19 April 2026: Database Analysis
Divergent Trends in Stroke Subtype Mortality Attributable to High Systolic Blood Pressure in China, 1990 to 2021: An Age-Period-Cohort Analysis and Projection
Yaoyao Li A 1, Linfeng Li B 1,2, Min Wang C 3, Qiaohong Xu D 4, Lixin Liu D 1,2, Xinyi Wang E 1,2, Xue Liu F 1,2, Hengjie Li A 1, Fahuan Song A 3, Wenwei Cai A 1*
DOI: 10.12659/MSM.951794
Med Sci Monit 2026; 32:e951794
Abstract
BACKGROUND: High systolic blood pressure (HSBP) is a major contributor to stroke mortality in China. This study analyzed trends in HSBP-attributable stroke mortality from 1990 to 2021 to elucidate the effects of age, period, and cohort and to project future trends for precision prevention.
MATERIAL AND METHODS: Using Global Burden of Disease 2021 estimates, temporal trends in age-standardized mortality rates were assessed by Joinpoint regression, age-period-cohort (APC) patterns were evaluated using APC modeling, and mortality trends were projected to 2030 within a Bayesian APC framework incorporating United Nations population projections. Uncertainty was summarized via 95% credible intervals.
RESULTS: From 1990 to 2021, HSBP-attributable stroke mortality declined overall (average annual percentage change [AAPC] -1.31%, 95% confidence interval [CI] -1.52 to -1.10), with pronounced subtype heterogeneity: ischemic stroke (IS) was relatively stable (AAPC -0.03%, 95% CI -0.29 to 0.24), whereas intracerebral hemorrhage (ICH) and subarachnoid hemorrhage (SAH) substantially declined (AAPC -1.75%, 95% CI -1.97 to -1.52; and -5.03%, 95% CI -5.29 to -4.76, respectively). IS trends diverged by sex, declining in women but increasing in men; mortality steeply increased according to age, with pronounced acceleration after 75 years. Cohort patterns suggested elevated risk in more recent birth cohorts.
CONCLUSIONS: Our analyses show that HSBP-attributable stroke mortality in China generally declined over the past 3 decades, but trends noticeably diverged among subtypes (ICH substantially declined and SAH most rapidly decreased, whereas IS remained relatively stable), underscoring the need for subtype- and population-specific prevention priorities, including sustained population-wide sodium reduction.
Keywords: Hypertension, Mortality, Retrospective Studies, Stroke
Introduction
Stroke remains a critical contributor to global mortality and enduring disability worldwide; annual figures reach approximately 12.2 million incident cases and 6.5 million fatalities [1–4]. In China, stroke causes more than 20% of total deaths, imposing severe socioeconomic consequences due to high recurrence rates and persistent disability burdens [5]. In addition to its high mortality, stroke constitutes a growing economic burden on the Chinese healthcare system, as indicated by rising hospitalization costs and rapidly increasing numbers of stroke admissions. According to the Global Burden of Disease (GBD) 2019 Study, elevated systolic blood pressure (SBP; ≥110–115 mmHg) contributed to 46.2% of global stroke-related deaths; this proportion reached 51.3% in China, reflecting a 1.1-fold excess compared with the global average [6].
Mechanistically, high systolic blood pressure (HSBP) exerts heterogeneous effects across stroke subtypes: ischemic stroke (IS) is predominantly linked to atherosclerotic thrombosis, whereas intracerebral hemorrhage (ICH) and subarachnoid hemorrhage (SAH) are more strongly associated with vascular fragility and rupture [7]. These distinct pathophysiological mechanisms are reflected by pronounced geographic variation in subtype distributions. Epidemiological surveys reveal that ICH causes only 10% to 15% of all strokes in high-income countries; this proportion reaches 25% to 55% in middle- and low-income countries, including China. Similarly, large-artery atherosclerotic stroke, a subtype of IS, represents 8% to 10% of cases in Western populations but 30% to 50% in Asian populations [6,8]. These large disparities indicate that prevention and management strategies developed in high-income settings – where IS predominates – cannot be directly extrapolated to China and underscore the need for a detailed, subtype-specific understanding of HSBP-attributable stroke mortality in this population. Furthermore, cohort-specific risks (eg, early-life famine exposure and urbanization-driven dietary changes) may amplify HSBP-related mortality in younger generations, but these dynamics are poorly quantified [9]. Such generational exposures may lead to persistent metabolic vulnerability and establish lifelong dietary and lifestyle patterns that influence blood pressure trajectories in ways not captured by cross-sectional analyses, making the Chinese population particularly suited to age-period-cohort (APC) investigation.
