="1">23.20%</td><td align="char" rowspan="1" colspan="1">220 394</td><td align="left" rowspan="1" colspan="1"></td></tr><tr><td align="left" rowspan="1" colspan="1">Non-household population</td><td align="char" rowspan="1" colspan="1">7913</td><td align="left" rowspan="1" colspan="1">50.90%</td><td align="char" rowspan="1" colspan="1">4028</td><td align="left" rowspan="1" colspan="1">NHH% = Svy%</td><td align="left" rowspan="1" colspan="1">23.20%</td><td align="char" rowspan="1" colspan="1">934</td><td align="left" rowspan="1" colspan="1"></td></tr><tr><td align="left" rowspan="1" colspan="1">Total population</td><td align="char" rowspan="1" colspan="1">1 874 267</td><td align="left" rowspan="1" colspan="1">50.90%</td><td align="char" rowspan="1" colspan="1">954 002</td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td><td align="char" rowspan="1" colspan="1">221 328</td><td align="left" rowspan="1" colspan="1">23.20%</td></tr><tr><td colspan="8" align="left" rowspan="1"><bold>Scenario B</bold></td></tr><tr><td align="left" rowspan="1" colspan="1">Population living in households</td><td align="char" rowspan="1" colspan="1">1 866 354</td><td align="left" rowspan="1" colspan="1">50.83%</td><td align="char" rowspan="1" colspan="1">948 726</td><td align="left" rowspan="1" colspan="1">Svy Est</td><td align="left" rowspan="1" colspan="1">23.20%</td><td align="char" rowspan="1" colspan="1">220 104</td><td align="left" rowspan="1" colspan="1"></td></tr><tr><td align="left" rowspan="1" colspan="1">Non-household population</td><td align="char" rowspan="1" colspan="1">7913</td><td align="left" rowspan="1" colspan="1">66.67%</td><td align="char" rowspan="1" colspan="1">5276</td><td align="left" rowspan="1" colspan="1">NHH% = 1.5*Svy%</td><td align="left" rowspan="1" colspan="1">34.80%</td><td align="char" rowspan="1" colspan="1">1836</td><td align="left" rowspan="1" colspan="1"></td></tr><tr><td align="left" rowspan="1" colspan="1">Total population</td><td align="char" rowspan="1" colspan="1">1 874 267</td><td align="left" rowspan="1" colspan="1">50.90%</td><td align="char" rowspan="1" colspan="1">954 002</td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td><td align="char" rowspan="1" colspan="1">221 940</td><td align="left" rowspan="1" colspan="1">23.26%</td></tr><tr><td colspan="8" align="left" rowspan="1"><bold>Scenario C</bold></td></tr><tr><td align="left" rowspan="1" colspan="1">Population living in households</td><td align="char" rowspan="1" colspan="1">1 866 354</td><td align="left" rowspan="1" colspan="1">50.80%</td><td align="char" rowspan="1" colspan="1">948 067</td><td align="left" rowspan="1" colspan="1">Svy Est</td><td align="left" rowspan="1" colspan="1">23.20%</td><td align="char" rowspan="1" colspan="1">219 952</td><td align="left" rowspan="1" colspan="1"></td></tr><tr><td align="left" rowspan="1" colspan="1">Non-household population</td><td align="char" rowspan="1" colspan="1">7913</td><td align="left" rowspan="1" colspan="1">75.00%</td><td align="char" rowspan="1" colspan="1">5935</td><td align="left" rowspan="1" colspan="1">NHH% = 2*Svy%</td><td align="left" rowspan="1" colspan="1">46.40%</td><td align="char" rowspan="1" colspan="1">2754</td><td align="left" rowspan="1" colspan="1"></td></tr><tr><td align="left" rowspan="1" colspan="1">Total population</td><td align="char" rowspan="1" colspan="1">1 874 267</td><td align="left" rowspan="1" colspan="1">50.90%</td><td align="char" rowspan="1" colspan="1">954 002</td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td><td align="char" rowspan="1" colspan="1">222 705</td><td align="left" rowspan="1" colspan="1">23.34%</td></tr></tbody></table><table-wrap-foot><fn id="nt105"><p>NHH: non-household; Svy: survey; Est: estimate.</p></fn><fn id="nt106"><p>Census data on the size of total population, size of non-household population and proportion of adults in the national population were obtained from India 2001 Census, Cambodia 2004 Inter-Censal Survey, Ghana 2000 Census, Uganda 2002 Census, Lesotho 2006 Census.</p></fn><fn id="nt107"><p>In India, the actual proportion of non-household adults in the census was 56.15%, much lower than 66.67% and 75.00% assumed in scenarios B and C.</p></fn><fn id="nt108"><p>In Ghana, the estimate of non-household population refers to homeless population only.</p></fn><fn id="nt109"><p>In Lesotho, the estimate of non-household population refers to institutional population only.