Introduction: Past events have demonstrated that crises and disasters can profoundly impact the mental health of those affected. To date, both practitioners and academics have focused on mitigating short-term adverse outcomes through psychosocial care, while medium- and long-term consequences have been relatively overlooked. Although a growing number of studies point to waves of mental health problems in the post-disaster timeline, little is known about predictors, especially in the early stages. This study aims to identify potential determinants of mental health for individuals at least 12 months after a disaster event. Methods: This literature review searches Medline, PsycInfo, PTSDpubs, Web of Science, and SocINDEX for studies published from January 1946 to July 2024. It includes studies that examine the prevalence of mental health problems, such as post-traumatic stress disorder, among survivors, along with information on predictors at various time points. Results: A variety of factors were identified, which can be categorized into two dominant groups: vulnerability-related factors (e.g., sociodemographic risk factors such as gender or age, prior health problems, and lack of social support) and exposure-based factors. The latter range from primary exposures (e.g., danger, loss of loved ones or property, time since exposure or loss) to secondary exposures (e.g., loss of income, property damage, and relocation). In most cases, the identified factors were linked to sociodemographic risk factors and measured concurrently with mental health issues. However, several studies provided information on predictors identified at earlier time points (cross-lagged). Conclusion: The factors identified in this study are valuable for policymakers, practitioners, and scholars seeking to better understand and address the mental health burden at various stages post-disaster, particularly for at-risk populations. Researchers should assess vulnerability and exposure-based factors more systematically in longitudinal monitoring programs to enhance the knowledge base regarding early predictors.
Verweij et al. (Sun,) studied this question.