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. 2026 May 11;28(4):1188–1203. doi: 10.1002/epd2.70264

Bayesian age‐period‐cohort analysis and trend prediction of epilepsy disease burden in China, 1990–2021

Chao Jiang 1,2, Keke Ju 3, Yiming You 1, Yan Zhao 1, Jian Wang 2, Chuang Guo 1,✉, Zhiqiang Cui 2,✉
PMCID: PMC13499261  PMID: 42113023

Abstract

Objective

This study analyzes the trends, age‐period‐cohort effects, and influencing factors of epilepsy burden in China from 1990 to 2021, and predicts future burden to support prevention strategies.

Methods

Data were sourced from the Global Burden of Disease Study (GBD) 2021 study. Joinpoint regression analyzed trends in incidence, prevalence, mortality, and disability‐adjusted life years (DALYs). The BAPC (Bayesian Age‐Period‐Cohort) model evaluated age, period, and cohort effects, and an ARIMA (Autoregressive Integrated Moving Average) model projected burden to 2036.

Results

In 2021, epilepsy cases reached 3.086 million (a 41.67% increase since 1990), while DALYs fell to 1.375 million person‐years (a 35.60% decrease). Age‐standardized incidence and prevalence increased (average annual percent change [AAPC] = 0.78% and 0.42%, respectively), whereas mortality and DALY rates declined (AAPC = −2.65% and −1.79%). After 2019, incidence and prevalence rose sharply, accompanied by a short‐term increase in DALY rates indicating a risk of rebound. Age‐specific burden shifted from a unimodal (0–25 years) to a bimodal distribution (0–14 and 30–34 years), with the highest burden now among working‐age adults. The DALY rate decreased slowest in youths (20–24 years) and fastest in middle‐aged groups (45–55 years). Projections indicate continued declines in mortality but stable yet fluctuating incidence through 2036.

Conclusion

Despite improved mortality and DALY rates, epilepsy incidence and prevalence have increased, and although the overall DALY rate has declined, a recent uptick since 2019 signals the need for continued vigilance and targeted intervention. The shifting burden toward working‐age populations underscores the need for differentiated prevention strategies focusing on young adults and the elderly, emphasizing early intervention and long‐term management.

Keywords: age‐period‐cohort analysis, disease burden, epilepsy, ILAE classification, trend prediction

Key points.

  • From 1990 to 2021, China's age‐standardized epilepsy incidence and prevalence increased, while mortality and DALY rates declined.

  • After 2019, incidence and prevalence rose sharply, with a reversal in DALY rates likely driven by COVID‐19‐related diagnostic backlog and stress.

  • The age‐specific burden shifted from a unimodal to a bimodal distribution, with rising burden in working‐age adults (30–39 years).

  • Young adults (20–24 years) showed the slowest decline in DALY rates, highlighting a “lagging” improvement in this population.

  • Differentiated prevention strategies focusing on young adults and the elderly, with early intervention and long‐term management, are urgently needed.

1. INTRODUCTION

Epilepsy, a chronic brain disorder characterized by an enduring predisposition to generate epileptic seizures, affects over 50 million people worldwide and is associated with significantly increased morbidity and mortality. 1 , 2 According to the 2017 International League Against Epilepsy (ILAE) classification, the etiology of epilepsy is categorized into six groups: structural, genetic, infectious, metabolic, immune, and unknown, which reflects a more precise framework for diagnosis and management. 3 The substantial health and economic burden it imposes makes epilepsy a critical global public health priority. 4 , 5

It is estimated that over 50 million people worldwide suffer from epilepsy, with more than 5 million new cases diagnosed annually, 80% of whom live in low‐ and middle‐income countries. 6 In China, epidemiological surveys indicate that there are approximately 10 million epilepsy patients, with about 400 000 to 600 000 new cases annually. 7 The 2019 Global Burden of Disease Study (GBD) report reveals that China's epilepsy burden accounts for 10% of global disability‐adjusted life years (DALYs) and 94% of the East Asia region, 8 highlighting China's pivotal role in the global landscape of epilepsy prevention and control.

Comprehensively, accurately, and dynamically monitoring the disease burden of epilepsy and its changing trends, deeply analyzing its driving factors, and predicting future trajectories are critical prerequisites for formulating effective prevention and control strategies and optimizing the allocation of health resources. The GBD, led by the Institute for Health Metrics and Evaluation (IHME) at the University of Washington, is currently the most authoritative and comprehensive index system for measuring the impact of diseases, injuries, and risk factors on health at global, regional, and national levels. 9 , 10 However, despite the GBD data providing a macrolevel picture of understanding the burden of epilepsy in China, the following research gaps still exist 1 : Insufficient refinement in long‐term, systematic trend analysis: A more in‐depth depiction based on the latest data is still needed for the detailed trajectory of changes in the burden of epilepsy in China (including incidence, prevalence, mortality, DALYs) since 1990, particularly the specific trends across different genders and age groups, and whether significant turning points (Joinpoints) exist. (2) Quantitative decomposition of driving factors urgently needs in‐depth exploration: The underlying driving forces behind the changes in epilepsy burden in China, namely the age structure effect (e.g., population aging), period effect (e.g., advancements in medical technology, policy interventions, and socioeconomic changes), and birth cohort effect (reflecting differences in cumulative exposure risks across different generational groups), have not been sufficiently deconstructed and quantitatively assessed. Clarifying the relative contributions of these three effects is crucial for precise intervention. (3) Lack of projections for future burden: There is a relative scarcity of studies utilizing predictive models (such as ARIMA, BAPC) to forecast the future burden of epilepsy in China (including incidence, prevalence, mortality, and DALY rates), which is crucial for prospective health resource planning and assessing the feasibility of prevention and control targets. Therefore, this study aims to leverage the latest GBD 2021 database (covering 1990–2021) in conjunction with advanced statistical modeling techniques to comprehensively depict the long‐term evolution of the epilepsy burden in China, deeply analyze the underlying age, period, and cohort effects, and scientifically predict the changes in burden up to 2036.