</p></fn></table-wrap-foot></table-wrap><p>In India, for example, under scenario B where the proportion of adults age 15–49 in the non-household population is assumed to be 67% and the HIV prevalence among non-household adults is assumed to be 10 times the prevalence among household adults (2.80%), the estimated HIV prevalence among all adults increases only slightly, from 0.28% to 0.31%. Under scenario C, where the proportion of adults in the non-household population is assumed to be 75% and the HIV prevalence among non-household adults is assumed to be 20 times the prevalence among household adults (5.60%), the estimated HIV prevalence among all adults increases to 0.35%. Similarly, in Cambodia, the observed HIV prevalence in the survey (0.62%) increases to 0.77% under scenario B and 0.98% under scenario C. In Ghana, Uganda and Lesotho, with much higher levels of HIV prevalence, estimated bias because of exclusion of non-household population groups tends to be relatively smaller.</p></sec></sec><sec id="s4"><title>DISCUSSION</title><p>HIV response rates for females were considerably higher than for males in all countries. The lower response rates for males mainly reflect more frequent absence of men from the households. In 12 of the 14 countries, the HIV non-response rate because of absence was two to four times greater for males than for females. Non-response rates were higher among urban, more-educated and wealthier respondents. These patterns of non-response are typical of most household surveys in developing countries. However, there were no clear patterns in non-response rates by various risk and protective factors. Chronically ill adults were equally or more likely to participate in the surveys, suggesting that differential participation of chronically ill adults is unlikely to be a major source of bias.</p><p>The non-responder males and females tend to have higher predicted HIV prevalence than those tested. In eight of the 14 countries for males and in seven of the 14 countries for females, non-responders have significantly higher predicted prevalence, but consistent with previous research, the overall effects of non-response on the observed national HIV prevalence estimates are small and insignificant in all 14 countries.<xref ref-type="bibr" rid="b4">4</xref> <xref ref-type="bibr" rid="b17">17</xref><sup>–</sup><xref ref-type="bibr" rid="b19">19</xref> The small effects of the non-response bias on the observed national estimates are due mainly to a much smaller proportion of non-responders than those who were tested in the surveys. The effects of non-response are somewhat greater among lower prevalence countries for both males and females.</p><p>Our analysis of potential bias in the national HIV prevalence estimates because of the exclusion of non-household population in five countries indicated that exclusion of non-household population groups in the surveys is likely to have only a minimal effect on the observed national HIV prevalence estimates. This bias is expected to be greater in countries with concentrated epidemics. Our analysis shows that even in countries with concentrated epidemics (for example, India with a survey HIV prevalence estimate of 0.28%), HIV prevalence in the non-household groups needs to be orders of magnitude higher for it to have any significant effect on the national estimate based on the household sample.</p><p>In the analysis of the non-response bias, a major limitation is that the estimates are only adjusted to the extent that the sociodemographic and behavioural characteristics included in the analysis are correlated with the risk of HIV infection. Despite including about 30 predictor variables in the regression models, only about 20% of variation in HIV prevalence is explained in most countries, indicating the limitation of such modelling in explaining behavioural health outcomes. Another limitation is that the adjustments for not interviewed, not tested respondents are based on limited information available from the household questionnaire. Future surveys should attempt to collect additional information on this group (mostly absentees) to better assess potential bias due to their exclusion.</p><p>Our analysis is based on de facto household-based sample of the national population. A de facto sample assumes that usual residents (de jure household members) who did not spend the previous night in their own household are, on average, interviewed in a household they may be visiting. A de facto sample maximises participation rates and avoids potential double counting of respondents. HIV seroprevalence estimates based on de facto samples may be biased to the extent some of the de jure household members who slept away may not be visiting another household and to the extent such people have differential HIV prevalence.