Peter P Vitaliano1, Annette L Fitzpatrick2,3, Lee E Williams4, Michalina A Montano3, Joan E Russo1. 1. Department of Psychiatry and Behavioral Sciences, University of Washington Seattle. 2. Department of Family Medicine, University of Washington Seattle. 3. Department of Epidemiology, University of Washington Seattle. 4. Department of Bioengineering, University of Washington Seattle.
Abstract
PURPOSE OF THE STUDY: (1a) We use the Cardiovascular Health Study (CHS), a multi-site heterogeneous sample of Medicare enrollees (N = 5,849) to provide rates for specific life events experienced within 6 months; (1b) We present rates for 29 other studies of community-residing older adults (N = 41,308); (2) For the CHS, we provide demographic-specific rates and predicted probabilities for age [young-old (65-75) vs old-old (≥75)], gender, race, marital status, and education. DESIGN/ METHODS: The CHS sample is 57.6% women, 84.2% white (15.8% black), and 66.3% married. Mean age is 72.8 years (standard deviation [SD] = 5.6, range = 65-100) and education is 13.7 years (SD = 4.8). Life events were interviewer-assessed. Regressions estimated associations of life event rates with demographic groups (e.g., age), controlling for other demographic variables (e.g., gender, etc.). RESULTS: (1a) CHS rates ranged from 44.7% (death of someone close) to 1.1% (retirement/work changes). (1b) Most life event studies used total scores and only 5 that met our inclusion criteria used time intervals <1 year; longer intervals were associated with higher rates. (2) In the CHS, the life event for illnesses was related to 5 demographic variables (net the other 4 demographic variables), difficulties caregiving to 4, and worse relationships to 3 demographic variables. Race was related to 8 life events, marital status to 7, education to 6, and age to 4 events. IMPLICATIONS: By identifying demographic groups at highest risk for life events, this research focuses on older adults at greatest risk for health problems. These data are necessary for translating research into interventions, practice, and policy.
PURPOSE OF THE STUDY: (1a) We use the Cardiovascular Health Study (CHS), a multi-site heterogeneous sample of Medicare enrollees (N = 5,849) to provide rates for specific life events experienced within 6 months; (1b) We present rates for 29 other studies of community-residing older adults (N = 41,308); (2) For the CHS, we provide demographic-specific rates and predicted probabilities for age [young-old (65-75) vs old-old (≥75)], gender, race, marital status, and education. DESIGN/ METHODS: The CHS sample is 57.6% women, 84.2% white (15.8% black), and 66.3% married. Mean age is 72.8 years (standard deviation [SD] = 5.6, range = 65-100) and education is 13.7 years (SD = 4.8). Life events were interviewer-assessed. Regressions estimated associations of life event rates with demographic groups (e.g., age), controlling for other demographic variables (e.g., gender, etc.). RESULTS: (1a) CHS rates ranged from 44.7% (death of someone close) to 1.1% (retirement/work changes). (1b) Most life event studies used total scores and only 5 that met our inclusion criteria used time intervals <1 year; longer intervals were associated with higher rates. (2) In the CHS, the life event for illnesses was related to 5 demographic variables (net the other 4 demographic variables), difficulties caregiving to 4, and worse relationships to 3 demographic variables. Race was related to 8 life events, marital status to 7, education to 6, and age to 4 events. IMPLICATIONS: By identifying demographic groups at highest risk for life events, this research focuses on older adults at greatest risk for health problems. These data are necessary for translating research into interventions, practice, and policy.
Entities:
Keywords:
Education; Gender; Life events; Married; Race; Stress
Authors: Anna C Phillips; Douglas Carroll; Victoria E Burns; Christopher Ring; John Macleod; Mark Drayson Journal: Brain Behav Immun Date: 2005-09-27 Impact factor: 7.217
Authors: Femke Rutters; Stefan Pilz; Anitra D M Koopman; Simone P Rauh; Frans Pouwer; Coen D A Stehouwer; Petra J Elders; Giel Nijpels; Jacqueline M Dekker Journal: Stress Date: 2015-07-17 Impact factor: 3.493
Authors: May Li Lim; Donovan Lim; Xinyi Gwee; Ma Shwe Zin Nyunt; Rajeev Kumar; Tze Pin Ng Journal: Aging Ment Health Date: 2015-01-06 Impact factor: 3.658
Authors: Teresa E Seeman; Burton H Singer; Carol D Ryff; Gayle Dienberg Love; Lené Levy-Storms Journal: Psychosom Med Date: 2002 May-Jun Impact factor: 4.312