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Elliot AJ, Hughes HE, Harcourt SE, Smith S, Loveridge P, Morbey RA, Bains A, Edeghere O, Jones NR, Todkill D, Smith GE. From Fax to Secure File Transfer Protocol: The 25-Year Evolution of Real-Time Syndromic Surveillance in England. J Med Internet Res 2024; 26:e58704. [PMID: 39288377 PMCID: PMC11445629 DOI: 10.2196/58704] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2024] [Revised: 06/04/2024] [Accepted: 06/11/2024] [Indexed: 09/19/2024] Open
Abstract
The purpose of syndromic surveillance is to provide early warning of public health incidents, real-time situational awareness during incidents and emergencies, and reassurance of the lack of impact on the population, particularly during mass gatherings. The United Kingdom Health Security Agency (UKHSA) currently coordinates a real-time syndromic surveillance service that encompasses 6 national syndromic surveillance systems reporting on daily health care usage across England. Each working day, UKHSA analyzes syndromic data from over 200,000 daily patient encounters with the National Health Service, monitoring over 140 unique syndromic indicators, risk assessing over 50 daily statistical exceedances, and taking and recommending public health action on these daily. This English syndromic surveillance service had its origins as a small exploratory pilot in a single region of England in 1999 involving a new pilot telehealth service, initially reporting only on "cold or flu" calls. This pilot showed the value of syndromic surveillance in England, providing advanced warning of the start of seasonal influenza activity over existing laboratory-based surveillance systems. Since this initial pilot, a program of real-time syndromic surveillance has evolved from the single-system, -region, -indicator pilot (using manual data transfer methods) to an all-hazard, multisystem, automated national service. The suite of systems now monitors a wide range of syndromes, from acute respiratory illness to diarrhea to cardiac conditions, and is widely used in routine public health surveillance and for monitoring seasonal respiratory disease and incidents such as the COVID-19 pandemic. Here, we describe the 25-year evolution of the English syndromic surveillance system, focusing on the expansion and improvements in data sources and data management, the technological and digital enablers, and novel methods of data analytics and visualization.
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Affiliation(s)
- Alex J Elliot
- Real-time Syndromic Surveillance Team, UK Health Security Agency, Birmingham, United Kingdom
- NIHR Health Protection Research Unit in Emergency Preparedness and Response, King's College London, London, United Kingdom
| | - Helen E Hughes
- Real-time Syndromic Surveillance Team, UK Health Security Agency, Birmingham, United Kingdom
| | - Sally E Harcourt
- Real-time Syndromic Surveillance Team, UK Health Security Agency, Birmingham, United Kingdom
| | - Sue Smith
- Real-time Syndromic Surveillance Team, UK Health Security Agency, Birmingham, United Kingdom
| | - Paul Loveridge
- Real-time Syndromic Surveillance Team, UK Health Security Agency, Birmingham, United Kingdom
| | - Roger A Morbey
- Real-time Syndromic Surveillance Team, UK Health Security Agency, Birmingham, United Kingdom
- NIHR Health Protection Research Unit in Emergency Preparedness and Response, King's College London, London, United Kingdom
| | - Amardeep Bains
- Real-time Syndromic Surveillance Team, UK Health Security Agency, Birmingham, United Kingdom
| | - Obaghe Edeghere
- Real-time Syndromic Surveillance Team, UK Health Security Agency, Birmingham, United Kingdom
| | - Natalia R Jones
- NIHR Health Protection Research Unit in Emergency Preparedness and Response, King's College London, London, United Kingdom
- School of Environmental Sciences, University of East Anglia, Norwich, United Kingdom
| | - Daniel Todkill
- Real-time Syndromic Surveillance Team, UK Health Security Agency, Birmingham, United Kingdom
| | - Gillian E Smith
- Real-time Syndromic Surveillance Team, UK Health Security Agency, Birmingham, United Kingdom
- NIHR Health Protection Research Unit in Emergency Preparedness and Response, King's College London, London, United Kingdom
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Hetland ML, Strangfeld A, Bonfanti G, Soudis D, Deuring JJ, Edwards RA. Machine learning prediction and explanatory models of serious infections in patients with rheumatoid arthritis treated with tofacitinib. Arthritis Res Ther 2024; 26:153. [PMID: 39192350 DOI: 10.1186/s13075-024-03376-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Accepted: 07/10/2024] [Indexed: 08/29/2024] Open
Abstract
BACKGROUND Patients with rheumatoid arthritis (RA) have an increased risk of developing serious infections (SIs) vs. individuals without RA; efforts to predict SIs in this patient group are ongoing. We assessed the ability of different machine learning modeling approaches to predict SIs using baseline data from the tofacitinib RA clinical trials program. METHODS This analysis included data from 19 clinical trials (phase 2, n = 10; phase 3, n = 6; phase 3b/4, n = 3). Patients with RA receiving tofacitinib 5 or 10 mg twice daily (BID) were included in the analysis; patients receiving tofacitinib 11 mg once daily were considered as tofacitinib 5 mg BID. All available patient-level baseline variables were extracted. Statistical and machine learning methods (logistic regression, support vector machines with linear kernel, random forest, extreme gradient boosting trees, and boosted trees) were implemented to assess the association of baseline variables with SI (logistic regression only), and to predict SI using selected baseline variables using 5-fold cross-validation. Missing values were handled individually per prediction model. RESULTS A total of 8404 patients with RA treated with tofacitinib were eligible for inclusion (15,310 patient-years of total follow-up) of which 473 patients reported SIs. Amongst other baseline factors, age, previous infection, and corticosteroid use were significantly associated with SI. When applying prediction modeling for SI across data from all studies, the area under the receiver operating characteristic (AUROC) curve ranged from 0.656 to 0.739. AUROC values ranged from 0.599 to 0.730 in data from phase 3 and 3b/4 studies, and from 0.563 to 0.643 in data from ORAL Surveillance only. CONCLUSIONS Baseline factors associated with SIs in the tofacitinib RA clinical trial program were similar to established SI risk factors associated with advanced treatments for RA. Furthermore, while model performance in predicting SI was similar to other published models, this did not meet the threshold for accurate prediction (AUROC > 0.85). Thus, predicting the occurrence of SIs at baseline remains challenging and may be complicated by the changing disease course of RA over time. Inclusion of other patient-associated and healthcare delivery-related factors and harmonization of the duration of studies included in the models may be required to improve prediction. TRIAL REGISTRATION ClinicalTrials.gov: NCT00147498; NCT00413660; NCT00550446; NCT00603512; NCT00687193; NCT01164579; NCT00976599; NCT01059864; NCT01359150; NCT02147587; NCT00960440; NCT00847613; NCT00814307; NCT00856544; NCT00853385; NCT01039688; NCT02187055; NCT02831855; NCT02092467.