</p><p>Furthermore, the adjustments for bias because of non-response and exclusion of non-household population groups do not account for a small proportion (usually 1–3%) of sampled households that were not interviewed in the surveys. Finally, the assumptions regarding HIV prevalence and the proportion of adults in the non-household population are arbitrary. However, in India where information on the age structure of non-household population was available from the census, the proportion of adults (15–49) in the non-household population was much lower (56%) than the assumed levels of 67% and 75% in the analysis. Moreover, because males tend to have lower prevalence than females and because a great majority of the institutional and homeless population tends to be males, our assumptions of 10 and 20 times greater prevalence among non-household adults seems reasonable.</p><p>Our analyses suggest that population-based surveys provide reliable, nationally representative direct estimates of HIV seroprevalence in countries with generalised epidemics. HIV prevalence data from population-based surveys can be useful in understanding the magnitude and spread of the epidemics and in calibrating estimates from sentinel surveillance.</p><boxed-text position="float"><sec id="s4a1"><title>Key messages</title><list list-type="bullet"><list-item><p>Non-response in national household surveys tends to have small, not significant effects on HIV seroprevalence estimates based on tested respondents.</p></list-item><list-item><p>Exclusion of institutional and homeless population in the surveys is likely to have only a minimal effect on national HIV seroprevalence estimates from household surveys.</p></list-item><list-item><p>Population-based surveys provide reliable, nationally representative direct estimates of HIV seroprevalence in countries with generalised epidemics.</p></list-item></list></sec></boxed-text></sec></body><back><ack><p>We acknowledge useful discussions with Martin Vaessen, Ann Way, Anne Cross and Benny Kottiri on the analysis. We thank Yuan Gu for research assistance.</p></ack><fn-group><fn fn-type="financial-disclosure"><p><bold>Funding:</bold> Funding for this research was provided by the United States Agency for International Development (USAID) and the President’s Emergency Plan for AIDS Relief (PEPFAR) through the MEASURE Demographic and Health Surveys project.</p></fn><fn fn-type="conflict"><p><bold>Competing interests:</bold> All four authors work for Macro International Inc, the agency that helps to conduct demographic and health surveys and AIDS indicator surveys around the world.</p></fn><fn fn-type="other"><p><bold>Contributors:</bold> VM planned and drafted the paper. RH and SK carried out the analysis and contributed to revising the paper. BB contributed to the conceptualisation of the analysis and revising the paper.</p><p><bold>Disclaimer:</bold> The views presented in this manuscript are those of the authors and do not necessarily represent the views of the funding agencies or the organisations to which the authors belong.</p></fn></fn-group><ref-list><title>REFERENCES</title><ref id="b1"><label>1</label><mixed-citation publication-type="book"><collab>WHO and UNAIDS</collab> <source>Guidelines for measuring national HIV prevalence in population-based surveys</source>. <publisher-loc>Geneva</publisher-loc>: <publisher-name>WHO and UNAIDS</publisher-name>, <year>2005</year></mixed-citation></ref><ref id="b2"><label>2</label><mixed-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Stover</surname><given-names>J</given-names></name><name><surname>Ghys</surname><given-names>PD</given-names></name><name><surname>Walker</surname><given-names>N</given-names></name></person-group> <article-title>Testing the accuracy of demographic estimates in countries with generalized epidemics.</article-title> <source>AIDS</source> <year>2004</year>;<volume>18</volume><issue>(Suppl 2)</issue>:<fpage>S67</fpage>–<lpage>73</lpage><pub-id pub-id-type="pmid">15319745</pub-id></mixed-citation></ref><ref id="b3"><label>3</label><mixed-citation publication-type="journal"><comment><ext-link ext-link-type="uri" xlink:href="http://www.measuredhs.com">http://www.measuredhs.com</ext-link> (accessed 25 April 2008)</comment></mixed-citation></ref><ref id="b4"><label>4</label><mixed-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Mishra</surname><given-names>V</given-names></name><name><surname>Vaessen</surname><given-names>M</given-names></name><name><surname>Boerma</surname><given-names>JT</given-names></name><etal></etal></person-group> <article-title>HIV testing in national population-based surveys: experience from the demographic and health surveys.