Abstract
Background:
Neurological diseases represent a growing challenge to the Chinese public health system. However, a comprehensive analysis of neurological diseases in China is lacking. This study aimed to analyze disease burden and risk factors of neurological diseases in China to identify priorities for disease control and prevention.
Methods:
Disease burden and risk factors in China were analyzed for 12 neurological disorders using data from the Global Burden of Diseases 2021 study and the Chinese Center for Disease Control and Prevention. Prevalence, deaths, years of life lost, years lived with disability (YLDs), and disability-adjusted life years (DALYs) were used as metrics.
Results:
Intracerebral hemorrhage (ICH) (1930.3 [95% uncertainty interval (UI): 1605.3–2296.7] per 100,000), ischemic stroke (1646.8 [95% UI: 1400.0–1893.1] per 100,000), Alzheimer’s disease and other dementias (dementia) (708.0 [95% UI: 347.7–1561.7] per 100,000) made the greatest contributions to DALY rates in China in 2021. The fastest growing contributors to DALY rates were dementia (208.2% [95% UI: 166.4–255.7%]), Parkinson’s disease (160.7% [95% UI: 121.8–208.3%]), and ischemic stroke (95.2% [95% UI: 56.9–140.6%]). Migraine was the leading contributor to DALY rates among populations aged 10–39 years, ICH for those aged 40–74 years, ischemic stroke for those aged 75–89 years, and dementia for those aged >90 years. Ischemic stroke accounted for the highest age-standardized DALY rates in North and Northeast China, whereas ICH ranked first in other regions. High systolic blood pressure had the highest attributable DALYs for all diseases combined. Metabolic risk factors, alcohol use, secondhand smoke, and low physical activity contributed to higher YLDs in females, whereas alcohol use, smoking, and a high-sodium diet contributed to higher YLDs in males.
Conclusions:
Neurological diseases present a growing public health challenge, characterized by significant disparities in their prevalence and presentation across age, sex, and geographic regions. Addressing these disparities requires coordinated strategies encompassing prevention, treatment, rehabilitation, and supportive care at the national level.
Keywords: Neurological disease, Burden of disease, Risk factors, Subgroup analysis, Disability-adjusted life years, Intracerebral hemorrhage, Ischemic stroke, Alzheimer’s disease
Introduction
Neurological diseases encompass a wide range of disorders with different etiologies and pose significant challenges to public health systems worldwide. As the population ages globally, the prevalence of these conditions continues to rise, placing an increasing burden on healthcare resources worldwide. In 2021, neurological diseases affected 3.40 billion individuals worldwide, corresponding to 43.1% (95% uncertainty interval [UI] 40.5–45.9%) of the total population.[1]
Rapid demographic shifts, urbanization, and lifestyle changes have contributed to increased rates of neurological disease in China.[2] The national population grew from 1.13 to 1.41 billion between 1990 and 2020, and the proportion over 60 years rose from 8.57% to 18.70%,[3] concomitant with increases in stroke, dementia, and Parkinson’s disease (PD),[2,4] representing a trend that is anticipated to continue.[5–9] Urbanization and modern lifestyles have precipitated a growing prevalence of headache disorders and stroke.[10–12] These shifts underscore the urgent need for a comprehensive understanding of neurological disease burden across regions to inform targeted public health interventions.
Previous reports on causes and consequences of neurological disease offer valuable insights but have often focused on individual diseases,[8,9,13–20] leaving knowledge gaps regarding disease burden due to different pathologies within regions or subpopulations. The current study analyzed the prevalence and disease burden of 12 neurological diseases in China and its provinces between 1990 and 2021 using data from the Global Burden of Diseases, Injuries, and Risk Factors (GBD) 2021 study. Subgroup analyses by age, sex, geographic region, and modifiable risk factors were performed. By doing so, we hope to highlight priorities for disease prevention and control in different subgroups and thus provide epidemiologic evidence for public health decisions.
Methods
Data acquisition and disease scope
The GBD 2021 study is a meta-analysis that collated data on 371 diseases in 204 countries and territories, including values for prevalence, disease severity, and death. Its original data sources included government websites, statistical annuals, demographic compendia, large-scale surveys, and collaborator input.[21] In this study, disease data at the national and provincial levels were acquired from the GBD 2021 website[22] and the Chinese Center for Disease Control and Prevention, respectively. The spatial and temporal coverage of the data sources are summarized in Supplementary Figures 1–4, http://links.lww.com/CM9/C831. Spatiotemporal Gaussian process regression and cause of death ensemble modeling (CODEm) were used by GBD modeling to infer missing data using values from geographically adjacent areas, populations with comparable demographic profiles, and longitudinal disease trends.[21]
Twelve neurological diseases (or categories) that belonged to the level of “most detail causes” in the GBD 2021 cause of death or injury database were included in the current study. These were ischemic stroke, intracerebral hemorrhage (ICH), subarachnoid hemorrhage (SAH), brain and central nervous system (CNS) cancers, Alzheimer’s disease and other dementias (dementia), PD, idiopathic epilepsy, multiple sclerosis (MS), motor neuron disease (MND), migraine, tension-type headache (TTH), and a category of other less common neurological disorders [Supplementary Tables 1 and 2, http://links.lww.com/CM9/C831]. Infectious diseases, such as tetanus, and posttraumatic conditions were not included as part of our study.
Statistical parameters
Data are presented as absolute numbers, rates (per 100,000), and age-standardized rates (per 100,000). Rates were calculated by dividing the absolute number by the population number of the subgroup. Age-standardized rates were computed with the GBD standard population structure.
A total of five metrics were used to describe disease burden, including prevalence, deaths, years of life lost (YLLs), years lived with disability (YLDs), and disability-adjusted life years (DALYs). Prevalence and death were defined as the number of cases or deaths in the population within a specified time. YLLs were calculated by multiplying the number of deaths with the corresponding loss of life expectancy for that age group. The standard life expectancy is calculated from the lowest age-specific mortality rate across countries. YLDs were calculated by multiplying the case number of different severities with their corresponding healthy life years that were lost to disability. Finally, DALYs are the sum of YLLs and YLDs. No deaths were attributed to headache, and DALY rates were equal to YLD rates for migraine and TTH.
