Evaluation of bias in HIV seroprevalence estimates from national household surveys
Identifieur interne : 000576 ( Istex/Corpus ); précédent : 000575; suivant : 000577Evaluation of bias in HIV seroprevalence estimates from national household surveys
Auteurs : V. Mishra ; B. Barrere ; R. Hong ; S. KhanSource :
- Sexually Transmitted Infections [ 1368-4973 ] ; 2008-08.
English descriptors
- KwdEn :
- Additional variables, Adults increases, Aids indicator surveys, Behavioural characteristics, Burkina faso, Cambodia, Clear patterns, Different assumptions, Eligible respondents, Female kenya, Generalised epidemics, Health surveys, Homeless population, Household adults, Household members, Household questionnaire, Household samples, Household survey, Household surveys, Households population, Individual interview, Individual interview response rates, Lesotho, Lower prevalence countries, Major challenge, Malawi, Minimal effect, National estimates, National household surveys, National population, National seroprevalence estimates, National surveys, Nonhousehold, Nonhousehold adults, Nonhousehold population, Nonhousehold population groups, Nonresponse, Nonresponse rates, Percentage points, Population groups, Population prevalence, Potential bias, Potential impact, Predictor variables, Prevalence, Prevalence estimate, Prevalence estimates, Prevalence levels, Protective factors, Response rate, Response rates, Royal government, Rural areas, Rwanda, Sampling weights, Scenario, Seroprevalence, Seroprevalence estimates, Significant effects, Standard laboratory, Supplement table, Survey estimates, Survey year, Total population, Transm, Uganda, Uganda bureau, Various risk, Wealthier respondents, Zimbabwe.
- Teeft :
- Additional variables, Adults increases, Aids indicator surveys, Behavioural characteristics, Burkina faso, Cambodia, Clear patterns, Different assumptions, Eligible respondents, Female kenya, Generalised epidemics, Health surveys, Homeless population, Household adults, Household members, Household questionnaire, Household samples, Household survey, Household surveys, Households population, Individual interview, Individual interview response rates, Lesotho, Lower prevalence countries, Major challenge, Malawi, Minimal effect, National estimates, National household surveys, National population, National seroprevalence estimates, National surveys, Nonhousehold, Nonhousehold adults, Nonhousehold population, Nonhousehold population groups, Nonresponse, Nonresponse rates, Percentage points, Population groups, Population prevalence, Potential bias, Potential impact, Predictor variables, Prevalence, Prevalence estimate, Prevalence estimates, Prevalence levels, Protective factors, Response rate, Response rates, Royal government, Rural areas, Rwanda, Sampling weights, Scenario, Seroprevalence, Seroprevalence estimates, Significant effects, Standard laboratory, Supplement table, Survey estimates, Survey year, Total population, Transm, Uganda, Uganda bureau, Various risk, Wealthier respondents, Zimbabwe.
Abstract
Objectives: 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. Methods: 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. Results: 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. Conclusions: 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
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ISTEX:5E3F50F0C60003DCBC1A292CFE52585BD9DAE61ELe document en format XML
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Mishra</name><affiliation><mods:affiliation>Macro International Inc, Calverton, Maryland, USA</mods:affiliation></affiliation></author><author><name sortKey="Barrere, B" sort="Barrere, B" uniqKey="Barrere B" first="B" last="Barrere">B. Barrere</name><affiliation><mods:affiliation>Macro International Inc, Calverton, Maryland, USA</mods:affiliation></affiliation></author><author><name sortKey="Hong, R" sort="Hong, R" uniqKey="Hong R" first="R" last="Hong">R. Hong</name><affiliation><mods:affiliation>Macro International Inc, Calverton, Maryland, USA</mods:affiliation></affiliation></author><author><name sortKey="Khan, S" sort="Khan, S" uniqKey="Khan S" first="S" last="Khan">S. 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type="ISSN">1368-4973</idno></seriesStmt></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Additional variables</term><term>Adults increases</term><term>Aids indicator surveys</term><term>Behavioural characteristics</term><term>Burkina faso</term><term>Cambodia</term><term>Clear patterns</term><term>Different assumptions</term><term>Eligible respondents</term><term>Female kenya</term><term>Generalised epidemics</term><term>Health surveys</term><term>Homeless population</term><term>Household adults</term><term>Household members</term><term>Household questionnaire</term><term>Household samples</term><term>Household survey</term><term>Household surveys</term><term>Households population</term><term>Individual