Affordable Access

Symptom burden predicts nursing home admissions among older adults.

Authors
  • Sheppard, Kendra D
  • Brown, Cynthia J
  • Hearld, Kristine R
  • Roth, David L
  • Sawyer, Patricia
  • Locher, Julie L
  • Allman, Richard M
  • Ritchie, Christine S
Publication Date
Oct 01, 2013
Source
eScholarship - University of California
Keywords
License
Unknown
External links

Abstract

Symptom burden has been associated with functional decline in community-dwelling older adults and may be responsive to interventions. Known predictors of nursing home (NH) admission are often nonmodifiable.To determine if symptom burden independently predicted NH admission among community-dwelling older adults over an eight and a half-year follow-up period.A random sample of community-dwelling Medicare beneficiaries in Alabama, stratified by race, gender, and rural/urban residence had baseline in-home assessments of sociodemographic measurements, Charlson comorbidity count, and symptoms. Symptom burden was derived from a count of 10 patient-reported symptoms. Nursing home admissions were determined from telephone interviews conducted every six months over the eight and a half years of study. Cox proportional hazard modeling was used to examine the significance of symptom burden as a predictor for NH admission after adjusting for other variables.The mean ± SD age of the sample (N = 999) was 75.3 ± 6.7 years, and the sample was 51% rural, 50% African American, and 50% male. Thirty-eight percent (n = 380) had symptom burden scores ≥2. Seventy-five participants (7.5%) had confirmed dates for NH admission during the eight and a half years of follow-up. Using Cox proportional hazard modeling, symptom burden remained an independent predictor of time to NH placement (hazard ratio = 1.11; P = 0.02), even after adjustment for comorbidity count, race, sex, and age.Symptom burden is an independent risk factor for NH admission. Aggressive management of symptoms in older adults may reduce or delay NH admission.

Report this publication

Statistics

Seen <100 times