The findings of this study will provide crucial evidence‐based support for gaining an in‐depth understanding of the evolving patterns of epilepsy burden in China, identifying key influencing factors, evaluating the effectiveness of previous prevention and control measures, and scientifically formulating future epilepsy prevention and control strategies as well as health resource allocation plans.

2. MATERIALS AND METHODS

2.1. Research object

The data of this study were derived from the GBD2021 database. This database covers 204 countries or regions, including the disease burden outcomes of 371 diseases and injuries and 88 risk factors, providing important and effective reference for formulating adaptive public health decisions and allocating health resources in different countries globally. In China, the sources of disease burden data are extensive, mainly including literature research, survey data and monitoring data, clinical data such as hospitalizations and outpatient visits 8 , 11 , 12 This study downloaded specific data through the Global Health Data Exchange (GHDx), an online data collection tool (accessible at: http://ghdx.healthdata.org/gbd‐results‐tool). The online GHDx query tool was utilized to obtain data on epilepsy cases in China from 1990 to 2021. The specific parameter settings for the search are as follows: the region is selected as China; the year is selected as All; the age is selected as All ages; the metric is selected as Number and Rate; the analysis indicators are determined as DALYs and Prevalence; the gender is set as Both; the disease cause is specified as Epilepsy. This study strictly adheres to the relevant provisions of the Declaration of Helsinki (revised version 2013).

2.2. The burden of epilepsy

In this study, the selected indicators of disease burden include the number of new cases (Incidence), the number of prevalent cases (Prevalence), the number of deaths (Deaths), DALYs, and their 95% uncertainty interval (95% UI). The corresponding crude rates (incidence rate, prevalence rate, mortality rate, DALYs) as well as the age‐standardized incidence rate (ASIR), age‐standardized prevalence rate (ASPR), age‐standardized mortality rate (ASMR), and age‐standardized DALY rate (ASDR) were included. The age‐standardized rates were calculated using the GBD 2021 global standard population structure. DALY is used to measure the total health loss caused by epilepsy, specifically the sum of years of life lost (YLL) and years lived with disability (YLD). In China, the epidemiological distribution of epilepsy is described by the number of incident cases, prevalent cases, deaths, and prevalence rate, while the disease burden is comprehensively evaluated using DALYs. The average annual percent change (AAPC) serves as a summary statistic for the trends in incidence rate, prevalence rate, mortality rate, and DALY standardized rate. Therefore, this study employs the AAPC to delineate the annual trends in the incidence, prevalence, mortality, and DALYs burden of epilepsy.

2.3. Statistical analysis

2.3.1. Joinpoint regression model analysis of disease burden trend changes

This study employed the Joinpoint regression model to assess the trends in the incidence, prevalence, mortality, and DALYs rates of epilepsy. The model identifies points of trend change in time series by fitting multiple linear segments (joinpoints). Its principle is to determine, among numerous possible trend combinations, the combination that statistically fits the data series better than a single trend line fitted by Poisson regression or time series models, thereby achieving an accurate assessment of changes in disease burden trends. In specific operations, the model was constructed using the Joinpoint Regression Program (version 5.0.2) developed by the National Cancer Institute (NCI). The number and location of joinpoints were determined through Monte Carlo permutation tests, which select the optimal number by balancing best data fit and minimizing overfitting. The annual percent change (APC) and AAPC were then calculated. 13 The optimal number of joinpoints was determined by permutation test to achieve the best data fit and minimize overfitting. To verify the statistical significance of trend changes, the AAPC was compared to zero, and a p‐value < 0.05 was considered indicative of a significant trend change. 14

2.3.2. BAPC model analysis of influencing factors

The Bayesian Age‐Period‐Cohort (BAPC) model is a multivariate analytical method based on a Poisson distribution framework. It decomposes the independent effects of age, period, and birth cohort within the temporal trends of disease incidence, prevalence, mortality, or DALY rates. 15 This study employed the “BAPC” package in R software (version 4.3.1) to construct the models. The intrinsic estimator (IE) method was applied to address the inherent collinearity issue among the age, period, and cohort effects. Within the model:

  1. Age Effect: Reflects the variation in risk for developing epilepsy across different age groups (e.g., higher incidence in infants and the elderly).

  2. Period Effect: Characterizes the immediate impact of external environmental factors (such as public health policies, advancements in diagnosis and treatment technology, accessibility of antiepileptic drugs, and socioeconomic changes) on the burden of epilepsy across the entire population.

  3. Cohort Effect: Represents the long‐term influence of differences in cumulative lifelong exposures (including social, environmental, nutritional, and healthcare factors) on epilepsy risk among different birth cohorts.

This study will construct separate BAPC models for the ASIR, ASPR, ASMR, and ASDR of epilepsy. This comprehensive approach aims to assess the driving factors underlying the burden of epilepsy in China.

2.3.3. ARIMA model for predicting disease burden

The Autoregressive Integrated Moving Average (ARIMA) model was employed to forecast trends in the burden of epilepsy in China from 2022 to 2036. Model construction utilized the statsmodels library (version 0.14.0) in Python, adhering to the following procedure:

  1. Stationarity Testing: The stationarity of the time series for the ASIR, ASPR, and ASDR was assessed using the Augmented Dickey‐Fuller (ADF) test (significance level α = 0.05). Differencing (d‐order) was applied when necessary to achieve stationarity.

