Summary
Background
Understanding the temporal trend of the disease burden of stroke and its attributable risk factors in China, especially at provincial levels, is important for effective prevention strategies and improvement. The aim of this analysis from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) is to investigate the disease burden of stroke and its risk factors at national and provincial levels in China from 1990 to 2019.
Methods
Following the methodology in the GBD 2019, the incidence, prevalence, mortality, and disability-adjusted life-years (DALYs) of stroke cases in the Chinese population were estimated by sex, age, year, stroke subtypes (ischaemic stroke, intracerebral haemorrhage, and subarachnoid haemorrhage), and across 33 provincial administrative units in China from 1990 to 2019. Attributable mortality and DALYs of underlying risk factors were calculated by a comparative risk assessment.
Findings
In 2019, there were 3·94 million (95% uncertainty interval 3·43–4·58) new stroke cases in China. The incidence rate of stroke increased by 86·0% (73·2–99·0) from 1990, reaching 276·7 (241·3–322·0) per 100 000 population in 2019. The age-standardised incidence rate declined by 9·3% (3·3–15·5) from 1990 to 2019. Among 28·76 million (25·60–32·21) prevalent cases of stroke in 2019, 24·18 million (20·80–27·87) were ischaemic stroke, 4·36 million (3·69–5·05) were intracerebral haemorrhage, and 1·58 million (1·32–1·91) were subarachnoid haemorrhage. The prevalence rate increased by 106·0% (93·7–118·8) and age-standardised prevalence rate increased by 13·2% (7·7–19·1) from 1990 to 2019. In 2019, there were 2·19 million (1·89–2·51) deaths and 45·9 million (39·8–52·3) DALYs due to stroke. The mortality rate increased by 32·3% (8·6–59·0) from 1990 to 2019. Over the same period, the age-standardised mortality rate decreased by 39·8% (28·6–50·7) and the DALY rate decreased by 41·6% (30·7–50·9). High systolic blood pressure, ambient particulate matter pollution exposure, smoking, and diet high in sodium were four major risk factors for stroke burden in 2019. Moreover, we found marked differences of stroke burden and attributable risk factors across provinces in China from 1990 to 2019.
Interpretation
The disease burden of stroke is still severe in China, although the age-standardised incidence and mortality rates have decreased since 1990. The stroke burden in China might be reduced through blood pressure management, lifestyle interventions, and air pollution control. Moreover, because substantial heterogeneity of stroke burden existed in different provinces, improved health care is needed in provinces with heavy stroke burden.
Funding
National Key Research and Development Program of China and Taikang Yicai Public Health and Epidemic Control Fund.
Introduction
Stroke is a leading cause of mortality and long-term disability, especially in low-income and middle-income countries.1 The Global Burden of Disease Study (GBD) 2017 estimated that deaths caused by stroke in China reached approximately 2 million in 2017, and stroke became the leading cause of years of life lost.2, 3 The implementation of effective prevention strategies is therefore essential to mitigate the disease burden of stroke in China.4
Estimating the temporal trends of the stroke burden is fundamental to guide disease intervention and improving public health. Although analyses have been done on the disease burden of stroke in China, an updated comprehensive analysis of its temporal trend over a long period at the provincial level is needed.5, 6 Moreover, previous studies from the GBD collaborators showed that approximately 90% of the disease burden of stroke could be attributed to important environmental and lifestyle risk factors.7, 8, 9 Tracking the disease burden attributable to these risk factors could help in formulating effective control measures to prevent stroke.
The GBD provides a unique framework to examine disease burden and attributable burdens across a series of risk factors, using disease surveillance, health administrative reports, vital registry information, and other sources.10, 11 We used data from the GBD 2019 to conduct a comprehensive analysis of the incidence, prevalence, deaths, and disability-adjusted life years (DALYs) of stroke at the national and regional levels of China in terms of number, rate, and age-standardised rate from 1990 to 2019, stratified by sex, age, and stroke subtype. We also estimated the attributable stroke burden for risk factors at national and provincial levels in China from 1990 to 2019.
Research in context.
Evidence before this study
We searched three databases (PubMed, Embase, and Web of Science) using search terms (“stroke” OR “ischemic stroke” OR “intracerebral hemorrhage” OR “subarachnoid hemorrhage”) AND (“prevalence” OR “incidence” OR “global burden of disease” OR “epidemiology”) AND (“China”) for studies on stroke in China, without language restrictions, until Dec 31, 2020. Although many studies have investigated the burden of stroke in China using data from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) or other data source, little is known regarding the regional distribution of stroke burden and its attributable risk factors. Moreover, because the ageing, growth, and sociodemographic characteristics of the Chinese population have changed during the past decades, the burden of stroke is expected to differ. We systematically analysed the temporal trend of stroke burden and its risk factors at the national and provincial levels of China from 1990 to 2019.
Added value of this study
We conducted a comprehensive estimation of stroke burden and attributable risk factors at national and provincial levels in China using data from GBD 2019. For the past three decades, although the age-standardised incidence and mortality rate of stroke decreased in China, the stroke burden has still been substantial. Regionally stratified estimates of stroke burden suggested the importance of targeted prevention and health care in different provinces. High systolic blood pressure, ambient particulate matter pollution exposure, smoking, and diet high in sodium were found to be the leading risk factors of stroke in 2019. To reduce the stroke burden in China, further efforts should focus on the management of risk factors, especially in provinces with high stroke burden and limited resources.
Implications of all the available evidence
Stroke remains the leading cause of mortality and long-term disability in China. Findings from this study provide updated evidence that could direct future research on stroke prevention and care, and could help guide the allocation of health-care resources in different provinces in China. Additionally, leading attributable risk factors could be first targeted to reduce the disease burden of stroke in China.
This manuscript was produced as part of the GBD Collaborator Network and in accordance with the GBD Protocol.
Methods
Overview
The GBD 2019 was designed to conduct a consistent comparison of the disease burden of stroke and its risk factors over time from 1990 to 2019, across locations, and by sex and age.10 These estimates in the GBD 2019 were updated using the addition of new data and changes in methods, which have been describe elsewhere.10 We used the data in the GBD 2019 to estimate the trends of disease burden of stroke in China with four standard epidemiological measures: deaths, prevalence, incidence, and disability-adjusted life-years (DALYs).10 DALYs represent a combined measure of health loss from both non-fatal and fatal outcomes, equal to years of life lost (YLLs) plus years lived with disability (YLDs).11, 12 YLLs are calculated as the product of counts of deaths caused by stroke and a standard remaining life expectancy at the age of death.13 YLDs are estimated by multiplying stroke prevalence with the corresponding disability weights.14 All data were from 33 provincial administrative units in China, including 22 provinces, four municipalities, five autonomous regions, and two special administrative regions. Municipalities, autonomous regions, and special administrative regions are all termed provincial administrative units in China, although they are not named provinces. All measures were stratified according to age (age groups in 5-year intervals from 15 years to ≥80 years), sex, and three subtypes of stroke (ischaemic stroke, subarachnoid haemorrhage, and intracerebral haemorrhage) from 1990 to 2019.