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Affiliation(s)
- Merete Lund Hetland
- Copenhagen Center for Arthritis Research (COPECARE), Center for Rheumatology and Spine Diseases, Centre of Head and Orthopaedics, Copenhagen University Hospital Rigshospitalet, Glostrup, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Anja Strangfeld
- Epidemiology and Health Services Research, German Rheumatism Research Centre (DRFZ), Berlin, Germany
- Department of Rheumatology and Clinical Immunology, Charité University Medicine Berlin, Berlin, Germany
| | | | | | - J Jasper Deuring
- Pfizer, Rotterdam, The Netherlands.
- Pfizer Netherlands GmbH, Rivium Westlaan, 142 2909 LD Capelle a/d IJssel, Rotterdam, The Netherlands.
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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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Amamou M, Ben-Ahmed K. Managing the COVID-19 pandemic in thirty-two policy measures in Saudi Arabia: A mixed-methods analysis. J Infect Public Health 2023; 16:1650-1658. [PMID: 37619476 DOI: 10.1016/j.jiph.2023.08.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Revised: 07/26/2023] [Accepted: 08/08/2023] [Indexed: 08/26/2023] Open
Abstract
BACKGROUND The Kingdom of Saudi Arabia has developed rigorous strategies to control and prevent the spread of COVID-19. However, the effectiveness of these measures in containing and mitigating the epidemic has yet to be studied. This paper aims to assess the efficiency of preventive policy initiatives that Saudi Arabia has taken to reduce the spread of COVID-19, which was rapid and progressive in nature. Information on the effectiveness of measures applies to help the Saudi government adjust policy responses when considering which measures to relax once the epidemic is controlled. METHODS Data for this study were retrieved via publicly available data sources such as the Saudi Arabia Ministry of Health and the government's official news agency-Saudi Press Agency (SPA) websites. Other datasets, such as prevention measures, were gathered from the Country Policy Tracker website. Our dataset's time component extends over 590 consecutive days from 20 January 2020-31 August 2021. Moreover, a mixed-method approach combining COVID-19 data and prevention measures was adopted to assess preventative measures practice. We compiled the dataset used in this study in a Microsoft Excel database. The significance of observed differences among implementing effective strategies was determined using ANOVA and Mixed methods approach. Noticeably, the statistical analysis was performed using the open-source statistical system R version 4.2 (available at http://cran.r-project.org). RESULTS Our analysis showed that only three out of the 32 (9.4%) measures significantly reduced the spread of COVID-19. Our results also show substantial variations in the spread of COVID-19 associated with preventive measures in Saudi Arabia. There was a significant positive correlation between activating and massive testing in communities and cases of COVID-19 (measure effect = 923.086 and p < 0.05). A similar result was found for complete curfew across the Kingdom and cases of COVID-19 (measure effect = 621.389 and p < 0.10). Removing slum areas interrupted the spread of Covid-19 (measure effect = 305.689 and p < 0.01). The other preventive measures did not significantly affect the COVID-19 pandemic distribution. These findings consistently concluded that activating and massive testing in communities, complete curfew across the Kingdom, and removal of slum areas were the most effective measures for reducing the impact of the COVID-19 pandemic. CONCLUSION Only by understanding these correlations will it be possible to control and reduce the rate of COVID-19 spread and, therefore, suggest a possible exit strategy once the epidemic is controlled.
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Affiliation(s)
- Meriam Amamou
- Department of Human Resources Management, College of Business, University of Jeddah, Saudi Arabia; Department of Management, Higher Institute of Management, ISG, University of Sousse, Tunisia.
| | - Kais Ben-Ahmed
- Department of Finance, College of Business, University of Jeddah, Saudi Arabia; Department of Quantitative Methods, Higher Institute of Management, ISG, University of Sousse, Tunisia.
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Leston M, Elson WH, Watson C, Lakhani A, Aspden C, Bankhead CR, Borrow R, Button E, Byford R, Elliot AJ, Fan X, Hoang U, Linley E, Macartney J, Nicholson BD, Okusi C, Ramsay M, Smith G, Smith S, Thomas M, Todkill D, Tsang RS, Victor W, Williams AJ, Williams J, Zambon M, Howsam G, Amirthalingam G, Lopez-Bernal J, Hobbs FDR, de Lusignan S. Representativeness, Vaccination Uptake, and COVID-19 Clinical Outcomes 2020-2021 in the UK Oxford-Royal College of General Practitioners Research and Surveillance Network: Cohort Profile Summary. JMIR Public Health Surveill 2022; 8:e39141. [PMID: 36534462 PMCID: PMC9770023 DOI: 10.2196/39141] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Revised: 10/27/2022] [Accepted: 11/02/2022] [Indexed: 11/05/2022] Open
Abstract
BACKGROUND The Oxford-Royal College of General Practitioners (RCGP) Research and Surveillance Centre (RSC) is one of Europe's oldest sentinel systems, working with the UK Health Security Agency (UKHSA) and its predecessor bodies for 55 years. Its surveillance report now runs twice weekly, supplemented by online observatories. In addition to conducting sentinel surveillance from a nationally representative group of practices, the RSC is now also providing data for syndromic surveillance. OBJECTIVE The aim of this study was to describe the cohort profile at the start of the 2021-2022 surveillance season and recent changes to our surveillance practice. METHODS The RSC's pseudonymized primary care data, linked to hospital and other data, are held in the Oxford-RCGP Clinical Informatics Digital Hub, a Trusted Research Environment. We describe the RSC's cohort profile as of September 2021, divided into a Primary Care Sentinel Cohort (PCSC)-collecting virological and serological specimens-and a larger group of syndromic surveillance general practices (SSGPs). We report changes to our sampling strategy that brings the RSC into alignment with European Centre for Disease Control guidance and then compare our cohort's sociodemographic characteristics with Office for National Statistics data. We further describe influenza and COVID-19 vaccine coverage for the 2020-2021 season (week 40 of 2020 to week 39 of 2021), with the latter differentiated by vaccine brand. Finally, we report COVID-19-related outcomes in terms of hospitalization, intensive care unit (ICU) admission, and death. RESULTS As a response to COVID-19, the RSC grew from just over 500 PCSC practices in 2019 to 1879 practices in 2021 (PCSC, n=938; SSGP, n=1203). This represents 28.6% of English general practices and 30.59% (17,299,780/56,550,136) of the population. In the reporting period, the PCSC collected >8000 virology and >23,000 serology samples. The RSC population was broadly representative of the national population in terms of age, gender, ethnicity, National Health Service Region, socioeconomic status, obesity, and smoking habit. The RSC captured vaccine coverage data for influenza (n=5.4 million) and COVID-19, reporting dose one (n=11.9 million), two (n=11 million), and three (n=0.4 million) for the latter as well as brand-specific uptake data (AstraZeneca vaccine, n=11.6 million; Pfizer, n=10.8 million; and Moderna, n=0.7 million). The median (IQR) number of COVID-19 hospitalizations and ICU admissions was 1181 (559-1559) and 115 (50-174) per week, respectively. CONCLUSIONS The RSC is broadly representative of the national population; its PCSC is geographically representative and its SSGPs are newly supporting UKHSA syndromic surveillance efforts. The network captures vaccine coverage and has expanded from reporting primary care attendances to providing data on onward hospital outcomes and deaths. The challenge remains to increase virological and serological sampling to monitor the effectiveness and waning of all vaccines available in a timely manner.
