Influence of socioeconomic events on cause-specific mortality in urban Shanghai, China, from 1974 to 2015: a population-based longitudinal study

Abstract

Background: Understanding how socioeconomic events influence cause-specific mortality is essential for optimizing disease-control strategies. We characterized long-term trends in cause-specific mortality in a stable population from a very large urban centre.

Methods: We derived population data from 1974 to 2015 on vital status, demographics and causes of death from the death registration system in Yangpu District, Shanghai, China. We examined temporal trends in mortality and assessed the effects of age, period and birth cohort.

Results: Over 41 879 864 person-years of follow-up, we analyzed 290 332 deaths: 3.80% from communicable conditions (group 1), 86.50% from noncommunicable diseases (group 2), and 5.56% from injuries (group 3). Age-standardized mortality decreased after 1988 for group 1 (average annual percentage change [AAPC] -6.7, 95% confidence interval [CI] -9.3 to -4.1), after 1995 for group 2 (AAPC -2.9, 95% CI -3.5 to -2.3), and after 1994 for group 3 (AAPC -5.4, 95% CI -6.3 to -4.5), after improvements in public health and clinical service infrastructure and the removal of polluting industries during the 1980s. We observed increased mortality from group 2 and group 3 causes in those born between 1955 and 1965, a period that included the Great Chinese Famine. Cause-specific mortality risks increased in those born after 1949 for cancer and diabetes only.

Interpretation: Birth cohorts exposed to extreme starvation in early life had increased premature cause-specific mortality in later life. Decreased cause-specific mortality followed improvements in public health, medical infrastructure and pollution control, but not for cancer or diabetes, likely because of exposure to new risk factors.

Figure 1:

Rate of increase of grain yield and natural population growth rate from 1949 to 2015 in mainland China, and change in dietary patterns in urban Shanghai, China. The data on grain yield and natural population growth were derived from the National Bureau of Statistics of China. The information on dietary patterns in urban Shanghai was obtained from a Shanghai Center for Disease Control database. Pure energy food, which includes oil, starch, sugar and alcohol, provides energy but lacks protein and other nutrients.

Figure 2:

Proportions of causes of death in Yangpu District, Shanghai, China, 1974–2015. Solid lines and empty bars represent group 1 causes (communicable, maternal, perinatal and nutritional conditions); coloured solid bars indicate group 2 causes (chronic noncommunicable diseases); dotted lines and empty bars indicate group 3 causes (injuries).

Figure 3:

Age-specific mortality rates (per 100 000) by period and birth cohort and age, period, and cohort effects for the mortality rates of major chronic diseases in Yangpu, Shanghai, China (1974–2015). Each row of plots, from left to right, are age-specific mortality rates by period, age-specific mortality by birth cohort, and an age–period–cohort Poisson (APC) regression plot. The APC regression plot has 3 curves depicting, from left to right, trends in mortality rate by age( yr) for the reference birth cohort (1949), the risk ratio of the cohort effect compared with the reference birth cohort (1949), and the risk ratio of the calendar period effect compared with the reference cohort (1980). Dotted lines show the 95% confidence intervals of the 3 components (solid lines).

Figure 4:

Pie charts of all causes of death in women and men from 1976 to 1980 and from 2011 to 2015, and predicted from 2026 to 2030, scaled to the number of deaths during each period, (A) for women; (B) for men.