Predictors of hospital admissions in the elderly: analysis of data from the Longitudinal Study on Aging

J Natl Med Assoc. 2003 Dec;95(12):1158-67.

Abstract

Healthcare for the elderly population presents enormous challenges, which are further complicated by ethnicity-related socioeconomic disparities in the United States. We set out to determine the predictors of hospital admissions in the elderly by conducting a retrospective cohort analysis of a nationally representative sample of community-dwelling individuals aged 70 and older in 1984 (n = 7541). Multivariate logistic regression analysis of data from the Longitudinal Study on Aging revealed that race, health status, type of family relationship, and activities of daily living (ADL) are significant predictors of hospitalization among the elderly. Older blacks are less likely to be admitted into the hospital, compared to their white counterparts (OR 0.68, 95%CI 0.52-0.89). Elderly persons who perceive their health status as being fair or poor are three times as likely to be hospitalized than those who perceived their health status as excellent (OR 2.99, 95%CI 2.15-4.15). Those with impairment in activities of daily living are twice as likely to be confined to the hospital than those without impairment (OR 1.78, 95%CI 1.64-1.96). Elderly persons living with nonrelatives are three times as likely to be admitted for short hospital stays than those living with spouses (OR 2.90, 95%CI 1.44-5.82). Future identification of predictors of hospital admissions in the elderly may help characterize those at risk and perhaps allow for focused and timely intervention.

Publication types

  • Comparative Study
  • Research Support, Non-U.S. Gov't
  • Research Support, U.S. Gov't, P.H.S.

MeSH terms

  • Activities of Daily Living
  • African Americans / statistics & numerical data
  • Aged / statistics & numerical data*
  • European Continental Ancestry Group / statistics & numerical data
  • Factor Analysis, Statistical
  • Female
  • Hospitalization / statistics & numerical data*
  • Humans
  • Longitudinal Studies
  • Male
  • Multivariate Analysis
  • Retrospective Studies
  • Risk
  • United States / epidemiology