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Affiliation(s)
- Meredith Leston
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - William H Elson
- 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, Colindale, London, United Kingdom
| | - Anissa Lakhani
- Immunisation and Vaccine Preventable Diseases Division, UK Health Security Agency, Colindale, London, United Kingdom
| | - Carole Aspden
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Clare R Bankhead
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Ray Borrow
- Vaccine Evaluation Unit, UK Health Security Agency, Manchester Royal Infirmary, Manchester, United Kingdom
| | - Elizabeth Button
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Rachel Byford
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Alex J Elliot
- Real-time Syndromic Surveillance Team, Field Service, UK Health Security Agency, Birmingham, United Kingdom
| | - Xuejuan Fan
- 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
| | - Ezra Linley
- Vaccine Evaluation Unit, UK Health Security Agency, Manchester Royal Infirmary, Manchester, United Kingdom
| | - Jack Macartney
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Brian D Nicholson
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Cecilia Okusi
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Mary Ramsay
- Immunisation and Vaccine Preventable Diseases Division, UK Health Security Agency, Colindale, London, United Kingdom
| | - Gillian Smith
- Real-time Syndromic Surveillance Team, Field Service, UK Health Security Agency, Birmingham, United Kingdom
| | - Sue Smith
- Real-time Syndromic Surveillance Team, Field Service, UK Health Security Agency, Birmingham, United Kingdom
| | - Mark Thomas
- Royal College of General Practitioners, London, United Kingdom
| | - Dan Todkill
- Real-time Syndromic Surveillance Team, Field Service, UK Health Security Agency, Birmingham, United Kingdom
| | - Ruby Sm Tsang
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - William Victor
- Royal College of General Practitioners, London, United Kingdom
| | - Alice J Williams
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - John Williams
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Maria Zambon
- Reference Microbiology, UK Health Security Agency, Colindale, London, United Kingdom
| | - Gary Howsam
- Royal College of General Practitioners, London, United Kingdom
| | - Gayatri Amirthalingam
- Immunisation and Vaccine Preventable Diseases Division, UK Health Security Agency, Colindale, London, United Kingdom
| | - Jamie Lopez-Bernal
- Immunisation and Vaccine Preventable Diseases Division, UK Health Security Agency, Colindale, London, United Kingdom
| | - F D Richard Hobbs
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Simon de Lusignan
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
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McGeoch LJ, Thornton HV, Blair PS, Christensen H, Turner NL, Muir P, Vipond B, Redmond NM, Turnbull S, Hay AD. Prognostic value of upper respiratory tract microbes in children presenting to primary care with respiratory infections: A prospective cohort study. PLoS One 2022; 17:e0268131. [PMID: 35552562 PMCID: PMC9098075 DOI: 10.1371/journal.pone.0268131] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2021] [Accepted: 04/22/2022] [Indexed: 12/02/2022] Open
Abstract
BACKGROUND The association between upper respiratory tract microbial positivity and illness prognosis in children is unclear. This impedes clinical decision-making and means the utility of upper respiratory tract microbial point-of-care tests remains unknown. We investigated for relationships between pharyngeal microbes and symptom severity in children with suspected respiratory tract infection (RTI). METHODS Baseline characteristics and pharyngeal swabs were collected from 2,296 children presenting to 58 general practices in Bristol, UK with acute cough and suspected RTI between 2011-2013. Post-consultation, parents recorded the severity of six RTI symptoms on a 0-6 scale daily for ≤28 days. We used multivariable hurdle regression, adjusting for clinical characteristics, antibiotics and other microbes, to investigate associations between respiratory microbes and mean symptom severity on days 2-4 post-presentation. RESULTS Overall, 1,317 (57%) children with complete baseline, microbiological and symptom data were included. Baseline characteristics were similar in included participants and those lacking microbiological data. At least one virus was detected in 869 (66%) children, and at least one bacterium in 783 (60%). Compared to children with no virus detected (mean symptom severity score 1.52), adjusted mean symptom severity was 0.26 points higher in those testing positive for at least one virus (95% CI 0.15 to 0.38, p<0.001); and was also higher in those with detected Influenza B (0.44, 0.15 to 0.72, p = 0.003); RSV (0.41, 0.20 to 0.60, p<0.001); and Influenza A (0.25, -0.01 to 0.51, p = 0.059). Children positive for Enterovirus had a lower adjusted mean symptom severity (-0.24, -0.43 to -0.05, p = 0.013). Children with detected Bordetella pertussis (0.40, 0.00 to 0.79, p = 0.049) and those with detected Moraxella catarrhalis (-0.76, -1.06 to -0.45, p<0.001) respectively had higher and lower mean symptom severity compared to children without these bacteria. CONCLUSIONS There is a potential role for upper respiratory tract microbiological point-of-care tests in determining the prognosis of childhood RTIs.
