Article Text
Abstract
Background Intraurban 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.
Methods This 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.
Results Neighbourhoods 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.
Conclusion Like 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.
- COVID-19
- spatial analysis
- social inequalities
- public health
- neighbourhood/place
Data availability statement
Our data are accessible to researchers upon reasonable request for data sharing to the corresponding author. Our dataset has been built based on publicly available data in the referred repositories.
This article is made freely available for use in accordance with BMJ’s website terms and conditions for the duration of the covid-19 pandemic or until otherwise determined by BMJ. You may use, download and print the article for any lawful, non-commercial purpose (including text and data mining) provided that all copyright notices and trade marks are retained.
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