IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v20y2023i3p1912-d1042080.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/20/3/1912/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/20/3/1912/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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).
    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. Christopher J Greenwood & George J Youssef & Primrose Letcher & Jacqui A Macdonald & Lauryn J Hagg & Ann Sanson & Jenn Mcintosh & Delyse M Hutchinson & John W Toumbourou & Matthew Fuller-Tyszkiewicz &, 2020. "A comparison of penalised regression methods for informing the selection of predictive markers," PLOS ONE, Public Library of Science, vol. 15(11), pages 1-14, November.
    2. Ida Kubiszewski & Kenneth Mulder & Diane Jarvis & Robert Costanza, 2022. "Toward better measurement of sustainable development and wellbeing: A small number of SDG indicators reliably predict life satisfaction," Sustainable Development, John Wiley & Sons, Ltd., vol. 30(1), pages 139-148, February.
    3. Christopher Kath & Florian Ziel, 2018. "The value of forecasts: Quantifying the economic gains of accurate quarter-hourly electricity price forecasts," Papers 1811.08604, arXiv.org.
    4. Kath, Christopher & Ziel, Florian, 2021. "Conformal prediction interval estimation and applications to day-ahead and intraday power markets," International Journal of Forecasting, Elsevier, vol. 37(2), pages 777-799.
    5. Kath, Christopher & Ziel, Florian, 2018. "The value of forecasts: Quantifying the economic gains of accurate quarter-hourly electricity price forecasts," Energy Economics, Elsevier, vol. 76(C), pages 411-423.
    6. Andreas Groll & Gerhard Tutz, 2017. "Variable selection in discrete survival models including heterogeneity," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 23(2), pages 305-338, April.
    7. Brunori, Paolo & Salas-Rojo, Pedro & Verme, Paolo, 2022. "Estimating Inequality with Missing Incomes," GLO Discussion Paper Series 1138, Global Labor Organization (GLO).
    8. Claude Renaux & Laura Buzdugan & Markus Kalisch & Peter Bühlmann, 2020. "Hierarchical inference for genome-wide association studies: a view on methodology with software," Computational Statistics, Springer, vol. 35(1), pages 1-40, March.
    9. Danhyang Lee & Jae Kwang Kim, 2022. "Semiparametric imputation using conditional Gaussian mixture models under item nonresponse," Biometrics, The International Biometric Society, vol. 78(1), pages 227-237, March.
    10. Jacqueline K Kueper & Daniel J Lizotte & Manuel Montero-Odasso & Mark Speechley & for the Alzheimer’s Disease Neuroimaging Initiative, 2020. "Cognition and motor function: The gait and cognition pooled index," PLOS ONE, Public Library of Science, vol. 15(9), pages 1-16, September.
    11. Andree,Bo Pieter Johannes, 2021. "Estimating Food Price Inflation from Partial Surveys," Policy Research Working Paper Series 9886, The World Bank.
    12. Ting‐Huei Chen & Hanaa Boughal, 2021. "A penalized structural equation modeling method accounting for secondary phenotypes for variable selection on genetically regulated expression from PrediXcan for Alzheimer's disease," Biometrics, The International Biometric Society, vol. 77(1), pages 362-371, March.
    13. Halewijn M. Drent & Barbara van den Hoofdakker & Jan K. Buitelaar & Pieter J. Hoekstra & Andrea Dietrich, 2022. "Factors Related to Perceived Stigma in Parents of Children and Adolescents in Outpatient Mental Healthcare," IJERPH, MDPI, vol. 19(19), pages 1-14, October.
    14. Monica E. Ellwood-Lowe & Susan Whitfield-Gabrieli & Silvia A. Bunge, 2021. "Brain network coupling associated with cognitive performance varies as a function of a child’s environment in the ABCD study," Nature Communications, Nature, vol. 12(1), pages 1-14, December.
    15. Elizabeth Weigensberg & Derekh Cornwell & Lindsey Leininger & Matthew Stagner & Sarah LeBarron & Jonathan Gellar & Sophie MacIntyre & Richard Chapman & Erin J. Maher & Peter J. Pecora & Kirk O’Brien, "undated". "Superutilization of Child Welfare, Medicaid, and Other Services," Mathematica Policy Research Reports caaff77fa722452aa241ace4b, Mathematica Policy Research.
    16. Helian Feng & Nicholas Mancuso & Alexander Gusev & Arunabha Majumdar & Megan Major & Bogdan Pasaniuc & Peter Kraft, 2021. "Leveraging expression from multiple tissues using sparse canonical correlation analysis and aggregate tests improves the power of transcriptome-wide association studies," PLOS Genetics, Public Library of Science, vol. 17(4), pages 1-21, April.
    17. Phung Khanh Lam & Tran Van Ngoc & Truong Thi Thu Thuy & Nguyen Thi Hong Van & Tran Thi Nhu Thuy & Dong Thi Hoai Tam & Nguyen Minh Dung & Nguyen Thi Hanh Tien & Nguyen Tan Thanh Kieu & Cameron Simmons , 2017. "The value of daily platelet counts for predicting dengue shock syndrome: Results from a prospective observational study of 2301 Vietnamese children with dengue," PLOS Neglected Tropical Diseases, Public Library of Science, vol. 11(4), pages 1-20, April.
    18. Tutz, Gerhard & Pößnecker, Wolfgang & Uhlmann, Lorenz, 2015. "Variable selection in general multinomial logit models," Computational Statistics & Data Analysis, Elsevier, vol. 82(C), pages 207-222.
    19. Noémi Kreif & Richard Grieve & Iván Díaz & David Harrison, 2015. "Evaluation of the Effect of a Continuous Treatment: A Machine Learning Approach with an Application to Treatment for Traumatic Brain Injury," Health Economics, John Wiley & Sons, Ltd., vol. 24(9), pages 1213-1228, September.
    20. Abhilash Bandam & Eedris Busari & Chloi Syranidou & Jochen Linssen & Detlef Stolten, 2022. "Classification of Building Types in Germany: A Data-Driven Modeling Approach," Data, MDPI, vol. 7(4), pages 1-23, April.

    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:20:y:2023:i:3:p:1912-:d:1042080. 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.