Evidence map›Paper›PMID 30350392›Full record

ArticleJournal of the International AIDS Society2018

Successes and challenges in optimizing the viral load cascade to improve antiretroviral therapy adherence and rationalize second-line switches in Swaziland.

David Etoori, Iza Ciglenecki, Mpumelelo Ndlangamandla, Celeste G Edwards, Kiran Jobanputra, Munyaradzi Pasipamire, Gugu Maphalala, Chunfu Yang, Inoussa Zabsonre, Serge M Kabore and 3 more

Abstract read
In one paragraph

Article in Journal of the International AIDS Society, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers, 5 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
42citing papers in PubMed, 5 pooled it
–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

42 citing papers in PubMed, 5 syntheses or guidelines pooled it.

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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.

David EtooriResearch Department, Médecins Sans Frontières, Mbabane, Swaziland.ORCID 0000-0002-2807-6272
Iza CigleneckiResearch Department, Médecins Sans Frontières, Geneva, Switzerland.
Mpumelelo NdlangamandlaResearch Department, Médecins Sans Frontières, Mbabane, Swaziland.
Celeste G EdwardsResearch Department, Médecins Sans Frontières, Mbabane, Swaziland.
Kiran JobanputraThe Manson Unit, Médecins Sans Frontières, London, United Kingdom.
Munyaradzi PasipamireSwaziland National AIDS Programme (SNAP), Ministry of Health, Mbabane, Swaziland.
Gugu MaphalalaSwaziland National Reference Laboratory (NRL), Ministry of Health, Mbabane, Swaziland.
Chunfu YangDivision of Global HIV/AIDS, The Centre for Disease Control, Atlanta, GA, USA.
Inoussa ZabsonreResearch Department, Médecins Sans Frontières, Mbabane, Swaziland.
Serge M KaboreResearch Department, Médecins Sans Frontières, Mbabane, Swaziland.
Javier GoiriResearch Department, Médecins Sans Frontières, Geneva, Switzerland.
Roger TeckResearch Department, Médecins Sans Frontières, Geneva, Switzerland.
Bernhard KerschbergerResearch Department, Médecins Sans Frontières, Mbabane, Swaziland.

Funding

Medical Research Council MR/P014313/1
6 · The paper itself

Abstract

introductionAs antiretroviral therapy (ART) is scaled up, more patients become eligible for routine viral load (VL) monitoring, the most important tool for monitoring ART efficacy. For HIV programmes to become effective, leakages along the VL cascade need to be minimized and treatment switching needs to be optimized. However, many HIV programmes in resource-constrained settings report significant shortfalls.

methodsFrom a public sector HIV programme in rural Swaziland, we evaluated the VL cascade of adults (≥18 years) on ART from the time of the first elevated VL (>1000 copies/mL) between January 2013 and June 2014 to treatment switching by December 2015. We additionally described HIV drug resistance for patients with virological failure. We used descriptive statistics and Kaplan-Meier estimates to describe the different steps along the cascade and regression models to determine factors associated with outcomes. RESULTS AND DISCUSSION: Of 828 patients with a first elevated VL, 252 (30.4%) did not receive any enhanced adherence counselling (EAC). Six hundred and ninety-six (84.1%) patients had a follow-up VL measurement, and the predictors of receiving a follow-up VL were being a second-line patient (adjusted hazard ratio (aHR): 0.72; p = 0.051), Hlathikhulu health zone (aHR: 0.79; p = 0.013) and having received two EAC sessions (aHR: 1.31; p = 0.023). Four hundred and ten patients (58.9%) achieved VL re-suppression. Predictors of re-suppression were age 50 to 64 (adjusted odds ratio (aOR): 2.02; p = 0.015) compared with age 18 to 34 years, being on second-line treatment (aOR: 3.29; p = 0.003) and two (aOR: 1.66; p = 0.045) or three (aOR: 1.86; p = 0.003) EAC sessions. Of 278 patients eligible to switch to second-line therapy, 120 (43.2%) had switched by the end of the study. Finally, of 155 successfully sequenced dried blood spots, 144 (92.9%) were from first-line patients. Of these, 133 (positive predictive value: 92.4%) had resistance patterns that necessitated treatment switching.

conclusionsPatients on ART with high VLs were more likely to re-suppress if they received EAC. Failure to re-suppress after counselling was predictive of genotypically confirmed resistance patterns requiring treatment switching. Delays in switching were significant despite the ability of the WHO algorithm to predict treatment failure. Despite significant progress in recent years, enhanced focus on quality care along the VL cascade in resource-limited settings is crucial.

Indexed as

Medication AdherenceViral LoadAdolescentAdultAnti-HIV AgentsEswatiniFemaleHIV InfectionsHumansMaleMiddle AgedRetrospective StudiesYoung AdultAnti-HIV AgentsARTgenotypingtreatment monitoringtreatment switchingviral load

Identifiers

PMID30350392
PMCPMC6198167

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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.