interview</term><term>Individual interview response rates</term><term>Lesotho</term><term>Lower prevalence countries</term><term>Major challenge</term><term>Malawi</term><term>Minimal effect</term><term>National estimates</term><term>National household surveys</term><term>National population</term><term>National seroprevalence estimates</term><term>National surveys</term><term>Nonhousehold</term><term>Nonhousehold adults</term><term>Nonhousehold population</term><term>Nonhousehold population groups</term><term>Nonresponse</term><term>Nonresponse rates</term><term>Percentage points</term><term>Population groups</term><term>Population prevalence</term><term>Potential bias</term><term>Potential impact</term><term>Predictor variables</term><term>Prevalence</term><term>Prevalence estimate</term><term>Prevalence estimates</term><term>Prevalence levels</term><term>Protective factors</term><term>Response rate</term><term>Response rates</term><term>Royal government</term><term>Rural areas</term><term>Rwanda</term><term>Sampling weights</term><term>Scenario</term><term>Seroprevalence</term><term>Seroprevalence estimates</term><term>Significant effects</term><term>Standard laboratory</term><term>Supplement table</term><term>Survey estimates</term><term>Survey year</term><term>Total population</term><term>Transm</term><term>Uganda</term><term>Uganda bureau</term><term>Various risk</term><term>Wealthier respondents</term><term>Zimbabwe</term></keywords><keywords scheme="Teeft" xml:lang="en"><term>Additional variables</term><term>Adults increases</term><term>Aids indicator surveys</term><term>Behavioural characteristics</term><term>Burkina faso</term><term>Cambodia</term><term>Clear patterns</term><term>Different assumptions</term><term>Eligible respondents</term><term>Female kenya</term><term>Generalised epidemics</term><term>Health surveys</term><term>Homeless population</term><term>Household adults</term><term>Household members</term><term>Household questionnaire</term><term>Household samples</term><term>Household survey</term><term>Household surveys</term><term>Households population</term><term>Individual interview</term><term>Individual interview response rates</term><term>Lesotho</term><term>Lower prevalence countries</term><term>Major challenge</term><term>Malawi</term><term>Minimal effect</term><term>National estimates</term><term>National household surveys</term><term>National population</term><term>National seroprevalence estimates</term><term>National surveys</term><term>Nonhousehold</term><term>Nonhousehold adults</term><term>Nonhousehold population</term><term>Nonhousehold population groups</term><term>Nonresponse</term><term>Nonresponse rates</term><term>Percentage points</term><term>Population groups</term><term>Population prevalence</term><term>Potential bias</term><term>Potential impact</term><term>Predictor variables</term><term>Prevalence</term><term>Prevalence estimate</term><term>Prevalence estimates</term><term>Prevalence levels</term><term>Protective factors</term><term>Response rate</term><term>Response rates</term><term>Royal government</term><term>Rural areas</term><term>Rwanda</term><term>Sampling weights</term><term>Scenario</term><term>Seroprevalence</term><term>Seroprevalence estimates</term><term>Significant effects</term><term>Standard laboratory</term><term>Supplement table</term><term>Survey estimates</term><term>Survey year</term><term>Total population</term><term>Transm</term><term>Uganda</term><term>Uganda bureau</term><term>Various risk</term><term>Wealthier respondents</term><term>Zimbabwe</term></keywords></textClass><langUsage><language ident="en">en</language></langUsage></profileDesc></teiHeader><front><div type="abstract">Objectives: 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. Methods: 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. Results: 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. Conclusions: 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.</div></front></TEI><istex><corpusName>bmj</corpusName><keywords><teeft><json:string>uganda</json:string><json:string>lesotho</json:string><json:string>seroprevalence</json:string><json:string>households population</json:string><json:string>scenario</json:string><json:string>population groups</json:string><json:string>response rates</json:string><json:string>transm</json:string><json:string>rwanda</json:string><json:string>prevalence</json:string><json:string>zimbabwe</json:string><json:string>malawi</json:string><json:string>potential bias</json:string><json:string>individual interview</json:string><json:string>seroprevalence estimates</json:string><json:string>nonresponse</json:string><json:string>nonhousehold</json:string><json:string>health surveys</json:string><json:string>prevalence estimates</json:string><json:string>household surveys</json:string><json:string>total population</json:string><json:string>cambodia</json:string><json:string>percentage