IDEAS home Printed from https://ideas.repec.org/a/bla/ijhplm/v37y2022i1p281-300.html
   My bibliography  Save this article

Association of COVID‐19 with lifestyle behaviours and socio‐economic variables in Turkey: An analysis of Google Trends

Author

Listed:
  • Gamze Bayın Donar
  • Seda Aydan

Abstract

This study aims to examine the relationship between COVID‐19 cases/deaths and Google data on lifestyle behaviours and socio‐economic variables in Turkey. The data of the research are composed of Google Trends search volume for various words related to socio‐economic conditions, nutritional attitudes, indoor behaviour, outdoor activities and confirmed COVID‐19 case and death data from the Ministry of Health from 31 December 2019 to 31 January 2021. Spearman correlation analysis was conducted to evaluate the relationship between the Google search volumes of selected keywords and COVID‐19 case and deaths. In addition, repeated ANOVA and Bonferroni post‐hoc tests were performed to compare the differences in search volumes of selected keywords before and during the COVID‐19 outbreak. Correlation analysis showed that the strongest variables in each category were vitamin C, zinc, Zoom, online shopping, hotel, market, gym, unemployment and unemployment benefit. Compared to previous years, during the pandemic, there was a significant increase or decrease in the search volumes of almost all words. These results showed that the COVID‐19 significantly changed people's online interests regarding lifestyle behaviours and socio‐economic conditions. It is thought that the findings can guide health policies to be followed in reducing the effects of both behavioural changes and negative socio‐economic consequences.

Suggested Citation

  • Gamze Bayın Donar & Seda Aydan, 2022. "Association of COVID‐19 with lifestyle behaviours and socio‐economic variables in Turkey: An analysis of Google Trends," International Journal of Health Planning and Management, Wiley Blackwell, vol. 37(1), pages 281-300, January.
    • Handle: RePEc:bla:ijhplm:v:37:y:2022:i:1:p:281-300
    DOI: 10.1002/hpm.3342
    as

