An Appraisal of Female Sex Work in Nigeria - Implications for Designing and Scaling Up HIV Prevention Programmes
Identifieur interne : 002B22 ( Pmc/Corpus ); précédent : 002B21; suivant : 002B23An Appraisal of Female Sex Work in Nigeria - Implications for Designing and Scaling Up HIV Prevention Programmes
Auteurs : Akudo Ikpeazu ; Amaka Momah-Haruna ; Baba Madu Mari ; Laura H. Thompson ; Kayode Ogungbemi ; Uduak Daniel ; Hafsatu Aboki ; Shajy Isac ; Marelize Gorgens ; Elizabeth Mziray ; Ndella Njie ; Francisca Ayodeji Akala ; Faran Emmanuel ; Willis Omondi Odek ; James F. BlanchardSource :
- PLoS ONE [ 1932-6203 ] ; 2014.
Abstract
The HIV epidemic in Nigeria is complex with diverse factors driving the epidemic. Accordingly, Nigeria's National Agency for the Control of AIDS is coordinating a large-scale initiative to conduct HIV epidemic appraisals across all states. These appraisals will help to better characterize the drivers of the epidemic and ensure that the HIV prevention programmes match the local epidemic context, with resources allocated to interventions that have the greatest impact locally. Currently, the mapping and size estimation of Female Sex Workers (FSWs) - a major component of the appraisal has been completed in seven states. These states are using the data generated to plan, prioritize and scale-up sub-national HIV prevention programmes.
It involved a two-level process of identifying and validating locations where FSWs solicit and/or meet clients (“hotspots”). In the first level, secondary key informants were interviewed to collect information about the geographic location and description of the hotspots. For the second level, FSWs were interviewed at each hotspot and information on population size estimates, typologies and operational dynamics of the FSWs were collected.
Across the seven states, a total of 17,266 secondary key informants and 5,732 FSWs were interviewed. 10,233 hotspots were identified with an estimated 126,489 FSWs ranging from 5,920 in Anambra to 46,691 in Lagos. The most common hotspots were bars/nightclubs (30%), hotels/lodges (29.6%), streets (16.6%), and brothels (14.6%). Furthermore, the population density of FSWs (per thousand adult men) across the states ranged from 2 in Anambra to 17 in the Federal Capital Territory.
FSW populations in Nigeria are large and diverse, with substantial differences between and within states. Improved understanding of the location, population size, density, organizational typologies and clients of sex work has informed and is central to Nigeria's planning process for scaling up focused HIV prevention programmes.
Url:
DOI: 10.1371/journal.pone.0103619
PubMed: 25118691
PubMed Central: 4131880
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<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">An Appraisal of Female Sex Work in Nigeria - Implications for Designing and Scaling Up HIV Prevention Programmes</title><author><name sortKey="Ikpeazu, Akudo" sort="Ikpeazu, Akudo" uniqKey="Ikpeazu A" first="Akudo" last="Ikpeazu">Akudo Ikpeazu</name><affiliation><nlm:aff id="aff1"><addr-line>National Agency for the Control of AIDS, Abuja, Nigeria</addr-line></nlm:aff></affiliation></author><author><name sortKey="Momah Haruna, Amaka" sort="Momah Haruna, Amaka" uniqKey="Momah Haruna A" first="Amaka" last="Momah-Haruna">Amaka Momah-Haruna</name><affiliation><nlm:aff id="aff2"><addr-line>Centre for Global Public Health, Department of Community Health Sciences, University of Manitoba, Winnipeg, Canada</addr-line></nlm:aff></affiliation></author><author><name sortKey="Madu Mari, Baba" sort="Madu Mari, Baba" uniqKey="Madu Mari B" first="Baba" last="Madu Mari">Baba Madu Mari</name><affiliation><nlm:aff id="aff2"><addr-line>Centre for Global Public Health, Department of Community Health Sciences, University of Manitoba, Winnipeg, Canada</addr-line></nlm:aff></affiliation></author><author><name sortKey="Thompson, Laura H" sort="Thompson, Laura H" uniqKey="Thompson L" first="Laura H." last="Thompson">Laura H. Thompson</name><affiliation><nlm:aff id="aff2"><addr-line>Centre for Global Public Health, Department of Community Health Sciences, University of Manitoba, Winnipeg, Canada</addr-line></nlm:aff></affiliation></author><author><name sortKey="Ogungbemi, Kayode" sort="Ogungbemi, Kayode" uniqKey="Ogungbemi K" first="Kayode" last="Ogungbemi">Kayode Ogungbemi</name><affiliation><nlm:aff id="aff1"><addr-line>National Agency for the Control of AIDS, Abuja, Nigeria</addr-line></nlm:aff></affiliation></author><author><name sortKey="Daniel, Uduak" sort="Daniel, Uduak" uniqKey="Daniel U" first="Uduak" last="Daniel">Uduak Daniel</name><affiliation><nlm:aff id="aff1"><addr-line>National Agency for the Control of AIDS, Abuja, Nigeria</addr-line></nlm:aff></affiliation></author><author><name sortKey="Aboki, Hafsatu" sort="Aboki, Hafsatu" uniqKey="Aboki H" first="Hafsatu" last="Aboki">Hafsatu Aboki</name><affiliation><nlm:aff id="aff1"><addr-line>National Agency for the Control of AIDS, Abuja, Nigeria</addr-line></nlm:aff></affiliation></author><author><name sortKey="Isac, Shajy" sort="Isac, Shajy" uniqKey="Isac S" first="Shajy" last="Isac">Shajy Isac</name><affiliation><nlm:aff id="aff2"><addr-line>Centre for Global Public Health, Department of Community Health Sciences, University of Manitoba, Winnipeg, Canada</addr-line></nlm:aff></affiliation></author><author><name sortKey="Gorgens, Marelize" sort="Gorgens, Marelize" uniqKey="Gorgens M" first="Marelize" last="Gorgens">Marelize Gorgens</name><affiliation><nlm:aff id="aff3"><addr-line>World Bank, Washington, DC, United States of America</addr-line></nlm:aff></affiliation></author><author><name sortKey="Mziray, Elizabeth" sort="Mziray, Elizabeth" uniqKey="Mziray E" first="Elizabeth" last="Mziray">Elizabeth Mziray</name><affiliation><nlm:aff id="aff3"><addr-line>World Bank, Washington, DC, United States of America</addr-line></nlm:aff></affiliation></author><author><name sortKey="Njie, Ndella" sort="Njie, Ndella" uniqKey="Njie N" first="Ndella" last="Njie">Ndella Njie</name><affiliation><nlm:aff id="aff3"><addr-line>World Bank, Washington, DC, United States of America</addr-line></nlm:aff></affiliation></author><author><name sortKey="Akala, Francisca Ayodeji" sort="Akala, Francisca Ayodeji" uniqKey="Akala F" first="Francisca Ayodeji" last="Akala">Francisca Ayodeji Akala</name><affiliation><nlm:aff id="aff3"><addr-line>World Bank, Washington, DC, United States of America</addr-line></nlm:aff></affiliation></author><author><name sortKey="Emmanuel, Faran" sort="Emmanuel, Faran" uniqKey="Emmanuel F" first="Faran" last="Emmanuel">Faran Emmanuel</name><affiliation><nlm:aff id="aff2"><addr-line>Centre for Global Public Health, Department of Community Health Sciences, University of Manitoba, Winnipeg, Canada</addr-line></nlm:aff></affiliation></author><author><name sortKey="Odek, Willis Omondi" sort="Odek, Willis Omondi" uniqKey="Odek W" first="Willis Omondi" last="Odek">Willis Omondi Odek</name><affiliation><nlm:aff id="aff2"><addr-line>Centre for Global Public Health, Department of Community Health Sciences, University of Manitoba, Winnipeg, Canada</addr-line></nlm:aff></affiliation></author><author><name sortKey="Blanchard, James F" sort="Blanchard, James F" uniqKey="Blanchard J" first="James F." last="Blanchard">James F. Blanchard</name><affiliation><nlm:aff id="aff2"><addr-line>Centre for Global Public Health, Department of Community Health Sciences, University of Manitoba, Winnipeg, Canada</addr-line></nlm:aff></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PMC</idno><idno type="pmid">25118691</idno><idno type="pmc">4131880</idno><idno type="url">http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4131880</idno><idno type="RBID">PMC:4131880</idno><idno type="doi">10.1371/journal.pone.0103619</idno><date when="2014">2014</date><idno type="wicri:Area/Pmc/Corpus">002B22</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Corpus" wicri:corpus="PMC">002B22</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">An Appraisal of Female Sex Work in Nigeria - Implications for Designing and Scaling Up HIV Prevention Programmes</title><author><name sortKey="Ikpeazu, Akudo" sort="Ikpeazu, Akudo" uniqKey="Ikpeazu A" first="Akudo" last="Ikpeazu">Akudo Ikpeazu</name><affiliation><nlm:aff id="aff1"><addr-line>National Agency for the Control of AIDS, Abuja, Nigeria</addr-line></nlm:aff></affiliation></author><author><name sortKey="Momah Haruna, Amaka" sort="Momah Haruna, Amaka" uniqKey="Momah Haruna A" first="Amaka" last="Momah-Haruna">Amaka Momah-Haruna</name><affiliation><nlm:aff id="aff2"><addr-line>Centre for Global Public Health, Department of Community Health Sciences, University of Manitoba, Winnipeg, Canada</addr-line></nlm:aff></affiliation></author><author><name sortKey="Madu Mari, Baba" sort="Madu Mari, Baba" uniqKey="Madu Mari B" first="Baba" last="Madu Mari">Baba Madu Mari</name><affiliation><nlm:aff id="aff2"><addr-line>Centre for Global Public Health, Department of Community Health Sciences, University of Manitoba, Winnipeg, Canada</addr-line></nlm:aff></affiliation></author><author><name sortKey="Thompson, Laura H" sort="Thompson, Laura H" uniqKey="Thompson L" first="Laura H." last="Thompson">Laura H. Thompson</name><affiliation><nlm:aff id="aff2"><addr-line>Centre for Global Public Health, Department of Community Health Sciences, University of Manitoba, Winnipeg, Canada</addr-line></nlm:aff></affiliation></author><author><name sortKey="Ogungbemi, Kayode" sort="Ogungbemi, Kayode" uniqKey="Ogungbemi K" first="Kayode" last="Ogungbemi">Kayode Ogungbemi</name><affiliation><nlm:aff id="aff1"><addr-line>National Agency for the Control of AIDS, Abuja, Nigeria</addr-line></nlm:aff></affiliation></author><author><name sortKey="Daniel, Uduak" sort="Daniel, Uduak" uniqKey="Daniel U" first="Uduak" last="Daniel">Uduak Daniel</name><affiliation><nlm:aff id="aff1"><addr-line>National Agency for the Control of AIDS, Abuja, Nigeria</addr-line></nlm:aff></affiliation></author><author><name sortKey="Aboki, Hafsatu" sort="Aboki, Hafsatu" uniqKey="Aboki H" first="Hafsatu" last="Aboki">Hafsatu Aboki</name><affiliation><nlm:aff id="aff1"><addr-line>National Agency for the Control of AIDS, Abuja, Nigeria</addr-line></nlm:aff></affiliation></author><author><name sortKey="Isac, Shajy" sort="Isac, Shajy" uniqKey="Isac S" first="Shajy" last="Isac">Shajy Isac</name><affiliation><nlm:aff id="aff2"><addr-line>Centre for Global Public Health, Department of Community Health Sciences, University of Manitoba, Winnipeg, Canada</addr-line></nlm:aff></affiliation></author><author><name sortKey="Gorgens, Marelize" sort="Gorgens, Marelize" uniqKey="Gorgens M" first="Marelize" last="Gorgens">Marelize Gorgens</name><affiliation><nlm:aff id="aff3"><addr-line>World Bank, Washington, DC, United States of America</addr-line></nlm:aff></affiliation></author><author><name sortKey="Mziray, Elizabeth" sort="Mziray, Elizabeth" uniqKey="Mziray