Data curation and modeling strategy of the GBD 2021 study
A rigorous process of data standardization was performed in the GBD 2021 study and reported previously.[21] Briefly, age and sex splitting based on the corresponding proportion of a standard population was performed for individual data sources that lacked sex or age information or those that could not be readily assigned to an age group. In addition, “garbage codes”, which are defined by GBD as nonspecific, implausible, or intermediate, rather than underlying cause of death codes from the International Classification of Diseases, were redistributed to appropriate targets to assign the underlying cause of death.[21] Data sources that had more than 50% of all deaths assigned to major garbage codes in a given location-year were excluded to control potential bias. However, a 5% buffer was applied to data sources that had been included in the GBD 2019 study to facilitate better consistency between cycles. This meant that up to 55% “garbage-coded” deaths could be tolerated.
After data assignment and standardization, two major strategies were applied to generate the final estimates showcased on the GBD 2021 website. Modeling approaches were individualized for the 12 neurological disorders based on differing underlying hypotheses (Appendix 1 of the GBD 2021 capstone paper[21]). The two major modeling strategies are summarized below.
First, the CODEm framework was used to generate most cause-specific death rates. Briefly, a set of plausible models was first applied to fit the whole data pool, 30% of the data was randomly excluded from the initial model fitting, and half of the excluded data (15% of the total) was used to evaluate the out-of-sample predictive ability of individual models. Models were assigned weights based on their performance and assembled into the final model for parameter estimation. A total of 1000 draws were made, and 0.025 and 0.975 quantiles gave the 95% UIs for each estimate. A lack of overlap in the 95% UIs between estimates may be interpreted as suggestive of a statistically significant difference, whereas substantial overlap indicates that the difference may not be statistically significant.
Second, the Disease Modelling Meta-Regression (DisMod-MR) 2.1 tool (Institute for Health Metrics and Evaluation, Population Health Building/Hans Rosling Center, 3980 15th Ave. NE, Seattle, WA, USA) was used to estimate nonfatal parameters and to ensure internal consistency between epidemiological parameters. DisMod-MR is a Bayesian hierarchical model capable of synthesizing disparate and heterogeneous data sources, such as incidence rates, prevalence, and relative mortality risk, into a coherent set of estimates. By imposing a compartmental model structure that reflects the disease’s natural history, DisMod-MR ensures that estimates for different parameters are biologically coherent and mathematically consistent. This is critical for diseases where mortality is not directly modeled via CODEm or where nonfatal estimates (e.g., prevalence from DisMod-MR) must be consistent with fatal outcomes.
Risk factors
Attributable YLL and YLD numbers, rates, and percentage of risk factors at the “most detailed risk” level were downloaded from the GBD 2021 database, where available. A total of 23 modifiable risk factors were acquired for YLLs and 21 for YLDs of 7 diseases, including ischemic stroke, ICH, SAH, dementia, PD, idiopathic epilepsy, and MS. Risk factors belonged to 3 major categories of environmental/occupational (low temperature, high temperature, lead exposure, household air pollution from solid fuels, and ambient particulate matter pollution), metabolic (kidney dysfunction, high low density lipoprotein [LDL] cholesterol, high systolic blood pressure, high body mass index, and high fasting plasma glucose), and behavioral (secondhand smoke, low physical activity, alcohol use, smoking, diet high in sugar-sweetened beverages, diet high in processed meat, diet low in polyunsaturated fatty acids, diet low in fiber, diet high in red meat, diet low in fruits, diet low in vegetables, and diet high in sodium).
Definition of geographic regions in China
In this study, these regions include North China (Beijing, Tianjin, Hebei, Shanxi, and Inner Mongolia), East China (Shanghai, Jiangsu, Zhejiang, Anhui, Fujian, Jiangxi, Shandong), South China (Guangdong, Guangxi, Hainan, Hong Kong, and Macao), Northwest China (Shaanxi, Gansu, Qinghai, Ningxia, and Xinjiang), Northeast China (Heilongjiang, Jilin, and Liaoning), Central China (Henan, Hubei, and Hunan), and Southwest China (Xizang, Sichuan, Chongqing, Guizhou, and Yunnan).
Statistical evaluation of temporal trends in disease prevalence
Temporal trends in disease prevalence rates were evaluated using joinpoint regression, as described previously.[23] The annual percent change (APC) with a 95% confidence interval (CI) was calculated for the entire study period. The joinpoint model was used to identify points where significant changes in trends occurred, allowing a maximum of two joinpoints. Model selection was performed by Monte Carlo Permutation. Trends were categorized as increasing (worsening) or decreasing (improving) if the APC differed significantly from zero. Otherwise, trends were considered to be stable. Country-level associations were assessed using sociodemographic index (SDI)-adjusted partial correlation coefficients (ρ). All joinpoint analyses were conducted using the Joinpoint Regression Program version 4.5.0.2 (National Institutes of Health National Cancer Institute, Surveillance Research Program, Division of Cancer Control and Population Sciences. Bethesda, MD, USA) from the surveillance, epidemiology, and end results program. Graphs and tables were generated using R software, version 4.4.1, https://www.r-project.org/foundation/.
Results
Overview of neurological disease burden in China, 2021
Neurological diseases with the highest prevalence rates in 2021 were TTH (19,948.3 [95% UI: 17,672.8–22,522.1] per 100,000), migraine (12,985.6 [95% UI: 11304.7–15,015.6] per 100,000), and ischemic stroke (1462.2 [95% UI: 1308.4–1616.3] per 100,000). The leading causes of death were ICH (93.0 [95% UI: 77.9–110.2] per 100,000), ischemic stroke (82.7 [95% UI: 69.4–96.5] per 100,000), and dementia (34.6 [95% UI: 8.8–93.5] per 100,000). Diseases with the highest DALY rates were ICH (1930.3 [95% UI: 1605.3–2296.7] per 100,000), ischemic stroke (1646.8 [95% UI: 1400.0–1893.1] per 100,000), and dementia (708.0 [95UI: 347.7–1561.7] per 100,000) [Supplementary Figure 5A and Table 1, http://links.lww.com/CM9/C831]. YLLs accounted for the majority of disease DALYs of ICH (1869.8/1930.3, 96.9%), SAH (143.4/161.4, 88.8%), ischemic stroke (1367.7/1646.8, 83.0%), CNS cancer (155.3/158.0, 98.3%), and MND (8.1/8.6, 94.2%); while YLDs took up the majority of DALYs of migraine (100.0%), TTH (100.0%), and MS (0.8/1.1, 72.7%). DALYs of dementia (YLL percentage 65.6%), PD (YLL percentage 66.3%), idiopathic epilepsy (YLL percentage 40.8%), and other neurological disorders (YLL percentage 46.3%) had relatively even proportions of both [Supplementary Figure 5B, http://links.lww.com/CM9/C831].
