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


Journal

The lancet. HIV
ISSN: 2352-3018
Titre abrégé: Lancet HIV
Pays: Netherlands
ID NLM: 101645355

Informations de publication

Date de publication:
04 2021
Historique:
received: 05 04 2020
revised: 07 10 2020
accepted: 13 10 2020
pubmed: 22 12 2020
medline: 23 4 2021
entrez: 21 12 2020
Statut: ppublish

Résumé

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. 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. 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. 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. National Institutes of Health.

Sections du résumé

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.

Identifiants

pubmed: 33347810
pii: S2352-3018(20)30279-4
doi: 10.1016/S2352-3018(20)30279-4
pmc: PMC8284441
mid: NIHMS1659067
pii:
doi:

Substances chimiques

Anti-HIV Agents 0
Creatinine AYI8EX34EU

Types de publication

Journal Article Research Support, N.I.H., Extramural

Langues

eng

Sous-ensembles de citation

IM

Pagination

e216-e224

Subventions

Organisme : NIMH NIH HHS
ID : K01 MH115789
Pays : United States
Organisme : NIAID NIH HHS
ID : R01 AI147752
Pays : United States
Organisme : NIAID NIH HHS
ID : R21 AI124719
Pays : United States

Informations de copyright

Copyright © 2021 Elsevier Ltd. All rights reserved.

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Auteurs

Monisha Sharma (M)

Department of Global Health, University of Washington, Seattle, WA, USA. Electronic address: msharma1@uw.edu.

Edinah Mudimu (E)

Department of Decision Sciences, University of South Africa, Pretoria, South Africa.

Kate Simeon (K)

Department of Medicine, University of Washington, Seattle, WA, USA; Department of Emergency Medicine, Denver Health, Denver, CO, USA.

Anna Bershteyn (A)

Department of Population Health, NYU School of Medicine, New York, NY, USA; Institute for Disease Modeling, Bellevue, WA, USA.

Jienchi Dorward (J)

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.

Lauren R Violette (LR)

Department of Global Health, University of Washington, Seattle, WA, USA.

Adam Akullian (A)

Department of Global Health, University of Washington, Seattle, WA, USA; Institute for Disease Modeling, Bellevue, WA, USA.

Salim S Abdool Karim (SS)

Centre for the AIDS Programme of Research in South Africa, Durban, South Africa; Department of Epidemiology, Columbia University, New York, NY, USA.

Connie Celum (C)

Department of Global Health, University of Washington, Seattle, WA, USA; Department of Medicine, University of Washington, Seattle, WA, USA.

Nigel Garrett (N)

Centre for the AIDS Programme of Research in South Africa, Durban, South Africa.

Paul K Drain (PK)

Department of Global Health, University of Washington, Seattle, WA, USA; Department of Medicine, University of Washington, Seattle, WA, USA.

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Classifications MeSH