Improving mortality prediction using biosocial surveys

Noreen Goldman, Dana A. Glei, Yu Hsuan Lin, Maxine Weinstein

Research output: Contribution to journalArticlepeer-review

17 Scopus citations


The authors used data from a nationally representative survey of 933 adults aged 54 years or older (mean age = 66.2 years; standard deviation, 8.0) in Taiwan to explore whether mortality prediction at older ages is improved by the use of 3 clusters of biomarkers: 1) standard cardiovascular and metabolic risk factors; 2) markers of disease progression; and 3) nonclinical (neuroendocrine and immune) markers. They also evaluated the extent to which these biomarkers account for the female advantage in survival. Estimates from logistic regression models of the probability of dying between 2000 and 2006 (162 deaths; mean length of follow-up = 5.8 years) showed that inclusion of each of the 3 sets of markers significantly (P = 0.024, P = 0.002, and P = 0.003, respectively) improved discriminatory power in comparison with a base model that adjusted for demographic characteristics, smoking, and baseline health status. The set of disease progression markers and the set of nonclinical markers each provided more discriminatory power than standard risk factors. Most of the excess male mortality resulted from the men being more likely than women to smoke, but each of 3 markers related to disease progression or inflammation (albumin, neutrophils, and interleukin-6) explained more than 10% of excess male mortality.

Original languageEnglish (US)
Pages (from-to)769-779
Number of pages11
JournalAmerican Journal of Epidemiology
Issue number6
StatePublished - Mar 2009

All Science Journal Classification (ASJC) codes

  • Epidemiology


  • Biological markers
  • Mortality
  • Risk factors
  • Sex factors
  • Taiwan


Dive into the research topics of 'Improving mortality prediction using biosocial surveys'. Together they form a unique fingerprint.

Cite this