E" first="Elizabeth" last="Mziray">Elizabeth Mziray</name><affiliation><nlm:aff id="aff3"><addr-line>World Bank, Washington, DC, United States of America</addr-line></nlm:aff></affiliation></author><author><name sortKey="Njie, Ndella" sort="Njie, Ndella" uniqKey="Njie N" first="Ndella" last="Njie">Ndella Njie</name><affiliation><nlm:aff id="aff3"><addr-line>World Bank, Washington, DC, United States of America</addr-line></nlm:aff></affiliation></author><author><name sortKey="Akala, Francisca Ayodeji" sort="Akala, Francisca Ayodeji" uniqKey="Akala F" first="Francisca Ayodeji" last="Akala">Francisca Ayodeji Akala</name><affiliation><nlm:aff id="aff3"><addr-line>World Bank, Washington, DC, United States of America</addr-line></nlm:aff></affiliation></author><author><name sortKey="Emmanuel, Faran" sort="Emmanuel, Faran" uniqKey="Emmanuel F" first="Faran" last="Emmanuel">Faran Emmanuel</name><affiliation><nlm:aff id="aff2"><addr-line>Centre for Global Public Health, Department of Community Health Sciences, University of Manitoba, Winnipeg, Canada</addr-line></nlm:aff></affiliation></author><author><name sortKey="Odek, Willis Omondi" sort="Odek, Willis Omondi" uniqKey="Odek W" first="Willis Omondi" last="Odek">Willis Omondi Odek</name><affiliation><nlm:aff id="aff2"><addr-line>Centre for Global Public Health, Department of Community Health Sciences, University of Manitoba, Winnipeg, Canada</addr-line></nlm:aff></affiliation></author><author><name sortKey="Blanchard, James F" sort="Blanchard, James F" uniqKey="Blanchard J" first="James F." last="Blanchard">James F. Blanchard</name><affiliation><nlm:aff id="aff2"><addr-line>Centre for Global Public Health, Department of Community Health Sciences, University of Manitoba, Winnipeg, Canada</addr-line></nlm:aff></affiliation></author></analytic><series><title level="j">PLoS ONE</title><idno type="eISSN">1932-6203</idno><imprint><date when="2014">2014</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><sec><title>Background</title><p>The HIV epidemic in Nigeria is complex with diverse factors driving the epidemic. Accordingly, Nigeria's National Agency for the Control of AIDS is coordinating a large-scale initiative to conduct HIV epidemic appraisals across all states. These appraisals will help to better characterize the drivers of the epidemic and ensure that the HIV prevention programmes match the local epidemic context, with resources allocated to interventions that have the greatest impact locally. Currently, the mapping and size estimation of Female Sex Workers (FSWs) - a major component of the appraisal has been completed in seven states. These states are using the data generated to plan, prioritize and scale-up sub-national HIV prevention programmes.</p></sec><sec><title>Methodology</title><p>It involved a two-level process of identifying and validating locations where FSWs solicit and/or meet clients (“hotspots”). In the first level, secondary key informants were interviewed to collect information about the geographic location and description of the hotspots. For the second level, FSWs were interviewed at each hotspot and information on population size estimates, typologies and operational dynamics of the FSWs were collected.</p></sec><sec><title>Results</title><p>Across the seven states, a total of 17,266 secondary key informants and 5,732 FSWs were interviewed. 10,233 hotspots were identified with an estimated 126,489 FSWs ranging from 5,920 in Anambra to 46,691 in Lagos. The most common hotspots were bars/nightclubs (30%), hotels/lodges (29.6%), streets (16.6%), and brothels (14.6%). Furthermore, the population density of FSWs (per thousand adult men) across the states ranged from 2 in Anambra to 17 in the Federal Capital Territory.</p></sec><sec><title>Conclusion</title><p>FSW populations in Nigeria are large and diverse, with substantial differences between and within states. Improved understanding of the location, population size, density, organizational typologies and clients of sex work has informed and is central to Nigeria's planning process for scaling up focused HIV prevention programmes.</p></sec></div></front><back><div1 type="bibliography"><listBibl><biblStruct><analytic><author><name sortKey="Mishra, S" uniqKey="Mishra S">S Mishra</name></author><author><name sortKey="Sgaier, Sk" uniqKey="Sgaier S">SK Sgaier</name></author><author><name sortKey="Thompson, Lh" uniqKey="Thompson L">LH Thompson</name></author><author><name sortKey="Moses, S" uniqKey="Moses S">S Moses</name></author><author><name sortKey="Ramesh, Bm" uniqKey="Ramesh B">BM Ramesh</name></author></analytic></biblStruct><biblStruct></biblStruct><biblStruct><analytic><author><name sortKey="Cuadros, Df" uniqKey="Cuadros D">DF Cuadros</name></author><author><name sortKey="Awad, Sf" uniqKey="Awad S">SF Awad</name></author><author><name sortKey="Abu Raddad, Lj" uniqKey="Abu Raddad L">LJ 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Khobragade</name></author><author><name sortKey="Adhikary, R" uniqKey="Adhikary R">R Adhikary</name></author><author><name sortKey="George, B" uniqKey="George B">B George</name></author></analytic></biblStruct></listBibl></div1></back></TEI><pmc article-type="research-article"><pmc-dir>properties open_access</pmc-dir>
<front><journal-meta><journal-id journal-id-type="nlm-ta">PLoS One</journal-id><journal-id journal-id-type="iso-abbrev">PLoS ONE</journal-id><journal-id journal-id-type="publisher-id">plos</journal-id><journal-id journal-id-type="pmc">plosone</journal-id><journal-title-group><journal-title>PLoS ONE</journal-title></journal-title-group><issn pub-type="epub">1932-6203</issn><publisher><publisher-name>Public Library of Science</publisher-name><publisher-loc>San Francisco, USA</publisher-loc></publisher></journal-meta><article-meta><article-id pub-id-type="pmid">25118691</article-id><article-id pub-id-type="pmc">4131880</article-id><article-id pub-id-type="publisher-id">PONE-D-13-44483</article-id><article-id pub-id-type="doi">10.1371/journal.pone.0103619</article-id><article-categories><subj-group subj-group-type="heading"><subject>Research Article</subject></subj-group><subj-group subj-group-type="Discipline-v2"><subject>Biology and Life Sciences</subject><subj-group><subject>Ecology</subject><subj-group><subject>Ecological Metrics</subject><subj-group><subject>Population Size</subject><subj-group><subject>Effective Population Size</subject></subj-group></subj-group></subj-group></subj-group><subj-group><subject>Microbiology</subject><subj-group><subject>Medical Microbiology</subject><subj-group><subject>Microbial Pathogens</subject><subj-group><subject>Viral Pathogens</subject><subj-group><subject>Immunodeficiency Viruses</subject><subj-group><subject>HIV</subject></subj-group></subj-group></subj-group></subj-group></subj-group></subj-group><subj-group><subject>Population Biology</subject><subj-group><subject>Population Metrics</subject></subj-group></subj-group></subj-group><subj-group subj-group-type="Discipline-v2"><subject>Medicine and health sciences</subject><subj-group><subject>Epidemiology</subject></subj-group><subj-group><subject>Infectious Diseases</subject></subj-group><subj-group><subject>Public and occupational health</subject><subj-group><subject>Preventive medicine</subject><subj-group><subject>HIV prevention</subject></subj-group></subj-group><subj-group><subject>Global Health</subject></subj-group></subj-group><subj-group><subject>Women's Health</subject></subj-group></subj-group><subj-group subj-group-type="Discipline-v2"><subject>People and Places</subject><subj-group><subject>Demography</subject></subj-group></subj-group><subj-group subj-group-type="Discipline-v2"><subject>Research and Analysis Methods</subject><subj-group><subject>Research Design</subject><subj-group><subject>Survey Research</subject><subj-group><subject>Survey Methods</subject></subj-group></subj-group></subj-group></subj-group><subj-group subj-group-type="Discipline-v2"><subject>Social Sciences</subject><subj-group><subject>Sociology</subject></subj-group></subj-group></article-categories><title-group><article-title>An Appraisal of Female Sex Work in Nigeria - Implications for Designing and Scaling Up HIV Prevention Programmes</article-title><alt-title alt-title-type="running-head">An Appraisal of Female Sex Work in Nigeria</alt-title></title-group><contrib-group><contrib contrib-type="author"><name><surname>Ikpeazu</surname><given-names>Akudo</given-names></name><xref ref-type="aff" rid="aff1"><sup>1</sup></xref></contrib><contrib contrib-type="author"><name><surname>Momah-Haruna</surname><given-names>Amaka</given-names></name><xref ref-type="aff" rid="aff2"><sup>2</sup></xref></contrib><contrib contrib-type="author"><name><surname>Madu Mari</surname><given-names>Baba</given-names></name><xref ref-type="aff" rid="aff2"><sup>2</sup></xref></contrib><contrib contrib-type="author"><name><surname>Thompson</surname><given-names>Laura H.</given-names></name><xref ref-type="aff" rid="aff2"><sup>2</sup></xref></contrib><contrib contrib-type="author"><name><surname>Ogungbemi</surname><given-names>Kayode</given-names></name><xref ref-type="aff" rid="aff1"><sup>1</sup></xref></contrib><contrib contrib-type="author"><name><surname>Daniel</surname><given-names>Uduak</given-names></name><xref ref-type="aff" rid="aff1"><sup>1</sup></xref></contrib><contrib contrib-type="author"><name><surname>Aboki</surname><given-names>Hafsatu</given-names></name><xref ref-type="aff" rid="aff1"><sup>1</sup></xref></contrib><contrib contrib-type="author"><name><surname>Isac</surname><given-names>Shajy</given-names></name><xref ref-type="aff" rid="aff2"><sup>2</sup></xref></contrib><contrib contrib-type="author"><name><surname>Gorgens</surname><given-names>Marelize</given-names></name><xref ref-type="aff" rid="aff3"><sup>3</sup></xref></contrib><contrib contrib-type="author"><name><surname>Mziray</surname><given-names>Elizabeth</given-names></name><xref ref-type="aff" rid="aff3"><sup>3</sup></xref></contrib><contrib contrib-type="author"><name><surname>Njie</surname><given-names>Ndella</given-names></name><xref ref-type="aff" rid="aff3"><sup>3</sup></xref></contrib><contrib contrib-type="author"><name><surname>Akala</surname><given-names>Francisca Ayodeji</given-names></name><xref ref-type="aff" rid="aff3"><sup>3</sup></xref></contrib><contrib contrib-type="author"><name><surname>Emmanuel</surname><given-names>Faran</given-names></name><xref ref-type="aff" rid="aff2"><sup>2</sup></xref></contrib><contrib contrib-type="author"><name><surname>Odek</surname><given-names>Willis Omondi</given-names></name><xref ref-type="aff" rid="aff2"><sup>2</sup></xref></contrib><contrib contrib-type="author"><name><surname>Blanchard</surname><given-names>James F.