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Gu X, Watson C, Agrawal U, Whitaker H, Elson WH, Anand S, Borrow R, Buckingham A, Button E, Curtis L, Dunn D, Elliot AJ, Ferreira F, Goudie R, Hoang U, Hoschler K, Jamie G, Kar D, Kele B, Leston M, Linley E, Macartney J, Marsden GL, Okusi C, Parvizi O, Quinot C, Sebastianpillai P, Sexton V, Smith G, Suli T, Thomas NPB, Thompson C, Todkill D, Wimalaratna R, Inada-Kim M, Andrews N, Tzortziou-Brown V, Byford R, Zambon M, Lopez-Bernal J, de Lusignan S. Postpandemic Sentinel Surveillance of Respiratory Diseases in the Context of the World Health Organization Mosaic Framework: Protocol for a Development and Evaluation Study Involving the English Primary Care Network 2023-2024. JMIR Public Health Surveill 2024; 10:e52047. [PMID: 38569175 PMCID: PMC11024753 DOI: 10.2196/52047] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Revised: 01/02/2024] [Accepted: 01/17/2024] [Indexed: 04/05/2024] Open
Abstract
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.
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Affiliation(s)
- Xinchun Gu
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Conall Watson
- Immunisation and Vaccine-Preventable Diseases Division, UK Health Security Agency, London, United Kingdom
| | - Utkarsh Agrawal
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Heather Whitaker
- Statistics, Modelling and Economics Department, UK Health Security Agency, London, United Kingdom
| | - William H Elson
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Sneha Anand
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Ray Borrow
- Vaccine Evaluation Unit, UK Health Security Agency, Manchester, United Kingdom
| | | | - Elizabeth Button
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Lottie Curtis
- Royal College of General Practitioners, London, United Kingdom
| | - Dominic Dunn
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Alex J Elliot
- Real-time Syndromic Surveillance Team, UK Health Security Agency, Birmingham, United Kingdom
| | - Filipa Ferreira
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Rosalind Goudie
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Uy Hoang
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Katja Hoschler
- Respiratory Virus Unit, UK Health Security Agency, London, United Kingdom
| | - Gavin Jamie
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Debasish Kar
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Beatrix Kele
- Respiratory Virus Unit, UK Health Security Agency, London, United Kingdom
| | - Meredith Leston
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Ezra Linley
- Vaccine Evaluation Unit, UK Health Security Agency, Manchester, United Kingdom
| | - Jack Macartney
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Gemma L Marsden
- Royal College of General Practitioners, London, United Kingdom
| | - Cecilia Okusi
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Omid Parvizi
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
- Respiratory Virus Unit, UK Health Security Agency, London, United Kingdom
| | - Catherine Quinot
- Immunisation and Vaccine-Preventable Diseases Division, UK Health Security Agency, London, United Kingdom
| | | | - Vanashree Sexton
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Gillian Smith
- Real-time Syndromic Surveillance Team, UK Health Security Agency, Birmingham, United Kingdom
| | - Timea Suli
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | | | - Catherine Thompson
- Respiratory Virus Unit, UK Health Security Agency, London, United Kingdom
| | - Daniel Todkill
- Real-time Syndromic Surveillance Team, UK Health Security Agency, Birmingham, United Kingdom
| | - Rashmi Wimalaratna
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | | | - Nick Andrews
- Immunisation and Vaccine-Preventable Diseases Division, UK Health Security Agency, London, United Kingdom
| | | | - Rachel Byford
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Maria Zambon
- Virus Reference Department, UK Health Security Agency, London, United Kingdom
| | - Jamie Lopez-Bernal
- Immunisation and Vaccine-Preventable Diseases Division, UK Health Security Agency, London, United Kingdom
| | - Simon de Lusignan
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
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Vasileiou E, Shi T, Kerr S, Robertson C, Joy M, Tsang R, McGagh D, Williams J, Hobbs R, de Lusignan S, Bradley D, OReilly D, Murphy S, Chuter A, Beggs J, Ford D, Orton C, Akbari A, Bedston S, Davies G, Griffiths LJ, Griffiths R, Lowthian E, Lyons J, Lyons RA, North L, Perry M, Torabi F, Pickett J, McMenamin J, McCowan C, Agrawal U, Wood R, Stock SJ, Moore E, Henery P, Simpson CR, Sheikh A. Investigating the uptake, effectiveness and safety of COVID-19 vaccines: protocol for an observational study using linked UK national data. BMJ Open 2022; 12:e050062. [PMID: 35165107 PMCID: PMC8844955 DOI: 10.1136/bmjopen-2021-050062] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/09/2021] [Accepted: 01/19/2022] [Indexed: 11/30/2022] Open
