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<title>Emanuele Del Fava</title>
<copyright>Copyright (c) 2012  All rights reserved.</copyright>
<link>http://works.bepress.com/emanuele_delfava</link>
<description>Recent documents in Emanuele Del Fava</description>
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<title>Joint Modeling of HCV and HIV Co-Infection among Injecting Drug Users in Italy and Spain Using Individual Cross-Sectional Data</title>
<link>http://works.bepress.com/emanuele_delfava/2</link>
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<pubDate>Sat, 06 Aug 2011 07:06:27 PDT</pubDate>
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	<p>The aim of the analysis presented in this paper is to study co-infection with hepatitis C virus (HCV) and human immunodeficiency virus (HIV) in injecting drug users (IDUs) using a joint modeling approach that makes use of multivariate statistical methods for current status data.</p>
<p>Using marginal models, we estimate association measures between HCV and HIV infections at individual level, i.e., odds ratios and correlation coefficients, and we regress them against some risk factors, e.g., the length of the injecting career, the age at first injection, the ever sharing of syringes, and the frequency of current injecting. In addition, we fit random-effects models that take into account the individual heterogeneity in the acquisition of the infections. For our analysis, we use cross-sectional data from two independent serological surveys, one carried out in Italy (IT) in 2005 on 856 subjects, and the other in three Spanish (ES) cities, between 2001 and 2003, on 589 subjects.</p>
<p>We found that the infections are positively associated within individuals, e.g., OR<sub>IT</sub>=2.56 with 95% confidence interval (CI) (1.43, 6.68) and OR<sub>ES</sub>= 2.42, with 95% CI (1.41, 4.30). We found that the odds ratio and the correlation between HCV and HIV infections increase positively with the length of the injecting career. Moreover, they are found to be significantly positive in case IDUs have never shared syringes or report low injecting frequencies. The variance of the individual random effects is positive, e.g., σ<sub>b</sub><sup>2</sup>=0.34 (0.14, 0.62), indicating that there is significant individual heterogeneity in the acquisition of the infections.</p>
<p>Our results show that a significant association between HCV and HIV infections within IDUs is related to significant individual heterogeneity in the acquisition of the infections. Indeed, the association between these infections in IDUs who report ever sharing syringes is not significant, which can be explained by a higher homogeneity in their behaviors and, therefore, in their acquisition of the infections.</p>

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<author>Emanuele Del Fava et al.</author>


<category>Categorical Data Analysis</category>

<category>Disease Modeling</category>

<category>Multivariate Analysis</category>

<category>Statistical Models</category>

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<item>
<title>Joint Modeling of HCV and HIV Infections among Injecting Drug Users in Italy Using Repeated Cross-Sectional Prevalence Data</title>
<link>http://works.bepress.com/emanuele_delfava/1</link>
<guid isPermaLink="true">http://works.bepress.com/emanuele_delfava/1</guid>
<pubDate>Sat, 06 Aug 2011 07:06:24 PDT</pubDate>
<description>
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	<p>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.</p>

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</description>

<author>Emanuele Del Fava et al.</author>


<category>Multivariate Analysis</category>

<category>Categorical Data Analysis</category>

<category>Bayesian modeling</category>

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