Discrepancies in neglected tropical diseases burden estimates in China: comparative study of real-world data and Global Burden of Disease 2021 data (2004-2020)

OBJECTIVES: To assess the discrepancies between real-world data and the Global Burden of Disease (GBD) 2021 estimates for six neglected tropical diseases in China. Additionally, to evaluate the applicability of the GBD model within the Chinese context and to assess the effectiveness of China's historical prevention and control policies for neglected tropical diseases. DESIGN: Comparative study of real-world data and GBD 2021 (2004-2020). MAIN OUTCOME MEASURES: Disability adjusted life years (DALYs). METHODS: DALYs based on reported data for leprosy, echinococcosis, schistosomiasis, visceral leishmaniasis, dengue, and rabies from 2004 to 2020 were compared with the estimated DALYs from the GBD 2021 database. Additionally, we combined and analysed China's historical policies on prevention and control of neglected tropical diseases with real-world DALYs. DATA SOURCES: Reported data were sourced from the Chinese Center for Disease Control and Prevention's China Public Health Science data centre and related reports. Data for GBD 2021 and GBD 2019 were obtained from GBD databases. These data included all of China's 31 provinces (including autonomous regions and municipalities) and the Xinjiang Production and Construction Corps. RESULTS: 67 000 for rabies. The ratios of the GBD estimates to the real-world DALYs for the six neglected tropical diseases were 17 for leprosy, 11 for echinococcosis, 1.5 for schistosomiasis, 280 for visceral leishmaniasis, 4.2 for dengue, and 1.4 for rabies. CONCLUSIONS: The findings indicate that reliance solely on global estimates, such as those of the GBD, may not sufficiently capture the dynamics of neglected tropical diseases in China. Integrating local epidemiological data into global health assessments is crucial to develop accurate and effective public health policies. This study highlights the importance of continuously updating and improving data collection and surveillance methods to adapt public health strategies to evolving disease patterns.