Abstract
BACKGROUND: This study examined temporal trends in laryngeal cancer (LC) incidence and mortality across urban and rural China from 2004–2019, with projections of disease burden through 2034. METHODS: Data on incidence, death, age-standardized incidence rate (ASIR), and age-standardized death rate (ASDR) of LC were obtained from Chinese Cancer Registry Annual Report from 2004 to 2019. Joinpoint regression analysis and age-period-cohort models were used to assess trends in the incidence and mortality rates of LC and interpret its epidemiological characteristics. Decomposition analysis assessed the contributions of demographic and epidemiological factors to the evolving burden of LC. Finally, the ASIR and ASDR of LC were projected for the next 15 years using a Bayesian Age-Period-cohort model. RESULTS: From 2004 to 2019, the number of LC cases and deaths increased in Chinese Cancer Registry dataset. During the study period, the ASIR of LC in urban and ASDR of LC in rural showed a down trend with estimate annual percent changes (EAPC) of -1.49% (95% confidence intervals [CI]: -2.11 to -0.87) and -1.32% (95% CI: -2.38 to -0.24). The impacts of age, period, and cohort on incidence and mortality rates varied between rural and urban areas. Population growth is the main driver of increased LC deaths in urban areas, while aging is the main driver in rural areas. The forecast predicts a decline in the ASIR of LC in urban areas, while it will rise in rural areas by 2034. The ASDR of LC is expected to decrease slightly in both urban and rural areas by 2034. CONCLUSIONS: This study reveals the complex epidemiological characteristics of LC in urban and rural areas of China. Population growth and aging are the primary drivers of LC deaths in China. By 2034, the ASIR of LC in rural areas is projected to increase to 2.11/100,000, necessitating the management of specific risk factors and the development of targeted public health strategies. However, these findings should be interpreted with caution, as variations in cancer registry coverage over time, differences in data quality between urban and rural areas, and assumptions inherent in projection models may influence the observed temporal trends and urban–rural comparisons. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13690-026-01851-0.