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Factors influencing the difference in HIV prevalence between antenatal clinic and general population in sub-Saharan Africa

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dc.contributor.author Glynn, J. R. en_US
dc.contributor.author Buvé, A. en_US
dc.contributor.author Caraël, M. en_US
dc.contributor.author Musonda, R. M. en_US
dc.contributor.author Kahindo, M. en_US
dc.contributor.author Macauley, I. en_US
dc.contributor.author Tembo, F. en_US
dc.contributor.author Zekeng, L. en_US
dc.date.accessioned 2007-12-06T14:47:03Z
dc.date.available 2007-12-06T14:47:03Z
dc.date.issued 2001 en_US
dc.identifier.issn 0269-9370 en_US
dc.identifier.other ITG-M2A en_US
dc.identifier.other MICRO en_US
dc.identifier.other U-HIVSTD en_US
dc.identifier.other JIF en_US
dc.identifier.other ABSTRACT en_US
dc.identifier.uri http://hdl.handle.net/10390/1773
dc.description Not the final published version
dc.description.abstract OBJECTIVE: To compare HIV prevalence in antenatal clinics (ANC) and the general population, and to identify factors determining the differences that were found. DESIGN: Cross-sectional surveys in the general population and in ANC in three cities. METHODS: HIV prevalence measured in adults in the community was compared with that measured by sentinel surveillance in ANC in Yaoundé, Cameroon, Kisumu, Kenya, and Ndola, Zambia. RESULTS: In Yaoundé and Ndola, the HIV prevalence in ANC attenders was lower than that in women in the population overall, and for age groups over 20 years. In Kisumu, the HIV prevalence in ANC attenders was similar to that in women in the population at all ages. The only factors identified that influenced the results were age, marital status, parity, schooling, and contraceptive use. The HIV prevalence in women in ANC was similar to that in the combined male and female population aged 15-40 years in Yaoundé and Ndola, but overestimated it in Kisumu. In Yaoundé and Ndola, the overall HIV prevalence in men was approximated by using the age of the father of the child reported by ANC attenders, but this method overestimated the HIV prevalence in Kisumu, and did not give good age-specific estimates. CONCLUSION: Few factors influenced the difference in HIV prevalence between ANC and the population, which could aid the development of adjustment procedures to estimate population HIV prevalence. However, the differences between cities were considerable, making standard adjustments difficult. The method of estimating male HIV prevalence should be tested in other sites. en_US
dc.language English en_US
dc.publisher Lippincott, Williams & Wilkins
dc.subject Viral diseases en_US
dc.subject HIV en_US
dc.subject Prevalence en_US
dc.subject Surveillance en_US
dc.subject Monitoring en_US
dc.subject Geographical distribution en_US
dc.subject Prenatal care en_US
dc.title Factors influencing the difference in HIV prevalence between antenatal clinic and general population in sub-Saharan Africa en_US
dc.type Article en_US
dc.citation.issue Africa en_US
dc.citation.jtitle AIDS en_US
dc.citation.volume 15 en_US
dc.citation.pages 1717-1725 en_US
dc.publisher.place Philadelphia
dc.identifier.pmid http://www.ncbi.nlm.nih.gov/pubmed/11546948
dc.citation.jabbreviation AIDS en_US


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