Dataset Information

Estimating past hepatitis C infection risk from reported risk factor histories: implications for imputing age of infection and modeling fibrosis progression.

ABSTRACT:

Background

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.

Methods

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.

Results

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.

Conclusion

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.

SUBMITTER: Bacchetti P

PROVIDER: S-EPMC2238758 | biostudies-literature | 2007 Dec

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Estimating past hepatitis C infection risk from reported risk factor histories: implications for imputing age of infection and modeling fibrosis progression.

Bacchetti Peter P Tien Phyllis C PC Seaberg Eric C EC O'Brien Thomas R TR Augenbraun Michael H MH Kral Alex H AH Busch Michael P MP Edlin Brian R BR

BMC infectious diseases 20071210

<h4>Background</h4>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.<h4>Methods</h4>We analyzed cross-sectional data on hepatitis C ant ...[more]

PMID: 18070362

Similar Datasets

Project description:Knowing the rate of liver fibrosis progression in hepatitis C virus (HCV)-infected persons can help inform patients and providers (clinicians, medical institutions or organizations, and third-party payers) in making treatment decisions.To determine the rate and factors associated with liver fibrosis progression and hepatic decompensation in persons after acquiring HCV infection.Secondary data analysis of persons in the Electronically Retrieved Cohort of HCV Infected Veterans (ERCHIVES), a national Veterans Affairs (VA) database, between 2002 and 2012. Among 610?514 persons in ERCHIVES (half were HCV positive), we identified those with an initial negative and subsequent positive test result for HCV antibody and positive HCV RNA test result (HCV+). Controls had 2 negative HCV antibody test results (HCV-) in a comparable time frame and were matched 1:1 on age (in 5-year blocks), race, and sex. We excluded persons with human immunodeficiency virus, hepatitis B, less than 24 months of follow-up, hepatocellular carcinoma, and cirrhosis at baseline.Progression of liver fibrosis as estimated by the Fibrosis-4 (FIB-4) index; development of cirrhosis, defined by a FIB-4 score greater than 3.5; and development of hepatic decompensation.The evaluable data set consisted of 1840 persons who were HCV+ and 1840 HCV- controls. The HCV+ persons were younger and had a lower mean (SD) body mass index (27.39 [5.51] vs 29.49 [6.16]; P?<?.001), a higher prevalence of alcohol and drug abuse and dependence diagnoses, and higher serum aminotransferase levels, but had a lower prevalence of diabetes and hypertension. Fibrosis progression started early after infection among HCV+ persons and tapered off after 5 years. A total of 452 cirrhosis and 85 hepatic decompensation events were recorded. After 10 years of follow-up, HCV+ persons were more likely to have a diagnosis of cirrhosis compared with HCV- controls (18.4% vs 6.1%). Nine years after diagnosis of cirrhosis, hepatic decompensation events were uncommon but had a higher rate in the HCV+ group (1.79% vs 0.33%).Persons who seroconverted for HCV have a more rapid progression of liver fibrosis and accelerated time to development of cirrhosis after seroconversion compared with HCV- controls. Fibrosis progression occurs early after infection; however, hepatic decompensation is uncommon after diagnosis of cirrhosis.

Project description:The relationship between the human gut microbiota and disease is of increasing scientific interest. Previous investigations have focused on the differences in intestinal bacterial abundance between control and affected groups to identify disease biomarkers. However, different types of intestinal bacteria may have interacting effects and thus be considered biomarker complexes for disease. To investigate this, we aimed to identify a new kind of biomarker for atopic dermatitis using structural equation modeling (SEM). The biomarkers identified were latent variables, which are complex and derived from the abundance data for bacterial marker candidates. Groups of females and males classified as healthy participants [normal control (NC) (female: 321 participants, male: 99 participants)], and patients afflicted with atopic dermatitis only [AS (female: 45 participants, male: 13 participants)], with atopic dermatitis and other diseases [AM (female: 75 participants, male: 34 participants)], and with other diseases but without atopic dermatitis [OD (female: 1,669 participants, male: 866 participants)] were used in this investigation. The candidate bacterial markers were identified by comparing the intestinal microbial community compositions between the NC and AS groups. In females, two latent variables (lv) were identified; for lv1, the associated components (bacterial genera) were Alistipes, Butyricimonas, and Coprobacter, while for lv2, the associated components were Agathobacter, Fusicatenibacter, and Streptococcus. There was a significant difference in the lv2 scores between the groups with atopic dermatitis (AS, AM) and those without (NC, OD), and the genera identified for lv2 are associated with the suppression of inflammatory responses in the body. A logistic regression model to estimate the probability of atopic dermatitis morbidity with lv2 as an explanatory variable had an area under the curve (AUC) score of 0.66 when assessed using receiver operating characteristic (ROC) analysis, and this was higher than that using other logistic regression models. The results indicate that the latent variables, especially lv2, could represent the effects of atopic dermatitis on the intestinal microbiome in females. The latent variables in the SEM could thus be utilized as a new type of biomarker. The advantages identified for the SEM are as follows: (1) it enables the extraction of more sophisticated information when compared with models focused on individual bacteria and (2) it can improve the accuracy of the latent variables used as biomarkers, as the SEM can be expanded.

Dataset Information

Estimating past hepatitis C infection risk from reported risk factor histories: implications for imputing age of infection and modeling fibrosis progression.

Background

Methods

Results

Conclusion

Publications

Estimating past hepatitis C infection risk from reported risk factor histories: implications for imputing age of infection and modeling fibrosis progression.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets