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Sociodemographic determinants of intraurban variations in COVID-19 incidence: the case of Barcelona
  1. Antonio López-Gay1,2,
  2. Jeroen Spijker2,
  3. Helen V S Cole3,
  4. Antonio G Marques4,
  5. Margarita Triguero-Mas5,6,
  6. Isabelle Anguelovski7,8,
  7. Marc Marí-Dell'Olmo9,10,11,
  8. Juan A Módenes2,
  9. Dolores Álamo-Junquera9,
  10. Fernando López-Gallego12,
  11. Carme Borrell9,10,11
  1. 1 Department of Geography, Autonomous University of Barcelona, Barcelona, Spain
  2. 2 Center for Demographic Studies, Bellaterra, Spain
  3. 3 Barcelona Lab for Urban Environmental Justice and Sustainability, Autonomous University of Barcelona, Barcelona, Spain
  4. 4 Department of Signal Theory and Communications, Rey Juan Carlos University, Madrid, Spain
  5. 5 Institute for Environmental Science and Technology—Barcelona Lab for Urban Environmental Justice and Sustainability, Autonomous University of Barcelona, Cerdanyola del Vallès, Spain
  6. 6 Department of Urban Studies and Planning—Mariana Arcaya's Research Lab, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
  7. 7 Autonomous University of Barcelona, Bellaterra, Spain
  8. 8 Catalan Institution for Research and Advanced Studies, Barcelona, Spain
  9. 9 Agència de Salut Pública de Barcelona, Barcelona, Spain
  10. 10 CIBER de Epidemiología y Salud Pública, Madrid, Spain
  11. 11 Institut d’Investigació Biomèdica Sant Pau (IIB Sant Pau), Barcelona, Spain
  12. 12 Heterogeneous Biocatalysis Laboratory, CIC biomaGUNE, San Sebastian, Spain
  1. Correspondence to Dr Antonio López-Gay, Department of Geography, Autonomous University of Barcelona, 08193 Bellaterra, Barcelona, Spain; tlopez{at}


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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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.

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  • Twitter @tonilopezga, @popageing, @hvscole, @ItaTrigueroMas, @ianguelovski, @epistatistic, @ModenesJA, @flopez_gallego, @carme1848

  • Contributors AGM, AL-G and FL-G conceived the study. AL-G, FL-G, MM-D and JAM collected data, calculated indicators and built the final dataset. AGM, AL-G, MM-D, JS and MT-M conducted the statistical analyses. IA, CB, HC, AGM, AL-G, JS and MT-M wrote the paper. AL-G was the principal investigator of the study. All authors contributed to the interpretation of data, and read, edited and approved the final manuscript.

  • Funding This project has been funded by the following programmes: H2020 European Research Council (GREEN LULUs SG/GA678034 and HEALIN CoG/GA864616); Ministerio de Ciencia e Innovación (CSO2016-79142-R; GLOBFAM/RTI2018-096730-B-I00I3; ‘Maria de Maeztu’ Program/CEX2019-000940-M; ‘Ramón y Cajal' Program/RYC-2013-14851; 'Juan de la Cierva' Program/FJCI-2017-33842 and IJC2018-035322-I); Agència de Gestió d'Ajuts Universitaris i de Recerca (DEMFAMS/2017 SGR 1454); Talent Research Program, Universitat Autònoma de Barcelona.

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  • Competing interests None declared.

  • Provenance and peer review Not commissioned; externally peer reviewed.

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