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. 2025 Aug 21;25(1):1050.
doi: 10.1186/s12879-025-11292-9.

Improving the accuracy of Respiratory Syncytial Virus (RSV) incidence estimates among hospitalised adults in Bristol, UK

Katie Lihou  1   2 , Robert Challen  2 , Anastasia Chatzilena  1   2 , George Qian  1   2 , Glenda Oben  1   2 , Jade King  3 , Serena McGuinness  1 , Begonia Morales-Aza  1 , Kaltun Duale  1 , Ainhoa Rodriguez Pereira  1 , William Healy  1 , Jennifer Oliver  1 , Nick Maskell  4 , Adam Finn  1 , Leon Danon  2 , Catherine Hyams  5   6 ; Avon C. A. P. Research Group
Collaborators, Affiliations

Improving the accuracy of Respiratory Syncytial Virus (RSV) incidence estimates among hospitalised adults in Bristol, UK

Katie Lihou et al. BMC Infect Dis. .

Abstract

Background: The burden of Respiratory Syncytial Virus (RSV) infection in adults is of interest in the context of recently-licensed vaccines. However, burden estimates are affected by test error associated with the testing platform, and number and type of samples tested.

Methods: We conducted a prospective cohort study of adults with acute lower respiratory tract disease (aLRTD) hospitalised in Bristol, UK, from April 2022-March 2023. RSV was detected by RT-PCR both by routine standard-of-care (SOC) testing, and by testing of additional nasopharyngeal swabs, saliva and sputum samples from a patient subset. Latent class analysis was used to quantify and adjust for test error rates, including effects of multiple testing. RSV test-positivity rates are reported, and after adjustment for test error, are used to calculate adult population incidence/1000 person-years.

Results: 6906/11445 aLRTD cases (60%) were tested and 251 were positive (3.6%; 251/6906). Test-positivity peaked in December (95%CI 7.9-12.7%). Among cases, 43% had pneumonia, 55% had non-pneumonic infection, 59% chronic respiratory disease exacerbations, and 16% heart failure. Test-positivity was highest in 75-84-year-olds, and 30-day mortality was highest in ≥ 75-year-olds (7.1%; 9/127). Due to low positivity-rates and imperfect specificity (0.98-1.00), test-positivity (3.6%) overestimated inferred true prevalence (2.3%). After adjustment for test error, we estimate overall adult population incidence/1000-person-years to be 0.33 (0.21-0.49), and 2.02 (1.10-3.06) in ≥ 75-year-olds.

Conclusions: RSV contributes significantly to hospitalised adult aLRTD, particularly among the elderly. The implementation of effective RSV vaccines could reduce morbidity, mortality and associated costs of disease. Adult RSV burden accuracy is improved by adjustment for test characteristics due to the impact of imperfect specificity when positivity-rates are low, and this is particularly important for out-of-season estimates. Multiple samples can improve burden estimation accuracy only when tests have near-perfect specificity.

Keywords: Cardiac failure; Lower respiratory tract infection; Pneumonia; RSV; Test error.

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Conflict of interest statement

Declarations. Ethics approval and consent to participate: This prospective observational cohort study of adults admitted to two large university hospitals (Southmead, and Bristol Royal Infirmary) in Bristol, UK, was conducted in accordance with the International Conference for Harmonisation of Good Clinical Practice (ICCH GCP), the UK Policy Framework for Health and Social Care Research, and the Declaration of Helsinki, and was approved by the East of England—Essex Research Ethics Committee, Health Research Authority reference 20/EE/0157, ISRCTN: 17354061. Informed consent was obtained from patients with capacity, and declarations for participation from consultees for individuals lacking capacity. If it was not practicable to approach individuals for consent, data were included using approval from the Clinical Advisory Group under Sect. 251 of the 2006 NHS Act. Clinical trial number: not applicable. Competing interests: CH is Principal Investigator of the AvonCAP study which is an investigator-led University of Bristol study funded by Pfizer and has previously received support from the NIHR in an Academic Clinical Fellowship. JO is a Co-Investigator on the AvonCAP Study. LD is further supported by UKRI through the JUNIPER consortium (grant number MR/V038613/1), MRC (grant number MC/PC/19067), EPSRC (EP/V051555/1 and The Alan Turing Institute, grant EP/N510129/1). AF was a member of the Joint Committee on Vaccination and Immunization (JCVI) until January 2024 and remains a member of the JCVI RSV subcommittee. In addition to receiving funding from Pfizer as Chief Investigator of this study, he leads another project investigating transmission of respiratory bacteria in families jointly funded by Pfizer and the Gates Foundation and is an investigator in trials of COVID-19 vaccines including ChAdOx1nCOV-19, Janssen, Sanofi and Valneva vaccines. NM is supported by the National Institute for Health and Care Research Bristol Biomedical Research Centre. The views expressed are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care. This study was conducted as a collaboration between The University of Bristol (study sponsor) and Pfizer (study funder).

Figures

Fig. 1
Fig. 1
Flowchart of methods used to calculate annual RSV population incidence in adults in Bristol between April 2022 – March 2023
Fig. 2
Fig. 2
Test and sample type combinations for testing of RSV across the study time period. Histogram of specimen combinations collected from each study participant (between April 2022 – March 2023); SOC NP/OP swab; research NP/OP swab; research saliva sample; and research sputum sample. Weekly counts of RSV, by a positive on any test, are shown by the blue line
Fig. 3
Fig. 3
RSV test-positivity over time. The proportion of RSV test-positives over time, overall between July – April (A) and stratified by age group between September—March (B). Points show observed test-positive-rates for each week (participants RSV test-positive/participants RSV tested), and the ribbons show estimated uncertainty from a locally fitted quasi-binomial model
Fig. 4
Fig. 4
Test-positivity vs inferred true prevalence in different participant subgroups. Point estimates of test-positivity (individuals positive by any test/individuals tested; orange) and inferred true prevalence from a latent class model accounting for test error and multiple testing (mean; green), run for different participant subgroups split by peak season (Nov-Feb), off-peak season, and age groups during the peak season. Bars show 95% CI’s (orange: Clopper and Pearson confidence intervals; green: credible interval)
Fig. 5
Fig. 5
Test characteristics by different combinations of respiratory specimens. UpSet diagrams showing individuals tested by different mutually exclusive test/sample combinations of standard of care (SOC) naso- or oropharyngeal (NP/OP) respiratory swabs and research samples: sputum, saliva or NP/OP respiratory swabs. A Percentage of RSV test-positive individuals (95%CI Clopper-Pearson). Numbers show number of participants tested. Dotted line shows true prevalence inferred from a latent class (LC) model; B Number of RSV test-positive individuals. Points show number of RSV-positives after adjustment for true prevalence inferred from the LC model (with 95% credible intervals); C Sensitivity and positive predictive values (PPV); D) Specificity and negative predictive values (NPV) from LC model (with 95% credible intervals)
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