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

Francesco Brizzi, Paul J. Birrell, Martyn T. Plummer, Peter Kirwan, Alison E. Brown, Valerie C. Delpech, O. Noel Gill and Daniela Angelis ()
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Francesco Brizzi: University of Cambridge
Paul J. Birrell: University of Cambridge
Martyn T. Plummer: IARC
Peter Kirwan: Public Health England
Alison E. Brown: Public Health England
Valerie C. Delpech: Public Health England
O. Noel Gill: Public Health England
Daniela Angelis: University of Cambridge

Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, 2019, vol. 25, issue 4, No 10, 757-780

Abstract: 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.

Keywords: Back-calculation; Multi-state model; Bayesian inference; Splines; Routinely collected data (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s10985-019-09465-1

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