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Community Risk Factors in the COVID-19 Incidence and Mortality in Catalonia (Spain). A Population-Based Study

Author

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  • Quim Zaldo-Aubanell

    (Environment and Human Health Laboratory (EH2 Lab), Forest Science and Technology Center of Catalonia, Ctra. de St. Llorenç de Morunys, km 2, 25280 Solsona, Spain
    Institute of Environmental Science and Technology (ICTA), Autonomous University of Barcelona (UAB), Z Building, ICTA-ICP, Carrer de les Columnes, UAB Campus, 08193 Bellaterra, Spain)

  • Ferran Campillo i López

    (Environment and Human Health Laboratory (EH2 Lab), Forest Science and Technology Center of Catalonia, Ctra. de St. Llorenç de Morunys, km 2, 25280 Solsona, Spain
    Pediatric Environmental Health Specialty Unit, Pediatric Team of Garrotxa and Ripollès Regions, Olot and Garrotxa Region Hospital Foundation, 17800 Olot, Spain)

  • Albert Bach

    (Environment and Human Health Laboratory (EH2 Lab), Forest Science and Technology Center of Catalonia, Ctra. de St. Llorenç de Morunys, km 2, 25280 Solsona, Spain)

  • Isabel Serra

    (Centre de Recerca Matemàtica, Edifici C, 08193 Bellaterra, Spain
    Barcelona Supercomputing Center, 08034 Barcelona, Spain)

  • Joan Olivet-Vila

    (Health Promotion Service in Girona, Agency of Public Health of Catalonia, Generalitat of Catalonia, 17003 Girona, Spain)

  • Marc Saez

    (Research Group on Statistics, Econometrics and Health (GRECS), University of Girona, 17003 Girona, Spain
    CIBER of Epidemiology and Public Health (CIBERESP), 28029 Madrid, Spain)

  • David Pino

    (Departament of Physics, Universitat Politècnica de Catalunya·BarcelonaTech, Esteve Terrades 5, 08034 Castelldefels, Spain
    Institut d’Estudis Espacials de Catalunya (IEEC-UPC), Gran Capità 2-4, 08034 Barcelona, Spain)

  • Roser Maneja

    (Environment and Human Health Laboratory (EH2 Lab), Forest Science and Technology Center of Catalonia, Ctra. de St. Llorenç de Morunys, km 2, 25280 Solsona, Spain
    Forest Science and Technology Center of Catalonia, Ctra. de St. Llorenç de Morunys, km 2, 25280 Solsona, Spain
    Geography Department, Autonomous University of Barcelona (UAB), B Building, UAB Campus, 08193 Bellaterra, Spain)

Abstract

The heterogenous distribution of both COVID-19 incidence and mortality in Catalonia (Spain) during the firsts moths of the pandemic suggests that differences in baseline risk factors across regions might play a relevant role in modulating the outcome of the pandemic. This paper investigates the associations between both COVID-19 incidence and mortality and air pollutant concentration levels, and screens the potential effect of the type of agri-food industry and the overall land use and cover (LULC) at area level. We used a main model with demographic, socioeconomic and comorbidity covariates highlighted in previous research as important predictors. This allowed us to take a glimpse of the independent effect of the explanatory variables when controlled for the main model covariates. Our findings are aligned with previous research showing that the baseline features of the regions in terms of general health status, pollutant concentration levels (here NO 2 and PM 10 ), type of agri-food industry, and type of land use and land cover have modulated the impact of COVID-19 at a regional scale. This study is among the first to explore the associations between COVID-19 and the type of agri-food industry and LULC data using a population-based approach. The results of this paper might serve as the basis to develop new research hypotheses using a more comprehensive approach, highlighting the inequalities of regions in terms of risk factors and their response to COVID-19, as well as fostering public policies towards more resilient and safer environments.

Suggested Citation

  • Quim Zaldo-Aubanell & Ferran Campillo i López & Albert Bach & Isabel Serra & Joan Olivet-Vila & Marc Saez & David Pino & Roser Maneja, 2021. "Community Risk Factors in the COVID-19 Incidence and Mortality in Catalonia (Spain). A Population-Based Study," IJERPH, MDPI, vol. 18(7), pages 1-20, April.
  • Handle: RePEc:gam:jijerp:v:18:y:2021:i:7:p:3768-:d:530122
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    References listed on IDEAS

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    1. Andree,Bo Pieter Johannes, 2020. "Incidence of COVID-19 and Connections with Air Pollution Exposure : Evidence from the Netherlands," Policy Research Working Paper Series 9221, The World Bank.
    2. K. Hron & P. Filzmoser & K. Thompson, 2012. "Linear regression with compositional explanatory variables," Journal of Applied Statistics, Taylor & Francis Journals, vol. 39(5), pages 1115-1128, November.
    3. Leonardo Setti & Fabrizio Passarini & Gianluigi De Gennaro & Pierluigi Barbieri & Alberto Pallavicini & Maurizio Ruscio & Prisco Piscitelli & Annamaria Colao & Alessandro Miani, 2020. "Searching for SARS-COV-2 on Particulate Matter: A Possible Early Indicator of COVID-19 Epidemic Recurrence," IJERPH, MDPI, vol. 17(9), pages 1-5, April.
    4. Heyuan You & Xin Wu & Xuxu Guo, 2020. "Distribution of COVID-19 Morbidity Rate in Association with Social and Economic Factors in Wuhan, China: Implications for Urban Development," IJERPH, MDPI, vol. 17(10), pages 1-14, May.
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