Changes of disease prevalence and DALYs in China from 1990 to 2021
From 1990 to 2021, increased prevalence rates were observed for most neurological disorders, except for idiopathic epilepsy, which gradually decreased with an upturn from 2019 to 2021. Age-standardized prevalence rates (ASPRs) of ICH and SAH decreased steadily despite an increase in total prevalence rates [Supplementary Figure 6, http://links.lww.com/CM9/C831]. The diseases showing the greatest increases in prevalence rate from 1990 to 2021 were PD (544.1% [95% UI: 496.6–589.9%]), dementia (249.1% [95% UI: 234.7–262.7%]), and ischemic stroke (161.5% [95% UI: 149.9–171.8%]) [Table 1 and Supplementary Figure 6, http://links.lww.com/CM9/C831]. Notably, a sharp increase in prevalence rates for several neurological diseases was seen from 2019 to 2021 [Figure 1 and Supplementary Figure 7, http://links.lww.com/CM9/C831]. Joinpoint analysis confirmed a significant shift in the trend of ASPRs for dementia (APC = 3.4% [95 CI: 3.0–3.9%]), idiopathic epilepsy (APC = 6.9% [95 CI: 5.2–8.6%]), MS (APC = 6.7% [95 CI: 6.0–7.5%]), and TTH (APC = 1.0% [95 CI: 0.6–1.4%]) during 2019–2021 [Supplementary Figure 7, http://links.lww.com/CM9/C831]. The timing of this inflection point coincided precisely with the onset of the coronavirus disease 2019 (COVID-19) pandemic in China.
Table 1.
Prevalence, deaths, YLLs, YLDs, and DALYs of neurological diseases in China from 1990 to 2021.
| Variables | Number (1000s) | Rate (per 100,000) | Age-standardized rate (per 100,000) | ||||||
|---|---|---|---|---|---|---|---|---|---|
| 1990 | 2021 | Change (%) | 1990 | 2021 | Change (%) | 1990 | 2021 | Change (%) | |
| Ischemic stroke | |||||||||
| Prevalence | 6577.2 (5875.4–7262.4) | 20,803.9 (18,615.9–22,995.5) | 216.3 (202.2–228.7) | 559.1 (499.4–617.3) | 1462.2 (1308.4–1616.3) | 161.5 (149.9–171.8) | 759.2 (675.2–850.3) | 1018.8 (918.5–1123.4) | 34.2 (29.9–38.8) |
| Deaths | 428.0 (362.3–506.4) | 1177.0 (986.9–1372.7) | 175.0 (116.1–246.6) | 36.4 (30.8–43.0) | 82.7 (69.4–96.5) | 127.4 (78.7–186.6) | 75.2 (64.5–88.2) | 64.5 (54.0–74.8) | –14.3 (–31.9 to 6.1) |
| DALYs | 9926.1 (8510.1–11656.2) | 23430.4 (19,918.9–26,933.9) | 136 (89.7–191.0) | 843.7 (723.4–990.8) | 1646.8 (1400.0–1893.1) | 95.2 (56.9–140.6) | 1387.9 (1188.7–1621.4) | 1181.0 (1009.7–1356.7) | –14.9 (–30.9 to 4.2) |
| YLLs | 8664.5 (7306.2–10320.8) | 19458.1 (15,988.0–22,936.2) | 124.6 (74.6–187.2) | 736.5 (621.0–877.3) | 1367.6 (1123.7–1612.1) | 85.7 (44.4–137.5) | 1240.3 (1051.4–1465.9) | 985.4 (818.3–1158.3) | –20.6 (–37.5 to 0.7) |
| YLDs | 1261.6 (896.6–1622.7) | 3972.3 (2812.5–5155.6) | 214.9 (200.1–229.9) | 107.2 (76.2–137.9) | 279.2 (197.7–362.4) | 160.4 (148.1–172.8) | 147.6 (104.9–189.2) | 195.6 (138.8–252.5) | 32.5 (27.6–37.4) |
| Intracerebral hemorrhage | |||||||||
| Prevalence | 3115.0 (2764.3–3518.3) | 4385.2 (3892.1–4906.6) | 40.8 (32.5–48.6) | 264.8 (235.0–299.1) | 308.2 (273.6–344.9) | 16.4 (9.6–22.9) | 308.4 (274.5–348.3) | 222.1 (200.1–246.5) | –28.0 (–30.6 to –25.3) |
| Deaths | 913.0 (784.4–1064.5) | 1322.9 (1108–1567.7) | 44.9 (16.2–79.6) | 77.6 (66.7–90.5) | 93 (77.9–110.2) | 19.8 (–3.9 to 48.5) | 139.7 (121.1–162.0) | 68.8 (57.6–81.2) | –50.7 (–60.3 to –39.9) |
| DALYs | 22,779.1 (19,630.5–26,510.8) | 27,463.7 (22,839.2–32,676.7) | 20.6 (–3.0 to 50.7) | 1936.2 (1668.6–2253.4) | 1930.3 (1605.3–2296.7) | –0.3 (–19.8 to 24.6) | 2830.0 (2441.8–3281.1) | 1351.6 (1129.1–1600.9) | –52.2 (–61.2 to –41.1) |
| YLLs | 22,182.1 (19,012.5–25,938.5) | 26,602.3 (22,015.6–31,818.3) | 19.9 (–4.1 to 50.8) | 1885.5 (1616.1–2204.8) | 1869.8 (1547.4–2236.4) | –0.8 (–20.7 to 24.7) | 2769.9 (2382.8–3224.4) | 1307.9 (1092.4–1558.4) | –52.8 (–61.9 to –41.5) |
| YLDs | 597.0 (409.8–784.6) | 861.4 (597.3–1116.9) | 44.3 (35.3–53.5) | 50.7 (34.8–66.7) | 60.5 (42.0–78.5) | 19.3 (11.9–26.9) | 60.1 (41.4–78.5) | 43.7 (30.3–57.0) | –27.3 (–30.1 to –24.3) |
| Subarachnoid hemorrhage | |||||||||
