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Bridging the gap between HIV epidemiology and antiretroviral resistance evolution: Modelling the spread of resistance in South Africa.

ABSTRACT: The scale-up of antiretroviral therapy (ART) in South Africa substantially reduced AIDS-related deaths and new HIV infections. However, its success is threatened by the emergence of resistance to non-nucleoside reverse-transcriptase inhibitors (NNRTI). The MARISA (Modelling Antiretroviral drug Resistance In South Africa) model presented here aims at investigating the time trends and factors driving NNRTI resistance in South Africa. MARISA is a compartmental model that includes the key aspects of the local HIV epidemic: continuum of care, disease progression, and gender. The dynamics of NNRTI resistance emergence and transmission are then added to this framework. Model parameters are informed using data from HIV cohorts participating in the International epidemiology Databases to Evaluate AIDS (IeDEA) and literature estimates, or fitted to UNAIDS estimates. Using this novel approach of triangulating clinical and resistance data from various sources, MARISA reproduces the time trends of HIV in South Africa in 2005-2016, with a decrease in new infections, undiagnosed individuals, and AIDS-related deaths. MARISA captures the dynamics of the spread of NNRTI resistance: high levels of acquired drug resistance (ADR, in 83% of first-line treatment failures in 2016), and increasing transmitted drug resistance (TDR, in 8.1% of ART initiators in 2016). Simulation of counter-factual scenarios reflecting alternative public health policies shows that increasing treatment coverage would have resulted in fewer new infections and deaths, at the cost of higher TDR (11.6% in 2016 for doubling the treatment rate). Conversely, improving switching to second-line treatment would have led to lower TDR (6.5% in 2016 for doubling the switching rate) and fewer new infections and deaths. Implementing drug resistance testing would have had little impact. The rapid ART scale-up and inadequate switching to second-line treatment were the key drivers of the spread of NNRTI resistance in South Africa. However, even though some interventions could have substantially reduced the level of NNRTI resistance, no policy including NNRTI-based first line regimens could have prevented this spread. Thus, by combining epidemiological data on HIV in South Africa with biological data on resistance evolution, our modelling approach identified key factors driving NNRTI resistance, highlighting the need of alternative first-line regimens.

SUBMITTER: Hauser A

PROVIDER: S-EPMC6611642 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature

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Bridging the gap between HIV epidemiology and antiretroviral resistance evolution: Modelling the spread of resistance in South Africa.

Hauser Anthony A Kusejko Katharina K Johnson Leigh F LF Wandeler Gilles G Riou Julien J Goldstein Fardo F Egger Matthias M Kouyos Roger D RD

PLoS computational biology 20190624 6

The scale-up of antiretroviral therapy (ART) in South Africa substantially reduced AIDS-related deaths and new HIV infections. However, its success is threatened by the emergence of resistance to non-nucleoside reverse-transcriptase inhibitors (NNRTI). The MARISA (Modelling Antiretroviral drug Resistance In South Africa) model presented here aims at investigating the time trends and factors driving NNRTI resistance in South Africa. MARISA is a compartmental model that includes the key aspects of ...[more]

PMID: 31233494

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Project description:INTRODUCTION:Significant gaps persist in providing HIV treatment to all who are in need. Restricting care delivery to healthcare facilities will continue to perpetuate this gap in limited resource settings. We assessed a large-scale community-based programme for effectiveness in identifying people living with HIV and linking them to antiretroviral treatment. METHODS:A retrospective secular trend study of 14 high burden local government areas of Nigeria was conducted in which two models of community antiretroviral treatment delivery were implemented: Model A (on-site initiation) and Model B (immediate referral) clusters. Model A cluster offered services within communities, from HIV diagnosis to immediate antiretroviral therapy initiation and some follow-up. Model B cluster offered services for HIV diagnosis up to baseline evaluation and provided referral for antiretroviral therapy initiation to nearest health facility providing HIV services. For controls, we selected and cluster-matched 34 local government areas where community antiretroviral treatment delivery was not implemented. Outcomes of interest were: the number of people identified as HIV positive and the number of HIV-positive individuals started on antiretroviral treatment; from June 2014 to May 2016. We used interrupted time-series analysis to estimate outcome levels and trends across the pre-and post-intervention periods. RESULTS:Before community antiretrovial treatment introduction, Model A cluster identified, per 100,000 catchment population, 500 HIV-positives (95% CI: 399.66 to 601.41) and initiated 216 HIV-positives on antiretroviral treatment (95% CI: 152.72 to 280.10). Model B cluster identified 32 HIV-positives (95% CI: 25.00 to 40.51) and initiated 8 HIV-positives on antiretroviral treatment (95% CI: 5.54 to 10.33). After commART introduction, Model A cluster showed an immediate significant increase in 744 HIV-positive persons (p = 0.00, 95% CI: 360.35 to 1127.77) and 560 HIV-positives initiated on treatment (p = 0.00, 95% CI: 260.56 to 859.64). Model B cluster showed an immediate significant increase in 30 HIV-positive persons identified (p = 0.01, 95% CI: 8.38 to 51.93) but not in the number of HIV-positives initiated on treatment. Model B cluster showed increased month-on-month trends of both outcomes of interest (3.4, p = 0.02, 95% CI: 0.44 to 6.38). CONCLUSION:Both community-models had similar population-level effectiveness for rapidly identifying people living with HIV but differed in effectively transitioning them to treatment. Comprehensiveness, integration and attention to barriers to care are important in the design of community antiretroviral treatment delivery.

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Dataset Information

Bridging the gap between HIV epidemiology and antiretroviral resistance evolution: Modelling the spread of resistance in South Africa.

Publications

Bridging the gap between HIV epidemiology and antiretroviral resistance evolution: Modelling the spread of resistance in South Africa.

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