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. 2019 Dec 15;33 Suppl 3(Suppl 3):S235-S244.
doi: 10.1097/QAD.0000000000002437.

The Estimation and Projection Package Age-Sex Model and the r-hybrid model: new tools for estimating HIV incidence trends in sub-Saharan Africa

Jeffrey W Eaton  1 , Tim Brown  2 , Robert Puckett  2 , Robert Glaubius  3 , Kennedy Mutai  4 , Le Bao  5 , Joshua A Salomon  6 , John Stover  3 , Mary Mahy  7 , Timothy B Hallett  1
Affiliations

The Estimation and Projection Package Age-Sex Model and the r-hybrid model: new tools for estimating HIV incidence trends in sub-Saharan Africa

Jeffrey W Eaton et al. AIDS. .

Abstract

Objectives: Improve models for estimating HIV epidemic trends in sub-Saharan Africa (SSA).

Design: Mathematical epidemic model fit to national HIV survey and ANC sentinel surveillance (ANC-SS) data.

Methods: We modified EPP to incorporate age and sex stratification (EPP-ASM) to more accurately capture the shifting demographics of maturing HIV epidemics. Secondly, we developed a new functional form for the HIV transmission rate, termed 'r-hybrid', which combines a four-parameter logistic function for the initial epidemic growth, peak, and decline followed by a first-order random walk for recent trends after epidemic stabilization. We fitted the r-hybrid model along with previously developed r-spline and r-trend models to HIV prevalence data from household surveys and ANC-SS in 177 regions in 34 SSA countries. We used leave-one-out cross validation with household survey HIV prevalence to compare model predictions.

Results: The r-hybrid and r-spline models typically provided similar HIV prevalence trends, but sometimes qualitatively different assessments of recent incidence trends because of different structural assumptions about the HIV transmission rate. The r-hybrid model had the lowest average continuous ranked probability score, indicating the best model predictions. Coverage of 95% posterior predictive intervals was 91.5% for the r-hybrid model, versus 87.2 and 85.5% for r-spline and r-trend, respectively.

Conclusion: The EPP-ASM and r-hybrid models improve consistency of EPP and Spectrum, improve the epidemiological assumptions underpinning recent HIV incidence estimates, and improve estimates and short-term projections of HIV prevalence trends. Countries that use general population survey and ANC-SS data to estimate HIV epidemic trends should consider using these tools.

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Figures

Fig. 1
Fig. 1
Examples of r-hybrid model fits (red) compared with r-spline (blue) model fitted using the EPP-ASM model for Kenya – Eastern (top), Malawi – Central Region, Ethiopia – Amhara Urban, and Mozambique – Maputo Province (bottom).
Fig. 2
Fig. 2
Posterior mean estimates of logistic function parameters for 177 Estimation and Projection Package regions.
See this image and copyright information in PMC

References

    1. The UNAIDS Reference Group on Estimates Modelling and Projections. Improved methods and assumptions for estimation of the HIV/AIDS epidemic and its impact: recommendations of the UNAIDS Reference Group on Estimates, Modelling and Projections. AIDS 2002; 16:W1–W14. - PubMed
    1. Ghys PD, Brown T, Grassly NC, Garnett G, Stanecki KA, Stover J, et al. The UNAIDS Estimation and Projection Package: a software package to estimate and project national HIV epidemics. Sex Transm Infect 2004; 80: Suppl 1: i5–i9. - PMC - PubMed
    1. Grassly NC, Morgan M, Walker N, Garnett G, Stanecki KA, Stover J, et al. Uncertainty in estimates of HIV/AIDS: the estimation and application of plausibility bounds. Sex Transm Infect 2004; 80:i31–i38. - PMC - PubMed
    1. Alkema L, Raftery AE, Clark SJ. Probabilistic projections of HIV prevalence using Bayesian melding. Ann Appl Stat 2007; 1:229–248.
    1. Bao L, Raftery AE. A stochastic infection rate model for estimating and projecting national HIV prevalence rates. Sex Transm Infect 2010; 86: Suppl 2: ii93–ii99. - PMC - PubMed

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