| Title | Hepatitis C: epidemiologic investigation and therapeutical treatment in Greece | |
| Authors | Marianna Diomidous1, Stelios Zimeras2, Dimitris Zikos1, Dimitris Charalabides3 and Ioannis Pistolis1
1. Department of Public Health, Faculty of Nursing, University of Athens, Athens, Hellas 2. Department of Statistics and Actuarial Financial Mathematics, University of the Aegean, Samos, Hellas 3. MD Saint Olga Hospital, N. Ionia, Athens, Hellas |
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| Citation | Diomidous, M., Zimeras, S., Zikos, D., Charalabides, D., Pistolis, I.: Hepatitis C: epidemiologic investigation and therapeutical treatment in Greece, Epitheorese Klin. Farmakol. Farmakokinet. 23(2): 51-57 (2009) | |
| Publication Date | Accepted for publication (Final version): July 1, 2009 | |
| Full Text Language | English | |
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| Keywords | Hepatitis C, interferon a, PCR, logistic regression, non-parametric test. | |
| Other Terms | review article | |
| Summary | The CV infection is a serious public health problem and according to WHO and CDC more than 500.000 individuals have been infected worldwide, from which the 85% have developed chronic hepatitis C. Despite the fact that hepatitis C causes diffuse necrosis and liver inflammation, the therapeutic approach of the disease with a combined interferon and ribavirin treatment, has improved survival rates. The aim of the present study is to analyze a sample of 265 patients with hepatitis C from the Hepatologic Centre of the ‘Elena’ hospital in Athens, Greece and to investigate potential virologic, clinical and demographic characteristics of the patients that may constitute important correlation factors. Advanced statistical techniques have been applied to investigate the significant factors that affect PCR values. Application of x2-test indicates that a significant difference was found between: PCR values vs SGPT (Fisher test, p=0.0001). With the use of factor analysis, a model has been implemented, including two factors which interpret the 74.35% of the initial information. Finally, a predictive logistic regression model has been used for the PCR and SGPT factors, predicting 92.4% of the corrected values of PCR. | |
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Online ISSN 1011-6575
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