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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/12235

Title: Joint Modeling of HCV and HIV Infections among Injecting Drug Users in Italy Using Repeated Cross-Sectional Prevalence Data
Authors: Del Fava, Emanuele
Kasim, Adetayo
Muhammed, Usman
Shkedy, Ziv
Hens, Niel
Aerts, Marc
Bollaerts, Kaatje
Scalia Tomba, Gianpaolo
Vickerman, Peter
Sutton, Andrew J.
Wiessing, Lucas
Kretzschmar, Mirjam
Issue Date: 2011
Citation: Statistical Communications in Infectious Diseases, 3(1) (ART N° 1), p. 1-30.
Abstract: During their injecting career, injecting drug users (IDUs) are exposed to some infections, like hepatitis C virus (HCV) infection and human immunodeficiency virus (HIV) infection, due to their injecting behavioral risk factors, such as sharing syringes or other paraphernalia containing infected blood, or sexual behavior risk factors. If we consider that these IDUs might belong to a social network of people where these behavioral risk factors are spread, then HCV and HIV infections might be associated at both the individual and the population level. In this paper, we study the association between HCV and HIV infection at the population level using aggregate data. Our aim is to define a hierarchy of structured models with which the association between HCV and HIV infection at population level and the time trend of prevalence can be investigated. The data analyzed in the paper are “diagnostic testing data,” which consist of repeated cross-sectional prevalence measurements from 1998 to 2006 for HCV and HIV infection, obtained from a sample of 515 drug treatment centers spread among the 20 regions in Italy, where subjects went for a serum diagnostic test. Since we do not have any individual data, it is not possible to relate these prevalence data to socio-demographic or behavioral risk data. Each region defines a cluster with repeated prevalence data for HCV and HIV infection over time. Several modeling approaches, such as generalized linear mixed models (GLMMs) and hierarchical Bayesian models are applied to the data. First, we test different covariance structures for the region-specific random effects in the GLMM context; second, a hierarchical Bayesian model is used to refit the best GLMM in order to obtain the posterior distribution for the parameters of primary interest. We found that the correlation at population level between HCV and HIV is approximately 0.68 and the prevalence of the two infections generally decreased over the years, compared to the situation in 1998.
URI: http://hdl.handle.net/1942/12235
DOI: 10.2202/1948-4690.1009
ISSN: 1948-4690
Category: A1
Type: Journal Contribution
Validation: vabb, 2014
Appears in Collections: Research publications

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