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Case-Control Study on the Routes of Transmission of SARS-CoV-2 after the Third Pandemic Wave in Tuscany, Central Italy

Author

Listed:
  • Miriam Levi

    (Epidemiology Unit, Department of Prevention, Central Tuscany Health Authority, 50135 Florence, Italy)

  • Giulia Cereda

    (Department of Statistics, Computer Science, Applications (DISIA), University of Florence, 50134 Florence, Italy)

  • Francesco Cipriani

    (Epidemiology Unit, Department of Prevention, Central Tuscany Health Authority, 50135 Florence, Italy
    Tuscany Regional Centre for Work-Related Injuries and Diseases (CeRIMP), 50135 Florence, Italy)

  • Fabio Voller

    (Epidemiology Unit, Regional Health Agency of Tuscany, 50141 Florence, Italy)

  • Michela Baccini

    (Department of Statistics, Computer Science, Applications (DISIA), University of Florence, 50134 Florence, Italy)

Abstract

The emergence of hyper-transmissible SARS-CoV-2 variants that rapidly became prevalent throughout the world in 2022 made it clear that extensive vaccination campaigns cannot represent the sole measure to stop COVID-19. However, the effectiveness of control and mitigation strategies, such as the closure of non-essential businesses and services, is debated. To assess the individual behaviours mostly associated with SARS-CoV-2 infection, a questionnaire-based case-control study was carried out in Tuscany, Central Italy, from May to October 2021. At the testing sites, individuals were invited to answer an online questionnaire after being notified regarding the test result. The questionnaire collected information about test result, general characteristics of the respondents, and behaviours and places attended in the week prior to the test/symptoms onset. We analysed 440 questionnaires. Behavioural differences between positive and negative subjects were assessed through logistic regression models, adjusting for a fixed set of confounders. A ridge regression model was also specified. Attending nightclubs, open-air bars or restaurants and crowded clubs, outdoor sporting events, crowded public transportation, and working in healthcare were associated with an increased infection risk. A negative association with infection, besides face mask use, was observed for attending open-air shows and sporting events in indoor spaces, visiting and hosting friends, attending courses in indoor spaces, performing sport activities (both indoor and outdoor), attending private parties, religious ceremonies, libraries, and indoor restaurants. These results might suggest that during the study period people maintained a particularly responsible and prudent approach when engaging in everyday activities to avoid spreading the virus.

Suggested Citation

  • Miriam Levi & Giulia Cereda & Francesco Cipriani & Fabio Voller & Michela Baccini, 2023. "Case-Control Study on the Routes of Transmission of SARS-CoV-2 after the Third Pandemic Wave in Tuscany, Central Italy," IJERPH, MDPI, vol. 20(3), pages 1-13, January.
  • Handle: RePEc:gam:jijerp:v:20:y:2023:i:3:p:1912-:d:1042080
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    References listed on IDEAS

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    1. van Buuren, Stef & Groothuis-Oudshoorn, Karin, 2011. "mice: Multivariate Imputation by Chained Equations in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 45(i03).
    2. Friedman, Jerome H. & Hastie, Trevor & Tibshirani, Rob, 2010. "Regularization Paths for Generalized Linear Models via Coordinate Descent," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 33(i01).
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