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Citation |
Wilson MJ, Sugg MM, Lane SJ. Int. J. Disaster Risk Reduct. 2019; 36: e101106. |
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Copyright |
(Copyright © 2019, Elsevier Publishing) |
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DOI |
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PMID |
unavailable |
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Abstract |
To identify nursing home vulnerability attributable to location using a triangulated approach that includes historic natural hazards, community vulnerability and nursing home attributes, we use an inductive-hierarchical vulnerability index construction model. Principal components analysis (PCA) is used for two inductive models of community (CLI) and natural hazard (HLI) vulnerability. Analytical hierarchy process (AHP) is used to determine weights, according to expert ranks, for a hierarchical model of nursing home facility level vulnerability (NHLI). These three sub-indices are combined using an equal weights hierarchical approach to create a multivariate nursing home vulnerability index (MNHVI). Hazard level vulnerability is predominantly attributable to storm surge, minor hurricanes, and inland flooding. Drivers of community level vulnerability were found to be poverty and minority population, age, income and housing, Hispanic population, family status, employment type and female gender, and nursing home population. Nursing home vulnerability is found to be higher for tracts and counties that house nursing home residents with decreased or limited mobility. The clusters throughout the study area that were identified as the most vulnerable for the MNHVI are predominantly attributable to their geographic location along the coastline. The mapped outputs can provide nursing homes with an easily distributable form of visual and quantitative information to share with emergency management agencies, family members or representatives of residents in nursing homes. This study can also assist administrators in risk assessment, development of policies and procedures, communication planning, and personnel training to comply with emergency preparedness regulations. Language: en |
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Keywords |
CMS datasets (MDS); Factor analysis; Hierarchical linear modeling; Long-term care; Natural disasters; Nursing homes; Quantitative research methods |