The Ethical Review Committee of the National Center for Chronic and Non-communicable Disease Control and Prevention of the Chinese Center for Disease Control and Prevention approved this study. This study complied with standards of reporting of neurological disorders guidelines.
Stroke definition
According to WHO criteria, stroke is defined as rapidly developing clinical signs of focal disturbance of cerebral function, lasting more than 24 h or leading to death, with no apparent cause other than that of vascular origin.15, 16 Incident stroke is defined as the occurrence of first-ever stroke according to WHO criteria. Stroke prevalence is the number of newly identified stroke cases and previous stroke cases during the study period. In this study, stroke was modelled in three subcategories, including ischaemic stroke and haemorrhagic stroke (intracerebral haemorrhage and subarachnoid haemorrhage).17 Ischaemic stroke is defined as all vascular events that lead to limited blood flow to brain tissue and result in infarction, thromboembolic strokes, or atherosclerotic strokes, but excluding intracerebral haemorrhage. Haemorrhagic strokes are defined as non-trauma induced subarachnoid or intracerebral haemorrhage.
Estimates of stroke mortality
Mortality data at the provincial levels of China were extracted mainly from surveillance systems (the Disease Surveillance Point system and the Maternal and Child Surveillance System), as well as from surveys, the China Cancer Registry, and the Chinese Center for Disease Control and Prevention cause-of-death reporting system.18 The ninth and tenth International Classification of Diseases codes that were used to estimate stroke mortality are shown in the appendix (p 3). Stroke mortality was generated by Cause of Death Ensemble modelling methods, a systematised tool that runs many different models on the same data and chooses an ensemble of models that best reflects all the available input data.10
Non-fatal estimates of stroke
Data on non-fatal outcomes of stroke were derived primarily from published studies, national surveys, cancer registries, the Chinese Center for Disease Control and Prevention cause-of-death reporting system, and hospital inpatient data.19 These data have been included in the Global Health Data Exchange, which is the largest repository of health data (appendix pp 5–9). We applied the DisMod-MR 2.1, a Bayesian meta-regression tool using the aforementioned sources to estimate non-fatal outcomes of stroke.11 In this model, a reference case definition that best quantifies stroke was set. If there was evidence of a systematic bias in data that used different stroke definitions compared with reference data, we adjusted those data points in DisMod. Details of this model are given in the appendix (p 4).
Risk factor estimation
Individual risk factors were assessed on four levels (appendix p 10).20 Definitions of risk factors (household air pollution from solid fuels, ambient particulate matter pollution, non-optimal temperature, and lead exposure) are shown in the appendix (pp 3–4). Attributable number and age-standardised rate of deaths and DALYs by selected risk factors were estimated according to a comparative risk assessment.21, 22 Briefly, this method included several specific steps: (1) identifying the inclusion of risk-outcome pairs; (2) estimation of relative risk as functional exposure; (3) estimation of exposures for each risk by age, sex, location, and year; (4) identification of the theoretical minimum risk exposure level (TMREL) and the counterfactual exposure; (5) estimation of attributable burden and population attributable fractions (PAFs); and (6) estimation of the deaths and DALYs attributable to various combinations of risk factors.22, 23 The PAF is the proportion of the outcome that would be removed if a risk factor had been reduced to the TMREL. For harmful risk factors, the TMREL was set to zero. For U-shaped and J-shaped associations between risk and outcome, TMREL was determined as the low point of the risk function. Attributable burden for each risk-outcome pair was calculated by multiplying the relevant cause measure by the PAF.
Standardisation and uncertainty intervals
Age-standardised rates for incidence, mortality, prevalence, YLLs, YLDs, and DALYs were computed with a global age structure from 2019. The uncertainty interval (UI) analysis was used in the GBD to address the possible heterogeneity from both sampling error and non-sampling variance. The 95% UIs were calculated by taking 1000 samples from the posterior distribution of the respective step in the modelling process and reported as the 2·5th and 97·5th values for each estimate.24
Role of the funding source
The funder of the study had no role in the study design, data collection, data analysis, data interpretation, or writing of the report.
Results
Of 3·94 million (95% UI 3·43–4·58) incident cases of stroke in China in 2019, 2·87 million (2·38–3·46) were ischaemic stroke, 0·85 million (0·70–1·01) were intracerebral haemorrhage, and 0·22 million (0·18–0·26) were subarachnoid haemorrhage (table 1). The incident cases of stroke and its subcategories increased from 1990 to 2019, with the largest increase for ischaemic stroke (226·5% [211·3–242·5]), followed by subarachnoid haemorrhage (35·6% [25·7–45.1]) and intracerebral haemorrhage (17·6% [12·1–23·7]; table 1). The incidence rate of stroke was 276·7 (241·3–322·0) per 100 000 population in 2019 (table 1). In age-specific analysis, the incidence rate of stroke decreased among each age group younger than 60 years and older than 80 years from 1990 to 2019 (appendix pp 11–12). Moreover, we found different trends of incidence rate stratified by age groups among different stroke subcategories from 1990 to 2019 (appendix pp 11–12). However, the age-standardised incidence rate of overall stroke decreased by 9·3% (3·3–15·5) from 1990 to 2019. Among different stroke subtypes, the age-standardised incidence rates of intracerebral haemorrhage and subarachnoid haemorrhage similarly decreased, but the age-standardised incidence rate of ischaemic increased from 1990 to 2019 (table 1).
Table 1.
