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The objective of this research is to identify sociodemographic predictors of depression for a rural population in the US during the COVID-19 pandemic to enhance mental health disaster preparedness.
This study uses t-tests to differentiate between gender and ethnicity groups regarding depression status; binary logistic regression to identify socio-demographic characteristics that predict depression status; and t-test to differentiate between average depression scores, measured by the PHQ-9, pre-COVID-19 pandemic (2019) and after it’s start (2020).
Results indicate that men were less likely than women to report depression. Clients who identified as Latinx/Hispanic were 2.8 times more likely than non-Hispanics to report depression and clients who did not reside in public housing were 19.9% less likely to report depression. There was a statistically significant difference between mean PHQ-9 scores pre- and post-pandemic, with pre-pandemic scores lower on average, with a small effect size.
Building on findings from this study, we propose ways to increase rural access to mental health services, through equitable access to telemedicine, to meet the needs of rural clients to increase disaster preparedness.
Literature explores which factors most impact resilience and how these factors impact an individual and communities’ ability to cope with disaster. Less research has focused on how age impacts resilience. This research adapts several previous conceptual models used to investigate resilience. To investigate the unique vulnerabilities faced by older individuals in post-disaster settings, this analysis was undertaken to investigate predictors of individual resilience.
Data for the study were derived from the Centers for Disease Control and Prevention (CDC) Gulf States Population Survey (GSPS). The final sample included 5,713 adult residents from 4 gulf-coast states. Multiple linear regression was used for the analysis.
All models (demographic, health, social, and combined) acted as significant predictors of individual resilience. Health and social resilience models accounted for more of the variance in resilience scores. In all models, age was negatively associated with resilience scores. Being female was protective across all models. The results of the model testing indicate inequitable disaster mitigation, with social and health indicators explaining the most variance in the resilience levels.
This research provides practitioners with the knowledge they need to focus their interventions on the areas where it is most needed to empower resilient individuals. (Disaster Med Public Health Preparedness. 2019;13:256–264)
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