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Maaß L, Angoumis K, Freye M, Pan CC. Mapping Digital Public Health Interventions Among Existing Digital Technologies and Internet-Based Interventions to Maintain and Improve Population Health in Practice: Scoping Review. J Med Internet Res 2024; 26:e53927. [PMID: 39018096 PMCID: PMC11292160 DOI: 10.2196/53927] [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: 10/24/2023] [Revised: 01/31/2024] [Accepted: 05/15/2024] [Indexed: 07/18/2024] Open
Abstract
BACKGROUND The rapid progression and integration of digital technologies into public health have reshaped the global landscape of health care delivery and disease prevention. In pursuit of better population health and health care accessibility, many countries have integrated digital interventions into their health care systems, such as web-based consultations, electronic health records, and telemedicine. Despite the increasing prevalence and relevance of digital technologies in public health and their varying definitions, there has been a shortage of studies examining whether these technologies align with the established definition and core characteristics of digital public health (DiPH) interventions. Hence, the imperative need for a scoping review emerges to explore the breadth of literature dedicated to this subject. OBJECTIVE This scoping review aims to outline DiPH interventions from different implementation stages for health promotion, primary to tertiary prevention, including health care and disease surveillance and monitoring. In addition, we aim to map the reported intervention characteristics, including their technical features and nontechnical elements. METHODS Original studies or reports of DiPH intervention focused on population health were eligible for this review. PubMed, Web of Science, CENTRAL, IEEE Xplore, and the ACM Full-Text Collection were searched for relevant literature (last updated on October 5, 2022). Intervention characteristics of each identified DiPH intervention, such as target groups, level of prevention or health care, digital health functions, intervention types, and public health functions, were extracted and used to map DiPH interventions. MAXQDA 2022.7 (VERBI GmbH) was used for qualitative data analysis of such interventions' technical functions and nontechnical characteristics. RESULTS In total, we identified and screened 15,701 records, of which 1562 (9.94%) full texts were considered relevant and were assessed for eligibility. Finally, we included 185 (11.84%) publications, which reported 179 different DiPH interventions. Our analysis revealed a diverse landscape of interventions, with telemedical services, health apps, and electronic health records as dominant types. These interventions targeted a wide range of populations and settings, demonstrating their adaptability. The analysis highlighted the multifaceted nature of digital interventions, necessitating precise definitions and standardized terminologies for effective collaboration and evaluation. CONCLUSIONS Although this scoping review was able to map characteristics and technical functions among 13 intervention types in DiPH, emerging technologies such as artificial intelligence might have been underrepresented in our study. This review underscores the diversity of DiPH interventions among and within intervention groups. Moreover, it highlights the importance of precise terminology for effective planning and evaluation. This review promotes cross-disciplinary collaboration by emphasizing the need for clear definitions, distinct technological functions, and well-defined use cases. It lays the foundation for international benchmarks and comparability within DiPH systems. Further research is needed to map intervention characteristics in this still-evolving field continuously. TRIAL REGISTRATION PROSPERO CRD42021265562; https://tinyurl.com/43jksb3k. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) RR2-10.2196/33404.
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Affiliation(s)
- Laura Maaß
- University of Bremen, SOCIUM Research Center on Inequality and Social Policy, Bremen, Germany
- Leibniz ScienceCampus Digital Public Health Bremen, Bremen, Germany
- Digital Health Section, European Public Health Association - EUPHA, Utrecht, Netherlands
| | - Konstantinos Angoumis
- University of Bielefeld, Bielefeld, Germany
- Leibniz Institute for Prevention Research and Epidemiology - BIPS, Bremen, Germany
| | - Merle Freye
- Leibniz ScienceCampus Digital Public Health Bremen, Bremen, Germany
- University of Bremen, Institute for Information, Health and Medical Law - IGMR, Bremen, Germany
| | - Chen-Chia Pan
- Leibniz ScienceCampus Digital Public Health Bremen, Bremen, Germany
- Leibniz Institute for Prevention Research and Epidemiology - BIPS, Bremen, Germany
- University of Bremen, Institute for Public Health and Nursing Research - IPP, Bremen, Germany
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Alahmari AA, Almuzaini Y, Alamri F, Alenzi R, Khan AA. Strengthening global health security through health early warning systems: A literature review and case study. J Infect Public Health 2024; 17 Suppl 1:85-95. [PMID: 38368245 DOI: 10.1016/j.jiph.2024.01.019] [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: 09/12/2023] [Revised: 01/29/2024] [Accepted: 01/31/2024] [Indexed: 02/19/2024] Open
Abstract
Disease transmission is dependent on a variety of factors, including the characteristics of an event, such as crowding and shared accommodations, the potential of participants having prolonged exposure and close contact with infectious individuals, the type of activities, and the characteristics of the participants, such as their age and immunity to infectious agents [1-3]. Effective control of outbreaks of infectious diseases requires rapid diagnosis and intervention in high-risk settings. As a result, syndromic and event-based surveillance may be used to enhance the responsiveness of the surveillance system [1]. In public health, surveillance is collecting, analyzing, and interpreting data across time to inform decision-making and aid policy implementation [1]. In this review article we aimed to provide an overview of the principles, types, uses, advantages, and limitations of surveillance systems and to highlight the importance of early warning systems in response to the information received by disease surveillance. The study conducted a comprehensive literature search using several databases, selecting, and reviewing 78 articles that covered different types of surveillance systems, their applications, and their impact on controlling infectious diseases. The article also presents a case study from the Hajj gathering, which highlighted the development, evaluation, and impact of early warning systems on response to the information received by disease surveillance. The study concludes that ongoing disease surveillance should be accompanied by well-designed early warning and response systems, and continuous efforts should be invested in evaluating and validating these systems to minimize the risk of reporting delays and reducing the risk of outbreaks.
