IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v18y2021i7p3768-d530122.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/18/7/3768/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1660-4601/18/7/3768/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Nikola Štefelová & Andreas Alfons & Javier Palarea-Albaladejo & Peter Filzmoser & Karel Hron, 2021. "Robust regression with compositional covariates including cellwise outliers," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 15(4), pages 869-909, December.
    2. Mauricio Velasquez, 2016. "Compositions vs Gini: A new metric to evaluate the effects of land-income disparities," 2016 Papers pve364, Job Market Papers.
    3. Janina Janurek & Sascha Abdel Hadi & Andreas Mojzisch & Jan Alexander Häusser, 2018. "The Association of the 24 Hour Distribution of Time Spent in Physical Activity, Work, and Sleep with Emotional Exhaustion," IJERPH, MDPI, vol. 15(9), pages 1-14, September.
    4. J. A. Martín-Fernández, 2021. "“Compositional Data Analysis in Practice” by Michael Greenacre Universitat Pompeu Fabra (Barcelona, Spain), Chapman and Hall/CRC, 2018," Journal of Classification, Springer;The Classification Society, vol. 38(1), pages 109-111, April.
    5. Andriansyah, Andriansyah & Messinis, George, 2016. "Intended use of IPO proceeds and firm performance: A quantile regression approach," Pacific-Basin Finance Journal, Elsevier, vol. 36(C), pages 14-30.
    6. Joseph Ching & Mizuo Kajino, 2020. "Rethinking Air Quality and Climate Change after COVID-19," IJERPH, MDPI, vol. 17(14), pages 1-11, July.
    7. Gómez-Lobo, Andrés & Gutiérrez, Mauro & Huamaní, Sandro & Marino, Diego & Serebrisky, Tomás & Solís, Ben, 2022. "Access to water and COVID-19: a regression discontinuity analysis for the peri-urban areas of Metropolitan Lima, Peru," IDB Publications (Working Papers) 12332, Inter-American Development Bank.
    8. Yongzhu Xiong & Yunpeng Wang & Feng Chen & Mingyong Zhu, 2020. "Spatial Statistics and Influencing Factors of the COVID-19 Epidemic at Both Prefecture and County Levels in Hubei Province, China," IJERPH, MDPI, vol. 17(11), pages 1-26, May.
    9. Zhao, T.; & Sutton, M.; & Meacock, M.;, 2023. "Use of compositional covariates in linear regression: problems and solutions," Health, Econometrics and Data Group (HEDG) Working Papers 23/16, HEDG, c/o Department of Economics, University of York.
    10. Jacob Fiksel & Scott Zeger & Abhirup Datta, 2022. "A transformation‐free linear regression for compositional outcomes and predictors," Biometrics, The International Biometric Society, vol. 78(3), pages 974-987, September.
    11. Bopaki Phogole & Kowiyou Yessoufou, 2023. "Greener Neighbourhoods Show Resilience to the Spread but Not Severity of COVID-19 Infection in South Africa," Sustainability, MDPI, vol. 15(19), pages 1-16, October.
    12. Biyun Guo & Taiping Xie & M.V. Subrahmanyam, 2019. "The Impact of China’s Grain for Green Program on Rural Economy and Precipitation: A Case Study of Yan River Basin in the Loess Plateau," Sustainability, MDPI, vol. 11(19), pages 1-18, September.
    13. Patrick Connerton & João Vicente de Assunção & Regina Maura de Miranda & Anne Dorothée Slovic & Pedro José Pérez-Martínez & Helena Ribeiro, 2020. "Air Quality during COVID-19 in Four Megacities: Lessons and Challenges for Public Health," IJERPH, MDPI, vol. 17(14), pages 1-24, July.
    14. Alberto Pivato & Gianni Formenton & Francesco Di Maria & Tatjana Baldovin & Irene Amoruso & Tiziano Bonato & Pamela Mancini & Giusy Bonanno Ferraro & Carolina Veneri & Marcello Iaconelli & Lucia Bonad, 2022. "SARS-CoV-2 in Atmospheric Particulate Matter: An Experimental Survey in the Province of Venice in Northern Italy," IJERPH, MDPI, vol. 19(15), pages 1-14, August.
    15. Thomas-Agnan, Christine & Morais, Joanna, 2019. "Covariates impacts in compositional models and simplicial derivatives," TSE Working Papers 19-1057, Toulouse School of Economics (TSE).
    16. Qian Liu & Wei Liu & Dexuan Sha & Shubham Kumar & Emily Chang & Vishakh Arora & Hai Lan & Yun Li & Zifu Wang & Yadong Zhang & Zhiran Zhang & Jackson T. Harris & Srikar Chinala & Chaowei Yang, 2020. "An Environmental Data Collection for COVID-19 Pandemic Research," Data, MDPI, vol. 5(3), pages 1-13, August.
    17. Lorenzo Gianquintieri & Maria Antonia Brovelli & Andrea Pagliosa & Rodolfo Bonora & Giuseppe Maria Sechi & Enrico Gianluca Caiani, 2021. "Geospatial Correlation Analysis between Air Pollution Indicators and Estimated Speed of COVID-19 Diffusion in the Lombardy Region (Italy)," IJERPH, MDPI, vol. 18(22), pages 1-18, November.
    18. Andy Hong & Sandip Chakrabarti, 2023. "Compact living or policy inaction? Effects of urban density and lockdown on the COVID-19 outbreak in the US," Urban Studies, Urban Studies Journal Limited, vol. 60(9), pages 1588-1609, July.
    19. Dorothea Dumuid & Željko Pedišić & Javier Palarea-Albaladejo & Josep Antoni Martín-Fernández & Karel Hron & Timothy Olds, 2020. "Compositional Data Analysis in Time-Use Epidemiology: What, Why, How," IJERPH, MDPI, vol. 17(7), pages 1-17, March.
    20. Huiwen Wang & Zhichao Wang & Shanshan Wang, 2021. "Sliced inverse regression method for multivariate compositional data modeling," Statistical Papers, Springer, vol. 62(1), pages 361-393, February.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jijerp:v:18:y:2021:i:7:p:3768-:d:530122. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.