| Prevalence | 1104.5 (961.7–1242.6) | 1323.3 (1176.1–1484.1) | 19.8 (14.4–25.2) | 93.9 (81.7–105.6) | 93.0 (82.7–104.3) | –0.9 (–5.4 to 3.5) | 107.9 (94.6–121.8) | 68.9 (61.5–76.9) | –36.2 (–38.3 to –34.2) |
| Deaths | 189.6 (90.8–249.0) | 91.8 (66.7–116.2) | –51.6 (–68.2 to –13.7) | 16.1 (7.7–21.2) | 6.5 (4.7–8.2) | –60.0 (–73.7 to –28.6) | 27.3 (12.8–36.1) | 4.7 (3.4–6.0) | –82.7 (–88.7 to –67.9) |
| DALYs | 5298.1 (2791.0–6806.3) | 2296.5 (1727.4–2847.4) | –56.7 (–70.2 to –28.0) | 450.3 (237.2–578.5) | 161.4 (121.4–200.1) | –64.2 (–75.3 to –40.5) | 616.8 (315.5–799.2) | 115.5 (86.9–142.5) | –81.3 (–87.2 to –68.4) |
| YLLs | 5084.6 (2582.1–6589.5) | 2040.4 (1483.1–2609.2) | –59.9 (–73.0 to –31.3) | 432.2 (219.5–560.1) | 143.4 (104.2–183.4) | –66.8 (–77.7 to –43.2) | 595.7 (294.2–776.2) | 102.1 (74.3–129.7) | –82.9 (–88.6 to –70.2) |
| YLDs | 213.5 (148.7–278.7) | 256.2 (181.0–336.6) | 20.0 (13.9–26.2) | 18.2 (12.6–23.7) | 18.0 (12.7–23.7) | –0.8 (–5.8 to 4.4) | 21.1 (14.7–27.6) | 13.4 (9.4–17.4) | –36.6 (–39.1 to –33.2) |
| Brain and central nervous system cancer | |||||||||
| Prevalence | 108.1 (77.3–131.0) | 305.1 (244.9–387.4) | 182.2 (123.1–270.4) | 9.2 (6.6–11.1) | 21.4 (17.2–27.2) | 133.3 (84.5–206.3) | 9.7 (6.9–11.8) | 21.2 (17.0–26.6) | 119.3 (73.4–181.5) |
| Deaths | 39.2 (28.5–49.2) | 68.9 (52.1–88.3) | 76.0 (35.5–126.1) | 3.3 (2.4–4.2) | 4.8 (3.7–6.2) | 45.5 (12.0–87.0) | 4.0 (3.0–5.0) | 3.6 (2.7–4.6) | –10.2 (–29.7 to 14.1) |
| DALYs | 1884.4 (1333.6–2298.1) | 2247.7 (1715.8–2880.8) | 19.3 (–8.2 to 55.3) | 160.2 (113.4–195.3) | 158.0 (120.6–202.5) | –1.4 (–24.1 to 28.4) | 174.4 (123.9–213.5) | 134.1 (102.9–171.5) | –23.1 (–40.4 to –0.6) |
| YLLs | 1869.1 (1323.2–2283.6) | 2209.5 (1687.6–2828.0) | 18.2 (–9.0 to 53.9) | 158.9 (112.5–194.1) | 155.3 (118.6–198.8) | –2.3 (–24.7 to 27.2) | 172.9 (123.0–212.1) | 131.8 (101.0–168.4) | –23.8 (–40.9 to –1.5) |
| YLDs | 15.3 (10.0–21.5) | 38.2 (25.3–54.5) | 149.7 (95.0–220.8) | 1.3 (0.8–1.8) | 2.7 (1.8–3.8) | 106.5 (61.2–165.3) | 1.5 (1.0– 2.1) | 2.3 (1.6–3.3) | 58.5 (23.9–102.6) |
| Alzheimer’s disease and other dementias | |||||||||
| Prevalence | 4024.5 (3446.4–4623.1) | 16,990.8 (14,488.5–19,672.7) | 322.2 (304.7–338.7) | 342.1 (292.9–393) | 1194.2 (1018.3–1382.7) | 249.1 (234.7–262.7) | 703.1 (608.4–809.5) | 900.8 (770.9–1043.2) | 28.1 (24.5–31.1) |
| Deaths | 119.8 (28.3–322.1) | 491.8 (125.0–1330.2) | 310.5 (248.0–403.8) | 10.2 (2.4–27.4) | 34.6 (8.8–93.5) | 239.4 (187.8–316.6) | 31.4 (7.6–83.6) | 30.8 (7.9–82.4) | –1.8 (–15.9 to 18.3) |
| DALYs | 2702.5 (1239.2–6085.4) | 10072.5 (4947.2–22,219.2) | 272.7 (222.2–330.1) | 229.7 (105.3–517.3) | 708.0 (347.7–1561.7) | 208.2 (166.4–255.7) | 534.5 (236.2–1190.6) | 562.4 (271.2–1238.8) | 5.2 (–9.1 to 21.9) |
| YLLs | 1894.0 (432.5–5188.0) | 6612.2 (1661.6–18,403.6) | 249.1 (193.3–327.4) | 161.0 (36.8–441.0) | 464.7 (116.8–1293.5) | 188.7 (142.5–253.4) | 388.7 (93.2–1021.2) | 376.8 (96.3–1033.1) | –3.1 (–17.4 to 17.6) |
| YLDs | 808.5 (545.7–1082.8) | 3460.3 (2394.3–4632.2) | 328.0 (308.2–346.8) | 68.7 (46.4–92.0) | 243.2 (168.3–325.6) | 253.9 (237.5–269.5) | 145.8 (99.2–193.2) | 185.6 (128.0–246.7) | 27.3 (23.9–30.4) |
| Parkinson’s disease | |||||||||
| Prevalence | 651.8 (546.5–786.5) | 5077.1 (4277.8–6049.7) | 678.9 (621.5–734.4) | 55.4 (46.5–66.9) | 356.8 (300.7–425.2) | 544.1 (496.6–589.9) | 91.8 (75.9–109.6) | 245.7 (208.3–289.2) | 167.8 (148.1–187.2) |
| Deaths | 32.6 (28.7–36.3) | 92.0 (75.9–108.1) | 182.5 (128.7–248.7) | 2.8 (2.4–3.1) | 6.5 (5.3–7.6) | 133.6 (89.1–188.4) | 6.1 (5.4–6.8) | 5.0 (4.2–5.9) | –17.7 (–32.9 to 0.4) |
| DALYs | 685.0 (597.9–765.4) | 2159.5 (1826.2–2521.3) | 215.3 (168.3–272.9) | 58.2 (50.8–65.1) | 151.8 (128.4–177.2) | 160.7 (121.8–208.3) | 105.3 (93.2–116.7) | 108.0 (91.1–125.5) | 2.6 (–12.9 to 20.9) |
| YLLs | 590.1 (517.2–661.1) | 1431.0 (1179.9–1689.1) | 142.5 (94.0–204.7) | 50.2 (44.0–56.2) | 100.6 (82.9–118.7) | 100.5 (60.4–151.9) | 92.1 (80.9–102.7) | 73.0 (60.2–86.0) | –20.8 (–36.0 to –1.8) |
