Incorporating incidence information within the UNAIDS estimation and projection package framework: A study based on simulated incidence assay data

Le Bao, Jingyi Ye, Timothy B. Hallett

Research output: Contribution to journalReview article

7 Citations (Scopus)

Abstract

Objectives: The objective of this study is to extend the UNAIDS incidence estimation model, the UNAIDS Estimation and Projection Package (EPP), so that it can incorporate data from incidence assays.

Methods: We propose combining the likelihood of the incidence assay data with the likelihood of other data, in a manner that is consistent with the biomarker-based incidence estimator using incidence assay data. Two calibrating parameters specify the performance of the incidence assays: The false recent rate and the mean duration of recent infection. We then use synthetic data, based on prevalence data obtained from antenatal clinic surveillances, and in some cases household surveys, from 24 countries, to examine the impact of including incidence assay data, under circumstances wherein the incidence assay data imply the same or a different incidence rate as that inferred from the prevalence data alone, and wherein incorrect calibrating parameters for the incidence assay data are used.

Results: Using incidence assay data, in addition to prevalence data, can improve estimate by narrowing uncertainty intervals in derived HIV incidence estimates, and by providing information on levels or trends in incidence that were not apparent in the prevalence data alone. However, the effect is relatively modest if the sample size of the incidence assay survey is small and results can be biased if the calibrating parameters for the incidence assay data are not known accurately.

Conclusion: Incorporating information from incidence assays in the manner proposed has the potential to improve estimates. Further work will examine in more detail the circumstances under which the contribution of incidence assay data would be most valuable.

Original languageEnglish (US)
Pages (from-to)S515-S522
JournalAIDS
Volume28
DOIs
StatePublished - Nov 11 2014

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Incidence
Sample Size
Uncertainty
Biomarkers
HIV
Infection

All Science Journal Classification (ASJC) codes

  • Immunology and Allergy
  • Immunology
  • Infectious Diseases

Cite this

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title = "Incorporating incidence information within the UNAIDS estimation and projection package framework: A study based on simulated incidence assay data",
abstract = "Objectives: The objective of this study is to extend the UNAIDS incidence estimation model, the UNAIDS Estimation and Projection Package (EPP), so that it can incorporate data from incidence assays.Methods: We propose combining the likelihood of the incidence assay data with the likelihood of other data, in a manner that is consistent with the biomarker-based incidence estimator using incidence assay data. Two calibrating parameters specify the performance of the incidence assays: The false recent rate and the mean duration of recent infection. We then use synthetic data, based on prevalence data obtained from antenatal clinic surveillances, and in some cases household surveys, from 24 countries, to examine the impact of including incidence assay data, under circumstances wherein the incidence assay data imply the same or a different incidence rate as that inferred from the prevalence data alone, and wherein incorrect calibrating parameters for the incidence assay data are used.Results: Using incidence assay data, in addition to prevalence data, can improve estimate by narrowing uncertainty intervals in derived HIV incidence estimates, and by providing information on levels or trends in incidence that were not apparent in the prevalence data alone. However, the effect is relatively modest if the sample size of the incidence assay survey is small and results can be biased if the calibrating parameters for the incidence assay data are not known accurately.Conclusion: Incorporating information from incidence assays in the manner proposed has the potential to improve estimates. Further work will examine in more detail the circumstances under which the contribution of incidence assay data would be most valuable.",
author = "Le Bao and Jingyi Ye and Hallett, {Timothy B.}",
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Incorporating incidence information within the UNAIDS estimation and projection package framework : A study based on simulated incidence assay data. / Bao, Le; Ye, Jingyi; Hallett, Timothy B.

In: AIDS, Vol. 28, 11.11.2014, p. S515-S522.

Research output: Contribution to journalReview article

TY - JOUR

T1 - Incorporating incidence information within the UNAIDS estimation and projection package framework

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AU - Bao, Le

AU - Ye, Jingyi

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N2 - Objectives: The objective of this study is to extend the UNAIDS incidence estimation model, the UNAIDS Estimation and Projection Package (EPP), so that it can incorporate data from incidence assays.Methods: We propose combining the likelihood of the incidence assay data with the likelihood of other data, in a manner that is consistent with the biomarker-based incidence estimator using incidence assay data. Two calibrating parameters specify the performance of the incidence assays: The false recent rate and the mean duration of recent infection. We then use synthetic data, based on prevalence data obtained from antenatal clinic surveillances, and in some cases household surveys, from 24 countries, to examine the impact of including incidence assay data, under circumstances wherein the incidence assay data imply the same or a different incidence rate as that inferred from the prevalence data alone, and wherein incorrect calibrating parameters for the incidence assay data are used.Results: Using incidence assay data, in addition to prevalence data, can improve estimate by narrowing uncertainty intervals in derived HIV incidence estimates, and by providing information on levels or trends in incidence that were not apparent in the prevalence data alone. However, the effect is relatively modest if the sample size of the incidence assay survey is small and results can be biased if the calibrating parameters for the incidence assay data are not known accurately.Conclusion: Incorporating information from incidence assays in the manner proposed has the potential to improve estimates. Further work will examine in more detail the circumstances under which the contribution of incidence assay data would be most valuable.

AB - Objectives: The objective of this study is to extend the UNAIDS incidence estimation model, the UNAIDS Estimation and Projection Package (EPP), so that it can incorporate data from incidence assays.Methods: We propose combining the likelihood of the incidence assay data with the likelihood of other data, in a manner that is consistent with the biomarker-based incidence estimator using incidence assay data. Two calibrating parameters specify the performance of the incidence assays: The false recent rate and the mean duration of recent infection. We then use synthetic data, based on prevalence data obtained from antenatal clinic surveillances, and in some cases household surveys, from 24 countries, to examine the impact of including incidence assay data, under circumstances wherein the incidence assay data imply the same or a different incidence rate as that inferred from the prevalence data alone, and wherein incorrect calibrating parameters for the incidence assay data are used.Results: Using incidence assay data, in addition to prevalence data, can improve estimate by narrowing uncertainty intervals in derived HIV incidence estimates, and by providing information on levels or trends in incidence that were not apparent in the prevalence data alone. However, the effect is relatively modest if the sample size of the incidence assay survey is small and results can be biased if the calibrating parameters for the incidence assay data are not known accurately.Conclusion: Incorporating information from incidence assays in the manner proposed has the potential to improve estimates. Further work will examine in more detail the circumstances under which the contribution of incidence assay data would be most valuable.

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