points</json:string><json:string>survey estimates</json:string><json:string>supplement table</json:string><json:string>national estimates</json:string><json:string>eligible respondents</json:string><json:string>individual interview response rates</json:string><json:string>predictor variables</json:string><json:string>prevalence levels</json:string><json:string>burkina faso</json:string><json:string>household survey</json:string><json:string>national surveys</json:string><json:string>homeless population</json:string><json:string>behavioural characteristics</json:string><json:string>nonhousehold population</json:string><json:string>aids indicator surveys</json:string><json:string>national population</json:string><json:string>generalised epidemics</json:string><json:string>various risk</json:string><json:string>household questionnaire</json:string><json:string>potential impact</json:string><json:string>survey year</json:string><json:string>national household surveys</json:string><json:string>household samples</json:string><json:string>national seroprevalence estimates</json:string><json:string>significant effects</json:string><json:string>response rate</json:string><json:string>major challenge</json:string><json:string>female kenya</json:string><json:string>rural areas</json:string><json:string>nonresponse rates</json:string><json:string>wealthier respondents</json:string><json:string>clear patterns</json:string><json:string>sampling weights</json:string><json:string>protective factors</json:string><json:string>prevalence estimate</json:string><json:string>additional variables</json:string><json:string>lower prevalence countries</json:string><json:string>household adults</json:string><json:string>adults increases</json:string><json:string>nonhousehold adults</json:string><json:string>minimal effect</json:string><json:string>nonhousehold population groups</json:string><json:string>household members</json:string><json:string>different assumptions</json:string><json:string>population prevalence</json:string><json:string>standard laboratory</json:string><json:string>royal government</json:string><json:string>uganda bureau</json:string></teeft></keywords><author><json:item><name>V Mishra</name><affiliations><json:string>Macro International Inc, Calverton, Maryland, USA</json:string></affiliations></json:item><json:item><name>B Barrere</name><affiliations><json:string>Macro International Inc, Calverton, Maryland, USA</json:string></affiliations></json:item><json:item><name>R Hong</name><affiliations><json:string>Macro International Inc, Calverton, Maryland, USA</json:string></affiliations></json:item><json:item><name>S Khan</name><affiliations><json:string>Macro International Inc, Calverton, Maryland, USA</json:string></affiliations></json:item></author><articleId><json:string>st30411</json:string></articleId><language><json:string>eng</json:string></language><originalGenre><json:string>research-article</json:string></originalGenre><abstract>Objectives: 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. Methods: 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. Results: 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. Conclusions: 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.</abstract><qualityIndicators><score>7.433</score><pdfVersion>1.2</pdfVersion><pdfPageSize>595.276 x 793.701 pts</pdfPageSize><refBibsNative>false</refBibsNative><keywordCount>0</keywordCount><abstractCharCount>1776</abstractCharCount><pdfWordCount>4457</pdfWordCount><pdfCharCount>32352</pdfCharCount><pdfPageCount>8</pdfPageCount><abstractWordCount>248</abstractWordCount></qualityIndicators><title>Evaluation of bias in HIV seroprevalence estimates from national household surveys</title><pmid><json:string>18647869</json:string></pmid><genre><json:string>research-article</json:string></genre><host><title>Sexually Transmitted Infections</title><language><json:string>unknown</json:string></language><issn><json:string>1368-4973</json:string></issn><eissn><json:string>1472-3263</json:string></eissn><publisherId><json:string>sti</json:string></publisherId><volume>84</volume><issue>Suppl 1</issue><pages><first>i63</first></pages><genre><json:string>journal</json:string></genre></host><categories><wos><json:string>science</json:string><json:string>infectious diseases</json:string></wos><scienceMetrix><json:string>health sciences</json:string><json:string>public health & health services</json:string><json:string>public health</json:string></scienceMetrix><inist><json:string>sciences appliquees, technologies et medecines</json:string><json:string>sciences biologiques et medicales</json:string><json:string>sciences