APC modeling offers a robust approach to dissect these multidimensional dynamics. By decomposing age effects (biological aging), period effects (eg, advancements in hypertension management), and cohort effects (generational risk exposure), APC modeling independently quantifies the contributions of each dimension to disease trends [10]. Within the GBD framework, previous studies have used APC-based approaches to characterize long-term temporal patterns in disease burden and mortality at national and global levels [11,12]. However, most existing GBD-based analyses have focused on aggregate stroke outcomes or overall cardiovascular disease burden. Consequently, HSBP-attributable mortality has not been systematically examined separately for IS, ICH, and SAH within an integrated APC framework; key aspects of heterogeneity in subtype-specific temporal dynamics remain insufficiently characterized. To address these critical gaps, we utilized GBD data for 1990 to 2021 to provide a comprehensive, subtype-specific assessment of HSBP-attributable stroke mortality in China. By integrating APC modeling with Joinpoint regression, Bayesian APC projections, and sex-stratified analyses, this study extends prior GBD-based work to systematically characterize heterogeneous temporal patterns of HSBP-attributable mortality across IS, ICH, and SAH; it projects these trends through 2030 [13]. The findings can be used to inform targeted, subtype-specific stroke prevention strategies in China.
Material and Methods
DATA SOURCES:
Data were retrieved from the Global Health Data Exchange using the online GBD Results Tool for the GBD 2021 Results (URL:
Mortality data for stroke subtypes (IS, ICH, and SAH) attributable to HSBP were obtained from the GBD 2021 database. The GBD study represents the most comprehensive global health assessment, providing standardized estimates across 204 countries and territories from 1990 to 2021. HSBP-attributable mortality was estimated using the GBD 2021 Comparative Risk Assessment framework. Briefly, exposure-response relationships (relative risks) for SBP and each stroke subtype were obtained from the Comparative Risk Assessment evidence synthesis based on systematic reviews and meta-analyses. SBP exposure was defined as the population distribution of SBP (mmHg) for each age-sex-location-year stratum and estimated using the GBD exposure-modeling framework from available population-based blood pressure measurements over time. The theoretical minimum risk exposure level for SBP was set at 110 to 115 mmHg. Population-attributable fractions were then computed by combining the estimated SBP exposure distribution with the exposure-response relationship; attributable deaths were calculated by applying population-attributable fractions to total stroke deaths for each subtype.
In China, stroke mortality data were primarily sourced from the China National Mortality Surveillance System (NMSS), a comprehensive surveillance network covering all 31 provinces and representing 99% of the national population [14]. The GBD study incorporates mortality data from vital registration and mortality surveillance systems (including China’s NMSS) that undergo predefined quality checks (eg, completeness of registration, cause-of-death coding quality, and internal consistency) and standardized preprocessing – such as International Classification of Diseases (ICD) code mapping and redistribution of ill-defined causes – prior to modeling. Additionally, the GBD modeling framework applies standardized adjustment and comparability procedures across subnational units to mitigate bias arising from heterogeneous data completeness and cause-of-death coding practices across provinces. The NMSS uses standardized data collection protocols and quality assurance measures to ensure data reliability and comparability across regions. Stroke subtypes were defined via the ICD-based cause hierarchy of the GBD framework. ICD refers to standardized disease coding and cause-of-death classification; it is distinct from psychiatric diagnostic frameworks such as the Diagnostic and Statistical Manual of Mental Disorders [15].
This study was conducted in accordance with the data-use policy of the GBD study and international norms for secondary analyses of publicly available health data. All data analyzed were obtained from the GBD 2021 database; they were fully aggregated and de-identified, with no access to individual-level records. Accordingly, ethics committee review and individual informed consent were not required, consistent with the GBD approach for population-level analyses. Given the use of aggregated data, individual-level covariate adjustment was not possible; the findings should be interpreted as population-level modeled estimates and projections, rather than individual-level causal effects [16].