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Affiliation(s)
- Ahmed A Alahmari
- Global Center of Mass Gatherings Medicine, Ministry of Health, Riyadh, Saudi Arabia.
| | - Yasir Almuzaini
- Global Center of Mass Gatherings Medicine, Ministry of Health, Riyadh, Saudi Arabia
| | - Fahad Alamri
- Global Center of Mass Gatherings Medicine, Ministry of Health, Riyadh, Saudi Arabia
| | | | - Anas A Khan
- Global Center of Mass Gatherings Medicine, Ministry of Health, Riyadh, Saudi Arabia; Department of Emergency Medicine, College of Medicine, King Saud University, Riyadh, Saudi Arabia
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Kasl P, Keeler Bruce L, Hartogensis W, Dasgupta S, Pandya LS, Dilchert S, Hecht FM, Gupta A, Altintas I, Mason AE, Smarr BL. Utilizing Wearable Device Data for Syndromic Surveillance: A Fever Detection Approach. SENSORS (BASEL, SWITZERLAND) 2024; 24:1818. [PMID: 38544080 PMCID: PMC10975930 DOI: 10.3390/s24061818] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/30/2024] [Revised: 02/29/2024] [Accepted: 03/06/2024] [Indexed: 04/01/2024]
Abstract
Commercially available wearable devices (wearables) show promise for continuous physiological monitoring. Previous works have demonstrated that wearables can be used to detect the onset of acute infectious diseases, particularly those characterized by fever. We aimed to evaluate whether these devices could be used for the more general task of syndromic surveillance. We obtained wearable device data (Oura Ring) from 63,153 participants. We constructed a dataset using participants' wearable device data and participants' responses to daily online questionnaires. We included days from the participants if they (1) completed the questionnaire, (2) reported not experiencing fever and reported a self-collected body temperature below 38 °C (negative class), or reported experiencing fever and reported a self-collected body temperature at or above 38 °C (positive class), and (3) wore the wearable device the nights before and after that day. We used wearable device data (i.e., skin temperature, heart rate, and sleep) from the nights before and after participants' fever day to train a tree-based classifier to detect self-reported fevers. We evaluated the performance of our model using a five-fold cross-validation scheme. Sixteen thousand, seven hundred, and ninety-four participants provided at least one valid ground truth day; there were a total of 724 fever days (positive class examples) from 463 participants and 342,430 non-fever days (negative class examples) from 16,687 participants. Our model exhibited an area under the receiver operating characteristic curve (AUROC) of 0.85 and an average precision (AP) of 0.25. At a sensitivity of 0.50, our calibrated model had a false positive rate of 0.8%. Our results suggest that it might be possible to leverage data from these devices at a public health level for live fever surveillance. Implementing these models could increase our ability to detect disease prevalence and spread in real-time during infectious disease outbreaks.
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Affiliation(s)
- Patrick Kasl
- Shu Chien-Gene Lay Department of Bioengineering, University of California San Diego, San Diego, CA 92093-0021, USA;
| | - Lauryn Keeler Bruce
- UC San Diego Health Department of Biomedical Informatics, University of California San Diego, San Diego, CA 92093-0021, USA;
| | - Wendy Hartogensis
- UCSF Osher Center for Integrative Health, University of California San Francisco, San Francisco, CA 92093-0021, USA; (W.H.); (L.S.P.); (F.M.H.); (A.E.M.)
| | - Subhasis Dasgupta
- San Diego Supercomputer Center, University of California San Diego, San Diego, CA 92093-0021, USA; (S.D.); (A.G.); (I.A.)
| | - Leena S. Pandya
- UCSF Osher Center for Integrative Health, University of California San Francisco, San Francisco, CA 92093-0021, USA; (W.H.); (L.S.P.); (F.M.H.); (A.E.M.)
| | - Stephan Dilchert
- Department of Management, Zicklin School of Business, Baruch College, The City University of New York, New York, NY 10010, USA;
| | - Frederick M. Hecht
- UCSF Osher Center for Integrative Health, University of California San Francisco, San Francisco, CA 92093-0021, USA; (W.H.); (L.S.P.); (F.M.H.); (A.E.M.)
| | - Amarnath Gupta
- San Diego Supercomputer Center, University of California San Diego, San Diego, CA 92093-0021, USA; (S.D.); (A.G.); (I.A.)
- Halıcıoğlu Data Science Institute, University of California San Diego, San Diego, CA 92093-0021, USA
| | - Ilkay Altintas
- San Diego Supercomputer Center, University of California San Diego, San Diego, CA 92093-0021, USA; (S.D.); (A.G.); (I.A.)
- Halıcıoğlu Data Science Institute, University of California San Diego, San Diego, CA 92093-0021, USA
| | - Ashley E. Mason
- UCSF Osher Center for Integrative Health, University of California San Francisco, San Francisco, CA 92093-0021, USA; (W.H.); (L.S.P.); (F.M.H.); (A.E.M.)
| | - Benjamin L. Smarr
- Shu Chien-Gene Lay Department of Bioengineering, University of California San Diego, San Diego, CA 92093-0021, USA;
- Halıcıoğlu Data Science Institute, University of California San Diego, San Diego, CA 92093-0021, USA
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Colucci M, Fonzo M, Miccolis L, Amoruso I, Mondino S, Trevisan A, Cazzaro R, Baldovin T, Bertoncello C. Emergency Department Syndromic Surveillance to Monitor Tick-Borne Diseases: A 6-Year Small-Area Analysis in Northeastern Italy. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:6822. [PMID: 37835091 PMCID: PMC10572455 DOI: 10.3390/ijerph20196822] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Revised: 09/11/2023] [Accepted: 09/19/2023] [Indexed: 10/15/2023]
Abstract
Tick-borne diseases (TBD) are endemic in Europe. However, surveillance is currently incomplete. Alternative strategies need to be considered. The aim of this study was to test an Emergency Department Syndromic Surveillance (EDSyS) system as a complementary data source to describe the impact of tick bites and TBD using a small-area analysis approach and to monitor the risk of TBD to target prevention. ED databases in the Local Health Authority 8 District (Veneto, Italy) were queried for tick-bite and TBD-related visits between January 2017 and December 2022. Hospitalisations were also collected. Events involving the resident population were used to calculate incidence rates. A total of 4187 ED visits for tick-bite and 143 for TBD were recorded; in addition, 62 TBD-related hospitalisations (of which 72.6% in over 50 s and 22.6% in over 65 s). ED visits peaked in spring and in autumn, followed by a 4-week lag in the increase in hospital admissions. The small-area analysis identified two areas at higher risk of bites and TBD. The use of a EDSyS system allowed two natural foci to be identified. This approach proved useful in predicting temporal and geographic risk of TBD and in identifying local endemic areas, thus enabling an effective multidisciplinary prevention strategy.