| YLDs | 94.9 (64.8–129.2) | 728.5 (505.9–977.1) | 667.5 (610.9–725.0) | 8.1 (5.5–11.0) | 51.2 (35.6–68.7) | 534.6 (487.8–582.2) | 13.1 (9.0–17.8) | 35.0 (24.4–46.9) | 166.9 (145.9–186.2) |
| Idiopathic epilepsy | |||||||||
| Prevalence | 2178.5 (1525.4–2896.3) | 3086.3 (2176.5–4020.7) | 41.7 (–1.2 to 99.3) | 185.2 (129.7–246.2) | 216.9 (153.0–282.6) | 17.1 (–18.3 to 64.8) | 189.3 (132.5–253.0) | 214.7 (150.1–278.6) | 13.4 (–18.7 to 59.1) |
| Deaths | 21.5 (18.3–26.0) | 11.9 (9.8–14.8) | –44.7 (–54.9 to –31.5) | 1.8 (1.6–2.2) | 0.8 (0.7–1.0) | –54.3 (–62.7 to –43.3) | 1.9 (1.6–2.2) | 0.8 (0.7–1.0) | –56.6 (–64.0 to –47.0) |
| DALYs | 2134.6 (1717.5–2651.2) | 1374.7 (969.5–1888.7) | –35.6 (–51.3 to –14.5) | 181.4 (146.0–225.4) | 96.6 (68.1–132.7) | –46.7 (–59.8 to –29.3) | 178.6 (143.4–220.6) | 101.4 (72.5–139.4) | –43.2 (–56.9 to –25.5) |
| YLLs | 1333.0 (1110.2–1587.4) | 560.7 (467.5–698.1) | –57.9 (–65.6 to –47.8) | 113.3 (94.4–134.9) | 39.4 (32.9–49.1) | –65.2 (–71.5 to –56.9) | 109.6 (90.5–129.4) | 43.8 (36.9–53.9) | –60.0 (–67.1 to –50.2) |
| YLDs | 801.6 (466.0–1253.2) | 814.0 (421.9–1302.2) | 1.6 (–39.2 to 59.8) | 68.1 (39.6–106.5) | 57.2 (29.7–91.5) | –16.0 (–49.7 to 32.1) | 69.0 (40.1–107.9) | 57.6 (30.2–92.9) | –16.5 (–49.9 to 29.4) |
| Multiple sclerosis | |||||||||
| Prevalence | 17.9 (13.4–23.7) | 40.2 (31.8–50.0) | 124.5 (105.7–143.5) | 1.5 (1.1–2.0) | 2.8 (2.2–3.5) | 85.6 (70.1 to 101.3) | 1.6 (1.2 to 2.0) | 2.2 (1.7 to 2.8) | 43.3 (37.0 to 50.2) |
| Deaths | 0 (0–0.1) | 0.1 (0.1–0.1) | 131.7 (36.8–296.0) | 0 | 0 | 91.6 (13.1–227.5) | 0 | 0 | 11.7 (–33.4 to 94.5) |
| DALYs | 7.0 (5.1–9.8) | 15.0 (11.0–19.6) | 113.9 (80.3–147) | 0.6 (0.4–0.8) | 1.1 (0.8–1.4) | 76.9 (49.1–104.3) | 0.6 (0.5–0.9) | 0.8 (0.6–1.1) | 33.6 (11.5–54.0) |
| YLLs | 2.0 (1.2–2.9) | 3.7 (2.9–4.7) | 87 (10.5–216.4) | 0.2 (0.1–0.3) | 0.3 (0.2–0.3) | 54.7 (–8.6 to 161.6) | 0.2 (0.1–0.3) | 0.2 (0.2–0.3) | 10.5 (–34.4 to 87.9) |
| YLDs | 5.0 (3.3 to 7.2) | 11.3 (7.5–16.0) | 124.4 (105.7–143.5) | 0.4 (0.3–0.6) | 0.8 (0.5–1.1) | 85.6 (70.1–101.3) | 0.4 (0.3–0.6) | 0.6 (0.4–0.9) | 43.3 (37.0–50.2) |
| Motor neuron disease | |||||||||
| Prevalence | 25.7 (20.3–32.0) | 33.3 (27.0–40.4) | 29.8 (19.2–44.1) | 2.2 (1.7–2.7) | 2.3 (1.9–2.8) | 7.3 (–1.5 to 19.2) | 2.1 (1.7 –2.6) | 2.3 (1.8–2.8) | 7.8 (2.7–14.5) |
| Deaths | 1.5 (0.8–2.0) | 3.5 (2.2–4.8) | 125.8 (38–302.2) | 0.1 (0.1–0.2) | 0.2 (0.2–0.3) | 86.7 (14.1–232.6) | 0.2 (0.1–0.2) | 0.2 (0.1–0.2) | 19.6 (–27.0 to 119.4) |
| DALYs | 87.6 (50.3–111.7) | 122.7 (81.0–167.3) | 40.1 (–14.7 to 155.8) | 7.4 (4.3–9.5) | 8.6 (5.7–11.8) | 15.8 (–29.5 to 111.6) | 8.0 (4.7–10.2) | 7.7 (4.9–10.1) | –4.0 (–43.0 to 77.6) |
| YLLs | 82.1 (44.6–105.8) | 115.6 (74.1–159.0) | 40.8 (–16.8 to 170.2) | 7.0 (3.8–9.0) | 8.1 (5.2 –11.2) | 16.4 (–31.2 to 123.4) | 7.5 (4.2–9.7) | 7.2 (4.4–9.5) | –4.7 (–45.5 to 83.9) |
| YLDs | 5.5 (3.6–7.8) | 7.1 (4.8–9.8) | 29.7 (19.2–44.1) | 0.5 (0.3–0.7) | 0.5 (0.3–0.7) | 7.3 (–1.5 to 19.2) | 0.5 (0.3–0.6) | 0.5 (0.3–0.7) | 7.8 (2.7–14.5) |
| Migraine | |||||||||
| Prevalence | 133,474.5 (114,199.4–153482.6) | 184,752.3 (160,836.5–213,634.0) | 38.4 (29.9–48) | 11345.5 (9707.1–13,046.2) | 12,985.6 (11,304.7–15,015.6) | 14.5 (7.4–22.4) | 10,948.5 (9428.8–12,586.1) | 11,777.5 (10,137.6–13,538.6) | 7.6 (3.5–11.4) |
| DALYs | 5028.8 (767.7–11262.3) | 6988.2 (1133.3–15,186.3) | 39.0 (28.6–53.3) | 427.5 (65.3–957.3) | 491.2 (79.7–1067.4) | 14.9 (6.4–26.8) | 413.0 (66.2–911) | 443.7 (66.9–971.7) | 7.4 (–0.2 to 11.5) |
| YLDs | 5028.8 (767.7–11,262.3) | 6988.2 (1133.3–15,186.3) | 39.0 (28.6–53.3) | 427.5 (65.3–957.3) | 491.2 (79.7–1067.4) | 14.9 (6.4–26.8) | 413.0 (66.2–911) | 443.7 (66.9–971.7) | 7.4 (–0.2 to 11.5) |
| Tension-type headache | |||||||||
| Prevalence | 204,064.3 (176,898.6–233,568.2) | 283,814.2 (251,438.7–320,431.6) | 39.1 (30.2–49.3) | 17,345.7 (15,036.5–19,853.5) | 19,948.3 (17,672.8–22,522.1) | 15.0 (7.7–23.5) | 17,174.5 (15,086.7–19,379.7) | 18,525.1 (16,380.9–20,958.7) | 7.9 (4.0–12.2) |