STATISTICAL ANALYSIS:
Joinpoint regression analysis was used to evaluate temporal trends in age-standardized mortality rates (ASMRs) from 1990 to 2021. Statistically significant inflection points were identified using Monte Carlo permutation tests [17]. Annual percentage change for each segment and average annual percentage change (AAPC) for the overall period were estimated with corresponding 95% confidence intervals (CIs) [18]. Regression was applied to time-ordered annual ASMRs within its standard segmented log-linear framework for temporal trend analysis. Joinpoint regression was performed using the Joinpoint Regression Program, with an a priori maximum of 5 joinpoints. The final model was selected using the standard model selection procedure implemented in the program, and corresponding AAPC estimates were reported. For sensitivity analysis, the models were re-fitted with the maximum number of allowable joinpoints set to 3.
APC analysis was conducted to disentangle the independent contributions of aging processes, temporal shifts in healthcare and policy environments, and birth cohort effects on mortality patterns. The analytical framework decomposes mortality trends into 3 distinct components: age effects representing biological aging processes and age-related physiological changes; period effects capturing temporal changes affecting all age groups simultaneously, such as healthcare policy reforms and therapeutic advances; and cohort effects reflecting variations driven by shared exposures among birth cohorts, including early-life nutritional status and environmental factors [19–21]. Mortality and population data were systematically aggregated into consecutive 5-year age groups and 5-year temporal intervals to ensure adequate statistical power while maintaining temporal resolution. APC analyses were performed using the NCI Age-Period-Cohort Web Tool (https://analysistools.cancer.gov/apc/). To address the intrinsic non-identifiability of the APC model (Age=Period-Cohort), we followed the estimable-functions approach implemented in the tool. Specifically, rather than attempting to estimate non-identifiable full sets of age, period, and cohort parameters, we reported uniquely estimable quantities, including the longitudinal age curve and period/cohort rate ratios defined relative to pre-specified reference categories, which provide identifiable summaries of age, period, and cohort patterns.
Future mortality trends (2022–2030) were projected using a Bayesian APC framework fitted to age-specific ASMRs from 1990 to 2021. The Bayesian APC model was used to estimate age, period, and cohort patterns and to generate projected age-specific mortality rates for 2022 to 2030 under continuity of the observed APC structure. United Nations population projections were incorporated to translate projected age-specific rates into expected death counts. Joinpoint regression was used to characterize recent temporal trends and identify statistically significant inflection points in ASMRs, whereas the APC model provided age-pattern summaries (eg, longitudinal age curves and period/cohort rate ratios relative to reference categories); together, these outputs informed the projection framework for 2022 to 2030. Projections assumed that the APC structure estimated from historical data would continue into the projection period; 95% credible intervals derived from the posterior predictive distributions reflect statistical and demographic uncertainty, without adjustment for potential structural changes (eg, major policy or healthcare system changes). Because age, period, and cohort components are inherently interdependent, we used a Bayesian APC specification with structured smoothing priors to stabilize estimation; inference focused on posterior summaries and predicted rates, rather than individual coefficients (Figure 1).
Results
Joinpoint regression showed distinct temporal patterns in ASMRs of HSBP-attributable stroke and its subtypes in China for the years 1990 to 2021 (Table 1; Figure 2A). From 1990 to 2021, overall stroke ASMR significantly declined (AAPC=−1.31%,
Local drift estimates further demonstrated strong age- and sex-specific heterogeneity in annual changes (Figure 3). Overall stroke exhibited a U-shaped pattern; the greatest reductions were evident in mid-to-late adulthood, and attenuation toward near-zero change was observed at the oldest ages. SAH showed consistently negative local drifts across all ages, as well as the largest decreases. ICH declined across most age groups, but the declines were smaller at older ages. In contrast, IS local drift increased with age and approached or exceeded zero at advanced ages – most prominently among men – indicating that improvements in IS mortality were least evident in older populations.
