Cardiovascular risk factors in China: a nationwide population-based cohort study

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Cardiovascular risk factors in China: a nationwide population-based cohort study

It is estimated that 4 million deaths are due to cardiovascular diseases each year in China. Comprehensive understanding about modifiable risk factors and how the risk differs across regions is needed to inform public health policies. We aimed to examine the geographical profile of cardiovascular disease risk across China.
In this study, we analysed data from a nationwide, population-based screening project, which covered 152 rural counties and 100 urban districts from 31 provinces in China. Between Sept 1, 2015, and Nov 30, 2019, standardised measurements were taken from participants aged 35–75 years who had lived in the region for at least 6 of the preceding 12 months to collect information on blood pressure, blood lipids, blood glucose, physical activity, tobacco smoking, alcohol use, overweight or obesity, and intake frequencies of fruits, vegetables, whole grains, legumes, and red meat. Individuals with a high risk of cardiovascular disease were identified according to medical history and WHO risk prediction charts.
983 476 individuals were included in this study. Among the participants included, 10·3% (95% CI 10·2–10·3) had a high cardiovascular disease risk after standardising age and sex, with a range of 3·1–24·9% across counties or districts. Among the seven regions in mainland China, the prevalence of high risk of cardiovascular disease was relatively high in northeast China (12·6% [12·4–12·8]) and north China (11·4% [11·3–11·6]), whereas it was low in south China (8·0% [7·8–8·2]). The geographical profiles of the 12 major cardiovascular disease risk factors were different. We found that the regions with high cardiovascular disease risk were facing challenges such as obesity and high blood pressure (north China) and consumption of unhealthy non-staple food (low intake of fruits and vegetables or high intake of red meat; northeast China). By contrast, south China—the region with the lowest cardiovascular disease risk—had the highest prevalence of unhealthy staple food (low intake of whole grains and beans), abnormal metabolism (glucose and lipid), and low physical activity in the country.
Risk for cardiovascular diseases varies geographically, and the major contributing risk factors are different across regions in China. Hence, geographically targeted interventions are needed to mitigate the risk and reduce the burden in such a vast country.
Ministry of Science and Technology, Ministry of Finance, and National Health Commission of China.
We searched PubMed for literature published in English before May 1, 2019, using the terms “CVD risk factor cluster”, “CVD risk factor pattern”, “geographical/regional disparity/difference/variation”, “lifestyle”, or “nationwide”. We excluded studies with less than 10 000 samples or studies done in a single province. We identified 43 articles (with participants enrolled between 1991 and 2017) that showed the distribution or association of major risk factors of cardiovascular disease in China. Of these studies, 17 included more than 100 000 participants, 20 covered all 31 provinces, 12 studied more than seven risk factors, and none reported the geographical discrepancies of clusters of major cardiovascular disease risk factors.
To our knowledge, this study is the largest population-based report of major risk factors of cardiovascular disease across all 31 provinces in mainland China. We evaluated geographical variations in cardiovascular disease risk and multiple cardiovascular risk factors. We found differences in the prevalence of high risk of cardiovascular disease across areas. Also, among the regions with similarly high population risk, the major contributing factors were not the same, and among regions with middle or even low risk, there were still some risk factors posing severe threats to population health. This study identified several clear classifications for the major risk factors of cardiovascular disease in the Chinese population; these risk factors occurred simultaneously and could be reduced to some clusters. These findings provided a detailed risk atlas, which highlights not only the high-risk areas, but also—more importantly—the priority factors.
The geographical profile of cardiovascular disease risk in China is complex, with a large variation in population prevalence and in contributor factors across regions. In view of the clustering of risk factors of cardiovascular disease in populations reported in this study, strategies and policies need to be targeted and based on comprehensive and practical data for delivering interventions.
We compared the risk profiles between rural counties and urban districts and between seven regions, including northeast China, north China, east China, central China, south China, northwest China, and southwest China, which were classified on the basis of geographical divisions of China (). We also searched grey literature, including statistical yearbooks, for information on the annual average ambient temperature, average altitude, and per capita gross domestic product (GDP) in 2017, to define environmental and socioeconomic characteristics of each study site.
We constructed maps for the rate of the populations with high cardiovascular disease and for the score of risk factor clusters, which had been standardised according to the 2010 population census of China. To show county-level or district-level distributions, we included 236 counties or districts after excluding those with less than 1000 eligible participants enrolled. Additionally, we generated prevalence or scores of each province by averaging the values of their corresponding counties or districts. Scatterplots and fitting lines were used to show the correlations of the risk factor clusters with environmental and socioeconomic characteristics across counties or districts, and Fisher z-transformation was applied to test the heterogeneity of correlation coefficients between rural and urban areas.
The proportion of participants missing data was 14·9% for MET, 3·8% for the number of cigarettes smoked, and less than 2% for all other risk factors (). We applied a multiple imputation method based on Markov Chain Monte Carlo by PROC multiple imputation procedure to impute the missing values, and the average of ten imputations was used to do the factor analysis (). We repeated the factor analysis among participants without any missing value as a sensitive analysis.
All analyses were done with SAS 9.4, and the maps were constructed with R 3.4.1.
The funder of the study had no role in study design, data collection, data analysis, data interpretation, or writing of the report. The corresponding author had full access to all the data in the study and had final responsibility for the decision to submit for publication.
Among all participants, 16·6% (95% CI 16·6–16·7) had a high risk of cardiovascular disease, ranging from 2·8% to 34·2% across the counties or districts included (data not shown). After standardising age and sex using the 2010 national census data, the overall rate of high risk was 10·3% (10·2–10·3), with a range of 3·1–24·9% among the counties or districts (). The median odds ratio of the rates of high cardiovascular disease risk at the county level or district level was 1·52 (95% CI 1·46–1·58).