  2. Parameter Selection: The optimal ARIMA (p, d, q) parameter combination (p = autoregressive order, d = differencing order, q = moving average order) was determined on the stationary series. Selection was guided by minimizing the Bayesian Information Criterion (BIC) and the Root Mean Square Error (RMSE).

  3. Model Validation: The randomness of the residual series was evaluated using the Ljung‐Box white noise test (p > 0.05 indicating good model fit and white noise residuals). Final forecasts are presented as the ASIR, ASPR, ASMR, and ASDR, along with their plausible confidence intervals. 16

3. RESULTS

3.1. Overall and sex‐specific burden of epilepsy in China from 1990 to 2021

In 2021, the total number of new epilepsy cases in China reached 360 000, an increase of 36.64% compared to 263 500 cases in 1990. The ASIR rose from 22.36 per 100 000 in 1990 to 28.19 per 100 000 in 2021, with a change rate of 26.07% (AAPC = 0.78, p < 0.05). From the perspective of gender distribution, in 2021, the number of new cases in males was 189 000, an increase of 39.74% compared to 135 300 in 1990, with the incidence rate rising from 22.30/100000 to 28.73/100000 (AAPC = 0.85, p < 0.05); the number of new cases in females was 170 900, an increase of 33.31% compared to 128 200 in 1990, with the incidence rate increasing from 22.42/100000 to 27.60/100000 (AAPC = 0.70, p < 0.05) (Table 1, Figure 1A).

TABLE 1.

Changes in the burden of epilepsy disease indicators in China, 1990–2021 (in ten thousands, 1/100000).

Indicator Group 1990 (×104) 1990 2021 (×104) 2021 AAPC
(95% UI) (per 100 000) (95% UI) (95% UI) (per 100 000) (95% UI) (95% CI)
Incidence Male 13.53 (8.98, 18.70) 22.30 (14.96, 30.85) 18.90 (13.05, 24.98) 28.73 (19.45, 38.19) 0.85* (0.75, 0.94)
Female 12.82 (8.54, 17.74) 22.42 (15.06, 30.98) 17.09 (11.81, 22.68) 27.60 (18.7, 37.05) 0.70* (0.54, 0.87)
Total population 26.35 (17.47, 36.28) 22.36 (15.04, 30.92) 36.00 (24.80, 47.57) 28.19 (19.03, 37.89) 0.78* (0.65, 0.91)
Prevalence Male 116.50 (81.76, 155.61) 196.75 (137.99, 263.44) 165.99 (116.58, 218.26) 227.38 (159.14, 295.24) 0.47* (0.33, 0.61)
Female 101.35 (70.21, 134.37) 181.33 (127.44, 242.01) 142.65 (101.46, 187.23) 201.24 (140.59, 261.78) 0.39* (0.21, 0.56)
Total population 217.85 (152.54, 289.63) 189.28 (132.48, 252.96) 308.63 (217.65, 402.07) 214.71 (150.11, 278.57) 0.42* (0.28, 0.59)
Mortality Male 1.31 (1.08, 1.61) 2.23 (1.85, 2.76) 0.77 (0.58, 1.00) 1.03 (0.8, 1.33) −2.44* (−2.70, −2.18)
Female 0.84 (0.61, 1.14) 1.51 (1.1, 2.06) 0.42 (0.31, 0.60) 0.58 (0.43, 0.82) −3.02* (−3.41, −2.62)
Total population 2.15 (1.83, 2.60) 1.86 (1.6, 2.25) 1.19 (0.98, 1.48) 0.81 (0.68, 1) −2.65* (−2.88, −2.42)
DALY Male 124.01 (100.53, 154.35) 199.92 (161.15, 248.04) 80.91 (58.23, 109.85) 116.41 (84.16, 156.74) −1.74* (−1.99, −1.49)
Female 89.45 (68.20, 113.91) 156.14 (119.47, 199.39) 56.56 (37.70, 80.58) 85.19 (58.08, 121.98) −1.89* (−2.08, −1.71)
Total population 213.46 (171.75, 265.12) 178.61 (143.44, 220.63) 137.47 (96.95, 188.87) 101.39 (72.51, 139.41) −1.79* (−1.96, −1.62)
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Abbreviation: AAPC, average annual percent change.

*

p < 0.05.

FIGURE 1.

FIGURE 1

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Burden of epilepsy in China from 1990 to 2021. (A) Incidence (number and rate); (B) prevalence (number and rate); (C) mortality (number and rate); and (D) disability‐adjusted life years (DALYs, number and rate). Data are presented for males and females.

The total number of prevalent cases of epilepsy in China in 2021 was 3 086 300, showing a significant 41.67% increase compared to 2 178 500 cases in 1990. The ASPR rose from 189.28 per 100 000 in 1990 to 214.71 per 100 000 in 2021, with a change rate of 13.43% (AAPC = 0.42, p < 0.05). From a gender analysis perspective, in 2021, the number of male patients was 1.6599 million, an increase of 42.50% compared to 1.1650 million in 1990, with the prevalence rate rising from 196.75 per 100 000 to 227.38 per 100 000 (AAPC = 0.47, p < 0.05); the number of female patients was 1.4265 million, an increase of 40.76% compared to 1.0135 million in 1990, with the prevalence rate increasing from 181.33 per 100 000 to 201.24 per 100 000 (AAPC = 0.39, p < 0.05).(Table 1, Figure 1B)The number of deaths attributable to epilepsy in China in 2021 was 11 900, marking a substantial 44.68% decrease from 21 500 deaths in 1990. The ASMR dropped markedly from 1.86 per 100 000 in 1990 to 0.81 per 100 000 in 2021, with a change rate of −56.45% (AAPC = −2.65, p < 0.05). By gender, the number of male deaths in 2021 was 7700, a decrease of 41.53% from 13 100 in 1990, with the mortality rate dropping from 2.23/100 000 to 1.03/100000 (AAPC = −2.44, p < 0.05). The number of female deaths was 4200, a decrease of 49.64% from 8400 in 1990, with the mortality rate decreasing from 1.51/100000 to 0.58/100000 (AAPC = −3.02, p < 0.05). (Table 1, Figure 1C).

