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

doi:10.21203/rs.3.rs-1892907/v1

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

https://doi.org/10.21203/rs.3.rs-1892907/v1

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published 06 Jun, 2023

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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.

COVID-19

Incidence

Social inequalities

risk factors

Social measures intended to contain the SARS-CoV-2 epidemic were applied according to indicators published by the French Public Health Agency Santé Publique France (SPF): incidence rate, hospitalisation rate and positivity rate. During the fourth quarter of 2020 there was a surge in the COVID-19 epidemic in France. Consequently, the government initially ordered a nationwide lockdown on October 29, 2020, which was lifted on December 15, 2021 while maintaining a country-wide curfew between 8pm and 6 am for the end-of-year celebrations.

In the Alpes-Maritimes department in South-eastern France, on November 16, 2021, the incidence rate for SARS-CoV-2 per 100 000 inhabitants exceeded the mean rate recorded at the national level (178/100 000 habitants versus 166/100 000 habitants). On December 18, it crossed the national alert threshold of 250/100,000 inhabitants set by the government. Thus, on January 2, 2021, as in 14 other French departments, the curfew was reinforced from 6pm to 6am.

Despite these measures aimed at controlling the epidemic, the SARS-CoV-2 incidence rate in the Alpes Maritimes department was the highest in metropolitan France at the start of 2021 and progressed between the first to the sixth week from 456 to 577 per 100 000 inhabitants, i.e. a continuously increasing rate which remained significantly higher than the government’s national alert threshold (1) (2).

Social and economic factors have always played a major role in epidemic dynamics (3) even in affluent countries (4) (5). Several studies have focused on their association with mortality and progression of the SARS-CoV-2 epidemic in Europe and worldwide (6) (7) (8) (9) (10) (11) (12). Recently, results of the first study on social inequality in France and COVID diagnosis highlighted the major role of social deprivation (13).

Each year, the French National Institute of Statistics and Economic Studies (Institut national de la statistique et des études économiques : INSEE) publishes socio-economic data grouped by census blocks (Ilots Regroupés pour l’Information Statistique : IRIS) (14). A census block is the smallest geographical unit for which INSEE data are available and cover relatively similar geographic areas with regard to socio-economic characteristics. The socioeconomic data were obtained from the 2017 national census and provided counts of population, households or residences at the census block level classified by social, economic and demographic characteristics.

The geographical boundaries for the census blocks were obtained from the National Geographic Institute. In 2017, the most recent year for which socio-economic data were available per age group and per census block, the population of the Alpes Maritimes department numbered 1 083 310, among whom 340 017 lived in the town of Nice which includes 146 census blocks with between 1142 and 4442 inhabitants (mean 2,328 inhabitants).

The town of Nice displayed major socio-economic inequalities, with over 50% of the population living below the poverty threshold in some districts (15).

The published socio-economic indicators were complete for 144 out of 146 census blocks. They included, among other variables, employment, income, housing, level of education or family structure (14).

Based on these data, the Surveillance centre for medical causes of death (Centre d'épidémiologie sur les causes médicales de Décès: CépiDc), which conducts statistical analyses of deaths in France, has developed a composite indicator of social disadvantage, i.e. the « French Deprivation index » (FDep) (16) which provides a synthetic view of social inequalities in health. It is computed from several socio-economic variables : the median annual income per consumption unit in the household, the percentage of workers in the active population, the percentage of graduates in the population over 14 years of age and the unemployment rate.

To our knowledge, the association between socio-economic characteristics and the incidence of SARS-CoV-2 in a large town has not been studied in France. We calculated the incidence rate of SARS-CoV-2 in Nice during the first 6 weeks of 2021 to measure the association between the progression of the epidemic and the various factors related to social inequality.

The study included residents of Nice with a first positive SARS-CoV-2 PCR or antigenic test result obtained between January 4 and February 14, 2021. Data were provided by the National information system for Covid 19 screening (Système d’information national de suivi du dépistage de la COVID-19: SI-DEP) which has been recording all positive SARS-CoV-2 tests in France since May 2020. Variables of interest included age, date of first positive test and residence coordinates. We considered a first positive test to be the one from a patient who had not tested positive during the previous 30 days.

