Extending Bayesian back-calculation to estimate age and time specific HIV incidence

Francesco Brizzi, Paul J. Birrell, Martyn T. Plummer, Peter Kirwan, Alison Brown, Valerie Delpech, Owen Gill, Daniela De Angelis

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

Abstract

CD4-based multi-state back-calculation methods are key for monitoring the HIV epidemic, providing estimates of HIV incidence and diagnosis rates by disentangling their inter-related contribution to the observed surveillance data. This paper, extends existing approaches to age-specific settings, permitting the joint estimation of age- and time-specific incidence and diagnosis rates and the derivation of other epidemiological quantities of interest. This allows the identification of specific age-groups at higher risk of infection, which is crucial in directing public health interventions. We investigate, through simulation studies, the suitability of various bivariate splines for the non-parametric modelling of the latent age- and time-specific incidence and illustrate our method on routinely collected data from the HIV epidemic among gay and bisexual men in England and Wales.

Original languageEnglish
Pages (from-to)757-780
Number of pages24
JournalLifetime Data Analysis
Volume25
Issue number4
DOIs
Publication statusPublished - 1 Oct 2019

Keywords

  • Back-calculation
  • Bayesian inference
  • Multi-state model
  • Routinely collected data
  • Splines

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