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Wilde H, Tomlinson C, Mateen BA, Selby D, Kanthimathinathan HK, Ramnarayan P, Du Pre P, Johnson M, Pathan N, Gonzalez-Izquierdo A, Lai AG, Gurdasani D, Pagel C, Denaxas S, Vollmer S, Brown K. Hospital admissions linked to SARS-CoV-2 infection in children and adolescents: cohort study of 3.2 million first ascertained infections in England. BMJ 2023; 382:e073639. [PMID: 37407076 PMCID: PMC10318942 DOI: 10.1136/bmj-2022-073639] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 05/18/2023] [Indexed: 07/07/2023]
Abstract
OBJECTIVE To describe hospital admissions associated with SARS-CoV-2 infection in children and adolescents. DESIGN Cohort study of 3.2 million first ascertained SARS-CoV-2 infections using electronic health care record data. SETTING England, July 2020 to February 2022. PARTICIPANTS About 12 million children and adolescents (age <18 years) who were resident in England. MAIN OUTCOME MEASURES Ascertainment of a first SARS-CoV-2 associated hospital admissions: due to SARS-CoV-2, with SARS-CoV-2 as a contributory factor, incidental to SARS-CoV-2 infection, and hospital acquired SARS-CoV-2. RESULTS 3 226 535 children and adolescents had a recorded first SARS-CoV-2 infection during the observation period, and 29 230 (0.9%) infections involved a SARS-CoV-2 associated hospital admission. The median length of stay was 2 (interquartile range 1-4) days) and 1710 of 29 230 (5.9%) SARS-CoV-2 associated admissions involved paediatric critical care. 70 deaths occurred in which covid-19 or paediatric inflammatory multisystem syndrome was listed as a cause, of which 55 (78.6%) were in participants with a SARS-CoV-2 associated hospital admission. SARS-CoV-2 was the cause or a contributory factor in 21 000 of 29 230 (71.8%) participants who were admitted to hospital and only 380 (1.3%) participants acquired infection as an inpatient and 7855 (26.9%) participants were admitted with incidental SARS-CoV-2 infection. Boys, younger children (<5 years), and those from ethnic minority groups or areas of high deprivation were more likely to be admitted to hospital (all P<0.001). The covid-19 vaccination programme in England has identified certain conditions as representing a higher risk of admission to hospital with SARS-CoV-2: 11 085 (37.9%) of participants admitted to hospital had evidence of such a condition, and a further 4765 (16.3%) of participants admitted to hospital had a medical or developmental health condition not included in the vaccination programme's list. CONCLUSIONS Most SARS-CoV-2 associated hospital admissions in children and adolescents in England were due to SARS-CoV-2 or SARS-CoV-2 was a contributory factor. These results should inform future public health initiatives and research.
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Affiliation(s)
- Harrison Wilde
- Department of Statistics, University of Warwick, Warwick, UK
- University College London (UCL) Institute of Health Informatics, UCL, London, UK
| | - Christopher Tomlinson
- University College London (UCL) Institute of Health Informatics, UCL, London, UK
- UCL UK Research and Innovation Centre for Doctoral Training in AI-enabled Healthcare Systems, UCL, London, UK
- University College London Hospitals Biomedical Research Centre, UCL, London, UK
| | - Bilal A Mateen
- University College London (UCL) Institute of Health Informatics, UCL, London, UK
- University College London Hospitals Biomedical Research Centre, UCL, London, UK
- Wellcome Trust, London, UK
| | - David Selby
- Department for Data Science and its Applications, German Research Centre for Artificial Intelligence (DFKI), Kaiserslautern, Germany
- Department of Computer Science, TU Kaiserslautern, Kaiserslautern, Germany
| | | | - Padmanabhan Ramnarayan
- Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London UK Imperial College London, London, UK
| | - Pascale Du Pre
- Biomedical Research Centre, Great Ormond Street Hospital for Children, London, UK
| | - Mae Johnson
- Biomedical Research Centre, Great Ormond Street Hospital for Children, London, UK
| | - Nazima Pathan
- University Department of Paediatrics, Cambridge University, Cambridge, UK
| | | | - Alvina G Lai
- University College London (UCL) Institute of Health Informatics, UCL, London, UK
| | - Deepti Gurdasani
- William Harvey Institute, Queen Mary University of London, London, UK
- Kirby Institute, University of New South Wales, Sydney, NSW, Australia
| | | | - Spiros Denaxas
- University College London (UCL) Institute of Health Informatics, UCL, London, UK
- University College London Hospitals Biomedical Research Centre, UCL, London, UK
| | - Sebastian Vollmer
