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

doi:10.1007/s10985-019-09465-1

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^*

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

7 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 language	English
Pages (from-to)	757-780
Number of pages	24
Journal	Lifetime Data Analysis
Volume	25
Issue number	4
DOIs	https://doi.org/10.1007/s10985-019-09465-1
Publication status	Published - 1 Oct 2019

Bibliographical note

Funding Information:
This work was presented at the symposium in celebration of Odd Aalen?s 70th birthday, and was supported by: the Medical Research Council (Unit Programme No. MC_UU_00002/11); the UK National Institute of Health Research Health Protection Units on Evaluation of Interventions; and Public Health England.

Publisher Copyright:
© 2019, The Author(s).

Keywords

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

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1007/s10985-019-09465-1

Cite this

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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.",

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AU - Kirwan, Peter

AU - Brown, Alison

AU - Delpech, Valerie

AU - Gill, Owen

AU - De Angelis, Daniela

N1 - Funding Information: This work was presented at the symposium in celebration of Odd Aalen?s 70th birthday, and was supported by: the Medical Research Council (Unit Programme No. MC_UU_00002/11); the UK National Institute of Health Research Health Protection Units on Evaluation of Interventions; and Public Health England. Publisher Copyright: © 2019, The Author(s).

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AB - 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.

KW - Back-calculation

KW - Bayesian inference

KW - Multi-state model

KW - Routinely collected data

KW - Splines

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

Abstract

Bibliographical note

Keywords

UN SDGs

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