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https://doi.org/10.21203/rs.3....
Preprint . 2020
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Oskar Bordeaux
Article . 2021
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Article . 2021
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Trends in reasons for emergency calls during the COVID-19 crisis in the department of Gironde, France using automatic classification.

Authors: Cédric Gil-Jardiné; Gabrielle Chenais; Catherine Pradeau; Eric Tentillier; Philipe Revel; Xavier Combes; Michel Galinski; +2 Authors

Trends in reasons for emergency calls during the COVID-19 crisis in the department of Gironde, France using automatic classification.

Abstract

Abstract Objectives During periods such as the COVID-19 crisis, there is a need for responsive public health surveillance indicators related to the epidemic and to preventative measures such as lockdown. The automatic classification of the content of calls to emergency medical communication centers could provide relevant and responsive indicators. Methods We retrieved all 796,209 free-text call reports from the emergency medical communication center of the Gironde department, France, between 2018 and 2020. We trained a natural language processing neural network model with a mixed unsupervised/supervised method to classify all reasons for calls in 2020. Validation and parameter adjustment were performed using a sample of 20,000 manually-coded free-text reports. Results The number of daily calls for flu-like symptoms began to increase from February 21, 2020 and reached an unprecedented level by February 28, 2020 and peaked on March 14, 2020, 3 days before lockdown. It was strongly correlated with daily emergency room admissions, with a delay of 14 days. Calls for chest pain, stress, but also those mentioning dyspnea, ageusia and anosmia peaked 12 days later. Calls for malaises with loss of consciousness, non-voluntary injuries and alcohol intoxications sharply decreased, starting one month before lockdown. Discussion This example of the COVID-19 crisis shows how the availability of reliable and unbiased surveillance platforms can be useful for a timely and relevant monitoring of all events with public health consequences. The use of an automatic classification system using artificial intelligence makes it possible to free itself from the context that could influence a human coder, especially in a crisis situation. Conclusion The content of calls to emergency medical communication centers is an efficient epidemiological surveillance data source that provides insights into the societal upheavals induced by a health crisis.

Country
France
Subjects by Vocabulary

Microsoft Academic Graph classification: History Coronavirus disease 2019 (COVID-19) business.industry Public relations business

Keywords

[SDV.MHEP] Life Sciences [q-bio]/Human health and pathology, [SDV.SPEE] Life Sciences [q-bio]/Santé publique et épidémiologie, COVID-19, Emergency medical communication, [SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologie, Emergency care, [SDV.MHEP]Life Sciences [q-bio]/Human health and pathology

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  • citations
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    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Average
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Average
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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
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