BibTex format
@article{Merson:2024:10.3390/epidemiologia5030039,
author = {Merson, L and Duque, S and Garcia-Gallo, E and Yeabah, TO and Rylance, J and Diaz, J and Flahault, A and Abdalasalam, S and Abdalhadi, AA and Abdalla, W and Abdalla, NR and Abdalrheem, AH and Abdalsalam, A and Abdeewi, S and Abdelgaum, EH and Abdelhalim, M and Abdelkabir, M and Abdukahil, SA and Abdulbaqi, LA and Abdulhamid, W and Abdulhamid, S and Abdulkadir, NN and Abdulwahed, E and Abdunabi, R and Abe, R and Abel, L and Abodina, AM and Abouelmagd, K and Abrous, A and Abu, Jabal K and Abu, Salah N and Abukhalaf, SMA and Abusalama, A and Abuzaid, TA and Acharya, S and Acker, A and Adem, S and Ademnou, M and Adewhajah, F and Adhikari, NKJ and Adrião, D and Yaw, Adu S and Afum-Adjei, Awuah A and Agbogbatey, M and Ageel, SA and Ahmed, MM and Ahmed, AM and Ahmed, S and Alaraji, ZA and Ahmed, Elhefnawy Enan A and Abdelhamid, Ahmed Khalil R and Ahmed, Mohamed Abdelaziz AM and Aiello, M and Ainscough, K and Airlangga, E and Aisa, T and Aisha, A and Aisha, B and Hssain, AA and Akimoto, T and },
doi = {10.3390/epidemiologia5030039},
journal = {Epidemiologia},
pages = {557--580},
title = {Optimising Clinical Epidemiology in Disease Outbreaks: Analysis of ISARIC-WHO COVID-19 Case Report Form Utilisation},
url = {http://dx.doi.org/10.3390/epidemiologia5030039},
volume = {5},
year = {2024}
}
RIS format (EndNote, RefMan)
TY - JOUR
AB - 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.
AU - Merson,L
AU - Duque,S
AU - Garcia-Gallo,E
AU - Yeabah,TO
AU - Rylance,J
AU - Diaz,J
AU - Flahault,A
AU - Abdalasalam,S
AU - Abdalhadi,AA
AU - Abdalla,W
AU - Abdalla,NR
AU - Abdalrheem,AH
AU - Abdalsalam,A
AU - Abdeewi,S
AU - Abdelgaum,EH
AU - Abdelhalim,M
AU - Abdelkabir,M
AU - Abdukahil,SA
AU - Abdulbaqi,LA
AU - Abdulhamid,W
AU - Abdulhamid,S
AU - Abdulkadir,NN
AU - Abdulwahed,E
AU - Abdunabi,R
AU - Abe,R
AU - Abel,L
AU - Abodina,AM
AU - Abouelmagd,K
AU - Abrous,A
AU - Abu,Jabal K
AU - Abu,Salah N
AU - Abukhalaf,SMA
AU - Abusalama,A
AU - Abuzaid,TA
AU - Acharya,S
AU - Acker,A
AU - Adem,S
AU - Ademnou,M
AU - Adewhajah,F
AU - Adhikari,NKJ
AU - Adrião,D
AU - Yaw,Adu S
AU - Afum-Adjei,Awuah A
AU - Agbogbatey,M
AU - Ageel,SA
AU - Ahmed,MM
AU - Ahmed,AM
AU - Ahmed,S
AU - Alaraji,ZA
AU - Ahmed,Elhefnawy Enan A
AU - Abdelhamid,Ahmed Khalil R
AU - Ahmed,Mohamed Abdelaziz AM
AU - Aiello,M
AU - Ainscough,K
AU - Airlangga,E
AU - Aisa,T
AU - Aisha,A
AU - Aisha,B
AU - Hssain,AA
AU - Akimoto,T
AU - Akmal,E
AU - Akwani,C
AU - Qasim,EA
AU - Alaa,Y
AU - Alajeeli,A
AU - Alali,A
AU - Alalqam,R
AU - Alameen,AM
AU - Al-Aquily,M
AU - Alaraji,ZA
AU - Albakry,K
AU - Albatni,S
AU - Alberti,A
AU - Al-Dabbous,T
AU - Aldhalia,A
AU - Aldoukali,A
AU - Alessi,M
AU - Alex,B
AU - Alexandre,K
AU - Al-Fares,A
AU - Alflite,A
AU - Alfoudri,H
AU - Alfroukh,KMA
AU - Alhadad,Q
AU - Alhaddad,HS
AU - Alhasan,MKMA
AU - Alhouri,AN
AU - Alhouri,H
AU - Ali,A
AU - Ali,MTM
AU - Ali,I
AU - Abbas,SA
AU - Abdelghafar,YA
AU - Shah,NA
AU - Alidjnou,KE
AU - Aljadi,M
AU - Aljamal,S
AU - Alkahlout,M
AU - Alkaraki,KJK
AU - Alkaseek,A
DO - 10.3390/epidemiologia5030039
EP - 580
PY - 2024///
SP - 557
TI - Optimising Clinical Epidemiology in Disease Outbreaks: Analysis of ISARIC-WHO COVID-19 Case Report Form Utilisation
T2 - Epidemiologia
UR - http://dx.doi.org/10.3390/epidemiologia5030039
VL - 5
ER -