Accès ouvert déclaré 2021 article

Assessing the capacity of symptom scores to predict COVID-19 positivity in Nigeria: a national derivation and validation cohort study

Kelly Elimian, Olaolu Moses Aderinola, Jack Gibson, Puja R Myles, Chinwe Lucia Ochu, Carina J. King, Tochi Joy Okwor, Giulia Gaudenzi, Adebola Olayinka, Habib Garba Zaiyad, Cornelius Ohonsi, Blessing Itohan Ebhodaghe, Chioma Dan-Nwafor, William E. Nwachukwu, Ismail Adeshina Abdus-Salam, Oluwatosin Wuraola Akande, Olanrewaju Falodun, Chinedu Chukwujekwu Arinze, Chidiebere Ezeokafor, Jafiya Abubakar, Anastacia Okwudili Ojimba, John Tunde Aremu, Emmanuel Joseph, Abimbola Bowale, Bamidele Paul Mutiu, Babatunde Akeem Saka, Arisekola Jinadu, Khadeejah Liman Hamza, Christian C. Ibeh, Shaibu Oricha Bello, Michael Chiemeli Asuzu, Nwando Mba, John Oladejo, Elsie A. Ilori, Tobias Alfvén, Ehimario Uche Igumbor, Chikwe A Ihekweazu

6Citations signalées, ce qui n’est pas une note de qualité

20Institutions déclarées

4Pays d’affiliation déclarés

Rattachement africain : se, Nigéria, gb, Afrique du Sud. Niveau de preuve : code pays fourni par la source.

Le résumé fourni par la source

OBJECTIVES: This study aimed to develop and validate a symptom prediction tool for COVID-19 test positivity in Nigeria. DESIGN: Predictive modelling study. SETTING: All Nigeria States and the Federal Capital Territory. PARTICIPANTS: A cohort of 43 221 individuals within the national COVID-19 surveillance dataset from 27 February to 27 August 2020. Complete dataset was randomly split into two equal halves: derivation and validation datasets. Using the derivation dataset (n=21 477), backward multivariable logistic regression approach was used to identify symptoms positively associated with COVID-19 positivity (by real-time PCR) in children (≤17 years), adults (18-64 years) and elderly (≥65 years) patients separately. OUTCOME MEASURES: Weighted statistical and clinical scores based on beta regression coefficients and clinicians' judgements, respectively. Using the validation dataset (n=21 744), area under the receiver operating characteristic curve (AUROC) values were used to assess the predictive capacity of individual symptoms, unweighted score and the two weighted scores. RESULTS: Overall, 27.6% of children (4415/15 988), 34.6% of adults (9154/26 441) and 40.0% of elderly (317/792) that had been tested were positive for COVID-19. Best individual symptom predictor of COVID-19 positivity was loss of smell in children (AUROC 0.56, 95% CI 0.55 to 0.56), either fever or cough in adults (AUROC 0.57, 95% CI 0.56 to 0.58) and difficulty in breathing in the elderly (AUROC 0.53, 95% CI 0.48 to 0.58) patients. In children, adults and the elderly patients, all scoring approaches showed similar predictive performance. CONCLUSIONS: The predictive capacity of various symptom scores for COVID-19 positivity was poor overall. However, the findings could serve as an advocacy tool for more investments in resources for capacity strengthening of molecular testing for COVID-19 in Nigeria.

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.

Le contrôle bibliographique ouvert

DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.

Titre Crossref: Assessing the capacity of symptom scores to predict COVID-19 positivity in Nigeria: a national derivation and validation cohort study
Date Crossref: 01/09/2021
Éditeur: BMJ
Type: journal-article

Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude, et il ne compte pas comme une seconde source scientifique indépendante.

Où se fait cette recherche

Karolinska Institutet Department of Global Public Health pays non établi dans la notice
Université ou école supérieure
Nigeria Centre for Disease Control Abuja, Nigéria (code pays fourni par la source)
Organisme public
University of Nottingham Department of Epidemiology and Public Health pays non établi dans la notice
Université ou école supérieure
World Health Organization - Nigeria Abuja, Nigéria (code pays fourni par la source)
Organisme public
University of Abuja Teaching Hospital University of Abuja Teaching Hospital, Nigéria (code pays fourni par la source)
Établissement de santé
Lagos State University Lagos State University, Nigéria (code pays fourni par la source)
Université ou école supérieure
Lagos State Ministry of Health Lagos State Ministry of Health, Nigéria (code pays fourni par la source)
Organisme public
University of Ilorin Teaching Hospital Department of Epidemiology and Community Health University of Ilorin Teaching Hospital, Nigéria (code pays fourni par la source)
Établissement de santé
National Hospital Abuja Department of Internal Medicine National Hospital Abuja, Nigéria (code pays fourni par la source)
Établissement de santé
National Agency for the Control of AIDS Abuja, Nigéria (code pays fourni par la source)
Organisme public
Federal Medical Centre Nigéria (code pays fourni par la source)
Établissement de santé
Gombe State University Gombe State University, Nigéria (code pays fourni par la source)
Université ou école supérieure

Department of Global Public Health — Karolinska Institutet, Nigeria Centre for Disease Control (Abuja, Nigéria) et Department of Epidemiology and Public Health — University of Nottingham, avec 9 autres affiliations. Pays d’affiliation : Nigéria, Afrique du Sud.

Une affiliation ne permet pas de déduire la nationalité d’un auteur.

Les sujets associés

COVID-19 Clinical Research StudiesLong-Term Effects of COVID-19SARS-CoV-2 detection and testing

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