    Download full text from publisher

    File URL: https://doi.org/10.1002/hpm.3342
    Download Restriction: no
    File URL: https://libkey.io/10.1002/hpm.3342?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Caperna, Giulio & Colagrossi, Marco & Geraci, Andrea & Mazzarella, Gianluca, 2020. "Googling Unemployment During the Pandemic: Inference and Nowcast Using Search Data," JRC Working Papers in Economics and Finance 2020-04, Joint Research Centre, European Commission.
    2. Caitlin Rivers & Jean-Paul Chretien & Steven Riley & Julie A. Pavlin & Alexandra Woodward & David Brett-Major & Irina Maljkovic Berry & Lindsay Morton & Richard G. Jarman & Matthew Biggerstaff & Micha, 2019. "Using “outbreak science” to strengthen the use of models during epidemics," Nature Communications, Nature, vol. 10(1), pages 1-3, December.
    3. Jeremy Ginsberg & Matthew H. Mohebbi & Rajan S. Patel & Lynnette Brammer & Mark S. Smolinski & Larry Brilliant, 2009. "Detecting influenza epidemics using search engine query data," Nature, Nature, vol. 457(7232), pages 1012-1014, February.
    4. Fajar, Muhammad & Prasetyo, Octavia Rizky & Nonalisa, Septiarida & Wahyudi, Wahyudi, 2020. "Forecasting unemployment rate in the time of COVID-19 pandemic using Google trends data (case of Indonesia)," MPRA Paper 105042, University Library of Munich, Germany, revised 30 Nov 2020.
    5. Chiara Sotis, 2021. "How do Google searches for symptoms, news and unemployment interact during COVID-19? A Lotka–Volterra analysis of google trends data," Quality & Quantity: International Journal of Methodology, Springer, vol. 55(6), pages 2001-2016, December.
    6. Sebastian Doerr & Leonardo Gambacorta, 2020. "Identifying regions at risk with Google Trends: the impact of Covid-19 on US labour markets," BIS Bulletins 8, Bank for International Settlements.
    7. Arora, Vishal S. & McKee, Martin & Stuckler, David, 2019. "Google Trends: Opportunities and limitations in health and health policy research," Health Policy, Elsevier, vol. 123(3), pages 338-341.
    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. Artur Strzelecki, 2020. "Google Medical Update: Why Is the Search Engine Decreasing Visibility of Health and Medical Information Websites?," IJERPH, MDPI, vol. 17(4), pages 1-13, February.
    2. Daniel E. O'Leary & Veda C. Storey, 2020. "A Google–Wikipedia–Twitter Model as a Leading Indicator of the Numbers of Coronavirus Deaths," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 27(3), pages 151-158, July.
    3. Simionescu, Mihaela & Cifuentes-Faura, Javier, 2022. "Can unemployment forecasts based on Google Trends help government design better policies? An investigation based on Spain and Portugal," Journal of Policy Modeling, Elsevier, vol. 44(1), pages 1-21.
    4. Soo Beom Choi & Juhyeon Kim & Insung Ahn, 2019. "Forecasting type-specific seasonal influenza after 26 weeks in the United States using influenza activities in other countries," PLOS ONE, Public Library of Science, vol. 14(11), pages 1-15, November.
    5. van der Wielen, Wouter & Barrios, Salvador, 2021. "Economic sentiment during the COVID pandemic: Evidence from search behaviour in the EU," Journal of Economics and Business, Elsevier, vol. 115(C).
    6. David H Chae & Sean Clouston & Mark L Hatzenbuehler & Michael R Kramer & Hannah L F Cooper & Sacoby M Wilson & Seth I Stephens-Davidowitz & Robert S Gold & Bruce G Link, 2015. "Association between an Internet-Based Measure of Area Racism and Black Mortality," PLOS ONE, Public Library of Science, vol. 10(4), pages 1-12, April.
    7. Xiaoli Wang & Shuangsheng Wu & C Raina MacIntyre & Hongbin Zhang & Weixian Shi & Xiaomin Peng & Wei Duan & Peng Yang & Yi Zhang & Quanyi Wang, 2015. "Using an Adjusted Serfling Regression Model to Improve the Early Warning at the Arrival of Peak Timing of Influenza in Beijing," PLOS ONE, Public Library of Science, vol. 10(3), pages 1-14, March.
    8. Ishani Chaudhuri & Parthajit Kayal, 2022. "Predicting Power of Ticker Search Volume in Indian Stock Market," Working Papers 2022-214, Madras School of Economics,Chennai,India.
    9. Yang, Xin & Pan, Bing & Evans, James A. & Lv, Benfu, 2015. "Forecasting Chinese tourist volume with search engine data," Tourism Management, Elsevier, vol. 46(C), pages 386-397.
    10. Kuchler, Theresa & Russel, Dominic & Stroebel, Johannes, 2022. "JUE Insight: The geographic spread of COVID-19 correlates with the structure of social networks as measured by Facebook," Journal of Urban Economics, Elsevier, vol. 127(C).
    11. Markowitz, Sara & Nesson, Erik & Robinson, Joshua J., 2019. "The effects of employment on influenza rates," Economics & Human Biology, Elsevier, vol. 34(C), pages 286-295.
    12. Bentzen, Jeanet Sinding, 2021. "In crisis, we pray: Religiosity and the COVID-19 pandemic," Journal of Economic Behavior & Organization, Elsevier, vol. 192(C), pages 541-583.
    13. Jesse T. Richman & Ryan J. Roberts, 2023. "Assessing Spurious Correlations in Big Search Data," Forecasting, MDPI, vol. 5(1), pages 1-12, February.
    14. Linus Schiöler & Marianne Fris�n, 2012. "Multivariate outbreak detection," Journal of Applied Statistics, Taylor & Francis Journals, vol. 39(2), pages 223-242, April.
    15. Sasikiran Kandula & Jeffrey Shaman, 2019. "Reappraising the utility of Google Flu Trends," PLOS Computational Biology, Public Library of Science, vol. 15(8), pages 1-16, August.
    16. Daniel E. O'Leary, 2024. "Toward an extended framework of exhaust data for predictive analytics: An empirical approach," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 31(2), June.
    17. Yangkun Huang & Xiaoping Xu & Sini Su, 2021. "Diverging from News Media: An Exploratory Study on the Changing Dynamics between Media and Public Attention on Cancer in China from 2011–2020," IJERPH, MDPI, vol. 18(16), pages 1-13, August.
    18. Vosen, Simeon & Schmidt, Torsten, 2012. "A monthly consumption indicator for Germany based on Internet search query data," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 19(7), pages 683-687.
    19. Klaus Ackermann & Simon D Angus & Paul A Raschky, 2017. "The Internet as Quantitative Social Science Platform: Insights from a Trillion Observations," Papers 1701.05632, arXiv.org.
    20. Edward L. Glaeser & Scott Duke Kominers & Michael Luca & Nikhil Naik, 2018. "Big Data And Big Cities: The Promises And Limitations Of Improved Measures Of Urban Life," Economic Inquiry, Western Economic Association International, vol. 56(1), pages 114-137, January.

    More about this item

    Statistics

    Access and download statistics

    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:bla:ijhplm:v:37:y:2022:i:1:p:281-300. 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: Wiley Content Delivery (email available below). General contact details of provider: http://www.blackwellpublishing.com/journal.asp?ref=0749-6753 .

    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.