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A Two-Week Vacation in the Tropics and Psychological Well-Being—An Observational Follow-Up Study

Author

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  • Tanja Laukkala

    (Department of Psychiatry, University of Helsinki and Acute Psychiatry and Consultations, HUS Helsinki University Hospital, 00029 Helsinki, Finland)

  • Tom Rosenström

    (Department of Psychology and Logopedics, Faculty of Medicine, University of Helsinki, 00014 Helsinki, Finland)

  • Anu Kantele

    (Meilahti Infectious Diseases and Vaccine Research Center, MeiVac, Department of Infectious Diseases, Inflammatory Center, University of Helsinki and Helsinki University Hospital, 00029 Helsinki, Finland)

Abstract

Despite the vast annual number of international visitors to the tropics, surprisingly little data are available on the psychological well-being associated with the travels or with travelers’ diarrhoea (TD). We herein recruited participants of a vaccination trial, OEV-123, before their 12-day holiday in Benin, West Africa. We assessed the travelers’ psychological distress with a general health questionnaire (GHQ-12) and retrieved data on TD from the trial database. The GHQ-12 was completed before (wave 0), at return (wave 1), and 1-month after (wave 2) the trip. Of the 174 participants, 73% were women, with a mean age 40 years. Moreover, 24% reported psychological distress before traveling, 10% immediately after, and 16% 1-month after the trip (GHQ-12, 3 or more; 0–12 scoring). The findings showed that psychological well-being increased after the tropical holiday. The GHQ-12 middle wave sum score differed from the wave 0 ( p < 0.001) and wave 2 ( p = 0.008) sum scores, with travelers reporting highest levels of well-being on their return, with evidence of a lasting improvement. TD was experienced by 71%, and it had a negative impact on psychological well-being only if experienced after travel.

Suggested Citation

  • Tanja Laukkala & Tom Rosenström & Anu Kantele, 2022. "A Two-Week Vacation in the Tropics and Psychological Well-Being—An Observational Follow-Up Study," IJERPH, MDPI, vol. 19(16), pages 1-9, August.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:16:p:10381-:d:893374
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    References listed on IDEAS

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    1. Carpenter, Bob & Gelman, Andrew & Hoffman, Matthew D. & Lee, Daniel & Goodrich, Ben & Betancourt, Michael & Brubaker, Marcus & Guo, Jiqiang & Li, Peter & Riddell, Allen, 2017. "Stan: A Probabilistic Programming Language," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 76(i01).
    2. 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).
    3. Zhou, Xiang & Reiter, Jerome P., 2010. "A Note on Bayesian Inference After Multiple Imputation," The American Statistician, American Statistical Association, vol. 64(2), pages 159-163.
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