</given-names></name><xref ref-type="aff" rid="aff2"><sup>2</sup></xref><xref ref-type="corresp" rid="cor1"><sup>*</sup></xref></contrib></contrib-group><aff id="aff1"><label>1</label><addr-line>National Agency for the Control of AIDS, Abuja, Nigeria</addr-line></aff><aff id="aff2"><label>2</label><addr-line>Centre for Global Public Health, Department of Community Health Sciences, University of Manitoba, Winnipeg, Canada</addr-line></aff><aff id="aff3"><label>3</label><addr-line>World Bank, Washington, DC, United States of America</addr-line></aff><contrib-group><contrib contrib-type="editor"><name><surname>Zunt</surname><given-names>Joseph Raymond</given-names></name><role>Editor</role><xref ref-type="aff" rid="edit1"></xref></contrib></contrib-group><aff id="edit1"><addr-line>University of Washington, United States of America</addr-line></aff><author-notes><corresp id="cor1">* E-mail: <email>james_blanchard@umanitoba.ca</email></corresp><fn fn-type="conflict"><p><bold>Competing Interests: </bold>The authors have declared that no competing interests exist.</p></fn><fn fn-type="con"><p>Conceived and designed the experiments: AI UD FAA EF WOO JFB. Analyzed the data: SI. Contributed reagents/materials/analysis tools: KO. Wrote the paper: AM-H BMM LHT HA MG EM NN JFB.</p></fn></author-notes><pub-date pub-type="collection"><year>2014</year></pub-date><pub-date pub-type="epub"><day>13</day><month>8</month><year>2014</year></pub-date><volume>9</volume><issue>8</issue><elocation-id>e103619</elocation-id><history><date date-type="received"><day>10</day><month>10</month><year>2013</year></date><date date-type="accepted"><day>30</day><month>6</month><year>2014</year></date></history><permissions><copyright-year>2014</copyright-year><copyright-holder>Ikpeazu et al</copyright-holder><license><license-p>This is an open-access article distributed under the terms of the <ext-link ext-link-type="uri" xlink:href="http://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution License</ext-link>, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.</license-p></license></permissions><abstract><sec><title>Background</title><p>The HIV epidemic in Nigeria is complex with diverse factors driving the epidemic. Accordingly, Nigeria's National Agency for the Control of AIDS is coordinating a large-scale initiative to conduct HIV epidemic appraisals across all states. These appraisals will help to better characterize the drivers of the epidemic and ensure that the HIV prevention programmes match the local epidemic context, with resources allocated to interventions that have the greatest impact locally. Currently, the mapping and size estimation of Female Sex Workers (FSWs) - a major component of the appraisal has been completed in seven states. These states are using the data generated to plan, prioritize and scale-up sub-national HIV prevention programmes.</p></sec><sec><title>Methodology</title><p>It involved a two-level process of identifying and validating locations where FSWs solicit and/or meet clients (“hotspots”). In the first level, secondary key informants were interviewed to collect information about the geographic location and description of the hotspots. For the second level, FSWs were interviewed at each hotspot and information on population size estimates, typologies and operational dynamics of the FSWs were collected.</p></sec><sec><title>Results</title><p>Across the seven states, a total of 17,266 secondary key informants and 5,732 FSWs were interviewed. 10,233 hotspots were identified with an estimated 126,489 FSWs ranging from 5,920 in Anambra to 46,691 in Lagos. The most common hotspots were bars/nightclubs (30%), hotels/lodges (29.6%), streets (16.6%), and brothels (14.6%). Furthermore, the population density of FSWs (per thousand adult men) across the states ranged from 2 in Anambra to 17 in the Federal Capital Territory.</p></sec><sec><title>Conclusion</title><p>FSW populations in Nigeria are large and diverse, with substantial differences between and within states. Improved understanding of the location, population size, density, organizational typologies and clients of sex work has informed and is central to Nigeria's planning process for scaling up focused HIV prevention programmes.</p></sec></abstract><funding-group><funding-statement>This surveillance and mapping effort was funded by the Government of Nigeria (bulk of the funding), with the World Bank providing technical support. The World Bank funded its own staff to support their involvement in technical support for the work and also contracted the University of Manitoba to provide technical support services to the Government to execute the epidemic appraisals, to support the States to analyse data and to write the reports. The Federal and State level governments in Nigeria financed the training, fieldwork, supervision and data analysis. The Government of Nigeria made the determination how to undertake the surveillance and mapping (with technical guidance from University of Manitoba), and the decision to undertake the surveillance and mapping effort. They invited the World Bank and University of Manitoba to be a co-author of the study results.</funding-statement></funding-group><counts><page-count count="9"></page-count></counts></article-meta></front><body><sec id="s1"><title>Introduction</title><p>Policy makers and programme implementers require accurate and timely guidance on the status and key epidemiologic drivers of ongoing local transmission to ensure that they maximize the impact of their investments in HIV prevention <xref rid="pone.0103619-Mishra1" ref-type="bibr">[1]</xref>. This is especially relevant in a country such as Nigeria with the second largest number of new HIV infections globally (nearly 300,000 annually) and substantial heterogeneity in HIV prevalence across different regions with diverse factors that drive the epidemic locally <xref rid="pone.0103619-Federal1" ref-type="bibr">[2]</xref>.</p><p>Given that an effective HIV prevention strategy must be guided by information about the underlying epidemiologic drivers and proximal sources of new infections, it is imperative that epidemic appraisals are conducted within local contexts to facilitate the identification of these relevant drivers of local HIV transmission <xref rid="pone.0103619-Mishra1" ref-type="bibr">[1]</xref>. In addition, there is growing evidence on the use of geographic clustering of HIV infections as a means to identify populations at higher risk of HIV infection, and subsequently targeting HIV prevention efforts on priority geographic areas to maximize the impact on the HIV epidemic <xref rid="pone.0103619-Cuadros1" ref-type="bibr">[3]</xref>. In Nigeria, it is estimated that the key populations considered to be most at risk for HIV transmission, Female Sex Workers (FSWs), Injecting Drug Users, and Men who have sex with Men and their partners contribute as much as 40% of new HIV infections, despite representing only about 3.4% of the adult population. Half of the infections contributed by these key populations and their partners are attributed to FSWs, their clients and clients' partners alone, highlighting a profound need for programmatic response focus on this sub-population group <xref rid="pone.0103619-National1" ref-type="bibr">[4]</xref>, <xref rid="pone.0103619-Eluwa1" ref-type="bibr">[5]</xref>. This is not surprising given the low levels of risk perception (39%) and knowledge about HIV (31%) reported among FSWs <xref rid="pone.0103619-National2" ref-type="bibr">[6]</xref>. Furthermore, it has been well documented that HIV transmission in the context of sex work plays a significant role in the expansion of HIV epidemics in many countries around the world <xref rid="pone.0103619-Emmanuel1" ref-type="bibr">[7]</xref>, <xref rid="pone.0103619-Jha1" ref-type="bibr">[8]</xref>, <xref rid="pone.0103619-Day1" ref-type="bibr">[9]</xref>.</p><p>In Nigeria, the national Integrated Biological and Behavioral Surveillance Survey (IBBSS) conducted across nine states reached FSWs by visiting hotspots identified by the State Agencies for Control of AIDS (SACAs) and Non-Governmental Organizations as locations where FSWs congregate. While this national survey did not derive the population size estimates of the FSW at these locations, it showed that the HIV prevalence among brothel-based FSWs in Nigeria varied considerably. It ranged from a low of 12% in Lagos state to a high of about 46% in Benue and Nasarawa state, incidentally states that also have high ANC prevalence (12.7% and 7.5% respectively) <xref rid="pone.0103619-Eluwa1" ref-type="bibr">[5]</xref>. This heterogeneity may be partially explained by regional differences in socioeconomic and cultural factors which may influence empowerment, opportunities, and stigma of FSWs, which in turn influence vulnerability to HIV infection <xref rid="pone.0103619-Onwuliri1" ref-type="bibr">[10]</xref>. Various prevention programmes have been implemented for FSWs by both governmental and nongovernmental organizations in Nigeria. These include programmes that are designed to increase their knowledge of HIV, increase condom use with clients through provision of free condoms and condom negotiation skills and offer free HIV voluntary counseling and testing (VCT) <xref rid="pone.0103619-National2" ref-type="bibr">[6]</xref>, <xref rid="pone.0103619-Society1" ref-type="bibr">[11]</xref>, <xref rid="pone.0103619-Society2" ref-type="bibr">[12]</xref>. Some of the key challenges for program design have been the lack of available information about the size and specific locations of key target populations, lack of a standardized, integrated package of services and poor coordination among implementers – all of which would be useful for determining the necessary scale and reach of HIV prevention programmes in order to maximize their impact.</p><p>Nigeria's National Agency for the Control of AIDS (NACA) is coordinating a large-scale national initiative to conduct rapid HIV epidemic appraisals across all states, which includes the mapping and size estimation of FSWs. The other components of the appraisal, Venue Profiling and Rural Appraisals were also conducted with the view of assessing the sexual behavior of other vulnerable populations in urban settings and the general population in rural settings respectively given that these target groups are also significant contributors to Nigeria's mixed epidemic. In this paper, we describe how the information obtained from the appraisal is providing the SACAs in Anambra, Benue, Cross River, Federal Capital Territory (FCT), Lagos, Nasarawa, and Ondo states with critical intelligence to plan, prioritise and scale up HIV prevention interventions for FSWs. These seven states pioneered the local epidemic appraisals in Nigeria.