Evidence map›Paper›PMID 39311356›Full record

ArticleEpidemiologia (Basel, Switzerland)2024

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

Laura Merson, Sara Duque, Esteban Garcia-Gallo, Trokon Omarley Yeabah, Jamie Rylance, Janet Diaz, Antoine Flahault, Isaric Clinical Characterisation Group

Abstract read
In one paragraph

Article in Epidemiologia (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Laura MersonISARIC, Pandemic Sciences Institute, University of Oxford, Oxford OX37LF, UK.ORCID 0000-0002-4168-1960
Sara DuqueISARIC, Pandemic Sciences Institute, University of Oxford, Oxford OX37LF, UK.
Esteban Garcia-GalloISARIC, Pandemic Sciences Institute, University of Oxford, Oxford OX37LF, UK.ORCID 0000-0002-8030-9985
Trokon Omarley YeabahNational Public Health Institute, Monrovia 1000, Liberia.ORCID 0000-0002-3790-8042
Jamie RylanceHealth Emergencies Program, World Health Organization, 1211 Geneva, Switzerland.ORCID 0000-0002-2323-3611
Janet DiazHealth Emergencies Program, World Health Organization, 1211 Geneva, Switzerland.
Antoine FlahaultInstitute of Global Health, Faculty of Medicine, University of Geneva, 1202 Geneva, Switzerland.ORCID 0000-0002-6957-2977
Isaric Clinical Characterisation GroupISARIC, Pandemic Sciences Institute, University of Oxford, Oxford OX37LF, UK.

Funding

Gates Foundation INV-063472UK Foreign, Commonwealth & Development Office 301542-403Wellcome TrustWellcome Trust 225288/Z/22/ZWellcome Trust 303666/Z/23/ZWorld Health Organization 001
6 · The paper itself

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.

Indexed as

clinical epidemiologycommon data elementsdata collectiondata managementinfectious disease outbreaksISARIC

Identifiers

PMID39311356
PMCPMC11417906

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.