Quality control consisted in eliminating duplicates, correcting inconsistent data, and completing missing addresses. The addresses of cases living in collective accommodation (nursing homes or long-term accommodation) were identified. Each address was entered in a Geographic Information System (ARCGIS 10®) to allocate it to a census block. All GIS techniques and map layouts were performed using ArcMap v.10.5 (ESRI, Redlands, CA, USA). We excluded the blocks containing the airport and the railway station as these are considered uninhabited, in which data for numerous socio-economic indicators were lacking.

For each of the 144 census blocks, we computed the social deprivation index according to 5 categories, using the method developed by Jenks (17) : FDep1[-2.84/-1,36], FDep2[-1.35/-0.48], FDep3[-0.47/-0.21], FDep4[0.22/0.91], FDep5[0.92/1.83].

For each FDep category, we calculated the SARS-CoV-2 incidence rate per age group, per week and for the overall study period. We also calculated the variation in mean weekly incidence rate and the Standardised Incidence Ratio (SIR) to check for a potentially excessive number of cases in the most deprived areas compared to the rest of the town (18).

We then calculated the correlation between the number of new cases per week and per census block and each of the socio-economic variables within these blocks (Pearson’s correlation coefficient). To characterise the effect of the association of all these variables on the incidence rate, we included them in a Generalized Additive Model with a Poisson distribution.

Statistical analyses were performed using SAS® et SPSS® software packages.

Incidence

During the first 6 weeks of 2021, 27 336 individuals with a known zip code in the Alpes-Maritimes had a positive SARS-CoV-2 PCR test result. Among these, 10 712 (39,1%) were living in Nice, and 10 078 of them had an identified address. Over this period, the town of Nice was an area of very high circulation of SARS-CoV-2, which markedly progressed from week 5 onwards (from 1/02 to 7/02) (Fig. 1). During the first three weeks of January, 4 762 cases (47.3%) were diagnosed, and 5 316 (52.7%) were identified over the three following weeks.

There was a significant drop in the mean age of cases between the first and the sixth week (46 ± 23 years versus 41 ± 22 years, p < 0,001). During the first four weeks, 10.3% of positive tests concerned patients above 80 years of age while this proportion dropped significantly during the last two weeks (6,7%, p < 0.001).

Incidence rate according to the social deprivation index

Figure 2 shows the distribution of the FDep index in the town of Nice. Darker colours show the most deprived census blocks.

The incidence rate, number of cases and their frequency for each FDep category are shown in Table 1. The number of observed cases in the FDep5 category (N = 2019) (worst level of social deprivation) was significantly higher than expected (N = 1384) SIR = 1.46 [1.40–1.52], p < 0.001. Over the 6-week study period, the incidence rate also increased significantly among residents within the FDep5 category, i.e. + 6.5% [5.2–7.1] p = 0.002) and to a lesser extent in the FDep3 category: + 2.7% [1.9%- 3.1%] p = 0,05.

Table 1

Trends in incidence rate according to social deprivation category
	Number of census blocks	Population within each category	Number of cases	Incidence rate /100 000 inhab	Mean variation in weekly incidence rate	95% CI	p
FDep1	18	46 372	1 136	2 490	1.6	[0.8–1.8]	0.1
FDep2	19	46 551	1 323	2 881	1.6	[0.8–1.7]	0.3
FDep3	42	95 082	2 527	2 767	2.7	[1.9–3.1]	0.05
FDep4	42	104 701	3 054	2 879	1.3	[0.9–1.5]	0.2
FDep5	20	51 514	2 019	4 001	6.5	[5.2–7.1]	0.002

Figure 3 shows the incidence rate per age group and for each social deprivation category. The highest incidence rate was observed in the most socially deprived areas (FDep5) for each age group (from 2 669 to 4 901 cases /100 000 inhabitants) and the lowest among those below 14 years of age (1 368 to 2 669 cases per 100 000/inhabitants). Conversely, the incidence rate among persons above 75 years of age living in the most deprived census blocks did not increase as much as among the remaining population (FDep 1 to 4).

