Long-term trends in the burden of breast cancer in China over three decades: a joinpoint regression and age-period-cohort analysis based on Global Burden of Disease 2021

We analyzed the trends in breast cancer (BC) morbidity, prevalence, and mortality among Chinese residents from 1990 to 2021. We then used joinpoint regression to further assess BC morbidity and mortality. We screened the morbidity, mortality, and prevalence of BC in Chinese residents (1990-2021) from the Global Burden of Disease. We used age-period-cohort (APC) modeling to assess the effects of age, period, and cohort on BC morbidity and mortality separately. We also used the joinpoint model to characterize trends in BC morbidity and mortality in China. From 1990 to 2021, age-standardized rates of morbidity have risen significantly, whereas mortality has declined. We discovered that the risk of morbidity and death rose with age by using the APC model. We also found that mortality and morbidity roughly continued to increase over time, and finally, we found that the later the birth cohort, the lower the mortality and the higher the morbidity. From 1990 to 2021, the burden of BC disease in China will continue to rise, and the situation of BC prevention and control will remain severe. Therefore, regular imaging and palpation examinations should be performed in the regular population over 40 years of age. When treating patients with BC, healthcare workers should develop individualized treatment plans to further reduce mortality.

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Publication Details

Journal
European Journal of Cancer Prevention
Published
2024-11-07
DOI
https://doi.org/10.1097/cej.0000000000000934
Citations
1
Primary Topic
Cancer Risks and Factors
Type
article
Field-Weighted Citation Impact
0.28
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article

Long-term trends in the burden of breast cancer in China over three decades: a joinpoint regression and age-period-cohort analysis based on Global Burden of Disease 2021

Li Pan, Jiacheng Yuan, Ming Yang
1 citations
European Journal of Cancer Prevention
Cancer Risks and Factors
0.28
article

Long-term trends in the burden of breast cancer in China over three decades: a joinpoint regression and age-period-cohort analysis based on Global Burden of Disease 2021

Li Pan, Jiacheng Yuan, Ming Yang
article en
1 citations

Abstract

We analyzed the trends in breast cancer (BC) morbidity, prevalence, and mortality among Chinese residents from 1990 to 2021. We then used joinpoint regression to further assess BC morbidity and mortality. We screened the morbidity, mortality, and prevalence of BC in Chinese residents (1990-2021) from the Global Burden of Disease. We used age-period-cohort (APC) modeling to assess the effects of age, period, and cohort on BC morbidity and mortality separately. We also used the joinpoint model to characterize trends in BC morbidity and mortality in China. From 1990 to 2021, age-standardized rates of morbidity have risen significantly, whereas mortality has declined. We discovered that the risk of morbidity and death rose with age by using the APC model. We also found that mortality and morbidity roughly continued to increase over time, and finally, we found that the later the birth cohort, the lower the mortality and the higher the morbidity. From 1990 to 2021, the burden of BC disease in China will continue to rise, and the situation of BC prevention and control will remain severe. Therefore, regular imaging and palpation examinations should be performed in the regular population over 40 years of age. When treating patients with BC, healthcare workers should develop individualized treatment plans to further reduce mortality.

European Journal of Cancer PreventionVol. 34(5)
Jilin University (CN), First Hospital of Jilin University (CN)
Good health and well-being
Openalex Percentile: Top 34%
Cancer Risks and Factors
0.28
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Long-term trends in the burden of breast cancer in China over three decades: a joinpoint regression and age-period-cohort analysis based on Global Burden of Disease 2021 — Li Pan, Jiacheng Yuan, et al. · European Journal of Cancer Prevention (2024) | TGRS Research Map | TGRS