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Affiliation(s)
- Luke J. McGeoch
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Hannah V. Thornton
- Centre for Academic Primary Care, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Peter S. Blair
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
- Bristol Randomised Trials Collaboration, Bristol Trials Centre, University of Bristol, Bristol, United Kingdom
| | - Hannah Christensen
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
- NIHR Health Protection Research Unit in Behavioural Science and Evaluation, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Nicholas L. Turner
- Bristol Randomised Trials Collaboration, Bristol Trials Centre, University of Bristol, Bristol, United Kingdom
| | - Peter Muir
- South West Regional Laboratory, National Infection Service, Public Health England, Bristol, United Kingdom
| | - Barry Vipond
- South West Regional Laboratory, National Infection Service, Public Health England, Bristol, United Kingdom
| | - Niamh M. Redmond
- Centre for Academic Primary Care, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
- Centre d’épidémiologie et de recherche en santé des populations (CERPOP), Université Toulouse III—Paul Sabatier, Toulouse, France
| | - Sophie Turnbull
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Alastair D. Hay
- Centre for Academic Primary Care, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
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Jaljaa A, Caminada S, Tosti ME, D'Angelo F, Angelozzi A, Isonne C, Marchetti G, Mazzalai E, Giannini D, Turatto F, De Marchi C, Gatta A, Declich S, Pizzarelli S, Geraci S, Baglio G, Marceca M. Risk of SARS-CoV-2 infection in migrants and ethnic minorities compared with the general population in the European WHO region during the first year of the pandemic: a systematic review. BMC Public Health 2022; 22:143. [PMID: 35057781 PMCID: PMC8771174 DOI: 10.1186/s12889-021-12466-1] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2021] [Accepted: 12/22/2021] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND Migrants and ethnic minorities have suffered a disproportionate impact of the COVID-19 pandemic compared to the general population from different perspectives. Our aim was to assess specifically their risk of infection in the 53 countries belonging to the World Health Organization European Region, during the first year of the pandemic. METHODS We conducted a systematic review following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines (PROSPERO CRD42021247326). We searched multiple databases for peer-reviewed literature, published on Medline, Embase, Scisearch, Biosis and Esbiobase in 2020 and preprints from PubMed up to 29/03/2021. We included cross-sectional, case-control, cohort, intervention, case-series, prevalence or ecological studies, reporting the risk of SARS-CoV-2 infection among migrants, refugees, and ethnic minorities. RESULTS Among the 1905 records screened, 25 met our inclusion criteria and were included in the final analysis. We found that migrants and ethnic minorities during the first wave of the pandemic were at increased exposure and risk of infection and were disproportionately represented among COVID-19 cases. However, the impact of COVID-19 on minorities does not seem homogeneous, since some ethnic groups seem to be more at risk than others. Risk factors include high-risk occupations, overcrowded accommodations, geographic distribution, social deprivation, barriers to access to information concerning preventive measures (due to the language barrier or to their marginality), together with biological and genetic susceptibilities. CONCLUSIONS Although mixed methods studies will be required to fully understand the complex interplay between the various biological, social, and cultural factors underlying these findings, the impact of structural determinants of health is evident. Our findings corroborate the need to collect migration and ethnicity-disaggregated data and contribute to advocacy for inclusive policies and programmatic actions tailored to reach migrants and ethnic minorities.
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Affiliation(s)
- Anissa Jaljaa
- Department of Public Health and Infectious Diseases, Sapienza University, Rome, Italy.
| | - Susanna Caminada
- Department of Public Health and Infectious Diseases, Sapienza University, Rome, Italy
- Italian Society of Migration Medicine (SIMM), Rome, Italy
| | - Maria Elena Tosti
- National Health Institute, National Centre for Global Health, Rome, Italy
| | - Franca D'Angelo
- National Health Institute, National Centre for Global Health, Rome, Italy
| | - Aurora Angelozzi
- Department of Public Health and Infectious Diseases, Sapienza University, Rome, Italy
| | - Claudia Isonne
- Department of Public Health and Infectious Diseases, Sapienza University, Rome, Italy
| | - Giulia Marchetti
- Italian Society of Migration Medicine (SIMM), Rome, Italy
- National Health Institute, National Centre for Global Health, Rome, Italy
| | - Elena Mazzalai
- Department of Public Health and Infectious Diseases, Sapienza University, Rome, Italy
| | - Dara Giannini
- Department of Public Health and Infectious Diseases, Sapienza University, Rome, Italy
- Italian Society of Migration Medicine (SIMM), Rome, Italy
| | - Federica Turatto
- Department of Public Health and Infectious Diseases, Sapienza University, Rome, Italy
| | - Chiara De Marchi
- Department of Public Health and Infectious Diseases, Sapienza University, Rome, Italy
| | - Angela Gatta
- Department of Public Health and Infectious Diseases, Sapienza University, Rome, Italy
| | - Silvia Declich
- National Health Institute, National Centre for Global Health, Rome, Italy
| | - Scilla Pizzarelli
- National Health Institute; Knowledge Service, Documentation and Library, Rome, Italy
| | - Salvatore Geraci
- Italian Society of Migration Medicine (SIMM), Rome, Italy
- Caritas of Rome, Health Area, Rome, Italy
| | - Giovanni Baglio
- Italian Society of Migration Medicine (SIMM), Rome, Italy
- AGENAS, Research and International Relations Office, Rome, Italy
| | - Maurizio Marceca
- Department of Public Health and Infectious Diseases, Sapienza University, Rome, Italy
- Italian Society of Migration Medicine (SIMM), Rome, Italy
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8
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Davidson JA, Banerjee A, Smeeth L, McDonald HI, Grint D, Herrett E, Forbes H, Pebody R, Warren-Gash C. Risk of acute respiratory infection and acute cardiovascular events following acute respiratory infection among adults with increased cardiovascular risk in England between 2008 and 2018: a retrospective, population-based cohort study. Lancet Digit Health 2021; 3:e773-e783. [PMID: 34823706 PMCID: PMC8628002 DOI: 10.1016/s2589-7500(21)00203-x] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2021] [Revised: 07/16/2021] [Accepted: 08/07/2021] [Indexed: 12/22/2022]
Abstract
BACKGROUND Although acute respiratory infections can lead to cardiovascular complications, the effect of underlying cardiovascular risk on the incidence of acute respiratory infections and cardiovascular complications following acute respiratory infection in individuals without established cardiovascular disease is unknown. We aimed to investigate whether cardiovascular risk is associated with increased risk of acute respiratory infection and acute cardiovascular events after acute respiratory infection using 10 years of linked electronic health record (EHR) data in England. METHODS In this retrospective, population-based cohort study we used EHRs from primary care providers registered on the Clinical Practice Research Datalink (CPRD) GOLD and Aurum databases in England. Eligible individuals were aged 40-64 years, did not have established cardiovascular disease or a chronic health condition that would make them eligible for influenza vaccination, were registered at a general practice contributing to the CPRD, and had linked Hospital Episode Statistics Admitted Patient Care data in England from Sept 1, 2008, to Aug 31, 2018. We classified cardiovascular risk on the basis of diagnosed hypertension and overall predicted cardiovascular risk, estimated by use of the QRISK2 risk-prediction tool (comparing a