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, Daniela De Angelis

OAI: oai:www.repository.cam.ac.uk:1810/290554 • DOI: 10.17863/CAM.37782

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.

Back-calculation Bayesian inference Multi-state model Routinely collected data Splines Adolescent Adult Bayes Theorem England HIV Infections Humans Incidence Likelihood Functions Male Middle Aged Population Surveillance prevalence Risk Assessment Time Factors Wales Young Adult