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BMC Public Health . Impact of social and demographic factors on the spread of the SARS-CoV-2 epidemic in the town of Nice

tetano

Editor, Senior Moderator
BMC Public Health


. 2023 Jun 6;23(1):1098.
doi: 10.1186/s12889-023-15917-z. Impact of social and demographic factors on the spread of the SARS-CoV-2 epidemic in the town of Nice

Eugènia Mariné Barjoan[SUP] 1 [/SUP], Amel Chaarana[SUP] 2 [/SUP], Julie Festraëts[SUP] 2 [/SUP], Carole Géloen[SUP] 2 [/SUP], Bernard Prouvost-Keller[SUP] 2 [/SUP], Kevin Legueult[SUP] 2 [/SUP], Christian Pradier[SUP] 2 3 [/SUP]



Affiliations
Abstract

Introduction: Socio-demographic factors are known to influence epidemic dynamics. The town of Nice, France, displays major socio-economic inequalities, according to the National Institute of Statistics and Economic Studies (INSEE), 10% of the population is considered to live below the poverty threshold, i.e. 60% of the median standard of living.
Objective: To identify socio-economic factors related to the incidence of SARS-CoV-2 in Nice, France.
Methods: The study included residents of Nice with a first positive SARS-CoV-2 test (January 4-February 14, 2021). Laboratory data were provided by the National information system for Coronavirus Disease (COVID-19) screening (SIDEP) and socio-economic data were obtained from INSEE. Each case's address was allocated to a census block to which we assigned a social deprivation index (French Deprivation index, FDep) divided into 5 categories. For each category, we computed the incidence rate per age and per week and its mean weekly variation. A standardized incidence ratio (SIR) was calculated to investigate a potential excess of cases in the most deprived population category (FDep5), compared to the other categories. Pearson's correlation coefficient was computed and a Generalized Linear Model (GLM) applied to analyse the number of cases and socio-economic variables per census blocks.
Results: We included 10,078 cases. The highest incidence rate was observed in the most socially deprived category (4001/100,000 inhabitants vs 2782/100,000 inhabitants for the other categories of FDep). The number of observed cases in the most social deprivated category (FDep5: N = 2019) was significantly higher than in the others (N = 1384); SIR = 1.46 [95% CI:1.40-1.52; p < 0.001]. Socio-economic variables related to poor housing, harsh working conditions and low income were correlated with the new cases of SARS-CoV-2.
Conclusion: Social deprivation was correlated with a higher incidence of SARS-CoV-2 during the 2021 epidemic in Nice. Local surveillance of epidemics provides complementary data to national and regional surveillance. Mapping socio-economic vulnerability indicators at the census block level and correlating these with incidence could prove highly useful to guide political decisions in public health.

Keywords: COVID-19; Incidence; Risk factors; Social inequalities.

 
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