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

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  • 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
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    1. 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.
    2. 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.
    3. Caperna, Giulio & Colagrossi, Marco & Geraci, Andrea & Mazzarella, Gianluca, 2020. "Googling Unemployment During the Pandemic: Inference and Nowcast Using Search Data," Working Papers 2020-04, Joint Research Centre, European Commission.
    4. 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.
    5. 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.
    6. 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.
    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.
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