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      Trends in reasons for emergency calls during the COVID-19 crisis in the department of Gironde, France using artificial neural network for natural language classification

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          During periods such as the COVID-19 crisis, there is a need for responsive public health surveillance indicators in order to monitor both the epidemic growth and potential public health consequences of preventative measures such as lockdown. We assessed whether the automatic classification of the content of calls to emergency medical communication centers could provide relevant and responsive indicators.


          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 39,907 manually-coded free-text reports.


          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 and stress and anxiety, peaked 12 days later. Calls for malaises with loss of consciousness, non-voluntary injuries and alcohol intoxications sharply decreased, starting one month before lockdown. No noticeable trends in relation to lockdown was found for other groups of reasons including gastroenteritis and abdominal pain, stroke, suicide and self-harm, pregnancy and delivery problems.


          The first wave of the COVID-19 crisis came along with increased levels of stress and anxiety but no increase in alcohol intoxication and violence. As expected, call related to road traffic crashes sharply decreased. The sharp decrease in the number of calls for malaise was more surprising.


          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. 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. The COVID-19 crisis and/or lockdown induced deep modifications in the population health profile.

          Supplementary Information

          The online version contains supplementary material available at 10.1186/s13049-021-00862-w.

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          Most cited references 12

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          A Novel Coronavirus from Patients with Pneumonia in China, 2019

          Summary In December 2019, a cluster of patients with pneumonia of unknown cause was linked to a seafood wholesale market in Wuhan, China. A previously unknown betacoronavirus was discovered through the use of unbiased sequencing in samples from patients with pneumonia. Human airway epithelial cells were used to isolate a novel coronavirus, named 2019-nCoV, which formed a clade within the subgenus sarbecovirus, Orthocoronavirinae subfamily. Different from both MERS-CoV and SARS-CoV, 2019-nCoV is the seventh member of the family of coronaviruses that infect humans. Enhanced surveillance and further investigation are ongoing. (Funded by the National Key Research and Development Program of China and the National Major Project for Control and Prevention of Infectious Disease in China.)
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            Digital technology and COVID-19

            The past decade has allowed the development of a multitude of digital tools. Now they can be used to remediate the COVID-19 outbreak.
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              Out-of-hospital cardiac arrest during the COVID-19 pandemic in Paris, France: a population-based, observational study

              Summary Background Although mortality due to COVID-19 is, for the most part, robustly tracked, its indirect effect at the population level through lockdown, lifestyle changes, and reorganisation of health-care systems has not been evaluated. We aimed to assess the incidence and outcomes of out-of-hospital cardiac arrest (OHCA) in an urban region during the pandemic, compared with non-pandemic periods. Methods We did a population-based, observational study using data for non-traumatic OHCA (N=30 768), systematically collected since May 15, 2011, in Paris and its suburbs, France, using the Paris Fire Brigade database, together with in-hospital data. We evaluated OHCA incidence and outcomes over a 6-week period during the pandemic in adult inhabitants of the study area. Findings Comparing the 521 OHCAs of the pandemic period (March 16 to April 26, 2020) to the mean of the 3052 total of the same weeks in the non-pandemic period (weeks 12–17, 2012–19), the maximum weekly OHCA incidence increased from 13·42 (95% CI 12·77–14·07) to 26·64 (25·72–27·53) per million inhabitants (p<0·0001), before returning to normal in the final weeks of the pandemic period. Although patient demographics did not change substantially during the pandemic compared with the non-pandemic period (mean age 69·7 years [SD 17] vs 68·5 [18], 334 males [64·4%] vs 1826 [59·9%]), there was a higher rate of OHCA at home (460 [90·2%] vs 2336 [76·8%]; p<0·0001), less bystander cardiopulmonary resuscitation (239 [47·8%] vs 1165 [63·9%]; p<0·0001) and shockable rhythm (46 [9·2%] vs 472 [19·1%]; p<0·0001), and longer delays to intervention (median 10·4 min [IQR 8·4–13·8] vs 9·4 min [7·9–12·6]; p<0·0001). The proportion of patients who had an OHCA and were admitted alive decreased from 22·8% to 12·8% (p<0·0001) in the pandemic period. After adjustment for potential confounders, the pandemic period remained significantly associated with lower survival rate at hospital admission (odds ratio 0·36, 95% CI 0·24–0·52; p<0·0001). COVID-19 infection, confirmed or suspected, accounted for approximately a third of the increase in OHCA incidence during the pandemic. Interpretation A transient two-times increase in OHCA incidence, coupled with a reduction in survival, was observed during the specified time period of the pandemic when compared with the equivalent time period in previous years with no pandemic. Although this result might be partly related to COVID-19 infections, indirect effects associated with lockdown and adjustment of health-care services to the pandemic are probable. Therefore, these factors should be taken into account when considering mortality data and public health strategies. Funding The French National Institute of Health and Medical Research (INSERM)

                Author and article information

                Scand J Trauma Resusc Emerg Med
                Scand J Trauma Resusc Emerg Med
                Scandinavian Journal of Trauma, Resuscitation and Emergency Medicine
                BioMed Central (London )
                31 March 2021
                31 March 2021
                : 29
                [1 ]GRID grid.412041.2, ISNI 0000 0001 2106 639X, Inserm, ISPED, University of Bordeaux, Bordeaux Population Health Research Center Inserm U1219 Injury Epidemiology Transport Occupation team, ; Bordeaux, France
                [2 ]GRID grid.42399.35, ISNI 0000 0004 0593 7118, University Hospital of Bordeaux, Pole of Emergency Medicine, ; Bordeaux, France
                © The Author(s) 2021

                Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

                Funded by: FundRef http://dx.doi.org/10.13039/501100001665, Agence Nationale de la Recherche;
                Award ID: ANR-20-COV1-0004-01
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                © The Author(s) 2021


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