Associations of socioeconomic factors with cause-specific Mortality and burden of cardiovascular diseases: findings from the vital registration in urban Shanghai, China, during 1974-2015

Abstract

Background: Cardiovascular disease (CVD) is the leading cause of mortality worldwide. The effect of socioeconomic factors on cause-specific mortality and burden of CVD is rarely evaluated in low- and middle-income countries, especially in a rapidly changing society.

Methods: Original data were derived from the vital registration system in Yangpu, a representative, population-stable district of urban Shanghai, China, during 1974-2015. Temporal trends for the mortality rates and burden of CVD during 1974-2015 were evaluated using Joinpoint Regression Software. The burden was evaluated using age-standardized person years of life loss per 100,000 persons (SPYLLs). Age-sex-specific CVD mortality rates were predicted by using age-period-cohort Poisson regression model.

Results: A total of 101,822 CVD death occurred during 1974-2015, accounting for 36.95% of total death. Hemorrhagic stroke, ischemic heart disease, and ischemic stroke were the 3 leading causes of CVD death. The age-standardized CVD mortality decreased from 144.5/100,000 to 100.7/100,000 in the residents (average annual percentage change [AAPC] -1.0, 95% confidence interval [CI] -1.7 to - 0.2), which was mainly contributed by women (AAPC -1.3, 95% CI - 2.0 to - 0.7), not by men. Hemorrhagic stroke, the major CVD death in the mid-aged population, decreased dramatically after 1991. The crude mortality of ischemic heart disease kept increasing but its age-adjusted mortality decreased continually after 1997. SPYLLs of CVD death increased from 1974 to 1986 (AAPC 2.1, 95% CI 0.4 to 3.8) and decreased after 1986 (AAPC 1.8, 95% CI - 2.3 to - 1.3). These changes were in concert with the implementation of policies including extended medical insurance coverage, pollution control, active prophylaxis of CVD including lifestyle promotion, and national health programs. The mortality of CVD increased in those born during 1937-1945, a period of the Japanese military occupation, and during 1958-1965, a period including the Chinese Famine. Sequelae of CVD and ischemic heart disease are predicted to be the leading causes of CVD death in 2029.

Conclusions: Exposure to serious malnutrition in early life might increase CVD mortality in later life. Improvements in medical services, pollution control, and lifestyle could decrease CVD death. New strategy is needed to prevent the aging-related CVD death and burden in the future.

Keywords: Burden; Cardiovascular disease; Lifestyle; Low- and middle-income countries; Malnutrition; Medical insurance; Mortality; Pollution control; Sociodemographic index.

Fig. 1

Proportion of age-standardized mortality rates per 100,000 persons for the major causes of CVD in Yangpu, Shanghai, China, 1974–2015

Fig. 2

Average age of the residents died of the major causes of CVD in Yangpu district, Shanghai, China, 1974–2015. Note: AAPC, average annual percentage change

Fig. 3

Trends in crude and age-standardized mortality rates of the major causes of CVD in both sexes in Yangpu, Shanghai, China, 1974–2015. Note: a trend in crude mortality rates in all residents; b trend in age-standardized mortality rates in all residents; c trend in crude mortality rates in women; d trend in age-standardized mortality rates in women; e trend in crude mortality rates in men; f trend in age-standardized mortality rates in men

Fig. 4

Age-specific mortality rates (per 100,000) by period and birth cohort and age, period, and cohort effects for the mortality rates of the major CVD in Yangpu, Shanghai, China, 1974–2015. Note: 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)

Fig. 5

Trend in age-standardized CVD person years of life lost (SPYLLs) per 100,000 persons in Yangpu district, Shanghai, China, 1974–2015. Note: Solid lines, mean value; dotted lines, standard error

Fig. 6

Changes in SPYLLs and AYLL for the major causes of CVD in Yangpu, Shanghai, China. Note: Solid lines are “increases” and dashed lines are “decreases”. For the time period 1974–1995 and 1995–2015, two measures of change are shown: percent change in SPYLLs, change in SPYLLs, change in AYLL. Statistically significant changes are shown with *. SPYLLs = age-standardized person years of life lost per 100,000 persons. AYLL = average years of life lost

Fig. 7

Trends in SPYLLs of the major causes of CVD in both sexes in Yangpu district, Shanghai, China, 1974–2015. Note: SPYLLs = Age-standardized person years of life lost per 100,000 persons

Fig. 8

Pie charts of cause-specific CVD death in women and men from 1976 to 1980 and from 2011 to 2015, and predicted from 2025 to 2030, scaled to the number of deaths during each period, (a) for women; (b) for men