score of ≥10% [increased risk] with a score of <10% [low risk]). Using multivariable Poisson regression models, we calculated incidence rate ratios (IRRs) for systemic acute respiratory infection. Among individuals who had an acute respiratory infection, we used multivariable Cox regression to calculate hazard ratios (HRs) for the risk of acute cardiovascular events within 1 year of infection. FINDINGS We identified 6 075 321 individuals aged 40-64 years with data in the CPRD and linked data in the Hospital Episode Statistics Admitted Patient Care database between Sept 1, 2008, and Aug 31, 2018. Of these individuals, 4 212 930 (including 526 480 [12·5%] with hypertension and 607 087 [14·4%] with a QRISK2 score of ≥10%) were included in the assessment of the incidence of acute respiratory infection. After adjusting for confounders (age, sex, ethnicity, socioeconomic status, body-mass index, alcohol consumption, smoking status, and consultation frequency in the hypertension analysis; and alcohol consumption and consultation frequency in the QRISK2 analysis), the incidence of acute respiratory infection was higher in individuals with hypertension than those without (IRR 1·04 [95% CI 1·03-1·05]) and higher in those with a QRISK2 score of 10% or higher than in those with a QRISK2 score of less than 10% (1·39 [1·37-1·40]). Of the 442 408 individuals who had an acute respiratory infection, 4196 (0·9%) had an acute cardiovascular event within 1 year of infection. After adjustment (for age, sex, ethnicity, socioeconomic status, body-mass index, alcohol consumption, and smoking status in the hypertension analysis; and for alcohol consumption in the QRISK2 analysis), hypertension (HR 1·98 [95% CI 1·83-2·15]) and a QRISK2 score of 10% or higher (3·65 [3·42-3·89]) were associated with a substantially increased risk of acute cardiovascular events after acute respiratory infection. INTERPRETATION People with increased cardiovascular risk but without diagnosed cardiovascular disease, measured by diagnosed hypertension or overall predicted cardiovascular risk, could benefit from influenza and pneumococcal vaccine prioritisation to reduce their risk of both acute respiratory infection and cardiovascular complications following an acute respiratory infection. FUNDING British Heart Foundation and the Wellcome Trust.
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Affiliation(s)
- Jennifer A Davidson
- Department of Non-Communicable Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK.
| | - Amitava Banerjee
- Institute of Health Informatics, University College London, London, UK
| | - Liam Smeeth
- Department of Non-Communicable Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK; National Institute for Health Research Health Protection Research Unit in Immunisation, London School of Hygiene & Tropical Medicine, London, UK
| | - Helen I McDonald
- Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK; National Institute for Health Research Health Protection Research Unit in Immunisation, London School of Hygiene & Tropical Medicine, London, UK
| | - Daniel Grint
- Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
| | - Emily Herrett
- Department of Non-Communicable Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
| | - Harriet Forbes
- Department of Non-Communicable Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK; Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Richard Pebody
- National Infection Service, Public Health England, London, UK
| | - Charlotte Warren-Gash
- Department of Non-Communicable Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
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9
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Rahman A, Kuddus MA, Ip RHL, Bewong M. A Review of COVID-19 Modelling Strategies in Three Countries to Develop a Research Framework for Regional Areas. Viruses 2021; 13:2185. [PMID: 34834990 PMCID: PMC8623457 DOI: 10.3390/v13112185] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2021] [Revised: 10/18/2021] [Accepted: 10/26/2021] [Indexed: 12/17/2022] Open
Abstract
At the end of December 2019, an outbreak of COVID-19 occurred in Wuhan city, China. Modelling plays a crucial role in developing a strategy to prevent a disease outbreak from spreading around the globe. Models have contributed to the perspicacity of epidemiological variations between and within nations and the planning of desired control strategies. In this paper, a literature review was conducted to summarise knowledge about COVID-19 disease modelling in three countries-China, the UK and Australia-to develop a robust research framework for the regional areas that are urban and rural health districts of New South Wales, Australia. In different aspects of modelling, summarising disease and intervention strategies can help policymakers control the outbreak of COVID-19 and may motivate modelling disease-related research at a finer level of regional geospatial scales in the future.
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Affiliation(s)
- Azizur Rahman
- School of Computing, Mathematics and Engineering, Charles Sturt University, Wagga Wagga, NSW 2678, Australia; (M.A.K.); (R.H.L.I.); (M.B.)
- Institute for Land, Water and Society (ILWS), Charles Sturt University, Albury, NSW 2640, Australia
| | - Md Abdul Kuddus
- School of Computing, Mathematics and Engineering, Charles Sturt University, Wagga Wagga, NSW 2678, Australia; (M.A.K.); (R.H.L.I.); (M.B.)
- Australian Institute of Tropical Health and Medicine, James Cook University, Townsville, QLD 4814, Australia
- Department of Mathematics, University of Rajshahi, Rajshahi 6205, Bangladesh
| | - Ryan H. L. Ip
- School of Computing, Mathematics and Engineering, Charles Sturt University, Wagga Wagga, NSW 2678, Australia; (M.A.K.); (R.H.L.I.); (M.B.)
| | - Michael Bewong
- School of Computing, Mathematics and Engineering, Charles Sturt University, Wagga Wagga, NSW 2678, Australia; (M.A.K.); (R.H.L.I.); (M.B.)
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10
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Bella A, Akbar MT, Kusnadi G, Herlinda O, Regita PA, Kusuma D. Socioeconomic and Behavioral Correlates of COVID-19 Infections among Hospital Workers in the Greater Jakarta Area, Indonesia: A Cross-Sectional Study. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:5048. [PMID: 34064580 PMCID: PMC8151868 DOI: 10.3390/ijerph18105048] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/07/2021] [Revised: 05/05/2021] [Accepted: 05/07/2021] [Indexed: 12/23/2022]
Abstract
(1) Background: because of close contacts with COVID-19 patients, hospital workers are among the highest risk groups for infection. This study examined the socioeconomic and behavioral correlates of COVID-19 infection among hospital workers in Indonesia, the country hardest-hit by the disease in the Southeast Asia region. (2) Methods: we conducted a cross-sectional study, which collected data from 1397 hospital staff from eight hospitals in the Greater Jakarta area during April-July 2020. The data was collected using an online self-administered questionnaire and Reverse Transcription-Polymerase Chain Reaction (RT-PCR) tests. We employed descriptive statistics and adjusted and unadjusted logistic regressions to analyze the data of hospital workers as well as the subgroups of healthcare and non-healthcare workers. (3) Results: from a total of 1397 hospital staff in the study, 22 (1.6%) were infected. In terms of correlates, being a healthcare worker (adjusted odds ratio (AOR) = 8.31, 95% CI 1.27-54.54) and having a household size of more than five (AOR = 4.09, 1.02-16.43) were significantly associated with a higher risk of infection. On the other hand, those with middle- and upper-expenditure levels were shown to have a lower risk of infection (AOR = 0.06, 0.01-0.66). Behavioral factors associated with COVID-19 infection among healthcare and non-healthcare workers included knowledge of standard personal protective equipment (PPE) (AOR = 0.08, 0.01-0.54) and application of the six-step handwashing technique (AOR = 0.32, 0.12-0.83). (4) Conclusion: among hospital staff, correlates of COVID-19 infection included being a healthcare worker, household size, expenditure level, knowledge and use of PPE, and application of appropriate hand washing techniques.