Number, rate, and age-standardised rate for stroke incidence in 2019 and percentage changes from 1990 and by sex and stroke subcategories in China
|
Incidence (95% UI), in thousands |
Incidence rate per 100 000 (95% UI) |
Age-standardised incidence rate per 100 000 (95% UI) |
||||
|---|---|---|---|---|---|---|
| 2019 | Percentage change, 1990–2019 | 2019 | Percentage change, 1990–2019 | 2019 | Percentage change, 1990–2019 | |
| Stroke | ||||||
| Male | 1951 (1714 to 2261) | 124·8% (109·8 to 141·6) | 269·2 (236·4 to 311·9) | 89·2% (76·6 to 103·4) | 209·8 (185·8 to 239·4) | −8·0% (−14·3 to −1·7) |
| Female | 1984 (1719 to 2323) | 122·3% (106·3 to 137·8) | 284·5 (246·5 to 333·0) | 82·8% (69·6 to 95·5) | 194·5 (169·7 to 225·2) | −10·1% (−16·3 to −3·5) |
| Total | 3935 (3432 to 4580) | 123·5% (108·2 to 139·1) | 276·7 (241·3 to 322·0) | 86·0% (73·2 to 99·0) | 200·8 (177·0 to 230·8) | −9·3% (−15·5 to −3·3) |
| Ischaemic stroke | ||||||
| Male | 1331 (1100 to 1607) | 237·7% (219·2 to 258·3) | 183·6 (151·8 to 221·7) | 184·3% (168·7 to 201·6) | 141·1 (118·9 to 168·6) | 39·5% (33·5 to 46·2) |
| Female | 1539 (1270 to 1871) | 217·3% (199·4 to 235·7) | 220·6 (182·0 to 268·3) | 160·9% (146·2 to 176·0) | 149·4 (124·7 to 179·8) | 31·4% (24·7 to 37·7) |
| Total | 2869 (2376 to 3459) | 226·5% (211·3 to 242·5) | 201·7 (167·0 to 243·2) | 171·7% (159·0 to 185·1) | 144·8 (121·6 to 173·2) | 34·7% (29·4 to 40·4) |
| Intracerebral haemorrhage | ||||||
| Male | 520 (433 to 621) | 30·7% (24·2 to 38·1) | 71·8 (59·8 to 85·6) | 10·1% (4·6 to 16·2) | 57·7 (48·6 to 67·7) | −46·9% (−50·4 to −43·5) |
| Female | 327 (269 to 392) | 1·4% (−4·1 to 7·6) | 46·9 (38·5 to 56·1) | −16·6% (−21·2 to −11·5) | 33·1 (27·6 to 39·5) | −60·2% (−63·2 to −57·2) |
| Total | 847 (702 to 1010) | 17·6% (12·1 to 23·7) | 59·6 (49·4 to 71·0) | −2·1% (−6·7 to 2·9) | 44·6 (37·5 to 52·7) | −53·1% (−56·4 to −49·8) |
| Subarachnoid haemorrhage | ||||||
| Male | 100 (83 to 118) | 31·5% (20·7 to 40·5) | 13·8 (11·5 to 16·3) | 10·7% (1·6 to 18·2) | 11·0 (9·4 to 12·8) | −39·8% (−45·3 to −35·7) |
| Female | 119 (98 to 142) | 39·2% (28·5 to 49·9) | 17·0 (14·1 to 20·3) | 14·5% (5·7 to 23·2) | 12·0 (10·2 to 14·2) | −38·7% (−43·8 to −34·4) |
| Total | 219 (182 to 260) | 35·6% (25·7 to 45·1) | 15·4 (12·8 to 18·3) | 12·8% (4·6 to 20·8) | 11·5 (9·8 to 13·6) | −39·3% (−44·0 to −35·2) |
UI=uncertainty interval.
In 2019, there were 2·19 million (95% UI 1·89 to 2·51) deaths due to stroke (table 2). The number of deaths from overall stroke increased by 59·0% (30·5 to 91·1), as did that of ischaemic stroke (171·1% [108·7 to 228·3]) and intracerebral haemorrhage (37·4% [9·6 to 66·2]) from 1990 to 2019 (table 2). However, the proportion of deaths caused by subarachnoid haemorrhage decreased by 58·7% (18·6 to 70·8) during the same study period (table 2). The crude mortality rate of stroke was 153·9 (132·6 to 176·8) per 100 000 population in 2019 and increased by 32·3% (8·6 to 59·0) from 1990 to 2019 (table 2). Moreover, the mortality rate of stroke and its subtypes (intracerebral haemorrhage and subarachnoid haemorrhage) decreased among all age groups from 1990 to 2019, with the greatest decrease among those aged 15–19 years, 45–49 years, 50–54 years, 55–59 years, and 60–64 years (appendix pp 13–14). The age-standardised mortality rate decreased by 39·8% (28·6 to 50·7) from 211·4 (187·7 to 243·8) per 100 000 population in 1990, to 127·2 (110·2 to 144·9) per 100 000 population in 2019. The largest decrease in age-standardised mortality rate was in subarachnoid haemorrhage (–84·1% [–88·7 to –69·4]), followed by intracerebral haemorrhage (–48·1% (–58·9 to –37·8]) from 1990 to 2019. Furthermore, the number of deaths and mortality rate were much higher among males than females for all stroke subcategories in 2019.
Table 2.
Number, rate, and age-standardised rate for stroke deaths in 2019 and percentage changes from 1990 and by sex and stroke subcategories in China
|
Deaths (95% UI), in thousands |
Mortality rate per 100 000 population (95% UI) |
Age-standardised mortality rate per 100 000 population (95% UI) |
||||
|---|---|---|---|---|---|---|
| 2019 | Percentage change, 1990–2019 | 2019 | Percentage change, 1990–2019 | 2019 | Percentage change, 1990–2019 | |
| Stroke | ||||||
| Male | 1261 (1035 to 1509) | 76·9 (36·2 to 129·1) | 174·0 (142·9 to 208·2) | 48·9 (14·7 to 92·9) | 170·0 (141·9 to 200·2) | −30·8 (−45·8 to −13·4) |
| Female | 928 (747 to 1117) | 39·7 (7·0 to 81·2) | 133·1 (107·2 to 160·2) | 14·9 (−12·0 to 49·0) | 97·5 (78·9 to 117·0) | −48·2 (−60·2 to −33·1) |
| Total | 2190 (1886 to 2514) | 59·0 (30·5 to 91·1) | 153·9 (132·6 to 176·8) | 32·3 (8·6 to 59·0) | 127·2 (110·2 to 144·9) | −39·8 (−50·7 to −28·6) |
| Ischaemic stroke | ||||||
| Male | 579 (474 to 692) | 195·7 (114·0 to 284·9) | 79·9 (65·4 to 95·5) | 149·0 (80·2 to 224·0) | 83·6 (69·4 to 97·4) | 8·0 (−22·1 to 35·5) |
| Female | 450 (359 to 544) | 144·9 (83·3 to 222·8) | 64·6 (51·5 to 78·0) | 101·3 (50·7 to 165·4) | 48·3 (38·7 to 58·1) | −14·0 (−35·5 to 12·9) |
| Total | 1030 (881 to 1177) | 171·1 (108·7 to 228·3) | 72·4 (62·0 to 82·7) | 125·6 (73·7 to 173·2) | 62·2 (53·3 to 70·7) | −3·3 (−25·6 to 16·4) |
| Intracerebral haemorrhage | ||||||
| Male | 630 (511 to 763) | 53·4 (14·3 to 102·5) | 86·9 (70·5 to 105·2) | 29·2 (−3·8 to 70·5) | 80·4 (66·3 to 95·4) | −40·6 (−54·6 to −23·9) |
| Female | 439 (357 to 529) | 19·6 (−9·8 to 56·7) | 63·0 (51·2 to 75·9) | −1·7 (−25·9 to 28·9) | 45·3 (36·9 to 54·4) | −55·7 (−66·5 to −42·3) |
| Total | 1069 (925 to 1237) | 37·4 (9·6 to 66·2) | 75·2 (65·0 to 86·9) | 14·4 (−8·8 to 38·3) | 60·1 (52·3 to 69·0) | −48·1 (−58·9 to −37·8) |
| Subarachnoid haemorrhage | ||||||
| Male | 52 (34 to 71) | −51·0 (−69·4 to 87·1) | 7·2 (4·7 to 9·8) | −58·8 (−74·2 to 57·5) | 6·4 (4·2 to 8·7) | −80·9 (−88·0 to −27·4) |
| Female | 39 (29 to 49) | −65·9 (−76·1 to −50·8) | 5·5 (4·2 to 7·1) | −71·9 (−80·3 to −59·5) | 3·9 (3·0 to 5·0) | −86·9 (−90·9 to −81·2) |
| Total | 90 (70 to 113) | −58·7 (−70·8 to −18·6) | 6·4 (4·9 to 7·9) | −65·6 (−75·7 to −32·3) | 5·0 (3·9 to 6·2) | −84·1 (−88·7 to −69·4) |
UI=uncertainty interval.
