Estimating past hepatitis C infection risk from reported risk factor histories: implications for imputing age of infection and modeling fibrosis progression
© Bacchetti et al; licensee BioMed Central Ltd. 2007
Received: 22 March 2007
Accepted: 10 December 2007
Published: 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.
Chronic hepatitis C virus (HCV) infection is prevalent both in the United States, with perhaps 3 million persons infected , and worldwide . It often causes progressive hepatic fibrosis. This can eventually become cirrhosis, causing hepatocellular carcinoma, liver failure, a need for liver transplant, or death. Fibrosis measured by liver biopsy is believed to provide the most accurate measurement of the extent of liver damage . Considerable effort has been devoted to estimating risk factors for infection [4–13], as well as rates of progression through fibrosis stages to cirrhosis [14–21]. Studies of HCV progression often utilize liver biopsies from persons who may have been originally infected many years or decades earlier. The exact time of infection is often unknown, so the subjects' self-reported risk factor histories are used to impute a presumed time of infection. A common practice is to assume that infection occurred at the time of first reported injection drug use (IDU) [14, 19, 22]. Studies of infection risk factors support this practice to a limited extent by showing that IDU is a strong risk factor [4, 5, 7, 8, 12]. Here, we assess in more detail how accurate such imputation is likely to be. We utilize cross-sectional HCV antibody status data and self-reported risk-factor histories, similar to the situation in progression studies where time of infection must be imputed. In contrast to the usual statistical modeling in risk factor studies, which utilizes logistic regression with potential risk factors modeled as fixed covariates, we use survival analysis methods with time-varying covariates. This is possible even though every observation is either left-censored (infection occurred at some unknown time in the past) or right censored (infection has not yet occurred). An advantage of this approach is that it permits reconstruction of past risk year by year, which facilitates assessment of possible biases or inaccuracies in the usual imputation strategy, along with the implications for modeling of factors that influence fibrosis progression.
We analyzed data from two large studies that performed HCV antibody (anti-HCV) testing and collected subject reports concerning history of IDU. The Women's Interagency HIV Study (WIHS), as previously described [23, 24], includes women with or at risk for HIV infection. This study performed anti-HCV testing of initial participants, mainly in 1994–1995, and additional participants who were recruited in 2001–2002. The results used here were obtained using a second generation HCV enzyme immunoassay for most subjects, with a few of the newer recruits having data from a third generation test. The Urban Health Study (UHS) was composed of street-recruited injection drug users in the San Francisco Bay Area and has also been described previously [25–27]. Participants included here were recruited from 1987 to 2002, and second generation anti-HCV testing was performed for specimens from 1987 and from 1998–2002, as well as the initial specimens regardless of year for all subjects who reported IDU duration of < 10 years. For our analyses of both studies, we included only the first anti-HCV result from each participant and did not include any subsequent longitudinal follow up. This was to reduce cohort participation bias [12, 28, 29], and to approximate the common situation in cross-sectional studies of patients presenting with chronic HCV infection with no prior HCV test results. We also required that subjects have valid data on: sex; race; HIV antibody; ages of first and last IDU, if any; age and calendar year of HCV test; and for those with a history of IDU, whether they typically injected every day. For UHS, we assumed that subject's reported current injection frequency was typical of their entire IDU history because no other information was available about frequency of injection. WIHS subjects were asked to report a single typical frequency for all the time when they were injecting. Histories of needle sharing practices were not collected in either study. Because needle sharing behavior may have changed substantially over time due to the emergence of HIV and subsequent efforts to prevent its spread, current sharing would not be an adequate surrogate for past practices; we therefore did not assess this risk factor. Data on blood transfusion history was not required for inclusion in our analyses because it was only partially ascertained in WIHS (transfusions in the years 1975–1985, only among those recruited in 1993–94) and was not collected in UHS.
The available data indicate only whether or not each subject was infected with HCV at some point in the past, a form of information known in the statistical literature as current status data [30–33]. We used maximum full likelihood to fit parameters of discrete-time survival models, using year of age as the time scale. Beginning with age 1 (where risk was very small in all models), the log-odds of HCV infection given no previous infection was modeled as a linear function of covariates that pertained to that year, including age, calendar year, and whether the subject reported using injection drugs during that year. The fitted log-odds were then converted to hazards and combined across years to obtain a predicted probability of being HCV seropositive at the age tested. The NLMIXED procedure in the SAS statistical package (version 9.1, SAS Institute, Cary, NC) was used to obtain the parameter estimates that maximized the resulting likelihood, along with their confidence intervals (CI) and p-values. This approach is similar to Cox's discrete-time partial likelihood model , except that the role of time (in this case age) is modeled instead of being conditioned out in a partial likelihood. It can also be considered a form of pooled logistic regression  with a parsimonious model for the effect of time instead of an unstructured model. Estimated effects of covariates are presented as odds ratios (OR's). We provide in Additional File 1 the SAS NLMIXED code for fitting one of the models presented below.
