ISSN: 1080-6059
Volume 14, Number 4–April 2008
Appendix
Research
Seroprevalence and Risk Factors for Human Herpesvirus 8 Infection, Rural Egypt1
Sam M. Mbulaiteye,* 
Ruth M. Pfeiffer,* Bryan Dolan,* Victor
C.W. Tsang,† John Noh,† Nabiel N.H. Mikhail,‡ Mohamed Abdel-Hamid,§¶ Mohamed
Hashem,§¶ Denise Whitby,# G. Thomas Strickland,¶ and James J. Goedert*
*National Cancer Institute, Bethesda, Maryland, USA;
†Centers for Disease Control and Prevention, Atlanta, Georgia, USA; ‡Assiut
University, Cairo, Egypt; §National Hepatology and Tropical Medicine Research
Institute, Cairo, Egypt; ¶University of Maryland School of Medicine, Baltimore,
Maryland, USA; and #National Cancer Institute-Frederick, Frederick, MD, USA
Suggested citation for this article
Statistical Methods
Because there is no standard for testing for human herpesvirus 8 (HHV-8) infection, we defined infection status by using a statistical mixture, or latent class, model for the optical density (OD) readings of the K8.1 assay (1). Briefly, the model considered the OD assay readings for each participant to arise from either an uninfected (I = 0) or an infected (I = 1) population. The probability density function of y = (xλ-1)/ λ, where x denoted the assay readings of the K8.1 assay, is g(y) = pf(y;α0) + (1-p) f(y;α1). The probability density function f(y;α1) reflected the infected population, whereas f(y;α0) was the probability density function corresponding to uninfected. P is the proportion of uninfected persons in the population. Details on parameters of the component densities and the estimation of the parameters are described elsewhere (1). Patients were classified as infected, I = 1, if the posterior probability of infection, given x, i.e., pr(I = 1| x) = pf(y;α0) / { pf(y;α0) + (1-p) f(y; α1)} was >0.5 and as uninfected otherwise. This classification rule minimized the overall misclassification probability based on the mixture model when discriminating infected from uninfected subjects.
The association of HHV-8 seropositivity with demographic, behavioral, and clinical risk factors was determined by fitting logistic regression models to computed odds ratios (OR) for infection status (PROC GENMOD, SAS 8.0, SAS, Cary, NC, USA). Because HHV-8 seropositivity is age dependent, we performed analyses separately for children and adults to minimize confounding from age. In addition, because we found significant differences by sex and age in the seroepidemiology of schistosomiasis (all p valus <0.05), which we had postulated a priori would be associated with HHV-8, we performed overall and sex-specific analyses for the HHV-8 associations among adults. Because many exposures are age dependent, we constructed multivariable models including variables that were significant at p<0.1 in univariate analyses separately for children, men, and women to determine the independent contribution of variables to HHV-8 seropositivity. Age was fitted with a trend whenever this resulted in a statistically significant (p<0.05) improved model fit; otherwise, it was fitted as a categoric variable with dummy values for the categories. Schistosomal seropositivity was included in all models because we postulated a priori that it was associated with HHV-8 seropositivity. We used generalized estimation equations to calculate 95% confidence intervals to account for correlations between participants living in the same household (2). We assumed an equally correlated working correlation matrix when computing the variances of the OR estimates, but other working correlations yielded similar results. We assumed that a 2-tailed p value <0.05 was statistically significant and that p values between 0.1 and 0.05 represented a trend.
Sensitivity Analyses
Appendix Tables 1, 2, 3 show our sensitivity analyses to address the concern that different model parameterizations may give very different estimates of posterior probabilities of infection. Appendix Table 1 shows HHV-8 seropositivity classification based on the mixture models with the Box Cox transformation and a) normal component densities, b) polynomial degree 1 and 2, c) polynomial degree 2, which was used in the paper. The table shows that no large variations in HHV-8 prevalence are observed, and that the classification of infection based on the posterior probabilities of the respective models leads to different classification of at most 4/734 individuals.
Appendix Table 2 addresses the concern that the degree of uncertainty in the models may lead to widely varying estimates of HHV-8 prevalence and thus, widely varying odds ratios in multivariable association models. The adjusted logistic regression associations before and after exclusion of individuals whose posterior probability was between 0.4-0.6 (38/734 subjects) and using HHV-8 status based on classification from 2 other parameterizations are presented in Appendix Tables 2 and 3. These tables show that the results presented in the manuscript do not appear to be overly sensitive to inclusion of "indeterminate range" individuals (Appendix Table 2) nor to the specific parameterization of the mixture model (Appendix Table 3). The different models gave very similar associations. The stability in the results obtained suggests that the associations that we present are likely valid.
References
- Pfeiffer RM, Carroll RJ, Wheeler W, Whitby D, Mbulaiteye S. Combining assays for estimating prevalence of human herpesvirus 8 infection using multivariate mixture models. Biostatistics. 2008;9:137–51.
- Zeger SL, Liang KY. Longitudinal data analysis for discrete and continuous outcomes. Biometrics. 1986;42:121–30.
Appendix Tables
Appendix Table 1. Cross-tabulation
of classification of patients by different models to estimate human
herpesvirus 8 seropositivity
Appendix Table 2. Adjusted human herpesvirus 8
associations with and without indeterminate participants
Appendix Table 3. Adjusted human herpesvirus 8 associations
by using alternative models
Suggested Citation for this Article
Mbulaiteye SM, Pfeiffer RM, Dolan B, Tsang VCW, Noh J, Mikhail NNH, et al. Seroprevalence and risk factors for human herpesvirus 8 infection, rural Egypt. Emerg Infect Dis [serial on the Internet]. 2008 April [date cited]. Available from http://www.cdc.gov/EID/content/14/4/586.htm
1Results were presented, in part, at the 9th International Workshop on Kaposi's Sarcoma–associated Herpesvirus (KSHV) and Related Agents, Cape Cod, Massachusetts, USA, July 12–15, 2006.
Please use the form below to submit correspondence to the authors or contact them at the following address:
Sam M. Mbulaiteye, 6120 Executive Blvd, Executive Plaza South, Rm 7080, Rockville, MD 20852-7248, USA; email: mbulaits@mail.nih.gov
Please contact the EID Editors at eideditor@cdc.gov
The opinions expressed by authors contributing to this journal do not necessarily reflect the opinions of the U.S. Department of Health and Human Services, the Public Health Service, the Centers for Disease Control and Prevention, or the authors' affiliated institutions. Use of trade names is for identification only and does not imply endorsement by any of the groups named above.
This page posted March 21, 2008
This page last reviewed March 21, 2008
Centers for Disease Control and Prevention, 1600 Clifton Rd, Atlanta, GA 30333, U.S.A
Tel: (404) 639-3311 / Public Inquiries: (404) 639-3534 / (800) 311-3435