Evidence map›Paper›PMID 41015562›Full record

ArticleZhong nan da xue xue bao. Yi xue ban = Journal of Central South University. Medical sciences2025

Gender differences in the burden of near vision loss in China: An analysis based on GBD 2021 data.

Yu Liu, Liping Zhu, Yanhui Lin, Yanbing Wang, Kun Xiong, Xuhong Li, Wenguang Yan

Abstract read
In one paragraph

Article in Zhong nan da xue xue bao. Yi xue ban = Journal of Central South University. Medical sciences, 2025. 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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5 · Who and what money

Authors and funding

7 authors.

Yu LiuDepartment of Ophthalmology, Third Xiangya Hospital, Central South University, Changsha 410013. 962651976@qq.com.
Liping ZhuDepartment of Rehabilitation Medicine, Third Xiangya Hospital, Central South University, Changsha 410013.
Yanhui LinHealth Management Medicine Center, Third Xiangya Hospital, Central South University, Changsha 410013.
Yanbing WangDepartment of Ophthalmology, Second Xiangya Hospital, Central South University, Changsha 410011.
Kun XiongXiangya School of Medicine, Central South University, Changsha 410013, China.
Xuhong LiDepartment of Rehabilitation Medicine, Third Xiangya Hospital, Central South University, Changsha 410013.
Wenguang YanDepartment of Rehabilitation Medicine, Third Xiangya Hospital, Central South University, Changsha 410013. 2402632247@qq.com.

Funding

Changsha Natural Science Foundation kq2403057Hunan Provincial Natural Science Foundation-Hengyang City Joint Fund Project 2025JJ70129the Natural Science Foundation of Hunan Province 2023JJ30817This is an open access article under Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International CC BY-NC-ND 4.0 and //creativecommons.org/licenses/by-nc-nd/4.0/
6 · The paper itself

Abstract

objectivesNear vision loss (NVL) is one of the leading causes of visual impairment worldwide, exerting a profound impact on individual quality of life and socio-economic development. This study aims to analyze the burden of NVL in China by sex and age groups from 1990 to 2021 and to project trends over the next 15 years.

methodsUsing data from the Global Burden of Disease (GBD) 2021 database, we conducted descriptive analyses of NVL prevalence in China, calculated age-standardized prevalence rates (ASPR) and age-standardized disability-adjusted life years rates (ASDR) to compare burden differences between sexes and age groups, and applied an autoregressive integrated moving average (ARIMA) model to predict NVL trends for the next 15 years. The model selection was based on best-fit criteria to ensure reliable projections.

resultsFrom 1990 to 2021, China's ASPR of NVL rose from 10 096.24/100 000 to 15 624.54/100 000, and ASDR increased from 101.75/100 000 to 158.75/100 000. In 2021, ASPR (16 551.70/100 000) and ASDR (167.69/100 000) were higher among females than males (14 686.21/100 000 and 149.76/100 000, respectively). China ranked highest globally in both NVL cases and disability-adjusted life years (DALYs), with female burden significantly exceeding male burden. Projections indicated this trend and sex gap will persist until 2036. Compared with 1990, the prevalence cases and DALYs increased by 239.20% and 238.82%, respectively in 2021, with the highest burden among females and the 55-59 age group. The ARIMA model predicted continued increases in prevalence and DALYs by 2036, with females maintaining a higher burden than males.

conclusionsThis study reveals a marked increase in the NVL burden in China and predicts continued growth in the coming years. Public health policies should prioritize NVL prevention and control, with special attention to women and middle-aged populations to mitigate long-term societal and health impacts.

Indexed as

Vision DisordersAdolescentAdultAgedAged, 80 and overChildChild, PreschoolChinaCost of IllnessDisability-Adjusted Life YearsFemaleGlobal Burden of DiseaseHumansInfantMaleMiddle Agedautoregressive integrated moving average modelChinagender differencesGlobal Burden of Disease databasenear vision loss

Identifiers

PMID41015562
PMCPMC12464932

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