Because HCV risk associated with IDU may vary depending on duration of IDU [7, 12, 36], we defined separate time-varying indicator variables for the reported first year of use, the second or third year, and the fourth or greater year. In defining the hazard at each age, we assumed that IDU began in the middle of the reported age of first use; instead assuming that IDU began at the beginning produced qualitatively similar results, but may be a less accurate assumption. We also examined further breakdown of duration into fourth to tenth year versus 11th or greater year. For the time-varying numeric covariates age and calendar year, we examined linear models, quadratic models, and more flexible models based on parametric cubic splines defined by the ns() function in S-Plus (Insightful Corporation, Seattle, WA), choosing a model by likelihood ratio testing of nested alternatives. We examined plausible interactions one at a time by adding the product of the two predictors to the model with main effects only. To limit the complexity of interactions with reported IDU, we assumed common interactions for the reported first, second-third, and fourth or greater years of IDU.
We used the fitted multivariate models to calculate estimated distributions of age at HCV infection given each subject's age at first positive HCV antibody test and reported risk factor profile. For each subject and each possible age of infection, this produced a probability that infection occurred at that age. To compare these to the usual imputation of age at HCV infection as the reported age of first IDU, we calculated the mean age at infection from the estimated probabilities and calculated the usual imputation's bias as the age of first IDU minus this mean. We also calculated the probability that infection occurred at the reported age of first IDU or the next year, considering this to be the probability that the usual imputation would be reasonably accurate.
Characteristics of WIHS and UHS subjects analyzed for this study.
N in category (% of total)
WIHS (N = 2248)
UHS (N = 4623)
Year of HCV test
Age at HCV test
Age of First IDU
Year of First IDU
HCV Antibody status by duration of injection drug use (IDU) at time of testing.
HCV seropositive/Total (%)
Duration of IDU
> 30 years
Models for HCV infection risk
Multivariate models of HCV infection risk.
Women's Interagency HIV Study
San Francisco Urban Health Study
Injection drug use (versus not injecting)
1st year of use
163 to 588
21 to 177
2nd & 3rd year
65 to 404
1.94 to 50
4th or later year
2.1 to 24.2
3.2 to 8.4
Daily IDU (versus less frequent IDU)
0.73 to 1.77
1.40 to 1.81
0.67 to 0.85
See Figure 1a
See Figure 1a
See Figure 1b
See Figure 1b
Race/ethnicity (versus Caucasian)
0.76 to 0.97
1.09 to 2.5
1.15 to 1.81
0.66 to 2.9
0.76 to 1.16
Area (versus San Francisco)
0.96 to 2.1
(All San Francisco area)
0.58 To 1.39
0.70 To 1.73
0.51 to 1.26
1.11 to 2.6
1.16 to 2.1
1.09 to 1.47
We examined a number of possible additions or refinements to the models shown. Including blood transfusion history in the WIHS model would result in losing 995 subjects with missing data for this variable, and it did not appear to be an important predictor (OR 1.10, 95% CI 0.74 to 1.65, p = 0.64). Allowing for differing effects of age when reportedly injecting versus not injecting did not produce substantially better fits to the data (WIHS p = 0.38, UHS p = 0.21 by likelihood ratio tests). Other interaction terms examined included calendar year by reported IDU (WIHS p = 0.64, UHS p = 0.39), year by age (WIHS p = 0.37, UHS p = 0.065), race by IDU (WIHS p = 0.13, UHS p = 0.29), and sex by IDU (UHS p = 0.54). We also examined models with IDU effects differing for the 4th to 10th years of reported IDU versus beyond the 10th (WIHS p = 0.33, UHS p = 0.17). We note that the confidence intervals were not narrow enough to rule out potentially important interactions, but in the absence of strong evidence for such interactions we focus on the simpler models without them. One interaction that did reach statistical significance was reported IDU by location in the WIHS study (p = 0.029 overall). This was mainly due to reported IDU being estimated to be less risky in the Bronx (IDU OR's estimated to be smaller by a factor of 0.38, 95% CI 0.16 to 0.91) and Brooklyn (OR's smaller by a factor of 0.42, 95% CI 0.16 to 1.14). The OR's for the main effects of these locations were higher when the interaction was included than those shown in Table 3 (Bronx OR 2.3, Brooklyn OR 1.41). These differences make the Bronx and Brooklyn more similar to the UHS model, with lower OR's for reported IDU and a higher background risk. Nevertheless, the OR for the reported first year of IDU in the Bronx remains nearly 3-fold larger than that in the UHS model. Because we already show details for the UHS model, we do not provide any further details on this WIHS model with interaction terms.
