Optimising Clinical Epidemiology in Disease Outbreaks: Analysis of ISARIC-WHO COVID-19 Case Report Form Utilisation

Standardised forms for capturing clinical data promote consistency in data collection and analysis across research sites, enabling faster, higher-quality evidence generation. ISARIC and the World Health Organization have developed case report forms (CRFs) for the clinical characterisation of several infectious disease outbreaks. To improve the design and quality of future forms, we analysed the inclusion and completion rates of the 243 fields on the ISARIC-WHO COVID-19 CRF. Data from 42 diverse collaborations, covering 1886 hospitals and 950,064 patients, were analysed. A mean of 129.6 fields (53%) were included in the adapted CRFs implemented across the sites. Consistent patterns of field inclusion and completion aligned with globally recognised research priorities in outbreaks of novel infectious diseases. Outcome status was the most highly included (95.2%) and completed (89.8%) field, followed by admission demographics (79.1% and 91.6%), comorbidities (77.9% and 79.0%), signs and symptoms (68.9% and 78.4%), and vitals (70.3% and 69.1%). Mean field completion was higher in severe patients (70.2%) than in all patients (61.6%). The results reveal how clinical characterisation CRFs can be streamlined to reduce data collection time, including the modularisation of CRFs, to offer a choice of data volume collection and the separation of critical care interventions. This data-driven approach to designing CRFs enhances the efficiency of data collection to inform patient care and public health response.

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Publication Details

Journal
Epidemiologia
Published
2024-08-30
DOI
https://doi.org/10.3390/epidemiologia5030039
Citations
1
Primary Topic
Machine Learning in Healthcare
Type
article
Field-Weighted Citation Impact
0.35

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article

Optimising Clinical Epidemiology in Disease Outbreaks: Analysis of ISARIC-WHO COVID-19 Case Report Form Utilisation

Trokon Omarley Yeabah, Esteban García-Gallo, Janet Dı́az, Jamie Rylance et al.
1 citations
Epidemiologia
Machine Learning in Healthcare
0.35
article

Optimising Clinical Epidemiology in Disease Outbreaks: Analysis of ISARIC-WHO COVID-19 Case Report Form Utilisation

Trokon Omarley Yeabah, Esteban García-Gallo, Janet Dı́az, Jamie Rylance, Sara Duque, Laura Merson, Antoine Flahault
article en
1 citations

Abstract

Standardised forms for capturing clinical data promote consistency in data collection and analysis across research sites, enabling faster, higher-quality evidence generation. ISARIC and the World Health Organization have developed case report forms (CRFs) for the clinical characterisation of several infectious disease outbreaks. To improve the design and quality of future forms, we analysed the inclusion and completion rates of the 243 fields on the ISARIC-WHO COVID-19 CRF. Data from 42 diverse collaborations, covering 1886 hospitals and 950,064 patients, were analysed. A mean of 129.6 fields (53%) were included in the adapted CRFs implemented across the sites. Consistent patterns of field inclusion and completion aligned with globally recognised research priorities in outbreaks of novel infectious diseases. Outcome status was the most highly included (95.2%) and completed (89.8%) field, followed by admission demographics (79.1% and 91.6%), comorbidities (77.9% and 79.0%), signs and symptoms (68.9% and 78.4%), and vitals (70.3% and 69.1%). Mean field completion was higher in severe patients (70.2%) than in all patients (61.6%). The results reveal how clinical characterisation CRFs can be streamlined to reduce data collection time, including the modularisation of CRFs, to offer a choice of data volume collection and the separation of critical care interventions. This data-driven approach to designing CRFs enhances the efficiency of data collection to inform patient care and public health response.

EpidemiologiaVol. 5(3)
University of Geneva (CH), Universidad de La Sabana (CO), University of Oxford (GB), World Health Organization (CH), National Public Health Institute of Liberia (LR)
Bill and Melinda Gates Foundation, Wellcome Trust, Sunnybrook Research Institute, Medical Research Charities Group, European Federation of Pharmaceutical Industries and Associations, National Institute for Health and Care Research, European Commission, University College Dublin, Institut National de la Santé et de la Recherche Médicale, Ministero della Salute, Conselho Nacional de Desenvolvimento Científico e Tecnológico, Kementerian Pendidikan Nasional, Norges Forskningsråd, University of Cape Town, All-India Institute of Medical Sciences, Kementerian Kesihatan Malaysia, Prince Charles Hospital Foundation, National Institutes of Health, Canadian Institutes of Health Research, Medical Research Council, Instituto de Salud Carlos III, JST-Mirai Program
Openalex Percentile: Top 34%
Machine Learning in Healthcare
0.35
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