基于Joinpoint回归及年龄-时期-队列模型分析鼻咽癌患者的死亡率发展趋势
To analyze the mortality trend of nasopharyngeal carcinoma based on Joinpoint regression and an age-peri-od-cohort model
代红磊 1王秋阳 2马文学 1官兵 1齐静静1
作者信息
- 1. 荆门市人民医院荆楚理工学院附属中心医院 耳鼻喉科,湖北 荆门 448000
- 2. 湖北医药学院附属襄阳市第一人民医院 耳鼻喉科,湖北 襄阳 441000
- 折叠
摘要
目的 探讨 1990～2019 年我国鼻咽癌患者的死亡率长期发展趋势.方法 数据来源于GHDx数据库,采用Joinpoint回归模型分析我国鼻咽癌患者死亡率趋势进行阶段性分析,并利用年龄-时期-队列模型进一步估计我国鼻咽癌患者死亡风险的年龄效应、时期效应和队列效应.结果 1990～2019 年我国鼻咽癌患者死亡率呈逐年下降趋势,整体变化趋势划分为五段,AAPC值为-2.454%.男性鼻咽癌患者死亡率变化趋势划分为五段,AAPC值为-1.958%;女性鼻咽癌患者死亡率变化趋势划分为六段,AAPC值为-3.498%.随着年龄的增长,鼻咽癌患者死亡率的年龄效应明显增加,并且死亡率的周期效应总体呈上升趋势.结论 队列效应的降低可能导致癌症死亡率的降低,而年龄效应和周期效应的增加可能导致癌症患者的死亡率增加.
Abstract
Objective To explore the long-term development trend of nasopharyngeal carcinoma mortality in China from 1990 to 2019.Methods Data obtained from the GHDx database established by the Global Burden of Disease Project was analyzed in this study.The Joinpoint regression model was used to analyze the trend of NPC mortality in China,and the age-period-cohort model was used to estimate the age effect,period effect,and cohort effect on nasopharyngeal carcinoma mortality risk in China.Results Overall,from 1990 to 2019,the nasopharyngeal carcinoma mortality rate in China showed a downward trend year by year.The Join-point regression model showed that the overall trend was divided into five segments,and the AAPC value was-2.454%.The trend of nasopharyngeal carcinoma mortality in males could be divided into five segments,with an AAPC value of-1.958%.The change trend of female nasopharyngeal carcinoma mortality can be divided into six segments,and the AAPC value was-3.498%.Addition-ally,increasing age significantly increases nasopharyngeal carcinoma mortality,and the cycle effect of mortality generally shows an upward trend.Conclusion The cohort reduction effect may lead to a decrease in nasopharyngeal carcinoma mortality,while an in-crease in age and the cycle effect may lead to an increase in cancer mortality.
关键词
鼻咽癌/死亡率/Joinpoint回归模型/年龄-时期-队列模型Key words
Nasopharyngeal carcinoma/Mortality/Joinpoint regression model/Age-period-ohort model引用本文复制引用
出版年
2024
NETL
NSTL
维普
万方数据