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Affiliation(s)
- Adrianna Bella
- Center for Indonesia’s Strategic Development Initiatives (CISDI), Jakarta 10350, Indonesia; (A.B.); (M.T.A.); (G.K.); (O.H.); (P.A.R.)
| | - Mochamad Thoriq Akbar
- Center for Indonesia’s Strategic Development Initiatives (CISDI), Jakarta 10350, Indonesia; (A.B.); (M.T.A.); (G.K.); (O.H.); (P.A.R.)
| | - Gita Kusnadi
- Center for Indonesia’s Strategic Development Initiatives (CISDI), Jakarta 10350, Indonesia; (A.B.); (M.T.A.); (G.K.); (O.H.); (P.A.R.)
| | - Olivia Herlinda
- Center for Indonesia’s Strategic Development Initiatives (CISDI), Jakarta 10350, Indonesia; (A.B.); (M.T.A.); (G.K.); (O.H.); (P.A.R.)
| | - Putri Aprilia Regita
- Center for Indonesia’s Strategic Development Initiatives (CISDI), Jakarta 10350, Indonesia; (A.B.); (M.T.A.); (G.K.); (O.H.); (P.A.R.)
| | - Dian Kusuma
- Centre for Health Economics & Policy Innovation, Imperial College Business School, London SW7 2AZ, UK
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11
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Parimalanathan V, Joy M, Van Dam PJ, Fan X, de Lusignan S. Association between Influenza Vaccine Administration and Primary Care Consultations for Respiratory Infections: Sentinel Network Study of Five Seasons (2014/2015-2018/2019) in the UK. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:ijerph18020523. [PMID: 33435229 PMCID: PMC7827078 DOI: 10.3390/ijerph18020523] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/25/2020] [Revised: 01/03/2021] [Accepted: 01/04/2021] [Indexed: 11/25/2022]
Abstract
Influenza, a vaccine preventable disease, is a serious global public health concern which results in a considerable burden on the healthcare system. However, vaccine hesitancy is increasingly becoming a global problem. One prevalent misconception is that influenza vaccinations can cause the flu. We carried out this study to determine whether people undertaking influenza vaccination presented less with acute respiratory tract infection (ARTI) and influenza-like-illness (ILI) following vaccination. We utilised the Oxford Royal College of General Practitioners Research and Surveillance Centre sentinel database to examine English patients who received vaccination between 2014/2015 and 2018/2019. Of the 3,841,700 influenza vaccinations identified, vaccination details and primary care respiratory consultation counts were extracted to calculate the relative incidence (RI) per exposure risk period using the self-controlled case series methodology. Results showed a significant increase in the RI of respiratory consultation rates within fourteen days of vaccination across all five years. Less than 6.2% of vaccinations led to consultations for ARTI or ILI in primary care (crude consultation rate 6196 per 100,000). These findings, particularly if confirmed in further research, may reduce the risk of cross-infection between waiting patients and increase uptake of influenza vaccine.
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Affiliation(s)
- Vaishnavi Parimalanathan
- Tasmanian School of Medicine, College of Health and Medicine, University of Tasmania, Hobart, TAS 7000, Australia; (V.P.); (P.J.V.D.)
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Woodstock Road, Oxford OX2 6GG, UK; (M.J.); (X.F.)
- Royal College of General Practitioners Research and Surveillance Centre, 30 Euston Square, London NW1 2FB, UK
| | - Mark Joy
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Woodstock Road, Oxford OX2 6GG, UK; (M.J.); (X.F.)
| | - Pieter Jan Van Dam
- Tasmanian School of Medicine, College of Health and Medicine, University of Tasmania, Hobart, TAS 7000, Australia; (V.P.); (P.J.V.D.)
| | - Xuejuan Fan
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Woodstock Road, Oxford OX2 6GG, UK; (M.J.); (X.F.)
| | - Simon de Lusignan
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Woodstock Road, Oxford OX2 6GG, UK; (M.J.); (X.F.)
- Royal College of General Practitioners Research and Surveillance Centre, 30 Euston Square, London NW1 2FB, UK
- Correspondence: ; Tel.: +44-1865-617-283
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12
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de Lusignan S, Liyanage H, McGagh D, Jani BD, Bauwens J, Byford R, Evans D, Fahey T, Greenhalgh T, Jones N, Mair FS, Okusi C, Parimalanathan V, Pell JP, Sherlock J, Tamburis O, Tripathy M, Ferreira F, Williams J, Hobbs FDR. COVID-19 Surveillance in a Primary Care Sentinel Network: In-Pandemic Development of an Application Ontology. JMIR Public Health Surveill 2020; 6:e21434. [PMID: 33112762 PMCID: PMC7674143 DOI: 10.2196/21434] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2020] [Revised: 10/02/2020] [Accepted: 10/02/2020] [Indexed: 02/06/2023] Open
Abstract
Background Creating an ontology for COVID-19 surveillance should help ensure transparency and consistency. Ontologies formalize conceptualizations at either the domain or application level. Application ontologies cross domains and are specified through testable use cases. Our use case was an extension of the role of the Oxford Royal College of General Practitioners (RCGP) Research and Surveillance Centre (RSC) to monitor the current pandemic and become an in-pandemic research platform. Objective This study aimed to develop an application ontology for COVID-19 that can be deployed across the various use-case domains of the RCGP RSC research and surveillance activities. Methods We described our domain-specific use case. The actor was the RCGP RSC sentinel network, the system was the course of the COVID-19 pandemic, and the outcomes were the spread and effect of mitigation measures. We used our established 3-step method to develop the ontology, separating ontological concept development from code mapping and data extract validation. We developed a coding system–independent COVID-19 case identification algorithm. As there were no gold-standard pandemic surveillance ontologies, we conducted a rapid Delphi consensus exercise through the International Medical Informatics Association Primary Health Care Informatics working group and extended networks. Results Our use-case domains included primary care, public health, virology, clinical research, and clinical informatics. Our ontology supported (1) case identification, microbiological sampling, and health outcomes at an individual practice and at the national level; (2) feedback through a dashboard; (3) a national observatory; (4) regular updates for Public Health England; and (5) transformation of a sentinel network into a trial platform. We have identified a total of 19,115 people with a definite COVID-19 status, 5226 probable cases, and 74,293 people with possible COVID-19, within the RCGP RSC network (N=5,370,225). Conclusions The underpinning structure of our ontological approach has coped with multiple clinical coding challenges. At a time when there is uncertainty about international comparisons, clarity about the basis on which case definitions and outcomes are made from routine data is essential.