Correlation between number of cases and socio-economic indicators

An analysis of the number of cases according to socio-economic indicators per census block is shown in Table 2. A statistically significant association was found between the number of cases and income indicators: poverty rate, median disposable income, share of taxable households, single-parent families, or housing conditions, such as overcrowded housing, apartments, rented housing.

The number of cases was also correlated with the type of profession: farmers, craftsmen, shopkeepers, or entrepreneurs; with occupations in the so-called core sectors, such as intermediate occupations, a category that includes nurses and healthcare workers, and employees and manual workers. The unemployment level was also associated with the number of cases as well as the proportion of adults with at most a primary education. However, no statistically significant correlation was found between the number of cases and the professions where teleworking was possible, such as executives and higher intellectual professions. Similarly, we did not find a correlation between the number of cases and population density.

Table 2

Correlation between number of cases, density, income, housing conditions, working conditions and education level.
	R	p
Population density	0.04585	0.5826
Number of overcrowded main residences	0.47047	< .0001
House as main residence	0.08402	0.3133
Appartment as main residence	0.33198	< 0.0001
Owners of main residence	0.04065	0.6261
Tenants of main residence	0.48458	< .0001
Single-parent families	0.61178	< 0.0001
Poverty rate	0.20816	0.0162
Median disposable income	-0.29505	0.0003
Percentage of taxable households	-0.27553	0.0009
Farmers	0.20520	0.0130
Artisans, shopkeepers, company managers	0.17366	0.0361
Executives, Higher professions	-0.07670	0.3575
Intermediate professions	0.24280	0.0031
Employees	0.54404	< 0.0001
Workers	0.65369	< 0.0001
Unemployed	0.52363	< 0.0001
Population > 14 years with no diploma nor Primary school certificate	0.66709	< 0.0001

In the Generalized Additive Model (GAM), there was a higher number of cases correlated to variables linked to more deprived populations, including population density, overcrowded housing, poverty rate and certain occupations (farmers, artisans and shopkeepers, and employed manual workers) or the uneducated population. On the other hand, no correlation was found between single-parent families and the number of cases. Table 3

Table 3

Generalized Additive Model
	R	p-value
Population density	0.04585	0.0004
Number of overcrowded main residences	0.47047	0.0152
House as main residence	0.08402	0.8602
Appartment as main residence	0.33198	0.4181
Owners of main residence	0.04065	0.4494
Tenants of main residence	0.48458	0.4239
Single-parent families	0.61178	0.1435
Poverty rate	0.20816	0.0121
Median disposable income	-0.29505	0.8341
Percentage of taxable households	-0.27553	0.2081
Farmers	0.20520	0.0065
Artisans, shopkeepers, company managers	0.17366	0.0002
Executives, higher professions	-0.07670	0.0019
Intermediate professions	0.24280	0.3053
Employees	0.54404	0.0331
Workers	0.65369	0.0448
Unemployed	0.52363	0.1555
Population > 14 years with no diploma nor Primary school certificate	0.66709	< .0001

This study was conducted at a time when the town of Nice had one of the highest SARS-CoV-2 incidence rates in France. It shows that the incidence rate was significantly higher in the most deprived census blocks regardless of the age group. We also report a more rapid weekly progression among the most deprived population. (FDep5). Lastly, we observed a decrease in the number of cases in the over-80 age group, starting in the 5th week of 2021, which could reflect an early effect of the administration of the vaccine that was initiated in this population at the end of December 2020.

In the GAM model, a statistically significant association was found between the number of cases and the various socio-economic indicators that characterize the most disadvantaged populations, such as crowding, type of housing, living environment or occupation. These results are in line with studies showing a correlation between population density and the spread of the epidemic (19) (20) (21) (22), and with European studies highlighting an increase in the number of SARS-CoV-2 cases among low-income earners (7) (23).