| DALYs | 489.5 (151.7–1687.1) | 716.2 (224.4–2174.7) | 46.3 (20.3–70.8) | 41.6 (12.9–143.4) | 50.3 (15.8–152.9) | 21.0 (–0.6 to 41.2) | 41.8 (13.1–141.4) | 43.5 (13.1–141.3) | 4.1 (–3.3 to 19.2) |
| YLDs | 489.5 (151.7–1687.1) | 716.2 (224.4–2174.7) | 46.3 (20.3–70.8) | 41.6 (12.9–143.4) | 50.3 (15.8–152.9) | 21.0 (–0.6 to 41.2) | 41.8 (13.1–141.4) | 43.5 (13.1–141.3) | 4.1 (–3.3 to 19.2) |
| Other neurological disorders | |||||||||
| Prevalence | 2.4 (1.5–3.5) | 3.6 (2.3–4.9) | 45.9 (23.5–75.4) | 0.2 (0.1–0.3) | 0.2 (0.2–0.3) | 20.6 (2.1–45.1) | 0.2 (0.1–0.3) | 0.2 (0.2–0.3) | 6.1 (–0.5 to 13.7) |
| Deaths | 0.7 (0.6–0.9) | 7.4 (5.7–9.3) | 906.1 (542.8–1393.2) | 0.1 (0–0.1) | 0.5 (0.4–0.7) | 731.9 (431.5–1134.7) | 0.1 (0.1–0.1) | 0.4 (0.3–0.6) | 546.5 (320.9–854.5) |
| DALYs | 168.1 (122.3–227.3) | 582.6 (459.1–721.5) | 246.6 (167.6–356.8) | 14.3 (10.4–19.3) | 41.0 (32.3–50.7) | 186.6 (121.3–277.7) | 15.2 (11.2–20.2) | 38.7 (30.3–49.0) | 155.2 (94.9–235.1) |
| YLLs | 44.7 (35.7–55.4) | 269.6 (204.5–338.4) | 503.5 (280.7–792.4) | 3.8 (3.0–4.7) | 18.9 (14.4–23.8) | 399.0 (214.8–637.9) | 3.8 (3.1–4.8) | 19.4 (14.7–24.1) | 406.5 (223.1–641.5) |
| YLDs | 123.4 (80.0–182.8) | 313.0 (204.8–430.8) | 153.6 (91.1–236.3) | 10.5 (6.8–15.5) | 22.0 (14.4–30.3) | 109.7 (58.1–178.1) | 11.3 (7.5–16.5) | 19.3 (12.3–28.5) | 70.4 (25.0–125.8) |
Data are shown as mean (95% uncertainty interval). DALYs: Disability-adjusted life years; YLDs: Years lived with disability; YLLs: Years of life lost.
Figure 1.
Upticks in neurological disease prevalence from 2019 to 2021. (A) Change in prevalence rates of dementia, (B) idiopathic epilepsy, (C) MS, and (D) TTH. Upticks in prevalence rate may be observed from 2019 to 2021. MS: Multiple sclerosis; TTH: Tension-type headache.
In terms of DALY rates, SAH and idiopathic epilepsy saw steadily decreasing absolute and age-standardized DALY rates (ASDRs) between 1990 and 2021. ICH and CNS cancers had decreasing ASDRs but stable DALY rates. Ischemic stroke, dementia, and PD had increasing DALY rates but stable ASDRs. MS, migraine, and TTH showed steadily increasing absolute and ASDRs [Supplementary Figure 6, http://links.lww.com/CM9/C831]. Significant increases in DALY rates were seen for dementia (208.2% [95% UI: 166.4–255.7%]) and PD (160.7% [95% UI: 121.8–208.3%]), in alignment with increasing prevalence [Table 1].
Age and sex patterns of neurological disease prevalence and DALYs in China, 2021
The prevalence of neurological diseases varied for different age groups. TTH and migraine were the most prevalent diseases for the population aged under 75 years. The prevalence rates of dementia and ischemic stroke were higher than migraine between the ages of 75 and 84 years, coming second and third after TTH. Dementia, TTH, and ischemic stroke were the most prevalent in the age >85 years [Supplementary Figure 8A and Supplementary Table 3, http://links.lww.com/CM9/C831].
Differences in DALY rate patterns among age groups could be attributed to the highly prevalent TTH and migraine, which accounted for much smaller proportions of DALY rates. In contrast, ICH had a relatively low prevalence rate but made a major contribution to disease burden. Idiopathic epilepsy and CNS cancers were the dominant causes of disease burden in children under 10 years. Migraine was the leading contributor to disease burden in individuals aged 10–39 years, and ICH and ischemic stroke ranked first and second for those aged 40–74 years. The burden of ischemic stroke and dementia increased with increasing age until it exceeded that of ICH. Ischemic stroke and dementia made the greatest contribution to DALYs in the 75–89 years age group, and in the age >90 years, dementia became the largest disease burden [Supplementary Figure 8A and Supplementary Table 3, http://links.lww.com/CM9/C831].