</article-title> <source>Bull World Health Organ</source> <year>2006</year>;<volume>84</volume>:<fpage>537</fpage>–<lpage>45</lpage><pub-id pub-id-type="pmid">16878227</pub-id></mixed-citation></ref><ref id="b5"><label>5</label><mixed-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Walker</surname><given-names>N</given-names></name><name><surname>Grassly</surname><given-names>NC</given-names></name><name><surname>Garnett</surname><given-names>GP</given-names></name><etal></etal></person-group> <article-title>Estimating the global burden of HIV/AIDS: what do we really know about the HIV pandemic?</article-title> <source>Lancet</source> <year>2004</year>;<volume>363</volume>:<fpage>2180</fpage>–<lpage>5</lpage><pub-id pub-id-type="pmid">15220043</pub-id></mixed-citation></ref><ref id="b6"><label>6</label><mixed-citation publication-type="journal"><person-group person-group-type="author"><name><surname>García-Calleja</surname><given-names>JM</given-names></name><name><surname>Gouws</surname><given-names>E</given-names></name><name><surname>Ghys</surname><given-names>PD</given-names></name></person-group> <article-title>National population based HIV prevalence surveys in sub-Saharan Africa: results and implications for HIV and AIDS estimates.</article-title> <source>Sex Transm Infect</source> <year>2006</year>;<volume>82</volume><issue>(Suppl 3)</issue>:<fpage>iii64</fpage>–<lpage>70</lpage><pub-id pub-id-type="pmid">16735296</pub-id></mixed-citation></ref><ref id="b7"><label>7</label><mixed-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Boerma</surname><given-names>JT</given-names></name><name><surname>Ghys</surname><given-names>PD</given-names></name><name><surname>Walker</surname><given-names>N</given-names></name></person-group> <article-title>Estimates of HIV-1 prevalence from national population-based surveys as a new gold standard.</article-title> <source>Lancet</source> <year>2003</year>;<volume>362</volume>:<fpage>1929</fpage>–<lpage>31</lpage><pub-id pub-id-type="pmid">14667753</pub-id></mixed-citation></ref><ref id="b8"><label>8</label><mixed-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Buve</surname><given-names>A</given-names></name><name><surname>Lagarde</surname><given-names>E</given-names></name><name><surname>Carael</surname><given-names>M</given-names></name><etal></etal></person-group> <article-title>Interpreting sexual behavior data: validity issues in the multicentre study on factors determining the differential spread of HIV in four African cities.</article-title> <source>AIDS</source> <year>2001</year>;<volume>15</volume><issue>(Suppl 4)</issue>:<fpage>S117</fpage>–<lpage>26</lpage><pub-id pub-id-type="pmid">11686460</pub-id></mixed-citation></ref><ref id="b9"><label>9</label><mixed-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Lydié</surname><given-names>N</given-names></name><name><surname>Robinson</surname><given-names>NJ</given-names></name><name><surname>Ferry</surname><given-names>B</given-names></name><etal></etal></person-group> <article-title>Mobility, sexual behavior, and HIV infection in an urban population in Cameroon.</article-title> <source>J Acquir Immune Defic Syndr</source> <year>2004</year>;<volume>35</volume>:<fpage>67</fpage>–<lpage>74</lpage><pub-id pub-id-type="pmid">14707795</pub-id></mixed-citation></ref><ref id="b10"><label>10</label><mixed-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Lagarde</surname><given-names>E</given-names></name><name><surname>Schim van der Loeff</surname><given-names>M</given-names></name><name><surname>Enel</surname><given-names>C</given-names></name><etal></etal></person-group> <article-title>Mobility and the spread of human immunodeficiency virus into rural areas of West Africa.</article-title> <source>Int J Epidemiol</source> <year>2003</year>;<volume>32</volume>:<fpage>744</fpage>–<lpage>52</lpage> <comment>Comment in: Int J Epidemiol 2003;<bold>32</bold>:753–4</comment></mixed-citation></ref><ref id="b11"><label>11</label><mixed-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Lurie</surname><given-names>MN</given-names></name><name><surname>Williams</surname><given-names>BG</given-names></name><name><surname>Zuma</surname><given-names>K</given-names></name><etal></etal></person-group> <article-title>The impact of migration on HIV-1 transmission in South Africa: a study of migrant and nonmigrant men and their partners.</article-title> <source>Sex Transm Dis</source> <year>2003</year>;<volume>30</volume>:<fpage>149</fpage>–<lpage>56</lpage><pub-id pub-id-type="pmid">12567174</pub-id></mixed-citation></ref><ref id="b12"><label>12</label><mixed-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Faisel</surname><given-names>A</given-names></name><name><surname>Cleland</surname><given-names>J</given-names></name></person-group> <article-title>Migrant men: a priority for HIV control in Pakistan?