Literature DB >> 10797514

Estimating HIV incidence using dates of both HIV and AIDS diagnoses.

J Cui1, N G Becker.   

Abstract

Knowledge of HIV incidence is important to formulate sensible strategies aimed at controlling the HIV/AIDS epidemic. Back-projection is one of the methods for reconstructing the HIV incidence curve from AIDS incidence data. However, because of the low risk of developing AIDS during the first few years after infection, precise estimates of HIV incidence for the recent past are unlikely if we use AIDS incidence data only. As a result there have been recent attempts to use, not only the date of AIDS diagnosis, but also to use the date of their first positive HIV test. The objective of this paper is to incorporate into back-projection the additional information provided by those individuals who have tested HIV positive but have not yet developed AIDS. This adds information on a very large number of other individuals, and provides the hope that the precision of back-projection is improved considerably. The date of a positive HIV test or an AIDS diagnosis of an individual, whichever comes first, is used in a generalized convolution equation for the purpose of back-projection. The method is illustrated by an application to Australian HIV and AIDS data. Study results show that dramatic improvement in precision is gained for estimates of HIV incidence in recent years when both HIV and AIDS diagnosis dates are used on all individuals. Copyright 2000 John Wiley & Sons, Ltd.

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Year:  2000        PMID: 10797514     DOI: 10.1002/(sici)1097-0258(20000515)19:9<1165::aid-sim419>3.0.co;2-7

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


  10 in total

Review 1.  How can we better identify early HIV infections?

Authors:  Nora E Rosenberg; Christopher D Pilcher; Michael P Busch; Myron S Cohen
Journal:  Curr Opin HIV AIDS       Date:  2015-01       Impact factor: 4.283

2.  The status of national HIV case surveillance, United States 2006.

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

3.  Estimation of HIV incidence in the United States.

Authors:  H Irene Hall; Ruiguang Song; Philip Rhodes; Joseph Prejean; Qian An; Lisa M Lee; John Karon; Ron Brookmeyer; Edward H Kaplan; Matthew T McKenna; Robert S Janssen
Journal:  JAMA       Date:  2008-08-06       Impact factor: 56.272

4.  Monitoring the severe acute respiratory syndrome epidemic and assessing effectiveness of interventions in Hong Kong Special Administrative Region.

Authors:  P H Chau; P S F Yip
Journal:  J Epidemiol Community Health       Date:  2003-10       Impact factor: 3.710

5.  A Bayesian hierarchical model with novel prior specifications for estimating HIV testing rates.

Authors:  Qian An; Jian Kang; Ruiguang Song; H Irene Hall
Journal:  Stat Med       Date:  2015-11-15       Impact factor: 2.373

6.  Characterizing trends in HIV infection among men who have sex with men in Australia by birth cohorts: results from a modified back-projection method.

Authors:  Handan Wand; David Wilson; Ping Yan; Andrea Gonnermann; Ann McDonald; John Kaldor; Matthew Law
Journal:  J Int AIDS Soc       Date:  2009-09-18       Impact factor: 5.396

7.  Is back-projection methodology still relevant for estimating HIV incidence from national surveillance data?

Authors:  Kylie-Ann Mallitt; David P Wilson; Ann McDonald; Handan Wand
Journal:  Open AIDS J       Date:  2012-09-07

8.  Declining trend in HIV new infections in Guangxi, China: insights from linking reported HIV/AIDS cases with CD4-at-diagnosis data.

Authors:  Xiaodan Sun; Wenmin Yang; Sanyi Tang; Mingwang Shen; Tianyang Wang; Qiuying Zhu; Zhiyong Shen; Shuai Tang; Huanhuan Chen; Yuhua Ruan; Yanni Xiao
Journal:  BMC Public Health       Date:  2020-06-12       Impact factor: 3.295

9.  Estimating the incidence and diagnosed proportion of HIV infections in Japan: a statistical modeling study.

Authors:  Hiroshi Nishiura
Journal:  PeerJ       Date:  2019-01-15       Impact factor: 2.984

Review 10.  Modeling methods for estimating HIV incidence: a mathematical review.

Authors:  Xiaodan Sun; Hiroshi Nishiura; Yanni Xiao
Journal:  Theor Biol Med Model       Date:  2020-01-22       Impact factor: 2.432

  10 in total

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