We also showed the living environment to be an important determinant of viral spread: census blocks most affected by the virus were those where cases lived in appartments and overcrowded dwellings. This has also been reported in a study on SARS-CoV-2 seroprevalence on a representative sample of the French population (24). The major role of the urban model and housing type had already been reported for other infectious diseases (5).

The number of cases was also found to be associated with the type of occupation, with more cases among artisans, shopkeepers, company managers, employees and workers and fewer cases among executives and professions requiring a higher level of education. The first category includes most « essential » professions, i.e. people who could not work remotely and who were therefore in direct, unavoidable, and repeated contact with the rest of the population (25) (26). These jobs are more frequent among residents of the most deprived census blocks and may have thus favoured the spread of SARS-CoV-2 in these areas. Farming was also a profession associated with the number of cases; in Nice, there are very few and they mainly reside in one of the deprived census blocks. There was no correlation with case numbers according to the GAM model for so-called intermediate professions which include non-medical health porfessionals; this may be due to an early effect of the vaccine which became available on January 3rd, along with masks, gloves and personal protective equipment (27). A study conducted in New York in March 2020, and another in the United States as a whole, also found a link between the type of occupation and the risk of contamination (28) (29).

Our results on the incidence rate of SARS CoV-2 in deprived areas are also in line with the study on Covid-19-related mortality conducted at the start of the epidemic by the Health office for the Ile de France region (Office Régionalde Santé Ile-de-France), in which excess mortality was closely linked to the social and urban geography of the population (30).

The degree of social deprivation would thus contribute to the increase in contaminations related to overcrowded living conditions, the cohabitation of generations in the same dwelling and the greater frequency of housing in apartments, particularly the resulting over-occupation. Limited possibilities of working remotely, inherent in the type of profession of this section of the population, would also be among the factors favouring contamination (23). By forcing people living in high-incidence areas such as the town of Nice to remain at home from 6:00 pm to 6:00 am, the curfew may have contributed to a greater risk of SARS-CoV-2 transmission among such most deprived populations (25).

Complying with hygiene measures, conforming to a strict lockdown or isolating oneself in an apartment without outdoor spaces is undoubtedly more complicated than in a house with a garden, the more so when it comes to over-crowded apartments or limited surfaces. Our results could warrant surveillance at a smaller scale than a department, even within municipalities, to accurately chart the progression of Covid-19 or any other epidemic. This could thus lead to the rapid implementation of intervention strategies targeting the areas that need them most.

The methodology chosen for this study may have induced certain limitations regarding our results.

The SIDEP database which we used offers the advantage of providing all cases recorded in a given geographical area. Nevertheless, among the 10 712 recorded cases and checked with a verified zipcode for the town of Nice, 1 525 (12%) lacked an address allowing geolocation (missing number, collective accommodation lacking an address, missing addresses). Following further verification, the number of missing addresses was reduced to 634 (6%). This may have biased our results. However, in a similar study, focusing on France as a whole, for which geolocation was essential, the authors also reported a missing address in 20.5% of cases. To compensate for these missing data, they chose to allocate cases to census block on a probability basis (13). Thanks to the local presence of our team and a number of cases allowing manual verification, we were able to improve the accuracy and completeness of the data, achieving geolocation without having to resort to an estimate. In addition to regional or national surveillance, it appears useful to maintain territorial management of epidemiological surveillance.

Although the study was conducted in 2021, we chose to use the 2017 INSEE data, which was the most recent year for which distribution per age group and census block was available, as well as the related socio-economic data. The delay between the study period and the one for which socio-economic data are available has already been mentioned in other French studies (13). Between 2017 and 2019, the latest year for which the overall population in Nice is known, the number of inhabitants increased by 1.6%, from 340 017 to 345 528. We thus considered that this bias did not compromise the validity of our results, despite potential minor changes in socio-economic data over the 4-year period.