Abstract
INTRODUCTION The novel coronavirus SARS-CoV-2, which emerged in December 2019, has caused millions of deaths and severe illness worldwide. Numerous vaccines are currently under development of which a few have now been authorised for population-level administration by several countries. As of 20 September 2021, over 48 million people have received their first vaccine dose and over 44 million people have received their second vaccine dose across the UK. We aim to assess the uptake rates, effectiveness, and safety of all currently approved COVID-19 vaccines in the UK. METHODS AND ANALYSIS We will use prospective cohort study designs to assess vaccine uptake, effectiveness and safety against clinical outcomes and deaths. Test-negative case-control study design will be used to assess vaccine effectiveness (VE) against laboratory confirmed SARS-CoV-2 infection. Self-controlled case series and retrospective cohort study designs will be carried out to assess vaccine safety against mild-to-moderate and severe adverse events, respectively. Individual-level pseudonymised data from primary care, secondary care, laboratory test and death records will be linked and analysed in secure research environments in each UK nation. Univariate and multivariate logistic regression models will be carried out to estimate vaccine uptake levels in relation to various population characteristics. VE estimates against laboratory confirmed SARS-CoV-2 infection will be generated using a generalised additive logistic model. Time-dependent Cox models will be used to estimate the VE against clinical outcomes and deaths. The safety of the vaccines will be assessed using logistic regression models with an offset for the length of the risk period. Where possible, data will be meta-analysed across the UK nations. ETHICS AND DISSEMINATION We obtained approvals from the National Research Ethics Service Committee, Southeast Scotland 02 (12/SS/0201), the Secure Anonymised Information Linkage independent Information Governance Review Panel project number 0911. Concerning English data, University of Oxford is compliant with the General Data Protection Regulation and the National Health Service (NHS) Digital Data Security and Protection Policy. This is an approved study (Integrated Research Application ID 301740, Health Research Authority (HRA) Research Ethics Committee 21/HRA/2786). The Oxford-Royal College of General Practitioners Clinical Informatics Digital Hub meets NHS Digital's Data Security and Protection Toolkit requirements. In Northern Ireland, the project was approved by the Honest Broker Governance Board, project number 0064. Findings will be made available to national policy-makers, presented at conferences and published in peer-reviewed journals.
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Affiliation(s)
| | - Ting Shi
- The University of Edinburgh, Usher Institute, Edinburgh, UK
| | - Steven Kerr
- The University of Edinburgh, Usher Institute, Edinburgh, UK
| | - Chris Robertson
- Department of Mathematics and Statistics, University of Strathclyde, Glasgow, UK
- Public Health Scotland, Glasgow, UK
| | - Mark Joy
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Ruby Tsang
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Dylan McGagh
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - John Williams
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Richard Hobbs
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Simon de Lusignan
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Declan Bradley
- School of Medicine, Dentistry and Biomedical Sciences, Queen's University Belfast, Belfast, UK
| | - Dermot OReilly
- School of Medicine, Dentistry and Biomedical Sciences, Queen's University Belfast, Belfast, UK
| | - Siobhan Murphy
- School of Medicine, Dentistry and Biomedical Sciences, Queen's University Belfast, Belfast, UK
| | - Antony Chuter
- BREATHE - The Health Data Research Hub for Respiratory Health, London, UK
| | - Jillian Beggs
- BREATHE - The Health Data Research Hub for Respiratory Health, London, UK
| | - David Ford
- Population Data Science, Swansea University Medical School, Swansea, UK
| | - Chris Orton
- Population Data Science, Swansea University Medical School, Swansea, UK
| | - Ashley Akbari
- Population Data Science, Swansea University Medical School, Swansea, UK
