Seroprevalence of Human Herpesvirus 8 Infection in Individuals From Health Care Centers in Mozambique: Potential for Endemic and Epidemic Kaposi`s Sarcoma

Human herpesvirus 8 (HHV-8) infection is common in sub-Saharan Africa, but its prevalence in Mozambique is unknown. The seroprevalence of HHV-8 in a cohort of individuals seen at public health centers in Northern (n = 208), Central (n = 226), or Southern (n = 318) Mozambique was examined. All individuals were interviewed to obtain socioeconomic, demographic and clinical data and were tested for serum anti-HHV-8 antibodies using an immunofluorescence assay. The overall frequency of HHV-8 antibodies was 21.4% and, in spite of the diversity of epidemiological characteristics of the tested individuals, did not differ significantly among regions: 18.7%, 24.3% and 21.4% in the North, Center, and South, respectively (chi(2), 2.37; P = 0.305). The variables that were associated significantly with the presence of HHV-8 antibodies were gender, age, level of education, number of siblings and HIV serostatus, but these differed across the regions. In the North, although tested individuals lived under poor socioeconomic conditions, no association between HHV-8 infection and household variables was detected, with the exception of the number of siblings (P = 0.042). In the Central region, HHV-8 infection was associated with gender (P = 0.010), the number of household members (P = 0.031), and the place of attendance (P = 0.021). In the South, HHV-8 infection was associated with the number of siblings (P = 0.023) and HIV status (P = 0.002). The overall prevalence of HHV-8 seropositivity increased with age. These results demonstrate that Mozambique is another country in Africa with endemic HHV-8 infection, and, because of the AIDS epidemic, continued access to antiretroviral treatment is necessary to avert an outbreak of AIDS-Kaposi`s sarcoma. J. Med. Virol. 82:1216-1223, 2010. (C) 2010 Wiley-Liss, Inc.

Heteroscedastic Nonlinear Regression Models

In this article, we present a generalization of the Bayesian methodology introduced by Cepeda and Gamerman (2001) for modeling variance heterogeneity in normal regression models where we have orthogonality between mean and variance parameters to the general case considering both linear and highly nonlinear regression models. Under the Bayesian paradigm, we use MCMC methods to simulate samples for the joint posterior distribution. We illustrate this algorithm considering a simulated data set and also considering a real data set related to school attendance rate for children in Colombia. Finally, we present some extensions of the proposed MCMC algorithm.