The total DALYs lost due to epilepsy in China in 2021 were 1.3747 million person‐years, indicating a 35.60% decrease from 2.1346 million person‐years in 1990. The ASDR significantly declined from 178.61 per 100 000 in 1990 to 101.39 per 100 000 in 2021, with a change rate of −43.24% (AAPC = −1.79, p < 0.05). Gender‐specific analysis revealed that in 2021, the DALY loss for males was 809 100 person‐years, a decrease of 34.76% from 1 240 100 person‐years in 1990, with the DALY rate declining from 199.92 per 100 000 to 116.41 per 100 000 (AAPC = −1.74, p < 0.05). For females, the DALY loss was 565 600 person‐years, a decrease of 36.77% from 894 500 person‐years in 1990, with the DALY rate dropping from 156.14 per 100 000 to 85.19 per 100 000 (AAPC = −1.89, p < 0.05). (Table 1, Figure 1D).

3.2. Trends in the burden of epilepsy in China, 1990–2021

Through an in‐depth analysis of the burden of epilepsy in China using the Joinpoint regression model, it was found that between 1990 and 2021, the ASIR and ASPR of epilepsy in China exhibited a fluctuating upward trend, while the ASMR and ASDR showed a significant downward trend (all p < 0.05).

The trend of ASIR: From 1990 to 2021, it showed an overall upward trend (AAPC = 0.78%), but with significant periodic fluctuations: 1990–1994: Rapid increase phase (APC = 4.48%), with a sharp rise in incidence; 1994–2002: Slowed growth (APC = 0.84%), maintaining a moderate increase; 2002–2019: Entered a declining phase (APC = −0.70%), with a continuous decrease in incidence; 2019–2021: A significant rebound occurred (APC = 6.07%), with incidence rising rapidly again. (Figure 2A).

FIGURE 2.

FIGURE 2

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Joinpoint regression analysis of age‐standardized rates of epilepsy in China, 1990–2021. (A) Age‐standardized incidence rate (ASIR); (B) age‐standardized prevalence rate (ASPR); (C) age‐standardized mortality rate (ASMR); and (D) age‐standardized DALY rate (ASDR). Solid lines = observed rates; segmented lines = joinpoint trends with annual percent change (APC). Asterisks (*) indicate APC is significantly different from zero (p < .05). Shaded areas = 95% uncertainty intervals.

The trend of ASPR: Similar to the ASIR trend, the overall ASPR has shown an upward trajectory (AAPC = 0.43%), with the following stage characteristics: 1990–1994: A period of rapid growth (APC = 4.43%), with a swift rise in prevalence; 1994–2005: A narrowing increase (APC = 0.38%), with a significant slowdown in the rate of ascent; 2005–2019: A phase of decline (2005–2009 APC = −2.33%, 2009–2019 APC = −1.19%); 2019–2021: A strong rebound (APC = 6.87%), with a sharp increase in prevalence. (Figure 2B).

The trend of ASMR: ASMR showed a continuous downward trend (AAPC = −2.65%), with key turning points including: 1992–2003: Rapid decline phase (APC = −2.76%), with a significant reduction in mortality; 2003–2007: Accelerated decline (APC = −5.72%), reaching the historical peak in reduction; 2010–2013: Another sharp decline (APC = −4.12%); 2013–2021: Maintained a steady decline (APC = −1.56%). (Figure 2C).

The trend of ASDR: The ASDR showed a significant overall decline (AAPC = −1.79%), with distinct phases characterized as follows: 1993–2003: a steady decrease (APC = −1.95%), with continuous reduction in health loss; 2003–2007: an accelerated decline (APC = −4.26%), with disease burden diminishing at a faster rate; 2007–2019: a maintained downward trend (APC = −2.64% from 2007 to 2013 and APC = −1.90% from 2013 to 2019); 2019–2021: a short‐term increase (APC = 2.51%), indicating a risk of rebound in health loss; a brief increase was also observed from 1990 to 1993 (APC = 1.24%). (Figure 2D).

The incidence and burden of epilepsy in China have generally increased over the past three decades, with a particularly significant acceleration in the rate of increase after 2019. Meanwhile, the burden of mortality and health loss has continued to improve, but an uptick in ASDR observed during 2019–2021 serves as a critical warning that these gains may be fragile, necessitating vigilance against the risk of a rebound in disease burden (Table 2).

TABLE 2.

Joinpoint regression analysis of epilepsy in China, 1990–2021.

