Evidence map›Paper›PMID 41959661›Full record

ArticleOpen forum infectious diseases2026

Effect of the Scale-Up of Dolutegravir on Retention in Care, Risk of Developing Tuberculosis and Viral Load Suppression Among People Living With HIV: Analysis of Routine HIV Clinical Data in Rural KwaZulu-Natal, South Africa (2019-23).

Reuben Christopher Moyo, Elphas Okango, Larisse Bolton, Margot Otto, Ntombifuthi Blose, Tumelo Sereo, Letoao Shoeshoe, Wilfred Otambo, Dickman Gareta, Peter S Nyasulu and 3 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Open forum infectious diseases, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

13 authors.

Reuben Christopher MoyoSouth African Centre for Epidemiological Modelling and Analysis (SACEMA), Centre for Epidemic Response and Innovation (CERI), School for Data Science and Computational Thinking, Stellenbosch University, Stellenbosch, South Africa.ORCID https://orcid.org/0000-0003-4308-0103
Elphas OkangoAfrica Health Research Institute, Durban, South Africa.
Larisse BoltonSouth African Centre for Epidemiological Modelling and Analysis (SACEMA), Centre for Epidemic Response and Innovation (CERI), School for Data Science and Computational Thinking, Stellenbosch University, Stellenbosch, South Africa.ORCID https://orcid.org/0000-0001-7879-2173
Margot OttoSouth African Centre for Epidemiological Modelling and Analysis (SACEMA), Centre for Epidemic Response and Innovation (CERI), School for Data Science and Computational Thinking, Stellenbosch University, Stellenbosch, South Africa.
Ntombifuthi BloseSouth African Centre for Epidemiological Modelling and Analysis (SACEMA), Centre for Epidemic Response and Innovation (CERI), School for Data Science and Computational Thinking, Stellenbosch University, Stellenbosch, South Africa.ORCID https://orcid.org/0000-0003-0787-6702
Tumelo SereoSouth African Centre for Epidemiological Modelling and Analysis (SACEMA), Centre for Epidemic Response and Innovation (CERI), School for Data Science and Computational Thinking, Stellenbosch University, Stellenbosch, South Africa.ORCID https://orcid.org/0000-0002-6261-8717
Letoao ShoeshoeSouth African Centre for Epidemiological Modelling and Analysis (SACEMA), Centre for Epidemic Response and Innovation (CERI), School for Data Science and Computational Thinking, Stellenbosch University, Stellenbosch, South Africa.ORCID https://orcid.org/0009-0002-1086-6011
Wilfred OtamboSouth African Centre for Epidemiological Modelling and Analysis (SACEMA), Centre for Epidemic Response and Innovation (CERI), School for Data Science and Computational Thinking, Stellenbosch University, Stellenbosch, South Africa.
Dickman GaretaAfrica Health Research Institute, Durban, South Africa.
Peter S NyasuluDepartment of Global Health, Division of Epidemiology and Biostatistics, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa.
Paul MeeLincoln Institute for Rural and Coastal Health, College of Health and Science, University of Lincoln, Lincoln, UK.ORCID https://orcid.org/0000-0003-0912-0984
Richard J LessellsKwaZulu-Natal Research and Innovation Sequencing Platform (KRISP), University of KwaZulu-Natal, Durban, KwaZulu-Natal, South Africa.ORCID https://orcid.org/0000-0003-0926-710X
Frank Courteney TanserSouth African Centre for Epidemiological Modelling and Analysis (SACEMA), Centre for Epidemic Response and Innovation (CERI), School for Data Science and Computational Thinking, Stellenbosch University, Stellenbosch, South Africa.ORCID https://orcid.org/0000-0001-9797-0000

Funding

The changing face of HIV in the era of COVID-19: Maximising HIV incidence reduction through dynamic targeting of current and future distributions of acquisition risk.R01MH131480 · NIMH · STELLENBOSCH UNIVERSITY · PI Henry Godwell Mwambi, Frank Courteney Tanser · 2023 to 2026
$2.0M
NIMH NIH HHS R01 MH131480Wellcome Trust
6 · The paper itself

Abstract

Background: There is limited evidence on how the rollout of dolutegravir (DTG) has affected retention in care, tuberculosis (TB) disease risk, and viral load suppression (VLS) among people living with HIV (PLHIV) in routine program settings. This study evaluated associations between DTG rollout and VLS, risk of developing TB disease, and retention in care in rural KwaZulu-Natal (KZN), South Africa. Methods: We employed a retrospective cohort study of PLHIV aged 15 and above, followed up from 1 October 2019 to 31 December 2023 in a rural sub-district of KZN. We grouped antiretroviral therapy (ART) regimens into DTG-containing and non-DTG-containing regimens. We classified PLHIV as virally suppressed or non-suppressed based on a VLS threshold of <400 copies/mL. We used Kaplan-Meier survival curves to describe the transition to DTG over time. We applied Cox proportional-hazards models to evaluate associations between DTG rollout and VLS, the risk of developing TB, and retention in care. Results: Of the 69 919 PLHIV included in the DTG rollout cohort, approximately 70% (n = 48 598) transitioned to DTG-containing regimens during the 4-year follow-up period. Compared with non-DTG regimens, DTG use was associated with a greater likelihood of VLS (aHR 1.24, 95% CI 1.10-1.29), retention in care (aHR 1.20, 95% CI 1.11-1.30), and lower risk of TB disease (aHR 0.68, 95% CI .54-.87). Conclusions: These findings support the sustained rollout of DTG-based regimens and emphasize the importance of continuous monitoring to assess their long-term associations and programmatic performance in comparable settings.

Indexed as

DolutegravirHIVKwaZulu-NatalSouth Africa

Identifiers

PMID41959661
PMCPMC13059684

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.