Reconstruction of yearly past infection probabilities for those infected
Summaries of fits from multivariate models described by Table 3 and Figure 1. Except as noted, area is San Francisco and sex is female.
Age of Infection
Age of Infection
Age of Infection
Year of Birth
HCV Ab + Test
Probability First IDU Accurate*
Probability First IDU Accurate*
2 – LA‡
9 – LA‡
2 – Ch§
9 – Ch§
2 – Male
9 – Male
Using modern statistical methods and large data sets, we were able to obtain models for HCV infection risk that can be used to produce year-by-year estimates of past infection risk. Similar to many epidemiological studies, we found high risk in the reported first year of IDU [4, 5, 7, 8, 12] and decreasing risk in recent calendar years [22, 37–40]. For a patient or research subject newly discovered to be HCV-infected, these models permit calculating the estimated probability that infection occurred at each year in the past based on IDU history and other characteristics. For the subjects studied here, results of such calculations suggest that the common approach of imputing the age of infection as the reported age of first IDU is unreliable.
Some implications for modeling of fibrosis progression
Although the average difference between reported age of first IDU and the fitted mean infection times from the models was small for both WIHS and UHS, this does not imply that modeling of fibrosis progression will be accurate if it assumes infection at reported age of first IDU. Large errors in one direction cancelled large errors in the other, and there was usually a considerable chance that infection occurred before or after the first reported year of IDU. Such uncertainty necessitates a multiple imputation strategy  or other advanced statistical method to avoid both biased estimates and inappropriately narrow confidence intervals. Most importantly, the strong correlation of errors with reported age at first IDU implies that the usual imputation can lead to spurious associations of fibrosis progression with age at first IDU. For example, consider one patient matching scenario 1 in Table 4 who shows Metavir  fibrosis stage 4 (cirrhosis) by liver biopsy at age 40, and another patient matching scenario 3 who shows fibrosis stage 2 at age 40. If we calculate progression rate as observed stage divided by duration of infection , we get very different comparisons depending on what we assume about age of infection. Using reported age of first IDU, the patient reporting first IDU at age 12 has a rate of 0.143 fibrosis points per year, while the one reporting first IDU at age 35 has a rate of 0.400, nearly three times as fast. But using the fitted means from the UHS model completely eliminates this difference, producing rates of 0.161 and 0.148.
Additional sources of similar possible bias
The above bias toward making earlier age of HCV infection look spuriously protective against rapid progression is further compounded by other sources of bias. Many clinic-based progression studies have utilized patients whose HCV infection is discovered due to related symptoms. This may exaggerate progression rates compared to the entire population of HCV-infected persons , but it can also produce a spurious protective effect of earlier age of infection. Consider 4 persons who all would develop symptoms and have their HCV infection discovered once they reach fibrosis stage 3. Two are fast progressors who reach stage 3 in 15 years, one infected at age 15 and one at age 40. The other two are slow progressors who will reach stage 3 in 45 years, again with one infected at age 15 and one at age 40. Among these 4 persons, progression is not associated with age at infection. Nevertheless, mortality risk unrelated to HCV makes the slow progressor infected at age 40 much less likely than the one infected at age 15 to ever be included in a clinic-based study, while the difference in unrelated mortality risk for the fast progressors is considerably less. This differential selection pressure implies that in clinic-based studies slow progressors will be under-represented among those infected at older ages compared to those infected at younger ages, producing an apparent protective effect of younger age at infection.
An additional difficulty is distinguishing a fixed effect due to age at infection from a time-varying effect that changes as a patient ages. For example, progression may accelerate with increasing age so that the rate of progression is 2-fold faster at age 60 than it would be at age 30, given the same duration of disease and other characteristics. Those infected at younger ages would then have more of their disease course occur at younger ages when progression is slower, causing them to progress more slowly overall even if age at infection itself has no direct effect and even though they experience the same acceleration once they reach older ages. This again could produce a misleading protective effect of younger age at infection. Carefully distinguishing a fixed versus time-varying role for age in fibrosis progression will likely require multi-state modeling algorithms  that allow for time-varying covariates, which are currently not available. An analysis of similar issues concerning the role of age in variant Creutzfeldt-Jakob disease  required customized analysis to reveal that previous analyses had reached apparently mistaken conclusions. Similar efforts may be worthwhile for HCV.
