Cecilia Okusi

Background The World Health Organisation recommends integrating viral genome sequences and sentinel surveillance data. We report progress in linking clinical, virology, and sequence data to enable genomic surveillance of influenza, respiratory syncytial virus (RSV), and severe-acute-respiratory-syndrome coronavirus-2 (SARS-CoV-2). Methods We linked individual-level clinical data from the Oxford-Royal College of General Practitioners (RCGP) Research and Surveillance Centre (RSC) sentinel network to virology results from the UK Health Security Agency (UKHSA) reference virology laboratory. We identify where publicly accessible repositories, the Global Initiative on Sharing All Influenza Data (GISAID), or others hold viral genome sequence data from test-positive cases. Our metadata also identifies test-negative controls contemporaneous to test-positive cases. We summarise the scope of data availability in the Wellcome Quinquagenarian (QQG) biomedical resource. Results We report respiratory virus sampling for influenza, RSV, and SARS-CoV-2 between 1992 and 2023. Samples were collected from a nationally representative subset of RSC general practices participating in the virological surveillance programme. QQG contains 13,665 positive influenza samples, 3,791 positive RSV samples, and 5,068 positive SARS-CoV2 samples. There were 2,819 sequenced influenza genomes, of which 97.1% were linked to clinical records, 1,251 sequenced RSV genomes of which 96.8 were linked to clinical records, and 2,486 sequenced SARS-CoV-2 genomes of which 98.9% were linked to clinical records. Conclusion We have described the scale of QQG, created to enable genomic surveillance linked to clinical metadata to facilitate research on the impact of different viral variants on clinical outcomes, vaccine effectiveness, and therapeutic strategies.

Background: Influenza and respiratory syncytial virus (RSV) predominantly circulate during the winter season and cause acute respiratory illness (ARI). Deploying molecular point-of-care testing (POCT) in primary care can inform whether a patient presenting with an ARI has influenza or RSV. An early virological diagnosis could facilitate appropriate use of antivirals and enable better antimicrobial stewardship. Objective: This study aimed to report the impact of POCT for influenza and RSV on antimicrobial prescribing, including antiviral therapy in primary care. Methods: The impact of POCT for influenza on antimicrobial stewardship (PIAMS) in UK primary care was a nested cohort study undertaken from January 20 to May 31, 2023, after the period of peak virus circulation, within practices that contribute data to the English sentinel network. People presenting with ARI had a nasopharyngeal swab performed and were tested for influenza and RSV with a molecular POCT analyzer located within the practice. Data on antimicrobial prescribing and other study outcomes were collected by linking information from the analyzer to coded data from the patient’s computerized medical record. Results: In total, 323 swabs were collected from 10 PIAMS study practices. In total, 59.7% (197/323) of swabbed patients were female, and the mean age was 37.28 (SD 25.05) years. Furthermore, 2.9% (9/323) of all swabs were positive, with 0.3% (1/323) positive for influenza A, 1.6% (5/323) positive for influenza B, and 0.9% (3/323) positive for RSV. In total, 80 patients were prescribed antibiotics 7 days following POCT testing. There were no instances of antiviral prescribing in the 7 days post testing. A statistically significant difference in antibiotic prescribing given a positive POCT result compared with a negative test was not found with an unadjusted odds ratio (OR) of 7 days post testing. A statistically significant difference in antibiotic prescribing given a positive POCT result compared with a negative test was not found with an unadjusted OR of 1.54 (95% CI 0.38‐6.30; P=.55) and adjusted OR of 1.21 (95% CI 0.00‐1.78). Conclusions: This study illustrates the risk of having a narrow study window; our observation period was not aligned with when influenza was circulating. The peak of weekly incidence of influenza in the sentinel network was in the last week of 2022, and RSV was circulating before this. Further evidence is needed to assess the impact of POCT on antimicrobial prescribing. The viruses tested for using POCT could be aligned with the circulating viruses identified by the sentinel network.

