Fabrice Carrat1,2, Mathilde Touvier3, Gianluca Severi4,5, Laurence Meyer6,7, Florence Jusot8, Nathanael Lapidus9,10, Delphine Rahib11, Nathalie Lydié11, Marie-Aline Charles12, Pierre-Yves Ancel13,14, Alexandra Rouquette5,6, Xavier de Lamballerie15, Marie Zins16,17, Nathalie Bajos18. 1. Sorbonne Université, Inserm, Institut Pierre-Louis d'Epidémiologie et de Santé Publique, 27 rue Chaligny, 75571 CEDEX 12, Paris, France. fabrice.carrat@iplesp.upmc.fr. 2. Département de Santé Publique, APHP. Sorbonne Université, Paris, France. fabrice.carrat@iplesp.upmc.fr. 3. Sorbonne Paris Nord University, Inserm U1153, Inrae U1125, Cnam, Nutritional Epidemiology Research Team (EREN), Epidemiology and Statistics Research Center - University of Paris (CRESS), Bobigny, France. 4. CESP UMR1018, Université Paris-Saclay, UVSQ, Inserm, Gustave Roussy, Villejuif, Paris, France. 5. Department of Statistics, Computer Science and Applications, University of Florence, Florence, Italy. 6. Université Paris Saclay, Inserm, CESP U1018, Le Kremlin Bicêtre, Paris, France. 7. Service de Santé Publique, APHP. Paris Saclay, Le Kremlin Bicêtre, France. 8. Université Paris-Dauphine, PSL-Research University, LEDa, Paris, France. 9. Sorbonne Université, Inserm, Institut Pierre-Louis d'Epidémiologie et de Santé Publique, 27 rue Chaligny, 75571 CEDEX 12, Paris, France. 10. Département de Santé Publique, APHP. Sorbonne Université, Paris, France. 11. Santé publique France, Saint-Maurice, France. 12. Ined, Inserm, EFS, UMS Elfe, Aubervilliers, Paris, France. 13. Obstetrical, Perinatal and Pediatric Epidemiology Research Team, Center for Epidemiology and Statistics Sorbonne Paris Cité, INSERM U1153, Paris Descartes University, Paris, France. 14. Clinical Research Unit, Center for Clinical Investigation P1419, Cochin Broca Hôtel-Dieu Hospital, Paris, France. 15. Unité des Virus Emergents, UVE: Aix Marseille Univ, IRD 190, INSERM 1207, IHU Méditerranée Infection, 13005, Marseille, France. 16. Paris University, Paris, France. 17. Paris Saclay University, Inserm UMS 11, Villejuif, France. 18. IRIS, Inserm/EHESS/CNRS, Aubervilliers, France.
Abstract
BACKGROUND: Our main objectives were to estimate the incidence of illnesses presumably caused by SARS-CoV-2 infection during the lockdown period and to identify the associated risk factors. METHODS: Participants from 3 adult cohorts in the general population in France were invited to participate in a survey on COVID-19. The main outcome was COVID-19-Like Symptoms (CLS), defined as a sudden onset of cough, fever, dyspnea, ageusia and/or anosmia, that lasted more than 3 days and occurred during the 17 days before the survey. We used delayed-entry Cox models to identify associated factors. RESULTS: Between April 2, 2020 and May 12, 2020, 279,478 participants were invited, 116,903 validated the questionnaire and 106,848 were included in the analysis. Three thousand thirty-five cases of CLS were reported during 62,099 person-months of follow-up. The cumulative incidences of CLS were 6.2% (95% Confidence Interval (95%CI): 5.7%; 6.6%) on day 15 and 8.8% (95%CI 8.3%; 9.2%) on day 45 of lockdown. The risk of CLS was lower in older age groups and higher in French regions with a high prevalence of SARS-CoV-2 infection, in participants living in cities > 100,000 inhabitants (vs rural areas), when at least one child or adolescent was living in the same household, in overweight or obese people, and in people with chronic respiratory diseases, anxiety or depression or chronic diseases other than diabetes, cancer, hypertension or cardiovascular diseases. CONCLUSION: The incidence of CLS in the general population remained high during the first 2 weeks of lockdown, and decreased significantly thereafter. Modifiable and non-modifiable risk factors were identified.
BACKGROUND: Our main objectives were to estimate the incidence of illnesses presumably caused by SARS-CoV-2 infection during the lockdown period and to identify the associated risk factors. METHODS:Participants from 3 adult cohorts in the general population in France were invited to participate in a survey on COVID-19. The main outcome was COVID-19-Like Symptoms (CLS), defined as a sudden onset of cough, fever, dyspnea, ageusia and/or anosmia, that lasted more than 3 days and occurred during the 17 days before the survey. We used delayed-entry Cox models to identify associated factors. RESULTS: Between April 2, 2020 and May 12, 2020, 279,478 participants were invited, 116,903 validated the questionnaire and 106,848 were included in the analysis. Three thousand thirty-five cases of CLS were reported during 62,099 person-months of follow-up. The cumulative incidences of CLS were 6.2% (95% Confidence Interval (95%CI): 5.7%; 6.6%) on day 15 and 8.8% (95%CI 8.3%; 9.2%) on day 45 of lockdown. The risk of CLS was lower in older age groups and higher in French regions with a high prevalence of SARS-CoV-2 infection, in participants living in cities > 100,000 inhabitants (vs rural areas), when at least one child or adolescent was living in the same household, in overweight or obesepeople, and in people with chronic respiratory diseases, anxiety or depression or chronic diseases other than diabetes, cancer, hypertension or cardiovascular diseases. CONCLUSION: The incidence of CLS in the general population remained high during the first 2 weeks of lockdown, and decreased significantly thereafter. Modifiable and non-modifiable risk factors were identified.
Entities:
Keywords:
COVID-19; Cohort; General population; Incidence; Risk factors; SARS-CoV-2
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