Estimating past hepatitis C infection risk from reported risk factor histories: implications for imputing age of infection and modeling fibrosis progression
1 Box 0560, Department of Epidemiology and Biostatistics, University of California, San Francisco, CA 94143, USA
2 Department of Medicine, University of California, San Francisco and Infectious Disease Section, 111W, Veterans Affairs Medical Center, San Francisco, CA 94121, USA
3 Department of Epidemiology, Johns Hopkins University, Bloomberg School of Public Health, Baltimore, MD, USA
4 Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
5 SUNY Downstate Medical Center, Division of Infectious Diseases, 450 Clarkson Avenue, Box 56, Brooklyn, NY 11203, USA
6 RTI International, San Francisco Regional Office; and Department of Family and Community Medicine, University of California, San Francisco, CA 94143, USA
7 Blood Systems Research Institute, San Francisco and the Department of Laboratory Medicine, University of California, San Francisco, CA 94143, USA
8 Center for the Study of Hepatitis C, Weill Medical College of Cornell University, 411 East 69th St., Room KB-319, New York, NY 10021, USA
9 Department of Family and Community Medicine, University of California, San Francisco, CA 94143, USA
BMC Infectious Diseases 2007, 7:145 doi:10.1186/1471-2334-7-145Published: 10 December 2007
Chronic hepatitis C virus infection is prevalent and often causes hepatic fibrosis, which can progress to cirrhosis and cause liver cancer or liver failure. Study of fibrosis progression often relies on imputing the time of infection, often as the reported age of first injection drug use. We sought to examine the accuracy of such imputation and implications for modeling factors that influence progression rates.
We analyzed cross-sectional data on hepatitis C antibody status and reported risk factor histories from two large studies, the Women's Interagency HIV Study and the Urban Health Study, using modern survival analysis methods for current status data to model past infection risk year by year. We compared fitted distributions of past infection risk to reported age of first injection drug use.
Although injection drug use appeared to be a very strong risk factor, models for both studies showed that many subjects had considerable probability of having been infected substantially before or after their reported age of first injection drug use. Persons reporting younger age of first injection drug use were more likely to have been infected after, and persons reporting older age of first injection drug use were more likely to have been infected before.
In cross-sectional studies of fibrosis progression where date of HCV infection is estimated from risk factor histories, modern methods such as multiple imputation should be used to account for the substantial uncertainty about when infection occurred. The models presented here can provide the inputs needed by such methods. Using reported age of first injection drug use as the time of infection in studies of fibrosis progression is likely to produce a spuriously strong association of younger age of infection with slower rate of progression.