In 2019, 28·8 million (95% UI 25·6–32·2) strokes occurred in China, 147·5% (132·8–162·9) higher than the number in 1990 (appendix pp 15–16). Among stroke subtypes, the largest increase in prevalence was in ischaemic stroke (195·2% [173·7–218·7]), followed by subarachnoid haemorrhage (54·8% [46·3–61·6]) and intracerebral haemorrhage (43·0% [37·5–48·7]) from 1990 to 2019 (appendix pp 15–16). In 2019, the prevalence was 2022·0 (1799·8–2264·8) per 100 000 population for stroke. The prevalence of stroke decreased among each age group younger than 60 years, but increased among groups older than 60 years from 1990 to 2019 (appendix pp 17–18). Trends of prevalence rate stratified by age groups differed greatly among different stroke subtypes from 1990 to 2019 (appendix pp 17–18). The age-standardised prevalence of stroke increased by 13·2% (7·7–19·1), as did that of ischaemic stroke (33·5% [25·6–42·4]) from 1990 to 2019. However, the age-standardised prevalence of subarachnoid haemorrhage decreased by 31·9% (28·6–35·2) and that of intracerebral haemorrhage by 21·9% (18·8–26·4) from 1990 to 2019.
The DALYs of stroke increased by 36·7% (95% UI 13·8 to 63·6), from 33·6 million (29·9 to 38·0) in 1990 to 45·9 million (39·8 to 52·3) in 2019 (figure 1; appendix pp 19–20). The changes in DALYs of stroke were inconsistent among different subtypes (appendix pp 19–20). The DALYs of ischaemic stroke increased by 138·6% (93·5 to 181·1), but the DALYs of subarachnoid haemorrhage decreased by 59·0% (24·5 to 70·4). Similar trends were also observed for YLLs from 1990 to 2019 (appendix pp 21–22). YLDs due to stroke and its subtypes increased from 1990 to 2019, with the highest increase in ischaemic stroke (202·7% [181·2 to 226·0]; appendix p 23). Despite the DALYs of stroke having increased steadily since 1990, we found a substantial reduction (–41·6% [–50·9 to –30·7]) in the age-standardised DALY rate from 1990 to 2019, with a greater reduction among females (–49·4% [–59·6 to –36·3%]) than among males (–34·5% [–49·0 to –16·5%]; appendix pp 19–20). Moreover, the age-standardised YLD rate increased by 15·9% (9·9 to 22·2), but the age-standardised YLL rate decreased by 45·7% (34·0 to 55·6) from 1990 to 2019 (appendix pp 21–23).
Figure 1.
Trends in annual number and age-standardised rate for stroke DALYs by sex in China, 1990–2019
DALY=disability-adjusted life-year.
The attributable DALYs by all risk factors of stroke were 40·55 million (95% UI 34·89–46.23) in 2019 (table 3). The leading risk factors for males in 2019 were high systolic blood pressure, ambient particulate matter pollution, smoking, and diet high in sodium (table 3). The leading risk factors for females in 2019 were high systolic blood pressure, ambient particulate matter pollution, high body-mass index (BMI), and diet high in sodium. We found little difference in the leading risk factors for stroke subcategories (appendix pp 26–31). Other than common risk factors for all stroke subtypes (high systolic blood pressure, ambient particulate matter pollution, smoking, and diet high in sodium), high LDL cholesterol was most strongly associated with ischaemic stroke, and high BMI was most strongly associated with intracerebral haemorrhage and subarachnoid haemorrhage.
Table 3.
Attributable DALYs and age-standardised DALY rate by stroke risk factors in 2019
|
DALYs (95% UI), in thousands |
Age-standardised DALY rate per 100 000 population (95% UI) |
||||||
|---|---|---|---|---|---|---|---|
| Total | Male | Female | Total | Male | Female | ||
| All risk factors | 40 554 (34 894 to 46233) | 24 471 (20 050 to 29 381) | 16 083 (13 202 to 19 060) | 2089·6 (1805·1 to 2374·8) | 2687·3 (2218·0 to 3194·6) | 1577·7 (1295·0 to 1869·0) | |
| Environmental or occupational risks | |||||||
| Ambient particulate matter pollution | 12 847 (10 372 to 15 357) | 7957 (6073 to 9871) | 4891 (3742 to 6151) | 647·6 (525·4 to 772·8) | 840·8 (648·3 to 1035·7) | 474·2 (362·9 to 597·6) | |
| Household air pollution from solid fuels | 3248 (1665 to 5497) | 1701 (757 to 3069) | 1547 (815 to 2502) | 163·1 (84·2 to 275·8) | 177·5 (79·4 to 318·4) | 149·5 (79·1 to 242·2) | |
| High temperature | 42 (−35 to 179) | 26 (−21 to 116) | 16 (−13 to 67) | 2·3 (−1·9 to 9·8) | 3·1 (−2·4 to 13·6) | 1·6 (−1·3 to 6·8) | |
| Low temperature | 3986 (2950 to 5237) | 2456 (1753 to 3336) | 1530 (1099 to 2051) | 208·6 (155·5 to 273·3) | 276·1 (199·2 to 371·5) | 151·6 (109·1 to 202·4) | |
| Lead exposure | 2650 (1603 to 3792) | 1828 (1135 to 2607) | 822 (436 to 1254) | 135·0 (82·5 to 193·2) | 200·3 (125·8 to 281·7) | 80·1 (42·3 to 122·2) | |
| Tobacco smoking | |||||||
