How community vulnerability factors jointly affect multiple health outcomes after catastrophic storms. (January 2020)

• Record Type: Journal Article
• Authors: Zhang, Wangjian; Kinney, Patrick L.; Rich, David Q.; Sheridan, Scott C.; Romeiko, Xiaobo Xue; Dong, Guanghui; Stern, Eric K.; Du, Zhicheng; Xiao, Jianpeng; Lawrence, Wayne R.; Lin, Ziqiang; Hao, Yuantao; Lin, Shao
• Is Part Of: Environment international. Volume 134(2020)
• Publication Date: 2020-01
• Subjects: Catastrophic storms -- Multiple health outcomes -- Community vulnerability index -- ROC; CVI community vulnerability index -- BRT boosted regression tree -- LVC low vulnerability communities -- HVC high vulnerability communities; Environmental protection -- Periodicals; Environmental health -- Periodicals; Environmental monitoring -- Periodicals; Environmental Monitoring -- Periodicals; Environnement -- Protection -- Périodiques; Hygiène du milieu -- Périodiques; Environnement -- Surveillance -- Périodiques; Environmental health; Environmental monitoring; Environmental protection; Periodicals; 333.705
• DOI: 10.1016/j.envint.2019.105285
• Languages: English
• ISSNs: 0160-4120

Abstract

Highlights: Substance abuse was the most sensitive disease to less optimal community indicators. Respiratory diseases were higher in communities with better social environment. Vulnerability factors include living in group quarters or areas prone to flooding. The community vulnerability index has an appropriate predictive performance. Abstract: Background: While previous studies uncovered individual vulnerabilities to health risks during catastrophic storms, few evaluated the population vulnerability which is more important for identifying areas in greatest need of intervention. Objectives: We assessed the association between community factors and multiple health outcomes, and developed a community vulnerability index. Methods: We retained emergency department visits for several health conditions from the 2005–2014 New York Statewide Planning and Research Cooperative System. We developed distributed lag nonlinear models at each spatial cluster across eight counties in downstate New York to evaluate the health risk associated with Superstorm Sandy (10/28/2012–11/9/2012) compared to the same period in other years, then defined census tracts in clusters with an elevated risk as "risk-elevated communities", and all others as "unelevated". We used machine-learning techniques to regress the risk elevation status against community factors to determine the contribution of each factor on population vulnerability, and developed a community vulnerability index (CVI). Results: Overall, community factors had positive contributions to increased community vulnerabilities to Sandy-related substance abuse (91.35%), injuries (70.51%), cardiovascular diseases (8.01%), and mental disorders (2.71%) but reversely contributed to respiratory diseases (−34.73%). The contribution of low per capita income (max: 22.08%), the percentage of residents living in group quarters (max: 31.39%), the percentage of areas prone to flooding (max: 38.45%), and the percentage of green coverage (max: 29.73%) tended to be larger than other factors. The CVI based on these factors achieved an accuracy of 0.73–0.90 across outcomes. Conclusions: Our findings suggested that substance abuse was the most sensitive disease susceptible to less optimal community indicators, whereas respiratory diseases were higher in communities with better social environment. The percentage of residents in group quarters and areas prone to flooding were among dominant predictors for community vulnerabilities. The CVI based on these factors has an appropriate predictive performance.