Evidence map›Paper›PMID 41291465›Full record

SynthesisBMC infectious diseases2025

Risk prediction models for targeted testing of HIV, hepatitis B and hepatitis C: a systematic review and meta-analysis.

Werner Leber, Hamzah Z Farooq, Jasmina Panovska-Griffiths, Harriet Larvin, Rebecca F Baggaley, Pip Divall, Dominic Haigh, Emma A Davies, Saher Choudhry, Scott Rory Hicks and 13 more

Abstract readSystematic ReviewMeta-Analysis
PubMed Full text at PMC Publisher
In one paragraph

Synthesis in BMC infectious diseases, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

23 authors.

Werner Leber *Wolfson Institute of Population Health, Queen Mary University of London, 58 Turner Street, London, E1 2AB, UK. w.leber@qmul.ac.uk.
Hamzah Z Farooq *Blizard Institute, Queen Mary University of London, London, UK.
Jasmina Panovska-GriffithsThe Pandemic Sciences Institute, University of Oxford, Oxford, UK.
Harriet LarvinWolfson Institute of Population Health, Queen Mary University of London, 58 Turner Street, London, E1 2AB, UK.
Rebecca F BaggaleyNIHR Leicester Biomedical Research Centre, University of Leicester, Leicester, UK.
Pip DivallClinical Librarian Service, University Hospitals of Leicester NHS Trust, Leicester, UK.
Dominic HaighDepartment of Clinical Virology, Manchester University NHS Foundation Trust, Manchester, UK.
Emma A DaviesDepartment of Clinical Virology, Manchester University NHS Foundation Trust, Manchester, UK.
Saher ChoudhryDepartment of Microbiology, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, UK.
Scott Rory HicksDepartment of Infectious Diseases, Manchester University NHS Foundation Trust, Manchester, UK.
Lynsey GoodwinInstitute of Infection, Veterinary and Ecological Sciences, University of Liverpool, Liverpool, UK.
Graham R FosterBlizard Institute, Queen Mary University of London, London, UK.
Chloe M OrkinBlizard Institute, Queen Mary University of London, London, UK.
Dominik ZennerWolfson Institute of Population Health, Queen Mary University of London, 58 Turner Street, London, E1 2AB, UK.
Peter VickermanBristol Population Health Science Institute, Bristol Medical School, Bristol, UK.
Matthew HickmanBristol Population Health Science Institute, Bristol Medical School, Bristol, UK.
Leila ReidThe Hepatitis C Trust, London, UK.
Steve WorrallPositive East, London, UK.
Manish PareekNIHR Leicester Biomedical Research Centre, University of Leicester, Leicester, UK.
Shahana LaisWolfson Institute of Population Health, Queen Mary University of London, 58 Turner Street, London, E1 2AB, UK.
Jane AndersonBlizard Institute, Queen Mary University of London, London, UK.
John RobsonWolfson Institute of Population Health, Queen Mary University of London, 58 Turner Street, London, E1 2AB, UK.
Chris J GriffithsWolfson Institute of Population Health, Queen Mary University of London, 58 Turner Street, London, E1 2AB, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundDiagnosing human immunodeficiency virus (HIV), hepatitis B virus (HBV), and hepatitis C virus (HCV) infections in general population settings is challenging. We conducted a systematic review and meta-analysis of prediction tools designed to help identify individuals at risk of these blood-borne viruses.

methodsWe included studies on individuals of any age at risk of blood-borne viruses from healthcare, community settings, and national databases. We searched the Web of Science, MEDLINE, EMBASE, and CENTRAL databases (from database inception to 2023) and used the Prediction model Risk Of Bias ASsessment Tool (PROBAST) to evaluate the quality and systematic risk of bias of these studies. We extracted model accuracy using the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity. A mixed-effects model (for AUC) and bivariate random-effects model (for sensitivity/specificity) were used to generate pooled values for these studies.

resultsOf the 41,585 records, 71 were included, covering over 31 million participants and more than 65,000 cases of blood-borne viruses. We examined 67 models: 47 for HIV, 13 for HCV, 5 for HBV, and 2 from studies that assessed multiple viruses separately. The studies were conducted in 41 low- and middle-income and 30 high-income countries. They covered 11 different populations (including men who have sex with men, the general population, and women), 8 types of settings (including sexual health, secondary care, and primary care) and 7 types of risk factors (behavioural, clinical, and demographic). The methods comprised traditional regression (n = 50), machine-learning models (n = 17), and others (n = 4). The risk of bias was high in 64 studies and low in seven. Among 33 studies reporting mean and 95% CI, pooled AUC values were 0.73 (95% CI:0.67–0.80, [Formula: see text] = 74%) across HIV studies (including 8 machine-learning models), 0.80 (0.73–0.86, [Formula: see text] = 86%) for HCV (including 2 machine-learning models) and 0.79 (0.76–0.81, [Formula: see text] = 93%) for HBV (including 3 machine-learning models).

conclusionsSignificant heterogeneity exists in blood-borne virus prediction accuracy across diverse settings and populations, with a high risk of bias. Contributions from primary care were limited, and evaluation and reporting were inconsistent. Developing and evaluating effective models for the combined risk assessment of HIV, HBV, and HCV in general population settings remains a priority.

Indexed as

Hepatitis BHepatitis CHIV InfectionsFemaleHumansModels, StatisticalPrediction AlgorithmsRisk AssessmentSensitivity and SpecificityBlood-borne virusHepatitis BHepatitis CHuman immunodeficiency virusPredictive algorithmPrimary care screening

Identifiers

PMID41291465
PMCPMC12648812

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.