Nutrition risk factors for survival in the elderly living in Canadian long-term care facilities

J Am Geriatr Soc. 2004 Jan;52(1):59-65. doi: 10.1111/j.1532-5415.2004.52011.x.

Abstract

Objectives: To determine the role of nutritional parameters in influencing the risk of mortality in institutionalized elderly.

Design: A prospective cohort study in which subjects had several nutritional parameters measured at baseline and were followed for 19 months. Time to death and mortality were recorded starting immediately after enrollment.

Setting: Fourteen long-term care facilities (LTCFs).

Participants: Four hundred eight elderly long-term care residents aged 60 and older who resided in the facility for more than 6 weeks.

Measurements: At baseline, knee height, weight, mid-arm circumference (MAC), skin-fold thickness, and fat-free mass using bioelectric impedance analysis were measured. Covariates included demographic factors, length of stay in the facility, functional status, and medical diagnoses. Cox proportional hazards regression analysis was used to identify independent predictors of mortality. Results are reported as mean+/-standard error of the mean (SEM).

Results: Overall, mortality rate was 28.4%. Univariate predictors included male sex, body mass index, MAC, and triceps skin fold. In multivariate analysis, male sex (hazard ratio (HR)=1.7, 95% confidence interval (CI)=1.2-2.7, P=.0096) and MAC less than 26 cm were significantly associated with increased risk of mortality (HR=4.8, 95% CI: 2.8-8.3, P<.0001).

Conclusion: Among this elderly population living in LTCFs, MAC is the best nutritional predictor of mortality.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Aged
  • Aged, 80 and over
  • Anthropometry / methods*
  • Arm / anatomy & histology*
  • Chi-Square Distribution
  • Female
  • Humans
  • Long-Term Care*
  • Male
  • Mortality*
  • Nutritional Status*
  • Ontario / epidemiology
  • Predictive Value of Tests
  • Proportional Hazards Models
  • Prospective Studies
  • Regression Analysis
  • Risk Factors