| Smoking | 10 099 (8318 to 12 172) | 9019 (7223 to 11 011) | 1080 (840 to 1370) | 490·5 (405·6 to 589·9) | 905·6 (729·2 to 1100·0) | 103·3 (80·5 to 130·5) | |
| Second-hand smoke | 1948 (1409 to 2569) | 855 (579 to 1184) | 1093 (783 to 1444) | 97·6 (71·5 to 127·2) | 89·8 (62·5 to 123·8) | 105·5 (76·4 to 139·1) | |
| Dietary risks | |||||||
| Diet high in red meat | 4941 (3262 to 6554) | 3045 (1914 to 4196) | 1897 (1232 to 2580) | 247·7 (163·6 to 327·6) | 316·2 (198·1 to 429·3) | 184·4 (120·2 to 250·0) | |
| Diet high in sodium | 9990 (4492 to 16 397) | 6817 (3264 to 11 043) | 3173 (1053 to 5828) | 486·1 (214·7 to 807·5) | 686·7 (323·7 to 1141·0) | 301·1 (98·1 to 559·1) | |
| Diet low in fibre | 926 (221 to 1884) | 542 (125 to 1141) | 384 (93 to 804) | 48·1 (11·6 to 97·0) | 58·6 (14·1 to 122·0) | 38·5 (9·4 to 79·7) | |
| Diet low in fruit | 2601 (1341 to 4279) | 1626 (834 to 2753) | 975 (482 to 1641) | 133·4 (68·5 to 219·1) | 174·7 (89·0 to 296·5) | 96·2 (47·6 to 161·8) | |
| Diet low in vegetables | 124 (89 to 202) | 74 (50 to 127) | 50 (35 to 79) | 6·7 (4·7 to 11·2) | 8·9 (5·8 to 16·3) | 5·0 (3·5 to 8·1) | |
| Diet low in wholegrains | 1061 (257 to 1636) | 608 (149 to 965) | 453 (113 to 709) | 54·7 (13·3 to 83·8) | 66·5 (16·4 to 104·7) | 44·8 (11·1 to 70·0) | |
| Low physical activity | 656 (102 to 1916) | 295 (37 to 969) | 360 (62 to 970) | 38·3 (6·0 to 107·8) | 39·6 (4·8 to 125·4) | 37·6 (6·6 to 99·7) | |
| Alcohol use | 3749 (2570 to 5117) | 3540 (2360 to 4865) | 209 (40 to 405) | 186·4 (127·5 to 255·4) | 373·0 (248·9 to 512·4) | 20·3 (3·9 to 39·5) | |
| Phsyiological factors | |||||||
| High fasting plasma glucose | 7533 (5073 to 11 086) | 4528 (2986 to 6781) | 3006 (1948 to 4491) | 386·0 (256·1 to 577·3) | 496·7 (320·5 to 763·4) | 292·5 (189·0 to 434·9) | |
| High LDL cholesterol | 4428 (2418 to 7710) | 2430 (1353 to 4274) | 1998 (1006 to 3563) | 232·8 (119·6 to 416·2) | 273·6 (136·4 to 498·9) | 199·7 (98·0 to 366·1) | |
| High systolic blood pressure | 25 176 (20 272 to 30 148) | 15 003 (11 241 to 18 824) | 10 174 (7670 to 12 790) | 1279·8 (1028·2 to 1531·4) | 1614·4 (1225·9 to 2022·0) | 987·4 (748·7 to 1247·7) | |
| High body-mass index | 8188 (3891 to 13 434) | 4999 (2303 to 8263) | 3189 (1484 to 5383) | 397·5 (188·6 to 654·5) | 493·5 (231·7 to 820·2) | 301·8 (138·7 to 509·2) | |
| Impaired kidney function | 3673 (2904 to 4452) | 2003 (1511 to 2520) | 1670 (1276 to 2114) | 184·4 (143·9 to 223·6) | 211·0 (159·7 to 267·1) | 161·0 (121·7 to 204·3) | |
| Cluster of risk factors* | |||||||
| AIr pollution | 16 095 (13 485 to 18 895) | 9658 (7742 to 11 758) | 6437 (5248 to 7790) | 810·7 (682·1 to 945·7) | 1018·3 (823·0 to 1236·2) | 623·7 (507·8 to 753·1) | |
| Non-optimal temperature | 4026 (3022 to 5283) | 2481 (1770 to 3345) | 1545 (1115 to 2082) | 210·8 (158·5 to 273·9) | 279·1 (202·2 to 372·0) | 153·1 (110·8 to 205·8) | |
| Tobacco smoking | 11 677 (9752 to 13 938) | 9569 (7674 to 11 701) | 2108 (1669 to 2647) | 570·4 (477·5 to 679·0) | 965·7 (781·0 to 1171·8) | 202·7 (160·0 to 253·8) | |
| Dietary risks | 16 729 (11 517 to 22 375) | 10 742 (7373 to 14 763) | 5987 (3889 to 8481) | 833·8 (572·5 to 1119·9) | 1115·1 (764·2 to 1539·3) | 578·6 (375·1 to 818·4) | |
| Behaviourial risks | 25 254 (20 354 to 30 596) | 17 535 (13 999 to 21 554) | 7719 (5597 to 10 219) | 1264·3 (1017·5 to 1534·5) | 1841·5 (1475·3 to 2256·6) | 748·3 (542·8 to 990·0) | |
| Environmental or occupational risks | 20 266 (17 179 to 23 407) | 12 319 (9998 to 14 923) | 7947 (6416 to 9539) | 1031·2 (874·8 to 1190·2) | 1324·6 (1083·1 to 1586·6) | 774·3 (626·4 to 928·7) | |
| Metabolic risks | 31 582 (26 245 to 36 814) | 18 680 (14 763 to 22 960) | 12 902 (10 208 to 15 689) | 1617·8 (1344·7 to 1882·4) | 2031·4 (1608·0 to 2481·6) | 1259·8 (995·3 to 1534·2) | |
DALY=disability-adjusted life-year. UI=uncertainty interval.
Air pollution cluster includes ambient particulate matter pollution and household air pollution from solid fuels. Non-optimal temperature cluster includes low temperature and high temperature. Tobacco smoking cluster includes smoking and second-hand smoking. Dietary risks cluster includes diet high in red meat, diet high in sodium, diet low in fibre, diet low in fruits, diet low in vegetables, diet low in whole grains, and alcohol use. Behavioural risks cluster includes tobacco smoking cluster, dietary risks cluster, and low physical activity. Environmental or occupational risks cluster includes air pollution cluster, non-optimal temperature cluster, and lead exposure. Metabolic risks cluster includes high fasting plasma glucose, high LDL cholesterol, high systolic blood pressure, high body-mass index, and impaired kidney function.