The current status data available in this cross-sectional study are less informative than knowing exact times of HCV infection from a longitudinal study, but the large sample sizes allowed our methods to produce models with many plausible features, reasonably narrow confidence intervals, and many small p-values. Importantly, this cross-sectional situation matches that of many progression studies where subjects are found to be chronically HCV infected without any direct information on when they became infected [14–21]. Although potentially counterintuitive, this type of data does permit estimation of effects of calendar time before the year of the first HCV antibody test and of the background risk without IDU despite the lack of anyone with no IDU in the UHS study. This is because the current status data provide information about cumulative risk over all years before the measurement of the outcome, and because we had considerable variation in the ages, calendar years, and reported durations of IDU at the time of HCV antibody testing. For example, information on background risk when reportedly not injecting can be obtained by comparing the HCV prevalence of persons with 1 year of IDU tested at age 20 versus persons with 1 year of IDU tested at age 40, because they have different amounts of non-IDU time. This comparison also provides information on the risk of the first year of IDU, because this is the risk the two groups have in common after the difference in background risk is accounted for. Our analyses synthesize many such comparisons to produce the estimates.
The self-reported risk factor histories we have used may be inaccurate, due to inaccurate recall or social desirability bias. Indeed, some (or most) of the apparent background risk of infection in the absence of IDU could be due to inaccurate recall or report of ages of IDU. Our results therefore do not necessarily imply substantial risk from sources other than IDU and therefore should not be interpreted as contradicting the established belief that IDU is the overwhelmingly predominant source of chronic HCV infections. Some of the 8.6% prevalence of HCV seropositivity among WIHS subjects reporting no IDU could reflect infections caused by forgotten or unreported IDU. More importantly, the estimated risk in the absence of IDU is very high in UHS, much higher than in WIHS, reaching incidence of 3.4% per year for the riskiest calendar year and riskiest age. This is reflected in Figure 2b, where 31% of the predicted mean infection ages are before the reported age of first IDU (points above the horizontal line). Utilizing the entire fitted distributions instead of just the predicted means, we obtained an estimate of 17% of UHS subjects being infected before their reported age of first IDU. Although there is likely to be some risk in the absence of IDU, particularly in this high-risk population, and one study found a similarly high prevalence of HCV infection among users of non-injection drugs , we believe that much of the risk predicted by the model may reflect inaccurate reporting of ages of first IDU. Thus, our models' fitted background risks may reflect risk in the absence of reported IDU, but they may be inaccurate estimates of risk when there really is no IDU. Fortunately, this fits with our primary purpose of studying imputation of age of infection based on reported risk factor history, which is what must often be done in studies of fibrosis progression. For this purpose, it is irrelevant whether risk of infection before reported age of first IDU results from non-IDU sources of risk or from unreported IDU. In either case, imputation may be inaccurate.
One aspect that does not match the situation in progression studies is the use of HCV serology as the outcome. The reconstructed yearly risks summarized in Table 4 and Figure 2 are conditional on HCV seropositivity instead of being conditional on confirmed chronic HCV infection (by HCV RNA), which will be the case in progression studies. The proportion of HCV seropositive persons who clear the virus and become HCV RNA- is thought to be about 20% overall  and 15% among HIV-infected persons . A model of clearance probabilities utilizing age and other characteristics could in principle be synthesized with the models presented here to produce more relevant reconstructed past infection probabilities that are conditional on chronic HCV infection. In addition, some chronically HCV infected persons may test HCV seronegative, particularly by the second generation assay mainly used here. In one study of HIV-infected persons, 37 of 617 (6%) with HCV infection were observed to be HCV negative by second generation antibody assay . We note that some of these 37 cases may have been recently infected and not yet produced antibodies, which would not be typical of subjects in HCV progression studies. Exposure to HCV that results in neither chronic infection nor HCV seropositivity  does not affect the validity of our results, because such events do not initiate progression of HCV-related liver damage and are correctly treated as such in our analyses.
The necessary exclusion of persons who died before the conduct of the study could result in biased estimates of risk factors, and any such "survivor bias" may be worse in the WIHS, where participants also had to survive to the visit where IDU history was assessed. For example, if those who die of overdose in the first year of IDU are much more likely to have been HCV infected, then their exclusion will result in an underestimate of the HCV risk during the first year of IDU. We note, however, that cross-sectional progression studies will also necessarily have excluded persons who die before being tested for HCV. Thus, our estimates remain relevant for our primary purpose of assessing estimation of past infection times in such studies.