Generally, the standardised rates of participants with high risk of cardiovascular disease were high in northeast China (12·6% [95% CI 12·4–12·8]) and north China (11·4% [11·3–11·6]), with 95% CIs higher than 11%. In contrast, south China (8·0% [7·8–8·2]) had low standardised rates, with a 95% CI lower than 9%. In between were east China (9·6% [9·4–9·7]), northwest China (9·6% [9·5–9·8]), southwest China (10·0% [9·8–10·1]), and central China (10·7% [10·5–10·9]; ).
In the factor analysis, six components with eigenvalues greater than 1·0 were retained. Of the total variance, the obesity factor (factor 1) accounted for 16·2%, the blood pressure factor (factor 2) for 13·6%, the staple food factor (factor 3) for 9·5%, the non-staple food factor (factor 4) for 8·8%, the smoking and alcohol use factor (factor 5) for 7·9%, and the metabolic and physical activities factor (factor 6) for 7·4% (). After varimax orthogonal rotation, the first component, the obesity factor, had factor pattern loadings exceeding 0·9 for BMI and waist circumference. The second component, the blood pressure factor, had factor pattern loadings of nearly 0·9 for systolic blood pressure and diastolic blood pressure. The third component was termed staple food factor because it mainly included intake of whole grains (with loadings of 0·8) and beans (0·7). The fourth component was termed non-staple food factor because it mainly included intake of fruit (0·6), vegetables (0·8), and red meat (0·6). The fifth component was termed smoking and alcohol use factor because of its high loadings on these two risk factors (0·8 for both). The last component was termed metabolic and physical activities factor with its high loadings on blood glucose (0·6), blood lipid (0·7), and physical activity (0·5). In the separate analyses for male and female individuals, we found the same six components and similar factor patterns. The findings did not change in the sensitivity analyses that were done using data without missing imputations ().
Large regional variations in prevalence of major risk factors of cardiovascular diseases were observed (). Moreover, geographical distributions of the six risk factor clusters were complex (; ). Among the high-risk regions, north China was particularly affected by the obesity factor and blood pressure factor, whereas northeast China was highest for the non-staple food factor. Nevertheless, south China, although being the region with the lowest risk, had the highest values for the staple food factor and the metabolic and physical activities factor ().
Analyses looking at annual average ambient temperature, altitude, and per capita GDP also showed differences between regions (; ). Higher per capita GDP was associated with lower risk in non-staple food factor in urban areas and higher risk in metabolic and physical activities factor in rural areas (p<0·05 for both). Notably, the correlation between per capita GDP and the smoking and alcohol use factor differed significantly between rural and urban regions (p=0·042; ).
We found an eight-time difference in the rate of the population at high risk for cardiovascular diseases across counties or districts, with the highest rate in northeast China and north China, and the lowest rate in south China. Moreover, the risk factors were clustered and had uneven spatial distributions through the country. These variations could be partly explained by regional environmental and socioeconomic characteristics.
Findings in the current study should be interpreted in the context of several potential limitations. First, some cardiovascular disease risk factors, such as sodium intake, were missing owing to absence of reliable measurement in the screening visit. However, this limitation could have little influence on our findings because we used factor analysis with potentially related but more immediate risk factors included (eg, blood pressure). Second, the study network has not been established on the basis of a random sampling design, which prohibited estimation of national or regional prevalence. Nevertheless, most of our analysis focused on the diversity rather than the average values, which might be generalisable within a particular area. Third, as in other large-scale studies, our data on some risk factors, including physical activities, diet, tobacco smoking, and alcohol use, were collected using self-reports, which might have been influenced by recall bias and social desirability. Finally, it is notable that the WHO risk prediction algorithm, although developed and dedicated for east Asia, might not fit the heterogeneous population in China.
In conclusion, the geographical profile of cardiovascular disease risk in China is complex; the population risk levels can vary substantially across regions. Thus, China needs geographically targeted intervention strategies considering environmental and socioeconomic factors to control cardiovascular disease risk and reduce the burden of cardiovascular diseases.
This online publication has been corrected. The corrected version first appeared at thelancet.com/public-health on April 26, 2021
SH and JLi conceived the China Patient-centered Evaluative Assessment of Cardiac Events Million Persons Project and take responsibility for all aspects of it. XL and CW designed the study. XL and CW wrote the first draft of the Article, with further contributions from JLu, BC, YL, YY, JLi, and SH. CW and BC did the statistical analysis. All authors interpreted the data and approved the final version of the Article. XL, CW, JLu, BC, JLi, and SH had access to the raw data.
We acknowledge funding by the National Key Research and Development Program (2018YFC1312404, 2017YFC1310803, 2017YFC1310801) from the Ministry of Science and Technology of China, the Chinese Academy of Medical Sciences Innovation Fund for Medical Science (2017-I2M-1–003, 2017-I2M-2–002), and the Ministry of Finance of China and National Health Commission of China. We thank the contributions that have been made by study teams at the Chinese National Center for Cardiovascular Diseases, and the local sites in the collaborative network in the realms of study design and operations, particularly data collection by Jianlan Cui, Wei Xu, and Bo Gu. We thank Danwei Zhang, Aoxi Tian, and Xingyi Zhang from the Chinese National Center for Cardiovascular Diseases for their support in manuscript coordinating and editing.
Editorial note: the Lancet Group takes a neutral position with respect to territorial claims in published maps and institutional affiliations.
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