The geographical heterogeneity in the number, rate, and age-standardised rate of stroke burden was observed at the provincial levels from 1990 to 2019 (figure 2; appendix pp 32–45). The largest increases in crude incident rate of stroke (all ages) were found in Heilongjiang (160·4% [95% UI 138·4 to 183·4]) and Liaoning (145·3% [123·6 to 164·3]) from 1990 to 2019 (appendix pp 32–34). The age-standardised incidence rate decreased in most provinces from 1990 to 2019, with the greatest decreases in Fujian (–38·6% [–43·8 to –32·9]), Jiangxi (–30·0% [–35·1 to –24·5]), and Shanxi (–25·2% [–31·0 to –18·9]; appendix pp 32–34). The greatest decreases in mortality rate and age-standardised mortality rate were observed in higher-income provinces such as Beijing, Shanghai, and Guangzhou (appendix pp 35–36). However, the greatest increase in mortality rate was observed in Sichuan (101·0% [57·5–151·7]), Gansu (93·0% [49·8–142·6]), and Qinghai (77·2% [37·3–126·04]), which have less developed economies (appendix pp 35–36). The number, rate, and age-standardised rate of stroke prevalence increased in most provinces from 1990 to 2019 (appendix pp 37–38). The highest age-standardised prevalence rates were observed in Heilongjiang (2049·5 [1819·1–2282·0] per 100 000 population) and Liaoning (2020·8 [1790·0–2257·5] per 100 000 population) in 2019 (appendix pp 37–38). The trends of DALYs and YLLs were generally consistent with those of deaths from 1990 to 2019 (appendix pp 39–40, 43–45). The number and rate of YLDs increased in all provinces, but the age-standardised YLD rate decreased in a few provinces such as Beijing, Hong Kong, Macao, and Shanghai.
Figure 2.
Age-standardised incidence, mortality, prevalence, and DALY rates of stroke at provincial levels of China in 2019
(A) Age-standardised incidence rate in 2019. (B) Age-standardised mortality rate in 2019. (C) Age-standardised prevalence rate in 2019. (D) Age-standardised DALY rate in 2019.
The attributable number and age-standardised rate of deaths and DALYs caused by high systolic blood pressure, ambient particulate matter pollution, smoking, and diet high in sodium at provincial levels were further analysed (table 4; appendix pp 46–59). In 2019, the highest attributable age-standardised mortality and DALY rates by high systolic blood pressure were observed in Qinghai and Tibet (table 4). From 1990 to 2019, the attributable age-standardised mortality and DALY rates of high systolic blood pressure decreased in all provinces (except Yunnan), with the largest decrease in Beijing (table 4). The age-standardised DALY rates attributable to ambient particulate matter pollution substantially decreased in Beijing (–54·4% [95% UI –68·5 to –24·6]), Macao (–48·5% [–68·0 to –7·4%]), and Shanghai (–47·6% [–65·3 to –5·2]), but substantially increased in Guangxi (89·2% [3·4 to 340·2]), Guizhou (111·6% [6·2 to 448·2]), Hebei (80·9% [4·3 to 278·0]), and Yunnan (121·4% [12·6 to 445·4]) from 1990 to 2019 (appendix pp 50–51). Attributable age-standardised mortality and DALY rates attributed to smoking decreased in all provinces from 1990 to 2019 (appendix pp 52–55). The attributable age-standardised DALY rates attributable to diet high in sodium also decreased in all provinces, with the greatest decreases in Beijing (–75·3% [–83·6 to –66·7]), Fujian (–62·0% [–73·9 to –48.4%]), Jilin (–68·3% [–78·1 to –58·6%]), and Shanghai (–66·2% [–76·7 to –54·0]) from 1990 to 2019.
Table 4.
DALYs and age-standardised DALY rates attributable to high systolic blood pressure at provincial levels in China, 1990–2019
|
DALYs (95% UI), in thousands |
Age-standardised DALY rate (95% UI) per 100 000 population |
|||||
|---|---|---|---|---|---|---|
| 1990 | 2019 | Percentage change, 1990–2019 | 1990 | 2019 | Percentage change, 1990–2019 | |
| Anhui | 811·4 (604·5 to 1056·6) | 1140·9 (866·7 to 1456·3) | 40·6 (1·1 to 92·3) | 2097·9 (1596 to 2711·9) | 1261·3 (965·6 to 1599·5) | −39·9 (−56·1 to −20·2) |
| Beijing | 202·7 (155·0 to 253·5) | 237·1 (178·9 to 297·8) | 17·0 (−13·0 to 59·2) | 2186·3 (1675·2 to 2706·6) | 705·7 (533·9 to 882·3) | −67·7 (−75·7 to −56·5) |
| Chongqing | 200·7 (142·9 to 285·4) | 513·3 (379·0 to 661·8) | 155·7 (81·4 to 268·0) | 1708·8 (1223·0 to 2399·9) | 1211·8 (909·1 to 1543·6) | −29·1 (−48·8 to −0·9) |
| Fujian | 286·4 (205·8 to 388·5) | 380·1 (289·3 to 481) | 32·7 (−1·0 to 82·0) | 1491·3 (1082·6 to 1995·4) | 778·8 (587·4 to 992·4) | −47·8 (−60·8 to −28·8) |
| Gansu | 212·5 (150·9 to 286·3) | 486·4 (374·0 to 600·3) | 128·9 (67·7 to 223·5) | 1564·6 (1128·4 to 2104·6) | 1428·7 (1098 to 1760·9) | −8·7 (−32·2 to 24·8) |
