Evidence map›Paper›PMID 42135718›Full record

SynthesisBMC public health2026

Simulation modeling for the design and evaluation of HIV drug resistance interventions: a systematic review of literature.

Beatrice Godwin Aiko, Leonor Guariguata, David Myemba, George Bwire, Goodluck Nyondo, Raphael Z Sangeda, Japhet Killewo, Nico Vandaele, Catherine Decouttere

Abstract readSystematic Review
In one paragraph

Synthesis in BMC public health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

9 authors.

Beatrice Godwin AikoFaculty of Economics and Business, Access to Medicine Research Center, KU Leuven, Vlamingenstraat 83, Leuven, 3000, Belgium. beatriceaiko59@gmail.com.
Leonor GuariguataFaculty of Economics and Business, Access to Medicine Research Center, KU Leuven, Vlamingenstraat 83, Leuven, 3000, Belgium.
David MyembaFaculty of Economics and Business, Access to Medicine Research Center, KU Leuven, Vlamingenstraat 83, Leuven, 3000, Belgium.
George BwireDepartment of Pharmaceutical Microbiology, School of Pharmacy, Muhimbili University of Health and Allied Sciences, Dar es Salaam, 65013, Tanzania.
Goodluck NyondoDepartment of Medicinal Chemistry, School of Pharmacy, Muhimbili University of Health and Allied Sciences, Dar es Salaam, 65013, Tanzania.
Raphael Z SangedaDepartment of Pharmaceutical Microbiology, School of Pharmacy, Muhimbili University of Health and Allied Sciences, Dar es Salaam, 65013, Tanzania.
Japhet KillewoDepartment of Epidemiology and Biostatistics, School of Public Health and Social Sciences, Muhimbili University of Health and Allied Sciences, Dar es Salaam, 65015, Tanzania.
Nico VandaeleFaculty of Economics and Business, Access to Medicine Research Center, KU Leuven, Vlamingenstraat 83, Leuven, 3000, Belgium.
Catherine DecouttereFaculty of Economics and Business, Access to Medicine Research Center, KU Leuven, Vlamingenstraat 83, Leuven, 3000, Belgium.

Funding

Flemish Interuniversity Council - University Development Cooperation (VLIROUS) TZ2022TEA530A101
6 · The paper itself

Abstract

backgroundThe utility of antiretroviral therapy (ART) in the management of human immunodeficiency virus (HIV) is being challenged by growing HIV drug resistance (HIVDR). Although simulation modelling is useful for understanding complex problems, the extent to which it is used in HIVDR is unknown. This review aimed to determine how modelling has been used to inform HIVDR interventions and how its use can align with the World Health Organization (WHO) Global Action Plan (GAP) for HIVDR 2017-2021.

methodsThis review involved a literature search across PubMed, Scopus, Web of Science, and Embase databases. Articles published after the introduction of ART, 1997 to 28th November 2025, were considered. Relevant information, including metadata, model descriptions, interventions, and their outcomes, was extracted. Findings from included papers were categorized according to their area of focus within the five strategic objectives of the WHO GAP for HIVDR 2017-2021.

resultsA total of 2346 articles were screened, and 17 articles were included in the final analysis. Most studies modelled HIVDR in sub-Saharan Africa (n = 13). Acquired resistance (n = 15) was assessed in most of the studies, followed by transmitted resistance (n = 7) and pretreatment resistance (n = 3). Most of the models were stochastic models(n = 11), with about one third of them analyzing cost effectiveness(n = 6). Ten models focused on the WHO GAP HIVDR strategic objective of prevention and response, four aligned with the objective of monitoring and surveillance, while the remaining three assessed a combination of the two objectives. None of the models assessed the remaining three objectives: research and innovation, laboratory capacity, or governance and enabling mechanisms.

conclusionThis review identifies a need for more cross-cutting analyses of multiple strategic objectives for HIVDR, including system-wide models to provide holistic insights into the complexity surrounding HIVDR. Employing patient and public involvement (PPI) in model development and intervention design would further strengthen model validity and transition to real-world settings (PROSPERO ID: CRD42024553557).

Indexed as

Anti-HIV AgentsAnti-Retroviral AgentsComputer SimulationDrug Resistance, ViralHIV InfectionsHumansModels, TheoreticalAnti-HIV AgentsAnti-Retroviral AgentsHIV Drug ResistanceHIV Drug Resistance Intervention ModelsInterventionsSimulation ModelingSystematic Review

Identifiers

PMID42135718
PMCPMC13348690

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.