</p></sec><sec sec-type="methods" id="s2"><title>Methods</title><p>A “female sex worker” was defined as any female who receives money or other valuable gifts/incentives from a man in exchange for sex in areas such as brothels, bars, hotels, nightclubs, restaurants, or on the street <xref rid="pone.0103619-National2" ref-type="bibr">[6]</xref>. The rationale behind the mapping methodology that the Government adopted was based largely on the understanding that most FSWs, particularly those who are most active, congregate and/or meet clients in definable geographic locations. Accordingly, the Government's approach entailed focusing on identifying these locations, characterizing each location in terms of specific “spots” within that location and the operational characteristics of sex work there (i.e. how and where FSWs meet clients and where transactions occur), and estimating the number of FSWs that frequent the location and spots. This approach is an adaptation of the PLACE (priorities for local AIDS control efforts) methodology, which identified locations where individuals frequented to acquire new sexual partners <xref rid="pone.0103619-National3" ref-type="bibr">[13]</xref>, <xref rid="pone.0103619-Weir1" ref-type="bibr">[14]</xref>.</p><p>To ensure the methodology was applicable to the local context, the National Prevention Technical Working Group held a series of consultative meetings to reach consensus on the methodology and instruments to be used for the geographic mapping. Thereafter, a core team of master trainers from each state was trained on the geographic mapping approach and they in turn trained their field teams at state level with support from the technical team. The field operations of the geographic mapping comprised of two major phases, referred to as “levels”. In Level 1, secondary key informants – people knowledgeable about the area (e.g., taxi drivers, commercial motorcyclists, shopkeepers etc) were interviewed about the geographic locations (“hot spots”) where FSWs congregate and solicit clients. To facilitate Level 1 data collection, all of the states except FCT and Lagos selected major towns in the state and divided these towns into smaller zones based on population estimates, physical features and landmarks. Lagos and FCT were divided into zones, as there are no well-defined towns in these states. Typically, 60 key informant interviews were conducted in each zone with a total of 17,266 interviews conducted across the seven states. The product of Level 1 activity was a comprehensive list of unique spots where FSWs may be found, the typology of the spot (e.g. brothels, street, hotels etc) and estimated minimum, and maximum numbers of FSWs at each spot.</p><p>This information was validated in Level 2 by interviewing 5732 FSWs at the identified hotspots. A few FSWs were recruited in each of the towns mapped to assist data collectors with identifying the members of their groups at the identified spots for validation interviews. The validation process determined the existence of a spot, whether or not the spot was frequented by FSWs, the estimated minimum and maximum number of FSWs who frequented active spots on usual and peak days and the operational dynamics of each spot (peak and non-peak times). In the validation, spots that were mentioned by the least number of secondary key informants at Level 1 were given priority, because these were the most likely to have been incorrectly identified.</p><p>To generate the population size estimate of FSWs at each spot, the average of the minimum and maximum number of FSWs that frequented each spot was derived. For the spots validated in Level 2, only validated data were used in generating population size estimates while for those spots not re-visited, an average of the minimum and maximum population size estimates obtained from secondary key informants in Level 1 were used. These average population size estimates obtained from Level 1 were adjusted by applying the correction factor derived by analyzing the differences in the estimate given by secondary key informants (level 1) and spot validation data (level 2) for the validated spots.</p><p>Level 2 interviews also sought other locations in the vicinity not identified by Level 1 secondary key informants. Following the validation of the hotspot, the interviewer obtained the GIS coordinates of the location, which were used for the spatial representation of the hotspot on the map of the state. The FSW density per 1000 adult males was obtained by dividing the FSW population estimate by the total population of adult males (>18 years) in the state.</p><sec id="s2a"><title>Ethics statement</title><p>Interviewers were required to read out a consent form to each participant. This form explained the objectives of the study, enquired as to whether the participant was willing to respond to the questions and required him/her to indicate their agreement through verbal consent. Participants provided verbal informed consent to participate in the study. They were not required to disclose any personal information and no biological specimen was obtained. The study protocol and consent procedure was reviewed and approved by the State Ministry of Health's Ethical Committee in Anambra, Cross River, Benue, Nasarawa, FCT, Lagos and Ondo states.</p></sec><sec id="s2b"><title>Data analysis</title><p>Data were collected from April to June 2012 across the seven states and processing was done using a Microsoft Access database with in-built quality checks. The same software was used to generate a list of unique spots and calculate FSW population size estimates by spot typology, zone and town. The analysis provided minimum and maximum estimates for each spot and to arrive at a point estimate, averages of the minimum and maximum estimates were calculated.</p></sec></sec><sec id="s3"><title>Results</title><sec id="s3a"><title>Key informant interviews</title><p>A total of 17,266 secondary key informants and 5732 primary key informants (FSWs) were interviewed to identify spots frequented by FSWs, with more than 1,000 key informants per state (<xref ref-type="table" rid="pone-0103619-t001">Table 1</xref>). The secondary key informants were predominantly male (75%) and 78 per cent of them had secondary or higher level of education. They included petty shop owners (12%), and taxi/commercial motorcycle drivers (21%) while majority were involved in “other” professions (46%) (<xref ref-type="table" rid="pone-0103619-t002">Table 2</xref>).</p><table-wrap id="pone-0103619-t001" orientation="portrait" position="float"><object-id pub-id-type="doi">10.1371/journal.pone.0103619.t001</object-id><label>Table 1</label><caption><title>Number of Key Informant Interviews by State, Sex and Educational level.</title></caption><alternatives><graphic id="pone-0103619-t001-1" xlink:href="pone.0103619.t001"></graphic><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 align="left" rowspan="1" colspan="1">State</td><td align="left" rowspan="1" colspan="1">Number of KI Interviews</td><td colspan="2" align="left" rowspan="1">Sex</td><td colspan="5" align="left" rowspan="1">Education Level</td></tr><tr><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1">Male</td><td align="left" rowspan="1" colspan="1">Female</td><td align="left" rowspan="1" colspan="1">Primary</td><td align="left" rowspan="1" colspan="1">Secondary/vocational</td><td align="left" rowspan="1" colspan="1">Tertiary</td><td align="left" rowspan="1" colspan="1">None</td><td align="left" rowspan="1" colspan="1">Did not disclose</td></tr></thead><tbody><tr><td align="left" rowspan="1" colspan="1">FCT</td><td align="left" rowspan="1" colspan="1">2190</td><td align="left" rowspan="1" colspan="1">73%</td><td align="left" rowspan="1" colspan="1">27%</td><td align="left" rowspan="1" colspan="1">10%</td><td align="left" rowspan="1" colspan="1">53%</td><td align="left" rowspan="1" colspan="1">20%</td><td align="left" rowspan="1" colspan="1">3%</td><td align="left" rowspan="1" colspan="1">14%</td></tr><tr><td align="left" rowspan="1" colspan="1">ANAMBRA</td><td align="left" rowspan="1" colspan="1">1672</td><td align="left" rowspan="1" colspan="1">64%</td><td align="left" rowspan="1" colspan="1">36%</td><td align="left" rowspan="1" colspan="1">27%</td><td align="left" rowspan="1" colspan="1">51%</td><td align="left" rowspan="1" colspan="1">15%</td><td align="left" rowspan="1" colspan="1">3%</td><td align="left" rowspan="1" colspan="1">4%</td></tr><tr><td align="left" rowspan="1" colspan="1">BENUE</td><td align="left" rowspan="1" colspan="1">1844</td><td align="left" rowspan="1" colspan="1">76%</td><td align="left" rowspan="1" colspan="1">24%</td><td align="left" rowspan="1" colspan="1">15%</td><td align="left" rowspan="1" colspan="1">55%</td><td align="left" rowspan="1" colspan="1">22%</td><td align="left" rowspan="1" colspan="1">5%</td><td align="left" rowspan="1" colspan="1">3%</td></tr><tr><td align="left" rowspan="1" colspan="1">CROSS RIVER</td><td align="left" rowspan="1" colspan="1">2308</td><td align="left" rowspan="1" colspan="1">74%</td><td align="left" rowspan="1" colspan="1">26%</td><td align="left" rowspan="1" colspan="1">13%</td><td align="left" rowspan="1" colspan="1">61%</td><td align="left" rowspan="1" colspan="1">24%</td><td align="left" rowspan="1" colspan="1">1%</td><td align="left" rowspan="1" colspan="1">1%</td></tr><tr><td align="left" rowspan="1" colspan="1">LAGOS</td><td align="left" rowspan="1" colspan="1">4940</td><td align="left" rowspan="1" colspan="1">82%</td><td align="left" rowspan="1" colspan="1">18%</td><td align="left" rowspan="1" colspan="1">14%</td><td align="left" rowspan="1" colspan="1">75%</td><td align="left" rowspan="1" colspan="1">8%</td><td align="left" rowspan="1" colspan="1">2%</td><td align="left" rowspan="1" colspan="1">1%</td></tr><tr><td align="left" rowspan="1" colspan="1">NASARAWA</td><td align="left" rowspan="1" colspan="1">2811</td><td align="left" rowspan="1" colspan="1">79%</td><td align="left" rowspan="1" colspan="1">21%</td><td align="left" rowspan="1" colspan="1">13%</td><td align="left" rowspan="1" colspan="1">52%</td><td align="left" rowspan="1" colspan="1">21%</td><td align="left" rowspan="1" colspan="1">13%</td><td align="left" rowspan="1" colspan="1">0%</td></tr><tr><td align="left" rowspan="1" colspan="1">ONDO</td><td align="left" rowspan="1" colspan="1">1501</td><td align="left" rowspan="1" colspan="1">68%</td><td align="left" rowspan="1" colspan="1">32%</td><td align="left" rowspan="1" colspan="1">12%</td><td align="left" rowspan="1" colspan="1">58%</td><td align="left" rowspan="1" colspan="1">26%</td><td align="left" rowspan="1" colspan="1">3%</td><td align="left" rowspan="1" colspan="1">1%</td></tr><tr><td align="left" rowspan="1" colspan="1">Total</td><td align="left" rowspan="1" colspan="1">17266</td><td align="left" rowspan="1" colspan="1">76%</td><td align="left" rowspan="1" colspan="1">24%</td><td align="left" rowspan="1" colspan="1">14%</td><td align="left" rowspan="1" colspan="1">61%</td><td align="left" rowspan="1" colspan="1">17%</td><td align="left" rowspan="1" colspan="1">4%</td><td align="left" rowspan="1" colspan="1">3%</td></tr></tbody></table></alternatives></table-wrap><table-wrap id="pone-0103619-t002" orientation="portrait" position="float"><object-id pub-id-type="doi">10.1371/journal.pone.0103619.t002</object-id><label>Table 2</label><caption><title>Number of key informants by type of key informant and by state.