Projecting the number of new HIV infections to formulate the "Getting to Zero" strategy in Illinois, USA

Aditya Subhash Khanna; Mert Edali; Jonathan Ozik; Nicholson Collier; Anna Hotton; Abigail Skwara; Babak Mahdavi Ardestani; Russell Brewer; Kayo Fujimoto; Nina Harawa; John A. Schneider; Aditya Subhash Khanna; Mert Edali; Jonathan Ozik; Nicholson Collier; Anna Hotton; Abigail Skwara; Babak Mahdavi Ardestani; Russell Brewer; Kayo Fujimoto; Nina Harawa; John A. Schneider

doi:10.3934/mbe.2021196

Mathematical Biosciences and Engineering

2021, Volume 18, Issue 4: 3922-3938. doi: 10.3934/mbe.2021196

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Research article Special Issues

Projecting the number of new HIV infections to formulate the "Getting to Zero" strategy in Illinois, USA

1.
Center for Alcohol and Addiction Studies, Brown University, Providence RI, USA
2.
Department of Behavioral and Social Sciences, Brown University School of Public Health, Providence RI, USA
3.
Chicago Center for HIV Elimination, The University of Chicago, Chicago, IL, USA
4.
Department of Medicine, The University of Chicago, Chicago, IL, USA
5.
Department of Industrial Engineering, Yildiz Technical University, Besiktas, Istanbul 34349, Turkey
6.
Decision and Infrastructure Sciences Division, Argonne National Laboratory, Lemont, IL, USA
7.
Center for Health Promotion and Prevention Research, The University of Texas Health Science Center at Houston (UTHealth), Houston, TX, USA
8.
Department of Psychiatry and Human Behavior, Charles R. Drew University, Los Angeles, CA, USA
9.
Department of Epidemiology, University of California, Los Angeles, CA, USA

Received: 19 February 2021 Accepted: 08 April 2021 Published: 06 May 2021

Objectives
Getting to Zero (GTZ) initiatives focus on expanding use of antiretroviral treatment (ART) and pre-exposure prophylaxis (PrEP) to eliminate new HIV infections. Computational models help inform policies for implementation of ART and PrEP continuums. Such models, however, vary in their design, and may yield inconsistent predictions. Using multiple approaches can help assess the consistency in results obtained from varied modeling frameworks, and can inform optimal implementation strategies.
Methods
A study using three different modeling approaches is conducted. Two approaches use statistical time series analysis techniques that incorporate temporal HIV incidence data. A third approach uses stochastic stimulation, conducted using an agent-based network model (ABNM). All three approaches are used to project HIV incidence among a key population, young Black MSM (YBMSM), over the course of the GTZ implementation period (2016–2030).
Results
All three approaches suggest that simultaneously increasing PrEP and ART uptake is likely to be more effective than increasing only one, but increasing ART and PrEP by 20% points may not eliminate new HIV infections among YBMSM. The results further suggest that a 20% increase in ART is likely to be more effective than a 20% increase in PrEP. All three methods consistently project that increasing ART and PrEP by 30% simultaneously can help reach GTZ goals.
Conclusions
Increasing PrEP and ART uptake by about 30% might be necessary to accomplish GTZ goals. Such scale-up may require addressing psychosocial and structural barriers to engagement in HIV and PrEP care continuums. ABNMs and other flexible modeling approaches can be extended to examine specific interventions that address these barriers and may provide important data to guide the successful intervention implementation.
- HIV infections,
- pre-exposure prophylaxis,
- computer simulation,
- sexual and gender minorities,
- preventive medicine
Citation: Aditya Subhash Khanna, Mert Edali, Jonathan Ozik, Nicholson Collier, Anna Hotton, Abigail Skwara, Babak Mahdavi Ardestani, Russell Brewer, Kayo Fujimoto, Nina Harawa, John A. Schneider. Projecting the number of new HIV infections to formulate the 'Getting to Zero' strategy in Illinois, USA[J]. Mathematical Biosciences and Engineering, 2021, 18(4): 3922-3938. doi: 10.3934/mbe.2021196

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Abstract

Objectives

Getting to Zero (GTZ) initiatives focus on expanding use of antiretroviral treatment (ART) and pre-exposure prophylaxis (PrEP) to eliminate new HIV infections. Computational models help inform policies for implementation of ART and PrEP continuums. Such models, however, vary in their design, and may yield inconsistent predictions. Using multiple approaches can help assess the consistency in results obtained from varied modeling frameworks, and can inform optimal implementation strategies.

Methods

A study using three different modeling approaches is conducted. Two approaches use statistical time series analysis techniques that incorporate temporal HIV incidence data. A third approach uses stochastic stimulation, conducted using an agent-based network model (ABNM). All three approaches are used to project HIV incidence among a key population, young Black MSM (YBMSM), over the course of the GTZ implementation period (2016–2030).

Results

All three approaches suggest that simultaneously increasing PrEP and ART uptake is likely to be more effective than increasing only one, but increasing ART and PrEP by 20% points may not eliminate new HIV infections among YBMSM. The results further suggest that a 20% increase in ART is likely to be more effective than a 20% increase in PrEP. All three methods consistently project that increasing ART and PrEP by 30% simultaneously can help reach GTZ goals.

Conclusions

Increasing PrEP and ART uptake by about 30% might be necessary to accomplish GTZ goals. Such scale-up may require addressing psychosocial and structural barriers to engagement in HIV and PrEP care continuums. ABNMs and other flexible modeling approaches can be extended to examine specific interventions that address these barriers and may provide important data to guide the successful intervention implementation.

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