Variable Years APC (%, 95% CI) AAPC (%, 95% CI) t p
ASIR 1990–2021 0.7807* (0.6502, 0.9114) 11.7619 <0.000001
1990–1994 4.4815* (3.9442, 5.0216) 17.6825 <0.000001
1994–2002 0.8380* (0.6286, 1.0477) 8.3514 <0.000001
2002–2019 −0.6968* (−0.7548, −0.6388) −24.8888 <0.000001
2019–2021 6.0686* (4.3911, 7.7730) 7.6859 <0.000001
ASPR 1990–2021 0.4323* (0.2794, 0.5855) 5.5482 <0.000001
1990–1994 4.4258* (3.9212, 4.9328) 18.7839 <0.000001
1994–2005 0.3816* (0.2675, 0.4958) 7.0366 0.000001
2005–2009 −2.3280* (−3.0418, −1.609) −6.7471 0.000003
2009–2019 −1.1928* (−1.3282, −1.0572) −18.383 <0.000001
2019–2021 6.8668* (5.2043, 8.5556) 8.8989 <0.000001
ASMR 1990–2021 −2.6494* (−2.8758, −2.4225) −22.606 <0.000001
1990–1992 −0.9926 (−2.4819, 0.5194) −1.4029 0.181003
1992–2003 −2.7608* (−2.8649, −2.6565) −55.6889 <0.000001
2003–2007 −5.7241* (−6.367, −5.0768) −18.3609 <0.000001
2007–2010 −0.5699 (−2.0285, 0.9106) −0.8242 0.42274
2010–2013 −4.1183* (−5.5961, −2.6174) −5.771 0.000037
2013–2021 −1.5552* (−1.7357, −1.3744) −18.2034 <0.000001
ASDR 1990–2021 −1.7901* (−1.9605, −1.6194) −20.3876 <0.000001
1990–1993 1.2430* (0.6317, 1.8580) 4.3479 0.000574
1993–2003 −1.9508* (−2.0652, −1.8363) −35.9821 <0.000001
2003–2007 −4.2610* (−4.9308, −3.5865) −13.2196 <0.000001
2007–2013 −2.6377* (−2.9586, −2.3158) −17.2597 <0.000001
2013–2019 −1.8998* (−2.2498, −1.5485) −11.4367 <0.000001
2019–2021 2.5132* (0.6679, 4.3924) 2.9126 0.010718
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*

p < 0.05.

3.3. Evolution of age‐specific burden of epilepsy in China, 1990–2021

From 1990 to 2021, the burden of epilepsy in China exhibited a significant evolution in age distribution (Figure 3). In terms of incidence burden, the number of cases in 1990 displayed a unimodal distribution, peaking in the 0–25 age group and decreasing with age. By 2021, the incidence pattern had shifted to a bimodal distribution, with the primary burden transitioning to the 0–14 age group of children and adolescents and the 30–34 age group of young adults. The prevalence pattern demonstrated a characteristic of intergenerational shift: in 1990, the distribution of prevalent cases was similar to the incidence trend, while by 2021, it showed a uniform distribution across all age groups, with a significant reduction in the number of prevalent cases in the <5 age group of young children but a notable increase in the burden of prevalent cases in the elderly group aged 55 and above.

FIGURE 3.

FIGURE 3

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Evolution of age‐specific burden of epilepsy in China, 1990 vs. 2021. (A) Number of incident cases and (B) age‐specific incidence rate; (C) number of prevalent cases and (D) age‐specific prevalence rate; (E) number of deaths and (F) age‐specific mortality rate; and (G) number of DALYs and (H) age‐specific DALY rate. Blue bars represent data from 1990; orange bars represent data from 2021.

The burden of death and health loss has undergone a structural transformation. Although the number of deaths and DALYs lost in 2021 significantly decreased compared to 1990, there has been a fundamental shift in the age distribution: in 1990, both exhibited a “J‐shaped” distribution, with the peak concentrated in the <5 years old infant and toddler group; whereas in 2021, the peak of mortality burden and DALYs lost shifted to the 30–39 years old core labor force group. Analysis from the disease burden rate indicators reveals that: (1) The incidence rate maintained a stable age pattern, showing a “U‐shaped” curve in both periods, with high incidence in the <5 years old and ≥ 95 years old groups, and a gradient decline with increasing age in the 25–94 years old range; (2) The prevalence rate exhibited a “bathtub‐shaped” distribution, decreasing with age in the 0–54 years old group and significantly increasing in the above 55 years old group, with the overall prevalence level in 2021 higher than in 1990; (3) The mortality rate achieved a historic breakthrough, with a significant overall decline, and the mortality rate in the <5 years old group dropping to a level comparable with other age groups; (4) The DALY rate reflected a transformation in disease burden, shifting from being dominated by health loss in infants and toddlers in 1990 to a dual burden pattern in 2021 characterized by health loss in the young labor force (20–24 years old) and the very elderly population (≥95 years old).

3.4. The APC model of DALY rate risk for epilepsy in China, 1990–2021

The APC model showed a net drift value of −3.423% (95% CI: −3.586% to −3.26%, p < 0.001), indicating a slight decline in the DALY rate of epilepsy. The age effect of the APC model revealed a significant age effect of epilepsy in China, presenting a J‐shaped risk distribution. The age‐specific relative risk peaked in infants under 5 years of age, decreased to its lowest value during young adulthood (57.5 years), rebounded significantly after 80 years, and reached its peak in the very elderly aged 90 and above. The period effect, demonstrated by Period RR, showed a significant downward trend in the burden of epilepsy in China. Taking 2007 as the reference (RR = 1), the relative risk was highest in 1992 and decreased to its lowest in 2021. The cohort effect was represented by Cohort RR, indicating a significant intergenerational decline in the lifetime risk of epilepsy in China. The cohort born in 1900 exhibited the highest relative risk, which decreased to the lowest level for the cohort born in 2020. Local Drifts With Net Drift analysis revealed significant age differences in the rate of decline of epilepsy burden in China (Figure 4D). The young adult group (20–24 years) showed the slowest decline, while the oldest age group (≥85 years) experienced the fastest decline. Notably, the working‐age population (45–55 years) demonstrated a sharp decline, significantly faster than the overall trend (Figure 4A). This pattern of “accelerated in the middle and lagging at both ends” suggests the necessity of formulating differentiated age‐targeted prevention and control strategies (Table 3).