medicales</json:string></inist></categories><publicationDate>2008</publicationDate><copyrightDate>2008</copyrightDate><doi><json:string>10.1136/sti.2008.030411</json:string></doi><id>5E3F50F0C60003DCBC1A292CFE52585BD9DAE61E</id><score>1</score><fulltext><json:item><extension>pdf</extension><original>true</original><mimetype>application/pdf</mimetype><uri>https://api.istex.fr/document/5E3F50F0C60003DCBC1A292CFE52585BD9DAE61E/fulltext/pdf</uri></json:item><json:item><extension>zip</extension><original>false</original><mimetype>application/zip</mimetype><uri>https://api.istex.fr/document/5E3F50F0C60003DCBC1A292CFE52585BD9DAE61E/fulltext/zip</uri></json:item><istex:fulltextTEI uri="https://api.istex.fr/document/5E3F50F0C60003DCBC1A292CFE52585BD9DAE61E/fulltext/tei"><teiHeader><fileDesc><titleStmt><title level="a" type="main" xml:lang="en">Evaluation of bias in HIV seroprevalence estimates from national household surveys</title></titleStmt><publicationStmt><authority>ISTEX</authority><publisher>BMJ Publishing Group Ltd</publisher><availability><p>2008 BMJ Publishing Group Ltd</p></availability><date>2008-07-22</date></publicationStmt><sourceDesc><biblStruct type="inbook"><analytic><title level="a" type="main" xml:lang="en">Evaluation of bias in HIV seroprevalence estimates from national household surveys</title><author xml:id="author-1"><persName><forename type="first">V</forename><surname>Mishra</surname></persName><affiliation>Macro International Inc, Calverton, Maryland, USA</affiliation></author><author xml:id="author-2"><persName><forename type="first">B</forename><surname>Barrere</surname></persName><affiliation>Macro International Inc, Calverton, Maryland, USA</affiliation></author><author xml:id="author-3"><persName><forename type="first">R</forename><surname>Hong</surname></persName><affiliation>Macro International Inc, Calverton, Maryland, USA</affiliation></author><author xml:id="author-4"><persName><forename type="first">S</forename><surname>Khan</surname></persName><affiliation>Macro International Inc, Calverton, Maryland, USA</affiliation></author></analytic><monogr><title level="j">Sexually Transmitted Infections</title><title level="j" type="abbrev">Sex Transm Infect</title><idno type="pISSN">1368-4973</idno><idno type="eISSN">1472-3263</idno><imprint><publisher>BMJ Publishing Group Ltd</publisher><date type="published" when="2008-08"></date><biblScope unit="volume">84</biblScope><biblScope unit="issue">Suppl 1</biblScope><biblScope unit="page" from="63">i63</biblScope></imprint></monogr><idno type="istex">5E3F50F0C60003DCBC1A292CFE52585BD9DAE61E</idno><idno type="DOI">10.1136/sti.2008.030411</idno><idno type="href">sextrans-84-i63.pdf</idno><idno type="ArticleID">st30411</idno><idno type="PMID">18647869</idno><idno type="local">sextrans;84/Suppl_1/i63</idno></biblStruct></sourceDesc></fileDesc><profileDesc><creation><date>2008-07-22</date></creation><langUsage><language ident="en">en</language></langUsage><abstract><p>Objectives: 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. Methods: 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. Results: 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. Conclusions: 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></abstract></profileDesc><revisionDesc><change when="2008-07-22">Created</change><change when="2008-08">Published</change></revisionDesc></teiHeader></istex:fulltextTEI><json:item><extension>txt</extension><original>false</original><mimetype>text/plain</mimetype><uri>https://api.istex.fr/document/5E3F50F0C60003DCBC1A292CFE52585BD9DAE61E/fulltext/txt</uri></json:item></fulltext><metadata><istex:metadataXml wicri:clean="corpus bmj" wicri:toSee="no header"><istex:xmlDeclaration>version="1.0" encoding="UTF-8" standalone="no"</istex:xmlDeclaration><istex:docType PUBLIC="-//NLM//DTD Journal Archiving and Interchange DTD v2.3 20070202//EN" URI="archivearticle.dtd" name="istex:docType"></istex:docType><istex:document><article article-type="research-article" xml:lang="EN"><front><journal-meta><journal-id journal-id-type="hwp">sextrans</journal-id><journal-id journal-id-type="nlm-ta">Sex Transm Infect</journal-id><journal-id journal-id-type="publisher-id">sti</journal-id><journal-title>Sexually Transmitted Infections</journal-title><abbrev-journal-title abbrev-type="publisher">Sex Transm Infect</abbrev-journal-title><issn pub-type="ppub">1368-4973</issn><issn pub-type="epub">1472-3263</issn><publisher><publisher-name>BMJ Publishing Group Ltd</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="publisher-id">st30411</article-id><article-id pub-id-type="doi">10.1136/sti.2008.030411</article-id><article-id pub-id-type="other">sextrans;84/Suppl_1/i63</article-id><article-id pub-id-type="pmid">18647869</article-id><article-id pub-id-type="other">i63</article-id><article-id pub-id-type="other">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" xlink:type="simple"><name name-style="western"><surname>Mishra</surname><given-names>V</given-names></name></contrib><contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Barrere</surname><given-names>B</given-names></name></contrib><contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Hong</surname><given-names>R</given-names></name></contrib><contrib