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Socioeconomic Differentials in Mortality among the Oldest Old in China. Haiyan Zhu Yu Xie University of Michigan. Background. Well documented inverse relationship: Higher SES Lower mortality However, magnitude varies by age.
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Socioeconomic Differentials in Mortality among the Oldest Old in China Haiyan Zhu Yu Xie University of Michigan
Background • Well documented inverse relationship: Higher SES Lower mortality • However, magnitude varies by age
Yu Xie (1994). “Log-multipliticative Models for Discrete Time, Discrete Covariate Event-History Data”
SES differentials in mortality diminish with age (Convergence hypothesis)? • The oldest old are detached from economic activities and thus immune from some causal mechanisms (e.g., job hazards, work stress) • Biological determinants, rather than social determinants, play a predominant role • The oldest old are a select group with respect to unobserved health traits
Research Questions: • Does SES still affect mortality among the oldest old Chinese? • Do SES differentials in mortality disappear beyond an old age (e.g., 80, 90, or 100)?
Importance • Previous studies have not specifically studied these questions • This study focuses on these questions using a sample of oldest old in China
Data and Measures • The Chinese Healthy Longevity Survey:1998 and 2000 cohort data • SES: composite of education and urbanity • Illiterate, rural • Illiterate, urban • Literate, rural • Literate, urban • Covariates: age, sex, ethnicity, region, self-rated health, ADL, and “time-interval” • “time-interval”: time since the first interview
Analyses • Three alternative target populations 80 and older, 90 and older, and 100 and older • Logit, discrete time analysis
Descriptive Statistics Table 1 Distribution of SES and Mortality by SES, female Note: numbers are unweighted; percentages are weighted.
Descriptive Statistics (Continued) Table 2. Distribution of SES and Mortality by SES, male Note: numbers are unweighted; percentages are weighted.
Results Table 3. Effects of SES on Mortality (controlling ethnicity and region) * p<.05; ** p<.01; *** p<.001
Results (Continued) Table 4. Effects of SES on Mortality (controlling health, ethnicity and region) P<0.1; * p<.05; ** p<.01; *** p<.001
time1 time2 time3 1st cohort 80 81 82 2nd cohort 81 82 83 3rd cohort 82 83 84
Summary • SES differentials in mortality persist, using all three operationalizations of “old age” • Convergence was not supported • Future studies