Anosmia and other SARS-CoV-2 positive test-associated symptoms, across three national, digital surveillance platforms as the COVID-19 pandemic and response unfolded: an observation study.


Journal

medRxiv : the preprint server for health sciences
Titre abrégé: medRxiv
Pays: United States
ID NLM: 101767986

Informations de publication

Date de publication:
16 Dec 2020
Historique:
pubmed: 24 12 2020
medline: 24 12 2020
entrez: 23 12 2020
Statut: epublish

Résumé

Multiple participatory surveillance platforms were developed across the world in response to the COVID-19 pandemic, providing a real-time understanding of community-wide COVID-19 epidemiology. During this time, testing criteria broadened and healthcare policies matured. We sought to test whether there were consistent associations of symptoms with SARS-CoV-2 test status across three national surveillance platforms, during periods of testing and policy changes, and whether inconsistencies could better inform our understanding and future studies as the COVID-19 pandemic progresses. Four months (1st April 2020 to 31st July 2020) of observation through three volunteer COVID-19 digital surveillance platforms targeting communities in three countries (Israel, United Kingdom, and United States). Logistic regression of self-reported symptom on self-reported SARS-CoV-2 test status (or test access), adjusted for age and sex, in each of the study cohorts. Odds ratios over time were compared to known changes in testing policies and fluctuations in COVID-19 incidence. Anosmia/ageusia was the strongest, most consistent symptom associated with a positive COVID-19 test, based on 658,325 tests (5% positive) from over 10 million respondents in three digital surveillance platforms using longitudinal and cross-sectional survey methodologies. During higher-incidence periods with broader testing criteria, core COVID-19 symptoms were more strongly associated with test status. Lower incidence periods had, overall, larger confidence intervals. The strong association of anosmia/ageusia with self-reported SARS-CoV-2 test positivity is omnipresent, supporting its validity as a reliable COVID-19 signal, regardless of the participatory surveillance platform or testing policy. This analysis highlights that precise effect estimates, as well as an understanding of test access patterns to interpret differences, are best done only when incidence is high. These findings strongly support the need for testing access to be as open as possible both for real-time epidemiologic investigation and public health utility. NIH, NIHR, Alzheimer's Society, Wellcome Trust.

Sections du résumé

Background UNASSIGNED
Multiple participatory surveillance platforms were developed across the world in response to the COVID-19 pandemic, providing a real-time understanding of community-wide COVID-19 epidemiology. During this time, testing criteria broadened and healthcare policies matured. We sought to test whether there were consistent associations of symptoms with SARS-CoV-2 test status across three national surveillance platforms, during periods of testing and policy changes, and whether inconsistencies could better inform our understanding and future studies as the COVID-19 pandemic progresses.
Methods UNASSIGNED
Four months (1st April 2020 to 31st July 2020) of observation through three volunteer COVID-19 digital surveillance platforms targeting communities in three countries (Israel, United Kingdom, and United States). Logistic regression of self-reported symptom on self-reported SARS-CoV-2 test status (or test access), adjusted for age and sex, in each of the study cohorts. Odds ratios over time were compared to known changes in testing policies and fluctuations in COVID-19 incidence.
Findings UNASSIGNED
Anosmia/ageusia was the strongest, most consistent symptom associated with a positive COVID-19 test, based on 658,325 tests (5% positive) from over 10 million respondents in three digital surveillance platforms using longitudinal and cross-sectional survey methodologies. During higher-incidence periods with broader testing criteria, core COVID-19 symptoms were more strongly associated with test status. Lower incidence periods had, overall, larger confidence intervals.
Interpretation UNASSIGNED
The strong association of anosmia/ageusia with self-reported SARS-CoV-2 test positivity is omnipresent, supporting its validity as a reliable COVID-19 signal, regardless of the participatory surveillance platform or testing policy. This analysis highlights that precise effect estimates, as well as an understanding of test access patterns to interpret differences, are best done only when incidence is high. These findings strongly support the need for testing access to be as open as possible both for real-time epidemiologic investigation and public health utility.
Funding UNASSIGNED
NIH, NIHR, Alzheimer's Society, Wellcome Trust.

Identifiants

pubmed: 33354683
doi: 10.1101/2020.12.15.20248096
pmc: PMC7755145
pii:
doi:

Types de publication

Preprint

Langues

eng

Commentaires et corrections

Type : UpdateIn

Auteurs

Carole H Sudre (CH)

MRC Unit for Lifelong health and Ageing at UCL, Department of Population Science and Experimental Medicine, University College London, London, UK.
Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK.
School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK.

Ayya Keshet (A)

Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot, Israel.
Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel.

Mark S Graham (MS)

School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK.

Amit D Joshi (AD)

Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School. Boston, Massachusetts, USA.
Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School. Boston, Massachusetts, USA.

Smadar Shilo (S)

Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot, Israel.
Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel.
Pediatric Diabetes Unit, Ruth Rappaport Children's Hospital, Rambam Healthcare Campus, Haifa, Israel.

Hagai Rossman (H)

Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot, Israel.
Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel.

Benjamin Murray (B)

School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK.

Erika Molteni (E)

School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK.

Kerstin Klaser (K)

School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK.

Liane D Canas (LD)

School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK.

Michela Antonelli (M)

School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK.

Marc Modat (M)

School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK.

Joan Capdevila Pujol (J)

Department of Twin Research and Genetic Epidemiology, King's College London, UK.

Sajaysurya Ganesh (S)

Department of Twin Research and Genetic Epidemiology, King's College London, UK.

Jonathan Wolf (J)

Department of Twin Research and Genetic Epidemiology, King's College London, UK.

Tomer Meir (T)

Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot, Israel.
Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel.

Andrew T Chan (AT)

Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School. Boston, Massachusetts, USA.
Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School. Boston, Massachusetts, USA.

Claire J Steves (CJ)

Department of Twin Research and Genetic Epidemiology, King's College London, UK.

Tim D Spector (TD)

Department of Twin Research and Genetic Epidemiology, King's College London, UK.
Zoe Global Limited.

John S Brownstein (JS)

Computational Epidemiology Lab, Boston Children's Hospital, Boston, MA, USA.

Eran Segal (E)

Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot, Israel.
Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel.

Sebastien Ourselin (S)

School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK.

Christina M Astley (CM)

Computational Epidemiology Lab, Boston Children's Hospital, Boston, MA, USA.
Division of Endocrinology, Boston Children's Hospital, Boston, MA, USA.
Broad Institute of Harvard and MIT, Cambridge, MA, USA.

Classifications MeSH