Friday, 14 January 2005 - 12:00 PM

This presentation is part of: Poster Session I

Depression Screening in Medically Ill Home Care Elderly Using the CES-D-11 Short Form

Despite the growth of geriatric home health care services, little is known about the mental health needs of elderly home care recipients. Evidence is clear that one of the most common psychiatric disorders in older adults is depression. Yet, the measurement of depressive symptoms in home care elderly is a concern to clinicians and researchers seeking reliable and valid indicators of mental health status in this population. Helping home health care professionals to identify and treat late life depression is an important evidence-based practice step in improving overall care. The purpose of this study was to determine the feasibility of screening for depression and functional impairment in a large certified home health care clinic. This is the first known empirical report using a brief CESD measure in this frail population. Over a 12 month period, home health care professionals screened patients (65 years or older) for depressive symptoms as part of their usual care registration assessment protocol. Using Structural Equation Modeling (SEM), we empirically validated a short-form CES-D (11 items) in medically ill homebound elderly (N=618) and examined the influence of age, functional disability (IADLs) and self-reported health status on depressive symptoms. The results supported the factor structures of the measure with medically ill home care elderly, which was internally consistent, reliable, and factorially valid. Functional disability was associated with higher CES-D somatic subscale scores. Health status was the strongest predictor of high depressive symptoms across all four subscales. In this sample, elderly participants who had high scores on the somatic subscale had high scores on the other three subscales as well. For older persons, brief and rapid measurement tools reduce respondent burden, are critical for attaining satisfactory response rates, and minimize drop-outs from longitudinal studies.

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