RESUMO
BackgroundStudies have already shown that many environmental factors are associated with COVID-19 incidence. However, none have studied a very large set of socio-economic indicators and analysed to what extent these factors could highlight populations at high risk for COVID-19. We propose here a new approach, a socio-economic wide study, to pinpoint subgroups with a high incidence of COVID-19, and illustrated this approach using hospitalized cases in Paris area. MethodsWe extracted 303 socio-economic indicators from French census data for the 855 residential units in Paris and assessed their association with COVID-19 hospitalization risk. We then fitted a predictive model using a penalized regression on these indicators to predict the incidence of patient hospitalization for COVID-19 in Paris. FindingsThe most associated indicator was income, corresponding to the 3rd decile of the population (OR= 0.11, CI95% [0.05; 0.20]). A model including only income achieves a high performance in both the training set (AUC=0.78, CI95%: 0.72-0.85) and the test set (AUC=0.79 (CI95%: 0.71-0.87). Overall, the 45% most deprived areas gathered 86% of the areas with a high incidence of COVID-19 hospitalized cases. InterpretationDuring the first wave of the epidemic, income predicted Paris areas at risk for a high incidence of patients hospitalized for COVID-19 with a high performance. Socio-economic wide association studies, collecting passively data from hospitalized cases, therefore not necessitating any effort for health caregivers, is of particular interest in such a period of hospital overcrowding as it provides real-time indirect information on populations having high COVID-19 incidence.
