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.

Highlights

  • It is well established that individuals who are displaced by disaster and war—a figure currently at 70.8 million [1]—suffer from high rates of psychopathology

  • We propose that association rule learning (ARL), a big data statistical method [17,21] can be used as an alternative to network analysis and cluster analysis for the purpose of identifying profiles of traumas and stressful life events that predict psychopathology

  • We aim to demonstrate the utility of ARL in identifying profiles of trauma and stressors that predict psychopathology by using the technique to identify such profiles in survivors of the Sri Lankan civil war [29]

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Summary

Objectives

We aim to demonstrate the utility of ARL in identifying profiles of trauma and stressors that predict psychopathology by using the technique to identify such profiles in survivors of the Sri Lankan civil war [29]

Results
Discussion
Conclusion

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