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Affiliation(s)
- Massimiliano Colucci
- Hospital Direction, Local Health Authority 8 (Azienda ULSS Berica), Veneto Region, 36100 Vicenza, Italy
| | - Marco Fonzo
- Hygiene and Public Health Unit, Department of Cardiac Thoracic Vascular Sciences and Public Health, University of Padova, 35131 Padova, Italy
| | - Liana Miccolis
- Hygiene and Public Health Unit, Department of Cardiac Thoracic Vascular Sciences and Public Health, University of Padova, 35131 Padova, Italy
| | - Irene Amoruso
- Hygiene and Public Health Unit, Department of Cardiac Thoracic Vascular Sciences and Public Health, University of Padova, 35131 Padova, Italy
| | - Sara Mondino
- Hospital Direction, Local Health Authority 8 (Azienda ULSS Berica), Veneto Region, 36100 Vicenza, Italy
| | - Andrea Trevisan
- Hygiene and Public Health Unit, Department of Cardiac Thoracic Vascular Sciences and Public Health, University of Padova, 35131 Padova, Italy
| | - Romina Cazzaro
- Hospital Direction, Local Health Authority 8 (Azienda ULSS Berica), Veneto Region, 36100 Vicenza, Italy
| | - Tatjana Baldovin
- Hygiene and Public Health Unit, Department of Cardiac Thoracic Vascular Sciences and Public Health, University of Padova, 35131 Padova, Italy
| | - Chiara Bertoncello
- Hygiene and Public Health Unit, Department of Cardiac Thoracic Vascular Sciences and Public Health, University of Padova, 35131 Padova, Italy
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Morbey RA, Todkill D, Watson C, Elliot AJ. Machine learning forecasts for seasonal epidemic peaks: Lessons learnt from an atypical respiratory syncytial virus season. PLoS One 2023; 18:e0291932. [PMID: 37738241 PMCID: PMC10516409 DOI: 10.1371/journal.pone.0291932] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Accepted: 09/10/2023] [Indexed: 09/24/2023] Open
Abstract
Seasonal peaks in infectious disease incidence put pressures on health services. Therefore, early warning of the timing and magnitude of peak activity during seasonal epidemics can provide information for public health practitioners to take appropriate action. Whilst many infectious diseases have predictable seasonality, newly emerging diseases and the impact of public health interventions can result in unprecedented seasonal activity. We propose a Machine Learning process for generating short-term forecasts, where models are selected based on their ability to correctly forecast peaks in activity, and can be useful during atypical seasons. We have validated our forecasts using typical and atypical seasonal activity, using respiratory syncytial virus (RSV) activity during 2019-2021 as an example. During the winter of 2020/21 the usual winter peak in RSV activity in England did not occur but was 'deferred' until the Spring of 2021. We compare a range of Machine Learning regression models, with alternate models including different independent variables, e.g. with or without seasonality or trend variables. We show that the best-fitting model which minimises daily forecast errors is not the best model for forecasting peaks when the selection criterion is based on peak timing and magnitude. Furthermore, we show that best-fitting models for typical seasons contain different variables to those for atypical seasons. Specifically, including seasonality in models improves performance during typical seasons but worsens it for the atypical seasons.
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Affiliation(s)
- Roger A. Morbey
- Real-Time Syndromic Surveillance Team, Field Services, Health Protection Operations, UK Health Security Agency, Birmingham, United Kingdom
| | - Daniel Todkill
- Real-Time Syndromic Surveillance Team, Field Services, Health Protection Operations, UK Health Security Agency, Birmingham, United Kingdom
| | - Conall Watson
- Immunisation and Vaccine Preventable Diseases Division, UK Health Security Agency, London, United Kingdom
| | - Alex J. Elliot
- Real-Time Syndromic Surveillance Team, Field Services, Health Protection Operations, UK Health Security Agency, Birmingham, United Kingdom
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Díaz-Cao JM, Liu X, Kim J, Clavijo MJ, Martínez-López B. Evaluation of the application of sequence data to the identification of outbreaks of disease using anomaly detection methods. Vet Res 2023; 54:75. [PMID: 37684632 PMCID: PMC10492347 DOI: 10.1186/s13567-023-01197-3] [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: 05/06/2022] [Accepted: 07/04/2023] [Indexed: 09/10/2023] Open
Abstract
Anomaly detection methods have a great potential to assist the detection of diseases in animal production systems. We used sequence data of Porcine Reproductive and Respiratory Syndrome (PRRS) to define the emergence of new strains at the farm level. We evaluated the performance of 24 anomaly detection methods based on machine learning, regression, time series techniques and control charts to identify outbreaks in time series of new strains and compared the best methods using different time series: PCR positives, PCR requests and laboratory requests. We introduced synthetic outbreaks of different size and calculated the probability of detection of outbreaks (POD), sensitivity (Se), probability of detection of outbreaks in the first week of appearance (POD1w) and background alarm rate (BAR). The use of time series of new strains from sequence data outperformed the other types of data but POD, Se, POD1w were only high when outbreaks were large. The methods based on Long Short-Term Memory (LSTM) and Bayesian approaches presented the best performance. Using anomaly detection methods with sequence data may help to identify the emergency of cases in multiple farms, but more work is required to improve the detection with time series of high variability. Our results suggest a promising application of sequence data for early detection of diseases at a production system level. This may provide a simple way to extract additional value from routine laboratory analysis. Next steps should include validation of this approach in different settings and with different diseases.
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Affiliation(s)
- José Manuel Díaz-Cao
- Center for Animal Disease Modeling and Surveillance (CADMS), Department of Medicine & Epidemiology, School of Veterinary Medicine, University of California, Davis, USA.
- Departamento de Patoloxía Animal, Facultade de Veterinaria de Lugo, Universidade de Santiago de Compostela, Lugo, Spain.
| | - Xin Liu
- Department of Computer Science, University of California, Davis, USA
| | - Jeonghoon Kim
- Department of Computer Science, University of California, Davis, USA
| | - Maria Jose Clavijo
- Department of Veterinary Diagnostic and Production Animal Medicine, College of Veterinary Medicine, Iowa State University, Ames, USA
| | - Beatriz Martínez-López
- Center for Animal Disease Modeling and Surveillance (CADMS), Department of Medicine & Epidemiology, School of Veterinary Medicine, University of California, Davis, USA
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Love NK, Douglas A, Gharbia S, Hughes H, Morbey R, Oliver I, Smith GE, Elliot AJ. Understanding the impact of the COVID-19 pandemic response on GI infection surveillance trends in England, January 2020-April 2022. Epidemiol Infect 2023; 151:e147. [PMID: 37622322 PMCID: PMC10540168 DOI: 10.1017/s095026882300136x] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Revised: 07/13/2023] [Accepted: 08/02/2023] [Indexed: 08/26/2023] Open
Abstract
Stepwise non-pharmaceutical interventions and health system changes implemented as part of the COVID-19 response have had implications on the incidence, diagnosis, and reporting of other communicable diseases. Here, we established the impact of the COVID-19 outbreak response on gastrointestinal (GI) infection trends using routinely collected surveillance data from six national English laboratory, outbreak, and syndromic surveillance systems using key dates of governmental policy to assign phases for comparison between pandemic and historic data. Following decreases across all indicators during the first lockdown (March-May 2020), bacterial and parasitic pathogens associated with foodborne or environmental transmission routes recovered rapidly between June and September 2020, while those associated with travel and/or person-to-person transmission remained lower than expected for 2021. High out-of-season norovirus activity was observed with the easing of lockdown measures between June and October 2021, with this trend reflected in laboratory and outbreak systems and syndromic surveillance indicators. Above expected increases in emergency department (ED) attendances may have reflected changes in health-seeking behaviour and provision. Differential reductions across specific GI pathogens are indicative of the underlying routes of transmission. These results provide further insight into the drivers for transmission, which can help inform control measures for GI infections.
