• shareshare
  • link
  • cite
  • add
auto_awesome_motion View all 12 versions
Publication . Preprint . Article . 2020

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

Cédric Gil-Jardiné; Gabrielle Chenais; Catherine Pradeau; Eric Tentillier; Philipe Revel; Xavier Combes; Michel Galinski; +2 Authors
Open Access
Published: 13 Nov 2020
Publisher: HAL CCSD
Country: France

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.

Subjects by Vocabulary

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


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

Download fromView all 4 sources
License: cc-by
Providers: UnpayWall