- Department for Data Science and its Applications, German Research Centre for Artificial Intelligence (DFKI), Kaiserslautern, Germany
- Department of Computer Science, TU Kaiserslautern, Kaiserslautern, Germany
| | - Katherine Brown
- Institute of Cardiovascular Science, UCL, London, UK
- Biomedical Research Centre, Great Ormond Street Hospital for Children, London, UK
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Lee S, Doktorchik C, Martin EA, D'Souza AG, Eastwood C, Shaheen AA, Naugler C, Lee J, Quan H. Electronic Medical Record-Based Case Phenotyping for the Charlson Conditions: Scoping Review. JMIR Med Inform 2021; 9:e23934. [PMID: 33522976 PMCID: PMC7884219 DOI: 10.2196/23934] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2020] [Revised: 11/20/2020] [Accepted: 12/05/2020] [Indexed: 12/16/2022] Open
Abstract
Background Electronic medical records (EMRs) contain large amounts of rich clinical information. Developing EMR-based case definitions, also known as EMR phenotyping, is an active area of research that has implications for epidemiology, clinical care, and health services research. Objective This review aims to describe and assess the present landscape of EMR-based case phenotyping for the Charlson conditions. Methods A scoping review of EMR-based algorithms for defining the Charlson comorbidity index conditions was completed. This study covered articles published between January 2000 and April 2020, both inclusive. Embase (Excerpta Medica database) and MEDLINE (Medical Literature Analysis and Retrieval System Online) were searched using keywords developed in the following 3 domains: terms related to EMR, terms related to case finding, and disease-specific terms. The manuscript follows the Preferred Reporting Items for Systematic reviews and Meta-analyses extension for Scoping Reviews (PRISMA) guidelines. Results A total of 274 articles representing 299 algorithms were assessed and summarized. Most studies were undertaken in the United States (181/299, 60.5%), followed by the United Kingdom (42/299, 14.0%) and Canada (15/299, 5.0%). These algorithms were mostly developed either in primary care (103/299, 34.4%) or inpatient (168/299, 56.2%) settings. Diabetes, congestive heart failure, myocardial infarction, and rheumatology had the highest number of developed algorithms. Data-driven and clinical rule–based approaches have been identified. EMR-based phenotype and algorithm development reflect the data access allowed by respective health systems, and algorithms vary in their performance. Conclusions Recognizing similarities and differences in health systems, data collection strategies, extraction, data release protocols, and existing clinical pathways is critical to algorithm development strategies. Several strategies to assist with phenotype-based case definitions have been proposed.
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Affiliation(s)
- Seungwon Lee
- Centre for Health Informatics, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.,Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.,Alberta Health Services, Calgary, AB, Canada.,Data Intelligence for Health Lab, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Chelsea Doktorchik
- Centre for Health Informatics, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.,Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Elliot Asher Martin
- Centre for Health Informatics, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.,Alberta Health Services, Calgary, AB, Canada
| | - Adam Giles D'Souza
- Centre for Health Informatics, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.,Alberta Health Services, Calgary, AB, Canada
| | - Cathy Eastwood
- Centre for Health Informatics, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.,Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Abdel Aziz Shaheen
- Centre for Health Informatics, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.,Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.,Department of Medicine, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Christopher Naugler
- Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.,Department of Pathology and Laboratory Medicine, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Joon Lee
- Centre for Health Informatics, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.,Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.,Data Intelligence for Health Lab, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.,Department of Cardiac Sciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Hude Quan
- Centre for Health Informatics, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.,Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
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