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Affiliation(s)
- Nicola K. Love
- North East Field Services, Health Protection Operations, UK Health Security Agency, Newcastle upon Tyne, UK
- National Institute for Health Research Health Protection Research Unit (NIHR HPRU) in Gastrointestinal Infections, University of Liverpool, Liverpool, UK
| | - Amy Douglas
- Gastrointestinal Infections and Food Safety (One Health) Division, UK Health Security Agency, London, UK
| | - Saheer Gharbia
- Gastrointestinal Infections and Food Safety (One Health) Division, UK Health Security Agency, London, UK
| | - Helen Hughes
- National Institute for Health Research Health Protection Research Unit (NIHR HPRU) in Gastrointestinal Infections, University of Liverpool, Liverpool, UK
- Real-time Syndromic Surveillance Team, Field Service, Health Protection Operations, UK Health Security Agency, Birmingham, UK
- Farr Institute@HeRC, University of Liverpool, Liverpool, UK
| | - Roger Morbey
- Real-time Syndromic Surveillance Team, Field Service, Health Protection Operations, UK Health Security Agency, Birmingham, UK
- National Institute for Health Research Health Protection Research Unit (NIHR HPRU) in Emergency Preparedness and Response, King’s College London, London, UK
| | - Isabel Oliver
- Science Group, UK Health Security Agency, London, UK
- National Institute for Health Research Health Protection Research Unit (NIHR HPRU) in Behavioural Science and Evaluation, Population Health Sciences, University of Bristol, Bristol, UK
| | - Gillian E. Smith
- National Institute for Health Research Health Protection Research Unit (NIHR HPRU) in Gastrointestinal Infections, University of Liverpool, Liverpool, UK
- Real-time Syndromic Surveillance Team, Field Service, Health Protection Operations, UK Health Security Agency, Birmingham, UK
- National Institute for Health Research Health Protection Research Unit (NIHR HPRU) in Emergency Preparedness and Response, King’s College London, London, UK
| | - Alex J. Elliot
- National Institute for Health Research Health Protection Research Unit (NIHR HPRU) in Gastrointestinal Infections, University of Liverpool, Liverpool, UK
- Real-time Syndromic Surveillance Team, Field Service, Health Protection Operations, UK Health Security Agency, Birmingham, UK
- National Institute for Health Research Health Protection Research Unit (NIHR HPRU) in Emergency Preparedness and Response, King’s College London, London, UK
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Ondrikova N, Harris JP, Douglas A, Hughes HE, Iturriza-Gomara M, Vivancos R, Elliot AJ, Cunliffe NA, Clough HE. Predicting Norovirus in England Using Existing and Emerging Syndromic Data: Infodemiology Study. J Med Internet Res 2023; 25:e37540. [PMID: 37155231 DOI: 10.2196/37540] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2022] [Revised: 11/28/2022] [Accepted: 02/19/2023] [Indexed: 05/10/2023] Open
Abstract
BACKGROUND Norovirus is associated with approximately 18% of the global burden of gastroenteritis and affects all age groups. There is currently no licensed vaccine or available antiviral treatment. However, well-designed early warning systems and forecasting can guide nonpharmaceutical approaches to norovirus infection prevention and control. OBJECTIVE This study evaluates the predictive power of existing syndromic surveillance data and emerging data sources, such as internet searches and Wikipedia page views, to predict norovirus activity across a range of age groups across England. METHODS We used existing syndromic surveillance and emerging syndromic data to predict laboratory data indicating norovirus activity. Two methods are used to evaluate the predictive potential of syndromic variables. First, the Granger causality framework was used to assess whether individual variables precede changes in norovirus laboratory reports in a given region or an age group. Then, we used random forest modeling to estimate the importance of each variable in the context of others with two methods: (1) change in the mean square error and (2) node purity. Finally, these results were combined into a visualization indicating the most influential predictors for norovirus laboratory reports in a specific age group and region. RESULTS Our results suggest that syndromic surveillance data include valuable predictors for norovirus laboratory reports in England. However, Wikipedia page views are less likely to provide prediction improvements on top of Google Trends and Existing Syndromic Data. Predictors displayed varying relevance across age groups and regions. For example, the random forest modeling based on selected existing and emerging syndromic variables explained 60% variance in the ≥65 years age group, 42% in the East of England, but only 13% in the South West region. Emerging data sets highlighted relative search volumes, including "flu symptoms," "norovirus in pregnancy," and norovirus activity in specific years, such as "norovirus 2016." Symptoms of vomiting and gastroenteritis in multiple age groups were identified as important predictors within existing data sources. CONCLUSIONS Existing and emerging data sources can help predict norovirus activity in England in some age groups and geographic regions, particularly, predictors concerning vomiting, gastroenteritis, and norovirus in the vulnerable populations and historical terms such as stomach flu. However, syndromic predictors were less relevant in some age groups and regions likely due to contrasting public health practices between regions and health information-seeking behavior between age groups. Additionally, predictors relevant to one norovirus season may not contribute to other seasons. Data biases, such as low spatial granularity in Google Trends and especially in Wikipedia data, also play a role in the results. Moreover, internet searches can provide insight into mental models, that is, an individual's conceptual understanding of norovirus infection and transmission, which could be used in public health communication strategies.
