Addressing Disaster Exposure Measurement Issues With Latent Class Analysis.

Disaster exposure can put survivors at greater risk for subsequent mental health (MH) problems. Within the field of disaster MH research, it is important to understand how the choice of analytic approaches and their implicit assumptions may affect results when using a disaster exposure measure. We compared different analytic strategies for quantifying disaster exposure and included a new analytic approach, latent class analysis (LCA), in a sample of parents and youth. Following exposure to multiple floods in Texas, a sample of 555 parents and 486 youth were recruited. Parents were predominantly female (70.9%) and White (60.8%). Parents were asked to have their oldest child between the ages of 10 and 19 years old participate (M = 13.74 years, SD = 2.57; 52.9% male). Participants completed measures on disaster exposure, posttraumatic stress, depression, and anxiety. The LCA revealed four patterns of exposure in both parents and youth: high exposure (15.5% parent, 9.5% child), moderate exposure (19.8% parent, 28.2% child), community exposure (45.9% parent, 34.4% child), and low exposure (18.8% parent, 27.8% child). In terms of MH, there were similarities across analytic approaches, but the LCA highlighted a threshold effect, with the high exposure class doing worse than all others, d = 1.12. These results have important implications in understanding the different exposure experiences of survivors and the linkage to MH outcomes. The findings are also informative in the development and use of screening tools used in postdisaster contexts in determining who may or may not need MH services.