Being HIV positive was included as a fixed risk factor in multivariate models, because the timing of HIV infection was not known for most HIV-infected subjects. This assumption could make sense if HCV risk associated with being HIV infected was mainly due to other risk factors (such as membership in a risky social network) that are not directly available for modeling but are associated with HIV infection and were present even before HIV infection. If the mechanism of HIV-associated risk is directly causal, such as greater susceptibility to HCV when HIV infection is already present, then this effect would not be present before HIV infection occurred. In this case, our estimates based on HIV as a fixed risk factor would be attenuated compared to what would be estimated if the timing of past HIV infection were known. Although IDU might be expected to usually result in HCV infection before it causes HIV infection, sexual HIV risk is important among drug users , so HIV could precede HCV infection often enough that the possibility of attenuation cannot be ruled out.
Despite many qualitative similarities, the differences between the WIHS and UHS models leave considerable uncertainty about how to impute ages of infection for a progression study. Because inclusion of WIHS participants required retention in the study until IDU history was assessed, these may be less similar than UHS participants to cross-sectional progression study participants, while perhaps being more similar to subjects who are retained in longitudinal follow-up. A desirable design would be a large cross-sectional study ascertaining chronic HCV infection and risk factor histories, followed by fibrosis progression measurement in those found to be HCV-infected. Infection models fit to the cross-sectional data would then be directly applicable to the progression modeling. (The most desirable design, prospectively ascertaining incident infections and subsequently monitoring progression, would likely be prohibitively difficult.) In the absence of such directly relevant models of past risk, a sensible strategy may be to perform sensitivity analyses using both models presented here, along with the usual imputation based on first year of IDU. In addition, within each model different specific choices about whether to include and how to model each predictor, as well as whether to include some interaction terms, could in some cases be reasonable. This adds additional uncertainty about specific imputations, but additional uncertainty only adds further support for our conclusion that the common single-imputation strategy is dangerous.
We have shown that, in cross-sectional HCV progression studies that rely on risk factor histories to impute time of HCV infection, there is usually considerable uncertainty about when HCV infection may have occurred, even for patients or research subjects reporting a history of IDU. This should be accounted for in such progression studies, preferably using modern methods such as multiple imputation . To facilitate use of such methods, we provide, in Additional File 2, code that produces individuals' past infection probabilities year by year. Use of single imputation can not only produce confidence intervals that are too narrow and p-values that are too small, but also severely biased estimates of covariate effects. In particular, the usual strategy of imputing age of HCV infection as the age of first reported IDU is likely to produce bias toward finding slower progression associated with younger age of infection. This bias could be further exacerbated by differential selection effects according to age of infection, and by time-varying effects of current age being mis-modeled as fixed effects of age at infection. Some or all of the protective effect of younger age at infection [14, 19] found in cross-sectional studies of fibrosis progression could therefore be spurious.
hepatitis C virus
human immunodeficiency virus
injection drug use
San Francisco Urban Health Study
Women's Interagency HIV Study
This work was supported by grants R01 AI55085 and R01 AI069952 from the National Institute of Allergy and Infectious Diseases. The Women's Interagency HIV Study was funded by the National Institute of Allergy and Infectious Diseases, with supplemental funding from the National Cancer Institute, and the National Institute on Drug Abuse (grants U01-AI-35004, UO1-AI-31834, UO1-AI-34994, UO1-AI-34989, UO1-AI-34993, and UO1-AI-42590). Funding was also provided by the National Institute of Child Health and Human Development (UO1-HD-32632) and the National Center for Research Resources (MO1-RR-00071, MO1-RR-00079, MO1-RR-00083. The San Francisco Urban Health Study was funded by grants R01-DA13245, R01-DA12109, and R01-DA16159 from the National Institute on Drug Abuse, by contract #N02-CP-91027 from the National Cancer Institute, by SAMHSA grant #H79-TI12103 (Center for Substance Abuse Treatment), by the Intramural Research Program of the National Institutes of Health, National Cancer Institute, Division of Cancer Epidemiology and Genetics, the Universitywide AIDS Research Program, and by grants from the City and County of San Francisco Department of Public Health. The content of this publication does not necessarily reflect the views or policies of the Department of Health and Human Services, nor does mention of trade names, commercial products, or organizations imply endorsement by the U.S. Government.
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