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Affiliation(s)
- Nikola Ondrikova
- Institute of Infection, Ecological and Veterinary Sciences, University of Liverpool, Liverpool, United Kingdom
- National Institute for Health and Care Research Health Protection Research Unit in Gastrointestinal Infections, University of Liverpool, Liverpool, United Kingdom
- Institute for Risk and Uncertainty, University of Liverpool, Liverpool, United Kingdom
| | - John P Harris
- Field Service, Health Protection Operations, United Kingdom Health Security Agency, Liverpool, United Kingdom
| | - Amy Douglas
- Gastrointestinal Infections and Food Safety (One Health) Division, United Kingdom Health Security Agency, London, United Kingdom
| | - Helen E Hughes
- National Institute for Health and Care Research Health Protection Research Unit in Gastrointestinal Infections, University of Liverpool, Liverpool, United Kingdom
- Real-time Syndromic Surveillance Team, Health Protection Operations, United Kingdom Health Security Agency, Birmingham, United Kingdom
| | | | - Roberto Vivancos
- National Institute for Health and Care Research Health Protection Research Unit in Gastrointestinal Infections, University of Liverpool, Liverpool, United Kingdom
- Field Service, Health Protection Operations, United Kingdom Health Security Agency, Liverpool, United Kingdom
- National Institute for Health and Care Research Health Protection Research Unit in Emerging and Zoonotic Infections, University of Liverpool, Liverpool, United Kingdom
| | - Alex J Elliot
- National Institute for Health and Care Research Health Protection Research Unit in Gastrointestinal Infections, University of Liverpool, Liverpool, United Kingdom
- Real-time Syndromic Surveillance Team, Health Protection Operations, United Kingdom Health Security Agency, Birmingham, United Kingdom
| | - Nigel A Cunliffe
- Institute of Infection, Ecological and Veterinary Sciences, University of Liverpool, Liverpool, United Kingdom
- National Institute for Health and Care Research Health Protection Research Unit in Gastrointestinal Infections, University of Liverpool, Liverpool, United Kingdom
| | - Helen E Clough
- Institute of Infection, Ecological and Veterinary Sciences, University of Liverpool, Liverpool, United Kingdom
- National Institute for Health and Care Research Health Protection Research Unit in Gastrointestinal Infections, University of Liverpool, Liverpool, United Kingdom
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Glatman-Freedman A, Kaufman Z. Syndromic Surveillance of Infectious Diseases. Infect Dis (Lond) 2023. [DOI: 10.1007/978-1-0716-2463-0_1088] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/10/2023] Open
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10
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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: 20] [Impact Index Per Article: 10.0] [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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11
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Rahimi-Ardabili H, Magrabi F, Coiera E. Digital health for climate change mitigation and response: a scoping review. J Am Med Inform Assoc 2022; 29:2140-2152. [PMID: 35960171 PMCID: PMC9667157 DOI: 10.1093/jamia/ocac134] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Revised: 06/23/2022] [Accepted: 07/28/2022] [Indexed: 11/13/2022] Open
Abstract
OBJECTIVE Climate change poses a major threat to the operation of global health systems, triggering large scale health events, and disrupting normal system operation. Digital health may have a role in the management of such challenges and in greenhouse gas emission reduction. This scoping review explores recent work on digital health responses and mitigation approaches to climate change. MATERIALS AND METHODS We searched Medline up to February 11, 2022, using terms for digital health and climate change. Included articles were categorized into 3 application domains (mitigation, infectious disease, or environmental health risk management), and 6 technical tasks (data sensing, monitoring, electronic data capture, modeling, decision support, and communication). The review was PRISMA-ScR compliant. RESULTS The 142 included publications reported a wide variety of research designs. Publication numbers have grown substantially in recent years, but few come from low- and middle-income countries. Digital health has the potential to reduce health system greenhouse gas emissions, for example by shifting to virtual services. It can assist in managing changing patterns of infectious diseases as well as environmental health events by timely detection, reducing exposure to risk factors, and facilitating the delivery of care to under-resourced areas. DISCUSSION While digital health has real potential to help in managing climate change, research remains preliminary with little real-world evaluation. CONCLUSION Significant acceleration in the quality and quantity of digital health climate change research is urgently needed, given the enormity of the global challenge.
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Affiliation(s)
- Hania Rahimi-Ardabili
- Centre for Health Informatics, Australian Institute of Health Innovation, Macquarie University, Macquarie Park, NSW, Australia
| | - Farah Magrabi
- Centre for Health Informatics, Australian Institute of Health Innovation, Macquarie University, Macquarie Park, NSW, Australia
| | - Enrico Coiera
- Centre for Health Informatics, Australian Institute of Health Innovation, Macquarie University, Macquarie Park, NSW, Australia
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12
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Thiam MM, Simac L, Fougère E, Forgeot C, Meurice L, Naud J, Le Strat Y, Caserio-Schönemann C. Expert consultation using the on-line Delphi method for the revision of syndromic groups compiled from emergency data (SOS Médecins and OSCOUR®) in France. BMC Public Health 2022; 22:1791. [PMID: 36131273 PMCID: PMC9494916 DOI: 10.1186/s12889-022-14157-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2022] [Accepted: 09/08/2022] [Indexed: 11/10/2022] Open
Abstract
Background Consultation data from emergency general practitioners known as SOS Médecins and emergency departments (ED) from OSCOUR® network to the French syndromic surveillance system SurSaUD® (Surveillance sanitaire des urgences et décès). These data are aggregated and monitored on a daily basis through groupings of one or more medical symptoms or diagnoses (“syndromic groups” (SG)). The objective of this study was to evaluate, revise and enrich the composition of SGs through a consensus of experts who contributed or have experience in syndromic surveillance. Methods Three rounds of a Delphi survey were organised, involving 15 volunteers from SOS Médecins and 64 ED physicians in the OSCOUR® network as well as 8 international epidemiologists. Thirty-four SOS Médecins and 40 OSCOUR® SGs covering major medical specialities were put to the experts, along with their diagnostic codes and their surveillance objectives. In each round, the experts could retain or reject the codes according to the surveillance objective. The panel could also put forward new diagnostic codes in the 1st round, included in subsequent rounds. Consensus was reached for a code if 80% of participants had chosen to keep it, or less than 20% to reject it. Results A total of 12 SOS Médecins doctors (80%), 30 ED doctors (47%) and 4 international experts (50%) participated in the three rounds. All of the SGs presented to the panel included 102 initial diagnostic codes and 73 additional codes for SOS Médecins, 272 initial diagnostic codes and 204 additional codes for OSCOUR®. At the end of the 3 rounds, 14 SOS Médecins (40%) and 11 OSCOUR® (28%) SGs achieved a consensus to maintain all of their diagnostic codes. Among these, indicators of winter seasonal surveillance (bronchiolitis and gastroenteritis) were included. Conclusion This study involved a panel of national experts with international representation and a good level of involvement throughout the survey. In the absence of a standard definition, the Delphi method has been shown to be useful in defining and validating syndromic surveillance indicators. Supplementary Information The online version contains supplementary material available at 10.1186/s12889-022-14157-x.