</title></caption><alternatives><graphic id="pone-0103619-t002-2" xlink:href="pone.0103619.t002"></graphic><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 colspan="7" align="left" rowspan="1">Type of Key Informant</td></tr><tr><td align="left" rowspan="1" colspan="1">State</td><td align="left" rowspan="1" colspan="1">Taxi drivers/Commercial motorcyclists</td><td align="left" rowspan="1" colspan="1">Local food vendors</td><td align="left" rowspan="1" colspan="1">Security staff</td><td align="left" rowspan="1" colspan="1">Bar workers</td><td align="left" rowspan="1" colspan="1">Service providers</td><td align="left" rowspan="1" colspan="1">Gov/law enforcement officials</td><td align="left" rowspan="1" colspan="1">Others</td></tr></thead><tbody><tr><td align="left" rowspan="1" colspan="1">FCT</td><td align="left" rowspan="1" colspan="1">17%</td><td align="left" rowspan="1" colspan="1">5%</td><td align="left" rowspan="1" colspan="1">4%</td><td align="left" rowspan="1" colspan="1">5%</td><td align="left" rowspan="1" colspan="1">15%</td><td align="left" rowspan="1" colspan="1">4%</td><td align="left" rowspan="1" colspan="1">53%</td></tr><tr><td align="left" rowspan="1" colspan="1">Anambra</td><td align="left" rowspan="1" colspan="1">16%</td><td align="left" rowspan="1" colspan="1">8%</td><td align="left" rowspan="1" colspan="1">2%</td><td align="left" rowspan="1" colspan="1">6%</td><td align="left" rowspan="1" colspan="1">16%</td><td align="left" rowspan="1" colspan="1">2%</td><td align="left" rowspan="1" colspan="1">49%</td></tr><tr><td align="left" rowspan="1" colspan="1">Benue</td><td align="left" rowspan="1" colspan="1">29%</td><td align="left" rowspan="1" colspan="1">8%</td><td align="left" rowspan="1" colspan="1">2%</td><td align="left" rowspan="1" colspan="1">3%</td><td align="left" rowspan="1" colspan="1">7%</td><td align="left" rowspan="1" colspan="1">5%</td><td align="left" rowspan="1" colspan="1">44%</td></tr><tr><td align="left" rowspan="1" colspan="1">CrossRiver</td><td align="left" rowspan="1" colspan="1">28%</td><td align="left" rowspan="1" colspan="1">7%</td><td align="left" rowspan="1" colspan="1">2%</td><td align="left" rowspan="1" colspan="1">6%</td><td align="left" rowspan="1" colspan="1">14%</td><td align="left" rowspan="1" colspan="1">5%</td><td align="left" rowspan="1" colspan="1">37%</td></tr><tr><td align="left" rowspan="1" colspan="1">Lagos</td><td align="left" rowspan="1" colspan="1">23%</td><td align="left" rowspan="1" colspan="1">7%</td><td align="left" rowspan="1" colspan="1">4%</td><td align="left" rowspan="1" colspan="1">6%</td><td align="left" rowspan="1" colspan="1">8%</td><td align="left" rowspan="1" colspan="1">1%</td><td align="left" rowspan="1" colspan="1">48%</td></tr><tr><td align="left" rowspan="1" colspan="1">Nasarawa</td><td align="left" rowspan="1" colspan="1">18%</td><td align="left" rowspan="1" colspan="1">6%</td><td align="left" rowspan="1" colspan="1">1%</td><td align="left" rowspan="1" colspan="1">1%</td><td align="left" rowspan="1" colspan="1">13%</td><td align="left" rowspan="1" colspan="1">7%</td><td align="left" rowspan="1" colspan="1">52%</td></tr><tr><td align="left" rowspan="1" colspan="1">Ondo</td><td align="left" rowspan="1" colspan="1">13%</td><td align="left" rowspan="1" colspan="1">5%</td><td align="left" rowspan="1" colspan="1">1%</td><td align="left" rowspan="1" colspan="1">4%</td><td align="left" rowspan="1" colspan="1">13%</td><td align="left" rowspan="1" colspan="1">7%</td><td align="left" rowspan="1" colspan="1">34%</td></tr><tr><td align="left" rowspan="1" colspan="1">Total</td><td align="left" rowspan="1" colspan="1">21%</td><td align="left" rowspan="1" colspan="1">6%</td><td align="left" rowspan="1" colspan="1">3%</td><td align="left" rowspan="1" colspan="1">5%</td><td align="left" rowspan="1" colspan="1">12%</td><td align="left" rowspan="1" colspan="1">4%</td><td align="left" rowspan="1" colspan="1">46%</td></tr></tbody></table></alternatives></table-wrap></sec><sec id="s3b"><title>Female sex worker spots</title><p>In Level 2, 23.5% of the hotspots identified by secondary key informants in Level 1 were found to be either non-existent or not frequented by FSWs. A total of 10,233 active FSW spots were identified through the mapping exercise in Level 2, with the fewest in Anambra (618, 6%) and the most in Lagos (4,056, 39.6%) (<xref ref-type="table" rid="pone-0103619-t003">Table 3</xref>).</p><table-wrap id="pone-0103619-t003" orientation="portrait" position="float"><object-id pub-id-type="doi">10.1371/journal.pone.0103619.t003</object-id><label>Table 3</label><caption><title>Number of FSW spots by state.</title></caption><alternatives><graphic id="pone-0103619-t003-3" xlink:href="pone.0103619.t003"></graphic><table frame="hsides" rules="groups"><colgroup span="1"><col align="left" span="1"></col><col align="center" span="1"></col></colgroup><thead><tr><td align="left" rowspan="1" colspan="1">State</td><td align="left" rowspan="1" colspan="1">Number of spots</td></tr></thead><tbody><tr><td align="left" rowspan="1" colspan="1">Anambra</td><td align="left" rowspan="1" colspan="1">618</td></tr><tr><td align="left" rowspan="1" colspan="1">Cross River</td><td align="left" rowspan="1" colspan="1">692</td></tr><tr><td align="left" rowspan="1" colspan="1">Benue</td><td align="left" rowspan="1" colspan="1">825</td></tr><tr><td align="left" rowspan="1" colspan="1">FCT</td><td align="left" rowspan="1" colspan="1">1146</td></tr><tr><td align="left" rowspan="1" colspan="1">Ondo</td><td align="left" rowspan="1" colspan="1">1187</td></tr><tr><td align="left" rowspan="1" colspan="1">Nasarawa</td><td align="left" rowspan="1" colspan="1">1409</td></tr><tr><td align="left" rowspan="1" colspan="1">Lagos</td><td align="left" rowspan="1" colspan="1">4065</td></tr><tr><td align="left" rowspan="1" colspan="1"><bold>Total number of active spots</bold></td><td align="left" rowspan="1" colspan="1"><bold>10233</bold></td></tr></tbody></table></alternatives></table-wrap></sec><sec id="s3c"><title>FSW population estimates</title><p>There were an estimated 126,489 FSWs in the 7 states, ranging from 5,920 in Anambra to 46,691 in Lagos (<xref ref-type="table" rid="pone-0103619-t004">Table 4</xref>). Out of the 46,491 FSWs in Lagos, the majority (16%) were found in Alimosho, followed by Agege (8.2%) and Surulere (6.7%) Local Government Areas (LGAs). In FCT, a significant majority (70%) of FSWs were found in AMAC, which encompasses the Central Business District, followed by Gwagwalada (16%). In Nasarawa, the LGAs with the highest number of FSWs were Karu (5404, 27%) and Lafia (3,695, 19%). Markurdi with an estimated 1,962 FSWs accounted for 20 per cent of the FSWs in Benue, followed by Vandekiya and Gboko each accounting for 12 percent of the estimated FSW population. In Cross River, the majority of FSWs were in Calabar Municipality (2,472, 25%) and Ikom (1,112, 11%). Akure South LGA accounted for 28 per cent (n = 2,687) while Odigbo (n = 2,383) and Ondo West (n = 2006) LGAs accounted for 24 and 21 per cent, respectively, of the FSWs in Ondo. In Anambra, Onitsha north LGA had the largest FSW population estimate (1406, 24%) followed by Akwa South (1269, 21%) LGAs. <xref ref-type="table" rid="pone-0103619-t004">Table 4</xref> also shows that the estimated number of FSWs per 1000 adult males varied across the states with Anambra having the smallest estimated number of FSWs per 1000 adult males (8) and FCT had the highest ratio with an estimated 69 FSWs per 1,000 adult males. There was also considerable heterogeneity in total FSW estimates and estimated number of FSWs per 1,000 adult males between LGAs within a state. In the Federal Capital Territory (FCT), Abaji had the lowest FSW estimate (245) with an estimated 17 FSWs per 1,000 adult males while Abuja Municipal Council (AMAC) had the highest FSW estimate (17,117) with an estimated 88 FSWs per 1,000 adult males. Overall, Njikoka Local Government Area (LGA) in Anambra state had the lowest FSW estimate (61) with an estimated 2 FSWs per 1,000 adult males while Abuja Municipal Council (AMAC) in the FCT had the highest FSW estimate (17,117) with an estimated 88 FSWs per 1,000 adult males.</p><table-wrap id="pone-0103619-t004" orientation="portrait" position="float"><object-id pub-id-type="doi">10.1371/journal.pone.0103619.t004</object-id><label>Table 4</label><caption><title>Estimated number of FSWs by state, by LGA and average number of FSWs/1000 adult men per state, Nigeria, 2012.</title></caption><alternatives><graphic id="pone-0103619-t004-4" xlink:href="pone.0103619.t004"></graphic><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></colgroup><thead><tr><td align="left" rowspan="1" colspan="1">State</td><td align="left" rowspan="1" colspan="1">LGA</td><td align="left" rowspan="1" colspan="1">Estimated number of FSWs</td><td align="left" rowspan="1" colspan="1">Average number of FSWs/1000 adult men (2006)</td></tr></thead><tbody><tr><td align="left" rowspan="1" colspan="1">FCT</td><td align="left" rowspan="1" colspan="1">ABAJI</td><td align="left" rowspan="1" colspan="1">245</td><td align="left" rowspan="1" colspan="1">17</td></tr><tr><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1">BWARI</td><td align="left" rowspan="1" colspan="1">1317</td><td align="left" rowspan="1" colspan="1">23</td></tr><tr><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1">KUJE</td><td align="left" rowspan="1" colspan="1">1125</td><td align="left" rowspan="1" colspan="1">46</td></tr><tr><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1">AMAC</td><td align="left" rowspan="1" colspan="1">17117</td><td align="left" rowspan="1" colspan="1">88</td></tr><tr><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1">GWAGWALADA</td><td align="left" rowspan="1" colspan="1">3928</td><td align="left" rowspan="1" colspan="1">99</td></tr><tr><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1">KWALI</td><td align="left" rowspan="1" colspan="1">644</td><td align="left" rowspan="1" colspan="1">30</td></tr><tr><td align="left" rowspan="1" colspan="1"><bold>FCT</bold></td><td align="left" rowspan="1" colspan="1"><bold>Total</bold></td><td align="left" rowspan="1" colspan="1"><bold>24376</bold></td><td align="left" rowspan="1" colspan="1"><bold>69.3</bold></td></tr><tr><td align="left" rowspan="1" colspan="1">ANAMBRA</td><td align="left" rowspan="1" colspan="1">AGUATA</td><td align="left" rowspan="1" colspan="1">97</td><td align="left" rowspan="1" colspan="1">1</td></tr><tr><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1">EKWUSIGO</td><td align="left" rowspan="1" colspan="1">183</td><td align="left" rowspan="1" colspan="1">5</td></tr><tr><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1">NJIKOKA</td><td align="left" rowspan="1" colspan="1">61</td><td align="left" rowspan="1" colspan="1">2</td></tr><tr><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1">OGBARU</td><td