Additionally, disease burdens differed between sexes. Females had a significantly higher prevalence rate of dementia, but the difference in DALY rate was insignificant (prevalence rates: 1376.6 [95% UI: 1180.2–1588.2] per 100,000 for females and 846.3 [95% UI: 706.3–980.9] per 100,000 for males, DALY rates: 935.8 [95% UI: 456.6–1969.5] per 100,000 for females and 490.6 [232.8–1119.1] per 100,000 for males). Similarly, the prevalence rate of migraine was significantly higher in females (prevalence: 16,514.4 [95% UI: 14,231.2–19,013.6] per 100,000 for females and 9619.0 [95% UI: 8330.1–11,169.3] per 100,000 for males), but the difference in DALY rate was insignificant (DALY rates: 610.8 [95% UI: 79.0–1344.0] per 100,000 for females and 377.1 [95% UI: 79.9–803.7] per 100,000 for males). Males had borderline but insignificantly higher prevalence rate and DALY rate of PD (prevalence: 401.9 [95% UI: 336.8–478.6] per 100,000 for males and 309.6 [95% UI: 264.4–366.0] per 100,000 for females; DALY rates: 175.8 [95% UI: 145.3–212.7] per 100,000 for males and 126.6 [103.0–150.8] per 100,000 for females) [Supplementary Figure 8B and Supplementary Table 4, http://links.lww.com/CM9/C831].
Geographic variation of neurological disease burden in China, 2021
The rankings of ASDRs across regions and provinces provide insights into disease control priorities throughout China [Supplementary Figure 9, http://links.lww.com/CM9/C831]. ICH and ischemic stroke were the leading causes of ASDRs in China. Ischemic stroke ranked first in North and Northeast China, whereas ICH ranked first in other regions. The ASDR of ICH was approximately double that of ischemic stroke in Southwest and Northwest China. For example, the mean ASDR of ischemic stroke in Southwest China was 1278.9 per 100,000, but the mean ASDR of ICH reached 2838.6 per 100,000. Dementia and migraine showed little variation in their rankings across provinces, and usually occupied third and fourth place. The burden of SAH showed some variation and was lower in eastern provinces, such as Shanghai and Zhejiang, and in southern provinces, such as Guangdong and Hainan. Hebei had a particularly high ASDR of SAH (482.5 [95% UI: 208.3–745.2] per 100,000) compared with the national mean of 159.1 per 100,000 (which was calculated as the weighted average of provincial ASDRs), while Xizang had a higher ASDR of idiopathic epilepsy (378.1 [95% UI: 164.3–572] per 100,000) compared with the national mean of 112.7 per 100,000 [Supplementary Figure 10 and Supplementary Table 5, http://links.lww.com/CM9/C831]. Additional data on disease burden by province may be found in Supplementary Table 5, http://links.lww.com/CM9/C831.
Impact of modifiable risk factors on neurological disease burden
Out of 12 analyzed diseases seven had quantified risk factors that contributed to disease YLLs and YLDs, with ischemic stroke, ICH, SAH, and dementia having >20% of YLL and YLD rates attributable to these risk factors [Supplementary Table 6, http://links.lww.com/CM9/C831]. Ischemic stroke had the highest proportion of attributable YLLs (0.9 [95% UI: 0.8–0.9]), followed by ICH (0.8 [95% UI: 0.8–0.9]), SAH (0.8 [95% UI: 0.7–0.8]), and dementia (0.2 [95% UI: 0.1–0.4]). Similarly, diseases with the highest attributable YLD percentages were ischemic stroke (0.9 [95% UI: 0.8–0.9]), ICH (0.8 [95% UI: 0.7–0.9]), SAH (0.7 [95% UI: 0.6–0.8]), and dementia (0.2 [95% UI: 0.1–0.4]). The attributable percentage of YLL rates was higher than YLD rates for ICH and SAH, while they were similar for the other five diseases [Supplementary Figure 11, http://links.lww.com/CM9/C831].
When pooling the risk-attributable YLL or YLD rates of all diseases together, metabolic risk factors contributed the most to the YLL and YLDs, while ambient particulate matter pollution from environmental risk factors and smoking and diet high in sodium from lifestyle risk factors also stood out. Leading contributors to YLL rates were high systolic blood pressure, ambient particulate matter pollution, and smoking, while for YLD rates, high systolic blood pressure, high LDL cholesterol, and ambient particulate matter pollution were the top contributors. Smoking was associated with the burden of 6 diseases (except for idiopathic epilepsy), while interestingly, it was associated with decreased YLL and YLD rates of PD [Figure 2].
Figure 2.
Attributable YLL and YLD rates of risk factors to neurological diseases. (A) Attributable YLL rates of risk factors to neurological diseases. (B) Attributable YLD rates of risk factors to neurological diseases. The attributable YLLs/YLDs rates for individual risk factors were estimated independently and represent the theoretical reduction in burden achievable by eliminating each risk factor in isolation. As individuals may be exposed to multiple risks and estimates are not mutually exclusive, the sum of these estimates is not limited by the total disease burden. YLDs: Years lived with disability; YLLs: Years of life lost.
Sex differences were also noted when examining the impact of risk factors. For all risk factors combined, males had a significantly higher risk-attributable YLL rate of ICH compared with females (attributable YLL rates: 1933.4 [95% UI: 1481.3–2525.1] per 100,000 in males and 1146.7 [95% UI: 879.0–1469.5] per 100,000 in females), but the sex difference in risk-attributable YLD rate was insignificant. For individual risk factors, all risk factors except secondhand smoke had higher attributable YLL rates in males than in females [Supplementary Figure 12, http://links.lww.com/CM9/C831]. As for YLD rates, all metabolic risk factors (including kidney dysfunction, high LDL cholesterol, high systolic blood pressure, high body mass index [BMI], and high fasting plasma glucose) contributed to higher YLD rates in females, while in behavioral risk factors, secondhand smoke and low physical activity contributed more to YLDs in females, and alcohol use, smoking, and diet high in sodium contributed to higher YLD rates in males [Supplementary Figure 13, http://links.lww.com/CM9/C831].
Discussion
In comparison with previous studies on neurological diseases in China, this study was an effort to pool the data for major neurological diseases together for a comprehensive analysis, which offered the opportunity to compare disease burdens.
In this study, we found TTH and migraine to be the most prevalent diseases in China, although they caused relatively low DALYs per case. ICH, ischemic stroke, and dementia accounted for the most DALYs nationwide. In terms of disease trend, from 1990 to 2021, the DALY rates of dementia and ischemic stroke, which had relatively balanced proportions of YLLs and YLDs, grew the fastest. In comparison, the DALY rates of ICH and SAH, which had high percentages of YLLs, decreased steadily. These results reflect a trend of rising disability burden and decreasing death burden caused by neurological diseases from 1990 to 2021. Previous modeling studies based on GBD data have predicted a continued rise in both ASPR and ASDR of dementia and PD in China,[6,8,24] while the ASDRs of stroke will decline despite rising ASPR,[5,25] which emphasizes the rising importance of neurodegenerative diseases. To address the rising disability burden of neurological diseases, rehabilitation facilities specializing in elder care and poststroke rehabilitation may serve as a good supplement after initial treatment in hospitals.