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Affiliation(s)
| | - Leslie Simac
- Regional Division, Santé Publique France, Saint-Maurice, France.
| | - Erica Fougère
- Regional Division, Santé Publique France, Saint-Maurice, France
| | - Cécile Forgeot
- Data Science Division, Santé Publique France, Saint-Maurice, France
| | - Laure Meurice
- Regional Division, Santé Publique France, Saint-Maurice, France
| | - Jérôme Naud
- Data Science Division, Santé Publique France, Saint-Maurice, France
| | - Yann Le Strat
- Data Science Division, Santé Publique France, Saint-Maurice, France
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13
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Love NK, Elliot AJ, Chalmers RM, Douglas A, Gharbia S, McCormick J, Hughes H, Morbey R, Oliver I, Vivancos R, Smith G. Impact of the COVID-19 pandemic on gastrointestinal infection trends in England, February-July 2020. BMJ Open 2022; 12:e050469. [PMID: 35314468 PMCID: PMC8968111 DOI: 10.1136/bmjopen-2021-050469] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/28/2023] Open
Abstract
OBJECTIVE To establish the impact of the first 6 months of the COVID-19 outbreak response on gastrointestinal (GI) infection trends in England. DESIGN Retrospective ecological study using routinely collected national and regional surveillance data from seven UK Health Security Agency coordinated laboratory, outbreak and syndromic surveillance systems using key dates of UK governmental policy change to assign phases for comparison between 2020 and historic data. RESULTS Decreases in GI illness activity were observed across all surveillance indicators as COVID-19 cases began to peak. Compared with the 5-year average (2015-2019), during the first 6 months of the COVID-19 response, there was a 52% decrease in GI outbreaks reported (1544 vs 3208 (95% CI 2938 to 3478)) and a 34% decrease in laboratory confirmed cases (27 859 vs 42 495 (95% CI 40 068 to 44 922)). GI indicators began to rise during the first lockdown and lockdown easing, although all remained substantially lower than historic figures. Reductions in laboratory confirmed cases were observed across all age groups and both sexes, with geographical heterogeneity observed in diagnosis trends. Health seeking behaviour changed substantially, with attendances decreasing prior to lockdown across all indicators. CONCLUSIONS There has been a marked change in trends of GI infections in the context of the COVID-19 pandemic. The drivers of this change are likely to be multifactorial; while changes in health seeking behaviour, pressure on diagnostic services and surveillance system ascertainment have undoubtably played a role, there has likely been a true decrease in the incidence for some pathogens resulting from the control measures and restrictions implemented. This suggests that if some of these changes in behaviour such as improved hand hygiene were maintained, then we could potentially see sustained reductions in the burden of GI illness.
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Affiliation(s)
- Nicola K Love
- UK Health Security Agency, London, UK
- National Institute for Health Research Health Protection Research Unit in Gastrointestinal Infections, University of Liverpool, Liverpool, UK
| | - Alex J Elliot
- UK Health Security Agency, London, UK
- National Institute for Health Research Health Protection Research Unit in Gastrointestinal Infections, University of Liverpool, Liverpool, UK
- National Institute for Health Research Health Protection Research Unit in Emergency Preparedness and Response, King's College London, London, UK
| | - Rachel M Chalmers
- Cryptosporidium Reference Unit, Public Health Wales Microbiology and Health Protection, Singleton Hospital, Swansea, UK
| | | | | | | | - Helen Hughes
- UK Health Security Agency, London, UK
- National Institute for Health Research Health Protection Research Unit in Gastrointestinal Infections, University of Liverpool, Liverpool, UK
| | - Roger Morbey
- UK Health Security Agency, London, UK
- National Institute for Health Research Health Protection Research Unit in Emergency Preparedness and Response, King's College London, London, UK
| | - Isabel Oliver
- UK Health Security Agency, London, UK
- National Institute for Health Research Health Protection Research Unit in Behavioural Science and Evaluation, University of Bristol, Bristol, UK
| | - Roberto Vivancos
- UK Health Security Agency, London, UK
- National Institute for Health Research Health Protection Research Unit in Gastrointestinal Infections, University of Liverpool, Liverpool, UK
| | - Gillian Smith
- UK Health Security Agency, London, UK
- National Institute for Health Research Health Protection Research Unit in Gastrointestinal Infections, University of Liverpool, Liverpool, UK
- National Institute for Health Research Health Protection Research Unit in Emergency Preparedness and Response, King's College London, London, UK
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14
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Heymann DL, Legido-Quigley H. Two years of COVID-19: many lessons, but will we learn? EURO SURVEILLANCE : BULLETIN EUROPEEN SUR LES MALADIES TRANSMISSIBLES = EUROPEAN COMMUNICABLE DISEASE BULLETIN 2022; 27. [PMID: 35272747 PMCID: PMC8915402 DOI: 10.2807/1560-7917.es.2022.27.10.2200222] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Affiliation(s)
- David L Heymann
- London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Helena Legido-Quigley
- London School of Hygiene and Tropical Medicine, London, United Kingdom.,Saw Swee Hock School of Public Health, National University of Singapore, Singapore
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15
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Chan TC, Tang JH, Hsieh CY, Chen KJ, Yu TH, Tsai YT. Approaching precision public health by automated syndromic surveillance in communities. PLoS One 2021; 16:e0254479. [PMID: 34358241 PMCID: PMC8345830 DOI: 10.1371/journal.pone.0254479] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2020] [Accepted: 06/29/2021] [Indexed: 11/23/2022] Open
Abstract
Background Sentinel physician surveillance in communities has played an important role in detecting early signs of epidemics. The traditional approach is to let the primary care physician voluntarily and actively report diseases to the health department on a weekly basis. However, this is labor-intensive work, and the spatio-temporal resolution of the surveillance data is not precise at all. In this study, we built up a clinic-based enhanced sentinel surveillance system named “Sentinel plus” which was designed for sentinel clinics and community hospitals to monitor 23 kinds of syndromic groups in Taipei City, Taiwan. The definitions of those syndromic groups were based on ICD-10 diagnoses from physicians. Methods Daily ICD-10 counts of two syndromic groups including ILI and EV-like syndromes in Taipei City were extracted from Sentinel plus. A negative binomial regression model was used to couple with lag structure functions to examine the short-term association between ICD counts and meteorological variables. After fitting the negative binomial regression model, residuals were further rescaled to Pearson residuals. We then monitored these daily standardized Pearson residuals for any aberrations from July 2018 to October 2019. Results The results showed that daily average temperature was significantly negatively associated with numbers of ILI syndromes. The ozone and PM2.5 concentrations were significantly positively associated with ILI syndromes. In addition, daily minimum temperature, and the ozone and PM2.5 concentrations were significantly negatively associated with the EV-like syndromes. The aberrational signals detected from clinics for ILI and EV-like syndromes were earlier than the epidemic period based on outpatient surveillance defined by the Taiwan CDC. Conclusions This system not only provides warning signals to the local health department for managing the risks but also reminds medical practitioners to be vigilant toward susceptible patients. The near real-time surveillance can help decision makers evaluate their policy on a timely basis.