The Oxford-RCGP Research and Surveillance Centre (RSC) is one of the largest health surveillance systems in Europe, using real-world primary care data to inform public health policy and clinical practice. As UK general practice has evolved digitally and the demand for strong information governance (IG) has increased, the RSC has expanded its technological capabilities. This paper applies a Digital Maturity Model (DMM) to assess the RSC’s progress across five dimensions and ten key informatics and IG elements. The analysis highlights the RSC’s strengths in meeting public health needs and identifies areas for improvement. This review identifies predictive analytics, cloud-based services, multi-dimensional modelling, and advanced visualisations as areas for future development.

BACKGROUND Influenza and respiratory syncytial virus (RSV) predominantly circulate during the winter season and cause acute respiratory infections (ARI). Deploying molecular point-of-care testing (POCT) in primary care at can inform whether a patient presenting with an ARI has influenza or RSV. An early virological diagnosis, could facilitate appropriate use of antivirals and enable better antimicrobial stewardship. OBJECTIVE To report the impact of POCT for influenza and RSV on antimicrobial prescribing including antiviral therapy in primary care. METHODS The impact of POCT for influenza on antimicrobial stewardship (PIAMS) in UK primary care was an observational study undertaken 20th Jan–31st May 2023, in practices that contribute data to the English sentinel network. People presenting with ARI had nasopharyngeal swab performed and were tested for Influenza and RSV with a molecular POCT analyser located within the practice. Data on antimicrobial prescribing and other study outcomes were collected by linking information from the analyser to coded data from the patient’s computerised medical record (CMR). RESULTS In total 323 swabs were collected from ten PIAMS study practices. 59.7% (197/323) of swabbed patients were female, and the mean age was 37 years. 2.79% (9/323) of all swabs were positive, with 0.31% (1/323) positive for influenza A, 1.55% (5/323) postivie for influenza B and 0.93% (3/323) positive for RSV. In total 80 patients were prescribed antibiotics seven days following POCT testing. There were no instances of antiviral prescribing in the seven days post-testing. The odds ratio for antibiotic prescribing given a positive POCT result was 1.54 (95% CI: 0.38–6.30; P =0.55) compared with a negative test. CONCLUSIONS This study illustrates the risk of having a narrow study window, our observation period was not aligned with when influenza was circulating. The peak of weekly incidence of influenza in the sentinel network was in the last week of 2022, and RSV circulating prior to this. Further evidence is needed to assess the impact of POCT on antibimicrobial prescribing. The viruses tested for using POCT could be aligned with the circulating viruses identified by the sentinel network. CLINICALTRIAL Not applicable INTERNATIONAL REGISTERED REPORT RR2-10.2196/46938

Introduction: Respiratory sentinel surveillance systems leveraging computerised medical records (CMR) use phenotyping algorithms to identify cases of interest, such as acute respiratory infection (ARI). The Oxford-Royal College of General Practitioners Research and Surveillance Centre (RSC) is the English primary care-based sentinel surveillance network. Aim: This study describes and validates the RSC’s new ARI phenotyping algorithm. Methods: We developed the phenotyping algorithm using a framework aligned with international interoperability standards. We validated our algorithm by comparing ARI events identified during the 2022/23 influenza season in England through use of both old and new algorithms. We compared clinical codes commonly used for recording ARI. Results: The new algorithm identified an additional 860,039 cases and excluded 52,258, resulting in a net increase of 807,781 cases (33.84%) of ARI compared to the old algorithm, with totals of 3,194,224 cases versus 2,386,443 cases. Of the 860,039 newly identified cases, the majority (63.7%) were due to identification of symptom codes suggestive of an ARI diagnosis not detected by the old algorithm. The 52,258 cases incorrectly identified by the old algorithm were due to inadvertent identification of chronic, recurrent, non-infectious and other non-ARI disease. Conclusion: We developed a new ARI phenotyping algorithm that more accurately identifies cases of ARI from the CMR. This will benefit public health by providing more accurate surveillance reports to public health authorities. This new algorithm can serve as a blueprint for other CMR-based surveillance systems wishing to develop similar phenotyping algorithms.

