Sociodemographic determinants of intraurban variations in COVID-19 incidence: the case of Barcelona

Abstract

BackgroundIntraurban sociodemographic risk factors for COVID-19 have yet to be fully understood. We investigated the relationship between COVID-19 incidence and sociodemographic factors in Barcelona at a fine-grained geography.MethodsThis cross-sectional ecological study is based on 10 550 confirmed cases of COVID-19 registered during the first wave in the municipality of Barcelona (population 1.64 million). We considered 16 variables on the demographic structure, urban density, household conditions, socioeconomic status, mobility and health characteristics for 76 geographical units of analysis (neighbourhoods), using a lasso analysis to identify the most relevant variables. We then fitted a multivariate Quasi-Poisson model that explained the COVID-19 incidence by neighbourhood in relation to these variables.ResultsNeighbourhoods with: (1) greater population density, (2) an aged population structure, (3) a high presence of nursing homes, (4) high proportions of individuals who left their residential area during lockdown and/or (5) working in health-related occupations were more likely to register a higher number of cases of COVID-19. Conversely, COVID-19 incidence was negatively associated with (6) percentage of residents with post-secondary education and (7) population born in countries with a high Human Development Index.ConclusionLike other historical pandemics, the incidence of COVID-19 is associated with neighbourhood sociodemographic factors with a greater burden faced by already deprived areas. Because urban social and health injustices already existed in those geographical units with higher COVID-19 incidence in Barcelona, the current pandemic is likely to reinforce both health and social inequalities, and urban environmental injustice all together.

Effects of the built environment and human factors on the spread of COVID-19: A systematic literature review

“…In most of the reviewed papers, in the areas where the share of the population aged 60, 65, or 70 is higher, the number of cases is higher. This issue is not sensitive to the scale, as the results of studies on the country ( Sigler et al, 2021 ; Bijari et al, 2021 ), state ( Basellini and Camarda, 2021 ; Tchicaya et al, 2021 ; Ramírez-Aldana et al, 2020 ), county ( Kodera et al, 2020 ; Hamidi et al, 2020b ; Wu and Zhang, 2021 ), city ( Tieskens et al, 2021 ; Pequeno et al, 2020 ) and urban district ( Chan et al, 2021 ; López-Gay et al, 2022 ; Urban and Nakada, 2021 ) scales show the same outcomes. However, Ilardi et al (2020) ; Boterman (2020) and Mondal et al (2022) found age as an insignificant predictor of COVID-19 and Johnson et al (2021) showed that the population over 70 is a negative factor and reduces the number of cases in 299 local authorities in the UK.…”

“…Studies on urban scale have made a clear distinction between inter and intra-city mobility of the population and they mainly discussed that the internal movement of the population is not a significant factor but the mobility of the population between cities is a positive predictor ( Feng et al, 2020 ; Ma et al, 2021 ; J. Liu et al, 2021 ; Zhu et al, 2020 ). Studies in Washington ( M. Hu et al, 2021 ), Barcelona ( López-Gay et al, 2022 ) and Beijing ( Hao et al, 2020 ) also emphasized the negative impact of work and recreation commuting on the spread of COVID-19 cases.…”

Measuring the Impact of Future Outbreaks? A Secondary Analysis of Routinely Available Data in Spain

“…The increase in debt relative to the GDP was not related to any epidemiological indicators. This remains controversial, as some authors have found similar results [ 10 , 25 ] and others have found differences between deprived and more wealthy areas [ 29 ]. However, a systematic review that included domains and variables of social vulnerability affecting COVID-19 such as population aged 65 years or older, unemployment rate, population living below poverty line and public health infrastructures found that higher social vulnerability experienced greater mortality rates during the COVID-19 pandemic [ 24 ].…”

Temporal trends in within-city inequities in COVID-19 incidence rate by area-level deprivation in Madrid, Spain

“…overcrowding) ( Ahmad et al, 2020 ). Some neighborhood characteristics, such as population density, could also explain a higher exposure to COVID-19 in high-deprivation areas ( López-Gay et al, 2021 ). Regarding differential susceptibility, previous research has shown that stress linked to disadvantaged populations might be associated with the likelihood of developing respiratory infections after exposure ( Cohen et al, 1991 ); however, it is unclear that differential susceptibility can play a significant role in explaining differences in COVID-19 incidence rate.…”