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Economic and public health impact of decentralized HIV viral load testing: A modelling study in Kenya.

ABSTRACT: Kenya has the world's 4th largest HIV burden. Various strategies to control the epidemic have been implemented, including the implementation of viral load (VL) testing to monitor HIV patients on ARVs. Like many resource limited settings, Kenya's healthcare system faces serious challenges in effectively providing quality health services to its population. Increased investments to strengthen the country's capacity to diagnose, monitor and treat diseases, particularly HIV and TB, continue to be made but are still inadequate in the face of global health goals like the UNAIDS 90:90:90 which require scaling up of VL tests amid existing constraints. In Kenya, there is an increase in the demand for VL tests amidst these existing constraints. The GeneXpert system is a diagnostic point-of-care technology that can quantify, amongst others, HIV VL. Currently, GeneXpert technology is widely distributed in Kenya for testing of tuberculosis. This study aimed to determine the economic and public health impact of incorporating VL test modules on the existing GeneXpert infrastructure. Markov models were constructed for different populations (non-pregnant adults, pregnant women and children). The scenarios analysed were 100% centralized VL testing compared to 50% GeneXpert plus 50% centralized VL testing, with time horizons of 5 years for the adult and child populations, and 31 months for the pregnant population. Incremental effectiveness was measured in terms of the number of HIV transmissions or opportunistic infections avoided when implementing the GeneXpert scenario compared to a 100% centralized scenario. The model indicated that, for all three populations combined, the GeneXpert scenario resulted in 117 less HIV transmissions and 393 less opportunistic infections. The cost decreased by $21,978,755 for the non-pregnant and pregnant adults and $22,808,533 for non-pregnant adults, pregnant adults and children. The model showed that GeneXpert would cost less and be more effective in terms of total cost per HIV transmission avoided and the total cost per opportunistic infection avoided, except for the pregnant population, when considered separately.

SUBMITTER: de Necker M

PROVIDER: S-EPMC6392277 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Economic and public health impact of decentralized HIV viral load testing: A modelling study in Kenya.

de Necker M M de Beer J C JC Stander M P MP Connell C D CD Mwai D D

PloS one 20190227 2

Kenya has the world's 4th largest HIV burden. Various strategies to control the epidemic have been implemented, including the implementation of viral load (VL) testing to monitor HIV patients on ARVs. Like many resource limited settings, Kenya's healthcare system faces serious challenges in effectively providing quality health services to its population. Increased investments to strengthen the country's capacity to diagnose, monitor and treat diseases, particularly HIV and TB, continue to be mad ...[more]

PMID: 30811510

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Project description:IntroductionRoutinely monitoring the HIV viral load (VL) of people living with HIV (PLHIV) on anti-retroviral therapy (ART) facilitates intensive adherence counselling and faster ART regimen switch when treatment failure is indicated. Yet standard VL-testing in centralized laboratories can be time-intensive and logistically difficult in low-resource settings. This paper evaluates the outcomes of the first four years of routine VL-monitoring using Point-of-Care technology, implemented by Médecins Sans Frontières (MSF) in rural clinics in Malawi.MethodsWe conducted a retrospective cohort analysis of patients eligible for routine VL- testing between 2013 and 2017 in four decentralized ART-clinics and the district hospital in Chiradzulu, Malawi. We assessed VL-testing coverage and the treatment failure cascade (from suspected failure (first VL>1000 copies/mL) to VL suppression post regimen switch). We used descriptive statistics and multivariate logistic regression to assess factors associated with suspected failure.Results and discussionAmong 21,400 eligible patients, VL-testing coverage was 85% and VL suppression was found in 89% of those tested. In the decentralized clinics, 88% of test results were reviewed on the same day as blood collection, whereas in the district hospital the median turnaround-time for results was 85 days. Among first-line ART patients with suspected failure (N = 1544), 30% suppressed (VL<1000 copies/mL), 35% were treatment failures (confirmed by subsequent VL-testing) and 35% had incomplete VL follow-up. Among treatment failures, 80% (N = 540) were switched to a second-line regimen, with a higher switching rate in the decentralized clinics than in the district hospital (86% vs. 67%, p < 0.01) and a shorter median time-to-switch (6.8 months vs. 9.7 months, p < 0.01). Similarly, the post-switch VL-testing rate was markedly higher in the decentralized clinics (61% vs. 26%, p < 0.01). Overall, 79% of patients with a post-switch VL-test were suppressed.ConclusionsViral load testing at the point-of-care in Chiradzulu, Malawi achieved high coverage and good drug regimen switch rates among those identified as treatment failures. In decentralized clinics, same-day test results and shorter time-to-switch illustrated the game-changing potential of POC-based VL-testing. Nevertheless, gaps were identified along all steps of the failure cascade. Regular staff training, continuous monitoring and creating demand are essential to the success of routine VL-testing.