contrib-type="author" xlink:type="simple"><name name-style="western"><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>Dr Vinod Mishra, Demographic and Health Research Division, Macro International Inc, 11785 Beltsville Drive, Calverton, Maryland 20705, USA; <email xlink:type="simple">vinod.mishra@macrointernational.com</email></corresp></author-notes><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><volume>84</volume><volume-id pub-id-type="other">84</volume-id><volume-id pub-id-type="other">84</volume-id><issue>Suppl 1</issue><issue-id pub-id-type="other">sextrans;84/Suppl_1</issue-id><issue-id pub-id-type="other" content-type="supplement">Suppl_1</issue-id><issue-id pub-id-type="other">84/Suppl_1</issue-id><issue-title>Improved data, methods and tools for the 2007 HIV and AIDS estimates and projections</issue-title><fpage>i63</fpage><history><date date-type="accepted"><day>15</day><month>5</month><year>2008</year></date></history><permissions><copyright-statement>2008 BMJ Publishing Group Ltd</copyright-statement><copyright-year>2008</copyright-year><license license-type="open-access" xlink:type="simple"><p></p></license></permissions><self-uri content-type="pdf" xlink:role="full-text" xlink:href="sextrans-84-i63.pdf"></self-uri><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></article></istex:document></istex:metadataXml><mods version="3.6"><titleInfo lang="en"><title>Evaluation of bias in HIV seroprevalence estimates from national household surveys</title></titleInfo><titleInfo type="alternative" lang="en" contentType="CDATA"><title>Evaluation of bias in HIV seroprevalence estimates from national household surveys</title></titleInfo><name type="personal"><namePart type="given">V</namePart><namePart type="family">Mishra</namePart><affiliation>Macro International Inc, Calverton, Maryland, USA</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">B</namePart><namePart type="family">Barrere</namePart><affiliation>Macro International Inc, Calverton, Maryland, USA</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">R</namePart><namePart type="family">Hong</namePart><affiliation>Macro International Inc, Calverton, Maryland, USA</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">S</namePart><namePart type="family">Khan</namePart><affiliation>Macro International Inc, Calverton, Maryland, USA</affiliation><role><roleTerm type="text">author</roleTerm></role></name><typeOfResource>text</typeOfResource><genre type="research-article" displayLabel="research-article"></genre><originInfo><publisher>BMJ Publishing Group Ltd</publisher><dateIssued encoding="w3cdtf">2008-08</dateIssued><dateCreated encoding="w3cdtf">2008-07-22</dateCreated><copyrightDate encoding="w3cdtf">2008</copyrightDate></originInfo><language><languageTerm type="code" authority="iso639-2b">eng</languageTerm><languageTerm type="code" authority="rfc3066">en</languageTerm></language><physicalDescription><internetMediaType>text/html</internetMediaType></physicalDescription><abstract>Objectives: 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. Methods: 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. Results: 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. Conclusions: 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.</abstract><relatedItem type="host"><titleInfo><title>Sexually Transmitted Infections</title></titleInfo><titleInfo type="abbreviated"><title>Sex Transm Infect</title></titleInfo><genre type="journal">journal</genre><identifier type="ISSN">1368-4973</identifier><identifier type="eISSN">1472-3263</identifier><identifier type="PublisherID">sti</identifier><identifier type="PublisherID-hwp">sextrans</identifier><identifier type="PublisherID-nlm-ta">Sex Transm Infect</identifier><part><date>2008</date><detail type="title"><title>Improved data, methods and tools for the 2007 HIV and AIDS estimates and projections</title></detail><detail type="volume"><caption>vol.</caption><number>84</number></detail><detail type="issue"><caption>no.</caption><number>Suppl 1</number></detail><extent unit="pages"><start>i63</start></extent></part></relatedItem><identifier type="istex">5E3F50F0C60003DCBC1A292CFE52585BD9DAE61E</identifier><identifier type="DOI">10.1136/sti.2008.030411</identifier><identifier type="href">sextrans-84-i63.pdf</identifier><identifier type="ArticleID">st30411</identifier><identifier type="PMID">18647869</identifier><identifier type="local">sextrans;84/Suppl_1/i63</identifier><accessCondition type="use and reproduction" contentType="copyright">2008 BMJ Publishing Group Ltd</accessCondition><recordInfo><recordContentSource>BMJ</recordContentSource></recordInfo></mods></metadata><serie></serie></istex></record>Pour manipuler ce document sous Unix (Dilib)
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