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Affiliation(s)
- Ta-Chien Chan
- Research Center for Humanities and Social Sciences, Academia Sinica, Taipei, Taiwan
- Institute of Public Health, School of Medicine, Yang-Ming Chiao Tung University, Taipei, Taiwan
- * E-mail:
| | - Jia-Hong Tang
- Institute of Statistical Science, Academia Sinica, Taipei, Taiwan
| | - Cheng-Yu Hsieh
- Research Center for Humanities and Social Sciences, Academia Sinica, Taipei, Taiwan
| | - Kevin J. Chen
- Department of Health, Taipei City Government, Taipei, Taiwan
| | - Tsan-Hua Yu
- Department of Health, Taipei City Government, Taipei, Taiwan
| | - Yu-Ting Tsai
- Department of Health, Taipei City Government, Taipei, Taiwan
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16
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Cohen R, Béchet S, Gelbert N, Frandji B, Vie Le Sage F, Thiebault G, Kochert F, Cahn-Sellem F, Werner A, Ouldali N, Levy C. New Approach to the Surveillance of Pediatric Infectious Diseases From Ambulatory Pediatricians in the Digital Era. Pediatr Infect Dis J 2021; 40:674-680. [PMID: 33657594 DOI: 10.1097/inf.0000000000003116] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
Abstract
BACKGROUND Many ambulatory networks in several countries have established syndromic surveillance systems to detect outbreaks of different illnesses. Here, we describe a new Pediatric and Ambulatory Research in Infectious diseases network that combined automated data extraction from the computers of primary care pediatricians. METHODS Pediatricians who used the same software, AxiSanté 5-Infansoft for electronic medical records were specially trained in infectious diseases, encouraged to comply with French treatments' recommendations, use of point-of-care tests and vaccination guidelines. Infectious disease diagnoses in children <16 years old in the records triggered automatic data extraction of complete records. A quality control process and external validation were developed. RESULTS From September 2017 to February 2020, 107 pediatricians enrolled 57,806 children (mean age 2.9 ± 2.6 years at diagnosis) with at least one infectious disease diagnosis among those followed by the network. Among the 118,193 diagnoses, the most frequent were acute otitis media (n = 44,924, 38.0%), tonsillopharyngitis (n = 13,334, 11.3%), gastroenteritis (n = 12,367, 10.5%), influenza (n = 11,062, 9.4%), bronchiolitis (n = 10,531, 8.9%), enteroviral infections (n = 8474, 7.2%) and chickenpox (n = 6857, 5.8%). A rapid diagnostic test was performed in 84.7% of cases of tonsillopharyngitis and was positive in 44%. The antibiotic recommendations from French guidelines were strictly followed: amoxicillin was the most prescribed antibiotic and less than 10% of presumed viral infections were treated. CONCLUSIONS This "tailor-made" network set up with quality controls and external validation represents a new approach to the surveillance of pediatric infectious diseases in the digital era and could highly optimize pediatric practices.
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Affiliation(s)
- Robert Cohen
- From the AFPA, Association Française de Pédiatrie Ambulatoire, Orléans, France
- ACTIV, Association Clinique Thérapeutique Infantile du Val de Marne
- Université Paris Est, IMRB-GRC GEMINI, Créteil, France
- Clinical Research Center (CRC), Centre Hospitalier Intercommunal de Créteil, Créteil, France
| | - Stéphane Béchet
- ACTIV, Association Clinique Thérapeutique Infantile du Val de Marne
| | - Nathalie Gelbert
- From the AFPA, Association Française de Pédiatrie Ambulatoire, Orléans, France
| | | | | | - Georges Thiebault
- From the AFPA, Association Française de Pédiatrie Ambulatoire, Orléans, France
| | - Fabienne Kochert
- From the AFPA, Association Française de Pédiatrie Ambulatoire, Orléans, France
| | | | - Andreas Werner
- From the AFPA, Association Française de Pédiatrie Ambulatoire, Orléans, France
| | - Naim Ouldali
- ACTIV, Association Clinique Thérapeutique Infantile du Val de Marne
- Unité d'épidémiologie clinique, Assistance Publique-Hôpitaux de Paris, Hôpital Robert Debré, ECEVE INSERM UMR 1123, Paris, France
| | - Corinne Levy
- From the AFPA, Association Française de Pédiatrie Ambulatoire, Orléans, France
- ACTIV, Association Clinique Thérapeutique Infantile du Val de Marne
- Université Paris Est, IMRB-GRC GEMINI, Créteil, France
- Clinical Research Center (CRC), Centre Hospitalier Intercommunal de Créteil, Créteil, France
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Dórea FC, Revie CW. Data-Driven Surveillance: Effective Collection, Integration, and Interpretation of Data to Support Decision Making. Front Vet Sci 2021; 8:633977. [PMID: 33778039 PMCID: PMC7994248 DOI: 10.3389/fvets.2021.633977] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2020] [Accepted: 02/18/2021] [Indexed: 11/20/2022] Open
Abstract
The biggest change brought about by the “era of big data” to health in general, and epidemiology in particular, relates arguably not to the volume of data encountered, but to its variety. An increasing number of new data sources, including many not originally collected for health purposes, are now being used for epidemiological inference and contextualization. Combining evidence from multiple data sources presents significant challenges, but discussions around this subject often confuse issues of data access and privacy, with the actual technical challenges of data integration and interoperability. We review some of the opportunities for connecting data, generating information, and supporting decision-making across the increasingly complex “variety” dimension of data in population health, to enable data-driven surveillance to go beyond simple signal detection and support an expanded set of surveillance goals.
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Affiliation(s)
- Fernanda C Dórea
- Department of Disease Control and Epidemiology, National Veterinary Institute, Uppsala, Sweden
| | - Crawford W Revie
- Computer and Information Sciences, University of Strathclyde, Glasgow, United Kingdom
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18
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Radford AD, Singleton DA, Jewell C, Appleton C, Rowlingson B, Hale AC, Cuartero CT, Newton R, Sánchez-Vizcaíno F, Greenberg D, Brant B, Bentley EG, Stewart JP, Smith S, Haldenby S, Noble PJM, Pinchbeck GL. Outbreak of Severe Vomiting in Dogs Associated with a Canine Enteric Coronavirus, United Kingdom. Emerg Infect Dis 2021; 27:517-528. [PMID: 33496240 PMCID: PMC7853541 DOI: 10.3201/eid2702.202452] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
Abstract
The lack of population health surveillance for companion animal populations leaves them vulnerable to the effects of novel diseases without means of early detection. We present evidence on the effectiveness of a system that enabled early detection and rapid response a canine gastroenteritis outbreak in the United Kingdom. In January 2020, prolific vomiting among dogs was sporadically reported in the United Kingdom. Electronic health records from a nationwide sentinel network of veterinary practices confirmed a significant increase in dogs with signs of gastroenteric disease. Male dogs and dogs living with other vomiting dogs were more likely to be affected. Diet and vaccination status were not associated with the disease; however, a canine enteric coronavirus was significantly associated with illness. The system we describe potentially fills a gap in surveillance in neglected populations and could provide a blueprint for other countries.
