Laurent Castra1,2, Michael Genin1, Joséphine Escutnaire1,3, Valentine Baert1,3, Jean-Marc Agostinucci4, François Revaux5, Cécile Ursat6, Karim Tazarourte7, Frédéric Adnet4, Hervé Hubert1,3,8. 1. Department of Public Health, EA2694, University of Lille. 2. ARS Ile-de-France. 3. French National Out-of-Hospital Cardiac Arrest Registry Research Team, RéAC. 4. AP-HP, Department of Emergency Medicine, Avicenne Hospital, Inserm U942, Paris 13 University. 5. APHP, Department of Emergency Medicine, Henri Mondor University Hospital. 6. APHP, Department of Emergency Medicine, Raymond Poincaré University Hospital, Paris. 7. HCL, Department of Emergency Medicine, Lyon University Hospital, Claude Bernard University, Lyon, France. 8. GR-RéAC, French national out-of-hospital cardiac arrest registry research group, Lille.
Abstract
OBJECTIVE: Cardiac arrest (CA) is considered a major public health issue. Few studies have focused on geographic variations in incidence and socioeconomic characteristics. The aim of this study is to identify clusters of municipalities with high or low CA incidence, and find socioeconomic factors associated with them. PATIENTS AND METHODS: CA data from three Parisian counties, representing 123 municipalities, were extracted from the French CA registry. Socioeconomic data for each municipality were collected from the French national institute of statistics. We used a statistical approach combining Bayesian methods to study geographical CA incidence variations, and scan statistics, to identify CA incidence clusters of municipalities. Finally, we compared clusters of municipalities in terms of socioeconomic factors. RESULTS: Strong geographical variations were found among 123 municipalities: 34 presented a significantly increased risk of incidence and 37 presented a significantly low risk. Scan statistics identified seven significant spatial clusters of CA incidence, including three clusters with low incidence (the relative risk varied from 0.23 to 0.54) and four clusters with high incidence (the relative risk varied from 1.43 to 2). Clusters of municipalities with a high CA incidence are characterized by a lower socioeconomic status than the others (low and normal CA incidence clusters). Analysis showed a statistically significant relationship between social deprivation factors and high incidence. CONCLUSION: This study shows strong geographical variations in CA incidence and a statistically significant relationship between over-incidence and social deprivation variables.
OBJECTIVE:Cardiac arrest (CA) is considered a major public health issue. Few studies have focused on geographic variations in incidence and socioeconomic characteristics. The aim of this study is to identify clusters of municipalities with high or low CA incidence, and find socioeconomic factors associated with them. PATIENTS AND METHODS: CA data from three Parisian counties, representing 123 municipalities, were extracted from the French CA registry. Socioeconomic data for each municipality were collected from the French national institute of statistics. We used a statistical approach combining Bayesian methods to study geographical CA incidence variations, and scan statistics, to identify CA incidence clusters of municipalities. Finally, we compared clusters of municipalities in terms of socioeconomic factors. RESULTS: Strong geographical variations were found among 123 municipalities: 34 presented a significantly increased risk of incidence and 37 presented a significantly low risk. Scan statistics identified seven significant spatial clusters of CA incidence, including three clusters with low incidence (the relative risk varied from 0.23 to 0.54) and four clusters with high incidence (the relative risk varied from 1.43 to 2). Clusters of municipalities with a high CA incidence are characterized by a lower socioeconomic status than the others (low and normal CA incidence clusters). Analysis showed a statistically significant relationship between social deprivation factors and high incidence. CONCLUSION: This study shows strong geographical variations in CA incidence and a statistically significant relationship between over-incidence and social deprivation variables.
Authors: Anthony Turpin; Michael Genin; Mohamed Hebbar; Florent Occelli; Caroline Lanier; Francis Vasseur; Clotilde Descarpentries; Diane Pannier; Anne Ploquin Journal: Cancer Manag Res Date: 2019-09-13 Impact factor: 3.989
Authors: Martin Jonsson; Petter Ljungman; Juho Härkönen; Ben Van Nieuwenhuizen; Sidsel Møller; Mattias Ringh; Per Nordberg Journal: J Epidemiol Community Health Date: 2020-05-08 Impact factor: 3.710