Literature DB >> 9483724

The current status of methods for estimating the prevalence of human immunodeficiency virus in the United States of America.

J M Karon1, M Khare, P S Rosenberg.   

Abstract

The prevalence of human immunodeficiency virus (HIV) infection can be estimated by two distinct methods. One method, back-calculation, is a complex statistical procedure that estimates the HIV epidemic curve. The second method is based on data from population-based surveys, which provide estimates of the proportion of persons infected with HIV within subgroups, and on the known or estimated population totals for these subgroups. Estimates from these methods are subject to substantial uncertainty and bias, both of which are difficult to quantify. We review recent use of these procedures to estimate HIV prevalence in the United States of America. We also summarize new data on the uncertainty and the bias in these estimates. Reliable estimates of HIV prevalence can be made only by synthesizing estimates from several procedures and by a comprehensive evaluation of relevant data. Future estimates of HIV prevalence will require modifications of these methods or the development of new methods.

Entities:  

Mesh:

Year:  1998        PMID: 9483724     DOI: 10.1002/(sici)1097-0258(19980130)17:2<127::aid-sim756>3.0.co;2-r

Source DB:  PubMed          Journal:  Stat Med        ISSN: 0277-6715            Impact factor:   2.373


  6 in total

1.  Estimates of HIV prevalence in a highly endemic area of China: Dehong Prefecture, Yunnan Province.

Authors:  Yujiang Jia; Jiangping Sun; Lu Fan; Duan Song; Shuming Tian; Yuecheng Yang; Manhong Jia; Lin Lu; Xinhua Sun; Sanguo Zhang; Andrzej Kulczycki; Sten H Vermund
Journal:  Int J Epidemiol       Date:  2008-10-14       Impact factor: 7.196

2.  Interpopulation variation in HIV testing promptness may introduce bias in HIV incidence estimates using the serologic testing algorithm for recent HIV seroconversion.

Authors:  Edward White; Gary Goldbaum; Steven Goodreau; Thomas Lumley; Stephen E Hawes
Journal:  Sex Transm Infect       Date:  2010-06-24       Impact factor: 3.519

3.  Monitoring the incidence of HIV infection in the United States.

Authors:  Lisa M Lee; Matthew T McKenna
Journal:  Public Health Rep       Date:  2007       Impact factor: 2.792

4.  An HIV prevalence-based model for estimating urban risk populations of injection drug users and men who have sex with men.

Authors:  Spencer Lieb; Samuel R Friedman; Mary Beth Zeni; Dale D Chitwood; Thomas M Liberti; Gary J Gates; Lisa R Metsch; Lorene M Maddox; Tamara Kuper
Journal:  J Urban Health       Date:  2004-09       Impact factor: 3.671

5.  Sources of data for improved surveillance of HIV/AIDS in China.

Authors:  Yujiang Jia; Fan Lu; Xinhua Sun; Sten H Vermund
Journal:  Southeast Asian J Trop Med Public Health       Date:  2007-11       Impact factor: 0.267

6.  Estimating adult HIV prevalence in the UK in 2003: the direct method of estimation.

Authors:  C A McGarrigle; S Cliffe; A J Copas; C H Mercer; D DeAngelis; K A Fenton; B G Evans; A M Johnson; O N Gill
Journal:  Sex Transm Infect       Date:  2006-06       Impact factor: 3.519

  6 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.