align="left" rowspan="1" colspan="1">95</td><td align="left" rowspan="1" colspan="1">2</td></tr><tr><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1">ONITSHA SOUTH</td><td align="left" rowspan="1" colspan="1">188</td><td align="left" rowspan="1" colspan="1">5</td></tr><tr><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1">ORUMBA SOUTH</td><td align="left" rowspan="1" colspan="1">117</td><td align="left" rowspan="1" colspan="1">3</td></tr><tr><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1">AWKA NORTH</td><td align="left" rowspan="1" colspan="1">587</td><td align="left" rowspan="1" colspan="1">21</td></tr><tr><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1">IDEMILI NORTH</td><td align="left" rowspan="1" colspan="1">379</td><td align="left" rowspan="1" colspan="1">4</td></tr><tr><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1">IHIALA</td><td align="left" rowspan="1" colspan="1">443</td><td align="left" rowspan="1" colspan="1">6</td></tr><tr><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1">NNEWI NORTH</td><td align="left" rowspan="1" colspan="1">766</td><td align="left" rowspan="1" colspan="1">20</td></tr><tr><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1">ORUMBA NORTH</td><td align="left" rowspan="1" colspan="1">331</td><td align="left" rowspan="1" colspan="1">8</td></tr><tr><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1">AWKA SOUTH</td><td align="left" rowspan="1" colspan="1">1269</td><td align="left" rowspan="1" colspan="1">27</td></tr><tr><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1">ONITSHA NORTH</td><td align="left" rowspan="1" colspan="1">1406</td><td align="left" rowspan="1" colspan="1">45</td></tr><tr><td align="left" rowspan="1" colspan="1"><bold>ANAMBRA</bold></td><td align="left" rowspan="1" colspan="1"><bold>Total</bold></td><td align="left" rowspan="1" colspan="1"><bold>5920</bold></td><td align="left" rowspan="1" colspan="1"><bold>8.7</bold></td></tr><tr><td align="left" rowspan="1" colspan="1">BENUE</td><td align="left" rowspan="1" colspan="1">KWANDE</td><td align="left" rowspan="1" colspan="1">554</td><td align="left" rowspan="1" colspan="1">9</td></tr><tr><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1">OKPOKWU</td><td align="left" rowspan="1" colspan="1">430</td><td align="left" rowspan="1" colspan="1">10</td></tr><tr><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1">GBOKO</td><td align="left" rowspan="1" colspan="1">1229</td><td align="left" rowspan="1" colspan="1">14</td></tr><tr><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1">GWER-EAST</td><td align="left" rowspan="1" colspan="1">873</td><td align="left" rowspan="1" colspan="1">21</td></tr><tr><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1">KATSINA-ALA</td><td align="left" rowspan="1" colspan="1">760</td><td align="left" rowspan="1" colspan="1">13</td></tr><tr><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1">TAKAR</td><td align="left" rowspan="1" colspan="1">377</td><td align="left" rowspan="1" colspan="1">19</td></tr><tr><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1">UKUM</td><td align="left" rowspan="1" colspan="1">895</td><td align="left" rowspan="1" colspan="1">16</td></tr><tr><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1">MAKURDI</td><td align="left" rowspan="1" colspan="1">1962</td><td align="left" rowspan="1" colspan="1">26</td></tr><tr><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1">OTUKPO</td><td align="left" rowspan="1" colspan="1">1715</td><td align="left" rowspan="1" colspan="1">26</td></tr><tr><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1">VANDEKYA</td><td align="left" rowspan="1" colspan="1">1241</td><td align="left" rowspan="1" colspan="1">21</td></tr><tr><td align="left" rowspan="1" colspan="1"><bold>BENUE</bold></td><td align="left" rowspan="1" colspan="1"><bold>Total</bold></td><td align="left" rowspan="1" colspan="1"><bold>10034</bold></td><td align="left" rowspan="1" colspan="1"><bold>17.6</bold></td></tr><tr><td align="left" rowspan="1" colspan="1">CROSS RIVER</td><td align="left" rowspan="1" colspan="1">ABI</td><td align="left" rowspan="1" colspan="1">327</td><td align="left" rowspan="1" colspan="1">9</td></tr><tr><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1">AKPABUYO</td><td align="left" rowspan="1" colspan="1">315</td><td align="left" rowspan="1" colspan="1">5</td></tr><tr><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1">BIASE</td><td align="left" rowspan="1" colspan="1">238</td><td align="left" rowspan="1" colspan="1">6</td></tr><tr><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1">BOKI</td><td align="left" rowspan="1" colspan="1">423</td><td align="left" rowspan="1" colspan="1">9</td></tr><tr><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1">OBANLIKWU</td><td align="left" rowspan="1" colspan="1">134</td><td align="left" rowspan="1" colspan="1">5</td></tr><tr><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1">OBUDU</td><td align="left" rowspan="1" colspan="1">359</td><td align="left" rowspan="1" colspan="1">9</td></tr><tr><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1">ODUKPANI</td><td align="left" rowspan="1" colspan="1">221</td><td align="left" rowspan="1" colspan="1">5</td></tr><tr><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1">AKAMKPA</td><td align="left" rowspan="1" colspan="1">638</td><td align="left" rowspan="1" colspan="1">17</td></tr><tr><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1">BAKASSI</td><td align="left" rowspan="1" colspan="1">259</td><td align="left" rowspan="1" colspan="1">33</td></tr><tr><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1">BEKWARRA</td><td align="left" rowspan="1" colspan="1">262</td><td align="left" rowspan="1" colspan="1">10</td></tr><tr><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1">CALABAR SOUTH</td><td align="left" rowspan="1" colspan="1">444</td><td align="left" rowspan="1" colspan="1">9</td></tr><tr><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1">ETUNG</td><td align="left" rowspan="1" colspan="1">227</td><td align="left" rowspan="1" colspan="1">11</td></tr><tr><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1">OBUBRA</td><td align="left" rowspan="1" colspan="1">503</td><td align="left" rowspan="1" colspan="1">12</td></tr><tr><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1">YAKURR</td><td align="left" rowspan="1" colspan="1">594</td><td align="left" rowspan="1" colspan="1">12</td></tr><tr><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1">YALA</td><td align="left" rowspan="1" colspan="1">486</td><td align="left" rowspan="1" colspan="1">9</td></tr><tr><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1">CALABAR MUNICIPALITY</td><td align="left" rowspan="1" colspan="1">2472</td><td align="left" rowspan="1" colspan="1">54</td></tr><tr><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1">IKOM</td><td align="left" rowspan="1" colspan="1">1112</td><td align="left" rowspan="1" colspan="1">27</td></tr><tr><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1">OGOJA</td><td align="left" rowspan="1" colspan="1">822</td><td align="left" rowspan="1" colspan="1">19</td></tr><tr><td align="left" rowspan="1" colspan="1"><bold>CROSS RIVER</bold></td><td align="left" rowspan="1" colspan="1"><bold>Total</bold></td><td align="left" rowspan="1" colspan="1"><bold>9838</bold></td><td align="left" rowspan="1" colspan="1"><bold>13.6</bold></td></tr><tr><td align="left" rowspan="1" colspan="1">LAGOS</td><td align="left" rowspan="1" colspan="1">BADAGRY</td><td align="left" rowspan="1" colspan="1">716</td><td align="left" rowspan="1" colspan="1">12</td></tr><tr><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1">EPE</td><td align="left" rowspan="1" colspan="1">554</td><td align="left" rowspan="1" colspan="1">12</td></tr><tr><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1">IBEJU LEKKI</td><td align="left" rowspan="1" colspan="1">493</td><td align="left" rowspan="1" colspan="1">17</td></tr><tr><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1">IKEJA</td><td align="left" rowspan="1" colspan="1">2871</td><td align="left" rowspan="1" colspan="1">36</td></tr><tr><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1">OJO</td><td align="left" rowspan="1" colspan="1">1434</td><td align="left" rowspan="1" colspan="1">9</td></tr><tr><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1">AMUWO-ODOFIN</td><td align="left" rowspan="1" colspan="1">1218</td><td align="left" rowspan="1" colspan="1">15</td></tr><tr><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1">IFAKO-IJAIYE</td><td align="left" rowspan="1" colspan="1">2353</td><td align="left" rowspan="1" colspan="1">22</td></tr><tr><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1">IKORODU</td><td align="left" rowspan="1" colspan="1">1674</td><td align="left" rowspan="1" colspan="1">13</td></tr><tr><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1">KOSOFE</td><td align="left" rowspan="1" colspan="1">2273</td><td align="left" rowspan="1" colspan="1">13</td></tr><tr><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1">LAGOS MAINLAND</td><td align="left" rowspan="1" colspan="1">1642</td><td align="left" rowspan="1" colspan="1">20</td></tr><tr><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1">MUSHIN</td><td align="left" rowspan="1" colspan="1">2940</td><td align="left" rowspan="1" colspan="1">19</td></tr><tr><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1">OSHODI-ISOLO</td><td align="left" rowspan="1" colspan="1">3116</td><td align="left" rowspan="1" colspan="1">20</td></tr><tr><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1">SHOMOLU</td><td align="left" rowspan="1" colspan="1">1706</td><td align="left" rowspan="1" colspan="1">17</td></tr><tr><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1">AGEGE</td><td align="left" rowspan="1" colspan="1">3835</td><td align="left" rowspan="1" colspan="1">33</td></tr><tr><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1">AJEROMI IFELODUN</td><td align="left" rowspan="1" colspan="1">3333</td><td align="left" rowspan="1" colspan="1">19</td></tr><tr><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1">ALIMOSHO</td><td align="left" rowspan="1" colspan="1">5684</td><td align="left" rowspan="1" colspan="1">17</td></tr><tr><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1">APAPA</td><td align="left" rowspan="1" colspan="1">2694</td><td align="left" rowspan="1" colspan="1">48</td></tr><tr><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1">ETI-OSA</td><td align="left" rowspan="1" colspan="1">2925</td><td align="left" rowspan="1" colspan="1">41</td></tr><tr><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1">LAGOS ISLAND</td><td align="left" rowspan="1" colspan="1">1772</td><td align="left" rowspan="1" colspan="1">33</td></tr><tr><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1">SURULERE</td><td align="left" rowspan="1" colspan="1">3457</td><td