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Affiliation(s)
- Simon de Lusignan
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Harshana Liyanage
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Dylan McGagh
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Bhautesh Dinesh Jani
- General Practice and Primary Care, Institute of Health and Wellbeing, University of Glasgow, Glasgow, United Kingdom
| | - Jorgen Bauwens
- University Children's Hospital Basel, University of Basel, Basel, Switzerland
| | - Rachel Byford
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Dai Evans
- PRIMIS, University of Nottingham, Nottingham, United Kingdom
| | - Tom Fahey
- Department of General Practice, Royal College of Surgeons, Ireland, Dublin, Ireland
| | - Trisha Greenhalgh
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Nicholas Jones
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Frances S Mair
- General Practice and Primary Care, Institute of Health and Wellbeing, University of Glasgow, Glasgow, United Kingdom
| | - Cecilia Okusi
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Vaishnavi Parimalanathan
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Jill P Pell
- General Practice and Primary Care, Institute of Health and Wellbeing, University of Glasgow, Glasgow, United Kingdom
| | - Julian Sherlock
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Oscar Tamburis
- Department of Veterinary Medicine and Animal Productions, University of Naples Federico II, Naples, Italy
| | - Manasa Tripathy
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Filipa Ferreira
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - John Williams
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - F D Richard Hobbs
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
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13
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Saffary T, Adegboye OA, Gayawan E, Elfaki F, Kuddus MA, Saffary R. Analysis of COVID-19 Cases' Spatial Dependence in US Counties Reveals Health Inequalities. Front Public Health 2020; 8:579190. [PMID: 33282812 PMCID: PMC7690561 DOI: 10.3389/fpubh.2020.579190] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2020] [Accepted: 10/12/2020] [Indexed: 12/23/2022] Open
Abstract
On March 13, 2020, the World Health Organization (WHO) declared the 2019 coronavirus disease (COVID-19) caused by the novel coronavirus SARS-CoV2 a pandemic. Since then the virus has infected over 9.1 million individuals and resulted in over 470,000 deaths worldwide (as of June 24, 2020). Here, we discuss the spatial correlation between county population health rankings and the incidence of COVID-19 cases and COVID-19 related deaths in the United States. We analyzed the spread of the disease based on multiple variables at the county level, using publicly available data on the numbers of confirmed cases and deaths, intensive care unit beds and socio-demographic, and healthcare resources in the U.S. Our results indicate substantial geographical variations in the distribution of COVID-19 cases and deaths across the US counties. There was significant positive global spatial correlation between the percentage of Black Americans and cases of COVID-19 (Moran I = 0.174 and 0.264, p < 0.0001). A similar result was found for the global spatial correlation between the percentage of Black American and deaths due to COVID-19 at the county level in the U.S. (Moran I = 0.264, p < 0.0001). There was no significant spatial correlation between the Hispanic population and COVID-19 cases and deaths; however, a higher percentage of non-Hispanic white was significantly negatively spatially correlated with cases (Moran I = -0.203, p < 0.0001) and deaths (Moran I = -0.137, p < 0.0001) from the disease. This study showed significant but weak spatial autocorrelation between the number of intensive care unit beds and COVID-19 cases (Moran I = 0.08, p < 0.0001) and deaths (Moran I = 0.15, p < 0.0001), respectively. These findings provide more detail into the interplay between the infectious disease and healthcare-related characteristics of the population. Only by understanding these relationships will it be possible to mitigate the rate of spread and severity of the disease.
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Affiliation(s)
- T. Saffary
- Department of Mathematics, Engineering and Computer Science, Chemeketa Community College, Salem, OR, United States
| | - Oyelola A. Adegboye
- Evolution Equations Research Group, Ton Duc Thang University, Ho Chi Minh City, Vietnam
- Faculty of Mathematics and Statistics, Ton Duc Thang University, Ho Chi Minh City, Vietnam
| | - E. Gayawan
- Department of Statistics, Federal University of Technology, Akure, Nigeria
| | - F. Elfaki
- Department of Mathematics, Physics and Statistics, Qatar University, Doha, Qatar
| | - Md Abdul Kuddus
- Department of Mathematics, University of Rajshahi, Rajshahi, Bangladesh
| | - R. Saffary
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University, Stanford, CA, United States
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14
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Bility MT, Agarwal Y, Ho S, Castronova I, Beatty C, Biradar S, Narala V, Periyapatna N, Chen Y, Nachega J. WITHDRAWN: Can Traditional Chinese Medicine provide insights into controlling the COVID-19 pandemic: Serpentinization-induced lithospheric long-wavelength magnetic anomalies in Proterozoic bedrocks in a weakened geomagnetic field mediate the aberrant transformation of biogenic molecules in COVID-19 via magnetic catalysis. THE SCIENCE OF THE TOTAL ENVIRONMENT 2020:142830. [PMID: 33071142 PMCID: PMC7543923 DOI: 10.1016/j.scitotenv.2020.142830] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/07/2020] [Revised: 09/26/2020] [Accepted: 09/27/2020] [Indexed: 06/11/2023]
Abstract
This article has been withdrawn at the request of the authors and the editors. The Publisher apologizes for any inconvenience this may cause. The full Elsevier Policy on Article Withdrawal can be found at https://www.elsevier.com/about/our-business/policies/article-withdrawal.
