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Association Rule Learning Is an Easy and Efficient Method for Identifying Profiles of Traumas and Stressors that Predict Psychopathology in Disaster Survivors: The Example of Sri Lanka

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  • Nuwan Jayawickreme

    (Department of Psychology, Manhattan College, Bronx, NY 18966, USA)

  • Ehsan Atefi

    (Department of Mechanical Engineering, Manhattan College, Bronx, NY 18966, USA)

  • Eranda Jayawickreme

    (Department of Psychology, Wake Forest University, Winston-Salem, NC 27109, USA)

  • Jiale Qin

    (School of Business, Stevens Institute of Technology, Hoboken, NJ 07030, USA)

  • Amir H. Gandomi

    (Faculty of Engineering and Information Technology, University of Technology Sydney, Ultimo, NSW 2007, Australia)

Abstract

Research indicates that psychopathology in disaster survivors is a function of both experienced trauma and stressful life events. However, such studies are of limited utility to practitioners who are about to go into a new post-disaster setting as (1) most of them do not indicate which specific traumas and stressors are especially likely to lead to psychopathology; and (2) each disaster is characterized by its own unique traumas and stressors, which means that practitioners have to first collect their own data on common traumas, stressors and symptoms of psychopathology prior to planning any interventions. An easy-to-use and easy-to-interpret data analytical method that allows one to identify profiles of trauma and stressors that predict psychopathology would be of great utility to practitioners working in post-disaster contexts. We propose that association rule learning (ARL), a big data mining technique, is such a method. We demonstrate the technique by applying it to data from 337 survivors of the Sri Lankan civil war who completed the Penn/RESIST/Peradeniya War Problems Questionnaire (PRPWPQ), a comprehensive, culturally-valid measure of experienced trauma, stressful life events, anxiety and depression. ARL analysis revealed five profiles of traumas and stressors that predicted the presence of some anxiety, three profiles that predicted the presence of severe anxiety, four profiles that predicted the presence of some depression and five profiles that predicted the presence of severe depression. ARL allows one to identify context-specific associations between specific traumas, stressors and psychological distress, and can be of great utility to practitioners who wish to efficiently analyze data that they have collected, understand the output of that analysis, and use it to provide psychosocial aid to those who most need it in post-disaster settings.

Suggested Citation

  • Nuwan Jayawickreme & Ehsan Atefi & Eranda Jayawickreme & Jiale Qin & Amir H. Gandomi, 2020. "Association Rule Learning Is an Easy and Efficient Method for Identifying Profiles of Traumas and Stressors that Predict Psychopathology in Disaster Survivors: The Example of Sri Lanka," IJERPH, MDPI, vol. 17(8), pages 1-17, April.
    • Handle: RePEc:gam:jijerp:v:17:y:2020:i:8:p:2850-:d:348361
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    References listed on IDEAS

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    1. Jayawickreme, Nuwan & Mootoo, Candace & Fountain, Christine & Rasmussen, Andrew & Jayawickreme, Eranda & Bertuccio, Rebecca F., 2017. "Post-conflict struggles as networks of problems: A network analysis of trauma, daily stressors and psychological distress among Sri Lankan war survivors," Social Science & Medicine, Elsevier, vol. 190(C), pages 119-132.
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