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Looking for COVID side effects in the EU through the analysis of health and behavioural profiles

Author

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  • Aurea Grané

    (Universidad Carlos III de Madrid)

  • Irene Albarrán

    (Universidad Carlos III de Madrid)

  • Diego Peran

    (Universidad Carlos III de Madrid)

Abstract

More than two years after the great outbreak of COVID suffered in almost the whole world, and in particular in Europe, we have gradually learned about the direct effects of this virus on our health and what consequences it can have if we become infected. However, this pandemic also had great economic and social consequences that affected people in an indirect way, which we can call COVID side effects. In this work we carried out an innovative type of analysis based on the concept of archetypoids in order to find extreme observations in a database of mixed-type data and used them to classify individuals yielding to different health and behavioural profiles in coping with the COVID outbreak in the EU. We use data from the first COVID-19 Survey of the SHARE project (Survey on Health, Aging and Retirement in Europe). The resulting profiles are easier to interpret than others based on central observations, and help to understand how the situations of restrictions and lock-downs affected people since the outbreak of the pandemic. Another key point of the work was to analyse how determinant are some aspects such as gender, age group or even geographical location in how each person experienced the pandemic. The method that we propose is wide enough to be used in other health and wellbeing surveys.

Suggested Citation

  • Aurea Grané & Irene Albarrán & Diego Peran, 2024. "Looking for COVID side effects in the EU through the analysis of health and behavioural profiles," Quality & Quantity: International Journal of Methodology, Springer, vol. 58(6), pages 5225-5255, December.
    • Handle: RePEc:spr:qualqt:v:58:y:2024:i:6:d:10.1007_s11135-022-01606-3
    DOI: 10.1007/s11135-022-01606-3
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    References listed on IDEAS

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    1. Vinué, Guillermo & Epifanio, Irene & Alemany, Sandra, 2015. "Archetypoids: A new approach to define representative archetypal data," Computational Statistics & Data Analysis, Elsevier, vol. 87(C), pages 102-115.
    2. García-Prado, Ariadna & González, Paula & Rebollo-Sanz, Yolanda F., 2022. "Lockdown strictness and mental health effects among older populations in Europe," Economics & Human Biology, Elsevier, vol. 45(C).
    3. Guillermo Vinue & Irene Epifanio, 2021. "Robust archetypoids for anomaly detection in big functional data," 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(2), pages 437-462, June.
    4. Aurea Grané & Irene Albarrán & Roger Lumley, 2020. "Visualizing Inequality in Health and Socioeconomic Wellbeing in the EU: Findings from the SHARE Survey," IJERPH, MDPI, vol. 17(21), pages 1-18, October.
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