align="left" rowspan="1" colspan="1">27</td></tr><tr><td align="left" rowspan="1" colspan="1"><bold>LAGOS</bold></td><td align="left" rowspan="1" colspan="1"><bold>Total</bold></td><td align="left" rowspan="1" colspan="1"><bold>46691</bold></td><td align="left" rowspan="1" colspan="1"><bold>20.5</bold></td></tr><tr><td align="left" rowspan="1" colspan="1">NASARAWA</td><td align="left" rowspan="1" colspan="1">AWE</td><td align="left" rowspan="1" colspan="1">517</td><td align="left" rowspan="1" colspan="1">18</td></tr><tr><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1">KEANA</td><td align="left" rowspan="1" colspan="1">625</td><td align="left" rowspan="1" colspan="1">31</td></tr><tr><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1">TOTO</td><td align="left" rowspan="1" colspan="1">368</td><td align="left" rowspan="1" colspan="1">12</td></tr><tr><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1">AKWANGA</td><td align="left" rowspan="1" colspan="1">947</td><td align="left" rowspan="1" colspan="1">34</td></tr><tr><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1">DOMA</td><td align="left" rowspan="1" colspan="1">1067</td><td align="left" rowspan="1" colspan="1">31</td></tr><tr><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1">KEFFI</td><td align="left" rowspan="1" colspan="1">1882</td><td align="left" rowspan="1" colspan="1">81</td></tr><tr><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1">KOKONA</td><td align="left" rowspan="1" colspan="1">1486</td><td align="left" rowspan="1" colspan="1">55</td></tr><tr><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1">LAFIA</td><td align="left" rowspan="1" colspan="1">3695</td><td align="left" rowspan="1" colspan="1">45</td></tr><tr><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1">NASSARAWA</td><td align="left" rowspan="1" colspan="1">1191</td><td align="left" rowspan="1" colspan="1">25</td></tr><tr><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1">NASSARAWA EGGON</td><td align="left" rowspan="1" colspan="1">852</td><td align="left" rowspan="1" colspan="1">23</td></tr><tr><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1">OBI</td><td align="left" rowspan="1" colspan="1">1393</td><td align="left" rowspan="1" colspan="1">37</td></tr><tr><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1">WAMBA</td><td align="left" rowspan="1" colspan="1">526</td><td align="left" rowspan="1" colspan="1">29</td></tr><tr><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1">KARU</td><td align="left" rowspan="1" colspan="1">5404</td><td align="left" rowspan="1" colspan="1">100</td></tr><tr><td align="left" rowspan="1" colspan="1"><bold>NASARAWA</bold></td><td align="left" rowspan="1" colspan="1"><bold>Total</bold></td><td align="left" rowspan="1" colspan="1"><bold>19953</bold></td><td align="left" rowspan="1" colspan="1"><bold>42.7</bold></td></tr><tr><td align="left" rowspan="1" colspan="1">ONDO</td><td align="left" rowspan="1" colspan="1">AKOKO SOUTH-EAST</td><td align="left" rowspan="1" colspan="1">730</td><td align="left" rowspan="1" colspan="1">35</td></tr><tr><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1">ILAJE</td><td align="left" rowspan="1" colspan="1">860</td><td align="left" rowspan="1" colspan="1">12</td></tr><tr><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1">ONDO-WEST</td><td align="left" rowspan="1" colspan="1">2006</td><td align="left" rowspan="1" colspan="1">28</td></tr><tr><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1">OWO</td><td align="left" rowspan="1" colspan="1">1066</td><td align="left" rowspan="1" colspan="1">19</td></tr><tr><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1">AKURE-SOUTH</td><td align="left" rowspan="1" colspan="1">2687</td><td align="left" rowspan="1" colspan="1">30</td></tr><tr><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1">ODIGBO</td><td align="left" rowspan="1" colspan="1">2328</td><td align="left" rowspan="1" colspan="1">40</td></tr><tr><td align="left" rowspan="1" colspan="1"><bold>ONDO</bold></td><td align="left" rowspan="1" colspan="1"><bold>Total</bold></td><td align="left" rowspan="1" colspan="1"><bold>9677</bold></td><td align="left" rowspan="1" colspan="1"><bold>26.2</bold></td></tr></tbody></table></alternatives><table-wrap-foot><fn id="nt101"><label><inline-formula><inline-graphic xlink:href="pone.0103619.e001.jpg"></inline-graphic></inline-formula></label><p> Per 1000 adult men refers to men age >18 years of age.</p></fn></table-wrap-foot></table-wrap></sec><sec id="s3d"><title>Estimates of FSWs by typology</title><p>The organizational typology of sex work differed substantially between the states. <xref ref-type="table" rid="pone-0103619-t005">Table 5</xref> shows that the most commonly identified typology across all included states was hotel or lodge-based FSWs, accounting for 32%, and this ranged from 23% of the FSWs in Cross River to 39% in Lagos. FSWs based in bars, night clubs, and casinos were the next largest category, accounting for 27% of the estimated number of FSW across the states and ranging from 13.7% in Nasarawa to 37.5% in Cross River. Overall, brothels accounted for 17% of FSW typologies, and ranged from 7.8% in Cross River to 26% in Ondo. Fifteen percent of FSWs were based in streets and other public places, and this ranged from 3% in Ondo to 29.8% in Nasarawa. Five percent of FSWs were classified as “other” and this included those who operate from home, trailer and truck stops, massage parlours, hostels and campuses, and escort services.</p><table-wrap id="pone-0103619-t005" orientation="portrait" position="float"><object-id pub-id-type="doi">10.1371/journal.pone.0103619.t005</object-id><label>Table 5</label><caption><title>Percentage distribution of FSWs by type of spot by state, Nigeria, 2012.</title></caption><alternatives><graphic id="pone-0103619-t005-5" xlink:href="pone.0103619.t005"></graphic><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">State</td><td colspan="6" align="left" rowspan="1">Type of spot</td><td align="left" rowspan="1" colspan="1">Total</td></tr><tr><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1">Brothel</td><td align="left" rowspan="1" colspan="1">Street/public</td><td align="left" rowspan="1" colspan="1">Home-based</td><td align="left" rowspan="1" colspan="1">Bar/night club/casino</td><td align="left" rowspan="1" colspan="1">Hotel/lodge</td><td align="left" rowspan="1" colspan="1">Other</td><td align="left" rowspan="1" colspan="1"></td></tr></thead><tbody><tr><td align="left" rowspan="1" colspan="1">FCT</td><td align="left" rowspan="1" colspan="1">19.38%</td><td align="left" rowspan="1" colspan="1">17.63%</td><td align="left" rowspan="1" colspan="1">5.55%</td><td align="left" rowspan="1" colspan="1">26.77%</td><td align="left" rowspan="1" colspan="1">27.47%</td><td align="left" rowspan="1" colspan="1">3.19%</td><td align="left" rowspan="1" colspan="1">24,376</td></tr><tr><td align="left" rowspan="1" colspan="1">ANAMBRA</td><td align="left" rowspan="1" colspan="1">8.50%</td><td align="left" rowspan="1" colspan="1">17.72%</td><td align="left" rowspan="1" colspan="1">1.36%</td><td align="left" rowspan="1" colspan="1">33.97%</td><td align="left" rowspan="1" colspan="1">29.68%</td><td align="left" rowspan="1" colspan="1">8.77%</td><td align="left" rowspan="1" colspan="1">5,920</td></tr><tr><td align="left" rowspan="1" colspan="1">BENUE</td><td align="left" rowspan="1" colspan="1">9.97%</td><td align="left" rowspan="1" colspan="1">18.55%</td><td align="left" rowspan="1" colspan="1">6.48%</td><td align="left" rowspan="1" colspan="1">31.84%</td><td align="left" rowspan="1" colspan="1">28.33%</td><td align="left" rowspan="1" colspan="1">4.83%</td><td align="left" rowspan="1" colspan="1">10,034</td></tr><tr><td align="left" rowspan="1" colspan="1">CROSS RIVER</td><td align="left" rowspan="1" colspan="1">7.80%</td><td align="left" rowspan="1" colspan="1">22.27%</td><td align="left" rowspan="1" colspan="1">3.19%</td><td align="left" rowspan="1" colspan="1">37.58%</td><td align="left" rowspan="1" colspan="1">23.01%</td><td align="left" rowspan="1" colspan="1">6.16%</td><td align="left" rowspan="1" colspan="1">9,838</td></tr><tr><td align="left" rowspan="1" colspan="1">LAGOS</td><td align="left" rowspan="1" colspan="1">21.34%</td><td align="left" rowspan="1" colspan="1">5.94%</td><td align="left" rowspan="1" colspan="1">2.14%</td><td align="left" rowspan="1" colspan="1">26.91%</td><td align="left" rowspan="1" colspan="1">39.69%</td><td align="left" rowspan="1" colspan="1">3.98%</td><td align="left" rowspan="1" colspan="1">46,691</td></tr><tr><td align="left" rowspan="1" colspan="1">NASARAWA</td><td align="left" rowspan="1" colspan="1">10.09%</td><td align="left" rowspan="1" colspan="1">29.81%</td><td align="left" rowspan="1" colspan="1">9.25%</td><td align="left" rowspan="1" colspan="1">13.74%</td><td align="left" rowspan="1" colspan="1">29.55%</td><td align="left" rowspan="1" colspan="1">7.57%</td><td align="left" rowspan="1" colspan="1">19,953</td></tr><tr><td align="left" rowspan="1" colspan="1">ONDO</td><td align="left" rowspan="1" colspan="1">25.89%</td><td align="left" rowspan="1" colspan="1">3.02%</td><td align="left" rowspan="1" colspan="1">1.61%</td><td align="left" rowspan="1" colspan="1">38.23%</td><td align="left" rowspan="1" colspan="1">28.68%</td><td align="left" rowspan="1" colspan="1">2.58%</td><td align="left" rowspan="1" colspan="1">9,677</td></tr><tr><td align="left" rowspan="1" colspan="1"><bold>Total (N%)</bold></td><td align="left" rowspan="1" colspan="1">16.98%</td><td align="left" rowspan="1" colspan="1">14.56%</td><td align="left" rowspan="1" colspan="1">4.27%</td><td align="left" rowspan="1" colspan="1">27.22%</td><td align="left" rowspan="1" colspan="1">32.23%</td><td align="left" rowspan="1" colspan="1">4.75%</td><td align="left" rowspan="1" colspan="1">126,489</td></tr></tbody></table></alternatives></table-wrap></sec><sec id="s3e"><title>Findings from the venue profiling and rural appraisal</title><p>The other components of the appraisal revealed that 31.4% of the venues profiled had FSWs, and these venues accounted for 56% of the estimated number of males and females who frequent venues seeking casual sexual partners. While a large proportion of the venues in Lagos (55%), Benue (42%), Cross River and Nasarawa (38% each), Ondo (33%) facilitated FSW networks; this was the case for only 17% and 26% of venues in Anambra and FCT respectively. In the rural areas, more than one-third of the unmarried and married males reported ever having sex with a FSW with 14.5% and 13.6% of the unmarried and married males respectively reporting having sex with a FSW in the last 6 months.