Applying an Innovative Model of Disaster Resilience at the Neighborhood Level : The COPEWELL New York City Experience

在社区层面应用创新型灾害韧性模式:纽约市 COPEWELL 项目经验

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Abstract

Community resilience is a community's ability to maintain functioning (ie, delivery of services) during and after a disaster event. The Composite of Post-Event Well-Being (COPEWELL) is a system dynamics model of community resilience that predicts a community's disaster-specific functioning over time. We explored COPEWELL's usefulness as a practice-based tool for understanding community resilience and to engage partners in identifying resilience-strengthening strategies. In 2014, along with academic partners, the New York City Department of Health and Mental Hygiene organized an interdisciplinary work group that used COPEWELL to advance cross-sector engagement, design approaches to understand and strengthen community resilience, and identify local data to explore COPEWELL implementation at neighborhood levels. The authors conducted participant interviews and collected shared experiences to capture information on lessons learned. The COPEWELL model led to an improved understanding of community resilience among agency members and community partners. Integration and enhanced alignment of efforts among preparedness, disaster resilience, and community development emerged. The work group identified strategies to strengthen resilience. Searches of neighborhood-level data sets and mapping helped prioritize communities that are vulnerable to disasters (eg, medically vulnerable, socially isolated, low income). These actions increased understanding of available data, identified data gaps, and generated ideas for future data collection. The COPEWELL model can be used to drive an understanding of resilience, identify key geographic areas at risk during and after a disaster, spur efforts to build on local metrics, and result in innovative interventions that integrate and align efforts among emergency preparedness, community development, and broader public health initiatives.

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