Age effects were dominant, such that HSBP-attributable mortality for IS and ICH steadily increased with age and greatly accelerated after 75 years. Across the age range, men generally exhibited higher mortality rates than women (Figure 4). Period effects shifted downward over time, and the most pronounced reduction was observed for SAH (period RR: 3.22 [95% CI 3.07–3.38] in 1990–1994 vs 0.83 [0.80–0.88] in 2015–2019). Period rate ratios declined more modestly for total stroke (1.17 [1.13–1.22] to 0.80 [0.77–0.82]), IS (0.99 [0.94–1.03] to 0.92 [0.90–0.95]), and ICH (1.07 [1.04–1.11] to 0.73 [0.71–0.75]) over the same periods (Figure 5). Cohort effects indicated substantial generational improvement for SAH, with strongly elevated risks in the earliest birth cohorts followed by a steep decline in later cohorts (cohort rate ratio: 13.62 [95% CI 10.77–17.21] for the earliest birth cohort vs 0.18 [0.09–0.35] for the most recent birth cohort). Cohort risks for ICH also decreased across successive cohorts (cohort rate ratio: 1.97 [1.64–2.36] for the earliest birth cohort vs 0.79 [0.45–1.38] for the most recent birth cohort). In contrast, IS showed a modest upward shift in more recent cohorts (cohort rate ratio: 0.79 [0.67–0.93] for the earliest birth cohort vs 1.06 [0.34–3.27] for the most recent birth cohort), although the estimate for the most recent cohort was imprecise (Figure 6).
Projections based on APC modeling indicated heterogeneous trajectories across stroke subtypes. After 2021, the model projects a continued decline in HSBP-attributable ASMRs for total stroke and all subtypes. Total stroke ASMR is predicted to decrease further into the 2030s, with widening 95% credible intervals of predicted values over time (Figure 7A). Among subtypes, the SAH ASMR remains lowest and is projected to continue steadily declining after 2021; the ICH ASMR shows a more pronounced post-2021 decline compared with IS. The IS ASMR is projected to decrease more modestly, with greater uncertainty in longer-term forecasts (Figure 7B–7D). Age-stratified Bayesian APC projections for adults aged 45 to 84 years revealed substantial heterogeneity in future HSBP-attributable stroke mortality across stroke subtypes. For total stroke, IS, and ICH, most middle-aged and older age groups are projected to continue exhibiting declines in mortality after 2021, whereas several younger adult groups (45–59 years) show relatively flat to slightly increasing trajectories; nevertheless, absolute mortality rates remain highest in the oldest age groups (Figure 8A, 8C, 8D). In contrast, SAH mortality is projected to consistently decline across all examined age groups (Figure 8B). All projections are presented with 95% credible intervals; these findings highlight persistent age- and subtype-specific heterogeneity despite projected overall declines in ASMRs.
In summary, (1) age effects were dominant, with steep increases in mortality at older ages; (2) period effects generally declined over time, most prominently for SAH; and (3) cohort effects indicated strong generational improvement for SAH but a modest upward shift for IS in recent cohorts. Projections to 2030 suggest continued overall declines, although uncertainty widens toward the end of the projection horizon.
Discussion
LIMITATIONS:
This study has several limitations that should be acknowledged. First, all estimates were derived from the GBD 2021 Study and therefore represent modeled outputs, rather than directly observed mortality data. Although the GBD framework integrates multiple data sources and applies standardized correction and quality-control procedures, uncertainties related to data availability, cause-of-death misclassification, and model assumptions may persist, particularly for children and older adults. Second, because the analyses were based on aggregated population-level data, causal inferences at the individual level cannot be made. The observed associations between HSBP and stroke mortality should thus be interpreted as population-level relationships, rather than individual-level causal effects. Third, the APC-based projections rely on the assumption that temporal patterns and cohort effects will continue into the future. Although Bayesian APC modeling quantifies uncertainty through credible intervals, unexpected changes in risk factor distributions, clinical practice, or public health interventions could lead to deviations from the projected trends. These limitations should be considered when interpreting the present findings, particularly the long-term projections.
