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. 2014 Nov;28 Suppl 4(4):S415-25.
doi: 10.1097/QAD.0000000000000454.

Improvements in prevalence trend fitting and incidence estimation in EPP 2013

Tim Brown  1 , Le Bao, Jeffrey W Eaton, Daniel R Hogan, Mary Mahy, Kimberly Marsh, Bradley M Mathers, Robert Puckett
Affiliations

Improvements in prevalence trend fitting and incidence estimation in EPP 2013

Tim Brown et al. AIDS. 2014 Nov.

Abstract

Objective: Describe modifications to the latest version of the Joint United Nations Programme on AIDS (UNAIDS) Estimation and Projection Package component of Spectrum (EPP 2013) to improve prevalence fitting and incidence trend estimation in national epidemics and global estimates of HIV burden.

Methods: Key changes made under the guidance of the UNAIDS Reference Group on Estimates, Modelling and Projections include: availability of a range of incidence calculation models and guidance for selecting a model; a shift to reporting the Bayesian median instead of the maximum likelihood estimate; procedures for comparison and validation against reported HIV and AIDS data; incorporation of national surveys as an integral part of the fitting and calibration procedure, allowing survey trends to inform the fit; improved antenatal clinic calibration procedures in countries without surveys; adjustment of national antiretroviral therapy reports used in the fitting to include only those aged 15-49 years; better estimates of mortality among people who inject drugs; and enhancements to speed fitting.

Results: The revised models in EPP 2013 allow closer fits to observed prevalence trend data and reflect improving understanding of HIV epidemics and associated data.

Conclusion: Spectrum and EPP continue to adapt to make better use of the existing data sources, incorporate new sources of information in their fitting and validation procedures, and correct for quantifiable biases in inputs as they are identified and understood. These adaptations provide countries with better calibrated estimates of incidence and prevalence, which increase epidemic understanding and provide a solid base for program and policy planning.

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Figures

Fig. 1
Fig. 1
Prevalence fits and incidence trends produced by the variable-r, r-trend and r-spline models on the same urban data set from (a) Kenya urban and (b) Rwanda.
Fig. 2
Fig. 2
Flow chart for choice of model to use for fitting.
Fig. 3
Fig. 3
Top: an example of major changes in the best fit curve in a sparse data situation when an additional data point is added.
Fig. 4
Fig. 4
The EPP Data Check page allows users to compare reported AIDS case and HIV infection trends with those predicted by the model.
Fig. 5
Fig. 5
Percentage of population on ART over age 50 for 2005 to 2020 for various countries.
See this image and copyright information in PMC

References

    1. Schwartlander B, Stanecki KA, Brown T, Way PO, Monasch R, Chin J, et al. Country-specific estimates and models of HIV and AIDS: methods and limitations. AIDS 1999; 13:2445–2458. - PubMed
    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. Bao L, Salomon JA, Brown T, Raftery AE, Hogan DR. Modelling national HIV/AIDS epidemics: revised approach in the UNAIDS Estimation and Projection Package 2011. Sex Transm Infect 2012; 88 Suppl 2:i3–i10. - PMC - PubMed
    1. Stover J, Brown T, Marston M. Updates to the Spectrum/Estimation and Projection Package (EPP) model to estimate HIV trends for adults and children. Sex Transm Infect 2012; 88 Suppl 2:i11–i16. - PMC - PubMed
    1. Brown T, Grassly NC, Garnett G, Stanecki K. Improving projections at the country level: the UNAIDS Estimation and Projection Package 2005. Sex Transm Infect 2006; 82 Suppl 3:iii34–iii40. - PMC - PubMed

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