文章摘要
王姣锋,林文尧,江峰,孟炜,沈福民.江苏省海门市1993--2006年肝癌死亡率时间趋势分析[J].中华流行病学杂志,2010,31(7):727-732
江苏省海门市1993--2006年肝癌死亡率时间趋势分析
Analysis of time trend of hepatocellular carcinoma mortality in Haimen city of Jiangsu province from 1993 to 2006
收稿日期:2010-01-10  出版日期:2014-09-10
DOI:
中文关键词: 肝细胞肿瘤  时间趋势  死亡率
英文关键词: Hepatoma  Time trend  Mortality
基金项目:国际合作项目(国科遗办审字19991002],NCI 2P01CA40737)
作者单位E-mail
王姣锋 复旦大学公共卫生学院流行病学教研室公共卫生安全教育部重点实验室,上海 200032  
林文尧 复旦大学公共卫生学院流行病学教研室公共卫生安全教育部重点实验室,上海 200032  
江峰 复旦大学公共卫生学院流行病学教研室公共卫生安全教育部重点实验室,上海 200032  
孟炜 复旦大学公共卫生学院流行病学教研室公共卫生安全教育部重点实验室,上海 200032 fmshen@yahoo.com 
沈福民 江苏省海门市疾病预防控制中心  
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中文摘要:
      目的 预测海门市男性肝癌死亡趋势,并定量描述肝癌死亡率的年龄、时期和出生队列特征.方法 应用男性肝癌1993-2006年的年龄标化死亡率(SMR)构建GM(1,1)模型,预测SMR至2012年.同时,以每4年为一间隔的肝癌死亡密度(MD)拟合年龄-时期-队列(age-period-cohort,APC)模型的梯度模型,并进行模型拟合优度评价和模型比较,筛选最优模型;根据APC全模型和最优模型的拟合结果评估年龄、时期和出生队列效应;利用最优模型预测MD至2012年.结果 根据构建的GM(1,1)残差修正模型对SMR进行预测,结果显示,2007年SMR预测值为48.578/10万,相对误差为-1.267%;2008-2012年男性SMR轻微下降,最低值为45.578/10万(2012年).APC梯度模型拟合优度检验及模型比较结果显示,年龄-时期模型为最优模型(△G2=9.065,AIC=202.544).APC模型的曲率估计结果显示,曲率改变显著的年龄组为36.5~40.5岁(-0.368)和64.5~68.5岁(-0.489),队列作用的曲率改变主要发生在1956-1959年(C2 1949.5,1967.5=-0.492);年龄-时期模型的分层相对危险度估计结果显示,随年龄增加,年龄效应呈升高趋势,至64.5~68.5岁组后开始下降;时期越晚相对危险度越低.年龄-时期模型的预测结果显示2005-2012年肝癌死亡率呈下降趋势.结论 年龄、时期和部分队列效应可以预测肝癌死亡率趋势;未来几年肝癌死亡率会略微下降,但仍处于较高发病状态.
英文摘要:
      Objective To predict the trend of hepatocellular carcinoma (HCC) mortality and investigate the features of its mortality including age, period, and birth cohort in males living in Haimen city of Jiangsu province, China. Methods Grey model (GM) was modeled using standardized mortality rate (SMR) of HCC from 1993 to 2006, and was applied to predicting SMR until 2012. Based on the mortality density (MD) for a four-year period, the goodness-of-fit of models and comparisons between models were evaluated so as to obtain the best one among these models including the effects of intercept, age-period-cohort (APC), age-period (AP), age-cohort (AC),period-cohort(PC), and APC. Both APC full model and the best model were used to estimate effects of age, period, and cohort on HCC mortality. In addition, MD from 2005 to 2012 was predicted by the best model. Results Predictions based on GM (1,1 )showed that SMR was 48.578 per 100 000 population (relative error=-1.267% ) in 2007 year, which declined between 2008 and 2012. The lowest value was 45.578 per 100 000 people (in the 2012 year). The results of fitted models and comparisons between models showed that AP model was the best one (△G2=9.065,AIC=202.544). The curvatures of the effects of the three factors from APC model suggested that significances existed in changes of curvatures of 36.5-40.5 years old- (-0.368) and 64.5-68.5 years old-(-0.489) as well as in the change of 1956-1959 birth cohort (C21949.5. 1967.5=-0.492). The estimation of relative risks for AP model showed that the age effects were upward to 64.5-68.5 years old-, then downward; and that the period effects were found to be declined between 1993 and 2004. Predictions based on AP model suggested the decrease of HCC mortality. Conclusion The slightly decreasing trend of HCC mortality for males might be explained by age, period and a minor birth cohort effects in Haimen of China.
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