Ying Li1, Yiyang Pan1, Yuan Chen1, Pingyu Cui1. 1. Department of Social Medicine, School of Public Health, Zhejiang University, Hangzhou 310058, China.
Abstract
Background: The prevalence of dependency personality disorder is high among elderly individuals with a low level of social support. The objective of this study was to explore the dependency associated with important community resources among elderly individuals with a low level of social support from the perspective of resource demand. Methods: The population-based cross-sectional study was conducted in 22 locations in China. A total of 950 participants aged ≥60 years were selected using a complex multistage sampling design. All the data were collected using questionnaires via face-to-face interviews. The dependency was assessed using the standardized Chinese version of the Minnesota Multiphasic Personality Inventory-II. Community resources were assessed using 43 items. Logistic regression analysis was used to evaluate the association between dependency and important community resources. Results: Bivariate analysis showed that the level of social support was negatively associated with levels of income (p < 0.001) and education (p = 0.008) and was positively associated with social communication and interactions (p < 0.001). The logistic regression analysis showed that the emergency call or survival monitoring system (ECSMS) was the most important community resource that was significantly associated with the levels of dependency; the odds ratio was 2.64 (95% CI, 1.07-3.91; p = 0.031) among elderly individuals with a low level of social support. Conclusions: The levels of dependency were most significantly associated with the ECSMS among elderly individuals with a low level of social support. Our results suggest that improving the ECSMS can be the main problem in the development of community resources.
Background: The prevalence of dependency personality disorder is high among elderly individuals with a low level of social support. The objective of this study was to explore the dependency associated with important community resources among elderly individuals with a low level of social support from the perspective of resource demand. Methods: The population-based cross-sectional study was conducted in 22 locations in China. A total of 950 participants aged ≥60 years were selected using a complex multistage sampling design. All the data were collected using questionnaires via face-to-face interviews. The dependency was assessed using the standardized Chinese version of the Minnesota Multiphasic Personality Inventory-II. Community resources were assessed using 43 items. Logistic regression analysis was used to evaluate the association between dependency and important community resources. Results: Bivariate analysis showed that the level of social support was negatively associated with levels of income (p < 0.001) and education (p = 0.008) and was positively associated with social communication and interactions (p < 0.001). The logistic regression analysis showed that the emergency call or survival monitoring system (ECSMS) was the most important community resource that was significantly associated with the levels of dependency; the odds ratio was 2.64 (95% CI, 1.07-3.91; p = 0.031) among elderly individuals with a low level of social support. Conclusions: The levels of dependency were most significantly associated with the ECSMS among elderly individuals with a low level of social support. Our results suggest that improving the ECSMS can be the main problem in the development of community resources.
Entities:
Keywords:
community resources; dependency; elderly; low level of social support
Authors: Dilip V Jeste; Dan G Blazer; Kathleen C Buckwalter; Keri-Leigh K Cassidy; Len Fishman; Lisa P Gwyther; Saul M Levin; Christopher Phillipson; Ramesh R Rao; Ellen Schmeding; William A Vega; Julie A Avanzino; Danielle K Glorioso; John Feather Journal: Am J Geriatr Psychiatry Date: 2016-07-28 Impact factor: 4.105
Authors: Valerie A Yeager; Nir Menachemi; Grant T Savage; Peter M Ginter; Bisakha P Sen; Leslie M Beitsch Journal: Health Care Manage Rev Date: 2014 Jan-Mar