FIGURE 4.

FIGURE 4

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Age‐period‐cohort (APC) model analysis of DALY rate risk for epilepsy in China, 1990–2021. (A) Age deviations; (B) period relative risk (RR); (C) cohort relative risk (RR); and (D) local drifts with net drift.

TABLE 3.

Results of Wald χ2 test for estimable functions in the age‐period‐cohort (APC) model of DALY rate risk for epilepsy in China, 1990–2021.

Statistical measure AF/AFL
Wald χ2 p
Overall Net Drift = 0 1161.86 <0.001
Sum of Age Deviations = 0 3065.5385 <0.001
Sum of Period Deviations = 0 14.1827 0.0067
Sum of Cohort Deviations = 0 581.3811 <0.001
Reference Period RR = 1 1177.6622 <0.001
Reference Cohort RR = 1 1971.0759 <0.001
Net Drift ≡ Mean Local Drift 530.9579 <0.001
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3.5. Projected ASIR, ASMR, ASPR, and ASDR of epilepsy in China for the next 15 years

3.5.1. Model fitting and validation

Through systematic comparison of ARIMA model structures, this study identified the optimal models for predicting the burden of epilepsy in China from 2022 to 2036 (Table 4): the ASIR was best fit by ARIMA(1,0,2) (Figure 5A), the ASPR by ARIMA(1,1,0) (Figure 5B), the ASMR by ARIMA(0,2,3) (Figure 5C), and the age‐standardized DALY rate (ASDR) by ARIMA(1,1,0) (Figure 5D). All models passed the residual white noise test (Box‐Ljung test p > 0.27), with the ASMR model demonstrating the best fit (AIC = −161.87, RMSE = 0.014). The differences in model parameters indicate that the incidence of epilepsy is likely to maintain stable fluctuations over the next 15 years, while the mortality rate will continue its strong downward trend. (Figure 5).

TABLE 4.

Projection model indicators for epilepsy in China, 2022–2036.

Forecast model ASIR ASPR ASMR ASDR
Optimal ARIMA (p,d,q) ARIMA (1,0,2) ARIMA (1,1,0) ARIMA (0,2,3) ARIMA (1,1,0)
AIC 59.82 188.83 −161.87 119.81
BIC 67.14 191.7 −156.27 122.68
RMSE 0.4867796 4.666865 0.01356767 1.506402
Box‐Ljung test 0.2726 0.564 0.8131 0.9181
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FIGURE 5.

FIGURE 5

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Projected trends of age‐standardized rates for epilepsy in China, 2022–2036. (A) Projection of age‐standardized incidence rate (ASIR); (B) projection of age‐standardized prevalence rate (ASPR); (C) projection of age‐standardized mortality rate (ASMR); and (D) projection of age‐standardized DALY rate (ASDR). Solid lines = observed data; dashed lines = forecasted trends.

4. DISCUSSION

4.1. Overview of epilepsy burden trends in China

This study, for the first time, systematically analyzed the evolution of the burden of epilepsy in China from 1990 to 2021 using the Age‐Period‐Cohort (BAPC) framework. The results revealed that the incidence and prevalence of epilepsy continued to rise (ASIR AAPC = 0.78%, ASPR AAPC = 0.42%), while the mortality rate and health loss showed significant improvement (ASMR AAPC = −2.65%, ASDR AAPC = −1.79%). However, since 2019, multiple burden indicators have exhibited an abnormal rebound, indicating new challenges in disease prevention and control.

4.2. Explanations for increasing incidence and prevalence (1990–2021)

The long‐term increase in ASIR and ASPR from 1990 to 2021 can be explained by three main factors: better diagnostics, better access to healthcare providers, and reduced stigma. First, substantial improvements in diagnostic capabilities, which include the wider availability of neuroimaging and electroencephalography particularly in previously underserved regions, have led to increased detection of previously missed cases. 4 , 7 Second, enhanced healthcare infrastructure and targeted public health initiatives have progressively narrowed the epilepsy treatment gap in rural China, which once exceeded 60% in the early 2000s. 7 , 17 Third, increased public awareness and ongoing efforts to reduce the profound social stigma associated with epilepsy have encouraged more individuals to seek medical attention, thereby uncovering previously hidden prevalent cases. 4 , 7 While a true increase in etiology‐related risk factors cannot be entirely ruled out, the observed trend is predominantly driven by these healthcare system and societal improvements.

4.3. Impact of the COVID‐19 pandemic and recent trends

The sharp increase in incidence and prevalence rates observed during 2019–2021 (ASIR APC = 6.07%, ASPR APC = 6.87%) is unlikely to represent a true sudden rise in underlying disease incidence; rather, it predominantly reflects the multidimensional impact of the COVID‐19 pandemic and potential transient coding or reporting variations during the public health emergency. “The pandemic may have driven a short‐term surge in the burden of epilepsy through a cascade reaction mechanism of ‘diagnostic delay‐neurophysiological stress‐decompensation in high‐risk populations‐insufficient medical support.’ First, the disruption of healthcare service continuity led to delays in the diagnosis of new cases, creating a diagnostic backlog effect, which resulted in an abnormal increase in statistical incidence during the pandemic relief period.” A survey study revealed that during the COVID‐19 pandemic, over 33% of routine neurological assessments were forced to be postponed. 18 Secondly, psychosocial stressors intensified, directly triggering the deterioration of conditions by lowering the seizure threshold. Empirical studies have shown that insomnia, loss of economic income, and epilepsy‐specific fears during lockdowns were independent predictors of increased seizure frequency, while anxiety and depression states with a comorbidity rate exceeding 40% further undermined disease management efficacy. 19 , 20 Thirdly, the collapse of compensatory mechanisms in high‐risk populations and the strain on medical resources made patients with drug‐resistant epilepsy and tumor‐related epilepsy more susceptible to decompensation in seizure control, directly driving up the prevalence rate. 19 , 20 , 21 Finally, the systemic structural deficiencies of the support system manifest as disruptions in the antiepileptic drug supply chain (such as regional shortages of valproate and the diagnostic limitations of telemedicine; inability to perform necessary neuroelectrophysiological/imaging assessments for newly diagnosed epilepsy), creating a synergistic negative cycle. 19 , 20 , 21

