Many hepatitis C reinfections that spontaneously clear may be undetected: Markov-chain Monte Carlo analysis of observational study data

Rachel Sacks-Davis, Emma McBryde, Jason Grebely, Margaret Hellard, Peter Vickerman

Research output: Contribution to journalArticle (Academic Journal)

11 Citations (Scopus)
196 Downloads (Pure)

Abstract

Hepatitis C virus (HCV) reinfection rates are probably underestimated due to reinfection episodes occurring between study visits. A Markov model of HCV reinfection and spontaneous clearance was fitted to empirical data. Bayesian post-estimation was used to project reinfection rates, reinfection spontaneous clearance probability and duration of reinfection. Uniform prior probability distributions were assumed for reinfection rate (more than 0), spontaneous clearance probability (0-1) and duration (0.25-6.00 months). Model estimates were 104 per 100 person-years (95% CrI: 21-344), 0.84 (95% CrI: 0.59-0.98) and 1.3 months (95% CrI: 0.3-4.1) for reinfection rate, spontaneous clearance probability and duration, respectively. Simulation studies were used to assess model validity, demonstrating that the Bayesian model estimates provided useful information about the possible sources and magnitude of bias in epidemiological estimates of reinfection rates, probability of reinfection clearance and duration or reinfection. The quality of the Bayesian estimates improved for larger samples and shorter test intervals. Uncertainty in model estimates notwithstanding, findings suggest that HCV reinfections frequently and quickly result in spontaneous clearance, with many reinfection events going unobserved.

Original languageEnglish
Article number20141197
Number of pages13
JournalJournal of the Royal Society Interface
Volume12
Issue number104
Early online date14 Jan 2015
DOIs
Publication statusPublished - 6 Mar 2015

Keywords

  • epidemiology
  • hepatitis C infection
  • Monte Carlo Markov chain

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