Literature DB >> 27496012

A GIS Approach to Identifying Socially and Medically Vulnerable Older Adult Populations in South Florida.

Elizabeth Hames1,2, Justin Stoler3,4, Christopher T Emrich5,6, Sweta Tewary1,2, Naushira Pandya1,2.   

Abstract

Purpose of the Study: We define, map, and analyze geodemographic patterns of socially and medically vulnerable older adults within the tri-county region of South Florida. Design and
Methods: We apply principal components analysis (PCA) to a set of previously identified indicators of social and medical vulnerability at the census tract level. We create and map age-stratified vulnerability scores using a geographic information system (GIS), and use spatial analysis techniques to identify patterns and interactions between social and medical vulnerability.
Results: Key factors contributing to social vulnerability in areas with higher numbers of older adults include age, large household size, and Hispanic ethnicity. Medical vulnerability in these same areas is driven by disease burden, access to emergency cardiac services, availability of nursing home and hospice beds, access to home health care, and available mental health services. Age-dependent areas of social vulnerability emerge in Broward County, whereas age-dependent areas of medical vulnerability emerge in Palm Beach County. Older-adult social and medical vulnerability interact differently throughout the study area. Implications: Spatial analysis of older adult social and medical vulnerability using PCA and GIS can help identify age-dependent pockets of vulnerability that are not easily identifiable in a populationwide analysis; improve our understanding of the dynamic spatial organization of health care, health care needs, access to care, and outcomes; and ultimately serve as a tool for health care planning.
© The Author 2016. Published by Oxford University Press on behalf of The Gerontological Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Keywords:  GIS; Health care access; Medical vulnerability; Social vulnerability

Mesh:

Year:  2017        PMID: 27496012     DOI: 10.1093/geront/gnw106

Source DB:  PubMed          Journal:  Gerontologist        ISSN: 0016-9013


  2 in total

1.  Machine Learning in Aging Research.

Authors:  Michelle C Odden; David Melzer
Journal:  J Gerontol A Biol Sci Med Sci       Date:  2019-11-13       Impact factor: 6.053

2.  Caregiving and Place: Combining Geographic Information System (GIS) and Survey Methods to Examine Neighborhood Context and Caregiver Outcomes.

Authors:  Scott R Beach; Ellen Kinnee; Richard Schulz
Journal:  Innov Aging       Date:  2019-08-23
  2 in total

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