In our subgroup analysis of age, we found that the overall DALY rate of neurological diseases increased with age. Population aging has been shown to be a major contributor of increasing neurological disease DALYs in previous decomposition studies.[9,24,26] With gradual and continual population aging in China over the years, it is highly likely that the burden of neurological diseases will continue to rise. However, in the younger population between 10 and 39 years, migraine was the leading contributor of disease DALYs. This population is more susceptible to psychological stress and unhealthy lifestyles, such as sleep disruption and excessive use of electronic devices, which have been correlated with the onset of migraine.[11,27–29] Although these risk factors were not isolated as modifiable risk factors in the GBD 2021 database, they can be controlled with education and psychological counseling, which may ultimately improve neurological health in this younger population.
Sex differences in disease burden and the impact of modifiable risk factors were also uncovered. We found that females had significantly higher prevalence rates but similar DALY rates of dementia and migraine, whereas males had borderline but insignificantly higher prevalence and DALY rates of PD, which was consistent with other global reports.[1]
Among risk factors, metabolic risk factors contributed to higher YLD rates in females, whereas in behavioral risk factors, the attributable YLD rates of secondhand smoke and low physical activity were higher in females, and the attributable YLD rates of alcohol use, smoking, and diet high in sodium were higher in males. Previous cohort studies have also reported the effect of metabolic syndrome on stroke risk to be higher in females than in males (indicated by higher hazard ratios).[30,31] In terms of lifestyle, males are more likely to smoke and drink more alcohol,[32,33] yet meta-analytical data have suggested that smoking was a risk factor of equal strength in both males and females.[34] Differential effects of behavioral risk factors may thus result from different exposure levels between sexes.
Furthermore, we found geographic variations in the ranking of neurological disease DALYs. When broadly considering geographical regions, ischemic stroke had the highest ASDR in North and Northeast China, while ICH ranked first in other regions. The mortality risk of ICH has been shown to be higher than ischemic stroke in the initial few months post-onset, although mortality risk and functional recovery did not differ between the two types of stroke.[35–37] This difference may account for higher DALYs per individual patient, and the higher ASDR of ischemic stroke in North and Northeast China may be explained by its higher prevalence in these regions [Supplementary Figure 10, http://links.lww.com/CM9/C831]. At the provincial level, Hebei province had a much higher ASDR of SAH than the national average, and Xizang had a higher ASDR of idiopathic epilepsy. The high ASDR of SAH in Hebei province may be explained by high dietary salt consumption[38,39] and high prevalence of hypertension,[40] and the high ASDR of idiopathic epilepsy in Xizang may be due to less access to medical screening and proper medication.
Finally, our analysis identified a notable increase in the burdens of several neurological disorders, including dementia, idiopathic epilepsy, MS, and TTH during 2019–2021, coincident with the COVID-19 pandemic. While this temporal association warrants careful interpretation, it aligns with the broader conceptual framework adopted by the GBD study, which recognized the pandemic impact on public health via direct viral effects and indirect factors, such as deferred care seeking.[41] Emerging literature provides possible mechanisms that could partially explain these observations. For example, cognitive impairment has been documented to persist in some individuals for up to 12 months after COVID infection,[42] and neurophysiological similarities have been observed between post-COVID “brain fog” and early-stage Alzheimer’s disease.[43] In addition, descriptions of long-COVID headaches with migraine-like or tension-type phenotypes suggest potential diagnostic overlap.[44,45] Healthcare access and medication management were disrupted during the pandemic, and this may have exacerbated existing neurological conditions, such as epilepsy.[46] However, we emphasize that these are potential contributing factors rather than established causal pathways, and the complex interplay between severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, healthcare system stressors, and neurological outcomes requires further longitudinal investigation. Our findings highlight the importance of considering these broader pandemic-related impacts when interpreting neurological disease burden trends during this period.
We acknowledge several limitations in this study. First, 12 common noncommunicable neurological disorders were analyzed, and traumatic injuries, infectious diseases, neurodevelopmental disorders, and other CNS complications were excluded. The limited scope may particularly affect burden estimates in children under 10 years, among whom the excluded conditions constitute major contributors to global neurological disability.[1] Second, the present findings are constrained by the methodological limitations of the GBD framework. Although GBD use sophisticated statistical models to address data sparsity and ensure internal consistency, these estimates remain model-dependent and are influenced by the quality and quantity of the data source. Studies used to calculate nonfatal causes remain relatively sparse compared with those dealing with fatal outcomes [Supplementary Figures 1–4, http://links.lww.com/CM9/C831], which warrants careful interpretation. Third, the ecological nature of our study design precludes causal inference regarding the observed associations between the COVID-19 pandemic and increased neurological disease burden. Although we identified temporally coincident trends, our analysis could not account for potential confounding factors such as regional variations in vaccination coverage and individual-level healthcare disruptions. Fourth, the attributed burden for individual risk factors presented in Figure 2 should be interpreted with the understanding that they represent independent, theoretical maxima. The contributions of co-occurring risk factors are not mutually exclusive, meaning that combined public health strategies will have benefits that are not simply the sum of the individual estimates. Finally, all statistical inferences were based on examining the overlap of 95% UIs, in accordance with GBD analytical standards, rather than conventional significance testing.
In conclusion, our study systematically described the disease burdens and risk factors of neurological diseases in China and offers some important implications for public health planning. Heterogeneity in age, sex, and geography emphasizes the need for tailored prevention and intervention strategies that address the priorities of different subpopulations. To reduce the growing burden of neurological diseases, prompt and coordinated action in prevention, treatment, rehabilitation, and supportive care at the national level is essential.
Funding
This study was supported by grants from STI 2030 Major Projects (No. 2022ZD0211603), the National Natural Science Foundation of China (No. 82330099), the Key Area Research and Development Program of Guangdong Province (No. 2023B0303040003), Science and Technology Program of Guangzhou (No. 2023A03J0708), and Guangdong Provincial Clinical Medical Science Data Center (No. 2024B1212070015).
Conflicts of interest
None.
Supplementary Material
Footnotes
Qingyuan Dai and Queran Lin contributed equally to this work.
How to cite this article: Dai QY, Lin QR, Chen JX, Deng ZH, Jin WY, Ye PP, Zuo Y, Yang YX, Xiao SH, Tang YM. Prevalence and burden of neurological diseases in China: An analysis from the Global Burden of Disease Study 2021. Chin Med J 2026;139:2141–2151. doi: 10.1097/CM9.0000000000004051
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