入射(几何)
疾病负担
疾病负担
医学
人口学
队列
疾病
全球卫生
环境卫生
观察研究
公共卫生
老年学
人口
病理
物理
社会学
光学
作者
Li Deng,Siqi Fan,Haochen Zhao,Jiayi Song,Linfen Guo,Wei Li,Xuewen Xu,Qingfeng Li
标识
DOI:10.3389/fpubh.2025.1580221
摘要
Background Fungal skin diseases represent pervasive global health concerns, predominantly arising from dermatophytes, yeasts, and molds. Objective This study aimed to estimate the disease burden associated with fungal skin diseases in 2021. Additionally, it sought to analyze trends from 1990 to 2021 and forecast future patterns. Methods This observational study first utilized data from the Global Burden of Disease (GBD) database covering the years 1990 to 2021. We specifically used data from GBD 2021 to evaluate the global incidence, prevalence, and disability-adjusted life years (DALYs), disaggregated by age, gender, socio-demographic index (SDI), and GBD regions. Linear regression models were then employed to identify temporal trends, estimating the annual percentage change. Cluster analysis examined disparities across 45 GBD regions. To forecast future disease burden, we applied the age-period-cohort model and the autoregressive integrated moving average model. Conclusion In 2021, there were approximately 1.73 billion global cases of fungal skin diseases. Males had higher age-standardized rates for incidence, prevalence, and DALYs compared to females. Age-specific analyses showed that although younger groups experienced the highest incidence rates, ASRs increased with age, especially among older populations. Regionally, low and middle SDI areas faced the greatest burden, with Asia having the highest incidence and Oceania the lowest. Projections suggest significant increases in incidence, prevalence, and DALYs, notably in middle- and low-income regions. These results highlight meaningful spatiotemporal disparities in fungal skin diseases and emphasize the need for strategic allocation of resources to mitigate these challenges and reduce the growing burden across various global populations.
科研通智能强力驱动
Strongly Powered by AbleSci AI