| Guangdong | 544·5 (375·9 to 728·8) | 959·2 (747·6 to 1196·7) | 76·2 (26·9 to 147·9) | 1294·4 (908·8 to 1718·6) | 738·1 (572·1 to 924·8) | −43·0 (−59 to −20·7) |
| Guangxi | 420·0 (300·4 to 549·4) | 786·3 (564·2 to 1026·8) | 87·2 (31·1 to 164·6) | 1470·2 (1066·8 to 1919·0) | 1291·4 (929·9 to 1665·9) | −12·2 (−38·3 to 23·1) |
| Guizhou | 502·6 (369·6 to 651·0) | 833·2 (640·3 to 1046·1) | 65·8 (19·2 to 128·1) | 2411·0 (1782·5 to 3094·4) | 1902·1 (1478·2 to 2359·4) | −21·1 (−42·3 to 6·9) |
| Hainan | 60·3 (42·2 to 84·4) | 119·6 (89·5 to 152·9) | 98·3 (35·3 to 189·4) | 1407·9 (994·5 to 1968·1) | 1107·7 (826·6 to 1413·4) | −21·3 (−45·9 to 12·6) |
| Hebei | 1040·9 (782·4 to 1329·4) | 1987·4 (1524 to 2480·8) | 90·9 (38·6 to 161·3) | 2304·9 (1729·5 to 2916·7) | 1935·9 (1503·1 to 2402·5) | −16·0 (−38·3 to 15·4) |
| Heilongjiang | 778·2 (578·7 to 973·4) | 1184·8 (872·2 to 1490·9) | 52·2 (13·8 to 108·2) | 3612·1 (2752·3 to 4465·7) | 1920·1 (1418·2 to 2396·7) | −46·8 (−59·9 to −28·8) |
| Henan | 1216·5 (925·1 to 1523·6) | 2009·0 (1531·6 to 2555·2) | 65·1 (20·1 to 125·6) | 1986·1 (1507·4 to 2483·1) | 1613·7 (1231·1 to 2039·7) | −18·8 (−41·0 to 9·6) |
| Hong Kong | 33·0 (25·8 to 42·7) | 46·4 (33·6 to 64·3) | 40·8 (8·6 to 85·7) | 590·8 (463·2 to 758·2) | 327·7 (237·4 to 450·2) | −44·5 (−56·8 to −28·2) |
| Hubei | 867·0 (655·8 to 1080·9) | 1131·3 (862·2 to 1428·4) | 30·5 (−2·7 to 74·0) | 2282·3 (1748 to 2869·7) | 1312·6 (1010·5 to 1651·3) | −42·5 (−56·5 to −23·4) |
| Hunan | 740·4 (529·9 to 1000·9) | 1094·7 (838·0 to 1383·3) | 47·9 (6·3 to 110·9) | 1685·9 (1218·3 to 2281·6) | 1124·4 (861·2 to 1411) | −33·3 (−51·1 to −7·4) |
| Inner Mongolia | 348·9 (257·2 to 444·5) | 572·9 (444·4 to 717·3) | 64·2 (21·1 to 130·4) | 2697·8 (2042·6 to 3386·0) | 1601·4 (1238·7 to 1987·1) | −40·6 (−55·5 to −18·7) |
| Jiangsu | 719·6 (528·7 to 975·4) | 1170·2 (892·0 to 1516·3) | 62·6 (18·6 to 124·3) | 1343·9 (991·9 to 1818) | 875·4 (672·8 to 1137·1) | −34·9 (−52·5 to −10·7) |
| Jiangxi | 492·3 (361·5 to 641·4) | 598·6 (458·4 to 749·4) | 21·6 (−10·6 to 66·8) | 2052·6 (1535·6 to 2682·8) | 1057·9 (809·4 to 1311·2) | −48·5 (−61·9 to −30·3) |
| Jilin | 605·2 (466 to 738·5) | 712·7 (539·2 to 898·7) | 17·8 (−7·0 to 53·7) | 3743·9 (2954·4 to 4546·1) | 1624·6 (1235·1 to 2031·8) | −56·6 (−65·5 to −44·1) |
| Liaoning | 666·7 (489·3 to 872·7) | 1381·5 (1068·6 to 1735·0) | 107·2 (54·4 to 182·8) | 2314·1 (1723·2 to 3001·5) | 1786·5 (1380·9 to 2240·5) | −22·8 (−41·6 to 5·1) |
| Macao | 3·0 (2·4 to 3·8) | 5·2 (4·0 to 6·7) | 72·7 (32·5 to 121·1) | 1086·2 (852·4 to 1369·7) | 539·8 (416·3 to 705·6) | −50·3 (−61·7 to −36·4) |
| Ningxia | 44·9 (32·9 to 59·5) | 106·1 (79·8 to 134·4) | 136·4 (65·8 to 239·4) | 1906·6 (1435·4 to 2527·5) | 1468·5 (1107·0 to 1843·3) | −23·0 (−44·7 to 8·3) |
| Qinghai | 55·6 (39·9 to 73·0) | 134·6 (101·4 to 169·5) | 142·3 (73·3 to 241·9) | 2353·9 (1717·1 to 3022·2) | 2068·2 (1581·6 to 2535·7) | −12·1 (−35·1 to 19·1) |
| Shaanxi | 433·1 (307·0 to 581·6) | 843·0 (632·4 to 1072·2) | 94·6 (35·6 to 182·6) | 1950·0 (1408·2 to 2584·3) | 1585·2 (1197·4 to 1985·7) | −18·7 (−42·8 to 14·5) |
| Shandong | 1042·5 (777·4 to 1345·0) | 1697·0 (1302·1 to 2153·0) | 62·8 (17·9 to 124·4) | 1659·1 (1223·8 to 2122·5) | 1123·1 (864·2 to 1417) | −32·3 (−50·7 to −6·9) |
| Shanghai | 182·9 (137·5 to 241·4) | 234·3 (176·5 to 294·8) | 28·1 (−4·1 to 75·6) | 1363·1 (1033·3 to 1777·6) | 555·7 (420·2 to 706·1) | −59·2 (−69·3 to −44·7) |
| Shanxi | 429·4 (309·9 to 561·3) | 795·9 (594·7 to 1011·7) | 85·4 (31·2 to 162·4) | 2123·2 (1541·2 to 2747·4) | 1658·7 (1249·0 to 2091·5) | −21·9 (−43·9 to 9·3) |
| Sichuan | 1076·8 (777·3 to 1551·4) | 1773·8 (1338·5 to 2270·6) | 64·7 (19·2 to 127·9) | 1398·0 (1017·1 to 1973·6) | 1365·8 (1037·5 to 1745·5) | −2·3 (−28·1 to 31·0) |
| Tianjin | 137·5 (101·1 to 176·9) | 250·5 (183·3 to 317·0) | 82·2 (30·0 to 154·1) | 1886·7 (1391·1 to 2402·3) | 1153·1 (847·1 to 1458·7) | −38·9 (−56·3 to −16·1) |
| Tibet | 62·3 (44·9 to 81·4) | 87·5 (68·7 to 108·0) | 40·3 (3·0 to 95·1) | 4232·8 (3100·0 to 5460·6) | 3137·3 (2514 to 3829·6) | −25·9 (−44·4 to 1·0) |
| Xinjiang | 138·1 (92·7 to 191·8) | 386·9 (287·9 to 499·6) | 180·1 (97·2 to 318·8) | 1571·5 (1078·9 to 2194·3) | 1514·8 (1151·7 to 1925·1) | −3·6 (−31·7 to 39·8) |
| Yunnan | 364·7 (248·2 to 495·2) | 900·7 (688·0 to 1113·1) | 146·9 (73·2 to 258·1) | 1474·6 (1033·1 to 1987·9) | 1607·0 (1240·9 to 1986·0) | 9·0 (−23·9 to 53·1) |
| Zhejiang | 469·2 (347·1 to 666·2) | 615·4 (471·8 to 783·8) | 31·2 (−2·4 to 76·6) | 1408·9 (1018·0 to 2032·8) | 711·6 (545·4 to 910·8) | −49·5 (−62·5 to −32) |
UI=uncertainty interval. DALY=disability-adjusted life-year.