| | - Stuart Bedston
- Population Data Science, Swansea University Medical School, Swansea, UK
| | - Gareth Davies
- Population Data Science, Swansea University Medical School, Swansea, UK
| | - Lucy J Griffiths
- Population Data Science, Swansea University Medical School, Swansea, UK
| | - Rowena Griffiths
- Population Data Science, Swansea University Medical School, Swansea, UK
| | - Emily Lowthian
- Population Data Science, Swansea University Medical School, Swansea, UK
| | - Jane Lyons
- Population Data Science, Swansea University Medical School, Swansea, UK
| | - Ronan A Lyons
- Population Data Science, Swansea University Medical School, Swansea, UK
| | - Laura North
- Population Data Science, Swansea University Medical School, Swansea, UK
| | - Malorie Perry
- Vaccine Preventable Disease Programme, Public Health Wales, Cardiff, UK
| | - Fatemeh Torabi
- Population Data Science, Swansea University Medical School, Swansea, UK
| | | | | | - Colin McCowan
- School of Medicine, University of St Andrews, St Andrews, UK
| | - Utkarsh Agrawal
- School of Medicine, University of St Andrews, St Andrews, UK
| | - Rachael Wood
- The University of Edinburgh, Usher Institute, Edinburgh, UK
- Public Health Scotland, Edinburgh, UK
| | - Sarah Jane Stock
- The University of Edinburgh, Usher Institute, Edinburgh, UK
- Public Health Scotland, Edinburgh, UK
| | | | - Paul Henery
- MRC/CSO Social and Public Health Sciences Unit, University of Glasgow, Glasgow, UK
| | - Colin R Simpson
- The University of Edinburgh, Usher Institute, Edinburgh, UK
- Wellington School of Health, Faculty of Health, Victoria University of Wellington, Wellington, New Zealand
| | - Aziz Sheikh
- The University of Edinburgh, Usher Institute, Edinburgh, UK
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Pearce C, McLeod A, Supple J, Gardner K, Proposch A, Ferrigi J. Responding to COVID-19 with real-time general practice data in Australia. Int J Med Inform 2022; 157:104624. [PMID: 34741891 PMCID: PMC8564317 DOI: 10.1016/j.ijmedinf.2021.104624] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2021] [Revised: 09/14/2021] [Accepted: 10/21/2021] [Indexed: 11/25/2022]
Abstract
INTRODUCTION As SARS-CoV-2 spread around the world, Australia was no exception. Part of the Australian response was a robust primary care approach, involving changes to care models (including telehealth) and the widespread use of data to inform the changes. This paper outlines how a large primary care database responded to provide real-time data to inform policy and practice. Simply extracting the data is not sufficient. Understanding the data is. The POpulation Level Analysis and Reporting (POLAR) program is designed to use GP data for multiple objectives and is built on a pre-existing engagement framework established over a fifteen-year period. Initially developed to provide QA activities for general practices and population level data for General Practice support organisations, the POLAR platform has demonstrated the critical ability to design and deploy real-time data analytics solutions during the COVID-19 pandemic for a variety of stakeholders including state and federal government agencies. METHODS The system extracts and processes data from over 1,300 general practices daily. Data is de-identified at the point of collection and encrypted before transfer. Data cleaning for analysis uses a variety of techniques, including Natural Language Processing and coding of free text information. The curated dataset is then distilled into several analytic solutions designed to address specific areas of investigation of interest to various stakeholders. One such analytic solution was a model we created that used multiple data inputs to rank patient geographic areas by the likelihood of a COVID-19 outbreak. The model utilised pathology ordering, COVID-19 related diagnoses, indication of COVID-19 related concern (via progress notes) and also incorporated state based actual confirmed case figures. RESULTS Using the methods described, we were able to deliver real-time data feeds to practices, Primary Health Networks (PHN) and other agencies. In addition, we developed a COVID-19 geographic risk stratification based on local government areas (LGAs) to pro-actively inform the primary care response. Providing PHNs with a list of geographic priority hotspots allowed for better targeting and response of Personal Protective Equipment allocation and pop-up clinic placement. CONCLUSIONS The program summarised here demonstrates the ability of a well-designed system underpinned by accurate and reliable data, to respond in real-time to a rapidly evolving public health emergency in a way which supports and enhances the health system response.
