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

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.

Preventable Hospitalization Rates and Neighborhood Poverty among New York City Residents, 2008-2013.

Knowing which demographic groups have higher rates of preventable hospitalizations can help identify geographic areas where improvements in primary care access and quality can be made. This study assessed whether preventable hospitalization rates by neighborhood poverty decreased from 2008 to 2013 and whether the gap between very high and low poverty neighborhoods changed. We examined trends in age-adjusted preventable hospitalization rates and rate ratios by neighborhood poverty overall and by sex using JoinPoint regression. Prevention Quality Indicators (PQIs) developed by the Agency for Healthcare Research and Quality were applied to inpatient hospitalization data from the New York State Department of Health's Statewide Planning and Research Cooperative System. PQIs were classified into composites. From 2008 to 2013, preventable hospitalization rates per 100,000 adults across each poverty group decreased. For very high poverty neighborhoods (ZIP codes with ≥30 % of persons living below the federal poverty level (FPL)), there were significant decreases overall (3430.56 to 2543.10, annual percent change [APC] = -5.91 %), for diabetes (676.15 to 500.83, APC = -5.75 %), respiratory (830.78 to 660.29, APC = -4.85 %), circulatory (995.69 to 701.81, APC = -7.24 %), and acute composites (928.18 to 680.17, APC = -5.62 %). The rate ratios also decreased over time; however, in 2013, the rates for very high poverty neighborhoods were two to four times higher than low poverty neighborhoods (ZIP codes with <10 % of persons below the FPL). While preventable hospitalization rates have decreased over time, disparities still exist. These findings underscore the need to ensure adequate access to quality and timely primary care among individuals living in high poverty neighborhoods.