In our view, the FDep was the most suitable social deprivation index for our population and provided the most complete results for the town of Nice. Indeed, in contrast to the Townsend index for the United Kingdom population in 1987 (31), this index was specifically developed and validated for the French population. As for the Ecological Deprivation Index (EDI), developed for the French and European population, it included 10 weighted variables (32), while the INSEE data per census block in Nice were not complete for these 10 variables. The Fdep, with its 4 variables, could be computed almost completely for the whole of Nice.

The missing data for certain variables, such as the poverty index in 5 of the 144 studied census blocks, could have biased our results. However, since two of these census blocks were in the most deprived category, two in the most affluent category, and one in the middle category, we considered that these missing data had little impact on our results.

To classify the deprivation index values into categories, we chose to use the Jenks natural breaks classification method, rather than a classification based on quintiles as used in other studies. Indeed, this method does not classify data in an arbitrary fashion subdividing the population in groups of 20% as with quintiles, but according to their uniformity, with thresholds that we feel are more in line with reality. When applied to a transmissible disease, the Jenks algorithm can limit within-class variance while maximising variance between classes (17).

Until individual specificities are taken into account, the Fdep, which is a simple composite index, can provides the basis for a synthetic approach at the level of a particular territory. Considering that the socio-economic status is a multidimensional and complex concept, and with Khalatbari-Soltani (33), we believe that surveillance of infectious diseases should take socio-economic data into account. This would allow precise identification of at-risk groups in order to implement targeted and equitable public health policies (24).

This descriptive study of the SARS-CoV-2 epidemic according to socio-economic variables, conducted in the town of Nice over six weeks in 2021, is the first in France, to our knowledge, to show that people living in the most deprived areas of a large town were those most impacted by the COVID-19 epidemic and among whom the epidemic spread fastest. Control measures such as the curfew may have contributed to this higher incidence rate. As for other infectious or non-infectious conditions, social inequalities should be considered to ensure a comprehensive and targeted surveillance for future epidemics. Local surveillance of epidemics provides complementary data to national and regional surveillance. Mapping socio-economic vulnerability indicators such as that recorded by the INSEE at the census block level and correlating these with incidence rates could prove highly useful to guide political decisions in public health.

Ethics approval and consent to participate

Study in compliance with MR004 ref 2022-R004-001

Health Data Hub registry

ClinicalTrials.gov Identifier: NCT05379621. 18/05/22

https://clinicaltrials.gov/ct2/show/NCT05379621

All the protocols were approved by the Délégation territoriale des Alpes-Maritimes de l'Agence Régionale de Santé PACA

SIDEP data were obtained in accordance with privacy laws (General Data Protection Regulation [EU] 2016/679).

All methods were carried out in accordance with relevant guidelines and regulation

About informed consent from all subjects and/or their legal guardian(s), the french Law n° 2020-546, 05 11 2020 art 11, for state of health emergency, states :

« - By way of derogation from Article L. 1110-4 of the Public Health Code, for the sole purpose of combating the spread of the covid-19 epidemic… personal data concerning health relating to persons affected by this virus and persons who have been in contact with them may be processed and shared, if necessary without the consent of the persons concerned, within the framework of an information system created by decree in the Council of State and implemented by the Minister in charge of health. »

Consent to publish

NA. Agree Consent to publish

Availability of data and materials

Data are available from the authors [email protected]

Competing interests

Without competing interests

Funding : Not founding

Acknowledgements
Brigitte Dunais for the translation, Elisabeth Huynh-Van for help at the legal level and the Délégation territoriale des Alpes-Maritimes de l'Agence Régionale de Santé PACA for its support.

Authors contribution

Eugènia Mariné Barjoan : Project Manager, rédaction

Amel Chaarana : Statistician

Julie Festraëts : Geographer

Carole Geloen : Data manager

Bernard Prouvost-Keller: Reviewer

Kevin Legueult : Data manager

Christian Pradier: Reviewer

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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

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Introduction

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Incidence rate according to the social deprivation index

Correlation between number of cases and socio-economic indicators

Discussion

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