Wangjian Zhang1, Patrick L Kinney2, David Q Rich3, Scott C Sheridan4, Xiaobo Xue Romeiko5, Guanghui Dong6, Eric K Stern7, Zhicheng Du8, Jianpeng Xiao9, Wayne R Lawrence10, Ziqiang Lin11, Yuantao Hao12, Shao Lin13. 1. Department of Medical Statistics and Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou, China; Department of Environmental Health Sciences, University at Albany, State University of New York, Rensselaer, NY, USA. 2. Department of Environmental Health, School of Public Health, Boston University, MA, USA. 3. Department of Public Health Sciences, University of Rochester Medical Center, Rochester, NY, USA. 4. Department of Geography, Kent State University, Kent, OH, USA. 5. Department of Environmental Health Sciences, University at Albany, State University of New York, Rensselaer, NY, USA. 6. Department of Preventive Medicine, School of Public Health, Sun Yat-sen University, Guangzhou, China. 7. College of Emergency Preparedness, Homeland Security, and Cyber-Security, University at Albany, State University of New York, Albany, NY, USA. 8. Department of Medical Statistics and Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou, China. 9. Department of Occupational Health and Occupational Medicine, School of Public Health, Southern Medical University, Guangzhou, China. 10. Department of Epidemiology and Biostatistics, University at Albany, State University of New York, Rensselaer, NY, USA. 11. Department of Environmental Health Sciences, University at Albany, State University of New York, Rensselaer, NY, USA; Department of Mathematics, University at Albany, Albany, NY, USA. 12. Department of Medical Statistics and Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou, China. Electronic address: haoyt@mail.sysu.edu.cn. 13. Department of Environmental Health Sciences, University at Albany, State University of New York, Rensselaer, NY, USA. Electronic address: slin@albany.edu.
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.
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.
Authors: Wangjian Zhang; Scott C Sheridan; Guthrie S Birkhead; Daniel P Croft; Jerald A Brotzge; John G Justino; Neil A Stuart; Zhicheng Du; Xiaobo X Romeiko; Bo Ye; Guanghui Dong; Yuantao Hao; Shao Lin Journal: Chest Date: 2020-06-02 Impact factor: 9.410