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Abstract Natural disasters, such as hurricanes and floods, are increasing in frequency and scope. Youth exposed to disasters are at risk for developing posttraumatic stress symptoms (PTSS). However, not all youth who report initially elevated PTSS report persistent PTSS that last beyond the first three to six months postdisaster. Thus, it is crucial to understand how and why youth differ in their patterns of PTSS. This study reviewed the literature on children's postdisaster PTSS, evaluating the typical number and types of patterns for children's PTSS trajectories, as well as risk and protective factors predicting trajectory membership. This review identified eight empirical studies on youth PTSS trajectories following natural disasters; these studies included 8,306 children aged 3 to 18 years. All studies identified resilience, recovery, and chronic trajectories. Evidence for a delayed trajectory was mixed. Proportions of children falling into each trajectory varied widely across studies, but overall, resilience was the most prevalent trajectory. These findings were consistent across study factors (i.e., analytic strategy, assessment timing, and study selection criteria). Female gender, disaster exposure, negative coping, and lack of social support were significant risk factors for chronic trajectories across several studies. Future research should combine individual level participant data across studies of children's responses to disasters to better understand PTSS trajectories.
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Statistical learning to identify salient factors influencing FEMA public assistance outlays
Abstract Both the number of disasters in the U.S. and federal outlays following disasters are rising. FEMA’s Public Assistance (PA) is a key program for rebuilding damaged public infrastructure and aiding local and state governments in recovery. It is the primary post-disaster source of recovery funds. Between 2000 and 2019, more than $125B (adjusted, 2020 dollars) was awarded through PA. While all who qualify for PA should have equal opportunity to receive aid, not all do, and the factors influencing how the program has been administered are complex and multifaceted. Lacking an understanding of the factors positively associated with historical receipt of aid, there is little way to objectively evaluate the efficacy of the PA program. In this work, we evaluate the salient features that contribute to the number of county-level PA applicants and projects following disasters. We use statistical learning theory applied to repetitive flooding events in the upper Midwest between 2003 and 2018 as a case study. The results suggest that many non-disaster related indicators are key predictors of PA outlays, including the state in which the disaster occurred, the county’s prior experience with disasters, the county’s median income, and the length of time between the end of the disaster and the date when a disaster is declared. Our work suggests that indicators of PA aid are tied to exposure, bureaucratic attributes, and human behavior. For equitable distribution of aid, policymakers should explore more disaster-relevant indicators for PA distribution.
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- Award ID(s):
- 2145509
- PAR ID:
- 10502141
- Publisher / Repository:
- Springer Science + Business Media
- Date Published:
- Journal Name:
- Natural Hazards
- Volume:
- 120
- Issue:
- 12
- ISSN:
- 0921-030X
- Format(s):
- Medium: X Size: p. 10559-10582
- Size(s):
- p. 10559-10582
- Sponsoring Org:
- National Science Foundation
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