Conclusions
APC analyses with projections to 2030 indicate that ICH mortality substantially declined in China, but hemorrhagic stroke remains a major challenge in the context of rapid population aging; accordingly, prevention should prioritize intensive blood pressure control and stronger hemorrhagic stroke care for older adults, especially those aged 75 years and older. In parallel, the modest decline in IS mortality among women and modest increase among men – combined with less favorable cohort patterns in more recent generations – support closing sex-related gaps that affect risk detection and treatment, while shifting risk-factor control earlier into early adulthood and midlife. National priorities should include population-wide sodium reduction and strengthened primary care pathways for blood pressure screening and evidence-based cardiovascular prevention.
Figures
Figure 1. Statistical analysis workflow for estimating high systolic blood pressure-attributable stroke mortality. AAPC – average annual percentage change; APC – age-period-cohort; ASMR – age-standardized mortality rate; BAPC – Bayesian age-period-cohort; CRA – comparative risk assessment; CrI – credible interval; GBD – Global Burden of Disease; ICH – intracerebral hemorrhage; IS – ischemic stroke; NMSS – National Mortality Surveillance System; SAH – subarachnoid hemorrhage. 
Figure 2. Temporal trends in age-standardized mortality rates (ASMRs) of stroke subtypes attributable to high systolic blood pressure in China (1990–2021). (A) Comparative ASMR trajectories in China versus the global average. (B) Sex-specific ASMR trends in China. 
Figure 3. Age-specific annual percentage changes (local drift) in high systolic blood pressure-attributable stroke mortality. ICH – intracerebral hemorrhage; IS – ischemic stroke; SAH – subarachnoid hemorrhage. Local drift values for both sexes (A), women (B), and men (C). A local drift value below 0 indicates a decreasing trend; conversely, values above 0 indicate an increasing trend. Error bars denote 95% confidence intervals. 
Figure 4. Longitudinal age-specific mortality curves for high systolic blood pressure-attributable stroke. Death rates (per 100 000) across age groups for both sexes (A), women (B), and men (C). Error bars denote 95% confidence intervals. ICH – intracerebral hemorrhage; IS – ischemic stroke; SAH – subarachnoid hemorrhage. 
Figure 5. Period-specific rate ratios (RRs) for high systolic blood pressure-attributable stroke mortality. Period RRs relative to the reference period (2005–2009), adjusted for age and nonlinear cohort effects, for both sexes (A), women (B), and men (C). Error bars depict 95% confidence intervals. ICH – intracerebral hemorrhage; IS – ischemic stroke; SAH – subarachnoid hemorrhage. 
Figure 6. Birth cohort-specific rate ratios (RRs) for high systolic blood pressure-attributable stroke mortality. Cohort RRs compared with the 1950 birth cohort, adjusted for age and nonlinear period effects, for both sexes (A), women (B), and men (C). Error bars indicate 95% confidence intervals. ICH – intracerebral hemorrhage; IS – ischemic stroke; SAH – subarachnoid hemorrhage. 
Figure 7. Trends and predictions of age-standardized mortality rates attributable to high systolic blood pressure for stroke and its subtypes in China, 1990 to 2030. (A) Overall stroke, (B) subarachnoid hemorrhage (SAH), (C) ischemic stroke (IS), (D) intracerebral hemorrhage (ICH). Black dots with a solid line represent historical observed values (1990–2021); the solid line after the vertical dashed line shows predicted values (2022–2030). Blue shaded areas indicate 95% credible intervals (CIs) of the predicted values. All mortality rates are presented per 100 000 population. 
Figure 8. Age-specific trends and predictions of mortality rates attributable to high systolic blood pressure for stroke and its subtypes across 8 age groups in China, 1990 to 2030. (A) Overall stroke, (B) subarachnoid hemorrhage (SAH), (C) ischemic stroke (IS), (D) intracerebral hemorrhage (ICH). Each small panel represents a specific 5-year age group from 45–49 years to 80–84 years. Black dots with a solid line represent historical observed values (1990–2021); the solid line after the vertical dashed line shows predicted values (2022–2030). Blue shaded areas indicate 95% credible intervals (CIs) of the predicted values. Tables
Table 1. Joinpoint regression results for ASMRs of stroke and stroke subtypes attributable to high systolic blood pressure in China, 1990 to 2021 (maximum 5 joinpoints).
Table 2. Joinpoint regression results for ASMRs of stroke and stroke subtypes attributable to high systolic blood pressure in China, 1990 to 2021 (maximum 3 joinpoints).
