Predicting social and health vulnerability to floods in Bangladesh

Abstract
Floods are the most common and damaging natural disaster in Bangladesh, and the effects of floods on public health have increased significantly in recent decades, particularly among lower socioeconomic populations. Assessments of social vulnerability on flood-induced health outcomes typically focus on local to regional scales; a notable gap remains in comprehensive, large-scale assessments that may foster disaster management practices. In this study, socioeconomic, health, and coping capacity vulnerability and composite social-health vulnerability are assessed using both equal-weight and principal-component approaches using 26 indicators across Bangladesh. Results indicate that vulnerable zones exist in the northwest riverine areas, northeast floodplains, and southwest region, potentially affecting 42 million people (26 % of the total population). Subsequently, the vulnerability measures are linked to flood forecast and satellite inundation information to evaluate their potential for predicting actual flood impact indices (distress, damage, disruption, and health) based on the immense August 2017 flood event. Overall, the forecast-based equally weighted vulnerability measures perform best. Specifically, socioeconomic and coping capacity vulnerability measures strongly align with the distress, disruption, and health impact records observed. Additionally, the forecast-based composite social-health vulnerability index also correlates well with the impact indices, illustrating its utility in identifying predominantly vulnerable regions. These findings suggest the benefits and practicality of this approach to assess both thematic and comprehensive spatial vulnerabilities, with the potential to support targeted and coordinated public disaster management and health practices.
Funding Information
  • University of Wisconsin-Madison (Global Health Institute)
  • Wisconsin Alumni Research Foundation (UW2020)
  • National Aeronautics and Space Administration (NNX17AC50G)