Background: Thyroid cancer (TC) is a prevalent malignant tumor of the endocrine system in China. Current research primarily focuses on clinical diagnosis and treatment as well as underlying mechanisms, lacking epidemiological studies on the burden of the disease in China and worldwide.
Methods: The Global Burden of Disease Study 2021 was utilized to assess the incidence, prevalence, death, and disability-adjusted life years of TC in China and worldwide from 1990 to 2021 using the Joinpoint and R software.
Results: From 1990 to 2021, the incidence and prevalence rates of TC in China have been consistently rising, and their growth rates are far higher than the global average. In China, TC usually occurs in patients aged 50-59, and the crude death rate generally increases with age. The burden of death among females has gradually declined, while that among males has continued to increase and surpassed females at the beginning of the 21st century. The burden of TC is heavy among middle-aged and elderly populations and the younger populations is also rapidly rising. The increased number of TC is mainly attributed to epidemiological changes, while the increase of deaths in China is primarily due to aging and population. Additionally, we predict that the age-standardized incidence rate of TC in China will continue to grow slowly over the next decade, while the age-standardized death rate will gradually decline among females and stabilize among males.
Conclusion: It is imperative to avoid over-screening and over-treatments for TC. Meanwhile, we should also avoid missing aggressive types of TC that may have an impact on overall survival. Additionally, understanding the mechanisms of metastasis and improving clinical treatments should be prioritized for further investigation. TC remains a significant public health challenge in China, necessitating a careful balance of the cost-benefit ratio.
Keywords: ARIMA model; age-period-cohort analysis; disease burden; epidemiology; joinpoint regression; thyroid cancer.
Copyright © 2024 Meng, Pan, Yu, Shi, Liu, Xue, Wang and Ma.