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Figures
Figure 1. Statistical analysis workflow for estimating high systolic blood pressure-attributable stroke mortality. AAPC – average annual percentage change; APC – age-period-cohort; ASMR – age-standardized mortality rate; BAPC – Bayesian age-period-cohort; CRA – comparative risk assessment; CrI – credible interval; GBD – Global Burden of Disease; ICH – intracerebral hemorrhage; IS – ischemic stroke; NMSS – National Mortality Surveillance System; SAH – subarachnoid hemorrhage.Figure 2. Temporal trends in age-standardized mortality rates (ASMRs) of stroke subtypes attributable to high systolic blood pressure in China (1990–2021). (A) Comparative ASMR trajectories in China versus the global average. (B) Sex-specific ASMR trends in China.Figure 3. Age-specific annual percentage changes (local drift) in high systolic blood pressure-attributable stroke mortality. ICH – intracerebral hemorrhage; IS – ischemic stroke; SAH – subarachnoid hemorrhage. Local drift values for both sexes (A), women (B), and men (C). A local drift value below 0 indicates a decreasing trend; conversely, values above 0 indicate an increasing trend. Error bars denote 95% confidence intervals.Figure 4. Longitudinal age-specific mortality curves for high systolic blood pressure-attributable stroke. Death rates (per 100 000) across age groups for both sexes (A), women (B), and men (C). Error bars denote 95% confidence intervals. ICH – intracerebral hemorrhage; IS – ischemic stroke; SAH – subarachnoid hemorrhage.Figure 5. Period-specific rate ratios (RRs) for high systolic blood pressure-attributable stroke mortality. Period RRs relative to the reference period (2005–2009), adjusted for age and nonlinear cohort effects, for both sexes (A), women (B), and men (C). Error bars depict 95% confidence intervals. ICH – intracerebral hemorrhage; IS – ischemic stroke; SAH – subarachnoid hemorrhage.Figure 6. Birth cohort-specific rate ratios (RRs) for high systolic blood pressure-attributable stroke mortality. Cohort RRs compared with the 1950 birth cohort, adjusted for age and nonlinear period effects, for both sexes (A), women (B), and men (C). Error bars indicate 95% confidence intervals. ICH – intracerebral hemorrhage; IS – ischemic stroke; SAH – subarachnoid hemorrhage.Figure 7. Trends and predictions of age-standardized mortality rates attributable to high systolic blood pressure for stroke and its subtypes in China, 1990 to 2030. (A) Overall stroke, (B) subarachnoid hemorrhage (SAH), (C) ischemic stroke (IS), (D) intracerebral hemorrhage (ICH). Black dots with a solid line represent historical observed values (1990–2021); the solid line after the vertical dashed line shows predicted values (2022–2030). Blue shaded areas indicate 95% credible intervals (CIs) of the predicted values. All mortality rates are presented per 100 000 population.Figure 8. Age-specific trends and predictions of mortality rates attributable to high systolic blood pressure for stroke and its subtypes across 8 age groups in China, 1990 to 2030. (A) Overall stroke, (B) subarachnoid hemorrhage (SAH), (C) ischemic stroke (IS), (D) intracerebral hemorrhage (ICH). Each small panel represents a specific 5-year age group from 45–49 years to 80–84 years. Black dots with a solid line represent historical observed values (1990–2021); the solid line after the vertical dashed line shows predicted values (2022–2030). Blue shaded areas indicate 95% credible intervals (CIs) of the predicted values. Tables
Table 1. Joinpoint regression results for ASMRs of stroke and stroke subtypes attributable to high systolic blood pressure in China, 1990 to 2021 (maximum 5 joinpoints).Table 2. Joinpoint regression results for ASMRs of stroke and stroke subtypes attributable to high systolic blood pressure in China, 1990 to 2021 (maximum 3 joinpoints).Table 1. Joinpoint regression results for ASMRs of stroke and stroke subtypes attributable to high systolic blood pressure in China, 1990 to 2021 (maximum 5 joinpoints).Table 2. Joinpoint regression results for ASMRs of stroke and stroke subtypes attributable to high systolic blood pressure in China, 1990 to 2021 (maximum 3 joinpoints). In Press
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