Project description:Point-of-care (POC) technologies-including HIV viral load (VL) monitoring-are expanding globally, including in resource-limited settings. Modelling could allow decision-makers to consider the optimal strategy(ies) to maximize coverage and access, minimize turnaround time (TAT) and minimize cost with limited machines. Informed by formative qualitative focus group discussions with stakeholders focused on model inputs, outputs and format, we created an optimization model incorporating queueing theory and solved it using integer programming methods to reflect HIV VL monitoring in Kisumu County, Kenya. We modelled three scenarios for sample processing: (1) centralized laboratories only, (2) centralized labs with 7 existing POC 'hub' facilities and (3) centralized labs with 7 existing and 1-7 new 'hub' facilities. We calculated total TAT using the existing referral network for scenario 1 and solved for the optimal referral network by minimizing TAT for scenarios 2 and 3. We conducted one-way sensitivity analyses, including distributional fairness in each sub-county. Through two focus groups, stakeholders endorsed the provisionally selected model inputs, outputs and format with modifications incorporated during model-building. In all three scenarios, the largest component of TAT was time spent at a facility awaiting sample batching and transport (scenarios 1-3: 78.7%, 89.9%, 91.8%) and waiting time at the testing site (18.7%, 8.7%, 7.5%); transportation time contributed minimally to overall time (2.6%, 1.3%, 0.7%). In scenario 1, the average TAT was 39.8 h (SD: 2.9), with 1077 h that samples spent cumulatively in the VL processing system. In scenario 2, the average TAT decreased to 33.8 h (SD: 4.8), totalling 430 h. In scenario 3, the average TAT decreased nearly monotonically with each new machine to 31.1 h (SD: 8.4) and 346 total hours. Frequency of sample batching and processing rate most impacted TAT, and inclusion of distributional fairness minimally impacted TAT. In conclusion, a stakeholder-informed resource allocation model identified optimal POC VL hub allocations and referral networks. Using existing-and adding new-POC machines could markedly decrease TAT, as could operational changes.

Project description:In 2011 the Centers for Disease Control and Prevention (CDC) published guidelines for the use of population viral load (PVL), community viral load (CVL) and monitored viral load (MVL), defined as the average viral load (VL) of all HIV infected individuals in a population, of all diagnosed individuals, and of all individuals on antiretroviral treatment (ART), respectively. Since then, CVL has been used to assess the effectiveness of ART on HIV transmission and as a proxy for HIV incidence. The first objective of this study was to investigate how aggregate VL measures change with the HIV epidemic phase and the drivers behind these changes using a mathematical transmission model. Secondly, we aimed to give some insight into how well CVL correlates with HIV incidence during the course of the epidemic and roll out of ART. We developed a compartmental model for disease progression and HIV transmission with disease stages that differ in viral loads for epidemiological scenarios relevant to a concentrated epidemic in a population of men who have sex with men (MSM) in Western Europe (WE) and to a generalized epidemic in a heterosexual population in Sub-Saharan Africa (SSA). The model predicts that PVL and CVL change with the epidemic phase, while MVL stays constant. These dynamics are linked to the dynamics of infected subgroups (undiagnosed, diagnosed untreated and treated) in different disease stages (primary, chronic and AIDS). In particular, CVL decreases through all epidemic stages: before ART, since chronic population builds up faster than AIDS population and after ART, due to the build-up of treated population with low VL. The trends in CVL and incidence can be both opposing and coinciding depending on the epidemic phase. Before ART is scaled up to sufficiently high levels, incidence increases while CVL decreases. After this point, CVL is a useful indicator of changes in HIV incidence. The model predicts that during the ART scale-up HIV transmission is driven by undiagnosed and diagnosed untreated individuals, and that new infections decline due to the increase in the number of treated. Although CVL is not able to capture the contribution of undiagnosed population to HIV transmission, it declines due to the increase of people on ART too. In the scenarios described by our model, the present epidemic phase corresponds to declining trends in CVL and incidence.

Project description:BackgroundCommunity-based oral pre-exposure prophylaxis (PrEP) provision has the potential to expand PrEP coverage. HIV self-testing can facilitate PrEP community-based delivery but might have lower sensitivity than facility-based HIV testing, potentially leading to inappropriate PrEP use among people with HIV and subsequent development of drug resistance. We aimed to evaluate the impact of HIV self-testing use for PrEP scale-up.MethodsWe parameterised an agent-based network model, EMOD-HIV, to simulate generic tenofovir disoproxil fumarate and emtricitabine PrEP scale-up in western Kenya using four testing scenarios: provider-administered nucleic acid testing, provider-administered rapid diagnostic tests detecting antibodies, blood-based HIV self-testing, or oral fluid HIV self-testing. Scenarios were compared with a no PrEP counterfactual. Individuals aged 18-49 years with one or more heterosexual partners who screened HIV-negative were eligible for PrEP. We assessed the cost and health impact of rapid PrEP scale-up with high coverage over 20 years, and the budget impact over 5 years, using various HIV testing modalities.FindingsPrEP coverage of 29% was projected to avert approximately 54% of HIV infections and 17% of HIV-related deaths among adults aged 18-49 years over 20 years; health impacts were similar across HIV testing modalities used to deliver PrEP. The percentage of HIV infections with PrEP-associated nucleoside reverse transcriptase inhibitor (NRTI) drug resistance was 0·6% (95% uncertainty intervals 0·4-0·9) in the blood HIV self-testing scenario and 0·8% (0·6-1·0) in the oral HIV self-testing scenario, compared with 0·3% (0·2-0·3) in the antibody rapid diagnostic testing scenario and 0·2% (0·1-0·2) in the nucleic acid testing scenario. Accounting for background NRTI resistance, we found similarly low proportions of drug resistance across scenarios. The budget impact of implementing PrEP using HIV self-testing and provider-administered rapid diagnostic tests were similar, while nucleic acid testing was approximately 50% more costly.InterpretationScaling up PrEP using HIV self-testing has similar health impacts, costs, and low risk of drug resistance as provider-administered rapid diagnostic tests. Policy makers should consider leveraging HIV self-testing to expand PrEP access among those at HIV risk.FundingThe Bill and Melinda Gates Foundation.

Dataset Information

Economic and public health impact of decentralized HIV viral load testing: A modelling study in Kenya.

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Economic and public health impact of decentralized HIV viral load testing: A modelling study in Kenya.

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