Related Experiment Video
Updated: May 1, 2026

Sex Stratified Neuronal Cultures to Study Ischemic Cell Death Pathways
Published on: December 9, 2013
Sex differences in health and mortality in Moscow and Denmark
A Oksuzyan1, M Shkolnikova, J W Vaupel
1Max Planck Odense Center on the Biodemography of Aging, Winsloews Vej 9B, 5000, Odense, Denmark, aoksuzyan@health.sdu.dk.
Abstract:
In high income countries females outlive men, although they generally report worse health, the so-called male-female health-survival paradox. Russia has one of the world's largest sex difference in life expectancy with a male disadvantage of more than 10 years. We compare components of the paradox between Denmark and Moscow by examining sex differences in mortality and several health measures. The Human Mortality Database and the Russian Fertility and Mortality Database were used to examine sex differences in all-cause death rates in Denmark, Russia, and Moscow in 2007-2008. Self-reported health data were obtained from the Study of Middle-Aged Danish Twins (n = 4,314), the Longitudinal Study of Aging Danish Twins (n = 4,731), and the study of Stress, Aging, and Health in Russia (n = 1,800). In both Moscow and Denmark there was a consistent female advantage at ages 55-89 years in survival and a male advantage in self-rated health, physical functioning, and depression symptomatology. Only on cognitive tests males performed similarly to or worse than women. Nevertheless, Muscovite males had more than twice higher mortality at ages 55-69 years compared to Muscovite women, almost double the ratio in Denmark. The present study showed that despite similar directions of sex differences in health and mortality in Moscow and Denmark, the male-female health-survival paradox is very pronounced in Moscow suggesting a stronger sex-specific disconnect between health indicators and mortality among middle-aged and young-old Muscovites.
Related Concept Videos
Comparing the Survival Analysis of Two or More Groups
Relationship Formation
Regression Toward the Mean
Statistical Methods for Analyzing Epidemiological Data
McNemar's Test
Bias in Epidemiological Studies