Importantly, we cannot exclude the possibility that transient coding or reporting variations during the public health emergency contributed to the observed statistical fluctuation in 2019–2021. Such systematic variations in case ascertainment are common during health system disruptions and should be interpreted as potential methodological limitations rather than true epidemiological shifts.

4.4. Drivers of declining mortality and DALY rates

The observed decline in ASDR can be attributed to multiple factors. From 2003 to 2007, an accelerated downward trend was evident (APC = −4.26%). First, at the clinical level, advancements including improved seizure control with newer antiseizure medications, better management of acute life‐threatening complications like status epilepticus, and enhanced prevention of epilepsy‐related injuries have all prolonged survival. 5 Moreover, better control of cardiovascular and cerebrovascular risk factors in the general population may have indirectly reduced epilepsy‐related mortality, as cerebrovascular disease is a recognized cause of acquired epilepsy in older adults and accounts for approximately one‐fifth of all deaths in people with epilepsy in China. 22 The substantial decline in cerebrovascular disease mortality nationally (GBD estimates indicate >40% reduction in age‐standardized cerebrovascular mortality rate from 1990 to 2021) likely contributed to reduced epilepsy DALYs through two mechanisms 1 : decreased incidence of poststroke epilepsy due to improved primary prevention of stroke, and 2 reduced mortality among patients with existing epilepsy where cerebrovascular comorbidities were contributing causes of death. 22 , 23

Second, at the systems level, healthcare policy improvements have played a crucial role. The comprehensive implementation of the New Rural Cooperative Medical Scheme (NRCMS) with full coverage achieved by 2008 significantly increased hospitalization reimbursement rates (to ≥70%), alleviated financial barriers for rural epilepsy patients, promoted standardized use of antiseizure medications, and enhanced epilepsy management capacity at county‐level hospitals. 7 , 24 , 25 These complementary drivers, namely clinical progress and health system strengthening, jointly explain the accelerated decline in both mortality and DALY rates during this critical period.

This positive trend should also be viewed against the backdrop of a rapidly increasing overall life expectancy in China, which rose from 67.7 years in 1990 to 77.6 years in 2021. 26 , 27 However, notable urban–rural disparities persist: the rural–urban gap in life expectancy widened to 23.7 months (approximately 2 years) by 2021, and rural patients face greater barriers to accessing specialized epilepsy care. 28 The differential improvement in life expectancy between urban and rural areas may have partially moderated the overall DALY decline. Urban patients likely experienced greater reductions in mortality and disability, whereas rural patients may have had less improvement in outcomes. This disparity arises because rural populations bear a disproportionate burden of epilepsy risk factors, including traumatic brain injuries, occupational hazards, and limited healthcare access, thereby creating an uneven pattern of DALY reduction across the population. 7 , 17 , 28 Unfortunately, the GBD 2021 database does not provide urban–rural stratification, precluding direct quantification of this differential effect in our analysis.

While this reflects broad societal and healthcare advancements, the recent uptick in ASDR (2019–2021) serves as a critical warning that gains in mortality are fragile and that the nonfatal burden of epilepsy (morbidity) requires continued, targeted intervention.

4.5. Age‐period‐cohort effects, youth lag phenomenon, and intergenerational trends

The BAPC model analyzed the DALY rate, revealing a “J‐shaped age risk” of exposure and a notable disparity in the rate of decline across age groups, with young adults experiencing the slowest improvement, reflecting structural contradictions. The relative risk of epilepsy disease burden peaks during infancy (<5 years), decreases to its lowest value during youth (57 years), and significantly rebounds after 80 years, reaching its peak in the ultraelderly period (≥90 years). The peak during infancy is primarily due to perinatal brain injury and inherited epilepsy syndromes. 29 , 30 Early epidemiological surveys indicate that the incidence of epilepsy is age‐related, with the highest rates observed in those under 5 years and over 65 years, with more than 60 per 100 000 people in these age groups affected by epilepsy. 31 It is evident that epilepsy prevention and control strategies should be stratified by age, focusing on perinatal care for infants, healthy lifestyles for young adults, and chronic disease management for the elderly, with a rational allocation of medical resources.