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Affiliation(s)
- Christopher Pearce
- Director of Research, Outcome Health, Adjunct Associate Professor in General Practice, Monash University, 1 Chapel Street, Blackburn 3130, Australia.
| | - Adam McLeod
- Chief Executive Officer, Outcome Health, Australia
| | - Jamie Supple
- Director of Business Intelligence, Outcome Health, Australia
| | | | - Amanda Proposch
- Chief Executive Officer, Gippsland Primary Health Network, Australia
| | - Jason Ferrigi
- Chief Information Officer, Outcome Health, Australia
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Lin Z, Lin S, Neamtiu IA, Ye B, Csobod E, Fazakas E, Gurzau E. Predicting environmental risk factors in relation to health outcomes among school children from Romania using random forest model - An analysis of data from the SINPHONIE project. THE SCIENCE OF THE TOTAL ENVIRONMENT 2021; 784:147145. [PMID: 33901961 DOI: 10.1016/j.scitotenv.2021.147145] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/19/2021] [Revised: 03/30/2021] [Accepted: 04/10/2021] [Indexed: 06/12/2023]
Abstract
BACKGROUND Few studies have simultaneously assessed the health impact of school and home environmental factors on children, since handling multiple highly correlated environmental variables is challenging. In this study, we examined indoor home and school environments in relation to health outcomes using machine learning methods and logistic regression. METHODS We used the data collected by the SINPHONIE (Schools Indoor Pollution and Health: Observatory Network in Europe) project in Romania, a multicenter European research study that collected comprehensive information on school and home environments, health symptoms in children, smoking, and school policies. The health outcomes were categorized as: any health symptoms, asthma, allergy and flu-like symptoms. Both logistic regression and random forest (RF) methods were used to predict the four categories of health outcomes, and the methods prediction performance was compared. RESULTS The RF method we employed for analysis showed that common risk factors for the investigated categories of health outcomes, included: environmental tobacco smoke (ETS), dampness in the indoor school environment, male gender, air freshener use, residence located in proximity of traffic (<200 m), stressful schoolwork, and classroom noise (contributions ranged from 7.91% to 23.12%). Specificity, accuracy and area under the curve (AUC) values for most outcomes were higher when using RF compared to logistic regression, while sensitivity was similar in both methods. CONCLUSION This study suggests that ETS, dampness in the indoor school environment, use of air fresheners, living in proximity to traffic (<200 m) and noise are common environmental risk factors for the investigated health outcomes. RF pointed out better predictive values, sensitivity and accuracy compared to logistic regression.
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Affiliation(s)
- Ziqiang Lin
- Department of Psychiatry, New York University School of Medicine, One Park Ave, New York, NY 10016, United States of America; Department of Environmental Health Science, School of Public Health, University at Albany, State University of New York, 1 University Place, Rensselaer, NY 12144, United States of America
| | - Shao Lin
- Department of Environmental Health Science, School of Public Health, University at Albany, State University of New York, 1 University Place, Rensselaer, NY 12144, United States of America; Department of Epidemiology and Biostatistics, School of Public Health, University at Albany, State University of New York, 1 University Place, Rensselaer, NY 12144, United States of America
| | - Iulia A Neamtiu
- Health Department, Environmental Health Center, 58 Busuiocului Street, Cluj-Napoca, Romania; Faculty of Environmental Science and Engineering, Babes-Bolyai University, 30 Fantanele Street, Cluj-Napoca, Romania.
| | - Bo Ye
- Department of Environmental Health Science, School of Public Health, University at Albany, State University of New York, 1 University Place, Rensselaer, NY 12144, United States of America; Department of Epidemiology and Biostatistics, School of Public Health, University at Albany, State University of New York, 1 University Place, Rensselaer, NY 12144, United States of America
| | - Eva Csobod
- Regional Environmental Center for Central and Eastern Europe (REC), Ady Endre ut 9-11, 2000 Szentendre, Hungary
| | - Emese Fazakas
- Health Department, Environmental Health Center, 58 Busuiocului Street, Cluj-Napoca, Romania; Faculty of Environmental Science and Engineering, Babes-Bolyai University, 30 Fantanele Street, Cluj-Napoca, Romania
| | - Eugen Gurzau
- Health Department, Environmental Health Center, 58 Busuiocului Street, Cluj-Napoca, Romania; Faculty of Environmental Science and Engineering, Babes-Bolyai University, 30 Fantanele Street, Cluj-Napoca, Romania; Cluj School of Public Health - College of Political, Administrative and Communication Sciences, Babes-Bolyai University, Cluj-Napoca, Romania
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