Background: Influenza contributes to the surge in winter infections and the consequent winter pressures on the health service. Molecular point-of-care testing (POCT) for influenza may improve patient management by providing rapid and accurate clinical diagnosis to inform the timely initiation of antiviral therapy and reduce unnecessary admissions and antibiotics use. Aim: To explore factors that influence the adoption or non-adoption of POCT in English general practices and provide insights to enable its integration into routine practice workflows. Design & setting: A qualitative implementation evaluation was conducted in 10 general practices within the English national sentinel network (Oxford RCGP Research and Surveillance Centre), from April–July 2023. Method: Using the Non-adoption, Abandonment, Scale-up, Spread, and Sustainability (NASSS) framework, data collection and analysis were conducted across 10 practices. We made ethnographic observations of the POCT workflow and surveyed the practice staff for their perspectives on POCT implementation. Data were analysed using a mix of descriptive statistics, graphical modelling techniques, and framework analysis. Results: Ethnographic observations identified the following two modes of POCT integration into practice workflow: (1) clinician POCT workflow, which typically involved batch testing owing to time constraints; and (2) research nurse or healthcare assistant POCT workflow, which was characterised by immediate testing of individual patients. Survey data indicated that most primary care staff considered the POCT training offered was sufficient and these practices were ready for change. Some participants agreed that there was the capacity and resources to integrate POCT into workflows. It was uncertain as to whether POCT required changes to organisational routines and processes. Conclusion: General practices should demonstrate flexibility in the workflow and workforce they deploy to integrate POCT into routine clinical workflow.

Background: Prepandemic sentinel surveillance focused on improved management of winter pressures, with influenza-like illness (ILI) being the key clinical indicator. The World Health Organization (WHO) global standards for influenza surveillance include monitoring acute respiratory infection (ARI) and ILI. The WHO’s mosaic framework recommends that the surveillance strategies of countries include the virological monitoring of respiratory viruses with pandemic potential such as influenza. The Oxford-Royal College of General Practitioner Research and Surveillance Centre (RSC) in collaboration with the UK Health Security Agency (UKHSA) has provided sentinel surveillance since 1967, including virology since 1993. Objective: We aim to describe the RSC’s plans for sentinel surveillance in the 2023-2024 season and evaluate these plans against the WHO mosaic framework. Methods: Our approach, which includes patient and public involvement, contributes to surveillance objectives across all 3 domains of the mosaic framework. We will generate an ARI phenotype to enable reporting of this indicator in addition to ILI. These data will support UKHSA’s sentinel surveillance, including vaccine effectiveness and burden of disease studies. The panel of virology tests analyzed in UKHSA’s reference laboratory will remain unchanged, with additional plans for point-of-care testing, pneumococcus testing, and asymptomatic screening. Our sampling framework for serological surveillance will provide greater representativeness and more samples from younger people. We will create a biomedical resource that enables linkage between clinical data held in the RSC and virology data, including sequencing data, held by the UKHSA. We describe the governance framework for the RSC. Results: We are co-designing our communication about data sharing and sampling, contextualized by the mosaic framework, with national and general practice patient and public involvement groups. We present our ARI digital phenotype and the key data RSC network members are requested to include in computerized medical records. We will share data with the UKHSA to report vaccine effectiveness for COVID-19 and influenza, assess the disease burden of respiratory syncytial virus, and perform syndromic surveillance. Virological surveillance will include COVID-19, influenza, respiratory syncytial virus, and other common respiratory viruses. We plan to pilot point-of-care testing for group A streptococcus, urine tests for pneumococcus, and asymptomatic testing. We will integrate test requests and results with the laboratory-computerized medical record system. A biomedical resource will enable research linking clinical data to virology data. The legal basis for the RSC’s pseudonymized data extract is The Health Service (Control of Patient Information) Regulations 2002, and all nonsurveillance uses require research ethics approval. Conclusions: The RSC extended its surveillance activities to meet more but not all of the mosaic framework’s objectives. We have introduced an ARI indicator. We seek to expand our surveillance scope and could do more around transmissibility and the benefits and risks of nonvaccine therapies.