This study also found that the DALY rate for epilepsy exhibited a deceleration in decline among the 30–39 age group, characterized by a significant increase in the DALY burden among the 30–39 age group. This phenomenon may be closely related to the occupational characteristics and behavioral risk factors prevalent in this age group. Specifically, young adult males, as the main labor force, are exposed to high‐risk occupational environments (such as construction and other physically demanding jobs prone to traumatic brain injuries) and are in a high‐frequency stage of alcohol consumption. Furthermore, the overall higher disease burden in males compared to females, as revealed in this study, provides gender‐level evidence for this age‐specific pattern of slower improvement. Analyzing in the context of China's national conditions, the rural areas have a large population base, with a significant concentration of young and middle‐aged laborers aged 30–39 in the construction industry or heavy manual labor sectors, markedly increasing the risk of occupation‐related brain injuries. Simultaneously, this age group is also a high‐risk period for alcohol abuse. Occupational injuries and alcohol abuse, as established risk factors for epilepsy of unknown etiology, their dual overlap in this population collectively constitutes an important mechanism for the increased burden of DALYs among young and middle‐aged individuals aged 30–39. 32 , 33 , 34 Beyond age and period effects, cohort effects indicate that the lifetime risk of epilepsy in China shows a significant intergenerational downward trend. The cohort born in 1900 exhibited the highest relative risk, with the RR value decreasing to its lowest in the cohort born in 2020 after falling below 1 since 2003. This progressive risk reduction across birth cohorts likely reflects cumulative improvements in maternal and child health, nutritional status, and infectious disease control over the 20th century, complementing the period‐specific policy effects described above. Together, these age, period, and cohort dynamics underscore the need for differentiated, life‐course‐based prevention strategies.

4.6. Future projections and policy implications

The ARIMA model suggests that the ASIR will maintain fluctuations from 2022 to 2036 (with a need to be vigilant against a rebound), while the ASMR continues to decline. As China has the world's largest population of epilepsy patients, establishing epilepsy prevention and treatment as a key public health area has become an urgent need. Core strategies should focus on optimizing the diagnostic system, strengthening comorbid disease screening, implementing personalized treatment plans, and improving the hierarchical referral mechanism. Meanwhile, at the governmental level, it is necessary to establish a comprehensive public health intervention system that covers prevention, diagnosis, and management by increasing dedicated fiscal investment, systematically conducting health education, and enhancing societal scientific understanding of diseases. Given the significant importance of epilepsy prevention and control to national health, only by integrating medical resources, deepening policy support, and synergizing public education can the health and well‐being of this population be effectively improved.

5. LIMITATIONS AND PROSPECTS

This study has several limitations. First, the analysis did not stratify by urban–rural residency, potentially masking significant disparities in disease burden and access to care, as discussed in Section 3.4. While we identified a widening urban–rural gap in life expectancy (23.7 months by 2021) and its moderating effect on overall DALY decline, direct quantification of this differential effect was precluded by the lack of GBD 2021 urban–rural stratification. Second, while the GBD 2021 database offers a robust, standardized framework for global and temporal comparisons, it may not fully capture region‐specific epidemiological nuances or the most recent data from local Chinese registries and surveillance systems. 35 Future studies incorporating data from national cohorts or large‐scale hospital‐based networks in China would be valuable to validate and complement our findings. Finally, the ARIMA prediction model did not incorporate potential future intervention variables, such as the widespread adoption of novel neuromodulation techniques or changes in healthcare policy.

AUTHOR CONTRIBUTIONS

Chao Jiang: Conceptualization; investigation; writing—original draft; writing—review and editing. Keke Ju: Conceptualization; investigation; writing—original draft; writing—review and editing; visualization. Yiming You: Data curation; formal analysis; writing—review and editing. Yan Zhao: Data curation; formal analysis; writing—review and editing. Jian Wang: Data curation; formal analysis; writing—review and editing. Chuang Guo: Formal analysis; methodology; writing—review and editing; supervision. Zhiqiang Cui: Formal analysis; methodology; writing—review and editing; supervision. All authors have approved the final draft of the manuscript.

FUNDING INFORMATION

This work was supported by the Natural Science Foundation of China, grant number 32371037.

CONFLICT OF INTEREST STATEMENT

The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

PATIENT CONSENT

This study does not involve direct patient participation or collection of personally identifiable information; therefore, patient consent is not applicable.

Test Yourself Answers.

  1. Which of the following trends best describes the age‐standardized prevalence rate (ASPR) of epilepsy in China from 1990 to 2021?
    1. Continuous decline throughout the period
    2. Initial increase, followed by a stable decline, with a rebound after 2019
    3. Steady increase without any inflection points
    4. No significant change over the 30‐year period
  2. According to the age‐specific burden analysis, which age group became the new peak of epilepsy‐related disability‐adjusted life years (DALYs) in China by 2021?
    1. Children aged 0–14 years
    2. Working‐age adults aged 30–39 years
    3. Adults aged 50–64 years
    4. Elderly adults aged ≥75 years
  3. Based on the ARIMA forecasting model, what is the expected trend of the age‐standardized mortality rate (ASMR) for epilepsy in China from 2022 to 2036?
    1. A rapid increase due to population aging
    2. A continued gradual decline
    3. A sharp rebound to 1990 levels
    4. No significant change

Answers may be found in the Supporting Information

Supporting information

Test yourself answers

EPD2-28-1188-s001.docx (14.3KB, docx)

ACKNOWLEDGMENTS

We appreciate the Global Burden of Disease Study collaborators for their work.

Contributor Information

Chuang Guo, Email: guoc@mail.neu.edu.cn.

Zhiqiang Cui, Email: zhiqiangcui2008@hotmail.com.

DATA AVAILABILITY STATEMENT

The data used in this study are publicly available from the Global Burden of Disease (GBD) 2021 study through the Global Health Data Exchange (GHDx) at http://ghdx.healthdata.org/gbd‐results‐tool. All data generated or analyzed during this study are included in this published article.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Test yourself answers

EPD2-28-1188-s001.docx (14.3KB, docx)

Data Availability Statement

The data used in this study are publicly available from the Global Burden of Disease (GBD) 2021 study through the Global Health Data Exchange (GHDx) at http://ghdx.healthdata.org/gbd‐results‐tool. All data generated or analyzed during this study are included in this published article.

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