</article-title> <source>Sex Transm Infect</source> <year>2006</year>;<volume>82</volume>:<fpage>307</fpage>–<lpage>10</lpage><pub-id pub-id-type="pmid">16877580</pub-id></mixed-citation></ref><ref id="b13"><label>13</label><mixed-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Zuma</surname><given-names>K</given-names></name><name><surname>Lurie</surname><given-names>MN</given-names></name><name><surname>Williams</surname><given-names>BG</given-names></name><etal></etal></person-group> <article-title>Risk factors of sexually transmitted infections among migrant and non-migrant sexual partnerships from rural South Africa.</article-title> <source>Epidemiol Infect</source> <year>2005</year>;<volume>133</volume>:<fpage>421</fpage>–<lpage>8</lpage><pub-id pub-id-type="pmid">15962548</pub-id></mixed-citation></ref><ref id="b14"><label>14</label><mixed-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Nunn</surname><given-names>AJ</given-names></name><name><surname>Wagner</surname><given-names>HU</given-names></name><name><surname>Kamali</surname><given-names>A</given-names></name><etal></etal></person-group> <article-title>Migration and HIV-1 seroprevalence in a rural Ugandan population.</article-title> <source>AIDS</source> <year>1995</year>;<volume>9</volume>:<fpage>503</fpage>–<lpage>6</lpage><pub-id pub-id-type="pmid">7639976</pub-id></mixed-citation></ref><ref id="b15"><label>15</label><mixed-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Coffee</surname><given-names>MP</given-names></name><name><surname>Garnett</surname><given-names>GP</given-names></name><name><surname>Mlilo</surname><given-names>M</given-names></name><etal></etal></person-group> <article-title>Patterns of movement and risk of HIV infection in rural Zimbabwe.</article-title> <source>J Infect Dis</source> <year>2005</year>;<volume>191</volume><issue>(Suppl 1)</issue>:<fpage>S159</fpage>–<lpage>67</lpage><pub-id pub-id-type="pmid">15627226</pub-id></mixed-citation></ref><ref id="b16"><label>16</label><mixed-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Martínez-Donate</surname><given-names>AP</given-names></name><name><surname>Rangel</surname><given-names>MG</given-names></name><name><surname>Hovell</surname><given-names>MF</given-names></name><etal></etal></person-group> <article-title>HIV infection in mobile populations: the case of Mexican migrants to the United States.</article-title> <source>Rev Panam Salud Publica</source> <year>2005</year>;<volume>17</volume>:<fpage>26</fpage>–<lpage>9</lpage><pub-id pub-id-type="pmid">15720878</pub-id></mixed-citation></ref><ref id="b17"><label>17</label><mixed-citation publication-type="journal"><person-group person-group-type="author"><name><surname>McNaghten</surname><given-names>AD</given-names></name><name><surname>Herold</surname><given-names>JM</given-names></name><name><surname>Dube</surname><given-names>HM</given-names></name><etal></etal></person-group> <article-title>Response rates for providing a blood specimen for HIV testing in a population-based survey of young adults in Zimbabwe.</article-title> <source>BMC Public Health</source> <year>2007</year>;<volume>7</volume>:<fpage>145</fpage><pub-id pub-id-type="pmid">17612395</pub-id></mixed-citation></ref><ref id="b18"><label>18</label><mixed-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Bignami-Van Assche</surname><given-names>S</given-names></name><name><surname>Salomon</surname><given-names>JA</given-names></name><name><surname>Murray</surname><given-names>CJL</given-names></name></person-group> <source>Evidence from national population-based estimates of bias in HIV prevalence</source>. <comment>Presented at Population Association of America Annual Meeting.</comment> <comment>Philadelphia, 31 March–2 April</comment> <year>2005</year></mixed-citation></ref><ref id="b19"><label>19</label><mixed-citation publication-type="book"><person-group person-group-type="author"><name><surname>Obare</surname><given-names>F</given-names></name></person-group> <source>The effect of non-response on population-based HIV prevalence estimates: the case of rural Malawi</source>. <comment>Social Networks Project Working Paper.</comment> <publisher-loc>Philadelphia</publisher-loc>: <publisher-name>University of Pennsylvania</publisher-name>, <year>2005</year></mixed-citation></ref><ref id="b20"><label>20</label><mixed-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Dandona</surname><given-names>R</given-names></name><name><surname>Dandona</surname><given-names>L</given-names></name><name><surname>Gutierrez</surname><given-names>JP</given-names></name><etal></etal></person-group> <article-title>High risk of HIV in non-brothel based female sex workers in India.