Evidence map›Paper›PMID 37891514›Full record

ArticleBMC public health2023

A quantitative assessment of the consistency of projections from five mathematical models of the HIV epidemic in South Africa: a model comparison study.

Haroon Moolla, Andrew Phillips, Debra Ten Brink, Edinah Mudimu, John Stover, Loveleen Bansi-Matharu, Rowan Martin-Hughes, Nisaa Wulan, Valentina Cambiano, Jennifer Smith and 4 more

Abstract read
In one paragraph

Article in BMC public health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
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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

3 citing papers in PubMed.

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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

14 authors.

Haroon MoollaCentre for Infectious Disease Epidemiology and Research, Faculty of Health Sciences, University of Cape Town, Anzio Road, Cape Town, 7925, Observatory, South Africa. haroon.moolla@uct.ac.za.
Andrew PhillipsInstitute for Global Health, UCL, London, UK.
Debra Ten BrinkBurnet Institute, Melbourne, Australia.
Edinah MudimuDepartment of Decision Sciences, University of South Africa, Pretoria, South Africa.
John StoverAvenir Health, Glastonbury, USA.
Loveleen Bansi-MatharuInstitute for Global Health, UCL, London, UK.
Rowan Martin-HughesBurnet Institute, Melbourne, Australia.
Nisaa WulanBurnet Institute, Melbourne, Australia.
Valentina CambianoInstitute for Global Health, UCL, London, UK.
Jennifer SmithInstitute for Global Health, UCL, London, UK.
Anna BershteynDepartment of Population Health, NYU Grossman School of Medicine, New York, USA.
Gesine Meyer-RathCenter for Global Health and Development, Boston University, Boston, USA.
Lise JamiesonHealth Economics and Epidemiology Research Office, University of the Witwatersrand, Johannesburg, South Africa.
Leigh F JohnsonCentre for Infectious Disease Epidemiology and Research, Faculty of Health Sciences, University of Cape Town, Anzio Road, Cape Town, 7925, Observatory, South Africa.

Funding

Medical Research Council MR/T042796/1
6 · The paper itself

Abstract

backgroundMathematical models are increasingly used to inform HIV policy and planning. Comparing estimates obtained using different mathematical models can test the robustness of estimates and highlight research gaps. As part of a larger project aiming to determine the optimal allocation of funding for HIV services, in this study we compare projections from five mathematical models of the HIV epidemic in South Africa: EMOD-HIV, Goals, HIV-Synthesis, Optima, and Thembisa.

methodsThe five modelling groups produced estimates of the total population, HIV incidence, HIV prevalence, proportion of people living with HIV who are diagnosed, ART coverage, proportion of those on ART who are virally suppressed, AIDS-related deaths, total deaths, and the proportion of adult males who are circumcised. Estimates were made under a "status quo" scenario for the period 1990 to 2040. For each output variable we assessed the consistency of model estimates by calculating the coefficient of variation and examining the trend over time.

resultsFor most outputs there was significant inter-model variability between 1990 and 2005, when limited data was available for calibration, good consistency from 2005 to 2025, and increasing variability towards the end of the projection period. Estimates of HIV incidence, deaths in people living with HIV, and total deaths displayed the largest long-term variability, with standard deviations between 35 and 65% of the cross-model means. Despite this variability, all models predicted a gradual decline in HIV incidence in the long-term. Projections related to the UNAIDS 95-95-95 targets were more consistent, with the coefficients of variation below 0.1 for all groups except children.

conclusionsWhile models produced consistent estimates for several outputs, there are areas of variability that should be investigated. This is important if projections are to be used in subsequent cost-effectiveness studies.

Indexed as

EpidemicsHIV InfectionsAdultChildForecastingHumansIncidenceMaleModels, TheoreticalSouth AfricaCoefficient of variationEpidemic projectionsHIVMathematical modellingModel comparison

Identifiers

PMID37891514
PMCPMC10612295

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