Temporal trends in incidence and mortality of cervical cancer in China from 1990 to 2019 and predictions for 2034

Shuang Li & Fan Zhang et al. · 2023-11-06

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Objective

This study aimed to analyze long-term trends of cervical cancer (CC) burden in China based on the GBD 2019 data and provide information and data support for formulating corresponding policies to control CC.

Methods

Incidence and mortality rate data of CC in China were described using GBD 2019 data. The Joinpoint regression analysis and age-period-cohort model were implemented to describe temporal trends of CC in China over the past 30 years. ARIMA model was used to predict trends of disease burden of CC in China for the next 15 years.

Results

From 1990 to 2019, the relative percentage change in age-standardized incidence rate (ASIR) of CC in Chinese women was 30.91 (95% UI: −50.13 to 96.78), and the relative percentage change in age-standardized mortality rate (ASMR) was −12.37 (95% UI: −63.54 to 28.52). The age-period-cohort model had different impacts on incidence and mortality rates. Overall annual percentage change (APC) (net drift) in incidence risk was 1.22 (95% CI: 0.87–1.57), and the overall APC (net drift) in mortality risk was −0.143 (95% CI: −0.38 to 0.09). The ARIMA model predicted ASIR and ASMR trends of CC for the next 15 years.

Conclusion

From 1990 to 2019, the overall incidence risk of CC in Chinese has shown an upward trend, with an earlier occurrence in the high-incidence age groups, while mortality risk showed a downward trend. It is anticipated that over the next 15 years, the incidence rate will decrease, while the mortality rate will initially rise before decreasing.

TL;DR

The overall incidence risk of CC in Chinese has shown an upward trend, with an earlier occurrence in the high-incidence age groups, while mortality risk showed a downward trend, and it is anticipated that over the next 15 years, the incidence rate will decrease, while the mortality rate will initially rise before decreasing.

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Authors
Shuang Li, Min Huang, Yan Zhu, Hai Zeng, Fan Zhang