目标:在美国,子宫癌发病率正在上升,种族和少数民族经历了最大的增长。我们使用新方法进行了年龄-时期队列分析,以检查诊断时年龄的贡献(年龄),诊断年份(期间),和出生队列(队列),子宫癌发病率的趋势。
方法:我们使用了来自监测的子宫癌发病率数据,流行病学,和最终结果(SEER)12数据库(1992-2019年),并进行了子宫切除术矫正。我们生成了hexamaps来可视化年龄,period,和队列效应,并使用互信息来估计年龄的贡献百分比,period,和队列效应,单独和组合,子宫癌发病率,总体上,按种族和种族以及组织学划分。
结果:Hexamps显示子宫癌在后期增加,1933年左右的队列效应显示,与早期和晚期队列相比,发病率较低。年龄,period,和队列效应合计贡献了86.6%(95%CI:86.4%,86.9%)的发生率。年龄效应的贡献最大(65.1%,95%CI:64.3%,65.9),其次是队列(20.7%,95%CI:20.1%,21.3%)和期间(14.2%,95%CI:13.7%,14.8%)影响。近年来,非西班牙裔黑人和非子宫内膜样肿瘤的发病率较高。
结论:年龄效应对子宫癌发病率的贡献最大,其次是总体以及跨种族和族裔群体和组织学的队列和时期影响。
结论:这些发现可以为子宫癌建模研究提供信息,以针对可能因年龄而异的危险因素的干预措施的效果。period,或队列。
In the U.S., uterine cancer incidence is rising, with racial and ethnic minorities experiencing the largest increases. We performed age-period-cohort analyses using novel methods to examine the contribution of age at diagnosis (age), year of diagnosis (period), and birth cohort (cohort), to trends in uterine cancer incidence.
We used uterine cancer incidence data from the Surveillance, Epidemiology, and End Result (SEER) 12 database (1992-2019), and performed hysterectomy-correction. We generated hexamaps to visualize age, period, and cohort effects, and used mutual information to estimate the percent contribution of age, period, and cohort effects, individually and combined, on uterine cancer incidence, overall and by race and ethnicity and histology.
Hexamaps showed an increase in uterine cancer in later time periods, and a cohort effect around 1933 showing a lower incidence compared with earlier and later cohorts. Age, period, and cohort effects combined contributed 86.6% (95% CI: 86.4%, 86.9%) to the incidence. Age effects had the greatest contribution (65.1%, 95% CI: 64.3%, 65.9), followed by cohort (20.7%, 95% CI: 20.1%, 21.3%) and period (14.2%, 95% CI: 13.7%, 14.8%) effects. Hexamaps showed higher incidence in recent years for non-Hispanic Blacks and non-endometrioid tumors.
Age effects had the largest contribution to uterine cancer incidence, followed by cohort and period effects overall and across racial and ethnic groups and histologies.
These findings can inform uterine cancer modeling studies on the effects of interventions that target risk factors which may vary across age, period, or cohort.