Literature DB >> 2919245

Statistical methods for monitoring the AIDS epidemic.

S L Zeger1, L C See, P J Diggle.   

Abstract

This article describes statistical methods for monitoring the epidemic of the acquired immunodeficiency syndrome (AIDS). A log-linear model is proposed to estimate AIDS incidence and its growth rate while taking account of delays in case reporting. An empirical Bayes approach for estimating the epidemic growth rate in low prevalence subgroups is introduced. These methods are illustrated with an analysis of AIDS incidence trends for seven risk groups in each of six geographic regions using the Centers for Disease Control AIDS case registry data as of September 1987. The analysis finds that AIDS incidence is currently doubling about once every two years and that the relative composition of new cases is shifting away from the older epidemics such as in north-eastern homosexual communities.

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Year:  1989        PMID: 2919245     DOI: 10.1002/sim.4780080104

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


  7 in total

1.  Short-term predictions of HIV prevalence and AIDS incidence.

Authors:  J C Hendriks; G F Medley; S H Heisterkamp; G J Van Griensven; P J Bindels; R A Coutinho; J A Van Druten
Journal:  Epidemiol Infect       Date:  1992-08       Impact factor: 2.451

2.  Estimating the human immunodeficiency virus infection curve of intravenous drug users in Lombardia, Italy.

Authors:  A Salvaggio
Journal:  Eur J Epidemiol       Date:  1995-04       Impact factor: 8.082

3.  A change-point model for reporting delays under change of AIDS case definition.

Authors:  F Tabnak; H G Müller; J L Wang; J M Chiou; R K Sun
Journal:  Eur J Epidemiol       Date:  2000       Impact factor: 8.082

4.  Incidence of tuberculosis among reported AIDS cases in Quebec from 1979 to 1996.

Authors:  P Brassard; R S Remis
Journal:  CMAJ       Date:  1999-06-29       Impact factor: 8.262

5.  Estimating the Sizes of Populations At Risk of HIV Infection From Multiple Data Sources Using a Bayesian Hierarchical Model.

Authors:  Le Bao; Adrian E Raftery; Amala Reddy
Journal:  Stat Interface       Date:  2015-04-01       Impact factor: 0.582

6.  A Bayesian Approach to Real-Time Monitoring and Forecasting of Chinese Foodborne Diseases.

Authors:  Xueli Wang; Moqin Zhou; Jinzhu Jia; Zhi Geng; Gexin Xiao
Journal:  Int J Environ Res Public Health       Date:  2018-08-13       Impact factor: 3.390

7.  Bayesian spatiotemporal modeling with sliding windows to correct reporting delays for real-time dengue surveillance in Thailand.

Authors:  Chawarat Rotejanaprasert; Nattwut Ekapirat; Darin Areechokchai; Richard J Maude
Journal:  Int J Health Geogr       Date:  2020-03-03       Impact factor: 3.918

  7 in total

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