Discussion
We sought to provide up-to-date estimates of stroke burden from different regions in China to advance evidence-informed prevention plans. As such, we estimated the disease burden of stroke in China over a 30-year period from 1990 to 2019 using data from GBD 2019. We found that although the age-standardised incidence, mortality, and DALY rates of stroke decreased, the absolute numbers and crude rate of stroke burden increased from 1990 to 2019. The attributable burden by selected risk factors of stroke indicated that stroke management could be achieved by effectively controlling for these risk factors. Furthermore, targeted, effective prevention and treatment strategies for stroke are needed in provinces with heavy stroke burden and attributable burden by selected risk factors.
We also found that the age-standardised prevalence rate of stroke increased in China in this period, as well as the crude mortality rate of stroke. These findings might largely be explained by the shift in demographic structure (eg, ageing). In 2019, 164·5 million Chinese citizens were aged 65 years and older, and 26·0 million were aged 80 years or older.25 Although the child policy has been changed in China, the ageing trend is unlikely to be reversed in the near future. Thus, the huge stroke burden placed on the public health system will not soon disappear. Nevertheless, we also found some improvement in the disease burden of stroke, which might be attributed to advancements in public stroke awareness and use of emergency medical services, improvement in medical treatment, as well as risk factor prevention of stroke. Moreover, the decrease in the age-standardised mortality rate was larger than the decrease in age-standardised incidence rate in China from 1990 to 2019. We also found that the decrease in age-standardised DALYs attributable to stroke was mostly due to the decrease in YLLs.
In subgroup analyses, the age-standardised mortality and DALY rates due to stroke were greater among males than among females, but the age-standardised prevalence rate was higher in females than in males. These findings suggest the possibility of increased risk of disability and death caused by stroke in males and increased survival in females.26, 27 We also found different trends of stroke burden among different subtypes from 1990 to 2019. For example, the age-standardised mortality rate decreased by 84·1% for subarachnoid haemorrhage and by 48·1% for intracerebral haemorrhage from 1990 to 2019. However, the age-standardised mortality rate for ischaemic stroke, the most common type of stroke, did not substantially change from 1990 to 2019. One possible reason might be the different improvements in the clinical treatment of haemorrhagic stroke and ischemic stroke.28 The age-stratified analysis suggested that the incidence and prevalence rate of stroke decreased among younger age groups, but increased among older age groups from 1990 to 2019. This finding could be because the ratio of haemorrhagic stroke cases over all stroke cases is higher among younger populations, and because haemorrhagic stroke is more preventable than ischemic stroke.29, 30
Identifying risk factors is important for stroke prevention.31, 32 In 2019, the main risk factors for stroke-related mortality and disability in China included high systolic blood pressure, ambient particulate matter pollution exposure, smoking, and diet high in sodium. Targeting regions where metabolic risks (such as hypertension, hyperglycaemia, and hyperlipidaemia) are increasing might alleviate the disease burden of stroke. Another path to reduce stroke burden is by avoiding high air pollution exposure. Progress in air quality improvements has been made in China,33, 34 which could further contribute to the reduction of disease burden.35 Also, by strengthening education on the other risk factors among the general population, we can improve prevention of stroke.36
Despite observed improvements in the stroke burden at the national level in China, our analysis revealed a large regional disparity in stroke burden and its risk factors. The largest decline of stroke burden occurred among the most economically developed provinces such as Beijing, Shanghai, and Guangdong. However, the age-standardised DALY and mortality rates due to stroke were still high in less economically developed regions such as Hebei, Qinghai, and Tibet. Large geographical variations in stroke burden might be correlated with the differential quality of preventative care of stroke, acute stroke care, and stroke rehabilitation and risk factors of stroke across different regions in China. We analysed the attributable burden caused by leading risk factors of stroke in all provinces. We found that the attributable burden caused by high systolic blood pressure and diet high in sodium was large in Qinghai, Tibet, and Hebei in 2019. The attributable burden caused by ambient particulate matter pollution was severe in some northern provinces such as Hebei, Henan, and Liaoning. Stroke burden due to smoking was large in Guizhou, Heilongjiang, Qinghai, and Tibet; and stroke burden due to diet high in sodium was large in Hebei, Qinghai and Tibet. Our findings could help to guide priority setting and resource allocation in different regions of China, especially in provinces with high stroke burden and many attributable risk factors.
Stroke burden is still severe and remains the leading cause of death in China. Existing stroke prevention strategies in many provinces of China are not sufficiently effective. To reverse the increasing stroke burden, Chinese health systems should focus on new ways for preventing stroke, including measures to provide greater access to health care across all populations and interventions to improve outcomes and reduce mortality.
Our study has several limitations. First, deaths for stroke might be underestimated considering the difficulty in distinguishing mortality from stroke and death due to its comorbidities. Second, TOAST classification of ischaemic stroke was unavailable in this study. This classification is a useful set of criteria used to judge the severity of cerebral ischaemia in clinical practice. Future studies with more specific classifications of ischaemic stroke are needed to assess the severity of stroke among study populations. Third, the use of different studies in different regions might cause a compositional bias of regional estimates. Fourth, UIs represent data differences and sparsity in sample size across study locations, which might be underestimated in remote regions where no data on health registration are available. Therefore, more health surveys with additional details and more representative data for remote areas are needed. Fifth, we did not collect any data on existing prevention and management strategies and these types of data should be included in future studies. Sixth, economic development does not fully explain the province-specific results. Finally, joint effects of multiple risk factors of stroke remain unclear and require further investigation.
Further efforts for stroke prevention and control should focus on the management of stroke risk factors and interventions in provinces with substantial stroke burden. This study could help research on stroke prevention and care and guide health-care resource allocation of different regions in China.
Data sharing
The data used for the analyses are available by email request to the corresponding author.
Declaration of interests
We declare no competing interests.
Acknowledgments
Acknowledgments
We acknowledge funding by the China National Key Research and Development Program (2018YFC1315301) and Taikang Yicai Public Health and Epidemic Control Fund. The GBD is funded by the Bill & Melinda Gates Foundation.
Editorial note: the Lancet Group takes a neutral position with respect to territorial claims in published maps and institutional affiliations.
Contributors
MZ, HL, and YY conceived the study. YY and QM prepared the first draft and finalised the manuscript, with comments from all other authors. MZ, LW, and PY collected and analysed the data. HL, ZZ, and RL participated in the data preparation and verified the data. ZQ, SEM, CW, and MGV provided important comments on the manuscript. YW, CY, YR, and MC participated in data interpretation. SIH and MN verified the data and provided important comments on the manuscript. MZ and PY had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. All authors reviewed the drafted manuscript for critical content and approved the final version. MZ, HL, and YY contributed equally to this work and should be considered co-senior authors.
Supplementary Material
References
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The data used for the analyses are available by email request to the corresponding author.