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Limitations of the UNAIDS 90-90-90 metrics: a simulation-based comparison of cross-sectional and longitudinal metrics for the HIV care continuum

The Joint United Nations Programme on HIV/AIDS (UNAIDS) 90–90–90 and other cross-sectional metrics can lead to potentially counterintuitive conclusions when used to evaluate health systems’ performance. This study demonstrates how time and population dynamics impact UNAIDS 90–90–90 metrics in compar...

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Autores principales: Haber, Noah A., Lesko, Catherine R., Fox, Matthew P., Powers, Kimberly A., Harling, Guy, Edwards, Jessie K., Salomon, Joshua A., Lippman, Sheri A., Bor, Jacob, Chang, Angela Y., Anglemyer, Andrew, Pettifor, Audrey
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Lippincott Williams & Wilkins 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7253182/
https://www.ncbi.nlm.nih.gov/pubmed/32044844
http://dx.doi.org/10.1097/QAD.0000000000002502
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author Haber, Noah A.
Lesko, Catherine R.
Fox, Matthew P.
Powers, Kimberly A.
Harling, Guy
Edwards, Jessie K.
Salomon, Joshua A.
Lippman, Sheri A.
Bor, Jacob
Chang, Angela Y.
Anglemyer, Andrew
Pettifor, Audrey
author_facet Haber, Noah A.
Lesko, Catherine R.
Fox, Matthew P.
Powers, Kimberly A.
Harling, Guy
Edwards, Jessie K.
Salomon, Joshua A.
Lippman, Sheri A.
Bor, Jacob
Chang, Angela Y.
Anglemyer, Andrew
Pettifor, Audrey
author_sort Haber, Noah A.
collection PubMed
description The Joint United Nations Programme on HIV/AIDS (UNAIDS) 90–90–90 and other cross-sectional metrics can lead to potentially counterintuitive conclusions when used to evaluate health systems’ performance. This study demonstrates how time and population dynamics impact UNAIDS 90–90–90 metrics in comparison with a longitudinal analogue. DESIGN: A simplified simulation representing a hypothetical population was used to estimate and compare inference from UNAIDS 90–90–90 metrics and longitudinal metrics based on Kaplan–Meier-estimated 2-year probability of transition between stages. METHODS: We simulated a large cohort over 15 years. Everyone started out at risk for HIV, and then transitioned through the HIV care continuum based on fixed daily probabilities of acquiring HIV, learning status, entering care, initiating antiretroviral therapy (ART), and becoming virally suppressed, or dying. We varied the probability of ART initiation over three five-year periods (low, high, and low). We repeated the simulation with an increased probability of death. RESULTS: The cross-sectional probability of being on ART among persons who were diagnosed responded relatively slowly to changes in the rate of ART initiation. Increases in ART initiation rates caused apparent declines in the cross-sectional probability of being virally suppressed among persons who had initiated ART, despite no changes in the rate of viral suppression. In some cases, higher mortality resulted in the cross-sectional metrics implying improved healthcare system performance. The longitudinal continuum was robust to these issues. CONCLUSION: The UNAIDS 90–90–90 care continuum may lead to incorrect inference when used to evaluate health systems performance. We recommend that evaluation of HIV care delivery include longitudinal care continuum metrics wherever possible.
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spelling pubmed-72531822020-06-23 Limitations of the UNAIDS 90-90-90 metrics: a simulation-based comparison of cross-sectional and longitudinal metrics for the HIV care continuum Haber, Noah A. Lesko, Catherine R. Fox, Matthew P. Powers, Kimberly A. Harling, Guy Edwards, Jessie K. Salomon, Joshua A. Lippman, Sheri A. Bor, Jacob Chang, Angela Y. Anglemyer, Andrew Pettifor, Audrey AIDS Epidemiology and Social The Joint United Nations Programme on HIV/AIDS (UNAIDS) 90–90–90 and other cross-sectional metrics can lead to potentially counterintuitive conclusions when used to evaluate health systems’ performance. This study demonstrates how time and population dynamics impact UNAIDS 90–90–90 metrics in comparison with a longitudinal analogue. DESIGN: A simplified simulation representing a hypothetical population was used to estimate and compare inference from UNAIDS 90–90–90 metrics and longitudinal metrics based on Kaplan–Meier-estimated 2-year probability of transition between stages. METHODS: We simulated a large cohort over 15 years. Everyone started out at risk for HIV, and then transitioned through the HIV care continuum based on fixed daily probabilities of acquiring HIV, learning status, entering care, initiating antiretroviral therapy (ART), and becoming virally suppressed, or dying. We varied the probability of ART initiation over three five-year periods (low, high, and low). We repeated the simulation with an increased probability of death. RESULTS: The cross-sectional probability of being on ART among persons who were diagnosed responded relatively slowly to changes in the rate of ART initiation. Increases in ART initiation rates caused apparent declines in the cross-sectional probability of being virally suppressed among persons who had initiated ART, despite no changes in the rate of viral suppression. In some cases, higher mortality resulted in the cross-sectional metrics implying improved healthcare system performance. The longitudinal continuum was robust to these issues. CONCLUSION: The UNAIDS 90–90–90 care continuum may lead to incorrect inference when used to evaluate health systems performance. We recommend that evaluation of HIV care delivery include longitudinal care continuum metrics wherever possible. Lippincott Williams & Wilkins 2020-06-01 2020-02-14 /pmc/articles/PMC7253182/ /pubmed/32044844 http://dx.doi.org/10.1097/QAD.0000000000002502 Text en Copyright © 2020 The Author(s). Published by Wolters Kluwer Health, Inc. http://creativecommons.org/licenses/by/4.0 This is an open access article distributed under the Creative Commons Attribution License 4.0 (CCBY), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. http://creativecommons.org/licenses/by/4.0
spellingShingle Epidemiology and Social
Haber, Noah A.
Lesko, Catherine R.
Fox, Matthew P.
Powers, Kimberly A.
Harling, Guy
Edwards, Jessie K.
Salomon, Joshua A.
Lippman, Sheri A.
Bor, Jacob
Chang, Angela Y.
Anglemyer, Andrew
Pettifor, Audrey
Limitations of the UNAIDS 90-90-90 metrics: a simulation-based comparison of cross-sectional and longitudinal metrics for the HIV care continuum
title Limitations of the UNAIDS 90-90-90 metrics: a simulation-based comparison of cross-sectional and longitudinal metrics for the HIV care continuum
title_full Limitations of the UNAIDS 90-90-90 metrics: a simulation-based comparison of cross-sectional and longitudinal metrics for the HIV care continuum
title_fullStr Limitations of the UNAIDS 90-90-90 metrics: a simulation-based comparison of cross-sectional and longitudinal metrics for the HIV care continuum
title_full_unstemmed Limitations of the UNAIDS 90-90-90 metrics: a simulation-based comparison of cross-sectional and longitudinal metrics for the HIV care continuum
title_short Limitations of the UNAIDS 90-90-90 metrics: a simulation-based comparison of cross-sectional and longitudinal metrics for the HIV care continuum
title_sort limitations of the unaids 90-90-90 metrics: a simulation-based comparison of cross-sectional and longitudinal metrics for the hiv care continuum
topic Epidemiology and Social
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7253182/
https://www.ncbi.nlm.nih.gov/pubmed/32044844
http://dx.doi.org/10.1097/QAD.0000000000002502
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