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Showing content from http://www.ncbi.nlm.nih.gov/pubmed/33347810 below:

Cost-effectiveness of point-of-care testing with task-shifting for HIV care in South Africa: a modelling study

. 2021 Apr;8(4):e216-e224. doi: 10.1016/S2352-3018(20)30279-4. Epub 2020 Dec 18. Cost-effectiveness of point-of-care testing with task-shifting for HIV care in South Africa: a modelling study

Affiliations

¹ Department of Global Health, University of Washington, Seattle, WA, USA. Electronic address: msharma1@uw.edu.
² Department of Decision Sciences, University of South Africa, Pretoria, South Africa.
³ Department of Medicine, University of Washington, Seattle, WA, USA; Department of Emergency Medicine, Denver Health, Denver, CO, USA.
⁴ Department of Population Health, NYU School of Medicine, New York, NY, USA; Institute for Disease Modeling, Bellevue, WA, USA.
⁵ Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK; Centre for the AIDS Programme of Research in South Africa, Durban, South Africa.
⁶ Department of Global Health, University of Washington, Seattle, WA, USA.
⁷ Department of Global Health, University of Washington, Seattle, WA, USA; Institute for Disease Modeling, Bellevue, WA, USA.
⁸ Centre for the AIDS Programme of Research in South Africa, Durban, South Africa; Department of Epidemiology, Columbia University, New York, NY, USA.
⁹ Department of Global Health, University of Washington, Seattle, WA, USA; Department of Medicine, University of Washington, Seattle, WA, USA.
¹⁰ Centre for the AIDS Programme of Research in South Africa, Durban, South Africa.

PMID: 33347810
PMCID: PMC8284441
DOI: 10.1016/S2352-3018(20)30279-4

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Cost-effectiveness of point-of-care testing with task-shifting for HIV care in South Africa: a modelling study

Monisha Sharma et al. Lancet HIV. 2021 Apr.

. 2021 Apr;8(4):e216-e224. doi: 10.1016/S2352-3018(20)30279-4. Epub 2020 Dec 18. Authors Monisha Sharma¹, Edinah Mudimu², Kate Simeon³, Anna Bershteyn⁴, Jienchi Dorward⁵, Lauren R Violette⁶, Adam Akullian⁷, Salim S Abdool Karim⁸, Connie Celum⁹, Nigel Garrett¹⁰, Paul K Drain⁹ Affiliations

¹ Department of Global Health, University of Washington, Seattle, WA, USA. Electronic address: msharma1@uw.edu.
² Department of Decision Sciences, University of South Africa, Pretoria, South Africa.
³ Department of Medicine, University of Washington, Seattle, WA, USA; Department of Emergency Medicine, Denver Health, Denver, CO, USA.
⁴ Department of Population Health, NYU School of Medicine, New York, NY, USA; Institute for Disease Modeling, Bellevue, WA, USA.
⁵ Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK; Centre for the AIDS Programme of Research in South Africa, Durban, South Africa.
⁶ Department of Global Health, University of Washington, Seattle, WA, USA.
⁷ Department of Global Health, University of Washington, Seattle, WA, USA; Institute for Disease Modeling, Bellevue, WA, USA.
⁸ Centre for the AIDS Programme of Research in South Africa, Durban, South Africa; Department of Epidemiology, Columbia University, New York, NY, USA.
⁹ Department of Global Health, University of Washington, Seattle, WA, USA; Department of Medicine, University of Washington, Seattle, WA, USA.
¹⁰ Centre for the AIDS Programme of Research in South Africa, Durban, South Africa.

PMID: 33347810
PMCID: PMC8284441
DOI: 10.1016/S2352-3018(20)30279-4

Item in Clipboard

Abstract

Background: The number of people on antiretroviral therapy (ART) requiring treatment monitoring in low-resource settings is rapidly increasing. Point-of-care (POC) testing for ART monitoring might alleviate burden on centralised laboratories and improve clinical outcomes, but its cost-effectiveness is unknown.

Methods: We used cost and effectiveness data from the STREAM trial in South Africa (February, 2017-October, 2018), which evaluated POC testing for viral load, CD4 count, and creatinine, with task shifting from professional to lower-cadre registered nurses compared with laboratory-based testing without task shifting (standard of care). We parameterised an agent-based network model, EMOD-HIV, to project the impact of implementing this intervention in South Africa over 20 years, simulating approximately 175 000 individuals per run. We assumed POC monitoring increased viral suppression by 9 percentage points, enrolment into community-based ART delivery by 25 percentage points, and switching to second-line ART by 1 percentage point compared with standard of care, as reported in the STREAM trial. We evaluated POC implementation in varying clinic sizes (10-50 patient initiating ART per month). We calculated incremental cost-effectiveness ratios (ICERs) and report the mean and 90% model variability of 250 runs, using a cost-effectiveness threshold of US$500 per disability-adjusted life-year (DALY) averted for our main analysis.

Findings: POC testing at 70% coverage of patients on ART was projected to reduce HIV infections by 4·5% (90% model variability 1·6 to 7·6) and HIV-related deaths by 3·9% (2·0 to 6·0). In clinics with 30 ART initiations per month, the intervention had an ICER of $197 (90% model variability -27 to 863) per DALY averted; results remained cost-effective when varying background viral suppression, ART dropout, intervention effectiveness, and reduction in HIV transmissibility. At higher clinic volumes (≥40 ART initiations per month), POC testing was cost-saving and at lower clinic volumes (20 ART initiations per month) the ICER was $734 (93 to 2569). A scenario that assumed POC testing did not increase enrolment into community ART delivery produced ICERs that exceeded the cost-effectiveness threshold for all clinic volumes.

Interpretation: POC testing is a promising strategy to cost-effectively improve patient outcomes in moderately sized clinics in South Africa. Results are most sensitive to changes in intervention impact on enrolment into community-based ART delivery.

Funding: National Institutes of Health.

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Figures
Figure 1:. Influence of varying healthcare costs…

Figure 1:. Influence of varying healthcare costs on ICERS by clinic volume.

ART, antiretroviral therapy;…

Figure 1:. Influence of varying healthcare costs on ICERS by clinic volume.

ART, antiretroviral therapy; DALY, disability-adjusted life years; ICER, incremental cost-effectiveness ratio. Clinic volume is defined as number of ART initiations per month. The solid red line represents the cost-effectiveness threshold of $500 and per DALY averted and the dashed red line represents the threshold of $1,175 per DALY averted.

Figure 2:. Budget impact.

Undiscounted annual healthcare…

Figure 2:. Budget impact.

Undiscounted annual healthcare costs of implementing the point-of-care testing intervention at…

Figure 2:. Budget impact.

Undiscounted annual healthcare costs of implementing the point-of-care testing intervention at varying clinic volumes and standard-of-care laboratory testing for a population of 175,000 adults. Clinic volume refers to the number of patient initiating ART per month.