Literature DB >> 10649297

Joint analysis of HIV and AIDS surveillance data in back-calculation.

R Bellocco1, I C Marschner.   

Abstract

AIDS surveillance data are the main source of information to perform back-calculation of HIV incidence. We propose a method to incorporate additional information gained by linkage with an HIV surveillance system, containing data on the time of first positive HIV test. In this paper we generalize an earlier method that was developed to use HIV testing data available only for AIDS cases. The new method also makes use of cases with an HIV positive test who have not yet developed AIDS, typically a substantial proportion of the HIV-infected population. Furthermore, we use a more realistic model for the HIV testing rate, incorporating dependence on both time since infection and calendar time. The method makes use of an EM algorithm with generalized additive model smoothing, and is applied to data from Veneto, a region of northern Italy. Our results show that HIV incidence in Veneto peaked in the late 1980s, and decreased thereafter. Importantly, the HIV incidence estimates based on joint analysis of HIV and AIDS surveillance data are more efficient than estimates based on AIDS surveillance data alone. Our estimates also show a decreasing trend in the HIV testing rate over time, which leads to the conclusion that the interval between HIV infection and first positive test has lengthened over time. Furthermore, it is found that for infected individuals, the probability of seeking on HIV test is highest soon after infection. Copyright 2000 John Wiley & Sons, Ltd.

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Year:  2000        PMID: 10649297     DOI: 10.1002/(sici)1097-0258(20000215)19:3<297::aid-sim340>3.0.co;2-6

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


  4 in total

1.  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

2.  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

3.  Extending Bayesian back-calculation to estimate age and time specific HIV incidence.

Authors:  Francesco Brizzi; Paul J Birrell; Martyn T Plummer; Peter Kirwan; Alison E Brown; Valerie C Delpech; O Noel Gill; Daniela De Angelis
Journal:  Lifetime Data Anal       Date:  2019-02-27       Impact factor: 1.588

Review 4.  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

  4 in total

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