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Risk factors for psychological distress in Northern Ireland

Author

Listed:
  • Liam Mahedy
  • Flora Todaro-Luck
  • Brendan Bunting
  • Samuel Murphy
  • Karen Kirby

Abstract

Background: Stress-related mental ill health and its disorders are considered by the World Health Organization (WHO) to be the new world epidemic and their prevalence rates seem to be increasing worldwide. Aims: To examine and identify sub-populations at risk for psychological discomfort in Northern Ireland and map the relative impact of potential predictors. Methods: A sample of 4,638 respondents to the NIHSW-2001 survey was analysed with latent class analysis and latent class factorial analysis. Latent class multinomial logistic regression assessed the impact of a range of predictors on class membership. Results: Five sub-populations were differentiated. All subgroups at risk for anxiety and depression were characterized as being younger and female. Disability and adverse life events were strong predictors of risk. Long-standing illness and housing worries were predictors of medium and high risk membership. The effect of civil unrest was significant only for the medium-risk subgroup; marital status and income did not affect group membership. Conclusions: Because all five subgroups showed a different probability, but a similar profile of endorsing GHQ-12 items, it could be hypothesized that an underlying continuum dimension of anxiety and depression is present in the Northern Irish population.

Suggested Citation

  • Liam Mahedy & Flora Todaro-Luck & Brendan Bunting & Samuel Murphy & Karen Kirby, 2013. "Risk factors for psychological distress in Northern Ireland," International Journal of Social Psychiatry, , vol. 59(7), pages 646-654, November.
    • Handle: RePEc:sae:socpsy:v:59:y:2013:i:7:p:646-654
    DOI: 10.1177/0020764012450993
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    1. Vuong, Quang H, 1989. "Likelihood Ratio Tests for Model Selection and Non-nested Hypotheses," Econometrica, Econometric Society, vol. 57(2), pages 307-333, March.
    2. Stanley Sclove, 1987. "Application of model-selection criteria to some problems in multivariate analysis," Psychometrika, Springer;The Psychometric Society, vol. 52(3), pages 333-343, September.
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