Social deprivation and Sars-CoV-2 testing

A population-based analysis in a highly contrasted southern France region

Datos Bibliográficos

ID22067056
AutoresJordi Landier (0000-0001-8619-9775, Inserm, autor de correspondencia), Léa Bassez (Inserm), M K Bendiane (0000-0003-4329-8807, Inserm), Marc-Karim Bendiane, Pascal Chaud, Florian Franke (0000-0002-0755-9451, Santé Publique France), Steve Nauleau, Fabrice Danjou (Agence Régionale de Santé Ile-de-France), Philippe Malfait, Stanislas Rebaudet (0000-0001-5099-1947, Inserm), Jean Gaudart (0000-0001-9006-5729, Inserm)
Año2023
Volumen11
Páginas1162711-1162711
Fecha de publicación2023-05-12
Peer ReviewedSí
Open AccessSí
TipoARTICLE
RevistaFrontiers in Public Health (JOURNAL)
Identificadores de la revistaISSN: 2296-2565 • E-ISSN: 2296-2565
EditorialFrontiers Media SA (PUBLISHER • CH)
DOI10.3389/fpubh.2023.1162711
PMID37250096
OpenAlexW4376279581
IdiomaEN
Citas recibidas5
Referencias citadas32

Background: Testing was the cornerstone of the COVID-19 epidemic response in most countries until vaccination became available for the general population. Social inequalities generally affect access to healthcare and health behaviors, and COVID-19 was rapidly shown to impact deprived population more drastically. In support of the regional health agency in Provence-Alpes-Côte d'Azur (PACA) in South-Eastern France, we analyzed the relationship between testing rate and socio-demographic characteristics of the population, to identify gaps in testing coverage and improve targeting of response strategies. Methods: We conducted an ecological analysis of SARS-CoV-2/COVID-19 testing rate in the PACA region, based on data aggregated at the finest spatial resolution available in France (IRIS) and by periods defined by public health implemented measures and major epidemiological changes. Using general census data, population density, and specific deprivation indices, we used principal component analysis followed by hierarchical clustering to define profiles describing local socio-demographic characteristics. We analyzed the association between these profiles and testing rates in a generalized additive multilevel model, adjusting for access to healthcare, presence of a retirement home, and the age profile of the population. Results: We identified 6 socio-demographic profiles across the 2,306 analyzed IRIS spatial units: privileged, remote, intermediate, downtown, deprived, and very deprived (ordered by increasing social deprivation index). Profiles also ranged from rural (remote) to high density urban areas (downtown, very deprived). From July 2020 to December 2021, we analyzed SARS-CoV-2/COVID-19 testing rate over 10 periods. Testing rates fluctuated strongly but were highest in privileged and downtown areas, and lowest in very deprived ones. The lowest adjusted testing rate ratios (aTRR) between privileged (reference) and other profiles occurred after implementation of a mandatory healthpass for many leisure activities in July 2021. Periods of contextual testing near Christmas displayed the largest aTRR, especially during the last periods of 2021 after the end of free convenience testing for unvaccinated individuals. Conclusion: We characterized in-depth local heterogeneity and temporal trends in testing rates and identified areas and circumstances associated with low testing rates, which the regional health agency targeted specifically for the deployment of health mediation activities

Census · Disease · Downtown · Economic growth · Environmental health · Geography · Health care · Health geography · International health · Pandemic · Population · Population health · Public health · Residence · Social deprivation · Sociology · COVID-19 epidemiological studies · Demography · Medicine · SARS-CoV-2 and COVID-19 Research · Vaccine Coverage and Hesitancy · Gerontology · Health Policy