</p></sec></sec><sec id="s4"><title>Discussion/Conclusions</title><p>To our knowledge this study was the first large scale attempt to geographically map the location of female sex worker populations in Nigeria and estimate their population sizes in the locations identified through the study. Our results show that there are large and diverse populations of FSWs with substantial geographic heterogeneity operating within the selected states. Understandably, Lagos, which is the commercial nerve center and most populous state in the country, has the largest FSW size estimate. However, when comparing the number of FSWs with the adult male population, the less populous states such as FCT, Nasarawa and Ondo, which have one-third the population of Lagos, have larger populations of FSWs relative to adult men. Similar results were also found in Pakistan and Kenya, both of which have undertaken mapping and size estimation of FSWs. In Kenya, the capital Nairobi also had the largest population estimates of FSWs. In Pakistan, Karachi, the most populous city, is home to the largest absolute number of FSWs. However, the less populous city of Nawabshah has the largest population of FSWs relative to men <xref rid="pone.0103619-Emmanuel1" ref-type="bibr">[7]</xref>. While the broad based classification of FSW typologies used in both countries is similar to the Nigerian context, the majority of the FSWs in Pakistan were found to be home-based while in Kenya and Nigeria more than half of the FSWs were found to be based in venues such as bars, hotels, and nightclubs) <xref rid="pone.0103619-Emmanuel1" ref-type="bibr">[7]</xref>, <xref rid="pone.0103619-Odek1" ref-type="bibr">[15]</xref>.</p><p>The SACAs are finding the mapping data exceptionally valuable in the scale up of HIV prevention efforts within their states as it has enabled them to identify and describe locations where sex work is conducted at high densities, or ‘hot spots’ thus ensuring optimal distribution and reach of HIV prevention services at these sites. It has also enabled them to employ a more strategic and systemic approach in prioritizing locations for FSW interventions. This is clearly illustrated in FCT where Gwagwalada, a remote area council, an academic hub due to the presence of a university, but with few commercial activities is prioritized above AMAC, a more centrally located and commercially viable area based on the fact that it shows the most epidemic potential in relation to the population of FSW/per thousand men. This “LGA prioritization” process has been central to the development of the implementation roll out plan for the targeted FSW prevention programmes within the states. It ensures that locations where sex work may be a significant driver of the HIV epidemic in terms of the large FSW population or FSW density per thousand men are sufficiently saturated with interventions before other areas. Furthermore, in most of these states where majority of the HIV prevention response is donor driven, being the “gatekeepers” of this information has put the SACAs in the driver's seat on the coordination of implementation roll out among the multiple donor agencies implementing HIV prevention programmes for FSWs. This improved coordination effort has markedly reduced duplication of efforts among partners implementing programmes in the field.</p><p>Knowledge about the typologies and operational dynamics of female sex work within their states is also enabling the SACAs to determine which strategy to employ in designing targeted FSW programmes that are applicable to their own local context <xref rid="pone.0103619-Emmanuel1" ref-type="bibr">[7]</xref>, <xref rid="pone.0103619-Chersich1" ref-type="bibr">[16]</xref>. States such as Nasarawa and Cross River, where a significant proportion of sex workers operate from street/public places emphasize peer outreach and provision of appropriate clinical services while states where most sex workers solicit at hotels/lodges and bars/night clubs (e.g. Lagos, Ondo, Benue) use alternate strategies such as engaging pimps, hotel staff and bar/night club staff to facilitate outreach and services. This is a novel approach in most of these states, as prevention programmes traditionally only targeted brothel based FSWs due to the impression that they constitute the majority of FSWs operating within states <xref rid="pone.0103619-National2" ref-type="bibr">[6]</xref>. This has clearly been disproven as our data shows that out of the seven states mapped, only Cross River had a significant proportion of FSWs operating from brothels. In designing these targeted programmes, the SACAs have also taken the specific risk factors for the different sex worker typologies into consideration as the brothel based FSW have been found to have a higher HIV prevalence, higher number of clients and less correct knowledge about HIV transmission than the non brothel based FSW <xref rid="pone.0103619-National2" ref-type="bibr">[6]</xref>.</p><p>More importantly, the SACAs are using the mapping data to design an effective and cost efficient approach for program implementation as the information obtained is being used to determine the resources required to provide the necessary services to a high proportion of FSWs in a catchment area <xref rid="pone.0103619-Emmanuel2" ref-type="bibr">[17]</xref>. This information has enabled the SACAs to determine the cost of providing FSW interventions in particular hotspots thereby ensuring efficient allocation of resources to civil society organizations (CSOs) implementing FSW programmes in different parts of the state. This is exemplified in Anambra, where the cost of interventions for FSWs per spot in Akwa North with an FSW estimate of only 587 is significantly higher than in Akwa South with an FSW estimate of 1269 due to the fact that the average number of FSWs found per spot in Akwa North (15) is higher than Akwa South (9). It would have been impossible to make such detailed distinctions between both LGAs without the mapping data. The mapping goes even further by providing details about the specific typologies of sex work to be prioritized in each LGA. This information has enhanced the SACA's ability to effectively coordinate and monitor program implementation and thus prevent the “one size fits all” and “one off” approach normally used by CSOs implementing such HIV prevention programmes for FSWs <xref rid="pone.0103619-UNFPA1" ref-type="bibr">[18]</xref>. Furthermore, the mapping data has been instrumental in the design of the proposed impact evaluation that will evaluate the impact of Nigeria's targeted HIV prevention program on averting new HIV infections among FSWs, their clients and communities. The mapping data served as the sampling frame for the random allocation of the units of intervention.</p><p>The SACAs are also using the information obtained from the Venue Profiling and Rural Appraisal to determine the types of “networking venues” to target. Given that there is significant overlap between FSW and casual networking sites across the states, the SACAs are targeting venues where men and women seeking casual partners frequent, as this would be an efficient strategy for targeting FSWs. In the rural areas, there has been an identified need to design specifically targeted programmes for unmarried and married men to address the high rate of high risk sexual behaviors exhibited from the appraisals.</p><p>Our geographic mapping approach has several limitations. First, because the geographic mapping methodology relies on numeric estimates rather than a count of FSWs at the spots identified this may lead to variability in the estimates derived. The methodology addresses this limitation by averaging estimates for spots identified by a large number of secondary key informants, and validating estimates for spots identified by the least number of secondary key informants through interviews with the FSWs themselves. Secondly, since the methodology is not individually based, it could overestimate the size of FSWs if they frequent multiple locations. However, since the methodology is rapid and focuses on the minimum, maximum and usual number of FSWs at a spot on a given day, the range of estimates (minimum to maximum) is unlikely to be skewed substantially. Moreover, the final estimates derived are adjusted to reflect the extent to which FSWs frequent multiple spots, based on primary key informant interviews. A third limitation is that the mapping methodology relies on secondary key informants to identify spots frequented by key populations. It is possible that some spots are missed and that females engaging in transactional sex or FSWs who are not venue based (such as those who contact clients using cell phones or social media) are likely not captured.</p><p>Despite these limitations, the geographic mapping approach has provided these states with an important baseline useful for scaling up all components of an integrated HIV prevention programme for FSWs. It will also enable implementers to utilize the cluster model of implementation – a highly effective implementation strategy whereby, a cluster of hotspots within each LGA is identified and linked to health facilities offering HIV testing, STI management, PMTCT and ART; and other key population-friendly services thereby ensuring saturation of the hotspots with the full minimum package of prevention interventions. It will enable programme managers of HIV prevention programmes for FSWs to determine, in a detailed way, which implementer is implementing which component of the FSW program in which hotspot. The use of such novel approaches that can accelerate the rapid scaling up of prevention programmes is imperative as programmes in several other settings have shown that a few interventions, implemented at sufficient scale, can markedly reduce the burden of HIV among sex workers and their clients and make considerable population-level impact <xref rid="pone.0103619-Rojanapithayakorn1" ref-type="bibr">[19]</xref>, <xref rid="pone.0103619-Verma1" ref-type="bibr">[20]</xref>. In the future, there is a need to further explore the socio-demographic and economic profiles and sexual behaviours of the FSWs by operational typology as well as understand their unmet needs in order to inform the design and implementation of even more comprehensive HIV prevention programmes for FSWs.</p></sec></body><back><ack><p><bold>Disclaimer:</bold> The findings, interpretations, and conclusions in this paper are those of the authors and do not necessarily reflect the views of the Executive Directors of the World Bank or the governments they represent. The World Bank does not guarantee the accuracy of the data included in this work. The authors wish to acknowledge the efforts of the State Agencies for the Control of AIDS teams from Lagos, FCT, Ondo, Cross River, Benue, Nasarawa and Anambra in successfully conducting the mapping and size estimation. The authors also thank all members of the National Prevention Technical Working Group and the University of Manitoba – Center for Global Public Health for their technical assistance in designing the study and for facilitating the training and fieldwork. 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