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Marbus SD, van der Hoek W, van Dissel JT, van Gageldonk-Lafeber AB. Experience of establishing severe acute respiratory surveillance in the Netherlands: Evaluation and challenges. PUBLIC HEALTH IN PRACTICE 2020; 1:100014. [PMID: 34171043 PMCID: PMC7260511 DOI: 10.1016/j.puhip.2020.100014] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2019] [Revised: 05/02/2020] [Accepted: 05/12/2020] [Indexed: 11/24/2022] Open
Abstract
The 2009 influenza A (H1N1) pandemic prompted the World Health Organization (WHO) to recommend countries to establish a national severe acute respiratory infections (SARI) surveillance system for preparedness and emergency response. However, setting up or maintaining a robust SARI surveillance system has been challenging. Similar to other countries, surveillance data on hospitalisations for SARI in the Netherlands are still limited, in contrast to the robust surveillance data in primary care. The objective of this narrative review is to provide an overview, evaluation, and challenges of already available surveillance systems or datasets in the Netherlands, which might be used for near real-time surveillance of severe respiratory infections. Seven available surveillance systems or datasets in the Netherlands were reviewed. The evaluation criteria, including data quality, timeliness, representativeness, simplicity, flexibility, acceptability and stability were based on United States Centers for Disease Control and Prevention (CDC) and European Centre for Disease Prevention and Control (ECDC) guidelines for public health surveillance. We added sustainability as additional evaluation criterion. The best evaluated surveillance system or dataset currently available for SARI surveillance is crude mortality monitoring, although it lacks specificity. In contrast to influenza-like illness (ILI) in primary care, there is currently no gold standard for SARI surveillance in the Netherlands. Based on our experience with sentinel SARI surveillance, a fully or semi-automated, passive surveillance system seems most suited for a sustainable SARI surveillance system. An important future challenge remains integrating SARI surveillance into existing hospital programs in order to make surveillance data valuable for public health, as well as hospital quality of care management and individual patient care. Multiple surveillance systems or datasets are available in the Netherlands with potential use for SARI surveillance. There is currently no gold standard for SARI surveillance in the Netherlands. A potential sustainable SARI surveillance system for the long-term is a fully or semi-automated, passive surveillance system. SARI surveillance data should be valuable for both public health and individual patient care. An important future challenge remains integrating SARI surveillance into existing hospital programs.
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Affiliation(s)
- S D Marbus
- Centre for Infectious Disease Control, National Institute for Public Health and the Environment, Bilthoven, the Netherlands
| | - W van der Hoek
- Centre for Infectious Disease Control, National Institute for Public Health and the Environment, Bilthoven, the Netherlands
| | - J T van Dissel
- Centre for Infectious Disease Control, National Institute for Public Health and the Environment, Bilthoven, the Netherlands.,Department of Infectious Diseases and Internal Medicine, Leiden University Medical Center, Leiden, the Netherlands
| | - A B van Gageldonk-Lafeber
- Centre for Infectious Disease Control, National Institute for Public Health and the Environment, Bilthoven, the Netherlands
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Degeling C, Chen G, Gilbert GL, Brookes V, Thai T, Wilson A, Johnson J. Changes in public preferences for technologically enhanced surveillance following the COVID-19 pandemic: a discrete choice experiment. BMJ Open 2020; 10:e041592. [PMID: 33208337 PMCID: PMC7677347 DOI: 10.1136/bmjopen-2020-041592] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/12/2020] [Revised: 10/18/2020] [Accepted: 10/29/2020] [Indexed: 01/06/2023] Open
Abstract
OBJECTIVES As governments attempt to navigate a path out of COVID-19 restrictions, robust evidence is essential to inform requirements for public acceptance of technologically enhanced communicable disease surveillance systems. We examined the value of core surveillance system attributes to the Australian public, before and during the early stages of the current pandemic. DESIGN A discrete choice experiment was conducted in Australia with a representative group of respondents, before and after the WHO declared COVID-19 a Public Health Emergency of International Concern. We identified and investigated the relative importance of seven attributes associated with technologically enhanced disease surveillance: respect for personal autonomy; privacy/confidentiality; data certainty/confidence; data security; infectious disease mortality prevention; infectious disease morbidity prevention; and attribution of (causal) responsibility. Specifically, we explored how the onset of the COVID-19 outbreak influenced participant responses. SETTING AND PARTICIPANTS 2008 Australians (general public) completed the experiment: 793 before COVID-19 outbreak onset (mean age 45.9 years, 50.2% male) and 1215 after onset (mean age 47.2 years, 49% male). RESULTS All seven attributes significantly influenced respondents' preferences for communicable disease surveillance systems. After onset, participants demonstrated greater preference for a surveillance system that could prevent a higher number of illnesses and deaths, and were less concerned about their personal autonomy. However, they also increased their preference for a system with high data security. CONCLUSIONS Public acceptance of technology-based communicable disease surveillance is situation dependent. During an epidemic, there is likely to be greater tolerance of technologically enhanced disease surveillance systems that result in restrictions on personal activity if such systems can prevent high morbidity and mortality. However, this acceptance of lower personal autonomy comes with an increased requirement to ensure data security. These findings merit further research as the pandemic unfolds and strategies are put in place that enable individuals and societies to live with SARS-CoV-2 endemicity.
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Affiliation(s)
- Chris Degeling
- Australian Centre for Health Engagement, Evidence and Values, University of Wollongong, Wollongong, New South Wales, Australia
| | - Gang Chen
- Centre for Health Economics, Monash Business School, Monash University, Caufield East, Victoria, Australia
| | - Gwendolyn L Gilbert
- Sydney Health Ethics, Sydney School of Public Health, The University of Sydney Faculty of Medicine and Health, Sydney, New South Wales, Australia
- Marie Bashir Institute for Emerging Infectious Disease and Biosecurity, The University of Sydney, Sydney, New South Wales, Australia
| | - Victoria Brookes
- School of Animal and Veterinary Sciences, Charles Sturt University, Wagga Wagga, New South Wales, Australia
| | - Thi Thai
- Centre for Health Economics, Monash Business School, Monash University, Caufield East, Victoria, Australia
| | - Andrew Wilson
- Menzies Centre for Health Policy, The University of Sydney, Sydney, New South Wales, Australia
| | - Jane Johnson
- Marie Bashir Institute for Emerging Infectious Disease and Biosecurity, The University of Sydney, Sydney, New South Wales, Australia
- Department of Philosophy, Macquarie University, Sydney, New South Wales, Australia
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