ABSTRACT
IntroductionThe spread of the COVID-19 pandemic, and its severity, is spatially heterogenous. At the individual level, the socioeconomic status (SES) profile is known to be associated with COVID-19 incidence and severity. The aim of this geo epidemiological study was to investigate the link between SES profile and potential confounders, and COVID-19 incidence and hospitalization rates, at a fine geographical scale. MethodsWe analyzed COVID-19 incidence and severity during two epidemic waves between September 2020 and June 2021, in Provence Alpes Cotes dAzur, a 5 million inhabitants French region. The region is divided into sub-municipal areas that we have classified according to their SES profile. We then conducted a spatial analysis of COVID-19 indicators depending on SES profile, age structure, and health services provision. This analysis considered spatial autocorrelation between areas. ResultsCOVID-19 incidence rates in more deprived areas were similar to those in wealthiest ones. Hospitalization rates of COVID-19 cases in conventional care units were greater in more deprived vs wealthiest areas: Standardized Incidence Ratio (SIR) were respectively 1.34 [95% confidence interval 1.18 - 1.52] and 1.25 [1.13 - 1.38] depending on the epidemic wave. This gap was even greater regarding hospitalization rates of cases in critical care units: SIR = 1.64 [1.30 - 2.07] then 1.33 [1.14 - 1.55] depending on the epidemic wave. Hospitalization rates of COVID-19 cases in conventional care units were also greater in areas with high proportion of elderly people vs young people: SIR respectively 1.24 [1.11 - 1.38] and 1.22 [1.13 - 1.32] depending on the wave. ConclusionConsidering age structure and health services provision, a deprived SES profile is associated to a greater COVID-19 severity in terms of hospitals admissions, in conventional care units and in critical care units. This result implies targeting risk prevention efforts on these areas in pandemic situations, and highlights the need to develop access to healthcare to deprived populations in anticipation of periods of crisis. Key messagesWhat is already known on this topic - Socioeconomic status is associated to COVID-19 incidence and severity, at an individual scale or at a large spatial scale. What this study adds - We showed the positive relationship between deprivation and COVID-19 incidence and hospitalization rates at a fine sub-municipal geographical scale. We considered confusion factors like demographic structure and health services provision. How this study might affect research, practice or policy - These findings may help predict at a fine scale where the impact will be most severe in pandemic situations and make it possible to target risk prevention efforts on these areas.
Subject(s)
COVID-19 , ConfusionABSTRACT
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 behaviours, and COVID-19 was rapidly shown to impact deprived population more drastically. In support of the regional health agency in Provence-Alpes-Cote d'Azur (PACA) in South-Eastern France, we analysed 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 analysed 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 analysed 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 analysed 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. Conclusions 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.