Spectrum and epidemiology of rare diseases in a Chinese natural population of 14.31 million residents, 2012–2023

医学 流行病学 罕见病 入射(几何) 人口 疾病 儿科 内科学 环境卫生 物理 光学
作者
Mingjia Li,Qi Li,Miaomiao Zhao,Hanna Kim,Ruijia Feng,Moning Guo,Jeremy Heng,Jin‐Kui Yang,Chang Liu
出处
期刊:Orphanet Journal of Rare Diseases [BioMed Central]
卷期号:20 (1)
标识
DOI:10.1186/s13023-025-03933-8
摘要

Abstract Background Rare diseases, though individually uncommon, collectively affect a significant portion of the population. However, their epidemiology in China remains underexplored. A population-based rare disease registry comprising 14.31 million individuals was conducted between 2012 and 2023 by the Beijing Municipal Health Big Data and Policy Research Center. Rare disease cases were identified via ICD-10 codes mapped to China’s national rare disease lists (2018 and 2023) and international databases. Age-standardized incidence rates (ASIR) were calculated per 100,000 person-years with 95% confidence intervals. Results Our analysis identified 12,371 rare disease cases, with the overall ASIR increasing from 6.109 in 2012 to 7.394 in 2023. Rare neurologic diseases accounted for 52.12% of cases, followed by systemic and rheumatologic diseases (16.89%) and rare neoplastic diseases (9.99%). The most frequently diagnosed rare diseases included generalized myasthenia gravis, ANCA-associated vasculitis, and malignant melanoma. Significant sex-based differences were observed, with female patients more affected by systemic and rheumatologic conditions, while male patients showed a higher incidence of respiratory disorders. Pediatric patients predominantly presented with inborn errors of metabolism and rare immune diseases. Comparisons with global data revealed notable disparities, such as a higher prevalence of Wilson’s disease and a lower incidence of amyotrophic lateral sclerosis (ALS) in China. Conclusions This study represents the first large-scale, population-based analysis of rare diseases in China, revealing distinct epidemiological patterns. These findings underscore the critical need for healthcare policies that address the unique challenges posed by rare diseases in China.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
香蕉菠娜娜完成签到,获得积分20
刚刚
xuxu发布了新的文献求助10
1秒前
磊2024完成签到,获得积分10
1秒前
2秒前
hubo发布了新的文献求助10
3秒前
美好向日葵完成签到,获得积分10
5秒前
高贵菲菲发布了新的文献求助20
5秒前
MAVS完成签到,获得积分10
5秒前
动听的鞋垫完成签到,获得积分10
6秒前
yuxi2025完成签到 ,获得积分10
7秒前
xliiii完成签到,获得积分10
9秒前
共享精神应助刘大大采纳,获得10
10秒前
hubo完成签到,获得积分10
10秒前
dy1994完成签到,获得积分10
10秒前
windsea完成签到,获得积分0
10秒前
zxdw完成签到,获得积分10
11秒前
Song完成签到 ,获得积分10
14秒前
科研通AI6.4应助july7292采纳,获得10
15秒前
淡定的幻枫完成签到 ,获得积分10
16秒前
18秒前
灰太狼大王完成签到,获得积分10
18秒前
MHB完成签到,获得积分10
19秒前
香蕉觅云应助CaiBangrong采纳,获得10
19秒前
Hsia完成签到,获得积分10
20秒前
无辜的黄豆完成签到 ,获得积分10
20秒前
乐助发布了新的文献求助30
21秒前
21秒前
深情安青应助Fighting采纳,获得10
21秒前
小肥肉发布了新的文献求助10
23秒前
蛋黄啵啵完成签到 ,获得积分10
23秒前
刘大大发布了新的文献求助10
24秒前
25秒前
26秒前
比格大王完成签到,获得积分10
27秒前
Cherish发布了新的文献求助10
29秒前
shiyin完成签到 ,获得积分10
30秒前
高贵菲菲完成签到,获得积分10
30秒前
木耳发布了新的文献求助10
33秒前
北风完成签到,获得积分10
33秒前
芽芽配茄子完成签到,获得积分10
34秒前
高分求助中
Psychopathic Traits and Quality of Prison Life 1000
Chemistry and Physics of Carbon Volume 18 800
The formation of Australian attitudes towards China, 1918-1941 660
Signals, Systems, and Signal Processing 610
天津市智库成果选编 600
Forced degradation and stability indicating LC method for Letrozole: A stress testing guide 500
全相对论原子结构与含时波包动力学的理论研究--清华大学 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 工程类 有机化学 化学工程 生物化学 计算机科学 物理 内科学 复合材料 催化作用 物理化学 光电子学 电极 细胞生物学 基因 无机化学
热门帖子
关注 科研通微信公众号,转发送积分 6451364
求助须知:如何正确求助?哪些是违规求助? 8263320
关于积分的说明 17607293
捐赠科研通 5516169
什么是DOI,文献DOI怎么找? 2903669
邀请新用户注册赠送积分活动 1880634
关于科研通互助平台的介绍 1722651