Limitations of the UNAIDS 90-90-90 metrics: a simulation-based comparison of cross-sectional and longitudinal metrics for the HIV care continuum.
AIDS (London, England)
OBJECTIVES: The 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 a 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 ART, and becoming virally suppressed, or dying. Within simulations we only varied the probability of ART initiation. 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.
View details for DOI 10.1097/QAD.0000000000002502
View details for PubMedID 32044844
- Improving the Validity of Mathematical Models for HIV Elimination by Incorporating Empirical Estimates of Progression Through the HIV Treatment Cascade JAIDS-JOURNAL OF ACQUIRED IMMUNE DEFICIENCY SYNDROMES 2018; 79 (5): 596–604
Improving the validity of mathematical models for HIV elimination by incorporating empirical estimates of progression through the HIV treatment cascade.
Journal of acquired immune deficiency syndromes (1999)
BACKGROUND: Optimism regarding prospects for eliminating HIV by expanding antiretroviral treatment has been emboldened in part by projections from several mathematical modeling studies. Drawing from a detailed empirical assessment of rates of progression through the entire HIV care cascade, we quantify for the first time the extent to which models may overestimate health benefits from policy changes when they fail to incorporate a realistic understanding of the cascade.SETTING: Rural KwaZulu-Natal, South Africa METHODS:: We estimated rates of progression through stages of the HIV treatment cascade using data from a longitudinal population-based HIV surveillance system in rural KwaZulu-Natal. Incorporating empirical estimates in a mathematical model of HIV progression, infection transmission, and care, we estimated life expectancy and secondary infections averted under a range of treatment scale-up scenarios reflecting expanding treatment eligibility thresholds. We compared the results to those implied by the conventional assumptions that have been commonly adopted by existing models.RESULTS: Survival gains from expanding the treatment eligibility threshold from CD4 350 to 500 cells/muL and from 500 cells/muL to treating everyone irrespective of their CD4 count may be overestimated by 3.60 and 3.79 times in models that fail to capture realities of the care cascade. HIV infections averted from raising the threshold from CD4 200 to 350, 350 to 500, and 500 cells/muL to treating everyone may be overestimated by 1.10, 2.65, and 1.18 times.CONCLUSION: Models using conventional assumptions about cascade progression may substantially overestimate health benefits. As implementation of treatment scale-up proceeds, it is important to assess the effects of required scale-up efforts in a way that incorporates empirical realities of how people move through the HIV cascade.This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial License 4.0 (CCBY-NC) , where it is permissible to download, share, remix, transform, and buildup the work provided it is properly cited. The work cannot be used commercially without permission from the journal.
View details for PubMedID 30272631