Change in emotional distress, anxiety, depression and PTSD from pre- to post-flood exposure in women residing in low-income settings in South Africa

Extreme Weather, Vulnerable Populations, and Mental Health: The Timely Role of AI Interventions

Environmental disasters are becoming increasingly frequent and severe, disproportionately impacting vulnerable populations who face compounded risks due to intersectional factors such as gender, socioeconomic status, rural residence, and cultural identity. These events exacerbate mental health challenges, including post-traumatic stress disorder (PTSD), anxiety, and depression, particularly in low- and middle-income countries (LMICs) and underserved areas of high-income countries (HICs). Addressing these disparities necessitates inclusive, culturally competent, intersectional, and cost-effective strategies. Artificial intelligence (AI) presents transformative potential for delivering scalable and culturally tailored mental health interventions that account for these vulnerabilities. This perspective highlights the importance of co-designing AI tools with at-risk populations, integrating these solutions into disaster management frameworks, and ensuring their sustainability through research, training, and policy support. By embedding mental health resilience into climate adaptation strategies, stakeholders can foster equitable recovery and reduce the long-term mental health burden of environmental disasters.

Nothing about us without us: harnessing local voices in shaping community-based adaptation in the Pacific

As global temperatures rise, so does the frequency and intensity of severe weather events and their risks to people and assets. These risks are especially acute for Pacific Islanders in urban informal settlements, given their socio-economic vulnerabilities and limited political influence. There is growing awareness that national adaptation strategies may not fully meet the needs of these vulnerable communities, leading to a focus on community-led adaptation. However, these approaches are in their infancy and have been criticised for fostering paternalistic tendencies, prompting calls for external institutions to facilitate rather than direct community initiatives. This research utilises Photovoice, a method recognised for its cultural relevance and ability to amplify Indigenous and marginalised voices. It involves 42 households in Fiji's Greater Suva Urban Area, using resident-led photography and interviews to explore community-based flood adaptation. Through ethnographic content analysis and inductive coding, the study captures residents' experiences and strategies, identifying over 31 unique adaptation measures and underscoring the importance of resources, social networks, traditional knowledge, beliefs, and leadership in enhancing adaptive capacity. The findings demonstrate the complexity of factors influencing adaptation, with resource availability and social capital being crucial. The study advocates for adaptive processes that are community driven, calling for a shift in research and funding to support these programmes in a flexible, responsive, and inclusive manner. It also highlights the need to understand community dynamics to prevent paternalistic practices and integrate local insights effectively, ensuring community self-determination in adaptation efforts.

Prevalence and determinants of post-traumatic stress disorder five months after the 2019 huge flooding in Iran

BACKGROUND

Despite the high occurrence of floods in Iran, its psychological consequences have been less discussed. The present paper addresses the prevalence of Post-traumatic Stress Disorder (PTSD) and its determinants among the affected adults by the huge flood of 2019.

METHODS

An analytical cross-sectional study was conducted through household face-to-face surveys in August and September 2019. Individuals who were affected by floods and were at least 16 years old were randomly selected from three provinces in Iran: Lorestan and Khuzestan in the west and southwest, and Golestan in the northeast. The questionnaire of demographic and flood related variables in addition to the Impact of Event Scale-Revised (IES-R) were utilized to collect the data. We applied a complex sample analysis to describe the prevalence of PTSD and logistic regression analyses to find its determinants.

RESULTS

Out of the 2,305 individuals approached for surveys, 1,671 (72.5%) adults affected by the floods participated in the study. The majority of participants were housewives, married, had either no formal education or primary education, and resided in rural areas. The prevalence of PTSD in the participants was 24.8% (CI 95%: 20.7-28.8%) and was significantly higher in Lorestan province (39.7%, P < 0.001). Determinants of PTSD, were unemployment (adjusted odds ratio [AOR] = 3.53, CI 95%: 1.38-9.00), primary (AOR = 2.44, CI 95%: 1.10-5.41) or high school (AOR = 2.35, CI 95%: 1.25-4.40) education (vs. university), a history of mental disorders (AOR = 2.36, CI 95%: 1.22-4.58), high damage to assets (AOR = 2.29, CI 95%: 1.40-3.75), limited access to health care services after the flood (AOR = 1.95, CI 95%: 1.20-3.19), not receiving compensation for flood damage (AOR = 1.94, CI 95%: 1.01-3.83), high wealth index (AOR = 1.90, CI 95%: 1.23-2.93), and flooded house with a height of more than one meter (AOR = 1.66, CI 95%: 1.02-2.76).

CONCLUSION

Results show a notable prevalence of PTSD, especially in Lorestan province, among adults affected by floods. Determinants of PTSD include unemployment, lower education, psychiatric history, extensive property damage, limited post-flood healthcare access, lack of compensation, and increased flood exposure. We recommend adopting an inclusive screening approach for high-risk groups and developing appropriate therapeutic and supportive interventions.