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Addressing Disaster Exposure Measurement Issues With Latent Class Analysis.

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Disaster exposure significantly impacts mental health (MH). Latent class analysis (LCA) identified distinct exposure patterns, revealing that high exposure strongly correlates with worse MH outcomes in parents and youth.

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Area of Science:

  • Disaster mental health research
  • Psychological trauma and resilience
  • Quantitative analysis in social sciences

Background:

  • Disaster exposure increases risk for subsequent mental health problems.
  • Understanding analytic approaches is crucial for accurate disaster exposure measurement.
  • Previous methods may not fully capture the nuances of disaster exposure experiences.

Purpose of the Study:

  • To compare different analytic strategies for quantifying disaster exposure.
  • To introduce and evaluate latent class analysis (LCA) as a novel approach.
  • To examine the relationship between disaster exposure patterns and mental health outcomes in parents and youth.

Main Methods:

  • Recruited 555 parents and 486 youth exposed to multiple floods in Texas.
  • Utilized latent class analysis (LCA) to identify disaster exposure patterns.
  • Administered measures of disaster exposure, posttraumatic stress, depression, and anxiety.

Main Results:

  • LCA identified four distinct exposure patterns: high, moderate, community, and low exposure.
  • A threshold effect was observed, with the high exposure group exhibiting significantly worse mental health outcomes (d = 1.12).
  • Similarities in mental health findings were noted across different analytic approaches.

Conclusions:

  • Latent class analysis provides valuable insights into varying disaster exposure experiences.
  • High levels of disaster exposure are linked to poorer mental health outcomes.
  • Findings can inform the development of targeted screening tools for postdisaster mental health services.