Literature DB >> 28648550

Individual housing-based socioeconomic status predicts risk of accidental falls among adults.

Euijung Ryu1, Young J Juhn2, Philip H Wheeler2, Matthew A Hathcock1, Chung-Il Wi2, Janet E Olson1, James R Cerhan1, Paul Y Takahashi3.   

Abstract

PURPOSE: Accidental falls are a major public health concern among people of all ages. Little is known about whether an individual-level housing-based socioeconomic status measure is associated with the risk of accidental falls.
METHODS: Among 12,286 Mayo Clinic Biobank participants residing in Olmsted County, Minnesota, subjects who experienced accidental falls between the biobank enrollment and September 2014 were identified using ICD-9 codes evaluated at emergency departments. HOUSES (HOUsing-based Index of SocioEconomic Status), a socioeconomic status measure based on individual housing features, was also calculated. Cox regression models were utilized to assess the association of the HOUSES (in quartiles) with accidental fall risk.
RESULTS: Seven hundred eleven (5.8%) participants had at least one emergency room visit due to an accidental fall during the study period. Subjects with higher HOUSES were less likely to experience falls in a dose-response manner (hazard ratio: 0.58; 95% confidence interval: 0.44-0.76 for comparing the highest to the lowest quartile). In addition, the HOUSES was positively associated with better health behaviors, social support, and functional status.
CONCLUSIONS: The HOUSES is inversely associated with accidental fall risk requiring emergency care in a dose-response manner. The HOUSES may capture falls-related risk factors through housing features and socioeconomic status-related psychosocial factors.
Copyright © 2017 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Accidental falls; Epidemiology; HOUSES; Housing; Mayo clinic biobank; Risk; Socioeconomic status

Mesh:

Year:  2017        PMID: 28648550     DOI: 10.1016/j.annepidem.2017.05.019

Source DB:  PubMed          Journal:  Ann Epidemiol        ISSN: 1047-2797            Impact factor:   3.797


  16 in total

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Authors:  Joshua R Ehrlich; Shirin E Hassan; Brian C Stagg
Journal:  J Am Geriatr Soc       Date:  2018-11-13       Impact factor: 5.562

4.  Role of geographic risk factors and social determinants of health in COVID-19 epidemiology: Longitudinal geospatial analysis in a midwest rural region.

Authors:  Philip H Wheeler; Christi A Patten; Chung-Il Wi; Joshua T Bublitz; Euijung Ryu; Elizabeth H Ristagno; Young J Juhn
Journal:  J Clin Transl Sci       Date:  2021-12-27

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Authors:  Tom D Thacher; Daniel V Dudenkov; Kristin C Mara; Julie A Maxson; Chung-Il Wi; Young J Juhn
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8.  An Interpretable Machine Learning Approach to Predict Fall Risk Among Community-Dwelling Older Adults: a Three-Year Longitudinal Study.

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9.  Long-term incidence of glioma in Olmsted County, Minnesota, and disparities in postglioma survival rate: a population-based study.

Authors:  Conor S Ryan; Young J Juhn; Harsheen Kaur; Chung-Il Wi; Euijung Ryu; Katherine S King; Daniel H Lachance
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10.  Association between an individual housing-based socioeconomic index and inconsistent self-reporting of health conditions: a prospective cohort study in the Mayo Clinic Biobank.

Authors:  Euijung Ryu; Janet E Olson; Young J Juhn; Matthew A Hathcock; Chung-Il Wi; James R Cerhan; Kathleen J Yost; Paul Y Takahashi
Journal:  BMJ Open       Date:  2018-05-14       Impact factor: 2.692

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