Evidence map›Paper›PMID 42113023›Full record

ArticleEpileptic disorders : international epilepsy journal with videotape2026

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

Chao Jiang, Keke Ju, Yiming You, Yan Zhao, Jian Wang, Chuang Guo, Zhiqiang Cui

Abstract read
In one paragraph

Article in Epileptic disorders : international epilepsy journal with videotape, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Chao JiangKey Laboratory of Bioresource Research and Development of Liaoning Province, College of Life and Health Sciences, Institute of Neuroscience, Northeastern University, Shenyang, Liaoning, People's Republic of China.
Keke JuDepartment of Neurology, Shaanxi Provincial People's Hospital, Xi'an, Shaanxi, China.
Yiming YouKey Laboratory of Bioresource Research and Development of Liaoning Province, College of Life and Health Sciences, Institute of Neuroscience, Northeastern University, Shenyang, Liaoning, People's Republic of China.
Yan ZhaoKey Laboratory of Bioresource Research and Development of Liaoning Province, College of Life and Health Sciences, Institute of Neuroscience, Northeastern University, Shenyang, Liaoning, People's Republic of China.
Jian WangDepartment of Neurosurgery, Chinese People's Liberation Army of General Hospital, Beijing, China.
Chuang GuoKey Laboratory of Bioresource Research and Development of Liaoning Province, College of Life and Health Sciences, Institute of Neuroscience, Northeastern University, Shenyang, Liaoning, People's Republic of China.
Zhiqiang CuiDepartment of Neurosurgery, Chinese People's Liberation Army of General Hospital, Beijing, China.ORCID https://orcid.org/0009-0008-0444-8810

Funding

National Natural Science Foundation of China 32371037
6 · The paper itself

Abstract

objectiveThis 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.

methodsData 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.

resultsIn 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.

conclusionDespite 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.

Indexed as

Cost of IllnessDisability-Adjusted Life YearsEpilepsyAdolescentAdultAgedAge FactorsBayes TheoremChildChild, PreschoolChinaCohort StudiesFemaleHumansIncidenceInfantage‐period‐cohort analysisdisease burdenepilepsyILAE classificationtrend prediction

Identifiers

PMID42113023
PMCPMC13499261

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.