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Affiliation(s)
- Moses Turkle Bility
- Department of Infectious Diseases and Microbiology, Graduate School of Public Health, University of Pittsburgh, Public Health, 130 DeSoto Street, Pittsburgh, PA 15261, United States of America.
| | - Yash Agarwal
- Department of Infectious Diseases and Microbiology, Graduate School of Public Health, University of Pittsburgh, Public Health, 130 DeSoto Street, Pittsburgh, PA 15261, United States of America
| | - Sara Ho
- Department of Infectious Diseases and Microbiology, Graduate School of Public Health, University of Pittsburgh, Public Health, 130 DeSoto Street, Pittsburgh, PA 15261, United States of America
| | - Isabella Castronova
- Department of Infectious Diseases and Microbiology, Graduate School of Public Health, University of Pittsburgh, Public Health, 130 DeSoto Street, Pittsburgh, PA 15261, United States of America
| | - Cole Beatty
- Department of Infectious Diseases and Microbiology, Graduate School of Public Health, University of Pittsburgh, Public Health, 130 DeSoto Street, Pittsburgh, PA 15261, United States of America
| | - Shivkumar Biradar
- Department of Infectious Diseases and Microbiology, Graduate School of Public Health, University of Pittsburgh, Public Health, 130 DeSoto Street, Pittsburgh, PA 15261, United States of America
| | - Vanshika Narala
- Department of Infectious Diseases and Microbiology, Graduate School of Public Health, University of Pittsburgh, Public Health, 130 DeSoto Street, Pittsburgh, PA 15261, United States of America
| | - Nivitha Periyapatna
- Department of Infectious Diseases and Microbiology, Graduate School of Public Health, University of Pittsburgh, Public Health, 130 DeSoto Street, Pittsburgh, PA 15261, United States of America
| | - Yue Chen
- Department of Infectious Diseases and Microbiology, Graduate School of Public Health, University of Pittsburgh, Public Health, 130 DeSoto Street, Pittsburgh, PA 15261, United States of America
| | - Jean Nachega
- Department of Infectious Diseases and Microbiology, Graduate School of Public Health, University of Pittsburgh, Public Health, 130 DeSoto Street, Pittsburgh, PA 15261, United States of America; Department of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Public Health, 130 DeSoto Street, Pittsburgh, PA 15261, United States of America
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de Lusignan S, Dorward J, Correa A, Jones N, Akinyemi O, Amirthalingam G, Andrews N, Byford R, Dabrera G, Elliot A, Ellis J, Ferreira F, Lopez Bernal J, Okusi C, Ramsay M, Sherlock J, Smith G, Williams J, Howsam G, Zambon M, Joy M, Hobbs FDR. Risk factors for SARS-CoV-2 among patients in the Oxford Royal College of General Practitioners Research and Surveillance Centre primary care network: a cross-sectional study. THE LANCET. INFECTIOUS DISEASES 2020; 20:1034-1042. [PMID: 32422204 PMCID: PMC7228715 DOI: 10.1016/s1473-3099(20)30371-6] [Citation(s) in RCA: 396] [Impact Index Per Article: 99.0] [Reference Citation Analysis] [Abstract] [Download PDF] [Subscribe] [Scholar Register] [Received: 04/15/2020] [Revised: 04/26/2020] [Accepted: 04/29/2020] [Indexed: 12/15/2022]
Abstract
BACKGROUND There are few primary care studies of the COVID-19 pandemic. We aimed to identify demographic and clinical risk factors for testing positive for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) within the Oxford Royal College of General Practitioners (RCGP) Research and Surveillance Centre primary care network. METHODS We analysed routinely collected, pseudonymised data for patients in the RCGP Research and Surveillance Centre primary care sentinel network who were tested for SARS-CoV-2 between Jan 28 and April 4, 2020. We used multivariable logistic regression models with multiple imputation to identify risk factors for positive SARS-CoV-2 tests within this surveillance network. FINDINGS We identified 3802 SARS-CoV-2 test results, of which 587 were positive. In multivariable analysis, male sex was independently associated with testing positive for SARS-CoV-2 (296 [18·4%] of 1612 men vs 291 [13·3%] of 2190 women; adjusted odds ratio [OR] 1·55, 95% CI 1·27-1·89). Adults were at increased risk of testing positive for SARS-CoV-2 compared with children, and people aged 40-64 years were at greatest risk in the multivariable model (243 [18·5%] of 1316 adults aged 40-64 years vs 23 [4·6%] of 499 children; adjusted OR 5·36, 95% CI 3·28-8·76). Compared with white people, the adjusted odds of a positive test were greater in black people (388 [15·5%] of 2497 white people vs 36 [62·1%] of 58 black people; adjusted OR 4·75, 95% CI 2·65-8·51). People living in urban areas versus rural areas (476 [26·2%] of 1816 in urban areas vs 111 [5·6%] of 1986 in rural areas; adjusted OR 4·59, 95% CI 3·57-5·90) and in more deprived areas (197 [29·5%] of 668 in most deprived vs 143 [7·7%] of 1855 in least deprived; adjusted OR 2·03, 95% CI 1·51-2·71) were more likely to test positive. People with chronic kidney disease were more likely to test positive in the adjusted analysis (68 [32·9%] of 207 with chronic kidney disease vs 519 [14·4%] of 3595 without; adjusted OR 1·91, 95% CI 1·31-2·78), but there was no significant association with other chronic conditions in that analysis. We found increased odds of a positive test among people who are obese (142 [20·9%] of 680 people with obesity vs 171 [13·2%] of 1296 normal-weight people; adjusted OR 1·41, 95% CI 1·04-1·91). Notably, active smoking was linked with decreased odds of a positive test result (47 [11·4%] of 413 active smokers vs 201 [17·9%] of 1125 non-smokers; adjusted OR 0·49, 95% CI 0·34-0·71). INTERPRETATION A positive SARS-CoV-2 test result in this primary care cohort was associated with similar risk factors as observed for severe outcomes of COVID-19 in hospital settings, except for smoking. We provide evidence of potential sociodemographic factors associated with a positive test, including deprivation, population density, ethnicity, and chronic kidney disease. FUNDING Wellcome Trust.
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Affiliation(s)
- Simon de Lusignan
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK; Royal College of General Practitioners Research and Surveillance Centre, London, UK.
| | - Jienchi Dorward
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK; Centre for the AIDS Programme of Research in South Africa, University of KwaZulu-Natal, Durban, South Africa
| | - Ana Correa
- Institute for Global Health, University College London, London, UK; Section of Clinical Medicine, University of Surrey, Guildford, UK
| | - Nicholas Jones
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Oluwafunmi Akinyemi
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | | | | | - Rachel Byford
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | | | | | | | - Filipa Ferreira
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | | | - Cecilia Okusi
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | | | - Julian Sherlock
- 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
| | - Gary Howsam
- Royal College of General Practitioners Research and Surveillance Centre, London, UK
| | | | - Mark Joy
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - F D Richard Hobbs
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
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