</article-title> <source>BMC Public Health</source> <year>2005</year>;<volume>5</volume>:<fpage>87</fpage><pub-id pub-id-type="pmid">16111497</pub-id></mixed-citation></ref><ref id="b21"><label>21</label><mixed-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Sarkar</surname><given-names>K</given-names></name><name><surname>Bal</surname><given-names>B</given-names></name><name><surname>Mukherjee</surname><given-names>R</given-names></name><etal></etal></person-group> <article-title>Epidemiology of HIV infection among brothel-based sex workers in Kolkata, India.</article-title> <source>J Health Popul Nutr</source> <year>2005</year>;<volume>23</volume>:<fpage>231</fpage>–<lpage>5</lpage><pub-id pub-id-type="pmid">16262019</pub-id></mixed-citation></ref><ref id="b22"><label>22</label><mixed-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Chimphambano</surname><given-names>C</given-names></name><name><surname>Komolafe</surname><given-names>OO</given-names></name><name><surname>Muula</surname><given-names>AS</given-names></name></person-group> <article-title>HIV prevalence among prison inmates in a central prison in southern Malawi, 2005.</article-title> <source>Trop Doct</source> <year>2007</year>;<volume>37</volume>:<fpage>226</fpage>–<lpage>8</lpage><pub-id pub-id-type="pmid">17988486</pub-id></mixed-citation></ref><ref id="b23"><label>23</label><mixed-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Dolan</surname><given-names>K</given-names></name><name><surname>Kite</surname><given-names>B</given-names></name><name><surname>Black</surname><given-names>E</given-names></name><etal></etal></person-group> <article-title>HIV in prison in low-income and middle-income countries.</article-title> <source>Lancet Infect Dis</source> <year>2007</year>;<volume>7</volume>:<fpage>32</fpage>–<lpage>41</lpage> <comment>Comment in</comment>: <source>Lancet Infect Dis</source> <year>2007</year>;<volume>7</volume>:<fpage>1</fpage></mixed-citation></ref><ref id="b24"><label>24</label><mixed-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Wolitski</surname><given-names>RJ</given-names></name><name><surname>Kidder</surname><given-names>DP</given-names></name><name><surname>Fenton</surname><given-names>KA</given-names></name></person-group> <article-title>HIV, homelessness, and public health: critical issues and a call for increased action.</article-title> <source>AIDS Behav</source> <year>2007</year>;<volume>11</volume><issue>(Suppl 6)</issue>:<fpage>167</fpage>–<lpage>71</lpage><pub-id pub-id-type="pmid">17676279</pub-id></mixed-citation></ref><ref id="b25"><label>25</label><mixed-citation publication-type="book"><collab>Macro International</collab> <source>HIV testing field manual: demographic and health surveys</source>. <publisher-loc>Calverton, MD</publisher-loc>: <publisher-name>Macro International Inc</publisher-name>, <year>2007</year></mixed-citation></ref><ref id="b26"><label>26</label><mixed-citation publication-type="book"><collab>Registrar General of India</collab> <source>The census of India 2001</source>. <publisher-loc>New Delhi</publisher-loc>: <publisher-name>Office of the Registrar General and Census Commission, India</publisher-name>, <year>2001</year></mixed-citation></ref><ref id="b27"><label>27</label><mixed-citation publication-type="journal"><collab>Royal Government of Cambodia</collab> <source>Cambodia inter-censal population survey 2004, General report.</source> <comment>Royal Government of Cambodia</comment>, <year>2005</year></mixed-citation></ref><ref id="b28"><label>28</label><mixed-citation publication-type="journal"><comment><ext-link ext-link-type="uri" xlink:href="http://www.ghanainfo.org">http://www.ghanainfo.org</ext-link> (accessed 7 February 2008)</comment></mixed-citation></ref><ref id="b29"><label>29</label><mixed-citation publication-type="book"><collab>Uganda Bureau of Statistics</collab> <source>2002 Uganda population and housing census.</source> <comment><italic>Analytical Report</italic> Abridged version.</comment> <publisher-loc>Kampala, Uganda</publisher-loc>: <publisher-name>Uganda Bureau of Statistics</publisher-name>, <year>2006</year></mixed-citation></ref><ref id="b30"><label>30</label><mixed-citation publication-type="journal"><comment><ext-link ext-link-type="uri" xlink:href="http://www.bos.gov.ls">http://www.bos.gov.ls</ext-link> (accessed 7 February 2008)</comment></mixed-citation></ref></ref-list></back></pmc></record>Pour manipuler ce document sous Unix (Dilib)
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