Suppr超能文献

预测 2030 年加泰罗尼亚的卵巢癌负担:年龄-时期-队列模型。

Predicting Ovarian-Cancer Burden in Catalonia by 2030: An Age-Period-Cohort Modelling.

机构信息

Cancer Epidemiology Research Programme, Catalan Institute of Oncology, Av. Gran Vía 199-203, L'Hospitalet de Llobregat, 08908 Barcelona, Spain.

Bellvitge Biomedical Research Institute-IDIBELL, Av. Gran Vía 199-203, L'Hospitalet de Llobregat, 08908 Barcelona, Spain.

出版信息

Int J Environ Res Public Health. 2022 Jan 27;19(3):1404. doi: 10.3390/ijerph19031404.

Abstract

Ovarian cancer is the most lethal gynaecological cancer in very-high-human-development-index regions. Ovarian cancer incidence and mortality rates are estimated to globally rise by 2035, although incidence and mortality rates depend on the region and prevalence of the associated risk factors. The aim of this study is to assess changes in incidence and mortality of ovarian cancer in Catalonia by 2030. Bayesian autoregressive age-period-cohort models were used to predict the burden of OC incidence and mortality rates for the 2015-2030 period. Incidence and mortality rates of ovarian cancer are expected to decline in Catalonia by 2030 in women ≥ 45 years of age. A decrease in ovarian-cancer risk was observed with increasing year of birth, with a rebound in women born in the 1980s. A decrease in mortality was observed for the period of diagnosis and period of death. Nevertheless, ovarian-cancer mortality remains higher among older women compared to other age groups. Our study summarizes the most plausible scenario for ovarian-cancer changes in terms of incidence and mortality in Catalonia by 2030, which may be of interest from a public health perspective for policy implementation.

摘要

卵巢癌是高发人类发展指数地区女性中最致命的妇科癌症。据估计,全球卵巢癌的发病率和死亡率到 2035 年将上升,尽管发病率和死亡率取决于该地区和相关风险因素的流行程度。本研究旨在评估 2030 年加泰罗尼亚卵巢癌发病率和死亡率的变化。贝叶斯自回归年龄-时期-队列模型用于预测 2015-2030 年 OC 发病率和死亡率的负担。预计到 2030 年,45 岁及以上的加泰罗尼亚女性卵巢癌的发病率和死亡率将下降。随着出生年份的增加,卵巢癌的风险呈下降趋势,出生于 20 世纪 80 年代的女性呈反弹趋势。诊断期和死亡期的死亡率都有所下降。然而,与其他年龄组相比,老年女性的卵巢癌死亡率仍然较高。本研究总结了 2030 年加泰罗尼亚卵巢癌发病率和死亡率变化的最合理情况,从公共卫生的角度来看,这可能对政策的实施具有重要意义。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b006/8834772/d2a9faf9832b/ijerph-19-01404-g001.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验