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

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Abstract Introduction: Socio-demographic factors are known to influence epidemic dynamics. The town of Nice displays major socio-economic inequalities, with over 50% of the population living below the poverty threshold in some districts. Objective: To measure the association between the incidence of SARS-CoV-2 and various factors related to social inequality in Nice during the first 6 weeks of 2021. Material and Methods: The study included residents of Nice with a first positive SARS-CoV-2 test (January 4 - February 14, 2021). Data were provided by the National information system for Covid 19 screening (SIDEP), and socioeconomic data were obtained from the National Institute of Statistics and Economic Studies (INSEE). Each case’s address was allocated to a census block to which we assigned a social deprivation index categorized into 5 classes. For each category, we calculated the incidence rate per age, per week and for the overall study period and the Standardised Incidence Ratio between the most deprived category compared to the remaining categories. We then calculated the Pearson’s correlation coefficient between the number of cases and each of the socio-economic variables within these blocks. To characterise the effect of the association of all these variables on the incidence rate, these were included in a Generalized Additive Model with a Poisson distribution. Results: We included 10 078 cases. The highest incidence rate was observed in the most socially deprived areas for each age group (2 669 to 4 901 cases /100 000 inhabitants). The number of observed cases in the worst level of social deprivation category (N = 2019) was significantly higher than expected (N = 1384) SIR = 1.46 [1.40–1.52], p < 0.001. Several socio-economic variables related to housing, work and income were correlated with the number of cases. In the Generalized Additive Model, we found a correlation between the indicators and the more deprived populations (density, overcrowded housing, poverty rate, certain occupations and uneducated populations). Conclusion: 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 rates could prove highly useful to guide political decisions in public health.

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