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<p>Climate-induced flooding presents a growing threat to mental health, disproportionately affecting women in rural communities. This study investigates the relationship between flood exposure and depression prevalence among women in Kurianwala, Jhang, Pakistan, during the 2023 and 2024 flood events. A mixed-methods design was employed, combining quantitative PHQ-9 assessments of 150 women with qualitative insights from 30 purposively selected participants through semi-structured interviews. Flood duration data were sourced from government reports, local news, and survivor accounts, distinguishing between total and household-impactable flood periods to capture actual exposure. Qualitative data were analyzed using MAXQDA 2022 to identify key psychosocial stressors—such as flood-induced anxiety, loss of livelihood, and displacement trauma—as well as protective factors including coping strategies and community support. Each theme was systematically linked to the probability of clinically significant depression (PHQ-9 ≥10). These conditional probabilities were then integrated into a Dynamic Bayesian Network (DBN) model, enabling simulation of mental health outcomes under varying flood scenarios and social contexts. Results demonstrated a strong association between flood duration and depression severity, with mean PHQ-9 scores rising from 9.72 to 12.29 as total flood days increased from 35 to 40. DBN modeling revealed how psychosocial stressors amplify depression risk, while coping mechanisms and social support mitigate adverse outcomes. This integrative framework offers a scalable, evidence-based approach to assess climate-related mental health vulnerabilities, providing actionable insights for targeted interventions, gender-sensitive disaster response, and predictive early-warning systems. By linking environmental exposure, psychosocial dynamics, and probabilistic mental health modeling, this study establishes a foundation for informed policy, community resilience planning, and strategic resource allocation.</p>

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A Hybrid Dynamic Bayesian Network for Flood-Driven Health Vulnerability: Integrating Local Knowledge and Spatial Data

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年1月28日

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  • Ayesha Sohail,
  • Arooba Arshad,
  • Rehana Ali Naqvi,
  • Ying Zhang

摘要

Climate-induced flooding presents a growing threat to mental health, disproportionately affecting women in rural communities. This study investigates the relationship between flood exposure and depression prevalence among women in Kurianwala, Jhang, Pakistan, during the 2023 and 2024 flood events. A mixed-methods design was employed, combining quantitative PHQ-9 assessments of 150 women with qualitative insights from 30 purposively selected participants through semi-structured interviews. Flood duration data were sourced from government reports, local news, and survivor accounts, distinguishing between total and household-impactable flood periods to capture actual exposure. Qualitative data were analyzed using MAXQDA 2022 to identify key psychosocial stressors—such as flood-induced anxiety, loss of livelihood, and displacement trauma—as well as protective factors including coping strategies and community support. Each theme was systematically linked to the probability of clinically significant depression (PHQ-9 ≥10). These conditional probabilities were then integrated into a Dynamic Bayesian Network (DBN) model, enabling simulation of mental health outcomes under varying flood scenarios and social contexts. Results demonstrated a strong association between flood duration and depression severity, with mean PHQ-9 scores rising from 9.72 to 12.29 as total flood days increased from 35 to 40. DBN modeling revealed how psychosocial stressors amplify depression risk, while coping mechanisms and social support mitigate adverse outcomes. This integrative framework offers a scalable, evidence-based approach to assess climate-related mental health vulnerabilities, providing actionable insights for targeted interventions, gender-sensitive disaster response, and predictive early-warning systems. By linking environmental exposure, psychosocial dynamics, and probabilistic mental health modeling, this study establishes a foundation for informed policy, community resilience planning, and strategic resource allocation.